Patent Application
20100093100
The presently-disclosed subject matter relates to methods for condition diagnosis and monitoring, biomarker discovery, composition screening, and therapeutic monitoring using sample profiling. In particular, the presently-disclosed subject matter relates to sample fraction profiling methods that make use of fractionation techniques in combination with differential scanning calorimetery (DSC) and use of resulting thermograms. The presently-disclosed subject matter further relates to identification of sample components responsible for alteration of thermograms in subjects having a condition of interest.
Biological samples (e.g., plasma, serum, blood, urine, saliva, etc.) are complex samples that contain thousands of individual polypeptides that are present in quantities that range from picograms to tens of milligrams per milliliter. The expression of specific proteins and specific changes in protein expression levels in samples from a subject can be associated with specific conditions, e.g., disease, stage or progression of a condition, infection, etc. As such, analysis of protein levels and changes in protein levels can provide information useful for purposes such as condition diagnosis and therapeutic monitoring. In the clinical setting, certain diagnostic tests include obtaining proteomic profiles of biological samples collected from a patient. Such diagnostic tests search for protein biomarkers or changes in expression of certain proteins found in biological samples, which can often be easily obtained from patients using minimally invasive, safe procedures.
A number of FDA-approved plasma and serum diagnostic assays currently exist; for example, serum and plasma electrophoresis, and a variety of immunochemical assays can be used to monitor the concentrations of specific proteins in plasma and serum. These existing low-to-moderate resolution assays have had a practical impact on medical diagnosis. Such assays can provide useful information at early stages of a disease, allowing for intervention and improved outcomes for patients, with lower associated monetary costs. However, specific protein levels or changes in protein levels associated with conditions of interest can be small, relative to the overall levels of proteins in a given fluid sample. As such, the sensitivity of a method for analyzing protein levels should be such that relatively low levels and minor fluctuations can be detected.
Developments in proteomics have brought increased interest human biological samples containing protein, such as the human plasma and serum proteome, as a source for biomarkers of human disease. Higher resolution methods like 2-D electrophoresis and mass spectrometry, coupled with often elaborate protocols for sample preparation and fractionation, have made it possible to identify apparent changes in the composition of the less abundant proteins and peptides in plasma that correlate with particular diseases. Typically no single protein emerges from such analyses as a wholly reliable biomarker, but instead changes in the patterns of panels of proteins often serve as the best diagnostic for a particular malady. These patterns often involve protein or peptide components of plasma that are present in low concentrations.
Interest in the array of existing proteins in a patient's biological sample has thus evolved to consider in more detail the low molecular weight peptides the sample, e.g., serum, which represent a mixture of small intact proteins plus degradation fragments of larger proteins. The low molecular weight region of the serum proteome has been dubbed the “peptidome,” and has been touted as a “treasure trove of diagnostic information that has largely been ignored . . . ” See Liotta and Petricoin, J. Clin. Invest. (2006), and Liotta, et al., Nature (2003). Although some consider the peptidome “unidentified flying peptides,” and have questioned the reliability of peptidome SELDI (surface-enhanced laser desorption ionization) patterns as a meaningful diagnostic until the functions of all of the peptide peaks in the peptidome have been properly identified, mass spectrometry, in particular SELDI methods, have made the peptidome accessible for analysis. See Anderson, Proteomics (2005). Many components of the “peptidome” have been found to be complexed with more abundant serum proteins, particular human serum albumin (HAS) and immunoglobulins. Such findings led to the concept of an “interactome,” which introduces the added complexity that serum and plasma can be “comprised of a ‘network’ of protein-protein and peptide-protein interactions,” in which potential biomarkers are bound to the more abundant proteins within the fluid. See Zhou, et al., Electrophoresis (2004). Interestingly, the paper that introduced the “interactome” concept concludes by saying that “the discovery of novel biomarkers in serum/plasma requires new biochemical and analytical approaches, and, most importantly, it is clear that no single sample preparation or detection method will suffice if biomarker investigations are to be broadly successful using current technologies.” See Zhou, et al., Electrophoresis, (2004).
Ten proteins make up 90% of the mass of plasma (by weight). These are, in order of abundance: albumin, IgG, Fibrinogen, Transferrin, IgA, α2-macroglobulin, α1-antitrypsin, complement C3, IgM and Haptoglobin. Another 12 proteins account for another 9% of the plasma mass, the 3 most abundant of which are the apolipoproteins A1 and B, and α1-acid glycoprotein. Twenty-two proteins thus comprise 99% of the mass of plasma, making it challenging to fractionate and quantify the remaining 1%.
The FDA-approved serum protein electrophoresis method monitors changes in the most abundant protein population. See O'Connell, et al., Am. Fam. Physician (2005). However, this method has sensitivity limitations and does not adequately detect changes in less-abundant proteins. Additionally, the equipment necessary for practicing this serum protein electrophoresis method is costly to obtain and maintain.
