Patent Application
20210095348
The present disclosure relates to analysis of genetic variations in individuals, and in particular to the preparation and analysis of biological samples for identification and/or detection of markers genetic information in biological material.
Use of biological material to answer questions pertaining to legal situations, including criminal and civil cases, has rapidly integrated traditional techniques of forensic science that depend on qualitative expert opinion.
In particular, DNA and protein analysis provide techniques which constitute evidence with a sound scientific footing.
Despite the progress made in this field, challenges remain to develop methods of genetic variation analysis resulting in reliable results from a broad spectrum of biological samples, and in particular to develop methods of genetic variation analysis which minimize false positive and/or false negative results due to the specific features of the biological sample where the investigation is performed.
Provided herein are methods and systems to perform genetically variant protein analysis and related marker genetic protein variations and databases, which in several embodiments allow performing a reliable genetic variation protein analysis in biological samples of different types and conditions taking into account the features of the biological sample where the analysis is performed.
In particular, in several embodiments, the methods and systems and related marker genetic protein variations and databases herein described comprise and/or use marker genetic protein variations validated to be detectable in the biological sample where the genetic protein variation analysis is performed. In several embodiments, the methods and systems and related marker genetic protein variations and databases herein described use preparation methods which maximize recovery of processable protein from such biological sample.
According to a first aspect, a method to prepare a biological sample for proteomic analysis, is described. The method comprises applying to the biological sample an energy field to obtain a processed biological sample comprising solubilized proteins to be used in the proteomic analysis. In some preferred embodiments, applying to the biological sample an energy field is performed by sonication with an energy field ranging from 150 to 1,200 Watts and frequency ranging from 20 to 80 kHz. In another embodiment microwave energy of up to 1,200 Watts can be used to obtain a processed biological sample comprising solubilized proteins.
According to a second aspect, a method and system are described to provide a marker genetic protein variation for a biological organism and a marker genetic protein variation obtainable thereby. In the method and system, the provided marker genetic protein variation is validated to be detectable in a biological sample of an individual of the biological organism.
The method comprises: providing a marker exome sequence of the biological organism, the marker exome sequence comprising a marker genetic variation for the biological organism.
The method further comprises detecting peptide sequences in the biological sample of the individual of the biological organism by performing proteomic analysis of said biological sample to provide proteomically detected peptide sequences.
The method also comprises providing the marker genetic protein variation for the biological organism detectable in the sample of the biological organism by comparing the provided marker exome sequence with the proteomically detected peptide sequences to provide the marker genetic protein variation validated to be detectable in the biological sample of an individual of the biological organism.
The system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to provide a marker genetic protein variation validated for a biological sample herein described.
According to a third aspect, a method and system to detect a marker genetic protein variation in a biological sample are described. In the method and system, the marker genetic protein variation validated to be detectable in the biological sample.
The method comprises providing a marker mass spectrum of a marker peptide comprising a marker genetic protein variation corresponding to the genetic protein variation; and performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide.
The method further comprises comparing the mass spectrum of the fractionated digested peptide with the marker mass spectrum of a marker peptide comprising the marker genetic protein variation to detect the genetic protein variation in the biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to detect a marker genetic protein variation in a biological sample herein described.
According to a fourth aspect, a method and system to improve a marker genetic protein variation database system for a biological organism, and a database obtainable thereby, are described. In the method, system and database herein described, the marker genetic protein variation database system includes data for at least one biological organism and the improvement is the inclusion of one or more marker genetic protein validated to be detectable in a biological sample from an individual of the at least one biological organism.
The method comprises: producing a proteomic dataset from a biological sample from an individual of the at least one biological organism and comparing the proteomic dataset to a protein variant database to produce a set of proteomically detected proteins in the biological sample of the individual.
The method further comprises providing a set of represented genes proteomically detectable in the biological sample of the individual, the represented genes corresponding to the proteomically detected proteins in the biological sample of the individual.
The method also comprises: identifying a marker genetic protein variation validated for the biological sample of the individual, to be included in the marker genetic protein variation database system by providing a proteomically detectable genomic variation in the set of represented genes proteomically detectable in the biological sample of the individual, and providing the marker genetic protein variation validated for the biological sample by providing a proteomically detectable genetic protein variation corresponding to the detectable genomic variation in the biological sample of the individual.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to improve a marker genetic protein variation database system for a biological organism herein described.
According to a fifth aspect, a method and system to improve a pooled marker genetic protein variation database system and a pooled marker genetic protein variation database obtainable thereby. In the method and system and related database, the pooled marker genetic protein variation database system comprising marker genetic protein variations common to a plurality of individuals.
The method comprises: providing a number of proteomic datasets of individuals of the plurality of individuals, the number statistically significant for the plurality of individuals, identifying a protein common to the provided number of proteomic datasets; and selecting from the identified protein common to the provided proteomic datasets, a protein detectable in a biological sample of an individual of the plurality of individuals.
The method further comprises providing a number of exome datasets of the individuals of the plurality of individuals, the number statistically significant for the plurality of individuals; and identifying a genetic variation in the provided number of exome datasets.
The method also comprises selecting from the identified genetic variation, a genetic variation detectable in the biological sample; and comparing the selected proteins detectable in the biological sample with the selected genetic variations detectable in the biological sample, to provide a marker genetic protein variation common to a plurality of individuals of a biological organism type and validated to be detectable in the biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to improve a pooled marker genetic protein variation database system for a biological organism herein described.
According to a sixth aspect, a method and a system are described to detect a marker genetic variation for a biological organism validated to be detectable in a biological sample of an individual of the biological system.
The method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis; and fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample.
The method further comprises detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction; and detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction.
The method also comprises comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system herein described.
The system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to detect a marker genetic variation for a biological organism validated to be detectable in a biological sample of an individual of the biological system herein described.
According to a seventh aspect, a method to provide a marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample, the method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis.
The method further comprises fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample.
The method also comprises detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction and detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction.
The method additionally comprises combining the detected genetic protein variations and the detected genomic variation to provide the marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to provide the marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample herein described.
According to an eight aspect, a method and system are described to perform genetic analysis of a sample of a biological organism.
The method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis, and fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample.
The method also comprises digesting the solubilized proteins from the sample with a site specific proteolytic enzyme to obtain digested solubilized proteins from the sample; fractionating the digested solubilized proteins to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample and detecting a marker genetic variation of the fractionated digested peptides.
In the method, preparing the sample and/or detecting a genetic variation can be performed by any one of the methods according to any one of the first aspect to the seventh aspect of the instant disclosure. In particular, in methods according to the eighth aspect the preparing is performed by any one of the methods according to the first aspect herein described; and/or the detecting is performed by at least one of a first detecting method wherein the detecting is performed by any one of the methods according to the third aspect of the present disclosure; and a second detecting method wherein the detecting is performed by any one of the methods according to the sixth aspect of the present disclosure.
The system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to perform genetic analysis of a sample of a biological organism herein described.
In preferred embodiments of the marker genetic protein variations, databases, methods and systems and related genetic protein variation analysis herein described, performing a proteomic analysis is carried out by performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide.
In further preferred embodiments of the marker genetic protein variations, databases, methods and systems and related genetic protein variation analysis herein described, the sample is hair and/or skin.
The methods and systems and related marker genetic protein variations and databases herein described, allow in several embodiments performing a reliable genetic variation protein analysis in degraded samples, in samples from multiple contributors, in samples where genetic material is not present in detectable amounts, and/or in samples where the genetic material and/or protein material are present in low amounts, the reliable analysis performed.
In particular, the methods and systems and related marker genetic protein variations and databases herein described, allow in several embodiments to provide a sample for proteomic analysis with a reduced presence of fragments resulting from uncontrolled breaking of the protein, not due to the enzymatic digestion (e.g. through trypsin digestion).
Accordingly, the methods and systems and related marker genetic protein variations and databases herein described, allow in several embodiments performing proteolysis on samples including a small amount of processable material (e.g. single hair but also other kind of tissues possibly available in small amounts).
Additionally, the methods and systems and related marker genetic protein variations and databases herein described allow in several embodiments to provide a sample for proteomic analysis comprising a more representative/more complete detection of proteins present in the tissue sample per mass of tissue sample.
The methods and systems and related marker genetic protein variations and databases herein described, further allow, in several embodiments, to providing and/or using improved databases in view of inclusion of marker genetic protein variations validated for the biological sample where the genetic protein variation analysis is performed.
Accordingly, the methods and systems and related marker genetic protein variations and databases herein described, also allow, in several embodiments, to reduce false negatives present in databases built with a proteome-based discovery process.
Additionally, the methods and systems and related marker genetic protein variations and databases herein described which are based on marker genetic variation validated to be detectable in the biological sample of interest, also allow, in several embodiments, to provide and/or use a database customizable with validated markers genetically variant protein for an individual, a biological organisms or types of biological organism in accordance with the experimental design and particular query.
Furthermore, the methods and systems and related marker genetic protein variations and databases herein described, also allow, in several embodiments, to perform genetically variant protein analysis without the need of the “needle in a haystack” approach, in view of the ability to use proteomics to screen with validated marker genetic protein variation for an individual, alone or in combination with marker genomic variation (in nuclear and/or mitochondrial genomes), thus having a faster and reliable response to a specific query with respect to available methods to perform genetic variation analysis known to a skilled person.
Additionally, in view of the use of marker genetic protein variation validated for a biological sample analyzed, the methods and systems and related marker genetic protein variations and databases herein described, also allow, in several embodiments, to perform genetically variant protein analysis without the need to go through databases to obtain an output (even if such step could still be performed).
In view of the ability to perform combined analysis of genetic protein variation and nuclear and/or, preferably, mitochondrial genomic variation, the methods and systems and related marker genetic protein variations and databases herein described, also allow, in several embodiments, to provide a more accurate response to a query/increased ability to discriminate identity based on combined metrics from genetic protein variation and genomic variation following verification of proteomic as well as of genomic markers from a single biological sample (e.g. genomic mitochondrial markers herein also mtDNA markers).
In general, embodiments of the methods and systems and related marker genetic protein variations and databases herein described, which are based on at least one of the sample preparation methods herein described, the marker genetic protein variation validated for a specific sample herein described, and/or the combined analysis of genetic protein variation with nuclear and/or mitochondrial genomic variation herein described, provide a faster and/or more reliable genetic variation analysis for a specific biological sample with respect to methods, systems and databases available for a skilled person.
The methods and systems and related marker genetic protein variations and databases herein described, can be used in connection with various applications wherein an improved ability to perform genetic variation analysis of a biological sample is desired. For example, the methods and systems and related marker genetic protein variations and databases herein described can be used in several applications of forensic analysis, such as identification of individuals, biological organisms types and biological organism of interest from a biological sample, determining relatedness of individuals, paternity testing and additional forensic analysis applications identifiable by a skilled person. Additional exemplary applications include uses of the methods and systems in several fields wherein genetic variation analysis can be used including basic biology research, applied biology, bio-engineering, medical research, medical diagnostics, therapeutics, and in additional fields identifiable by a skilled person upon reading of the present disclosure.
The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.
The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate one or more embodiments of the present disclosure and, together with the detailed description and the examples, serve to explain the principles and implementations of the disclosure.
Provided herein are methods and systems to perform genetically variant protein analysis and related marker genetic protein variations and databases, which in several embodiments allow performing of a reliable genetic variation protein analysis in biological samples of different types and under different conditions, taking into account the features of the biological sample for which the analysis is performed.
The term “genetic variation” as used herein refers to diversity in gene frequencies and/or in gene sequences. In particular, genetic variation as used herein can refer to genes that are translated into corresponding proteins, which can result in diversity in corresponding protein frequency. Genetic variation in the sense of the disclosure can refer to differences between individuals or to differences between populations. Mutation is the ultimate source of genetic variation, but mechanisms such as sexual reproduction and genetic drift contribute to it as well.
Genetic variations in the sense of the disclosure comprise genomic variations (genetic variations in nuclear or mitochondrial DNA of individuals), and genetic protein variations (genetic variations within a genetically variant protein encoded by a non-synonymous variation in the protein coding region of the corresponding encoding gene).
Accordingly, the term “genetically variant protein”, or “GVP” as used herein refers to a protein encoded by a gene, wherein variants of the protein have a variation (e.g. a single amino acid polymorphisms (SAPs)) that is encoded by non-synonymous variation (e.g. a single nucleotide polymorphisms (nsSNPs)) in the protein-coding region of the gene (e.g., see
The term “single amino acid polymorphisms (SAPs))” refers to named amino acid variances derived from SNPs within coding regions. SAP can be quantitatively or qualitatively detected at the proteome level, with non-targeted or targeted proteomics as will be understood by a skilled person.
The term “single nucleotide polymorphism” or “SNP” refers to a variation in a single nucleotide that occurs at a specific position in the genome of an organism, where each variation occurs at a particular frequency within a population of the organism. For example, at a specific base position in the human genome, the base C appears in most individuals, but in a minority of individuals, the position is occupied by base A. There is a SNP at this specific base position, and the two possible nucleotide variations—C or A—are said to be alleles for this base position. SNPs can occur within protein-coding sequences of genes, non-coding regions of genes, or in the intergenic regions (regions between genes). The term “protein-coding” region, also referred to herein as the “coding region”, “coding DNA sequence” or “CDS” as used herein refers to the portion of a gene's DNA or RNA, composed of exons, that codes for protein. The region is bounded at the 5′ end by a start codon (typically ATG) and at the 3′ end with a stop codon (typically TAA, TAG, or TGA). The coding region in mRNA is bounded by the five prime untranslated region (5′-UTR) and the three prime untranslated region (3′-UTR), which are also parts of the exons. The CDS is the portion of an mRNA transcript that is translated by a ribosome.
As understood by those skilled in the art, SNPs within a protein-coding sequence do not necessarily change the amino acid sequence of the protein that is produced, due to degeneracy of the genetic code. SNPs in the coding region are of two types, synonymous and nonsynonymous SNPs. Synonymous SNPs do not alter the amino acid sequence of a protein while nonsynonymous SNPs change the amino acid sequence of a protein. The nonsynonymous SNPs are of two types: missense and nonsense. A missense mutation is a point mutation in which a SNP results in a codon that codes for a different amino acid. In contrast, a nonsense mutation is a point mutation in a sequence of DNA that results in a premature stop codon, also referred to as a nonsense codon, in the transcribed mRNA, and in a truncated, incomplete, and usually nonfunctional protein product.
The term “protein” as used herein indicates a polypeptide with a particular secondary and tertiary structure that can interact with another molecule and in particular, with other biomolecules including other proteins, polynucleotides such as DNA and RNA, lipids, metabolites, hormones, chemokines, and/or small molecules. The term “polypeptide” as used herein indicates an organic linear polymer composed of two or more amino acid monomers and/or analogs thereof. The term “polypeptide” includes amino acid polymers of any length including full-length proteins and peptides, as well as analogs and fragments thereof. A polypeptide of three or more amino acids is also called a protein oligomer, peptide, or oligopeptide. In particular, the terms “peptide” and “oligopeptide” usually indicate a polypeptide with less than 100 amino acid monomers. In particular, in a protein, the polypeptide provides the primary structure of the protein, wherein the term “primary structure” of a protein refers to the sequence of amino acids in the polypeptide chain covalently linked to form the polypeptide polymer. A protein “sequence” indicates the order of the amino acids that form the primary structure. Covalent bonds between amino acids within the primary structure can include peptide bonds or disulfide bonds, and additional bonds identifiable by a skilled person. Polypeptides in the sense of the present disclosure are usually composed of a linear chain of alpha-amino acid residues covalently linked by peptide bond or a synthetic covalent linkage. The two ends of the linear polypeptide chain encompassing the terminal residues and the adjacent segment are referred to as the carboxyl terminus (C-terminus) and the amino terminus (N-terminus) based on the nature of the free group on each extremity. Unless otherwise indicated, counting of residues in a polypeptide is performed from the N-terminal end (NH2-group), which is the end where the amino group is not involved in a peptide bond to the C-terminal end (—COOH group), which is the end where a COOH group is not involved in a peptide bond. Proteins and polypeptides can be identified by x-ray crystallography, direct sequencing, immunoprecipitation, and a variety of other methods as understood by a person skilled in the art. Proteins can be provided in vitro or in vivo by several methods identifiable by a skilled person. In some instances where the proteins are synthetic proteins, in at least a portion of the polymer two or more amino acid monomers and/or analogs thereof are joined through chemically-mediated condensation of an organic acid (—COOH) and an amine (—NH2) to form an amide bond or a “peptide” bond.
As used herein the term “amino acid”, “amino acid monomer”, or “amino acid residue” refers to organic compounds composed of amine and carboxylic acid functional groups, along with a side-chain specific to each amino acid. In particular, alpha- or α-amino acid refers to organic compounds composed of amine (—NH2) and carboxylic acid (—COOH), and a side-chain specific to each amino acid connected to an alpha carbon. Different amino acids have different side chains and have distinctive characteristics, such as charge, polarity, aromaticity, reduction potential, hydrophobicity, and pKa. Amino acids can be covalently linked to form a polymer through peptide bonds by reactions between the amine group of a first amino acid and the carboxylic acid group of a second amino acid. Amino acid in the sense of the disclosure refers to any of the twenty naturally occurring amino acids, non-natural amino acids, and includes both D and L optical isomers.
Methods and systems herein described and related marker genetic protein variations and databases herein described allow performance of genetic protein variation analysis of a sample of a biological organism taking into account the features of the biological sample where the analysis is performed as will be understood by a skilled person upon reading of the present disclosure.
The wording “biological organism” as used herein indicates an entity that exhibits the properties of life and that comprises a genome which is expressed and translated in a proteome. Exemplary biological organisms comprise multicellular animals, plants, and fungi; or unicellular microorganisms such as protists, bacteria, and archaea. In preferred embodiments the biological organism comprises animals and in particular higher animals and in particular vertebrates such as mammals and in particular human beings (Homo sapiens).
Genetic protein variation analysis typically comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis.
Existing methods of sample preparation for proteomics generally comprise performing techniques of cell and tissue disruption, protein solubilization, removal of contaminants, and protein enrichment methods [1].
In particular methods of cell and tissue disruption typically comprise homogenization of the sample. Homogenization methods used for the proteomics purposes can be divided into five major categories: mechanical, ultrasonic, pressure, freeze—thaw, and osmotic/detergent lysis. Mechanical homogenization can be performed using rotor—stator homogenizers, open blade mills, or glass-glass milling, among others known to those skilled in the art. Ultrasonic homogenizers, also called as disintegrators, sonicators, or sonificators, are based on the piezoelectric effect and on the principle of cavitation while generating the high energy or ultrasonic wave, interacting with the sample. More specifically, ultrasonic homogenizers generate sound energy electronically; this energy is converted to mechanical energy, and these changes result in the formation and implosion of small bubbles in the sample. Energy, resolved after explosion/implosion of gas microbubbles, effectively destroys solid particles such as cells, causing cell rupture and successful cell lysis.. Ultrasonic devices are mainly used to homogenize small pieces of soft tissues (e.g., brain, blood, liver). Pressure homogenization typically uses a French press device, and is an effective method for homogenization of cells in suspension, but ineffective towards tissues or organs without previous preparation in another type of homogenizer. Freeze-thaw homogenization uses the effect of ice crystal formation in the tissue during freezing process. Osmotic and detergent lysis methods of disruption of cells utilize osmotic pressure or detergent interactions to destroy cells' walls and membranes. Osmotic lysis is often used to disrupt blood cells. Examples of commonly used detergents are Triton X-100, Tween 80, Nonidet P-40 (NP 40) and saponin.
In a genetic protein variation analysis, a homogenized sample is subjected to protein solubilization. Proteins in their native state are often insoluble. Breaking interactions involved in protein aggregation, e.g. disulfide/hydrogen bonds, van der Waals forces, ionic and hydrophobic interactions, allows disruption of proteins into a solution of individual polypeptides and thus promotes their solubilization. To avoid protein modifications, aggregation or precipitation resulting in the occurrence of artifacts and subsequent protein loss, sample solubilization process typically involves the use of chaotropes (e.g. urea and/or thiourea), detergents (e.g. 3-[(3-Cholamidopropyl)-dimethyl-ammonio]-1-propane sulfonate (CHAPS) or Triton X-100), reducing agents (dithiothreitol/dithioerythritol (DTT/DTE) or tributylphosphine (TBP)) and protease inhibitors in a sample buffer. Their proper use, together with the optimized cell disruption method, dissolution and concentration techniques determines effectiveness of solubilization. Chaotropes disrupt hydrogen bonds and hydrophilic interactions enabling proteins to unfold with all ionizable groups exposed to solution. Detergents and amphipathic molecules disrupt hydrophobic interactions, thus enabling protein extraction and solubilization. With respect to the ionic character of the hydrophilic group, they are classified into several groups: ionic (e.g. anionic sodium dodecyl sulfate (SDS)), non-ionic (uncharged, e.g. octyl glucoside, dodecyl maltoside and Triton X-100) or zwitterionic (having both positively and negatively charged groups with a net charge of zero, e.g. CHAPS, 3-[(3-Cholamidopropyl) dimethylammonio]-2-hydroxy-1-propanesulfonate (CHAPSO), tetradecanoylamidopropyl-dimethylammoniobutanesulfonate (ASB-14)). Reductants disrupt disulfide bonds between cysteine residues and thus promote unfolding of proteins. Typically, sulfhydryl reducing agents such as dithothreitol (DTT), dithioerythritol (DTE) are applied in the sample preparation protocol. To minimize uncontrolled enzymatic proteolysis by proteases present in samples, protein degradation can be minimized by quick and small scale tissue extraction, boiling the sample in SDS buffer with the high-pH Tris-base, or, on the contrary, lowering the pH and performing ice-cold precipitation in, e.g. 20% trichloroacetic acid. Alternatively, denaturation by boiling in water, focused microwave irradiation, and the use of organic solvents can be applied to inhibit proteases activity. Addition of protease inhibitors can be used to prevent uncontrolled enzymatic protein degradation in a sample. Addition of specific protease inhibitors (e.g. phenylmethylsulfonyl fluoride (PMSF), aminoethyl benzylsulfonyl fluoride (AEBSF), ethylene diamine tetraacetic acid (EDTA), pepstatin, benzamidine, leupeptin, aprotinin) or cocktails with a broader activity spectrum can be used.
In a genetic protein variation analysis, methods of homogenization and/or solubilization techniques for a particular sample type are identifiable by persons skilled in the art. Exemplary methods of homogenization comprise mechanical, ultrasonic, pressure, freeze-thaw, and osmotic/detergent lysis approaches as described herein. Exemplary method of solubilization comprise methods described herein that use reagents comprising one or more chaotropes, detergents, reducing agents and/or protease inhibitors in a sample buffer, as well as other materials and methods identifiable by skilled persons upon reading the present disclosure.
For example, exemplary methods to perform preparing a hair sample to obtain a processed hair sample comprising solubilized proteins to be used in a proteomic analysis comprise milling, denaturation, reduction, and alkylation. Some tissue types such as teeth and bones require additional steps to demineralize the sample material prior to homogenization and solubilization of proteins. There are several ways to extract peptide information from tissues such as teeth and bones, including using a hand-drill, crushing the sample material under liquid nitrogen and demineralization with EDTA or 1.2 M hydrochloric acid, and other methods identifiable by skilled persons.
Genetic protein variation analysis typically further comprises fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample.
In a genetic protein variation analysis, fractionating the processed sample typically comprises removing buffers, salts, and detergent from the processed sample. The pH and ionic strength of sample solutions considerably influence protein solubility. Therefore, buffers, salts and detergents are included in sample solutions and often tend to interfere with further protein separation steps, inhibit the digestion process, interfere with the mass spectrometry analysis, or complicate data analysis significantly, and thus need to be removed. Salts removal can be accomplished using methods such as dialysis (e.g. using spin columns), ultrafiltration, gel filtration, precipitation with TCA or organic solvents, and solid-phase extraction, some of which are used in commercially available clean-up kits identifiable by those skilled in the art. Typical detergent removal methods include dialysis, gel filtration chromatography, hydrophobic adsorption chromatography and protein precipitation. Detergents such as SDS can be removed with nanoscale hydrophilic phase chromatography or acetone precipitation. Commercially available kits, e.g., detergent precipitation reagents or gels effective for binding and removal milligram quantities of various detergents from protein solutions can be used (e.g. Extracti-Gel D Detergent Removing Gel, ReadyPrep 2-D Cleanup Kit, and the SDS-Out SDS Precipitation Reagent and Kit, Pierce). Hydrophobic adsorption employing the use of insoluble resin (e.g. CALBIOSORB, Calbiochem) can also be used to remove excess detergent.