More recently, 2-D gel electrophoresis and mass spectrometry assays have been developed, which allow for detection of the least abundant components of plasma; however, samples must be prepared by following laborious prefractionation protocols to rid the plasma/serum of the proteins present in high concentrations. See Anderson, Proteomics (2005); Anderson and Anderson, Electrophoresis (1991); Gygi and Aebersold, Curr Opin Chem Biol (2000); Liotta, et al., JAMA (2001); Yates, Trends Genet (2000); and Adkin, et al., Mol Cell Proteomics (2002). Additionally, these assays are time consuming and the equipment necessary for practicing these methods can be costly to obtain and maintain.
Although, the proteomes of biological samples, e.g., plasma proteome, holds great promise as a convenient specimen for disease diagnosis and therapeutic monitoring, existing assays and technologies have various drawbacks, including sensitivity limitations, time and efficiency limitations, and associated costs that can be prohibitive. Additionally, existing assays and technologies do not fully exploit the biological samples as a source for biomarkers. For example, electrophoresis and mass spectrometry both separate plasma proteins based on protein size and charge, but assays and technologies based on other physical properties of protein are lacking.
Accordingly, there remains a need in the art for a method for obtaining and exploiting proteomic profiles of samples, which will address the above-mentioned drawbacks of existing technologies.
The presently-disclosed subject matter meets some or all of the above-identified needs, as will become evident to those of ordinary skill in the art after a study of information provided in this document.
This Summary describes several embodiments of the presently-disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently-disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.
The presently-disclosed subject matter includes a method of identifying biomarkers useful for diagnosing a condition of interest in a subject, which includes: providing a test sample associated with the condition of interest; fractionating the test sample to obtain fractions of the test sample; generating a signature thermogram for at least one fraction of the test sample; comparing the signature thermogram to a sibling standard thermogram; and determining whether the signature thermogram is a good simulation or a poor simulation of the sibling standard thermogram.
In some embodiments, the sibling standard thermogram is a sibling positive standard thermogram generated using a positive control sample including a candidate biomarker. In some embodiments, the candidate biomarker is selected from a protein, a nucleic acid, a phospho lipid, and a small organic molecule. In some embodiments, the candidate biomarker is identified as an actual biomarker when the signature thermogram of a fraction of the test sample is a good simulation of sibling positive standard thermogram of the candidate biomarker.
In some embodiments the method of identifying useful biomarkers includes providing a negative control sample associated with an absence of the condition of interest; fractionating the control sample to obtain sibling fractions of the control sample; wherein the sibling standard thermogram is a sibling negative standard thermogram generated for a sibling fraction of the negative control sample; and identifying a fraction of the test sample having a unique component relative to the sibling fraction of the negative control sample due to the signature thermogram being a poor simulation of the sibling negative standard thermogram. In some embodiments, the method also includes testing the fraction of the test sample having a unique component to determine the identity of the unique component; and classifying the identified unique component as a biomarker useful for diagnosing the condition of interest.
In some embodiments of the method of identifying biomarkers, the fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the chromatographic fractionation is conducted using gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, or high pressure liquid chromatography (HPLC). In some embodiments, the mass spectroscopy is conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. In some embodiments, the fractionation results in fraction including different size classes of proteins.
In some embodiments of the method of identifying biomarkers, the condition of interest is selected from the group consisting of: a cancer, an autoimmune disease, and a microbial infection. In some embodiments, the condition is a cancer selected from the group consisting of: brain cancer, central nervous system (CNS) cancer, cervical cancer, endometrial cancer, lung cancer, leukemia, lymphoma, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer. In some embodiments, the cancer is selected from: a cancer of glial cells, including astrocytes, oligodendrocytes, ependymal cells; a cancer of neurons; a cancer of lymphatic tissue; a cancer of blood vessels; a cancer of cranial nerves; a cancer of the brain envelope; a cancer of the pitutitary gland; a cancer of the pineal gland; a metastatic cancer of the brain, and a secondary cancer, wherein the primary cancer is a brain cancer. In some embodiments, the cancer is selected from: grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and glyoblastoma mutiforme. In some embodiments, the condition of interest is a stage of cervical cancer selected from: moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer. In some embodiments, the condition of interest is an autoimmune disease. In some embodiments, the autoimmune disease is selected from: rheumatoid arthritis, multiple sclerosis, and systemic lupus. In some embodiments, the condition of interest is caused by a bacterial infection. In some embodiments, the condition is Lyme disease. In some embodiments, the condition of interest is caused by a viral infection. In some embodiments, the condition is selected from: Dengue fever, and hepatitis. In some embodiments, the condition of interest is selected from: amyotrophic lateral sclerosis (ALS), anemia, cardiac disease, diabetes, and renal disease.
In some embodiments of the method of identifying biomarkers, the samples are selected from: plasma sample, serum sample, a blood sample, an ascites fluid sample, a cerebral spinal fluid sample, a peritoneal fluid sample, a saliva sample, a senovial fluid sample, an ocular fluid sample, and a urine sample.
The presently-disclosed subject matter includes a system for identifying biomarkers useful for diagnosing a condition of interest in a subject, comprising: means for accepting a sample; means for fractionating a test sample and/or a control to obtain fractions of the test sample and/or the control sample; means for generating a signature thermogram for at least one fraction of the test sample and/or a standard thermogram for at least one fraction of the control sample; means for comparing a signature thermogram to a sibling standard thermogram; and means for determining whether the signature thermogram is a good simulation or a poor simulation of the sibling standard thermogram. In some embodiments, the system also includes means for testing the fraction of the test sample having a unique component to determine the identity of the unique component, which unique component is identified as a biomarker useful for diagnosing the condition of interest.
The presently-disclosed subject matter includes a method of diagnosing or monitoring a condition of interest in a subject, which includes providing a test sample to a subject; fractionating the test sample to obtain fractions of the test sample; generating a signature thermogram for each fraction of the test sample; comparing a signature thermogram with a sibling standard thermogram and/or a sibling signature thermogram; and identifying a status of the subject. In some embodiments, the standard thermogram is selected from a positive standard thermogram associated with a presence of the condition of interest, and a negative standard thermogram associated with an absence of the condition of interest. In some embodiments, the method also includes providing multiple standard thermograms associated with different conditions of interest. In some embodiments, the multiple positive standard thermograms include positive standard thermograms for different stages of a condition of interest.
In some embodiments of the method of diagnosing or monitoring a condition of interest in a subject, the method also includes identifying the status of the subject as having the condition of interest when the signature thermogram of a fraction of the test sample is a poor simulation of the negative standard thermogram; and/or the signature thermogram of a fraction of the test sample is a good simulation of the positive standard thermogram; and identifying the status of the subject as lacking the condition of interest when the signature thermogram of a fraction of the test sample is a good simulation of the negative standard thermogram; and/or the signature thermogram of a fraction of the test sample is a poor simulation of the positive standard thermogram.
In some embodiments of the method of diagnosing or monitoring a condition of interest in a subject, the method also includes providing a second test sample obtained from the subject at a time point that is differs from a time point that the test sample is obtained; fractionating the second test sample to obtain fractions of the second test sample; generating a signature thermogram for a fraction of the second test sample; comparing the signature thermogram of the second test sample to the sibling signature thermogram of the test sample; and identifying the status of the subject has having changed if the signature thermogram of the second test sample is a poor simulation of the sibling standard thermogram of the test sample.
In some embodiments of the method of diagnosing or monitoring a condition of interest in a subject, the method also includes providing a control sample; fractionating the control sample to obtain sibiling fractions of the control sample; generating a sibling standard thermogram for the sibling fractions of the control sample; comparing the signature thermogram with the sibling standard thermogram. In some embodiments, the control sample is selected from: a positive control sample, wherein a series of sibling positive standard thermograms are generated; and a negative control sample, wherein a series of sibling negative standard thermograms are generated. In some embodiments, the method also includes identifying the status of the subject as having the condition of interest when at least one signature thermogram of a fraction of the test sample is a poor simulation of the sibling negative standard thermogram; and/or at least one signature thermogram of a fraction of the test sample is a good simulation of the sibling positive standard thermogram; and identifying the status of the subject as lacking the condition of interest when each signature thermogram of the fractions of the test sample is a good simulation of each sibling negative standard thermogram; and/or each signature thermogram of the fractions of the test sample is a poor simulation of each sibling positive standard thermogram.
In some embodiments of the method of diagnosing or monitoring a condition of interest, the fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the chromatographic fractionation is conducted using gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, or high pressure liquid chromatography (HPLC). In some embodiments, the mass spectroscopy is conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. In some embodiments, the fractionation results in fraction including different size classes of proteins.