In a genetic protein variation analysis, fractionating the processed sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample can further comprise removing abundant proteins from the processed sample. Protein concentration in biological samples can vary more than 10 orders of magnitude and thus proteomic analyses and detection of less abundant proteins can be hampered by those molecules present at higher concentration. In some cases, removal of abundant proteins can be performed to increase detection of other molecules present at low concentrations. Various techniques can be used for the removal of high-abundant proteins, such as those based on affinity chromatography employing dye-ligands, their derivatives, mimetic ligands, proteins A and G, and antibodies (immunoaffinity depletion), and specific kits (e.g., Proteome Purify Immunodepletion Kit) can be utilized. Numerous proteins are complexed with lipids, and this interaction reduces their solubility. Moreover, by forming complexes with detergents, lipids reduce protein enrichment/separation efficacy. The use of centrifugal filter devices and a sample buffer including CHAPS allows for efficient lipid and salt removal. In order to exclude polysaccharides from the sample, precipitation in TCA, acetone, ammonium sulfate or phenol/ammonium acetate, followed by centrifugation can be performed. In order to remove DNA and RNA, methods such as digestion with protease-free DNase and RNase, or alternatively, protein precipitation from the solution are typically performed.
In a genetic protein variation analysis, fractionating the processed sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample can comprise protein enrichment processes. Various protein enrichment methods can be used to reduce the complexity of the sample by its pre-fractionation, or to enrich it with proteins of interest. Pre-fractionation is performed to isolate a sample into distinguishable fractions containing restricted numbers of molecules. The sample can be fractionated using a variety of approaches including precipitation, centrifugation, liquid chromatography and electrophoresis-based methods, filtration, and velocity or equilibrium sedimentation, among others identifiable by skilled persons.
Fractionating the processed sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample can also comprise removing contaminants. Samples injected onto chromatographic columns cannot contain insoluble particles or dispersed molecules that may cause column clogging and malfunction. Such contaminants are typically removed by centrifugation and/or sample filtration using spin-filters (e.g., 45 μm pores). In addition, samples should not contain buffers affecting LC separation, e.g. samples injected onto column should not be dissolved in buffer with higher eluting strength than of mobile phase. High concentration of detergents should be avoided when using reverse phase separation whereas samples injected on the ion-exchange column should not contain high contraction of background salts and other ionic contaminants that might disturb ionic equilibrium. Volatile buffers such as ammonium acetate or ammonium bicarbonate, are typically used in this case.
In a genetic protein variation analysis, fractionating the processed sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample can comprise any materials and methods or combination of materials and methods for removal of contaminants such as salts, buffers and detergents from the sample, and methods of sample concentration, enrichment, fractionation, filtration, and other methods identifiable by skilled persons upon reading the present disclosure, as described herein or otherwise known in the art can be used to perform fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample.
Genetic protein variation analysis further comprises digesting the solubilized proteins from the sample to obtain digested solubilized proteins from the sample.
In a genetic protein variation analysis, digesting the solubilized proteins from the sample to obtain digested solubilized proteins from the sample can be performed non-enzymatically, e.g., with low pH or high temperatures, as well as enzymatically, e.g., by intra-molecular digestion or with a site specific proteolytic enzyme. In many methods, the digesting is performed with a site specific proteolytic enzyme.
In a genetic protein variation analysis, digesting the solubilized proteins from the sample with a site specific proteolytic enzyme to obtain digested solubilized proteins from the sample can be performed by any method identifiable to a skilled person. As understood by those skilled in the art, the terms “proteolytic enzyme”, “protease”, “peptidase”, and “proteinase” refers to any enzyme that performs proteolysis, wherein the term “proteolysis” as used herein refers to protein catabolism by hydrolysis of peptide bonds.
As understood by those skilled in the art, proteases can be classified into seven broad groups, comprising serine proteases, cysteine proteases, threonine proteases, aspartic proteases, glutamic proteases, metalloproteases, and asparagine peptide lyases.
As understood by those skilled in the art, proteolytic catalysis is achieved by one of two mechanisms, wherein aspartic, glutamic and metallo-proteases activate a water molecule which performs a nucleophilic attack on the peptide bond to hydrolyze it. In contrast, serine, threonine and cysteine proteases use a nucleophilic residue (usually in a catalytic triad). That residue performs a nucleophilic attack to covalently link the protease to the substrate protein, releasing the first half of the product. This covalent acyl-enzyme intermediate is then hydrolyzed by activated water to complete catalysis by releasing the second half of the product and regenerating the free enzyme.
The terms “site specific proteolytic enzyme”, “site specific protease”, “site specific peptidase”, and “site specific proteinase” refer to enzymes that perform proteolysis by cleavage of a protein substrate having a specific sequence. As understood by those skilled in the art, proteolysis can be highly promiscuous such that a wide range of protein substrates are hydrolyzed. This is the case for digestive enzymes such as trypsin which have to be able to cleave the array of proteins ingested into smaller peptide fragments. Promiscuous proteases typically bind to a single amino acid on the substrate and so only have specificity for that residue. For example, trypsin is specific for the sequences . . . KV\ . . . or . . . RV\. . . (‘\’=cleavage site). Conversely some proteases are highly specific and only cleave substrates with a certain sequence. Blood clotting (such as thrombin) and viral polyprotein processing (such as TEV protease) requires this level of specificity in order to achieve precise cleavage events. This is achieved by proteases having a long binding cleft or tunnel with several pockets along it which bind the specified residues. For example, TEV protease is specific for the sequence (SEQ ID No. 86) . . . ENLYFQ\S . . . (‘\’=cleavage site).
Materials and methods for digestion of proteins using various proteases are identifiable by those skilled in the art and described herein.
Genetic protein variation analysis also comprises fractionating the digested solubilized proteins to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample.
Methods to perform fractionating the digested solubilized proteins to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample comprise chromatographic methods. The term “chromatography” as used herein refers to a technique for the separation of a mixture. More specifically, the term “chromatography” is a physical method of separation that distributes components to separate between two phases, one stationary (stationary phase), the other (the mobile phase) moving in a definite direction.
In chromatography, a mixture is dissolved in a fluid called the mobile phase, which carries it through a structure holding another material called the stationary phase. The various constituents of the mixture travel at different speeds, causing them to separate. The separation is based on differential partitioning between the mobile and stationary phases. Subtle differences in a compound's partition coefficient result in differential retention on the stationary phase and thus affect the separation. Chromatography can be preparative or analytical. The purpose of preparative chromatography is to separate the components of a mixture for later use, and is thus a form of purification. Analytical chromatography is done normally with smaller amounts of material and is for establishing the presence or measuring the relative proportions of analytes in a mixture. The two are not mutually exclusive.
As understood by those skilled in the art, chromatography is based on the concept of partition coefficient, wherein any solute partitions between two immiscible solvents. The term “partition coefficient” as defined herein refer to the ratio of concentrations of a compound in a mixture of two immiscible phases at equilibrium, and represents a measure of the difference in solubility of the compound in these two phases. It is also referred to as “distribution coefficient”. When one solvent is made immobile (e.g., by adsorption on a solid support matrix) and another solvent is mobile it results in most common applications of chromatography. As understood by those skilled in the art, if the matrix support, or stationary phase, is polar (e.g. paper, silica etc.) it is referred to as “forward phase” or “normal phase” chromatography, and if it is non-polar (C-18) it is referred to as “reverse phase”.
Chromatography techniques can be categorized according to chromatographic bed shape, wherein “column chromatography” refers to a separation technique in which the stationary bed is within a tube, and “planar chromatography”, which refers to a separation technique in which the stationary phase is present as or on a plane, such as paper chromatography or thin layer chromatography. Accordingly, in some embodiments, any method using column chromatography or planar chromatography can be used to perform fractionating the digested solubilized proteins.
Chromatography techniques can also be categorized according to physical state of mobile phase. The term “gas chromatography” (GC), also sometimes known as “gas-liquid chromatography” (GLC), refers to a separation technique in which the mobile phase is a gas. The term “liquid chromatography” (LC) refers to a separation technique in which the mobile phase is a liquid. In particular, liquid chromatography that generally utilizes very small packing particles and a relatively high pressure is referred to as high performance liquid chromatography (HPLC). In HPLC the sample is forced by a liquid at high pressure (the mobile phase) through a column that is packed with a stationary phase composed of irregularly or spherically shaped particles, a porous monolithic layer, or a porous membrane. HPLC can be divided into two different sub-classes based on the polarity of the mobile and stationary phases. Methods in which the stationary phase is more polar than the mobile phase (e.g., toluene as the mobile phase, silica as the stationary phase) are termed “normal phase” or “forward phase” liquid chromatography, whereas the opposite (e.g., water-methanol mixture as the mobile phase and C18 (octadecylsilyl) as the stationary phase) is termed “reversed phase” liquid chromatography (RPLC).
Accordingly, gas chromatography or liquid chromatography can be used to perform fractionating the digested solubilized proteins in genetic protein variation analysis as will be understood by a skilled person.
Chromatography techniques can also be categorized according to separation mechanism. The term “ion exchange chromatography” refers to a technique that uses an ion exchange mechanism to separate analytes based on their respective charges. The term “size-exclusion chromatography” (SEC) also known as “gel permeation chromatography” (GPC) or “gel filtration chromatography” refers to a technique that separates molecules according to their size, or more accurately according to their hydrodynamic diameter or hydrodynamic volume. The term “expanded bed chromatographic adsorption” (EBA) refers to a biochemical separation process using a column that comprises a pressure equalization liquid distributor having a self-cleaning function below a porous blocking sieve plate at the bottom of the expanded bed, an upper part nozzle assembly having a backflush cleaning function at the top of the expanded bed, and a better distribution of the feedstock liquor added into the expanded bed ensuring that the fluid passed through the expanded bed layer displays a state of piston flow.
Accordingly, ion exchange chromatography, size-exclusion chromatography, or expanded bed chromatographic adsorption can be used to perform fractionating the digested solubilized proteins in genetic variation protein analysis of the instant disclosure. Other chromatography techniques can be used such as hydrophobic interaction chromatography, two-dimensional chromatography, simulated moving-bed chromatography, pyrolysis gas chromatography, fast protein liquid chromatography, countercurrent chromatography, periodic counter-current chromatography, aqueous normal-phase chromatography, or chiral chromatography, among others identifiable by persons skilled in the art can be used to perform fractionating the digested solubilized proteins.
In general, techniques identifiable by skilled persons that can be used to perform fractionating proteins or digested proteins of a biological sample comprise methods based on purification of peptides according to their isoelectric points (e.g., by running them through a pH graded gel or an ion exchange column), separation according to their size or molecular weight (e.g., via size exclusion chromatography or by SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) analysis), or separation by polarity/hydrophobicity (e.g., via high performance liquid chromatography or reversed-phase chromatography).
Additional methods for fractionating proteins or digested proteins of a biological sample that can be used in some embodiments described herein comprise affinity chromatography. The term “affinity chromatography” refers to a separation technique based upon molecular conformation, which frequently utilizes application specific resins. These resins have ligands attached to their surfaces which are specific for the compounds to be separated. For example, immunoaffinity chromatography uses the specific binding of an antibody-antigen to selectively purify the target protein. The procedure involves immobilizing a protein to a solid substrate (e.g. a porous bead or a membrane), which then selectively binds the target, while everything else flows through. The target protein can be eluted by changing the pH or the salinity. The immobilized ligand can be an antibody (such as Immunoglobulin G) or it can be a protein (such as Protein A), among others identifiable by those skilled in the art.
Genetic protein variation analysis also comprises detecting a marker genetic variation of the fractionated digested peptides.
Various techniques can be used to perform detecting a marker genetic variation of the fractionated digested peptides in a genetic variation protein analysis, such as mass spectrometry. Mass Spectrometry (MS) is an analytical technique that ionizes chemical species and sorts the ions based on their mass-to-charge ratio. In simpler terms, a mass spectrum measures the masses within a sample. Mass spectrometry is used in many different fields and is applied to pure samples as well as complex mixtures. A mass spectrum is a plot of the ion signal as a function of the mass-to-charge ratio. These spectra are used to determine the elemental or isotopic signature of a sample, the masses of particles and of molecules, and to elucidate the chemical structures of molecules, such as peptides and other chemical compounds.
The terms “liquid chromatography mass-spectrometry” or “LC-MS” as used herein refer to an analytical chemistry technique that combines the physical separation capabilities of liquid chromatography (LC, or high-performance liquid chromatography, HPLC, or ultra-high-performance liquid chromatography, UHPLC) with the mass analysis capabilities of mass spectrometry (MS). The terms “tandem mass spectrometry”, or “MS/MS” as used herein refers to a mass-spectrometry technique that involves more than one stage of mass spectrometry analysis, with a step form of fragmentation occurring in between the stages. In a tandem mass spectrometer, ions are formed in the ion source and separated by mass-to-charge ratio in the first stage of mass spectrometry (MS1). Ions of a particular mass-to-charge ratio (precursor ions) are selected and fragment ions (product ions) are created by collision-induced dissociation, ion-molecule reaction, photodissociation, or other processes. The resulting ions are then separated and detected in a second stage of mass spectrometry (MS2). Thus, the terms “tandem liquid chromatography mass-spectrometry” and “LC-MS/MS” as used herein refer to a technique that couples liquid chromatography and tandem mass-spectrometry.
Typically, for LC-MS/MS proteomic analysis, the stationary LC phase is a C18 reverse-phase column. The reverse-phase column uses the hydrophobicity of peptides for separation, utilizing a gradient from low to high organic-phase solvent. Acidified methanol and acetonitrile are commonly used as organic-phase, also known as “B” or “strong”, solvents because of their miscibility with aqueous solutions. Acidified water is most often the “weak” solvent, also known as “A”. Both buffers are acidified with the same acid, generally with formic acid or trifluoroacetic acid (TFA) at 0.1% or 0.01%, respectively.
Examples of tandem mass-spectrometry instruments used for LC-MS/MS proteomics analysis comprise sector instruments, time-of-flight instruments, quadrupole mass analyzers, ion traps, and orbitraps, among others identifiable by those skilled in the art.
In proteomic analysis using LC-MS/MS, following purification of proteins from tissue samples, the purified proteins are enzymatically digested by a protease, typically, trypsin, which cleaves the protein into smaller detectable peptides, with molecular weights of about 400 to 4000. The peptides are then resolved using very low flow rate liquid chromatography, such as reversed phase liquid chromatography, and are then ionized and vaporized using methods such as fast atom bombardment (FAB), chemical ionization (CI), atmospheric-pressure chemical ionization (APCI), electrospray ionization (ESI), and matrix-assisted laser desorption/ionization (MALDI). The charged peptide is then funneled using electric fields into the mass spectrometer where its mass is measured (MS1). The instrument then fragments individual peptide backbones using either collision-induced or electron transfer dissociation and the resulting fragment masses are also measured (MS2). Both of these fragmentation methods break the peptide backbone at regular points. This allows the amino acid sequence to be determined. The information from tandem liquid chromatography mass-spectrometry, therefore, has three dimensions: time of retention on reversed phase, peptide mass (MS1) and individual peptide fragmentation masses (MS2). Mass spectrometry has matured to the point where over 10,000 peptide fragmentations can be obtained per run. The mass accuracy of peptide and fragmentation masses is now 1 ppm in both MS and MS2, removing ambiguity from the analysis.
The fragmentation data can be resolved using the data within the sample, based on the intrinsic properties of the data related to the peptide fragmentation, to provide de novo sequence information through a de novo peptide identification algorithm for LC-MS/MS which infers peptide sequences without knowledge of genomic data. Examples of de novo sequencing algorithms comprise Cyclobranch, DeNovoX, DeNos, Lutefisk, Novor, PEAKS, and Supernovo, among others identifiable by those skilled in the art.
The fragmentation data can also be resolved through comparison with predicted sequences derived from genomic and protein databases such as GenBank and UniProt. This method provides a statistical measure of probability that any fragmentation dataset is the predicted amino acid sequence through a database search peptide identification algorithm for LC-MS/MS which takes place against a database containing all amino acid sequences assumed to be present in the analyzed sample. Examples of database search algorithms comprise Andromeda, Byonic, Comet, Tide, Greylag, InsPecT, Mascot, MassMatrix, MassWiz, MS Amanda, MS-GF+, MyriMatch, OMSSA, PEAKS DB, pFind, Phenyx, ProblD, ProteinPilot Software, Protein Prospector, RAId, SEQUEST, SIMS, Sim Tandem, SQID, and X!Tandem, among others identifiable by those skilled in the art.
The allelic frequencies associated with each nucleotide and amino acid polymorphism within the fragmentation data are a product of the reference populations used in the single nucleotide polymorphism (SNP) data bases. The term “allelic frequency” as defined herein refers to the relative frequency of an allele (variant of a gene) at a particular locus in a population, expressed as a fraction or percentage. Examples of databases of human SNPs and SAPs comprise dbSNP, which is a SNP database from the National Center for Biotechnology Information (NCBI), as well as the 1000 Genomes Project, UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, Ensembl, COSMIC, and dbSAP [2], among others identifiable by those skilled in the art.
Accordingly, in a genetic protein variation analysis, any method of mass-spectrometry identifiable by skilled persons can be used to perform detecting a marker genetic variation of the fractionated digested peptides, such as techniques that use time-of-flight instruments, quadrupole mass analyzers, ion traps, and orbitraps, among others identifiable by those skilled in the art, that use any ionization and vaporization methods such as fast atom bombardment (FAB), chemical ionization (CI), atmospheric-pressure chemical ionization (APCI), electrospray ionization (ESI), and matrix-assisted laser desorption/ionization (MALDI), among others identifiable by skilled persons. Additionally, any method of peptide fragmentation known in the art, such as collision-induced or electron transfer dissociation can be used to detect a marker genetic variation of the fractionated digested peptides, and any method of peptide fragmentation data deconvolution, such as de novo sequencing, or comparison of peptide fragmentation data with predicted sequences derived from genomic and protein databases such as GenBank and UniProt can be used to perform detecting a marker genetic variation of the fractionated digested peptides.
Additionally, in a genetic variation protein analysis any peptide identification algorithms that can be used in database searches, such as Andromeda, Byonic, Comet, Tide, Greylag, InsPecT, Mascot, MassMatrix, MassWiz, MS Amanda, MS-GF+, MyriMatch, OMSSA, PEAKS DB, pFind, Phenyx, ProblD, ProteinPilot Software, Protein Prospector, RAId, SEQUEST, SIMS, Sim Tandem, SQID, and X!Tandem, among others identifiable by those skilled in the art, or in de novo searches, such as Cyclobranch, DeNovoX, DeNos, Lutefisk, Novor, PEAKS, and Supernovo, among others identifiable by those skilled in the art, can be used to perform detecting a marker genetic variation of the fractionated digested peptides. Additionally, in some embodiments, any databases of human SNPs and SAPs such as dbSNP, 1000 Genomes Project, UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, Ensembl, COSMIC, and dbSAP [2], among others identifiable by those skilled in the art can be used to perform detecting a marker genetic variation of the fractionated digested peptides.
An exemplary genetic protein variation analysis including specific protocols for performance of the related steps is shown in the paper Parker et al 2016 [3] incorporated herein by reference in its entirety and supplementary information of Parker et al. (2016) incorporated herein by reference in its entirety.
In a genetic protein variation analysis performed with methods and systems in accordance with the present disclosure, preparing the sample and/or detecting a genetic variation can be performed by any one of the methods and/or using anyone of the systems and databases according to any one of the first aspect to the seventh aspect of the present disclosure.
Accordingly, in some embodiments, preparing a biological sample to obtain a processed biological sample comprising solubilized proteins to be used in proteomic analysis can be performed by the method to prepare a biological sample for proteomic analysis according to the first aspect of the present disclosure. The method comprises applying to the biological sample an energy field to obtain a processed biological sample comprising solubilized proteins to be used in the proteomic analysis.
In particular, the energy field applied in methods for preparing a biological sample according to the first aspect of the disclosure comprises electromagnetic fields applied with parameters selected to result in protein solubilization while reducing breakage of the intramolecular peptidic bonds of the proteins in the sample.
In a method for preparing a biological sample according to the first aspect of the disclosure, typically, energy is applied at the initial solubilization stage of sample processing. Sample solubilization process typically involves the use of chaotropes (e.g. urea and/or thiourea), detergents (e.g. 3-[(3-Cholamidopropyl)-dimethyl-ammonio]-1-propane sulfonate (CHAPS) or Triton X-100), reducing agents (dithiothreitol/dithioerythritol (DTT/DTE) or tributylphosphine (TBP)) and protease inhibitors in a sample buffer.
In some embodiments the sample buffer can comprise reducing agents such as DTT, Dodecyltrimethylammonium bromide (DTBA), Betamercatptoethanol (BME), tris(2-carboxyethyl)phosphine (TCEP), and DTE. In particular, the applying can be performed with detergent in concentration ranging from 0.001 M to 10 M; 0.05 M to 0.2 M more preferably; and most preferably 0.1 M. In preferred embodiments the detergent comprises DTT.
In some embodiments the sample buffer can comprise detergents such as SDD, SDS, CHAPS, a Triton X-100, Lithium Dodecyl Sulfate (LDS)Tergitol-type NP-40 (NP-40) which is nonyl phenoxypolyethoxylethanol, commercially available with CAS 9016-45-9. The detergent concentrations depend on temperature and ultrasonic treatment time as will be understood by a skilled person. Specifically, decreasing SDD concentration by 1% drastically increases time for solubilization (60 minutes to 24 hours), whereas decreasing ultrasonic treatment incubation temperature also increases time (every 5 degrees C. decreased requires two hours or more ultrasonic treatment time). Increasing detergent concentration past 2% does not result in significant decreased ultrasonic incubation time. In preferred embodiments, the detergent comprises SDD.
A skilled person will understand that the composition of the sample buffer can vary depending on the time and condition of applying to the biological sample an energy field and can be adjusted by a skilled person to optimize protein solubilization upon reading of the present disclosure.
The term “solubilize”, used herein with reference to solubilized proteins, refers to a transfer of proteins comprised within the biological sample to a solvent such as an aqueous solvent by disrupting the cells of the biological sample. Disruption of the cells of the biological sample can be performed by applying a force to the cell to alter the cell membrane continuity and integrity for a time and under condition to result in the lysis of the cell.
In some preferred embodiments, applying to the biological sample an energy field can be performed by sonication. The sonication process can be carried out using an ultrasonic processor operating at the ultrasound frequency of about 20-80 kHz and applying the sample the ultrasound for about 30-120 minutes. In some embodiments, the sonication process can be performed using an ultrasonic processor set to 1 to 100 kHz; preferably 5 to 50 kHz and more preferably 37 kHz.
In embodiments, wherein applying energy is performed by sonication, the power setting of the device can range from 1 to 100%; more preferably 50 to 100%; most preferably 100%.
In embodiments, wherein applying energy is performed by sonication, the applying can be performed by providing the energy with an ultrasonic mode selected from sweep, degas, and pulse. In preferred embodiments, applying energy can be performed by providing the energy with ultrasonic mode sweep.
In the preferred embodiments, wherein applying energy is performed by sonication, which includes any method for imparting acoustic energy to bring about cavitation of the sample including sonication baths, sonication probes/sonicators, or sonication flow-through systems are applicable. The biological sample can be subjected to sonication by placing a sample containing tube with a sonication bath or samples can be directly sonicated using a probe or by placing in a flow-through system directly.
As a person skilled in the art will understand, other mechanical cell disruption methods capable of creating high stress via pressure or abrasion with rapid agitation can also be used to mechanically disrupt the biological sample. Exemplary mechanical cell disruption methods include bead milling, cryomilling, microfluidizers, high pressure homogenizer, nitrogen cavitation, and others identifiable to a person skilled in the art.