In some embodiments of the method of diagnosing or monitoring a condition of interest, the condition of interest is selected from the group consisting of: a cancer, an autoimmune disease, and a microbial infection. In some embodiments, the condition is a cancer selected from the group consisting of: brain cancer, central nervous system (CNS) cancer, cervical cancer, endometrial cancer, lung cancer, leukemia, lymphoma, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer. In some embodiments, the cancer is selected from: a cancer of glial cells, including astrocytes, oligodendrocytes, ependymal cells; a cancer of neurons; a cancer of lymphatic tissue; a cancer of blood vessels; a cancer of cranial nerves; a cancer of the brain envelope; a cancer of the pitutitary gland; a cancer of the pineal gland; a metastatic cancer of the brain, and a secondary cancer, wherein the primary cancer is a brain cancer. In some embodiments, the cancer is selected from: grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and glyoblastoma mutiforme. In some embodiments, the condition of interest is a stage of cervical cancer selected from: moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer. In some embodiments, the condition of interest is an autoimmune disease. In some embodiments, the autoimmune disease is selected from: rheumatoid arthritis, multiple sclerosis, and systemic lupus. In some embodiments, the condition of interest is caused by a bacterial infection. In some embodiments, the condition is Lyme disease. In some embodiments, the condition of interest is caused by a viral infection. In some embodiments, the condition is selected from: Dengue fever, and hepatitis. In some embodiments, the condition of interest is selected from: amyotrophic lateral sclerosis (ALS), anemia, cardiac disease, diabetes, and renal disease.
In some embodiments of the method of diagnosing or monitoring a condition of interest, the samples are selected from: plasma sample, serum sample, a blood sample, an ascites fluid sample, a cerebral spinal fluid sample, a peritoneal fluid sample, a saliva sample, a senovial fluid sample, an ocular fluid sample, and a urine sample.
The presently-disclosed subject matter includes a system for diagnosing or monitoring a condition of interest in a subject, which includes means for accepting a sample; means for fractionating a test sample and/or a control to obtain fractions of the test sample and/or the control sample; means for generating a signature thermogram for at least one fraction of the test sample and/or a standard thermogram for at least one fraction of the control sample; means for comparing a signature thermogram with a sibling standard thermogram and/or a sibling signature thermogram; and means for determining whether the signature thermogram is a good simulation or a poor simulation of the sibling standard thermogram and/or a sibling signature thermogram.
The presently-disclosed subject matter includes a method of assessing a treatment program for a subject, comprising: providing a first test sample obtained from the subject at a first time point of interest; fractionating the first test sample to obtain a first series of fractions; generating a first series of signature thermograms for the first series of fractions of the first test sample; providing a second test sample obtained from the subject at a second time point of interest; fractionating the second test sample to obtain a second series of fractions; generating a second series of signature thermograms for the second series of fractions of the second test sample; comparing the first series of signature thermograms to the second series of signature thermograms; and identifying the presence or absence of a change in the condition of interest. In some embodiments, the first time point of interest occurs before the initiation of the treatment program, and the second time point of interest occurs after the initiation of the treatment program.
In some embodiments, the method of assessing a treatment program for a subject also includes identifying the treatment program as maintaining the status of the subject when each second signature thermogram of the fractions of the second sample is a good simulation of the sibling first signature thermograms of the sibling fractions of the first sample; and identifying the treatment program as changing the status of the subject when at least one second signature thermogram of the fractions of the second sample is a poor simulation of the sibling first signature thermogram of the sibling fraction of the first sample.
In some embodiments of the method of assessing a treatment program for a subject, the fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the chromatographic fractionation is conducted using gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, or high pressure liquid chromatography (HPLC). In some embodiments, the mass spectroscopy is conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. In some embodiments, the fractionation results in fraction including different size classes of proteins.
In some embodiments of the method of assessing a treatment program for a subject, the condition of interest is selected from the group consisting of: a cancer, an autoimmune disease, and a microbial infection. In some embodiments, the condition is a cancer selected from the group consisting of: brain cancer, central nervous system (CNS) cancer, cervical cancer, endometrial cancer, lung cancer, leukemia, lymphoma, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer. In some embodiments, the cancer is selected from: a cancer of glial cells, including astrocytes, oligodendrocytes, ependymal cells; a cancer of neurons; a cancer of lymphatic tissue; a cancer of blood vessels; a cancer of cranial nerves; a cancer of the brain envelope; a cancer of the pitutitary gland; a cancer of the pineal gland; a metastatic cancer of the brain, and a secondary cancer, wherein the primary cancer is a brain cancer. In some embodiments, the cancer is selected from: grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and glyoblastoma mutiforme. In some embodiments, the condition of interest is a stage of cervical cancer selected from: moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer. In some embodiments, the condition of interest is an autoimmune disease. In some embodiments, the autoimmune disease is selected from: rheumatoid arthritis, multiple sclerosis, and systemic lupus. In some embodiments, the condition of interest is caused by a bacterial infection. In some embodiments, the condition is Lyme disease. In some embodiments, the condition of interest is caused by a viral infection. In some embodiments, the condition is selected from: Dengue fever, and hepatitis. In some embodiments, the condition of interest is selected from: amyotrophic lateral sclerosis (ALS), anemia, cardiac disease, diabetes, and renal disease.
In some embodiments of the method of assessing a treatment program for a subject, the samples are selected from: plasma sample, serum sample, a blood sample, an ascites fluid sample, a cerebral spinal fluid sample, a peritoneal fluid sample, a saliva sample, a senovial fluid sample, an ocular fluid sample, and a urine sample.
The presently-disclosed subject matter includes a method of screening for a composition useful for treating a condition of interest, which includes interacting a sample associated with the condition of interest with a candidate composition; fractionating the sample to obtain a series of fractions; generating a series of signature thermograms for the series of fractions; comparing the series of signature thermograms to sibling standard thermograms; and determining the utility of the candidate composition.
In some embodiments of the method of screening for a composition useful for treating a condition of interest, the fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the chromatographic fractionation is conducted using gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, or high pressure liquid chromatography (HPLC). In some embodiments, the mass spectroscopy is conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. In some embodiments, the fractionation results in fraction including different size classes of proteins.
In some embodiments of the method of screening for a composition useful for treating a condition of interest, the condition of interest is selected from the group consisting of: a cancer, an autoimmune disease, and a microbial infection. In some embodiments, the condition is a cancer selected from the group consisting of: brain cancer, central nervous system (CNS) cancer, cervical cancer, endometrial cancer, lung cancer, leukemia, lymphoma, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer. In some embodiments, the cancer is selected from: a cancer of glial cells, including astrocytes, oligodendrocytes, ependymal cells; a cancer of neurons; a cancer of lymphatic tissue; a cancer of blood vessels; a cancer of cranial nerves; a cancer of the brain envelope; a cancer of the pitutitary gland; a cancer of the pineal gland; a metastatic cancer of the brain, and a secondary cancer, wherein the primary cancer is a brain cancer. In some embodiments, the cancer is selected from: grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and glyoblastoma mutiforme. In some embodiments, the condition of interest is a stage of cervical cancer selected from: moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer. In some embodiments, the condition of interest is an autoimmune disease. In some embodiments, the autoimmune disease is selected from: rheumatoid arthritis, multiple sclerosis, and systemic lupus. In some embodiments, the condition of interest is caused by a bacterial infection. In some embodiments, the condition is Lyme disease. In some embodiments, the condition of interest is caused by a viral infection. In some embodiments, the condition is selected from: Dengue fever, and hepatitis. In some embodiments, the condition of interest is selected from: amyotrophic lateral sclerosis (ALS), anemia, cardiac disease, diabetes, and renal disease.
In some embodiments of the method of screening for a composition useful for treating a condition of interest, the samples are selected from: plasma sample, serum sample, a blood sample, an ascites fluid sample, a cerebral spinal fluid sample, a peritoneal fluid sample, a saliva sample, a senovial fluid sample, an ocular fluid sample, and a urine sample. The presently-disclosed subject matter includes a system for screening for a composition useful for treating a condition of interest, which includes means for accepting a sample; means for fractionating the sample to obtain a series of fractions; means for generating a series of signature thermograms for the series of fractions; and means for comparing the series of signature thermograms to sibling standard thermograms.
The presently-disclosed subject matter includes a method of screening a composition for plasma protein interactions, comprising: interacting the composition with a first plasma sample; fractionating the first plasma sample to obtain a first series of fractions; generating a first series of signature thermograms for the first series of fractions; comparing the first series of signature thermograms to sibling negative standard thermograms associated with an absence of plasma protein interactions; and/or a sibling second series of signature thermogram generated using a second series of fractions from a second plasma sample not interacted with the composition; and identifying the composition as lacking substantial plasma protein interactions when the first series of signature thermograms are good simulations of the sibling negative standard thermograms, and/or the sibling second series of signature thermograms.
In some embodiments of the method of screening a composition for plasma protein interactions, the fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the chromatographic fractionation is conducted using gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, or high pressure liquid chromatography (HPLC). In some embodiments, the mass spectroscopy is conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS. In some embodiments, the fractionation results in fraction including different size classes of proteins.
The presently-disclosed subject matter includes a system for screening a composition for plasma protein interactions, which includes means for fractionating a plasma sample to obtain a first series of fractions; means for generating a first series of signature thermograms for the first series of fractions; means for comparing the first series of signature thermograms to sibling negative standard thermograms associated with an absence of plasma protein interactions; and/or a sibling second series of signature thermogram generated using a second series of fractions from a second plasma sample not interacted with the composition; and means for identifying the composition as lacking substantial plasma protein interactions when the first series of signature thermograms are good simulations of the sibling negative standard thermograms, and/or the sibling second series of signature thermograms.
The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.
While the terms used herein are believed to be well understood by those of ordinary skill in the art, definitions are set forth herein to facilitate explanation of the presently-disclosed subject matter.
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 presently-disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are now described.
Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.
Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.
As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.