In some other embodiments, applying to the biological sample an energy field through the application of microwaves can be performed by microwaving the biological sample using 500-1,200 Watt microwaves, wherein samples can be treated from 10 seconds to several minutes [4-7].
In some embodiments, applying energy can be performed with an incubation time ranging from 5 to 1,440 minutes; more preferably 20 to 90 minutes; most preferably 60 minutes.
In some embodiments, applying energy can be performed with temperature settings from 15 to 100° C.; more preferably 30 to 90° C.; most preferably 70° C.
The time and temperature of applying to the biological sample an energy field in accordance with the first aspect of the disclosure depend on the composition of the sample buffer as will be understood by a skilled person. For example, in embodiments where the applying is performed by sonication, presence and concentration of a detergent in the sample buffer depend on temperature and ultrasonic treatment time as will be understood by a skilled person. In particular, decreasing concentration of a detergent such as SDD, by 1% drastically increases time for solubilization (60 minutes to 24 hours). Whereas decreasing ultrasonic treatment incubation temperature also increases time (every 5 degrees C. decreased requires two hours or more ultrasonic treatment time). Increasing concentration of a detergent such as SDD in the sample buffer past 2% does not result in significant decreased ultrasonic incubation time. Additional adjustments and variations of the sample buffer compositions, time and temperature of applying to the biological sample an energy field in accordance with the first aspect of the disclosure are identifiable by a skilled person upon reading of the present disclosure.
In some embodiments the biological sample is a tissue sample. The term “tissue” as used herein refers to a cellular organizational level intermediate between cells and a complete organ or organism. A tissue is typically an ensemble of similar cells from the same origin that together carry out a specific function. Organs and organisms are then formed by the functional grouping together of multiple tissues. As used herein, the term tissue comprises ensembles of cells such as hair, skin, bone, teeth, blood and other body fluids, muscle, nerves, and other cellular material originating from one or more organisms, and also comprises artifacts originating from tissues such as fingerprints. In particular, as used herein, organisms from which tissues originate comprise mammals and in particular humans.
In some embodiments, the biological sample comprises hair. Hair is commonly found as trace evidence in crimes scene forensic investigations. Persistence of hair in the environment demonstrates the unique chemical stability that makes it an ideal biological material for analysis by forensic practices [8]. Largely, forensic analysis of hair evidence is performed by microscopic analysis of morphological characteristics and more recently mitochondrial DNA (mtDNA) sequencing. Both accepted techniques have intrinsic flaws (subjectivity and low discrimination, respectively) highlighting the essential need for development of new strategies to obtain information from hair evidence in the forensic communities [9, 10].
Specifically, proteomic analysis of hair has been shown to provide identification markers in the form of genetically variant peptides (GVPs) in human samples [3]. GVP detection targets mutations in protein amino acid sequences as a direct reflection of single-nucleotide polymorphisms (SNPs) found in DNA. The utility of this technique in forensic practice hinges on its ability to apply to practical sample sizes, for example a single hair.
In some embodiments, the biological sample can be a single hair. In some embodiments, the single-hair sample is about 0.1 to 20 cm in length, such as 2.5 cm, and 2-1630 μg in weight, such as 85 μg in some examples (see e.g. Example 2). Providing a single-hair sample can further comprise cutting the single-hair sample into pieces.
In some embodiments, the method of preparing the biological sample comprises providing a single-hair sample from an individual, dissolving the single-hair sample in a cell lysis solution, subjecting the cell lysis solution containing the single-hair sample to ultrasonication or thermolysis to provide a solubilized single-hair sample, and digesting the solubilized single-hair sample to obtain peptide samples. The obtained peptide samples are then subjected to proteomics analysis.
Exemplary methods to perform a proteomic tissue sample preparation using methods according to the first aspect and single hairs are described in Examples 2-4.
In some embodiments detecting a genetic variation can be performed with a method and system to provide a marker genetic protein variation for a biological organism and a marker genetic protein variation obtainable thereby according to the second aspect of the present disclosure. In these method and system, the provided marker genetic protein variation validated to be detectable and in particular proteomically detectable in a biological sample of an individual of the biological organism.
The method comprises: providing a marker exome sequence of the biological organism, the marker exome sequence comprising a marker genetic variation for the biological organism.
The method further comprises detecting peptide sequences in the biological sample of the individual of the biological organism by performing proteomic analysis of said biological sample to provide proteomically detected peptide sequences.
The method also comprises providing the marker genetic protein variation of the biological organism detectable in the sample of the biological organism by comparing the provided marker exome sequence with the proteomically detected peptide sequences to provide a marker genetic protein variation validated for the biological sample of an individual of the biological organism.
The system comprises exome sequence databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to provide a marker genetic protein variation validated for a biological sample herein described.
The term “exome” as used in the instant disclosure indicates the part of the genome of a biological organism composed of exons, the sequences which, when transcribed, remain within the mature RNA after introns are removed by RNA splicing and contribute to the final protein product encoded by that gene.
In some embodiments, providing at least one marker exome sequence from a genome each comprising a genetic variation of the genome comprises detecting exome sequences of the genome by sequencing exomes of the genome and detecting at least one marker exome sequence each comprising a genetic variation of the genome by comparing the detected exome sequences with a database of exome sequences of the biological organism.
The genome being sequenced for detecting exome sequences can be of the same individual of the biological organism where the biological sample is collected from for proteomic analysis, or a close relative of the individual who has a coefficient of relationship (r) of at least 0.5 with the individual. Herein, the coefficient of relationship is a measure of the degree of consanguinity or biological relationship between two individuals. For example, a parent and child pair have a value of r=0.5 and full siblings have a value of r=0.5.
Sequencing exomes of a genome can comprise collecting a sample from the individual and performing exome sequencing of the sample. In some instances, the sample is a blood sample or buccal sample. The type of sample collected from the individual for the exome sequencing can be the same or different from the type of sample collected for the proteomics analysis. For example, in some instances, the sample collected for the exome sequencing can be a blood sample while the biological sample collected for proteomic analysis can be a hair sample.
The exome sequencing can be performed by whole exome sequencing (WES or WXS). Whole exome sequencing typically comprises selecting the subset of DNA containing exons from the whole genome. Both array-based and in-solution capture techniques can be used to selectively capture the subset of DNA containing exons. The subset of DNA containing exons can then be sequenced using high-throughput DNA sequencing technology.
High-throughput DNA sequencing also referred to as next-generation sequencing (NGS) refers to a number of different modern nucleic acid sequencing technologies including Illumia™ sequencing, Roche 454™ sequencing, Ion torrent: Protein/PGM™ sequencing and SOLiD™ sequencing. Next-generation sequencing (NGS) generally refers to non-Sanger-based high-throughput DNA sequencing technologies. The NGS technologies can be based on immobilization of the nucleotide samples onto a solid support, cyclic sequencing reactions using automated fluidics devices and detection of molecular events by imaging. Cyclic array platforms achieve low costs by simultaneously decoding a two-dimensional array bearing millions or billions of distinct sequencing features, each containing one species of DNA physically immobilized on an array. In each cycle, an enzymatic process is applied to interrogate the identity of a single base position for all features in parallel. The enzymatic process is coupled to either the production of light or the incorporation of a fluorescent group. At the end of each cycle, data are acquired by imaging of the array. Subsequent cycles are typically performed interrogating different base position within the sequence. Detailed information about various next-generation sequencing approaches can be found in related literation and documents and will be understood by a person skilled in the art.
In some embodiments of the present disclosure exome sequencing can be performed by RNA exome sequencing e.g. with (e.g., with Illumina RNA Exome Capture Sequencing) as will be understood by a skilled person.
In particular, in certain tissue types (either coextracted in sample; e.g. skin or bone or from separate buccal swab) exome sequencing can be performed from RNA in the sample. In particular, in some embodiments the exome sequencing can be performed on the protein fraction of the sample wherein GVPs can be fractionated with their mRNA counterparts. In some embodiments exome sequencing can be performed following RNA extraction of samples (cell lysis, solubilization, purification) using a portion of a sample or a buccal swab and RNA-sequencing performed with technologies such as RNA-seq, RNA capture exome sequencing, and addition technologies identifiable by a skilled person RNA sequences can be translated into DNA subsequently and provide the presence/absence of missense SNPs that correspond to GVPs.
Detecting at least one marker exome sequence can be performed by comparing the detected exome sequences of the individual with a database of exome sequences of the biological organism. In general, the exome sequences generated from a sequencing procedure can be aligned to the sequence entries contained in the database of exome sequences of the biological organism using alignment/assembly tools identifiable by a person skilled in the art. Exemplary database of exome sequences of the biological organism includes the NHLBI Exome Sequencing Project (ESP) database.
In particular, the detected marker exome sequences are a set of exome sequences, each comprising one or more single nucleotide polymorphisms. Therefore, comparing the detected exome sequences of the individual with a database of exome sequences of the biological organism can identify one or more non-synonymous single nucleotide polymorphisms in the exome sequence of the individual.
The method further comprises detecting peptide sequences in the biological sample by performing proteomic analysis of the biological sample. The term “proteomic analysis” refers to the systematic identification and quantification of the complete set of proteins encoded in a biological system such as a cell, tissue, organ, biological fluid or organism. Proteomic analysis can be performed using mass spectrometry (MS) or liquid chromatography mass-spectrometry (LC-MS) as will be understood by a person skilled in the art. Performing proteomic analysis of the biological sample comprises fragmenting proteins in the biological sample into peptides, subjecting the fragmented sample to MS or LC-MS to obtain proteomic datasets, and analyzing the proteomic datasets to identify the peptide sequences of the biological sample. Analyzing the proteomic datasets can be performed using computational algorithm such as MASCOT, GPM or Petunia as will be understood by a person skilled in the art.
In certain embodiments, the proteomics analysis performed on the biological sample is shotgun proteomics analysis. Shotgun proteomic analysis refers to the use of bottom-up proteomics techniques in identifying proteins in complex mixtures using a combination of high performance liquid chromatography combined with mass spectrometry, and is an alternative to targeted proteomics and data-independent acquisition proteomics.
The method according to the second aspect of the instant disclosure, further comprises providing the marker genetic protein variation of the biological organism in the biological sample by comparing the detected marker exome sequence with the detected peptide sequences to provide a marker genetic protein variation validated for the biological organism.
The comparison can be performed by comparing each detected marker exome sequence comprising a generic variation of the genome such as SNPs with the detected peptide sequences stored in a database. The comparison can be carried out by any sequence comparison programs that compare a DNA sequence to a peptide sequence database, such as BLASTX. Such sequence comparison programs typically involve translating the DNA sequence in three frames and aligning the translated DNA sequence to each sequence in the peptide database, allowing gaps and frameshifts as will be understood by a person skilled in the art.
The detected marker exome sequence having a corresponding entry in the database containing the detected peptide sequences is then indicated as a marker genetic protein variation validated for the biological organism. The marker genetic protein variation validated for the biological organism can be further stored in a database which contains, for each data entry, a detected marker exome sequence comprising a genetic variation and a peptide sequence corresponding to the detected marker exome sequence. The data entry can further comprise an allele frequency for the genetic variation in the detected marker exome sequence.
In some embodiments, the biological organism is Homo sapiens. In some embodiments, the biological sample is a hair sample.
Exemplary validated marker exome sequences of Homo Sapiens are indicated in Examples 43 to 45 listing exemplary set of genes validated as being detectable in hair samples (Example 43, Table 8) bone samples (Example 44, Table 9) and skin samples (Example 45, Table 10) of a human being.
Exemplary validated marker genetic protein variations of Homo Sapiens are indicated in Examples 46 and Example 47 listing exemplary set of GVPs validated in hair samples (Example 46, Table 11) and skin samples (Example 47, Table 12) of a human being.
In some embodiments detecting a genetic variation can be performed with a method and system to detect a marker genetic protein variation in a biological sample according to a third aspect of the present disclosure. In the method and system, the marker genetic protein variation are validated to be detectable and in particular proteomically detectable in the biological sample.
The method comprises providing a marker mass spectrum of a marker peptide comprising a marker genetic protein variation corresponding to the genetic protein variation; and performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide.
The method further comprises comparing the mass spectrum of the fractionated digested peptide with a marker mass spectrum of a marker peptide comprising the marker genetic protein variation to detect the genetic protein variation in the biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to detect a marker genetic protein variation in a biological sample herein described. In preferred embodiments, the reagents comprise one or more marker peptides in accordance with the present disclosure.
In the method according to the third aspect, any method of performing mass spectrometry of a fractionated digested peptide of the biological sample as described herein or otherwise identifiable by persons skilled in the art can be used to obtain a mass spectrum of each of the fractionated digested peptides.
As understood by skilled persons, mass-spectrometry of fractionated digested peptides of a biological sample can produce a large number of mass spectra. In embodiments described herein, the term “mass spec dataset” is used to refer to a plurality of mass spectra obtained for a plurality of fractionated digested peptides of a biological sample (e.g., see
As understood by persons skilled in the art, mass spectrometry (MS) is an analytical technique that ionizes chemical species and sorts the ions based on their mass-to-charge ratio. In simpler terms, mass spectrometry measures the masses within a sample. Mass spectrometry is used in many different fields and is applied to pure samples as well as complex mixtures. The term “mass spectrum” as used herein refers to a plot reporting a signal of one or more ions as a function of mass-to-charge ratio of the ions. Accordingly, mass spectra can be used to determine the elemental or isotopic signature of a sample, the masses of particles and of molecules, and to elucidate the chemical structures of molecules, such as peptides and other chemical compounds.
The terms “tandem mass spectrometry”, or “MS/MS” as used herein refer to a mass-spectrometry technique that involves more than one stage of mass spectrometry analysis, with a step of fragmentation occurring in between the stages. In a tandem mass spectrometer, ions are formed in the ion source and separated by mass-to-charge ratio in the first stage of mass spectrometry (MS1). Ions of a particular mass-to-charge ratio (precursor ions) are selected and fragment ions (product ions) are created by collision-induced dissociation, ion-molecule reaction, photodissociation, or other processes. The resulting ions are then separated and detected in a second stage of mass spectrometry (MS2).
Accordingly, a mass spectrum of a peptide is a plot reporting a signal of one or more ions of a peptide as a function of mass-to-charge ratio of the ions. In particular, with reference to LC-MS/MS analysis of peptides (e.g. peptides produced by digesting proteins of a biological sample using a site-specific protease), a mass spectrum of a peptide can refer to a mass spectrum produced in the MS1 stage or the MS2 stage, wherein the mass spectrum produced in the MS1 stage refers to a mass spectrum of a peptide (e.g. a peptide produced by digesting a protein using a site specific protease) before fragmentation of the peptide occurs, and the mass spectrum produced in the MS2 stage refers to a mass spectrum produced after fragmentation of the peptide has occurred.
The term “marker peptide” as used herein refers to a peptide that comprises a genetic protein variation. In some embodiments, a marker peptide is a peptide produced by digesting a protein that comprises a genetic protein variation, wherein the marker peptide is the peptide produced by proteolytic digestion that comprises the genetic protein variation. In some embodiments, the genetic protein variation is encoded by a ‘rare’ non-synonymous single nucleotide polymorphism (nsSNP) having an allelic frequency lower than 0.5% or a ‘private’ nsSNP having an allelic frequency lower than 0.1% in a given population, wherein an allelic frequency is a product of the reference populations used in the single nucleotide polymorphism (SNP) data bases.
Accordingly, the terms “marker mass spectrum of a marker peptide” or “diagnostic LC-MS/MS spectrum” as used herein refer to a mass spectrum of a marker peptide. In some embodiments, the terms “marker mass spectrum of a marker peptide” or “diagnostic LC-MS/MS spectrum” as used herein refer to a mass spectrum of a marker peptide that is produced in the MS1 stage, or a mass spectrum of a marker peptide that is produced in the MS2 stage.
In some embodiments, the amino acid sequence of a marker peptide can be provided by first sequencing an exome of an individual, detecting a genetic variation comprised in a sequence of the exome of the individual, providing the corresponding encoded genetic protein variation by providing a translation of the exome sequence comprising the genetic variation, and providing the amino acid sequence of the peptide produced as a result of digesting the peptide using a site-specific protease (e.g. trypsin) (e.g., see
In some embodiments, the amino acid sequence of a marker peptide for identification of an individual can be provided by sequencing the exomes of individuals related to the individual. In some embodiments, the individuals related to the individual can form a mother-father-child relationship.
Exemplary marker peptides comprising genetic protein variations are indicated in Examples 46 and Example 47 indicating exemplary set of GVPs and related mutated peptides validated in hair (Example 46, Table 11) and skin (Example 47, Table 12) samples. The marker peptides of Table 11 and Table 12 can be used in connection with method performed on biological samples from a human being.
In particular exemplary marker peptides that can be preferably used or comprise in the method and system according to the third aspect, comprise any combination of the peptides having sequence SEQ ID NO: 146 to SEQ ID NO: 748 (Example 46, Table 11) for detection in hair samples of human beings, and any combination of the peptides having sequence SEQ ID NO: 749 to SEQ ID NO: 829 (Example 47, Table 12) for detection in skin samples of human beings.
In some embodiments, a marker mass spectrum of a marker peptide can be provided by synthesizing a marker peptide and analyzing the marker peptide using LC-MS/MS. For example, peptides can be synthesized using biosynthetic methods, such as cell-based methods or cell-free methods known to those skilled in the art. Peptide biosynthesis can be performed by translation of DNA or RNA polynucleotides encoding the peptide. Thus, protein biosynthesis can be performed by providing cell-based or cell-free peptide translation systems with DNA or RNA polynucleotides encoding the peptide. Peptides can also be produced by liquid phase or solid-phase chemical peptide synthetic methods known to those skilled in the art. In other embodiments, a marker mass spectrum of a marker peptide can be provided by generating the mass spectrum in silico based on the predicted fragmentation products of the peptide as would be produced in the MS2 stage.
With regard to the method to detect a genetic protein variation in a biological sample according to the third aspect of the present disclosure, any method of performing mass spectrometry of a fractionated digested peptide of the biological sample as described herein or otherwise identifiable by persons skilled in the art can be used to obtain a mass spectrum of each of the fractionated digested peptides.
As understood by skilled persons, mass-spectrometry of fractionated digested peptides of a biological sample can produce a large number of mass spectra. In embodiments described herein, the term “mass spec dataset” is used to refer to a plurality of mass spectra obtained for a plurality of fractionated digested peptides of a biological sample (e.g., see
In some embodiments, the step of comparing the mass spectrum of the fractionated digested peptides of the biological sample with a marker mass spectrum of a marker peptide as described herein can be performed without reference to a protein variant database.
In particular, in embodiments described herein, a mass spec data set produced from a set of fractionated digested peptides of a biological sample (e.g. an operational sample) can be spectrally searched directly with reference to a marker mass spectrum (e.g. see
In some embodiments, stable isotope labeled peptide standards can be used in the method to detect a genetic protein variation in a biological sample. For example, an internal standard of the marker peptide labeled with multiple stable isotopes (e.g., D replacing H residues in the peptide) can be added to the fractionated digested proteins of the biological sample analyzed by LC-MS/MS, so that the standard co-elutes with the native peptide to assist with identification, wherein the mass of the internal standard is shifted so that it doesn't interfere with the analysis. Stable isotopes of peptides can be obtained commercially (e.g., from Sigma Aldrich).
Accordingly, in some embodiments, a detected identity between the marker mass spectrum of a marker peptide and a mass spectrum of a peptide of the biological sample can be used to confirm the prior presence of an individual at a sample site (e.g., see
In some embodiments, in the case of a detected identity between the marker mass spectrum of a marker peptide and a mass spectrum of a peptide of the biological sample, the spectral matching can be used to confirm the prior presence of an individual at a sample site when the biological sample comprises proteins from a plurality of individuals (e.g., see
In some embodiments detecting a genetic variation can be performed with a database obtainable with methods and systems according to a fourth aspect of the present disclosure. According to the fourth aspect, a method and system to improve a marker genetic protein variation database system for a biological organism, and a database obtainable thereby, are described. In the method, system and database herein described, the marker genetic protein variation database system includes data for at least one biological organism and the improvement is inclusion of one or more marker genetic proteins validated to be detectable and in particular, proteomically detectable in the biological sample from an individual of the at least one biological organism.
In particular the methods and systems of the fourth aspect of the instant disclosure are based on a top-bottom exome-driven approach which begins with obtaining exome data, allowing identification of relevant SNPs, followed by proteomic validation of GVPs.
The method according to the fourth aspect comprises: producing a proteomic dataset from a biological sample from an individual of the at least one biological organism and comparing the proteomic dataset to a protein variant database to produce a set of proteomically detected proteins in the biological sample of the individual.
The method further comprises providing a set of represented genes proteomically detectable in the biological sample of the individual, the represented genes corresponding to the proteomically detected proteins in the biological sample of the individual.
The method also comprises: identifying a marker genetic protein variation validated for the biological sample of the individual, to be included in the marker genetic protein variation database system by providing a proteomically detectable genomic variation in the set of represented genes proteomically detectable in the biological sample of the individual, and providing the marker genetic protein variation validated genetic protein variation by providing a proteomically detectable genetic protein variation corresponding to the proteomically detectable genomic variation in the biological sample of the individual.
In some embodiments the proteomic data set is a mass spectrometry dataset.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to improve a marker genetic protein variation database system for a biological organism herein described.
Herein, “database” refers to an organized collection of information. “Database system” refers to a system that includes at least one computer for the creation and storage of a database in computer memory. The database system can be stand-alone, distributed (networked), cloud-based (i.e. networked in a cloud computing system), or any standard database configuration. The database system can be shared among applications or dedicated to a single application. The database system can be local or remote. The database can be navigational, relational, object model, document model, flat file, associative, array, multidimensional, semantic, or any other logical structure. “Protein variant database” refers to a database of variant proteins or protein isoforms that are members of a set of highly similar proteins that originate from a single gene or gene family and are the result of genetic differences.
Detected proteins from the biological sample are determined by a proteomic analysis of the mass spectra obtained from individual biological samples. That proteomic analysis involves one or more databases which contain the protein sequences and their accession numbers. The proteins identified in the sample are then related though their unique protein accession numbers to the genes that code for them (the represented genes). This permits linking the observed protein with the responsible gene and therefore the associated statistics for that gene (SNPs, frequencies, etc.).
The mass spectrometry dataset can be obtained by taking the biological sample, for example one prepared by as described herein by dissolving, ultrasonication, and digestion, and running it through a mass spectrometer to determine a mass spectrum of the sample. Mass spectrometry can include hard ionization, soft ionization, inductively coupled plasma, photoionization, glow discharge, or other techniques, which can be selected based on the type of sample provided and the data required. For example, tandem liquid chromatography mass spectrometry can be used for prepared hair samples.
The mass spectrometry dataset can be compared, using existing spectrometry data analysis tools, to existing or created libraries of known spectra of known proteins (e.g. RefSeq, UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, dbSNP, Ensembl, COSMIC, or a custom database containing all of the single amino acid polymorphisms above some threshold allelic frequency) to determine the protein content of the biological sample, a.k.a. the proteomically detected proteins.
The data can be formatted in a number of different well-known proteomic datafile formats: as examples, mzML, Mascot Generic Format (MGF), or any proprietary format.
The identified variations in the detected proteins provide markers for genetic information (e.g., identifying genetic information) which can be verified against the genomic variations detectable in the original biological sample. This, the validated genetic protein variation, can be produced by comparing the provided mass spectrometry dataset of the original biological sample with the proteomically detectable genetic protein variation.
Providing a proteomically detectable genomic variation in the set of represented genes proteomically detectable in the biological sample of the individual can be performed by providing exome sequence data of the individual and comparing the exome sequence data of the individual with sequences from the represented genes proteomically detectable in the biological sample of the individual to determine the proteomically detectable genomic variation in the biological sample of the individual. Providing the exome sequence data of the individual can, for example, be performed by the methods explained herein, or by other known methods. The exome data can be procured from the original biological sample, or from some other biological sample, even one of a different type (blood, hair, saliva, etc.) than the original. Additionally, the exome data can be procured from any genetically relevant source, such as a close family member of the individual. Additionally, the exome data can be procured from a database of already determined genetic data.
Furthermore, providing a proteomically detectable genetic protein variation corresponding to the proteomically detectable genomic variation in the biological sample of the individual, can be performed through single nucleotide polymorphism (SNP) annotation on the proteomically detectable genomic variation in the biological sample of the individual to produce a corresponding mutant/reference protein sequence; and providing the proteomically detectable genetic protein variation from the annotated proteomically detectable genomic variation in the biological sample of the individual.
“SNP annotation” (or “annotation”) as used herein refers to the process to predict the effect or function of an individual SNP by use of a tool (e.g., SNPeff, VEP, ANNOVAR, FATHMM, PhD-SNP, PolyPhen-2, SuSPect, F-SNP, AnnTools, SeattleSeq, SNPit, SCAN, Snap, SNPs&GO, LS-SNP, Snat, TREAT, TRAMS, Maviant, MutationTaster, SNPdat, Snpranker, NGS—SNP, SVA, VARIANT, SIFT, PhD-SNP and FAST-SNP). In annotation, biological information is extracted, collected, and displayed in a way that makes querying the data easier.
A genetic protein variation identity panel can be created by collecting the validated genetic protein variant proteomically detectable in the biological sample of the individual. This provides a genetic protein variation identity panel of the individual.
Exemplary represented genes and/or exome sequences of Homo Sapiens having a corresponding detected peptide sequence that can be used in the method and/or comprised in a database according to the fourth aspect are indicated in Examples 43 to 45 listing exemplary set of genes validated in hair samples (Example 43, Table 8) bone samples (Example 44, Table 9) and skin samples (Example 45, Table 10) of a human being.
Exemplary marker genetic protein variations validated in Homo Sapiens that can be used in the method and/or comprised in a database according to the fourth aspect if the instant disclosure, can comprise any one of the marker genetic protein variations indicated in Examples 46 and Example 47 listing exemplary set of GVPs validated in hair (Example 46, Table 11) and skin samples (Example 47, Table 12) of a human being.
In some embodiments, detecting a genetic variation can be performed with a pooled marker genetic variation database system obtainable with a method and system to improve a pooled marker genetic protein variation database system according to the fifth aspect of the present disclosure. In the method and system, the pooled marker genetic protein variation database system comprises marker genetic protein variations common to a plurality of individuals.
The method comprises: providing a number of proteomic datasets of individuals of the plurality of individuals, the number statistically significant for the plurality of individuals, identifying a protein common to the provided number of proteomic datasets; and selecting from the identified protein common to the provided proteomic datasets, a protein detectable in a biological sample of an individual of the plurality of individuals.
The method further comprises providing a number of exome datasets of the individuals of the plurality of individuals, the number statistically significant for the plurality of individuals; and identifying a genetic variation in the provided number of exome datasets.
The method also comprises selecting from the identified genetic variation, a genetic variation detectable in the biological sample; and comparing the selected proteins detectable in the biological sample with the selected genetic variations detectable in the biological sample, to provide a marker genetic protein variation common to a plurality of individuals of a biological organism type and validated to be detectable in the biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to improve a pooled marker genetic protein variation database system for a biological organism herein described.
The process for creating a marker genetic protein variation database system can be repeated for a plurality of individuals, preferably ones sharing the same genetic variant or variants to be cataloged in the database, to provide a database comprising validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals of that biological organism type.
This database can be formed by collecting the represented genes common to the individuals into a proteomically detectable gene pool, providing validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals of the biological organism from the collected common represented, and collecting the validated genetic protein variants proteomically detectable in the biological sample of the individuals, in a genetic protein variation panel comprising a genetic protein variation panel common to the individuals.
The proteomically detectable gene pool can contain data corresponding to proteins that are common to some or all the validated genetic protein variants proteomically detectable in the biological sample of a given individual. This can be set against a threshold limit, for example only proteins that are common in at least (or over) 50% of all individuals in the pool.
Providing validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals can be performed to only include genomic variation with a frequency greater than some threshold limit, for example 1%, in the plurality of the individuals into a proteomically detectable gene pool.
One aspect of a method to improve a marker genetic protein variation database system comprising marker genetic protein variations common to a plurality of individuals includes: providing a number of proteomic datasets of individuals of the plurality of individuals, the number statistically significant for the plurality of individuals, identifying one or more proteins common to the provided number of proteomic datasets; selecting from the identified proteins common to the provided proteomic datasets, a protein detectable in a biological sample (e.g., hair) of an individual of the plurality of individuals; providing a number of exome datasets of the individuals of the plurality of individuals, the number statistically significant for the plurality of individuals; identifying a genetic variation in the provided number of exome datasets; selecting from the identified genetic variation, a genetic variation detectable in the biological sample; and comparing the selected proteins detectable in the biological sample with the selected genetic variations detectable in the biological sample, to provide a marker genetic protein variation common to a plurality of individuals of a biological organism type and detectable in the biological sample.
The database system is realizable in a computer system, either as a single computer (processor, memory, etc.) or as a network of computers, including, as examples, cloud, intranet, internet, or parallel processing systems. The database system can be centralized and accessible by web-based searches, or stand-alone.
Once created, the database can be searched to create identity metrics for a questioned biological sample of the same type (hair, blood, saliva, etc.) by GVP matching.
The term “exome” as used herein refers to the part of the genome formed by exons, the sequences which when transcribed remain within the mature RNA after introns are removed by RNA splicing. It consists of all DNA that is transcribed into mature RNA in cells of any type as distinct from the transcriptome, which is the RNA that has been transcribed only in a specific cell population. For example, humans have about 180,000 exons, constituting about 1% of the human genome, or approximately 30 million base pairs.
Exome sequencing, also known as whole exome sequencing (WES or WXS), typically consists of two steps: the first step is to select only the subset of DNA consisting of exons. The second step is to sequence the exonic DNA using any high-throughput DNA sequencing technology. In the first step, target-enrichment methods allow the selective capture of genomic regions of interest from a DNA sample prior to sequencing. Both array-based and in-solution capture techniques can be used. In array-based capture, microarrays containing single-stranded oligonucleotides with sequences from a genome (e.g. human exome) tile the region of interest fixed to the surface. Genomic DNA is sheared to form double-stranded fragments. The fragments undergo end-repair to produce blunt ends and adaptors with universal priming sequences are added. These fragments are hybridized to oligos on the microarray. Unhybridized fragments are washed away and the desired fragments are eluted. The fragments can then be amplified using PCR. Next-generation sequencing techniques can also be used with array-based capture. For example, the Sequence Capture Human Exome 2.1M Array can be used to capture -180,000 coding exons. This method is both time-saving and cost-effective compared to PCR based methods. The Agilent Capture Array and the comparative genomic hybridization array are other methods that can be used for hybrid capture of target sequences. To capture genomic regions of interest using in-solution capture, a pool of custom oligonucleotides (probes) is synthesized and hybridized in solution to a fragmented genomic DNA sample. The probes (labeled with beads) selectively hybridize to the genomic regions of interest after which the beads (now including the DNA fragments of interest) can be pulled down and washed to clear excess material. The beads are then removed and the genomic fragments can be sequenced allowing for selective DNA sequencing of genomic regions (e.g., exons). In general, in the first step, any of a number of available exome enrichment platforms (e.g., Roche/NimbleGen's SeqCap EZ Human Exome Library, Illumina's Nextera Rapid Capture Exome, Agilent's SureSelect XT Human All Exon and Agilent's SureSelect QXT) can be used to allow the selective capture of genomic regions of interest from a DNA sample. In the second step, there are several sequencing platforms available in addition to classical Sanger sequencing. Other platforms include the Roche 454 sequencer, the Illumina Genome Analyzer II and the Life Technologies SOLiD & Ion Torrent, which can be used for exome sequencing. Any cellular material that contains genomic DNA can be used for exome sequencing, such as human blood samples, buccal sample and others identifiable by skilled persons.
Exome sequencing can also be performed by RNA exome sequencing (e.g., Illumina RNA Exome Capture Sequencing) according to approaches and techniques identifiable by a skilled person.
The term “exome-driven” as used herein refers to an approach of GVP discovery that begins with sequencing the exome of an individual, allowing identification of relevant SNPs, followed by proteomic validation of GVPs (see
In a typical “proteome-driven” GVP discovery approach that is used following existing methods and systems, a peptide mixture is obtained from a sample and is analyzed by LC-MS/MS. The resulting dataset is then analyzed with reference to a protein variant database using analysis software tools such as MASCOT, PEAKS, and GPM. Candidate GVPs in the observed proteins identified in the sample are screened using metrics such as match score, frequency, and qualitative assessment. The screened GVPs are then validated by confirming the GVPs comprise missense mutations genetically encoded by SNPs by genomic sequencing. The validated GVPs then are incorporated into a GVP database.
The term “validated GVP” as used herein refers to a GVP that comprises a variation (e.g. a SAP) that has been confirmed to correspond to a variation (e.g., a nsSNP) in the exome of the same individual.
A schematic summarizing the “exome-driven” GVP discovery approach is shown in
In some embodiments, the exome-driven GVP discovery approach described herein can be used to assemble a panel of validated GVPs for a population of individuals, referred to herein as a “Common GVP Panel” or “Pooled GVP Panel”. In particular, in the “Common GVP panel”, GVPs are down selected for common nsSNPs, and a consensus panel is assembled from a large cohort. As described herein, the term “common nsSNPs” refers to nsSNPs having a frequency >1% and having a worldwide distribution.
In some embodiments, the exome-driven GVP discovery approach described herein can be used to assemble a panel of validated GVPs for an individual, referred to herein as an “Individual GVP Panel”. In particular, for an ‘Individual GVP Panel’, GVPs can be down-selected based on low-frequency or ‘rare’ or ‘private’ nsSNPs and the GVP panel is unique to that individual (see
An exemplary “exome-driven” GVP discovery method, showing integration of exomic and proteomic data for building a “Pooled GVP Panel” or an “Individual GVP Panel” is described in Example 14.
In some embodiments, exome-driven discovery of GVPs from a diverse cohort allows discovery of markers that are informative of biogeographic background.
The exome-driven GVP discovery methods and systems described herein can be used for discovery of validated GVPs for any tissue type. For example, an exemplary exome-driven method of building a panel of validated GVPs for hair samples is described in Example 15 and an exemplary panel of validated GVPs for bone is described in Example 21.
The exome-driven GVP discovery methods and systems described herein can be used in several embodiments in combination with samples from any tissue type prepared using any method.
In some embodiments, application of the product rule can be used to estimate the probability of a combination of individual nsSNPs (otherwise referred to herein as a “nsSNP profile”) in a population. The term “product rule” as used herein refers to the multiplication of frequencies of individual nsSNPs in a profile in a population to calculate the overall frequency of the combination of nsSNPs in a nsSNP profile in the population.
As understood by those skilled in the art, linkage disequilibrium (LD) can affect calculation of the overall frequency of the combination of nsSNPs in a nsSNP profile in the population, and thus can affect theoretical genotype match probabilities. The term “linkage disequilibrium” refers to non-random association of alleles at different loci in a given population. In general, DNA sequences that are close together on a chromosome have a tendency to be inherited together during the meiosis phase of sexual reproduction. Two loci that are physically near to each other are unlikely to be separated onto different chromatids during chromosomal crossover, and are therefore said to be more linked than markers that are far apart. Loci are said to be in linkage disequilibrium when the frequency of association of their different alleles is higher or lower than what would be expected if the loci were independent and associated randomly. Because nearby loci are often inherited together, in some embodiments the product rule doesn't directly apply. For example, many loci for exemplary validated GVPs shown in
In some embodiments, strategies for identification of candidate GVPs comprise studying a larger and more diverse cohort, increased proteomic detection through instrumentation, and bioinformatic data mining of previously collected datasets, among others identifiable by skilled persons upon reading of the present disclosure. In exemplary embodiments of the methods and systems described herein, sample sets comprise protein and DNA sample sets from cohorts comprising n=200-250 European Americans, n=30-50 African Americans, n=30-50 Hispanic, n=100 East Asian, and n=60 parent/offspring.
In some embodiments, the panel of validated GVPs is an Individual GVP panel.
In some embodiments, the panel of validated GVPs is a Pooled GVP panel.
A schematic of an exemplary method of how to apply an Individual or Pooled GVP panel to operational samples is shown in
Exemplary represented validated genes and/or exome sequences of Homo Sapiens having a corresponding detected peptide sequence that can be used in the method and/or comprised in a database according to the fifth aspect of the instant disclosure are indicated in Examples 43 to 45 listing exemplary set of genes validated in hair samples (Example 43, Table 8) bone samples (Example 44, Table 8) and skin samples (Example 45, Table 10) of a human being.
Exemplary validated marker genetic protein variations that can be used in the method and/or comprised in a database according to the fifth aspect of the instant disclosure, can comprise any one of the marker genetic protein variations indicated in Examples 46 and Example 47 listing exemplary set of GVPs validated in hair (Example 46, Table 11) and skin (Example 47, Table 12) samples. The validated GVPs of Table 11, and Table 12 can preferably be used in connection with method performed on biological samples from a human being.
Further details concerning the methods and systems of the present disclosure will become more apparent hereinafter from the following detailed disclosure of examples by way of illustration only with reference to an experimental section.
In some embodiments detecting a genetic variation can be performed with a method and a system to detect a marker genetic variation for a biological organism validated to be detectable in a biological sample of an individual of the biological system, according to the sixth aspect of the present disclosure.
The method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis; and fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample.
The method further comprises detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction; and detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction.
The method also comprises comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system herein described.
The system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to detect a marker genetic variation for a biological organism validated to be detectable in a biological sample of an individual of the biological system herein described.
In embodiments of the method according to the sixth aspect, any method of preparing the biological sample identifiable by persons skilled in the art upon reading the present disclosure can be used in the method to detect a marker genetic variation in a biological sample of a biological organism.
. In embodiments of the method according to the sixth aspect, any method to perform fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample can be used in the method to detect a marker genetic variation in a biological sample of a biological organism.
In some embodiments, the fractionating can be performed for example by several methods of DNA purification from a solution containing protein and DNA. In general, successful nucleic acid purification requires effective disruption of cells or tissue or organ material, denaturation of nucleoprotein complexes, inactivation of nucleases such as DNase, and absence of contamination.
For example, commonly used procedures for DNA purification from detergents, proteins, salts and reagents used in sample preparation comprise alcohol precipitation, phenol-chloroform extraction, and mini-column purification, among other techniques known in the art. Alcohol precipitation can be performed using e.g., using ice-cold ethanol or isopropanol. Since DNA is insoluble in these alcohols, it will aggregate together, giving a pellet upon centrifugation. Precipitation of DNA can be improved by increasing of ionic strength, for example by adding sodium acetate. Phenol—chloroform extraction can be performed in which phenol denatures proteins in the sample. After centrifugation of the sample, denatured proteins remain in the organic phase while aqueous phase containing nucleic acid is mixed with the chloroform that removes phenol residues from solution. Mini-column purification can be performed, in which nucleic acids bind (adsorb) to a solid phase (e.g., silica or other) depending on the pH and the salt concentration of the buffer. For example, an exemplary method of performing fractionation of a biological sample into a DNA fraction and a protein fraction using mini-column purification is described in Example 7.
In embodiments of the method and system of combined mtDNA and proteomic analysis from a single sample, any method of sample preparation identifiable by those skilled in the art that can provide an extract of purified protein suitable for proteomic analysis and a mtDNA extract and/or nuclear DNA extract suitable for mtDNA and/or nuclear DNA analysis from a single biological sample can be used, and is not limited to exemplary methods described herein.
The exemplary procedures described herein reveal that protein identification markers (GVPs) can be detected from one-inch hair samples using LC-MS/MS of peptides. In exemplary embodiments described herein, protein extraction by ultrasonication and harsh detergents can fully dissolve the hair matrix, maximizing the ability of enzyme proteolysis and subsequently peptide concentration in samples. Additionally, the exemplary protein extraction procedure described herein is compatible with mtDNA extraction, copy number determination, and hyper-variable region sequencing (Example 7). Thus, in some embodiments, GVP discovery and mtDNA sequencing in combination provide a substantial measure of human identity because of the vast variation in allelic frequencies of SNPs. These exemplary embodiments illustrate the potential proteomic analysis of hair evidence has for becoming a widely implemented forensic tool.
As understood by skilled persons, the term “genome” refers to the total heritable genetic material of an organism, comprising DNA (or RNA in RNA viruses), wherein a genome comprises a plurality of genes.
In particular, in eukaryotes, and in particular in animals, the genome comprises both a “nuclear genome” and a “mitochondrial genome”. In plants, the genome also comprises a “chloroplast genome”. Thus, in embodiments herein described, the term “genome” can be applied specifically to mean the genes that are stored on a complete set of nuclear DNA (also referred to herein as the “nuclear genome”, typically arranged on chromosomes in a eukaryotic cell's nucleus) and can also be applied to specifically refer to the genes that are within organelles that contain their own DNA, as with the “mitochondrial genome” or the “chloroplast genome”, as identifiable by persons skilled in the art upon reading of the present disclosure.
The mitochondrial genome is the entirety of hereditary information contained in mitochondria. Mitochondrial DNA (mtDNA) is not transmitted through nuclear DNA (nDNA).
While DNA is degraded as a function of biological processes, mitochondrial DNA has a higher template number than nuclear DNA and is more likely to survive apoptotic and subsequent environmental processes[11]. Accordingly, for some tissue sample types, recovery of both protein and mtDNA from tissue samples would allow incorporation of both proteomic and mtDNA haplotype analysis into a single measure of discrimination.
The terms “haplotype” or “haploid genotype” as used herein refers to a group of genes in an organism that are inherited together from a single parent and the term “haplogroup” refers to a group of similar haplotypes that share a common ancestor with a single-nucleotide polymorphism mutation. Accordingly, for example, a human mitochondrial DNA haplogroup is a haplogroup defined by differences in human mitochondrial DNA. The letter names of the haplogroups (not just mitochondrial DNA haplogroups) run from A to Z. The human mitochondrial genome is the entirety of hereditary information contained in human mitochondria. Mitochondrial DNA (mtDNA) is not transmitted through nuclear DNA (nDNA). In humans, as in most multicellular organisms, mitochondrial DNA is inherited only from the mother's ovum. In humans, mitochondrial DNA (mtDNA) forms closed circular molecules that contain 16,569 DNA base pairs, with each such molecule normally containing a full set of the mitochondrial genes. In humans, the 16,569 base pairs of mitochondrial DNA encode for 37 genes. Human mitochondrial DNA was the first significant part of the human genome to be sequenced.
For example, the current best practice to gain forensically informative genetic information from hair shafts is to obtain the mitochondrial DNA haplotype and determine the probability of occurrence in reference sample populations[12]. Incorporation of both proteomic and mtDNA haplotype analysis into a single measure of discrimination, would maximize the probative value of a biological sample such as hair shafts.
As understood by skilled persons, a genome (and in particular a nuclear genome) can comprise polynucleotides comprising repetitive DNA elements such as interspersed repeats, retrotransposons, long terminal repeats, non-long-terminal repeats, long-interspersed elements, short interspersed elements, DNA transposons, and tandem repeats, among others identifiable by skilled persons.
The term “interspersed repeat” refers to polynucleotide elements such as transposable elements (TEs), and in some embodiments can also refer to some protein coding gene families and pseudogenes. Transposable elements are able to integrate into the genome at another site within the cell. TEs can be classified into two categories, Class 1 (retrotransposons) and Class 2 (DNA transposons), as would be understood by skilled persons. Retrotransposons can be transcribed into RNA, which are then duplicated at another site into the genome. Retrotransposons can be divided into Long terminal repeats (LTRs) and Non-Long Terminal Repeats (Non-LTR). Long interspersed elements (LINEs) typically encode two Open Reading Frames (ORFs) to generate transcriptase and endonuclease, which are essential in retrotransposition. Short interspersed elements (SINEs) are typically less than 500 base pairs in length and require the LINEs machinery to function as nonautonomous retrotransposons. For example, the Alu element is the most common SINE found in primates, it has a length of about 350 base pairs and takes about 11% of the human genome with around 1,500,000 copies.
In particular, the term “tandem repeat” refers to a repeating pattern of one or more nucleotides in DNA wherein the repetitions are directly adjacent to each other. In particular, the term “minisatellite” refers to a tandem repeat having typically between 14 and 60 repeated nucleotides, whereas tandem repeats having fewer repeated nucleotides are typically referred to as “microsatellites” or “short tandem repeats” or “STR”.
In particular, an STR is type of microsatellite consisting of a unit of 2-13 or more base pairs repeated hundreds of times in a row on the DNA strand. A microsatellite is a tract of repetitive DNA in which certain DNA motifs (ranging in length from 2-13 base pairs) are repeated, typically 5-50 times. Microsatellites occur at thousands of locations within an organism's genome; additionally, they have a higher mutation rate than other areas of DNA leading to high genetic diversity. Microsatellites are often grouped according to the length of the unit of repeated base pairs. For example, the sequence TATATATATA (SEQ ID NO: 134) is a dinucleotide microsatellite, and GTCGTCGTCGTCGTC (SEQ ID NO: 135) is a trinucleotide microsatellite (with A being Adenine, G Guanine, C Cytosine, and T Thymine). Repeat units of four and five nucleotides are referred to as tetra- and pentanucleotide motifs, respectively. Most eukaryotes have microsatellites, with the notable exception of some yeast species, and these microsatellites are distributed throughout the genome. The human genome for example contains 50,000-100,000 dinucleotide microsatellites, and lesser numbers of tri-, tetra- and pentanucleotide microsatellites. Many are located in non-coding parts of the human genome and therefore do not produce proteins, but they can also be located in regulatory regions and coding regions. Microsatellites and minisatellites together are classified as VNTR (variable number of tandem repeats) DNA.
STRs are often used in forensics because although the repeating sequence of base pairs of a specific microsatellite does not change from person to person, the number of times the sequence repeats does change. This allows the number of repeats of a sequence to identify a person through his/her DNA if the number of sequence repeats matches the initial DNA basis used for comparison. STRs can also be used to eliminate a person from suspicion or reduce the suspicion of a person if he/she does not have the same number of sequence repeats as the comparate DNA. STRs are widely used for DNA profiling in kinship analysis (such as paternity testing) and in forensic identification. They are also used in genetic linkage analysis/marker assisted selection to locate a gene or a mutation responsible for a given trait or disease. Microsatellites are also used in population genetics to measure levels of relatedness between subspecies, groups and individuals.
In particular, STR analysis is a tool in forensic analysis that evaluates specific STR regions found on nuclear DNA. STR analysis measures the exact number of repeating units. This method differs from restriction fragment length polymorphism analysis (RFLP) since STR analysis does not cut the DNA with restriction enzymes. Instead, probes are attached to desired regions on the DNA, and a polymerase chain reaction (PCR) is employed to discover the lengths of the short tandem repeats. This method uses highly polymorphic regions that have short repeated sequences of DNA (the most common is 4 bases repeated, but there are other lengths in use, including 3 and 5 bases). Because unrelated individuals typically have different numbers of repeat units, STRs can be used to discriminate between unrelated individuals. These STR loci (locations on a chromosome) are targeted with sequence-specific primers and amplified using PCR. The DNA amplicons that result are then separated and detected using electrophoresis methods, such as capillary electrophoresis and gel electrophoresis.
Several STR-based DNA-profiling systems are in use, identifiable by those skilled in the art. For example, in North America, systems that amplify the “CODIS 13 core loci” are almost universal, whereas in the United Kingdom the “DNA-17” 17 loci system is in use. Whichever system is used, many of the STR regions used are the same. These DNA-profiling systems typically use multiplex PCR, whereby many STR regions are tested at the same time. For example, the 13 loci that are currently used for discrimination in CODIS are independently assorted (having a certain number of repeats at one locus does not change the likelihood of having any number of repeats at any other locus), and therefore the product rule for probabilities can be applied.
Accordingly, in embodiments of the method according to the sixth aspect described herein, any method of genetic analysis identifiable by skilled persons can be used for detecting a genomic variation of the nuclear and/or mitochondrial genome.
In embodiments of the method according to the sixth aspect described herein, any method of combining the detected genetic protein variations and the detected genomic variation can be used to provide the marker genetic variation database system of the biological sample, the detected genetic protein variations and the detected genomic variation to provide the marker genetic variation database system of the biological sample.