The presently-disclosed subject matter includes a method of diagnosing a condition of interest in a subject; a method of monitoring a condition of interest in a subject; a method for assessing the efficacy of a treatment program for a subject; a method of screening for compositions useful for treating a condition of interest; a method of identifying biomarkers for diagnosing a condition of interest in a subject; and a method of screening a composition for plasma protein interactions, including tendency of the composition to bind serum albumin.
As used herein, the term condition of interest refers to a variety of conditions, including cancers, autoimmune diseases, and microbial infections.
In some embodiments, the condition of interest can be a cancer. The term “cancer” refers to all types of cancer or neoplasm or malignant tumors found in animals, including leukemias, carcinomas, melanoma, and sarcomas. Examples of cancers include brain cancer, including, cancer of astrocytes, oligodendrocytes, ependymal cells; a cancer of neurons; a cancer of lymphatic tissue; a cancer of blood vessels; a cancer of cranial nerves; a cancer of the brain envelope; a cancer of the pitutitary gland; CNS lymphoma, CNS leukemia, metastatic cancer found in the brain; and secondary cancers wherein the primary cancer is brain cancer. Additional examples of cancers include cancer of the bladder, breast, cervix, colon, central nervous system (CNS), endometrium, head and neck, kidney, lung, non-small cell lung, leukemia, lymphoma, melanoma, multiple myeloma, mesothelioma, ovary, prostate, sarcoma, stomach, uterus, vulva, and Medulloblastoma.
By “leukemia” is meant broadly progressive, malignant diseases of the blood-forming organs and is generally characterized by a distorted proliferation and development of leukocytes and their precursors in the blood and bone marrow. Leukemia diseases include, for example, acute nonlymphocytic leukemia, chronic lymphocytic leukemia, acute granulocytic leukemia, chronic granulocytic leukemia, acute promyelocytic leukemia, adult T-cell leukemia, aleukemic leukemia, a leukocythemic leukemia, basophylic leukemia, blast cell leukemia, bovine leukemia, chronic myelocytic leukemia, leukemia cutis, embryonal leukemia, eosinophilic leukemia, Gross' leukemia, hairy-cell leukemia, hemoblastic leukemia, hemocytoblastic leukemia, histiocytic leukemia, stem cell leukemia, acute monocytic leukemia, leukopenic leukemia, lymphatic leukemia, lymphoblastic leukemia, lymphocytic leukemia, lymphogenous leukemia, lymphoid leukemia, lymphosarcoma cell leukemia, mast cell leukemia, megakaryocytic leukemia, micromyeloblastic leukemia, monocytic leukemia, myeloblastic leukemia, myelocytic leukemia, myeloid granulocytic leukemia, myelomonocytic leukemia, Naegeli leukemia, plasma cell leukemia, plasmacytic leukemia, promyelocytic leukemia, Rieder cell leukemia, Schilling's leukemia, stem cell leukemia, subleukemic leukemia, and undifferentiated cell leukemia.
The term “carcinoma” refers to a malignant new growth made up of epithelial cells tending to infiltrate the surrounding tissues and give rise to metastases. Exemplary carcinomas include, for example, acinar carcinoma, acinous carcinoma, adenocystic carcinoma, adenoid cystic carcinoma, carcinoma adenomatosum, carcinoma of adrenal cortex, alveolar carcinoma, alveolar cell carcinoma, basal cell carcinoma, carcinoma basocellulare, basaloid carcinoma, basosquamous cell carcinoma, bronchioalveolar carcinoma, bronchiolar carcinoma, bronchogenic carcinoma, cerebriform carcinoma, cholangiocellular carcinoma, chorionic carcinoma, colloid carcinoma, comedo carcinoma, corpus carcinoma, cribriform carcinoma, carcinoma en cuirasse, carcinoma cutaneum, cylindrical carcinoma, cylindrical cell carcinoma, duct carcinoma, carcinoma durum, embryonal carcinoma, encephaloid carcinoma, epiennoid carcinoma, carcinoma epitheliale adenoides, exophytic carcinoma, carcinoma ex ulcere, carcinoma fibrosum, gelatiniform carcinoma, gelatinous carcinoma, giant cell carcinoma, carcinoma gigantocellulare, glandular carcinoma, granulosa cell carcinoma, hair-matrix carcinoma, hematoid carcinoma, hepatocellular carcinoma, Hurthle cell carcinoma, hyaline carcinoma, hypemephroid carcinoma, infantile embryonal carcinoma, carcinoma in situ, intraepidermal carcinoma, intraepithelial carcinoma, Krompecher's carcinoma, Kulchitzky-cell carcinoma, large-cell carcinoma, lenticular carcinoma, carcinoma lenticulare, lipomatous carcinoma, lymphoepithelial carcinoma, carcinoma medullare, medullary carcinoma, melanotic carcinoma, carcinoma molle, mucinous carcinoma, carcinoma muciparum, carcinoma mucocellulare, mucoepidermoid carcinoma, carcinoma mucosum, mucous carcinoma, carcinoma myxomatodes, naspharyngeal carcinoma, oat cell carcinoma, carcinoma ossificans, osteoid carcinoma, papillary carcinoma, periportal carcinoma, preinvasive carcinoma, prickle cell carcinoma, pultaceous carcinoma, renal cell carcinoma of kidney, reserve cell carcinoma, carcinoma sarcomatodes, schneiderian carcinoma, scirrhous carcinoma, carcinoma scroti, signet-ring cell carcinoma, carcinoma simplex, small-cell carcinoma, solanoid carcinoma, spheroidal cell carcinoma, spindle cell carcinoma, carcinoma spongiosum, squamous carcinoma, squamous cell carcinoma, string carcinoma, carcinoma telangiectaticum, carcinoma telangiectodes, transitional cell carcinoma, carcinoma tuberosum, tuberous carcinoma, verrmcous carcinoma, and carcinoma villosum.