In embodiments of the method according to the sixth aspect described herein, comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system can be performed with any methods identifiable by a skilled person
In embodiments of the method and system of combined mtDNA and proteomic analysis from a single sample, any method of sample preparation identifiable by those skilled in the art that can provide an extract of purified protein suitable for proteomic analysis and a mtDNA extract suitable for mtDNA analysis from a single tissue sample can be used, and is not limited to exemplary methods described herein.
The system comprises equipment, reagents, and samples required to perform the method of the combined mtDNA and proteomic analysis from a single sample.
In some embodiments of a genetic variation analysis, detecting a genetic variation in a genetic variation analysis can be performed using a marker genetic variation database according to a seventh aspect herein described. The related method to provide the marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample, comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis.
The method further comprises fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample.
The method also comprises detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction and detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction.
The method additionally comprises combining the detected genetic protein variations and the detected genomic variation to provide the marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample.
The system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases for simultaneous combined or sequential use in the method to provide the marker genetic variation database system comprising marker genetic variation validated to be detectable in a biological sample herein described.
In some embodiments wherein preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis, is performed by the method according to the first aspect.
In some embodiments detecting a genetic protein variation is performed by the method according to the sixth aspect.
Methods and systems and related marker genetic protein variations and databases herein described, can be used in several embodiments for proteomic information detection using liquid chromatography/mass spectrometry methods for forensic analysis of tissue samples to provide identity metrics of individuals. In several embodiments, the methods and systems described herein allow improved proteomic information recovery when genomic DNA is degraded or not available, and/or when there are multiple contributors to the sample.
In some embodiments of the instant disclosure a genetic analysis of a sample of a biological organism can be performed with methods and systems according to the eighth aspect of the disclosure. The method comprises
preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis;
fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample;
digesting the solubilized protein fraction from the sample to obtain digested peptides from the sample;
fractionating the digested peptides to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample.
detecting a marker genetic variation of the fractionated digested peptides from the sample; in which
preparing the sample is performed according to any one of the methods according to the first aspect of the disclosure, comprising any one of the related sets of embodiments ; and/or
detecting a genetic variation is performed by at least one of
a first detecting method directed to detect a genetic protein variation by performing any one of the methods according to the third aspect, comprising any one of the related sets and subsets of claims; and
a second detecting method directed to detect a genetic variation by performing any one of the methods according to the sixth aspect of the disclosure comprising any one of the related sets of embodiments.
In the method of the eighth aspect the genetic analysis is directed to detect one or more genetic variations in the sample, and preferably comprises detection of at least one genetically variant protein, which more preferably has been validated in the sample where detection is performed. Therefore in preferred embodiments of the method of the eighth aspect of the disclosure the genetic analysis is a genetic protein variation analysis directed to detect in the sample one or more genetic variations validated in the analyzed sample.
In some embodiments of the method according to the eight aspect, the preparing can be performed with existing methods of sample preparation for proteomics. Typically, these methods comprise performing cell and tissue disruption and performing protein solubilization according to approaches identifiable by a skilled person upon reading of the present disclosure. Typically these methods can also comprise performing removal of contaminants and/or performing protein enrichment following performing protein solubilization, according to approaches identifiable by a skilled person upon reading of the present disclosure.
In preferred embodiments of the method according to the eight aspect however, the preparing is performed by any one of the embodiments the method according to the first aspect of the present disclosure as will be understood by a skilled person.
In more preferred embodiments of the method of the eight aspect wherein the preparing is performed according to the method of the first aspect, the applying is performed by sonication, with a related processor preferably set at 5 to 50 kHz and more preferably at 37 kHz with a power setting preferably set at 50 to 100%; most preferably at 100%. In more preferred embodiments the applying is performed with an ultrasonic mode sweep.
In more preferred embodiments of the method of the eight aspect wherein the preparing is performed according to the method of the first aspect, the applying can be performed with an incubation time from 20 to 90 minutes; most preferably 60 minutes
In more preferred embodiments of the method of the eight aspect wherein the preparing is performed according to the method of the first aspect, the applying can be performed with temperature settings from 30 to 90° C.; most preferably 70° C.
In any one of the embodiments of the method of the present disclosure according to the eighth aspect, the digesting can be performed with any methods identifiable by a skilled person upon reading of the present disclosure.
In preferred embodiments of method of the present disclosure according to the eighth aspect, the digesting is performed enzymatically with one or more proteolytic enzymes identifiable by a skilled person.
In more preferred embodiments of the method according to the eighth aspect, the digesting comprises digesting the solubilized proteins from the sample with a site specific proteolytic enzyme to obtain digested solubilized proteins from the sample.
In those more preferred embodiments the digesting can be performed in a sample buffer comprising an enzyme capable to perform site specific protease digestion such as trypsin, chymotrypsin, Lys-C, Arg-C, Asp-N, and Glu-C, non-specific; pepsin, and proteinase K.
In particular in those more preferred embodiments of the method according to the eighth aspect, the enzyme can be comprised in the sample buffer at concentrations for digest ranging from 0.0001 to 1 μg/μL; more preferably 0.01 to 0.001 μg/μL; most preferably 0.005 μg/μL.
In even more preferred embodiments of the method according to the eighth aspect, the proteolytic enzyme is trypsin.
In preferred embodiments of the method according to the eighth aspect of the present disclosure, the digesting is preceded by fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample. In those embodiments, the solubilized proteins are fractionated in a solubilized protein fraction and digesting the solubilized proteins is performed by digesting the solubilized protein fraction. In those embodiments fractionating the solubilized proteins can be performed by any one of the methods identifiable by a skilled person upon reading of the present disclosure typically comprises removing buffers, salts, and detergent from the processed sample. In more preferred embodiments fractionating the solubilized proteins can further comprise removing abundant proteins from the processed sample, protein enrichment processes and/or removing contaminants which can be performed with any one of the methods identifiable by a skilled person upon reading of the present disclosure.
In any one of the embodiments of the method according to the eighth aspect of the present disclosure, the genetic analysis also comprises detecting a marker genetic variation of the digested peptides.
In preferred embodiments of the method according to the eighth aspect of the present disclosure, the detecting is performed by mass spectrometry according to methods identifiable by a skilled person upon reading of the present disclosure. In those embodiments, the concentration of proteolytic enzyme in the sample buffer used during the digesting is set taking into account that increased concentrations can cause suppression of sample detection, decrease LC column capacity; and decrease ability to observe sample peptides by overcrowding mass a spectrometry detector as will be understood by a skilled person.
In those preferred embodiments of the method of the eighth aspect, wherein the proteomic analysis is performed by Mass Spectrometry, the digesting can be performed in a buffer comprising mass spectrometry compatible surfactant, such as for example, Invitrosol, ProteaseMax, Rapigest SF, and PPS Silent Surfactant), in concentration (percent w/v) ranges broadly from 0.0001 to 1.0%; more preferably 0.001 to 0.2%; and most preferably 0.01%. Increasing concentrations can cause issues with electrospray efficiency during MS data acquisition. In preferred embodiments, the surfactant comprise ProteaseMax.
In preferred embodiments of the method according to the eighth aspect, the detecting is preceded by fractionating the digested solubilized proteins to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample. In those embodiments, the digested peptides are fractionated digested peptides and detecting a marker genetic variation of the digested peptides is performed by detecting a marker genetic variation of the fractionated digested peptides.
In those preferred embodiments of the method according to the eighth aspect, fractionating the digested solubilized proteins can be performed by any suitable method of fractionating proteins identifiable by a skilled person upon reading of the present disclosure. Preferably, fractionating the digested solubilized proteins can be performed by any chromatographic techniques identifiable by a skilled person upon reading of the present disclosure.
In more preferred embodiments of the method according to the eighth aspect, the fractionating is performed by liquid chromatography and the detecting is performed by mass spectrometry in an approach that combines the physical separation capabilities of liquid chromatography with the mass analysis capabilities of any mass spectrometry as will be understood by a skilled person upon reading of the present disclosure.
In even more preferred embodiments of the method according to the eighth of the present disclosure, the detecting is performed according to any one of the methods according to the third aspect or the sixth aspect of the instant disclosure and/or using any of the related databases.
In particular in some of the even more preferred embodiments of the method according to the eighth aspect, the detecting is performed according to the third aspect of the instant disclosure by
providing a marker mass spectrum of a marker peptide comprising a marker genetic protein variation corresponding to the genetic protein variation;
performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide; and
comparing the mass spectrum of the fractionated digested peptide with a marker mass spectrum of a marker peptide comprising the marker genetic protein variation to detect the genetic protein variation in the biological sample.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the third aspect of the present disclosure, the marker genetic protein variation is obtained by any one of the methods to provide a marker genetic protein variation for a biological organism according to the second aspect of the instant disclosure and/or is a marker genetic protein variation obtainable and/or obtained thereby.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the third aspect of the present disclosure, the marker genetic protein variation comprises a marker genetic protein variation from the marker genetic protein variation database system according to the fourth aspect of the instant disclosure.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the third aspect of the present disclosure, the marker genetic protein variation comprises a marker genetic protein variation from the marker genetic protein variation database system according to the fifth aspect of the instant disclosure.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the third aspect of the present disclosure, the marker peptide comprises one or more of the marker peptides comprising a validate genetic protein variations indicated in Examples 46 and Example 47 indicating exemplary set of GVPs and related mutated peptides validated in hair samples (Example 46, Table 11) and skin samples (Example 47, Table 12) and in particular in hair and skin samples of human beings.
In particular exemplary marker peptides that can be preferably used or comprise in the method and system according to the eighth aspect, comprise any combination of the peptides having sequence SEQ ID NO: 150 to SEQ ID NO: 748 (Example 46, Table 11) for detection in hair samples, in particular for hair samples of human beings, and any combination of the peptides having sequence SEQ ID NO: 749 to SEQ ID NO: 829 (Example 47, Table 12) for detection in skin samples, in particular for skin samples of human beings.
In some of the even more preferred embodiments of the method according to the eighth aspect, the detecting is performed according to the sixth aspect of the instant disclosure by
preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis;
fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample;
detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction;
detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction; and
comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system herein described.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the sixth aspect of the present disclosure, detecting a genetic protein variation is performed by detecting one or more marker genetic protein variations obtained by any one of the methods to provide a marker genetic protein variation for a biological organism according to the second aspect of the instant disclosure and/or is a marker genetic protein variation obtainable and/or obtained thereby.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the sixth aspect of the present disclosure, detecting a genetic protein variation is performed by detect a genetic protein variation in a biological sample according to any one of the methods according to the third aspect of the instant disclosure.
In some more preferred embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the sixth aspect of the present disclosure, in which detecting a genetic protein variation is performed by detect a genetic protein variation in a biological sample according to any one of the methods according to the third aspect of the instant disclosure, the marker genetic protein variation comprises a marker genetic protein variation from the marker genetic protein variation database system according to the fourth aspect or the fifth aspect of the instant disclosure.
In some embodiments of the even more preferred embodiments of the method according to the eighth aspect in which the detecting is performed by the method according to the third aspect of the present disclosure or the sixth aspect of the present disclosure, the marker genetic protein variation are peptide sequences corresponding to (translated from at least a portion of) a marker exome sequences indicated in Examples 43 to 45 listing exemplary set of genes validated in hair (Example 43, Table 8) bone (Example 44, Table 9) and skin samples (Example 45, Table 10) of a human being.
Preferred validated marker genetic protein variations of Homo Sapiens are indicated in Examples 46 and Example 47 listing exemplary set of GVPs validated in hair sample (Example 46, Table 11) and skin sample (Example 47, Table 12) of a human being.
Additional preferred embodiments of the method according to the eighth aspect are identifiable by a skilled person upon reading of the instant disclosure.
Any one of the embodiments of the method according to the eight aspect of the instant disclosure can be performed with components of the system according to the eighth aspect of the instant disclosure.
In any one of the systems according to the eight aspect, the system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, alone or in combination with reagents to perform proteomic analysis of the biological sample for simultaneous combined or sequential use in the method to perform genetic analysis of a sample of a biological organism herein described.
In embodiments of the system according to the eighth aspects configured to perform a method according to the eighth aspect of the disclosure wherein the preparing is performed by the method according to the first aspect of the present disclosure, the system comprises a sample buffer typically comprising chaotropes (e.g. urea and/or thiourea), detergents (e.g. 3-[(3-Cholamidopropyl)-dimethyl-ammonio]-1-propane sulfonate (CHAPS) or Triton X-100), reducing agents (dithiothreitol/dithioerythritol (DTT/DTE) or tributylphosphine (TBP)) and protease inhibitors. Preferred embodiments of the sample buffer are identifiable by a skilled person upon reading of the present disclosure
In embodiments of the system according to the eighth aspects configured to perform a method according to the eighth aspect of the disclosure wherein the detecting is performed according to any one of the methods according to the third aspect of the instant disclosure and/or using any of the related databases, the system comprises protein databases, and/or reagents to perform proteomic analysis of the biological sample in combination with exome sequence databases. In preferred embodiments, the reagents comprise a marker peptide in accordance with the present disclosure.
In embodiments of the system according to the eighth aspect, configured to perform a method according to the eighth aspect of the disclosure wherein the detecting is performed according to any one of the methods according to the sixth aspect of the instant disclosure and/or using any of the related databases, the system comprises exome sequences databases and/or reagents to detect exome sequences in an individual of the biological organism, in combination with reagents to perform proteomic analysis of the biological organism. In preferred embodiments, the reagents comprise a marker peptide in accordance with the present disclosure
In even more preferred embodiments of the system according to the eighth aspect in which the reagents in the system comprises a marker peptide, the marker peptide comprises one or more of the marker peptides comprising a genetic protein variations validated in Homo Sapiens indicated in Examples 46 and Example 47 indicating exemplary set of GVPs and related mutated peptides validated in hair (Example 46, Table 11) and skin (Example 47, Table 12) samples of human beings. In particular exemplary marker peptides that can be preferably used or comprise in the method and system according to the third aspect, comprise any combination of the peptides having sequence SEQ ID NO: 150 to SEQ ID NO: 748 (Example 46, Table 11) for detection in hair samples of human beings, and any combination of the peptides having sequence SEQ ID NO: 749 to SEQ ID NO: 829 (Example 47, Table 12) for detection in skin samples of human beings.
In view of the above exemplary systems of the instant disclosure according to the eight aspect of the instant disclosure, comprise:
In some embodiments, the one or more marker peptide can be labeled.
The terms “label” and “labeled” as used herein refer to a molecule capable of detection, including but not limited to radioactive isotopes, fluorophores, chemiluminescent dyes, chromophores, enzymes, enzymes substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, nanoparticles, metal sols, ligands (such as biotin, avidin, streptavidin or haptens) and the like. The term “fluorophore” refers to a substance or a portion thereof which is capable of exhibiting fluorescence in a detectable image. As a consequence, the wording “labeling signal” as used herein indicates the signal emitted from the label that allows detection of the label, including but not limited to radioactivity, fluorescence, chemoluminescence, production of a compound in outcome of an enzymatic reaction and the like.
Accordingly, in embodiments of the disclosure a labeled peptide is a peptide attaching a label making the peptide capable of detection.
The terms “detect” or “detection” as used herein indicates the determination of the existence, presence or fact of a target in a limited portion of space, including but not limited to a sample, a reaction mixture, a molecular complex and a substrate. The “detect” or “detection” as used herein can comprise determination of chemical and/or biological properties of the target, including but not limited to ability to interact, and in particular bind, other compounds, ability to activate another compound and additional properties identifiable by a skilled person upon reading of the present disclosure. The detection can be quantitative or qualitative. A detection is “quantitative” when it refers, relates to, or involves the measurement of quantity or amount of the target or signal (also referred as quantitation), which includes but is not limited to any analysis designed to determine the amounts or proportions of the target or signal. A detection is “qualitative” when it refers, relates to, or involves identification of a quality or kind of the target or signal in terms of relative abundance to another target or signal, which is not quantified.
In preferred embodiments of the disclosure, peptides comprised in one of any of the systems of the disclosure are isotopically labeled or chemically labeled.
In particular, in embodiments, wherein a peptide is isotopically labeled and the detecting is performed by MS, the peptide is preferably labeled at the C terminus amino acid if y-series fragments predominate the MSMS spectrum, and preferably labeled at the N terminus amino acid if b-series fragments predominate the MSMS spectrum.
In embodiments wherein the detecting is performed by mass spectrometry, the label can comprise tandem mass tags.
In embodiments of any systems of the disclosure, wherein one or more marker peptides are comprised in the system, reagents to similarly label the unknown sample can further be provided as component of the system as will be understood by a skilled person.
Additional components of the system according to any one of the systems herein described and in particular of the system according to the eight aspect of the disclosure can comprise:
In preferred embodiments of the marker genetic protein variations, databases, methods and systems and related genetic protein variation analysis herein described, performing a proteomic analysis is carried out by performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide.
In further preferred embodiments of the marker genetic protein variations, databases, methods and systems and related genetic protein variation analysis herein described, the sample is hair and/or skin.
Methods and systems and related marker genetic protein variations and databases herein described, also allow in several embodiments to provide more reliable results for a specific query (such as whether there is a match between a sample and a certain individual or groups of individuals linked together by common genetic features).
Methods and systems and related marker genetic protein variations and databases herein described, further allow in several embodiments to perform genetically variant protein analysis applicable to samples from all tissues and are therefore not limited to hair; also the targeted approaches can improve LC-MS/MS analysis of bulk sample as well as analysis of samples available in smaller amounts processable according to the first aspect with particular reference to forensics applications.
As used herein, the wordings “forensics”, “forensic science” or “forensic analysis” refers to the application of science to criminal and civil laws, and in particular with regard to criminal investigation, as governed by the legal standards of admissible evidence and criminal procedure. Additionally, as used herein, the wordings “forensics”, “forensic science” or “forensic analysis” also refer to the application of forensic techniques to other types of investigation, such as determination of relatedness of individuals, or bioarcheological research, among others identifiable by those skilled in the art upon reading of the present disclosure. Accordingly, forensics involves the collection, processing, and analysis of scientific evidence during the course of an investigation.
The systems herein disclosed can be provided in the form of kits of parts. In kit of parts for performing any one of the methods herein described, one or more marker peptide and/or other standards, and/or one or more databases can be included in the kit alone or in the presence of additional sequences, reagents such as labels, reducing agents, surfactants, detergents, enzymes, buffers, as well as additional components, such as columns, filters, templates, reference materials and/or statistical tools identifiable by a skilled person upon reading of the instant discloure.
In a kit of parts, the one or more marker peptide, standards, and/or databases and additional reagents identifiable by a skilled person are comprised in the kit independently possibly included in a composition together with suitable vehicle carrier or auxiliary agents. For example, one or more marker peptides can be included in one or more compositions together with reagents for detection also in one or more suitable compositions.
Additional components of kits of parts according to the disclosure are identifiable by a skilled person upon reading of the present disclosure.
In embodiments herein described, the components of the kit can be provided, with suitable instructions and other necessary reagents, in order to perform the methods here disclosed. The kit will normally contain the compositions in separate containers. Instructions, for example written or audio instructions, on paper or electronic support such as tapes, CD-ROMs, flash drives, or by indication of a Uniform Resource Locator (URL), which contains a pdf copy of the instructions for carrying out the assay, will usually be included in the kit. The kit can also contain, depending on the particular method used, other packaged reagents and materials (i.e. wash buffers and the like).
Further details concerning the identification of the suitable carrier agent or auxiliary agent of the compositions, and generally manufacturing and packaging of the kit, can be identified by the person skilled in the art upon reading of the present disclosure
The methods and systems herein described and related marker genetic protein variations and databases are further illustrated in the following examples, which are provided by way of illustration and are not intended to be limiting.
In particular, the following examples illustrate exemplary methods, systems and related marker genetic protein variations and databases described herein. A person skilled in the art will appreciate the applicability and the necessary modifications to adapt the features described in detail in the present section, to additional methods and systems according to embodiments of the present disclosure.
In particular
Also in
Single hair samples (1 inch; 25 mm) from three individuals were carefully measured and cut into four equal pieces. The cut hair was then placed into separate Protein LoBind Eppendorf tubes. 100 μL of extraction buffer containing 0.05 M ammonium bicarbonate (ABC), 0.1 M dithiothreitol (DTT), 2% sodium dodecanoate (SDD) was added to each tube. Samples were then incubated at 70° C. in an ultrasonic water bath (Elma) while being ultrasonicated at high energy and frequency settings for 60 minutes or until hair was completely dissolved into solution. SDD was removed by extraction with acidified ethyl acetate (pH 2-3, 0.75% trifluoroacetic acid). After addition of 100 uL acidified ethyl acetate to each tube, samples were quickly vortexed, incubated at room temperature for 5 min, and centrifuged for 5 min at max speed (20,000×g). The upper organic phase was removed, discarded to waste, and the extraction process was repeated once. The remaining lower aqueous phase was then readjusted to pH 8 with ABC [13]. Carbamidomethylation of free cysteines was performed by adding 6μL of iodoacetamide (1.0 M) and incubation for 60 min in the dark at 25° C. To further solubilize proteins, 0.01% protease max (3 μL of 1.0% w/v) was added to each sample. Prior to proteolysis, the solubilized protein solution was concentrated to 50uL using 10 kD molecular weight spin concentrators (Millipore). Trypsin (1 μL of 0.5 μg/μL) was then added to each protein sample. Protein digestion was performed at 25° C. for 20/22 hours while being continuously agitated by magnetic-bar stirring. Resulting peptide mixture is then filtered using 0.1 μm PTFE filter, and transferred into fresh vials for mass spectrometric analysis (stored at −4.0-20° C.). Additional step of speed vacuum (20 minutes at 60° C.) can be used to concentrate peptide fraction of samples.
Ultrasonic frequency of 37 kHz is used to maximize dissolving of hair as recommended for dissolving, mixing, dispersing in Elma Elmasonic P user manual. Lower frequency setting concentrates power throughout the water bath and results in better dissolving of hair than the higher option (80 kHz). Elevated temperature setting is used (70° C.) to achieve solubilization of hair matrix. Ultrasonic using sweep mode controls the sound pressure throughout the water bath. This setting applies a more homogeneous sounding of the cleaning bath by the continued displacement of the sound pressure maxima in the cleaning liquid, leading to a more uniform ultrasonic intensity throughout the ultrasonic tank and samples. Ultrasonic power setting of 100% is used for hair matrix solubilization to maximize the force applied. [Reference: www.imlab.be/imlab_n1/e1ma/Pdf/Elmasonic_P/Elmasonic_P_Operating_Instructions_ENG_Iml ab.pdf)
Lower temperature settings ranging from 50-65° C. increase the time needed for complete solubilization substantially (from average of 60 minutes to 12 hours), but can be used to dissolve hair. Time of ultrasonic treatment at 70° C. depends on each given sample. Average of 30 to 60 minutes is efficient for hair solubilization. Brief sonication (30 seconds to 5 minutes) at lower temperature 37° C. is commonly a technique used for protein extractions for various tissues [14-17]. Protein extraction procedure is implemented at atmospheric pressure however, increasing pressure could decrease the amount of time needed for extraction [18].
Adaptation of method to perform sample preparation for proteomic analysis herein described exemplified herein for single hair to bone, teeth, fingerprint and other sample types would be achieved in several ways. For bone and tooth samples, single-hair extraction buffer could be applied to samples prior to mechanical milling procedures. Acid etching could be performed using 1 M HCl. This would be amenable to SDD liquid-liquid extraction step in the single-hair method due to the need to acidify ethyl acetate for SDD removal [19, 20]. In this case, non-acidified ethyl acetate would be used to extract SDD from samples. For finger-print and other samples, the single-hair method can be implemented by decreasing ultrasonic incubation time and decreasing sonication temperature. Exemplary adaptation of the protocol described in the current example to bone and teeth are reported in the following Examples 3 and 4.