The term “sarcoma” generally refers to a tumor which is made up of a substance like the embryonic connective tissue and is generally composed of closely packed cells embedded in a fibrillar or homogeneous substance. Sarcomas include, for example, chondrosarcoma, fibrosarcoma, lymphosarcoma, melanosarcoma, myxosarcoma, osteosarcoma, Abemethy's sarcoma, adipose sarcoma, liposarcoma, alveolar soft part sarcoma, ameloblastic sarcoma, botryoid sarcoma, chloroma sarcoma, chorio carcinoma, embryonal sarcoma, Wilns' tumor sarcoma, endometrial sarcoma, stromal sarcoma, Ewing's sarcoma, fascial sarcoma, fibroblastic sarcoma, giant cell sarcoma, granulocytic sarcoma, Hodgkin's sarcoma, idiopathic multiple pigmented hemorrhagic sarcoma, immunoblastic sarcoma of B cells, lymphomas (e.g., Non-Hodgkin Lymphoma), immunoblastic sarcoma of T-cells, Jensen's sarcoma, Kaposi's sarcoma, Kupffer cell sarcoma, angiosarcoma, leukosarcoma, malignant mesenchymoma sarcoma, parosteal sarcoma, reticulocytic sarcoma, Rous sarcoma, serocystic sarcoma, synovial sarcoma, and telangiectaltic sarcoma.
The term “melanoma” is taken to mean a tumor arising from the melanocytic system of the skin and other organs. Melanomas include, for example, acral-lentiginous melanoma, amelanotic melanoma, benign juvenile melanoma, Cloudman's melanoma, S91 melanoma, Harding-Passey melanoma, juvenile melanoma, lentigo maligna melanoma, malignant melanoma, nodular melanoma subungal melanoma, and superficial spreading melanoma.
Additional cancers include, for example, Hodgkin's Disease, multiple myeloma, neuroblastoma, breast cancer, ovarian cancer, lung cancer, rhabdomyosarcoma, primary thrombocytosis, primary macroglobulinemia, small-cell lung tumors, primary brain tumors, stomach cancer, colon cancer, malignant pancreatic insulanoma, malignant carcinoid, premalignant skin lesions, testicular cancer, thyroid cancer, neuroblastoma, esophageal cancer, genitourinary tract cancer, malignant hypercalcemia, cervical cancer, endometrial cancer, and adrenal cortical cancer.
In some embodiments, the condition of interest can be an autoimmune disease. The term “autoimmune disease” refers to all types of conditions arising from an abnormal or excessive immune response in an animal to normal and/or non-foreign compounds, cells, or tissues in the body of the animal. Examples of autoimmune diseases include, but are not limited to, Dermatomyositis, Polymyositis, Pernicious anaemia, Primary biliary cirrhosis, Wegener's granulomatosis, Acute disseminated encephalomyelitis (ADEM), Addison's disease, Alopecia greata, Antiphospholipid antibody syndrome (APS), Autoimmune hepatitis, Crohns Disease, Diabetes mellitus type 1, Goodpasture's syndrome, Graves' disease, Narcolepsy, Guillain-Barré syndrome (GBS), Hashimoto's disease, Idiopathic thrombocytopenic purpura, Systemic lupus erythematosus, Mixed Connective Tissue Disease, Multiple sclerosis (MS), Myasthenia gravis, Pemphigus vulgaris, Rheumatoid arthritis, Sjögren's syndrome, Temporal arteritis, Ulcerative colitis, Autoimmune hemolytic anemia, Bullous pemphigoid, Vasculitis, Behçet's disease, and Coeliac disease.
In some embodiments, the condition of interest can be caused by an infection, such as a bacterial or a viral infection; such conditions include but are not limited to Lyme disease, Dengue fever, and hepatitis.
In some embodiments, the condition of interest can be another condition, including but not limited to amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, anemia, cardiac disease, diabetes, renal disease, or plasma cell dyscrasias and related disorders.