Associated soft tissue was resected from each rib and a 20 mg block of cortical bone, roughly 1×3×4 mm, resected using a dental drill (NSK NE-213G) equipped with a diamond tip blade at room temperature (25° C.). Each sample was transferred into milling tubes that contained 2.8 mm ceramic bead media (Omni-International, Kennesaw, Ga.). Acid etching was performed by milling for 3 min @ 6.00 m/s in the presence of 1.2 M HCl (200 μL), reducing by addition of 3 μmol DTT (1.0 M) and incubation at 56° C. for 60 min. The supernatant was neutralized to pH 7.5-8.0 with a threefold molar excess of ammonium bicarbonate. Carbamidomethylation was then conducted by adding 6 μmol iodoacetamide and incubating at 22° C. and for 60 min in the dark. The reaction was quenched by the addition of 6 μmol DTT for 5 min. Solubilized proteins were then digested with the addition of 0.5 μg trypsin (TPCK-treated, sequencing grade, Worthington Inc., Lakewood, N.J.), and 30μg ProteaseMAX™ (Promega Inc., Madison, Wis.). The protein digest was performed at 37° C. for 20 to 22 hr. After digestion, peptide samples were centrifuged (30 min, 16,300 g, 22° C.), the supernatant filtered using a centrifugal 0.1 μm PTFE filter (Millipore Inc., Billerica, Mass.), and transferred into autosampler vials for mass spectrometric analysis (stored at −4.0 to −20° C.).
The protocol for tooth sample processing was adapted from the Porto et al. manuscript published in 2011. Wisdom tooth enamel samples from individuals (5 female, 5 male, and 1 archaeological) were stored at -20° C. until they were re-sectioned using a diamond tip blade at room temperature (25° C.). Enamel and enamel-dentine junction were carefully separated from the dentine, weighed, and -20 mg was transferred into milling tubes that also contain milling beads.
Prior to milling, 200 μL of 1.2 M HCl was added to each sample. Samples were milled in acid for 3 min @ 6.00 m/s and then centrifuged at max speed (5 min, 16,300 g, 22° C.). The supernatant were neutralized by measuring pH using paper and adjusting it to 7.5-8.0 pH by adding 2 M ammonium bicarbonate 90 μL. Soluble proteins were reduced by adding of 3 μL DTT (1 M) and incubating at 56° C. for 60 min. Alkylation was performed by adding 6 μL of iodoacetamide (1 M) at 25° C. and incubating for 60 min in the dark. Carbamidomethylation reaction was quenched by the addition of 6 μL DTT (1 M) and incubating at room temperature for 5 min. To further solubilize proteins, 0.01% protease max (3 μL of 1.0% w/v) was added to each sample. Trypsin (1 of 0.5 μg/μL) was then added to each protein sample, and then incubated at 37° C. for 20/22 hr. After digestion, peptide samples were centrifuged (30 min, 16,300 g, 22° C.) to remove particulates, filtered using 0.45 μm PTFE filters into fresh vials for mass spectrometric analysis (stored at −4.0-20° C.).
Reference is made to [19, 20], each incorporated herein by reference in its entirety.
Various applicable methods can be used to perform proteolytic cleavage (and in particular trypsinization) of proteins as will be understood by a skilled person.
In particular, during protein solubilization reduction of cysteine disulfide bonds is achieved using 100 mM of reducing agent dithiothreitol (DTT). DTT concentrations can vary from 50 mM to 180 mM. Carbamidomethylation of free cysteines is performed by adding 6μL of iodoacetamide (1.0 M) and incubation for 60 min in the dark at 25° C. [21, 22]. Alkylation time can vary from 45-60 minutes, longer reaction times increase confidence in reaction completion.
To further solubilize proteins, 0.01% protease max (3 μL of 1.0% w/v) can be added to each sample. Prior to proteolysis, the solubilized protein solution was concentrated to 50 uL using 10 kD molecular weight spin concentrators (Millipore). Trypsin (1 μL of 0.5 μg/μL) is then added to each protein sample. Protein digestion is performed at 25° C. for 20/22 hours while being continuously agitated by magnetic-bar stirring.
Digestion time can range from 16-22 hours. Agitation can be achieved by other techniques including sample rotated, milling, and shaking [23].
Reference is also made to [1, 21-23], each of which is incorporated by reference in its entirety.
An exemplary method of single hair sample processing performed according to method to perform sample preparation herein described and subsequent proteomic analysis of GVPs is shown in the lower portion of the schematic of
In an exemplary single hair processing method according to the schematics of
For comparison, in an exemplary “Bulk” hair method (e.g., using 10 mg hair sample), performed with conventional sample preparation methods, the sample is initially denatured using dithiothreitol (DTT), ammonium bicarbonate (ABC), urea, and ProteaseMax reagent (Promega, P-max), followed by mechanical milling of the sample comprising multiple steps as described herein and identifiable by those skilled in the art together with cysteine protection. Following mechanical milling, the proteins present in the sample are proteolytically digested with trypsin in a reaction mixture together with DTT, ABC and P-max, followed by centrifugation and filtration before analysis by LC-MS/MS. In contrast, in the exemplary “Single hair” method (e.g., using 85 μg hair, 2.5 cm in length) the sample is initially dissolved using a reaction mixture comprising DTT, ABC and sodium dodecanoate (SDD) and sonication at 70° C.
After dissolving, the sample is separated into organic phase, which is discarded, and aqueous phase, which is retained and further processed for protection of free cysteines, and spin-filter concentration of solubilized proteins, prior to proteolytic digestion by trypsin and filtration, followed by proteomic analysis by LC-MS/MS.
Exemplary results of proteomic metrics for samples processed using the exemplary method to perform a proteomic tissue sample preparation using single hairs, compared to an exemplary “Bulk” hair processing method are shown in
In particular,
In particular
In particular, an optimization of the data illustrated in
As also indicated in other sections of the present disclosure although in the exemplary illustration of
The GVPs detected using the sample preparation method herein described can be comprised in databases of validated marker genetic variation herein described to the extent such GVPs are marker for biological organisms, type of biological organisms or individual thereof. Accordingly, an operational scenario is expected to also utilize inclusion/exclusion lists wherein the exclusion lists can refer to validated GVPs which are not marker for a specific query of interest.
An exemplary method sample processing for subsequent proteomic analysis of GVPs combined with analysis of mtDNA from a same sample is shown in the schematics of
In particular, in the schematic of
In particular single hair samples (25 mm) from three individuals were carefully measured and cut into four equal pieces. The cut hair was then placed into separate Protein LoBind Eppendorf tubes. 100 μL of extraction buffer containing 0.05 M ammonium bicarbonate (ABC), 0.1 M dithiothreitol (DTT), 2% sodium dodecanoate (SDD) was added to each tube. Samples were then incubated at 70° C. in an ultrasonic water bath (Elma) while being ultrasonicated at high energy and frequency settings, (here 330 W and 37 kHz respectively) for 60 minutes or until hair was completely dissolved into solution. SDD was removed by extraction with acidified ethyl acetate (pH 2-3, 0.75% trifluoroacetic acid).
After addition of 100 uL acidified ethyl acetate to each tube, samples were quickly vortexed, incubated at room temperature for 5 min, and centrifuged for 5 min at max speed (20,000×g). The upper organic phase was removed, discarded to waste, and the extraction process was repeated once.
The remaining lower aqueous phase was then readjusted to pH 8 with ABC [13]. Carbamidomethylation of free cysteines was performed by adding 6μL of iodoacetamide (1.0 M) and incubation for 60 min in the dark at 25° C. To further solubilize proteins, 0.01% ProteaseMax reagent (Promega, 3μL of 1.0% w/v) was added to each sample. Prior to proteolysis, the solubilized protein solution was concentrated to 50 μL using 10 kD molecular weight spin concentrators (Millipore). Trypsin (1 μL of 0.5 μg/μL) was then added to each protein sample. Protein digestion was performed at 25° C. for 20-22 hours while being continuously agitated by magnetic-bar stirring.
A protocol for isolation of DNA from tissues was provided by the Qiagen QlAamp DNA Micro Kit. The steps of the Qiagen QlAamp DNA Micro Kit manual were followed with exception that the lysis procedural steps that include adding proteinase K, addition of Qiagen proprietary buffer ‘ATL’, pulse-vortexing, overnight incubation at 56° C., and addition of Qiagen proprietary buffer ‘AL’ were omitted and the aforementioned trypsin incubation was substituted for these steps. Accordingly, ffollowing trypsin proteolysis, 100 μL of 100% ethanol was added to each sample as recommended by Qiagen QlAamp DNA Micro Kit instructions. Samples were then vortexed for 15 seconds, incubated at 25° C. for 5 minutes, then added into separate QIAmp miniElute columns. Columns were closed and centrifuged at 6000×g for one minute. Flow-through was collected as the peptide fraction of the extraction, filtered using a 0.1 μm PTFE filter, and transferred into fresh vials for mass spectrometric analysis (stored at +4.0 to −20° C., or +4 to −12). The bound DNA fraction was then washed according to Qiagen QlAamp DNA Micro Kit instructions and eluted twice into the same collection tube with 20 μL of warm (37° C.) water by centrifugation for one minute (20,000×g).
In the illustration of
The genetic material recovered with the process outlined in
An exemplary illustration of DNA amplification/sequencing is illustrated in
In particular the exemplary list of primers of
In a DNA amplification analysis of mtDNA, PCR was used for amplification of HV mtDNA regions. Amplicons were purified, quantified and sequenced using standard mtDNA protocols.
Exemplary results of PCR amplification of mtDNA recovered using the exemplary combined mtDNA and proteomic analysis sample processing protocol are shown in
The results of the above proteomic and genomic analysis can then be compared with databases to identify the validated marker GVPs to be detected and/or provided in databases herein described.
In particular in
Applicable methods to detect exome sequences of the sample of the biological organism are identifiable by a skilled person.
According to an exemplary protocol blood and buccal samples can be used to perform DNA collection from individuals. DNA is isolated from blood associated with each sample and was subsequently analyzed by Sanger sequencing (2016 Sorenson Genomics, LLC). Full exome sequencing of the extracted DNA was also obtained (10-0111_ACE Research Exome with Secondary Analysis; 8 Gb; Alignment, Variant Calling and Annotation; ©2016 Personalis Inc).
Comparison of detected exome sequences and a database of exome sequences of the biological organism can then be performed. Exemplary databases that can be used comprise protein and genome sequence databases such as Uniprot [24] (www.uniprot.org/), Exome Variant Server (evs.gs.washington.edu/EVS/) Swiss-Prot [25](www.ebi.ac.uk/swissprot/), Ensembl [26] (www.ensembl.org/index.html) can be used to identify genetically variant peptide sequences in proteins. Sequence alignment webservers including BLAST [27] (www.ncbi.nlm.nih.gov/BLAST/), Prowl [28]; (www.prowl.rockefeller.com), and Protein Information Resource [29, 30]; (pir.georgetown.edu/) can be used to determine if peptide sequences are unique to a single human gene.
References is also made to the following documents incorporated herein by reference in their entirety [25-30].
Applicable methods to perform proteomic analysis to detect the peptide sequences are identifiable by a skilled person inclusive of any possible ways to perform a) LC separation of peptides orb) tandem MS analysis (to generate the ‘raw MS data’) c) analysis methods other than LC-MS/MS, e.g. protein quantification, antibody based assays, gel purification/isolation (2d and other),and additional methods.
In an exemplary approach, data acquisition was performed using Thermo Scientific Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer fitted with Easy-nLC 1000 HPLC (Thermo Scientific, Asheville, N.C., USA). Various combinations of liquid-chromatography systems coupled to mass spectrometers, peptide fragmentation techniques, and ionization methods can be used to generate peptide sequence identifications [31, 32]. Peptides were separated by reversed-phase liquid chromatography using a mobile phase A (0.01% TFA in water) and mobile phase B (0.01% TFA in acetonitrile) in a 97 minute gradient. 2 μL of each sample were injected onto a C18 trap cartridge and preceded by an Easy-Spray™ nanoflow (1 mm×150 mm) column (Thermo Scientific, Asheville, N.C., USA) with a flow rate of 3 μL/min. Numerous reversed-phase columns are commercially produced and distributed that are applicable to perform proteomic analysis of peptide sequences [33-35]. Electrospray ionization was achieved in positive mode with a voltage of 2-4 kV. Dynamic exclusion data collection was implemented at a MS scan range of 180-1,800 m/z, top 10 precursor ions were chosen for subsequent MS/MS scans and excluded after 10 seconds.
Due to extremely small quantities of protein solubilized from extractions of a single hair, many conventional quantification assays have insufficient limits of detection for example Bradford assay and UV absorbance measurements at 280 nm [36, 37]. Peptide quantification via fluorometric assay (Pierce™) of small volumes using nano fluorospectrometer (NanoDrop™ 3300 Fluorospectrometer; Thermo Scientific™) is most applicable for the single-hair method [38].
References is also made to the following documents incorporated herein by reference in their entirety [31-38].
Liquid Chromatography and Mass Spectrometry data acquisition was performed using Thermo Scientific Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer fitted with Easy-nLC 1000 HPLC (Thermo Scientific, Asheville, N.C., USA). Peptides were separated by reversed-phase liquid chromatography using a mobile phase A (0.01% TFA in water) and mobile phase B (0.01% TFA in acetonitrile) in a 97 minute gradient. 2 μL of each sample were injected onto a C18 trap cartridge and preceded by an Easy-Spray™ nanoflow (1 mm×150 mm) column (Thermo Scientific, Asheville, N.C., USA) with a flow rate of 3 μL/min. Electrospray ionization was achieved in positive mode with a voltage of 2-4 kV. Dynamic exclusion data collection was implemented at a MS scan range of 180-1,800 m/z, top 10 precursor ions were chosen for subsequent MS/MS scans and excluded after 24 seconds.
Data Analysis was performed using PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) protein identification software was used to search each RAW data file to determine the specific proteins that were identified in each sample. Search settings included partial posttranslational modifications including oxidation of methionine, deamidation of asparagine and glutamine, and hydroxyproline. Precursor mass error of 15 ppm using monoisotopic mass was used for parent ion identifications and a 0.05 Da for fragment ions masses. A decoy database was generated within the software using a protein library of all human protein sequences exported from UniProtKB/Swiss-Prot knowledgebase (The UniProt Consortium; www.uniprot.org/). The decoy database is used to determine the false determination rate (FDR) of protein identifications. Protein identifications (IDs) were filtered by a 1% FDR. Filtered protein IDs found in each individual data file was outputted and aligned using Scaffold proteomics software [39]. IDs were then additionally filtered by having two or more unique peptides detected.
Characterization of genetically variant peptides (GVPs) was performed using the Global Proteome Machine webserver (GPM; www.thegpm.org). Raw data was exported and converted into mgf format using MSconvertGUl (Proteowizard 2.1.×; proteowizard.sourceforge.net) and submitted to the Global Proteome Machine webserver (GPM; www.thegpm.org). Default search settings were used with the exception of the human male NCBI reference protein database, a 20 ppm error for the primary scan, inclusion of complete cysteine carbamidomethylation (C+57), and partial modifications of oxidized methionine (M+16), and deamidation (N+1, Q+1). Results from this search were filtered by single nucleotide polymorphism (SNPs) accessions (rs numbers) to obtain a list of previously characterized potential GVPs.
Genetically Variant Peptide Confirmation from Genetic Sequencing was performed as follows: DNA was isolated from blood associated with each sample and was subsequently analyzed by Sanger sequencing (2016 Sorenson Genomics, LLC). Full exome sequencing of the extracted DNA was also obtained (10-0111_ACE Research Exome with Secondary Analysis; 8 Gb; Alignment, Variant Calling and Annotation; ©2016 Personalis Inc). Genotypes obtained by exome that corresponded to missense variants were used to validate the observation of GVPs in proteomic data. Potential GVP identifications were filtered to cases where proteomic detection of a GVP was correlated to the correct SNP genotype determined in exome sequence data.
Exome validated genetically variant peptides (GVPs) observed in each sample were directly correlated to corresponding genotypes of missense single nucleotide polymorphism (SNP) at each locus. Using the 1000 genomes project database (1000 Genomes Project Consortium, Phase 3) population, random match probabilities (RMP) were calculated for each possible genotype (p=probability allele 1, q=probability allele 2) where both alleles p and q are defined by equation 1.
Genotype frequencies for each locus was calculated depending on heterozygosity of where heterozygous genotypes (2pq) and for minor allele homozygous (p2). Individual profile frequencies (P) were then calculated by implementation of the product rule on each set of observed genotypes and their calculated RMP values (al and for the first locus a2 for the second . . . ; Equation 2)
P(a1a2)=P(p1q1|p12)×P(p2q2|p22) Eq. (2)
In cases where a heterozygous genotype was observed in the exome sequencing data and only one allele was detected in proteomic data, only the probability corresponding to the allele of the observed GVP was considered.
Applicable methods to perform comparing the detected marker exome sequence with the detected peptide sequences to provide a marker genetic protein variation validated for the same of the biological organism, are identifiable by a skilled person.
There are several approaches to validate detected genetically variant peptides. Exemplary methods comprise implementing different protein identification software algorithms, DNA sequencing techniques, and mass spectrometry peptide confirmation. Single-hair method implements program PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) for variant peptide detection.
A reference database created by translating polymorphisms (missense SNPs, insertions, deletions, and stops/gains) that influence protein sequences observed in exome results into mutated protein sequences are used for peptide identification within software parameters. Experimental conditions and instrumental capabilities inform parameters chosen for search. Search settings include partial posttranslational modifications including oxidation of methionine, deamidation of asparagine and glutamine, and carbamidomethylation of cysteine. Precursor mass error of 30 ppm using monoisotopic mass was used for parent ion identifications and a 0.05 Da for fragment ions masses.
Other parameter settings can be chosen depending on instrument dependent metrics including parents and fragment mass errors. Additionally, software program PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) protein identification software can be used to identify putative peptide variants using a specific capability called Spider [40] without using mutated reference databases. Another approach, outlined in [3] uses the Global Proteome Machine webserver (GPM; www.thegpm.org) to detect possible peptide variants. Genetic confirmation of detected peptide variants can be performed by Sanger sequencing [41], whole-exome DNA sequencing, or other DNA sequencing methods [42].
Alternatively, observed genetically variant peptides can be confirmed using synthetic peptide internal standards that can be isotopically labeled [43].
References is also made to the following documents incorporated herein by reference in their entirety [40-43].
Any detectable genetic protein variations can be used in methods and systems herein described as will be understood by a skilled person. Exemplary GVP comprise not only SAPS but also insertions, deletions, and stops variation as will be understood by a skilled person
In particular, insertions, deletions, and stop mutations observed in exome sequencing results can be directly translated into reference mutated databases. Peptide masses reflecting these polymorphisms can also be predicted using in silico proteolysis analysis and targeted mass spectrometry techniques [44]. Targeted mass-spectrometry based techniques including parallel reaction monitoring, selected ion monitoring, or mass inclusion list methods during mass-spectrometry data acquisition can be used to confirm presence of variant peptides in samples [45-47].
References is also made to the following documents incorporated herein by reference in their entirety [44-47]
A schematic comparison of the steps used to perform a top-down approach of the disclosure versus the conventional approaches to identify genetic protein variations is shown in
In particular,
As described herein, the proteome-based discovery approach begins with proteomic analysis, followed by candidate peptide identification, and DNA validation of identified GVPs.
Thus, the proteome-driven approach has limitations such as being a ‘needle in a haystack’ approach that is not compatible with targeted proteomic analysis and relies on manual MS interpretation to identify potential GVPs, wherein potential GVPs are then validated by separate individual genotyping experiments.
In contrast, the exome-driven approach begins with obtaining exome data, allowing identification of relevant SNPs, followed by proteomic validation of GVPs. Thus, the “exome-driven” approach features (1) obtaining exome sequence for each donor, (2) establishing a workflow to identify specific SNPs of interest, (3) targeted proteomic analysis allowing simplified identification of GVPs in raw MS data, and (4) allows a logic-driven GVP selection, identification, and validation process.
A more detailed exemplification of methods according to the bottom-up approach and the top-down approaches are illustrated in the following Examples 14 to 17.
An exemplary method to identify a pooled marker genetic variation database in accordance with embodiments herein described is illustrated in
In particular,
The screened GVPs are then validated by confirming the GVPs comprise missense mutations genetically encoded by SNPs by genomic sequencing to provide validated GVPs. The validated GVPs then are incorporated into a GVP database, which is used for analysis of operational samples, wherein matches to known GVPs provide identity metrics.
An exemplary top-down approach for identification of a panel of GVPs using an “exome-driven” discovery process are outlined in the schematic of
In particular,
The ‘Observed Gene Pool’ built according to the method exemplified in
In the exemplary method illustrated by the schematic of
An exemplary application of a GVP panel of validated markers GVP identified and/or detected using methods and systems herein described is shown in
According to the exemplified exome drive approach shown in
In the ‘Common GVP panel’, GVPs are down selected for common nsSNPs, and a consensus panel is assembled from a large cohort. As described herein, the term “common nsSNPs” refers to nsSNPs having a frequency >1% having a worldwide distribution. A Pooled GVP panel can be provided from a population of individuals, which can then be used for analysis of an operational sample (e.g. a questioned hair sample found at a crime scene), for example in cases where a DNA sample from an individual of interest is not available; thus, identity metrics (such as biogeographic information) can be obtained for the operational sample based on the ‘Pooled GVP Panel’.
An exemplary method to provide a pooled marker genetic variation protein database is shown by
In particular, the schematic of
According to the illustration of
Of these common msSNPs, 64 encoded proteins that were also encoded by genes identified in the ‘Observable Gene Pool’. A list of the exemplary 64 GVPs identified by the approach of
Amount of proteins and number of GVP detectable in a hair sample can be provided with the approach exemplified in the schematics of
According to the approach exemplified in
The related panel of proteins and GVPS is reported in Table 2 below
Identity metrics provide the theoretical probability that any two randomly selected profiles with a given number of loci will match (where each locus encodes a validated GVP and the median match probability for these loci is shown on the y-axis), assuming independence of each locus.
For example, in the illustration of
Accordingly, for a common panel of 64 validated GVPs,
In the exemplary application of the product rule of
For example, many loci for exemplary validated GVPs shown in
It is expected that GVP based identification can be expanded to additional tissue types, and that protein-based identification can be conducted with multiple forensically relevant protein sources, such as hair, bone, teeth, and fingerprint protein.
In an exemplary method illustrated by the schematic of
Accordingly. for an ‘Individual GVP Panel’, GVPs can be down-selected based on low-frequency or ‘rare’ or ‘private’ nsSNPs and the GVP panel is unique to that individual (see
In the exemplary method, proteinaceous material (such as hair, house dust, fingerprint residue, urine/fecal matter, etc.) is collected (“Collection”) from a target location (e.g. a crime scene), wherein in some embodiments the proteinaceous material can comprise proteins originating from multiple contributors. Proteomic analysis of the proteinaceous material then provides a large number of highly complex fragmentation patterns. Spectral matching to a custom identification profile (“Unique synthetic peptide profile”, generated for a particular individual, e.g., following the exemplary method shown in
Accordingly, in the exemplary method illustrated by the schematics of
Successful recovery of trace DNA was performed. In real-world data sets, there is 2% success rate at searchable profile from touch samples. 11% of rape kits result in successful prosecution. Table 3 shows examples of percentage of samples for which a profile is recovered [48].
Exemplary advantages and challenges of a protein-based approach comprise those in Table 4 below.
A large reservoir of genetic variation exists in the proteome: Up to 60 k common variants (>0.5%), an estimated >1700 in the hair proteome alone.
In particular,
This example describes exemplary improved data acquisition approaches to maximize GVP discovery.
Improvements in instrumentation can maximize GVP discovery, for example, use of an advanced hybrid mass spectrometer such as the Q-Exactive Plus, which features nano-LC and nanoelectrospray, and advanced hybrid mass-spectrometry (quadrupole-orbitrap).
Other improved data acquisition approaches comprise use of exclusion lists, wherein data for peaks already collected in previous runs are not collected, and focusing on weaker peaks. Also, use of inclusion lists, wherein data is only collected on a specific list of GVPs that have been previously discovered in other samples, and/or predicted from genomic or proteomic databases. Also, use of improved reference databases, such as those that include all SAPs, wherein more GVPs allow greater power of discrimination.
Incorporation of GVP profiles and DNA based measures of identity can be performed by integrating single tandem repeat (STR) and mitochondrial DNA (mtDNA) genetic information with GVPs, (see
It is expected that analysis of a diverse cohort will reveal markers that are informative of biogeographic background.
An exemplary method is illustrated by the schematic of
Inclusion of informative markers in likelihood ratio (L) and the biostatistical analytical model will enable prediction of biogeographic origin from proteomic data. The use of GVP markers will be validated to predict biogeographic background.
It is expected that comparison of MS data from two different protein samples from one individual will demonstrate the validity of the approaches described herein. For example, it is expected that GVP alleles will be consistent between physiological locations (e.g. hair from head versus body), and that GVP profiles will remain consistent with age, and/or chemical and/or environmental exposure.
In particular, in a study to identify chemical markers in hair that are indicative of exposures to hair dye, exemplary results indicate surfactants comprise the majority of chemicals in hair care products (see
GVP databases can be designed based on the indications provided in the present disclosure comprising marker GVPs for biological organism, a biological organism type or an individual thereof as will be understood by a skilled person.
An exemplary GVP database design is shown in
The above schematics can be implemented by developing a central database resource for GVP and SNP genotyping, comprising web-based queries and data entry, bulk loading of sequencing and LC/MS data, streamlined data access for analysis tools, implemented using Django, a Python-based framework for web/database application development in accordance with the illustration of
An exemplary GVP analysis workflow is shown in
An exemplary tooth sex-linked protein analysis workflow is shown in
In this example, both amelogenin isoforms were identified from modern and archaeological teeth samples.
Touch samples were collected from multiple surfaces, such as those comprising DNA-incompatible materials. Samples were extracted with techniques identifiable by a skilled person. Samples were analyzed for protein coverage (see
Cranial hair shafts and buffy coat DNA were collected from a cohort of 60 self-identifying unrelated European—Americans (EA1, Sorenson Forensics LLC, Salt Lake City). Genomic DNA from each subject was screened using the Investigative LEAD™ Ancestry DNA Test (Sorenson Forensics LLC, Salt Lake City, Utah) and genotype data was generated for 190 SNPs that are ‘Ancestry Informative Markers’, which span all 22 autosomal chromosomes[49]. Nine individuals had measurable non-European admixture and were excluded from the analysis. An additional collection was conducted using cranial hair shaft and nuclear DNA from another cohort of self-identified unrelated European—Americans (EA2, n=15). All material was collected using protocols, informed consents, and questionnaires that were approved by the Institutional Review Boards at Utah Valley University (IRB #00642) and Lawrence Livermore National Laboratory (IRB #11-007). Hair shaft material was also collected from a cohort of five African-American and five Kenyan subjects[50]. Cranial hair shafts were additionally collected from six individuals from two separate archaeological assemblages excavated in London and Kent: three individuals (S1-S3), dating from circa 1750-1850, and three individuals (S4-S6) from a cemetery in active use 1821-1853.
Hair from subjects was processed physically and biochemically and data was acquired as described. Briefly, hair was ground or milled; treated in a solution of urea, DTT, and detergent; alkylated; and then proteolyzed with trypsin. Resulting peptide mixtures were analyzed using tandem liquid chromatography mass spectrometry. The resulting proteomic datasets were converted to the Mascot generic format and analyzed using three different approaches: Mascot (software version 2.2.03, Matrix Science, Inc., Boston, Mass.), X!Tandem, using the GPM manager software (www.thegpm.org, release SLEDGEHAMMER (2013.09.01)), or X!Tandem using the Petunia Graphic User Interface (TANDEM CYCLONE TPP, download=2011.12.01.1—LabKey, Insilicos, ISB). A custom protein reference database was used (51 Methods; zenodo.org/record/58223: DOI: 10.5281/zenodo.58223) to ensure the identification of genetically variant peptides by both Mascot and the Petunia GUI peptide spectra matching algorithms[51]. Resulting peptide lists were screened for the presence of genetically variant peptides and identifications were collated for each subject. Inferences made through the use of GPM manager or the use of the customized reference database, in either X!Tandem or MASCOT, were compared for redundancy 0. The mass spectrometry proteomics data that has been submitted to the Global Proteome Machine (www.thegpm.org,) can be publicly accessed[52].
Identified candidate genetically variant peptides were filtered to reduce false-positive assignment using the following criteria for exclusion: low-quality expectation scores (X!Tandem, log(e)<−2; Mascot, expectation score >0.05), if the corresponding nsSNPs were distributed at less than 0.8% in the sample population (minor allelic frequency <0.4%), the presence of masses in a MS/MS fragmentation spectrum from a GVP consistent with the alternative allele, the incorporation of biological post-translational modifications in the assigned sequence (such as phosphorylation), and high variance between theoretical and observed primary masses (>0.2 Da). Amino acid polymorphisms assigned due to likely chemical modification or conversion were also excluded from the analysis (www.unimod.org)[53-55]. Rejected single amino acid polymorphisms include methionine to phenylalanine, asparagine to aspartate, glutamine to glutamate and cysteine to serine[53, 55, 56]. Peptides that were potentially derived from paralogous sequences, or that were potentially expressed in more than one gene product, were removed from the analysis. Inferred nsSNP loci were directly validated by Sanger sequencing of the subjects' nuclear DNA.
An estimation of the probability of a given inferred nsSNP allele profile being detected in a sample population was calculated using a frequentist estimation of allele frequency, or frequency of an allele combination, within the reading frame of a gene (Pr(inferred nsSNP allele gene combinationipopulation)), and a Bayesian application of the product-rule[57, 58]. The occurrence of alleles, or allele combinations, was counted in European (n=379) and African (n=246) sample populations (www.1000genomes.org; Phase 1)[59]. The 1000 Genome Project sample populations were selected as sample populations because the African population did not have European admixture. The final probability of an individual SNP, or SNP combination, occurring within a gene reading frame, was estimated as (x+½)/(n+1), where x is the number of individuals with a given SNP, or combination of SNPs, in a sample population of size n[60]. The above expression represents the Bayesian posterior mean of a binomial probability using the Jeffreys Beta (½, ½) prior, which has the advantage of giving a non-zero estimate of the population probability even for x=0[60, 61]. Full independence between genes was assumed.
The effect of observed allele variation on the overall profile probability was estimated by parametric bootstrap resampling from a binomial (n, (x +½)/(n+1)) distribution for each gene, multiplying the resulting probability estimates across genes, and taking the 5th and 95th percentiles of the resampling distribution (90% CI)[61]. A comparison of the inferred nsSNP profile probability in the sample European and African population was calculated as a likelihood (L) ratio (L=Pr(profilelEUR population)/Pr(profilelAFR population))[57].
An exemplary method is described to perform a same sample mitochondrial/proteomics genetic variation detection and database building according to the following steps of the instant disclosure.
Applicable method to perform preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis, are identifiably by a skilled person upon reading of the instant disclosure
In an exemplary approach using preparation methods of the instant disclosure, single hair samples (1 inch; 25 mm) from separate individuals were carefully measured and cut into four equal pieces. The cut hair was then placed into separate Protein LoBind Eppendorf tubes. 100 μL of extraction buffer containing 0.05 M ammonium bicarbonate (ABC), 0.1 M dithiothreitol (DTT), 2% sodium dodecanoate (SDD) was added to each tube. Samples were then incubated at 70° C. in an ultrasonic water bath (Elma) while being ultrasonicated at high energy and frequency settings for 60 minutes or until hair was completely dissolved into solution. SDD was removed by extraction with acidified ethyl acetate (pH 2-3, 0.75% trifluoroacetic acid). After addition of 100 uL acidified ethyl acetate to each tube, samples were quickly vortexed, incubated at room temperature for 5 min, and centrifuged for 5 min at max speed (20,000×g). The upper organic phase was removed, discarded to waste, and the extraction process was repeated once. The remaining lower aqueous phase was then readjusted to pH 8 with ABC [13]. Alternative step includes cold acetone precipitation overnight and resuspension of protein pellet into 0.05M ABC; 0.1M DTT; and 1% protease max. Carbamidomethylation of free cysteines was performed by adding 6 μL of iodoacetamide (1.0 M) and incubation for 60 min in the dark at 25° C. To further solubilize proteins, 0.01% protease max (3 μL of 1.0% w/v) was added to each sample. Prior to proteolysis, the solubilized protein solution was concentrated to 50uL using 10 kD molecular weight spin concentrators (Millipore). Trypsin (2 μL of 0.5 μg/μL) was then added to each protein sample. Protein digestion was performed at 25° C. for 20/22 hours while being continuously agitated by magnetic-bar stirring. Protocol for isolation of DNA from tissues was provided by the Qiagen Q1Aamp® DNA Micro Kit. Manual suggestions were following with exception to the lysis procedural steps that include adding proteinase K, additional of proprietary buffer ‘ATL’, pulse-vortexing, overnight incubation at 56° C., and addition of proprietary buffer ‘AL’. Previous trypsin incubation was substituted for these steps. Following trypsin proteolysis, 100 uL of 100% ethanol was added to each sample as recommended by Qiagen Q1Aamp® DNA Micro Kit instructions. Removing this set and not adding ethanol also yields amplifiable mtDNA from sample. Samples were then vortexed for 15 seconds, incubated at 25° C. for 5 minutes, then added into separate QIAmp miniElute columns. Columns were closed and centrifuged at 6000×g for one minute. Flow-through was collected as the peptide fraction of the extraction, filtered using 0.1 μm PTFE filter, and transferred into fresh vials for mass spectrometric analysis (stored at +4.0 -−20° C.). Additional step of speed vacuum (20 minutes at 60° C.) can be used to concentrate peptide fraction of samples. The bound mtDNA fraction was then washed according to Qiagen Q1Aamp® DNA Micro Kit instructions and eluted twice into the same collection tube with 25 uL of warm (37° C.) water by centrifugation for one minute (20,000×g).
Applicable method to perform fractionating the processed biological sample to obtain solubilized protein fraction and a solubilized DNA fraction can also be identified by a skilled person.
In particular a solubilized protein fraction comprising the solubilized proteins from the sample can be obtained by the following exemplary SDD extraction and protein concentration procedure step which includes cold acetone precipitation (−4° C.) overnight and resuspension of protein pellet into 0.05M ABC; 0.1M DTT; and 1% protease max. Additional step of speed vacuum (20 minutes at 60° C.) can be used to concentrate peptide fraction of samples subsequent to proteolysis step.
A solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample can be provided with the following exemplary method. Following trypsin proteolysis, 100 uL of 100% ethanol was added to each sample as recommended by Qiagen Q1Aamp® DNA Micro Kit instructions. Removing this set and not adding ethanol also yields amplifiable mtDNA from sample.
Applicable methods to perform detecting a genetic protein variation in the solubilized protein fraction from the sample by performing the proteomic analysis of the solubilized protein fraction are identifiable by a skilled person. in an exemplary method MS/MS data acquisition of peptide sequences was performed using Thermo Scientific Q Exactive Plus Hybrid Quadrupole-Orbitrap mass spectrometer fitted with Easy-nLC 1000 HPLC (Thermo Scientific, Asheville, N.C., USA). Peptides were separated by reversed-phase liquid chromatography using a mobile phase A (0.01% TFA in water) and mobile phase B (0.01% TFA in acetonitrile) in a 97 minute gradient. 2 of each sample were injected onto a C18 trap cartridge and preceded by an Easy-Spray™ nanoflow (1 mm×150 mm) column (Thermo Scientific, Asheville, N.C., USA) with a flow rate of 3 μL/min. Electrospray ionization was achieved in positive mode with a voltage of 2-4 kV. Dynamic exclusion data collection was implemented at a MS scan range of 180-1,800 m/z, top 10 precursor ions were chosen for subsequent MS/MS scans and excluded after 10 seconds.
Single-hair method implements program PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) for variant peptide detection. PEAKs software was used to search each RAW data file to determine the specific peptides that were identified in each sample. A reference database created by translating polymorphisms (missense SNPs, insertions, deletions, and stops/gains) that influence protein sequences observed in exome results into mutated protein sequences is used for peptide identification within software parameters. Experimental conditions and instrumental capabilities inform parameters chosen for search. Search settings include partial posttranslational modifications including oxidation of methionine, deamidation of asparagine and glutamine, and carbamidomethylation of cysteine. Precursor mass error of 30 ppm using monoisotopic mass was used for parent ion identifications and a 0.05 Da for fragment ions masses. A decoy database was generated within the software using a protein library of all human protein sequences exported from UniProtKB/Swiss-Prot knowledgebase (The UniProt Consortium; www.uniprot.org/). The decoy database is used to determine the false determination rate (FDR) of protein identifications. Protein identifications (IDs) were filtered by a 1% FDR. Data output from PEAKs searches including identified peptides, quality measures, and protein sequence position is then filtered for peptides containing predicted mutations using in-house text mining scripts.
Applicable method to perform detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction; including methods to detect mitochondrial DNA variation or STR variation are identifiable by a skilled person, in an exemplary method to amplify mitochondrial control regions, PCR amplification was carried out with the following set of primers: F15975 and R16410m for HV1, F015 and R389 for HV2, F403 and R635 for HV3 in 50 ul reaction volumes with Q5 Hot Start High-Fidelity 2× Master Mix (New England Biolabs, Inc, Ipswich, Mass., USA), containing 0.2 uM each forward and reverse primers and 5 ul genomic DNA. Amplification was carried out on a PTC-200 DNA Engine (MJ Research, Waltham, MA, USA) under the following conditions: 98° C. for 2 min; 15 cycles of 98° C. for 10 s, 56° C. for 30 s, 72° C. for 30 s; 25 cycles of 98° C. for 20 s, 56° C. for 30 s, 72° C. for 30 s+10 s/cycle; and a final extension at 72° C. for 2 min. PCR amplicons were gel purified on a 2.0% agarose gel using QlAquick Gel Extraction Kit (Qiagen Inc, Germantown, Md., USA) according to the manufacturer's instructions with the exception the DNA was eluted with 35 ul EB Buffer. Purified PCR amplicons were visualized via gel electrophoresis on 2.0% agarose and quantified using QuBit 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, Mass., USA). DNA sequencing was performed using a Big Dye Terminator v3.1 Cycle Sequencing Kit (Thermo Fisher Scientific, Waltham, Mass., USA) with the following cycling conditions: 96° C. for 1 min; 30 cycles of 96° C. for 10 s, 50° C. for 5 s, 60° C. for 2 min. Sequencing reactions were analyzed on an ABI 3500 Genetic Analyzer (Applied Biosystems). Primers used for sequencing were the appropriate primers used during amplification. The results were analyzed and de novo assembled using Geneious R9.1.8 (Biomatters Ltd, Auckland, NZ). To ensure sequence data quality, each genomic DNA was amplified and sequenced in duplicate.
mtDNA variants were detected by alignment using Clustal multiple sequence alignment tool [62, 63]. mtDNA mutation database MitoMaster [63] was used in addition to confirm prior record of the observed mutations.
Applicable methods to perform combining the detected genetic protein variations and the detected genomic variation to provide the marker genetic variation database system of the biological sample, are identifiable by a skilled person. in an exemplary method Mutant genotypic frequencies available in mtDNA mutation database MitoMaster (Brandon 2009) and Ensembl [26] (www.ensembl.org/index.html)corresponding to the observed genetic variations in both peptides and mtDNA hyper-variable control regions were combined by calculating random match probabilities for each individual.
Comparing the Detected Genetic Protein Variation and/or the Detected Genomic Variation with a Marker Genetic Protein Variation and/or of a Marker Genomic Variation
Applicable methods to perform comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system are identifiable by a skilled person.
Exemplary methods include a range of possibilities from simply taking the two comparisons as independent verification of identity match or exclusion between samples or it could include a combined statistical model that taken into account the appropriate statistical metrics (e.g. random match probability) of both the proteomic marker(s) and the genetic marker(s) to give an overall greater statistical measure.
An example GVP analysis for a sample tissue can be broken down into the following parts, as shown in
Part 5: Analyze “hits” Process steps 1-3 describe the data analysis process that is used to extract relevant genetic information from exome data and relating those to detectable proteins, thereby identifying genetic markers for potential detectable GVPs. Those process steps can be used to provide a proteomically detectable genomic variation in a set of represented genes proteomically detectable in the biological sample of the individual.
Applicable methods to perform providing a set of represented genes proteomically detectable in the biological sample of the individual, are identifiable by a skilled person upon reading of the instant disclosure, wherein the represented genes correspond to the proteomically detected proteins in the biological sample of the individual.
In an exemplary approach, for a single-hair approach herein described implements program PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) for variant peptide detection. A reference database created by translating polymorphisms (missense SNPs, insertions, deletions, and stops/gains) that influence protein sequences observed in exome results into mutated protein sequences are used for peptide identification within software parameters. Search settings include partial posttranslational modifications including oxidation of methionine, deamidation of asparagine and glutamine, and carbamidomethylation of cysteine. Precursor mass error of 30 ppm using monoisotopic mass was used for parent ion identifications and a 0.05 Da for fragment ions masses. Additionally, software program PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Ontario, Canada) protein identification software can be used to identify putative peptide variants using a specific capability called Spider [40] without using mutated reference databases. Another approach, outlined in [3] uses the Global Proteome Machine webserver (GPM; www.thegpm.org) to detect possible peptide variants.
In particular, process step 2 described the process to extract information of interest from exome results, down select to preferred mutations, add supporting information. This particular step filters the exome data to down select for proteins that we know we can see proteomically. This step can be used to perform selecting from the identified genetic variation, a genetic variation detectable in the sample of the biological organism.
Process step 4 describes the process for identifying peptides in proteomic data output from raw MS datafile analysis (e.g. using PEAKS, GPM or other commercial proteomic search tool) that contain mutations predicted by the exome data analysis performed in steps 1-2 (iiid above). This step can be used to perform providing the marker genetic protein variation validated by providing a proteomically detectable genetic protein variation corresponding to the proteomically detectable genomic variation in the biological sample of the individual.
Process step 5 describes combining results of hits identified in step 4 above, applying filters (e.g. peptide is only coded for by the identified gene). Results in a summary file that provides a pooled set of GVPs for a plurality of individuals. This step can be used to perform providing a number of proteomic datasets of individuals of the plurality of individuals, the number statistically significant for the plurality of individuals, including how to determine a statistically significant number of datasets.
Process step 5 also describes combining results of hits identified in step 4 above, applying filters (e.g. peptide is only coded for by the identified gene). Results in a summary file that provides a pooled set of GVPs for a plurality of individuals and includes information on commonality, allele frequency and any additional genetic or statistical information required. This step can be used to identify identifying a protein common to the provided number of proteomic datasets; including threshold and ranges of percentage of commonality of observed proteins.
Process step 5 further describes combining results of hits identified in 4 above, applying filters (e.g. peptide is only coded for by the identified gene). Results in a summary file that provides a pooled set of GVPs for a plurality of individuals and includes information on commonality, allele frequency and any additional genetic or statistical information required. This step can also be used to perform selecting from the identified protein common to the provided proteomic datasets, a protein detectable in the sample of the individuals of the plurality of individuals
The tissue file (e.g. Tissue.txt) can be created by picking genes that appear frequently in a set of MS files as taken for a range of samples of a given tissue type (e.g. skin300g.txt, hair691g.txt, skhr838g.txt).
An example tissue file content is shown in Table 5. The required fields in this example are the standard gene symbol and CHR (“standard gene symbol” has an entrezID number, as in hg19 or hg38).
PART 1: Extract Information of Interest from Exome Files and Annotate Under GRCh38
Read-in list of target genes—tissue.txt.
Read-in VCF file—fname.svindeLvar.vcfgz (gzipped version).
Read meta data to confirm genomic coordinates (expecting 37d.5): e.g. VCF file L2_0051 reference is: hs37d5.fa .
Create TAB IX if none exist—fname.svindeLvar.vcfgz.tbi .
Subset VCF to target genes.
Extract all mutations in the subset VCF, clean-up formatting and data types. Carry through exome quality metrics for each entry .
Remove entries with filter of LowQual or “.” (i.e. “to poor to call”). (See Table 6—note VQSR ranking).
Drop cases where ALT is a coma-delimited list. (See
“Lift over” genomic coordinates from GRCh37 to GRCh38 (This example uses GRCh38.10).
Error check to confirm all SNPs collected conform to GRCh38, drop any deviants.
Summarize: L2_0051 / hr691g—921 unique mutations processed.
Translate each surviving mutation into HGVS notation per varnomen.hgvs.org .
Write (no row names, no column names, one entry per row)—fname_tissue_hgvs.txt.
ZZ
18:g.46098362_46098363insACCCCC
18:g.63499047_63499048insTATATA
17:g.82081885_82081942de1
8:g.143729168C>G
2:g.131218699G>C
21:g.44627940C>T
Write companion file with linkage information for each mutation—fname_tissue_link.txt. The link file carries CHR, START, END, and rsID, which are not used beyond this point in the pipeline. (See
Submit fname_tissue_hgvs.txt to ensembl Variant Effects Predictor (VEP) for GRCh38
PART 2: Extract Information of Interest from Annotated VCF, Down Select to Preferred Mutations, Add Supporting Information
For the mutations submitted as L2_0051_hr691g_hgvs.txt , ensembl VEP replies are as shown in
Recover the annotation results from VEP fname_tissue_annt.txt. The VEP annotation might contain all available G1000 and ExAC. AFs, SIFT, and Polyphen scores can be added. Note: as of Aug. 24, 2017 G1000 remains, but ExAC is replaced by gnomAD.
Read-in annotations. An example is shown in
Down-select to:
From bioMart add: Swissprot PA number (if not, then trEMBL PA number), APPRIS rank, ensembl external_transcript_name. Where an rsID is not returned, use a shortened version of HGVS call as the mutation “name” under dbSNP. Carry through G1000, G1000_EUR, ExAC, ExAC_NFE (as of Aug. 24, 2017, carry through gnomAD_AF and gnomAD_NFE_AF).
Read in link file fname_tissue_link.txt, add-on related REF, ALT, GATK and exome quality metrics. (see
Summarize:
Write mutations to target—fname_tissue_extract.txt. Note:*extract.txt created to support workflows where *extract.txt from different exomes are combined into a *predicted*extract.txt. For example: Combine L4_0001 (P1) and L4_0002 (P2) to predict the child (L4_0003) as L4_0003_T1_p12xc_tissue_extract.txt for Triad1 parents predict child where child's exome is L4_0003.
PART 3: Mutate Protein Sequences and Create FASTA Files Suitable for Use with PEAKs
Assumptions applied in GEN I mutations code:
PEAKs identifies a transcript by and passes-through the AccessionlEntry_Name portion of the FASTA header:
Read in mutations to target—fname_tissue_extract.txt.
Convert all frameshifts to SNPs as X/* (“*” indicates a stop and “X” indicates a wild-card in the AA sequence) .
Detect multiple SNPs/codon events and compute change from combination, update mutations list.
Subset to genes that are mutated (i.e. drop genes that carry only synonymous mutations).
From bioMart upload AA sequences for all transcripts that may be called on. Within a gene: de-duplicate for transcripts that have identical AA sequences.Drop any transcript that carries an X (i.e. a wild-card AA).
Process the AA sequence for each transcript remaining. Apply stops (stop-gained and frameshifts) and trim AA sequence to length. Apply remaining SNPs that are in-range. Apply INDELs that are in range, process from tail-to-snout (as INDELs will accordion the sequence).
Generate FASTA headers for mutant and reference sequences.
Write: mutated AA sequences in FASTA format—fname_tissue_mutant_fasta.txt—and reference AA sequences in FASTA format—fname_tissue_ref_fasta.txt.
Submit for PEAKs analysis: use combination of “Base” protein list and mutant/ref FASTA.
PART 4: From PEAKs Result, Find Peptides that Carry Programed—For Mutations
Read-in PEAKs output (fname_tissueprotein-peptides.csv) and down-select to columns of interest (see
Extract peptide sequence (remove PTMs and any lead/tail AA) e.g. R.TSC(+57.02)SSRPC(+57.02)V.P becomes TSCSSRPCV (SEQ ID NO. 127).
Separate PA and symbol, replace any UniprotKB Entry_Name with the standard gene name (e.g. replace KTP11 with KRTAP1-1).
Down select to those Protein Groups that carry a called-unique peptide assigned to a mutated transcript, and where the Peptide Group contains only the one mutated gene (may be a combination of mutated, reference and base transcripts from the one gene). Meaning of Unique in PEAKs output: The peptide (sans PTM) was detected uniquely within the present analysis. Such a called-unique peptide can be assigned to more than one transcript and/or gene. Each called-unique peptide is assigned to one Protein Group. There may be more than one called-unique peptide in a Protein Group. There may be more the one gene in a Protein Group. Filter by gene since Uniprot Entry_Name may not be a valid gene symbol for purposes of this example.
Read-in mutated FASTAfname_tissue_mutant_fasta.txt. Within each transcript (mutant FASTA entry) and for the selected Protein Groups:
Read-in fname_tissue_extract.txt . For “hits” (i.e. a programmed-for mutation found in a called-unique peptide) update entry with information about the mutation (e.g. dbSNP, AF' s, etc.).
Write out documented hits (group, peptide w/wo PTMs, MS meta data, mutation, AFs, GATK . . . )—fname_tissue_resu.txt.
Read in hits results across a sample family—L4_0001_hair691g_resu.txt through L4_0063_hair691g_resu.txt (say)
Determine which peptides that carry the hits are unique within some test protein set:
Write—L4_resu_summary.txt (symbol, dbSNP, peptide sans PTM, no match, MS meta data, mutation meta data, AFs, GATK, file tag . . . )
Write—L4_resu_exec_summary.txt (symbol, dbSNP, no match in dnSNP, AFs, all file tags carrying this mutation) (see
Create a tissue set
Retention time analysis/prediction
Exclusion list
An example GVP analysis for a sample tissue can also be broken down into the following parts, generally described as:
Parts 1 to 5 of the present example can be performed with methods similar to the ones indicated in Example 41 modified in view of the indications provided in the present example as will be understood by a skilled person upon reading of the present disclosure.
An exemplary set of genes that can be used in methods and systems herein described as well as in related databases is reported herein. In particular, the exemplary set of genes comprises genes validated as proteomically detectable in hair samples of Homo Sapiens which can be used in methods and systems to detect a genetic variation and/or perform a genetic variation analysis where the biological organism is a human being, as well as in related databases, in accordance with the various aspects of the present disclosure.
Specifically, Table 8 shows a list of exemplary genes that appear in MS files taken for samples of a hair of a human being. The fields in this example indicate the preference (X=more preferred), the standard gene symbol (gene symbol), the chromosome where the gene is located (chr), a description of the gene (gene description) and the gene identifier in the database Ensembl at the date of filing of the instant disclosure (Ensembl Gene Identifier).
The exemplary genes of Table 8 can therefore be used in methods and systems of the disclosure wherein the sample comprises an hair sample from human beings,
An exemplary set of genes that can be used in methods and systems herein described as well as in related databases is reported herein. In particular, the exemplary set of genes comprises genes validated as proteomically detectable in bone samples of a Homo Sapiens which can be used in methods and systems to detect a genetic variation and/or perform a genetic variation analysis wherein the biological organism is a human being, as well as in related databases, in accordance with the various aspects of the present disclosure.
Specifically, Table 9 shows a list of exemplary genes that appear in MS files taken for samples of a bone of human beings. The fields in this example are the preference (X=more preferred), the standard gene symbol (gene symbol), the chromosome where the gene is located (chr), a description of the gene (gene description) and the gene identifier in the database Ensembl at the date of filing of the instant disclosure (Ensembl Gene Identifier).
The exemplary genes of Table 9 can be therefore used in particular in methods and systems of the disclosure wherein the sample comprises a bone sample from human beings.
An exemplary set of genes that can be used in methods and systems herein described as well as in related databases is reported herein. In particular, the exemplary set of genes comprises genes validated as proteomically detectable in skin samples of Homo Sapiens which can be used in methods and systems to detect a genetic variation and/or perform a genetic variation analysis, as well as in related databases, in accordance with the various aspects of the present disclosure.
Specifically, Table 10 shows a list of exemplary genes that appear in MS files taken for skin samples of human beings. The fields in this example are the preference (X=more preferable), the standard gene symbol (gene symbol), the chromosome wherein the gene is located (chr), a description of the gene (gene description) and an identifier in the database Ensembl at the date of filing of the instant disclosure (Ensembl Gene Identifier).
The exemplary genes of Table 10 can be used in particular in methods and system of the disclosure wherein the sample comprises a skin sample from human beings.
An exemplary set of GVPs that can be used in methods and systems herein described as well as in related databases is reported herein. In particular, the exemplary set of GVP comprises genes validated as proteomically detectable in hair samples of a Homo Sapiens which can be used in methods and systems to detect a genetic variation and/or perform a genetic variation analysis, as well as in related databases, in accordance with the various aspects of the present disclosure.
Specifically, Table 11 shows a list of exemplary GVP detectable in hair samples of human beings. The fields in Table 11 are the chromosome where the gene is located (CHR), the gene name (gene name), mutation identifier (mutation ID), the sequence of the corresponding mutated peptide (mutated peptide (GVP)), the related sequence identifier in the sequence listing of the instant disclosure (SEQ ID NO), and the subpopulations including all populations (ALL), Non-Finnish European subpopulation (NFE), African subpopulation (AFR), East Asian subpopulation (EAS), South Asian subpopulation (SAS), and Latino subpopulation (AMR).
The exemplary GVPs of Table 11 can be therefore be used in methods and systems of the disclosure wherein the sample comprises hair samples from human beings.
An exemplary set of GVPs that can be used in methods and systems herein described as well as in related databases is reported herein. In particular, the exemplary set of GVPs comprises genes validated as proteomically detectable in skin samples of a Homo Sapiens which can be used in methods and systems to detect a genetic variation and/or perform a genetic variation analysis, as well as in related databases, in accordance with the various aspects of the present disclosure.
Specifically, Table 12 shows a list of exemplary GVP detectable in skin samples. The fields in Table 12 are the name of the gene (gene name), mutation identifier (mutation ID), sequence of the mutated peptide (mutated peptide (GVP)), sequence identifier in the sequence listing of the instant disclosure (SEQ ID NO), and the subpopulations including all populations (ALL), Non-Finnish European subpopulation (NFE), African subpopulation (AFR), East Asian subpopulation (EAS), South Asian subpopulation (SAS), and Latino subpopulation (AMR).
The exemplary GVPs of Table 12 can be used in method and systems of the instant disclosure wherein the sample comprises a skin sample from human beings.
In summary according to the first aspect, a method is described to prepare a biological sample for proteomic analysis, the method comprising applying to the biological sample an energy field resulting in an increased thermodynamic or total energy of the sample to obtain a processed biological sample comprising solubilized proteins to be used in the proteomic analysis.
In a first set of embodiments of the method of the first aspect, applying to the biological sample an energy field is performed by sonication and in particular by sonication baths, sonication probes, or flow-through sonication systems. In a second set of embodiments of the method of the first aspect which can comprise the method of the first aspect performed according to the first set of embodiments, the biological sample is hair and/or skin. In a third set of embodiments of the method of the first aspect which can comprise the method of the first aspect performed according to the first set of embodiments of the method of the first aspect, the biological sample can be bone or teeth.
In summary according to the second aspect, a method is described to provide a marker genetic protein variation of a biological organism in a biological sample of the biological organism.
detecting exome sequences of the sample of the biological organism by sequencing exomes of a genome from the sample of the biological organism;
detecting a marker exome sequence comprising a genetic variation of the genome of the biological organism by comparing the detected exome sequences with a database of exome sequences of the biological organism;
detecting peptide sequences of the sample of the biological organism by performing proteomic analysis of the sample of the biological organism; and providing the marker genetic protein variation of the biological organism in the sample of the biological organism by comparing the detected marker exome sequence with the detected peptide sequences to provide a marker genetic protein variation validated for the same of the biological organism.
In a first set of embodiments of the method of the second aspect, the biological organism is Homo sapiens. In a second set of embodiments of the method of the second aspect which can comprise the method of the second aspect performed according to the first set of embodiments, the biological sample is hair.
According to the second aspect of the disclosure, a marker genetic protein variation of a biological organism is also described. The marker genetic protein variation of the second aspect is validated for a sample of the biological organism, and is obtainable and obtained by any one of the method according to the second aspect.
In summary according to the third aspect, a method is described to improve a marker genetic protein variation database system including data for at least one biological organism. The method comprises
producing a mass spectrometry dataset from a biological sample from an individual of the at least one biological organism;
comparing the mass spectrometry dataset to a protein variant database to produce a set of proteomically detected proteins in the biological sample of the individual;
providing a set of represented genes proteomically detectable in the biological sample of the individual, the represented genes corresponding to the proteomically detected proteins in the biological sample of the individual; and
identifying a marker genetic protein variation validated for the biological sample of the individual, to be included in the marker genetic protein variation database system by
providing a proteomically detectable genomic variation in the set of represented genes proteomically detectable in the biological sample of the individual, and
providing the marker genetic protein variation validated genetic protein variation by providing a proteomically detectable genetic protein variation corresponding to the proteomically detectable genomic variation in the biological sample of the individual.
In a first set of embodiments of the method of the third aspect, providing the marker validated genetic protein variation, further comprises: providing a mass spectrometry dataset from the biological sample of the individual; and comparing the provided mass spectrometry dataset with the proteomically detectable genetic protein variation to provide the validated genetic protein variation.
In a second set of embodiments of the method of the third aspect which can comprise the method of the third aspect performed according to the first set of embodiments, providing a proteomically detectable genomic variation in the set of represented genes proteomically detectable in the biological sample of the individual is performed by providing exome sequence data of the individual; and comparing the exome sequence data of the individual with sequences from the represented genes proteomically detectable in the biological sample of the individual to determine the proteomically detectable genomic variation in the biological sample of the individual.
In a third set of embodiments of the method of the third aspect which can comprise the method of the third aspect performed according to the first set of embodiments or the second set of embodiments, providing a proteomically detectable genetic protein variation corresponding to the proteomically detectable genomic variation in the biological sample of the individual, is performed by: performing annotation on the proteomically detectable genomic variation in the biological sample of the individual to produce a corresponding mutant/reference protein sequence; and providing the proteomically detectable genetic protein variation from the annotated proteomically detectable genomic variation in the biological sample of the individual.
In a fourth set of embodiments of the method of the third aspect, which can comprise the method of the third aspect performed according to the first set of embodiments, the second set of embodiments or the third set of embodiments, the method further comprises creating a genetic protein variation identity panel by collecting the validated genetic protein variant proteomically detectable in the biological sample of the individual to provide a genetic protein variation identity panel of the individual.
In a fifth set of embodiments of the method of the third aspect, which can comprise the method of the third aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments or the fourth set of embodiments, the steps are repeated for a plurality of individuals of the at least one biological organism, to provide a database comprising validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals of the biological organism type.
In a first subset of embodiments of the fifth set of embodiments of the method according to the third aspect, the method further comprises: collecting the represented genes common to the plurality of the individuals into a proteomically detectable gene pool; providing validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals of the at least one biological organism from the collected common represented; and collecting the validated genetic protein variant proteomically detectable in the biological sample of the plurality of individuals, in the genetic protein variation panel is a genetic protein variation panel common to the plurality of individuals.
In a second subset of embodiments of the fifth set of embodiments of the method according the third aspect, the proteomically detectable gene pool contains data corresponding to proteins that are common to over 50% of all the validated genetic protein variant proteomically detectable in the biological sample of the individual.
In some embodiments of the first subset of embodiments or the second subset of embodiments of the fifth set of embodiments of the method according to the third aspect, the providing validated genetic protein variations proteomically detectable in the biological sample of the plurality of individuals is performed to only include genomic variation with a frequency greater than 1% in the plurality of the individuals into a proteomically detectable gene pool.
In a sixth set of embodiments of the method of the third aspect, which can comprise the method of the third aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments, the fourth set of embodiments or the fifth set of embodiments comprising any related subsets of embodiments, the at least one biological organism is Homo sapiens.
In a seventh set of embodiments of the method of the third aspect, which can comprise the method of the third aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments, the fourth set of embodiments, the fifth set of embodiments comprising any related subsets of embodiments, or the sixth set of embodiments, the biological sample is hair or skin.
According to the third aspect, a marker genetic protein variation database system is also described obtainable and/or obtained by the methods according to third aspect, which comprises the method of the third aspect performed according to any one of the related sets or subsets of embodiments.
In summary according to the fourth aspect, a method is described to improve a marker genetic protein variation database system comprising marker genetic protein variations common to a plurality of individuals. The method comprises
providing a number of proteomic datasets of individuals of the plurality of individuals, the number statistically significant for the plurality of individuals;
identifying a protein common to the provided number of proteomic datasets;
selecting from the identified protein common to the provided proteomic datasets, a protein detectable in a biological sample of an individual of the plurality of individuals;
providing a number of exome datasets of the individuals of the plurality of individuals, the number statistically significant for the plurality of individuals;
identifying a genetic variation in the provided number of exome datasets;
selecting from the identified genetic variation, a genetic variation detectable in the biological sample; and
comparing the selected proteins detectable in the biological sample with the selected genetic variations detectable in the biological sample,
to provide a marker genetic protein variation common to a plurality of individuals of a biological organism type and detectable in the biological sample.
In a first set of embodiments of the method of the fourth aspect, the individual is a Homo sapiens.
In a second set of embodiments of the method of the fourth aspect which can comprise the method of the fourth aspect performed according to the first set of embodiments, the biological sample is hair.
According to the fourth aspect, a marker genetic protein variation database system is also described, comprising genetic protein variations common to a plurality of individuals. The genetic protein variation database system is obtainable by the method according to sixth aspect, which comprises the method of the fourth aspect performed according to any one of the related sets of embodiments.
In summary, according to the fifth aspect a method is described to detect a genetic protein variation in a biological sample. The method comprises
providing a marker mass spectrum of a marker peptide comprising a marker genetic protein variation corresponding to the genetic protein variation;
performing mass spectrometry of a fractionated digested peptide of the biological sample to obtain a mass spectrum of each of the fractionated digested peptide; and
comparing the mass spectrum of the fractionated digested peptide with a marker mass spectrum of a marker peptide comprising the marker genetic protein variation to detect the genetic protein variation in the biological sample.
In a first set of embodiments of the method according to the fifth aspect, the fractionated digested peptides are obtained by preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in the protein analysis, fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample, digesting the solubilized proteins from the sample with a site specific proteolytic enzyme to obtain digested solubilized proteins from the sample, and fractionating the digested solubilized proteins to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample.
In a first subset of embodiments of the first set of embodiments of the method of the fifth aspect preparing the biological sample is performed according to the method of the first aspect of the disclosure comprising any one of the related sets of embodiments.
In a second set of embodiments of the method of the fifth aspect which can comprise the method of the fifth aspect performed according to the first set of embodiments, the marker peptide comprises a plurality of marker peptides each comprising a marker genetic protein variation.
In a third set of embodiments of the method of the fifth aspect which can comprise the method of the fifth aspect performed according to the first set of embodiments or the second set of embodiments, the marker genetic protein variation comprises a marker genetic protein variation according to the second aspect of the disclosure.
In a fourth set of embodiments of the method of the fifth aspect which can comprise the method of the fifth aspect performed according to the first set of embodiments, the second set of embodiments or the third set of embodiments, the marker genetic protein variation comprises a marker genetic protein variation from a marker genetic protein variation database system according to the third aspect of the disclosure comprising any one of the related sets of embodiments.
In a fifth set of embodiments of the method of the fifth aspect which can comprise the method of the fifth aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments or the fourth set of embodiments, the marker genetic protein variation comprises a marker genetic protein variation from a marker genetic protein variation database system according to the fourth aspect of the disclosure comprising any one of the related sets of embodiments.
In summary according to the sixth aspect, a method is described to provide a marker genetic variation database system for a biological sample. The method comprises:
preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis.
fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample and a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample;
detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction;
detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction; and combining the detected genetic protein variations and the detected genomic variation to provide the marker genetic variation database system of the biological sample.
In a first set of embodiments of the method according to the sixth aspect, preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis, is performed by the method of the first aspect, comprising any one of the related sets of embodiments.
In a second set of embodiments of the method of the sixth aspect which can comprise the method of the sixth aspect performed according to the first set of embodiments, detecting a genetic protein variation is performed by the method according to the fifth aspect comprising any one of the related sets and subsets of embodiments.
In a third set of embodiments of the method of the sixth aspect which can comprise the method of the sixth aspect performed according to the first set of embodiments or second sets of embodiments, the genetic protein variation is a single amino acid polymorphism (SAP), an amino acid deletion and/or an amino acid insertion.
In a fourth set of embodiments of the method of the sixth aspect which can comprise the method of the sixth aspect performed according to the first set of embodiments, second sets of embodiments or third sets of embodiments, the genomic variation is a single nucleotide polymorphism (SNP), a nucleotide deletion or a nucleotide insertion.
In a fifth set of embodiments of the method of the sixth aspect which can comprise the method of the sixth aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments or the fourth set of embodiments, the genomic variation is within the short tandem repeat (STR) regions of the genome.
In a sixth set of embodiments of the method of the sixth aspect which can comprise the method of the sixth aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments the fourth set of embodiments or the fifth set of embodiments, the genomic variation is within the mitochondrial DNA.
According to the sixth aspect, a marker genetic variation database system is also described obtainable by the method according to the sixth aspect of the disclosure, comprising any one of the related sets of embodiments.
In summary according to the seventh aspect, a method is described to detect a marker genetic variation in a biological sample of a biological organism. The method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis;
fractionating the processed biological sample to obtain
a solubilized protein fraction comprising the solubilized proteins from the sample and
a solubilized DNA fraction comprising solubilized nuclear and/or mitochondrial genome from the sample;
detecting a genetic protein variation in the solubilized proteins from the sample by performing the proteomic analysis of the solubilized protein fraction;
detecting a genomic variation of the nuclear and/or mitochondrial genome by performing a genetic analysis of the solubilized DNA fraction; and
comparing the detected genetic protein variation and/or the detected genomic variation with a marker genetic protein variation and/or of a marker genomic variation respectively from the marker genetic variation database system of the sixth aspect of the disclosure.
In a first set of embodiments of the method according to the seventh aspect, preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis, is performed by the method according to the first aspect of the disclosure comprising any one of the related sets of embodiments.
In a second set of embodiments of the method of the seventh aspect which can comprise the method of the seventh aspect performed according to the first set of embodiments, detecting a genetic protein variation is performed by the method according to the fifth aspect of the disclosure comprising any one of the related sets and subsets of embodiments.
In a third set of embodiments of the method of the seventh aspect which can comprise the method of the seventh aspect performed according to the first set of embodiments or second sets of embodiments, the genetic protein variation is a single amino acid polymorphism (SAP), an amino acid deletion and/or an amino acid insertion.
In a fourth set of embodiments of the method of the seventh aspect which can comprise the method of the seventh aspect performed according to the first set of embodiments, second sets of embodiments or third sets of embodiments, the genomic variation is a single nucleotide polymorphism (SNP), a nucleotide deletion or a nucleotide insertion.
In a fifth set of embodiments of the method of the seventh aspect which can comprise the method of the seventh aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments or the fourth set of embodiments, the genomic variation is within the short tandem repeat (STR) regions of the genome.
In a sixth set of embodiments of the method of the seventh aspect which can comprise the method of the seventh aspect performed according to the first set of embodiments, the second set of embodiments, the third set of embodiments or the fourth set of embodiments, the genomic variation is within the mitochondrial DNA.
In summary according to the eight aspect of the disclosure, a method is described to perform genetic analysis of a sample of a biological organism. The method comprises preparing the biological sample to obtain a processed biological sample comprising solubilized proteins to be used in a proteomic analysis;
fractionating the processed biological sample to obtain a solubilized protein fraction comprising the solubilized proteins from the sample;
digesting the solubilized protein fraction from the sample to obtain digested peptides from the sample;
fractionating the digested peptides to obtain fractionated digested peptides from the digested solubilized proteins from the biological sample.
detecting a marker genetic variation of the fractionated digested peptides from the sample; in which
preparing the sample is performed according to any one of the methods according to the first aspect of the disclosure, comprising any one of the related sets of embodiments ; and/or
detecting a genetic variation is performed by at least one of
the method to detect a genetic protein variation of any one of the methods according to the fifth aspect, comprising any one of the related sets and subsets of claims; and
the method to detect a genetic variation of any one of the methods according to the seventh aspect of the disclosure comprising any one of the related sets of embodiments.
Preferably in any one of the embodiments of the method to perform genetic analysis of a sample of a biological organism of the eight aspect the preparing is performed according to any one of the methods according to the first aspect of the disclosure, comprising any one of the related sets of embodiments and the detecting is performed at least one of the method to detect a genetic protein variation of any one of the methods according to the fifth aspect, comprising any one of the related sets and subsets of claims; and the method to detect a genetic variation of any one of the methods according to the seventh aspect of the disclosure comprising any one of the related sets of embodiments.
In view of the above, in summary described herein are methods and systems to perform genetically variant protein analysis and related marker genetic protein variations and databases, which in several embodiments allow performing a reliable genetic variation protein analysis in biological samples of different types and conditions taking into account the features of the biological sample where the analysis is performed. The examples set forth above are provided to give those of ordinary skill in the art a complete disclosure and description of how to perform the embodiments of the methods and systems of the disclosure, and are not intended to limit the scope of what the inventors regard as their disclosure. Those skilled in the art will recognize how to adapt the features of the exemplified methods and systems herein disclosed to additional methods and systems according to various embodiments and scope of the claims.
All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the disclosure pertains.
The entire disclosure of each document cited (including patents, patent applications, journal articles, abstracts, laboratory manuals, books, or other disclosures) in the Background, Summary, Detailed Description, and Examples is hereby incorporated herein by reference. All references cited in this disclosure are incorporated by reference to the same extent as if each reference had been incorporated by reference in its entirety individually. However, if any inconsistency arises between a cited reference and the present disclosure, the present disclosure takes precedence. Further, the computer readable form of the sequence listing of the ASCII text file IL-13212-Sequence-Listing_ST25 is incorporated herein by reference in its entirety.
The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the disclosure claimed. Thus, it should be understood that although the disclosure has been specifically disclosed by embodiments, exemplary embodiments and optional features, modification and variation of the concepts herein disclosed can be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this disclosure as defined by the appended claims.
It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. The term “plurality” includes two or more referents unless the content clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure pertains.
When a Markush group or other grouping is used herein, all individual members of the group and all combinations and possible sub-combinations of the group are intended to be individually included in the disclosure. Every combination of components or materials described or exemplified herein can be used to practice the disclosure, unless otherwise stated. One of ordinary skill in the art will appreciate that methods, system elements, and materials other than those specifically exemplified may be employed in the practice of the disclosure without resort to undue experimentation. All art-known functional equivalents, of any such methods, device elements, and materials are intended to be included in this disclosure. Whenever a range is given in the specification, for example, a temperature range, a frequency range, a time range, or a composition range, all intermediate ranges and all subranges, as well as, all individual values included in the ranges given are intended to be included in the disclosure. Any one or more individual members of a range or group disclosed herein may be excluded from a claim of this disclosure. The disclosure illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein.
A number of embodiments of the disclosure have been described. The specific embodiments provided herein are examples of useful embodiments of the disclosure and it will be apparent to one skilled in the art that the disclosure can be carried out using a large number of variations of the genetic circuits, genetic molecular components, and methods steps set forth in the present description. As will be obvious to one of skill in the art, methods and systems useful for the present methods and systems may include a large number of optional composition and processing elements and steps.
In particular, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. Accordingly, other embodiments are within the scope of the following claims.
Analytical and bioanalytical chemistry, 2007. 389(4): p. 1017-1031.
The present application claims priority to U.S. Provisional Application No. 62/555,001, entitled “Methods and Systems to Perform Genetically Variant Protein Analysis, and Related Marker Genetic Protein Variations and Databases” filed on Sep. 6, 2017 with docket number IL-13212, the content of which is incorporated herein by reference in its entirety.
The invention was made with Government support under Contract No. DE-AC52-07NA27344 between the U.S. Department of Energy and Lawrence Livermore National Security, LLC, for the operation of Lawrence Livermore National Security. The Government may have certain rights to the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2018/049775 | 9/6/2018 | WO | 00 |
| Number | Date | Country | |
|---|---|---|---|
| 62555001 | Sep 2017 | US |