In some embodiments, the condition of interest can be a particular stage of a condition, for example, a particular stage of cervical cancer, such as moderate cervical dysplasia (CIN II), early stage cervical cancer, or stage IVB cervical cancer. For another example, a particular stage of brain cancer, such as grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and grade 4 astrocytoma (grade 3 and/or grade 4 astrocytoma are sometimes referred to as glyoblastoma mutiforme).
As used herein, the term “subject” refers to both human and animal subjects. Thus, veterinary therapeutic uses are provided in accordance with the presently-disclosed subject matter. As such, the presently-disclosed subject matter provides for the treatment of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans or animals used for scientific research, such as rabbits, rats, and mice; and/or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; rodents such as guinea pigs and hamsters; primates such as monkeys; arthropods including insects, arachnids and crustaceans; fish; mollusks; ruminants and/or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Also provided is the treatment of birds, including the treatment of those kinds of birds that are endangered and/or kept in zoos, as well as fowl, and more particularly domesticated fowl, i.e., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like, as they are also of economic importance to humans. Thus, also provided is the treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), poultry, and the like.
The methods of the presently-disclosed subject matter make use of a unique calorimetric process for obtaining profiles of components of samples (e.g., proteins, nucleic acids, phospholipids, small molecules, and other components), including fractionated samples.
Although some examples provided herein are related to the use of plasma samples, and protein components of samples, the same techniques and procedures can be applied to the analysis of other sample types and for examination of other sample components, such as nucleic acids, phospholipids, small organic molecules, etc. and their interactions.
Calorimetry provides a direct means for detecting what is perhaps the most fundamental property of chemical and biochemical reactions—heat changes. Biological calorimetry dates from the time of Lavoisier (1743-1794), who invented a calorimetric method for measuring the heats of metabolism of living animals. The presently-disclosed subject matter can make use of the high sensitivity of modern microcalorimeters, which can reliably measure heat changes of about 0.1 microcalories.
With reference to
Differential scanning calorimetry (DSC) can be used for thermodynamic studies of protein denaturation. The thermodynamics of thermal-induced unfolding of proteins can be measured as directly as possible by DSC. With reference to
Every protein has, under a given set of buffer conditions, a characteristic denaturation thermogram that is unique, and which provides a fundamental thermodynamic signature for that protein. Thermograms can be more complex than the simple two-state melting shown in
A primary DSC thermogram is an extensive property of a protein solution, and as such it is directly proportional to the mass of the protein in solution. If the weight concentration of the protein is doubled, for example, the calorimetric heat response will double. Similarly, in a solution of mixtures of proteins, the heat response will be proportional to the mass of each protein component in the mixture. Mixtures of proteins can be resolved with respect to the fundamental characteristic melting curves of their component proteins. Each protein in a noninteracting mixture will denature at its characteristic melting temperature (Tm) and with its characteristic melting enthalpy. The observed overall thermogram will be the weighted sum of all of the individual protein thermograms, weighted according to the mass of each component. For example,
Although some examples provided herein are related to the use of plasma samples, and protein components of samples, the same techniques and procedures can be applied to the analysis of other sample types and for examination of other sample components, such as nucleic acids, phospholipids, small organic molecules, etc. and their interactions.
Samples obtained from subjects include mixtures of proteins, including low molecular weight proteins, nucleic acids, phospholipids, small organic molecules, and other compounds that can be unique to a particular condition of interest, i.e., “biomarkers.”
As used herein, the term “protein” means any polymer comprising any of the 20 protein amino acids, regardless of its size. Although “protein” is often used in reference to relatively large polypeptides, and “peptide” is often used in reference to small polypeptides, usage of these terms in the art overlaps and varies. The term “protein” as used herein refers to peptides, polypeptides and proteins, unless otherwise noted. As used herein, the terms “protein”, “polypeptide,” and “peptide” are used interchangeably herein.
As used herein, the term “nucleic acid” refers to deoxyribonucleotides (DNA), ribonucleotides (RNA), including messenger RNA (mRNA) and microRNA (miRNA), and polymers thereof in either single or double stranded form.
As used herein, the term “phospholipids” means any amphipathic compounds arranged in a way that the ‘head’ is hydrophilic and the lipophilic ‘tail’ is hydrophobic.
As used herein, the term “small organic molecule” means any carbon based molecule that is not a polymer. In some embodiments, protein components of a sample are of interest.
The presence of and the expression level of specific proteins in a mixture of proteins found in a sample can be referred to as the proteomic profile of the sample. The proteomic profile of a sample obtained from a subject having a condition differs from the proteomic profile of a normal subject, i.e., condition-free subject. As such, information about a subject of unknown status (having condition vs. normal/lacking condition) can be obtained by comparing a thermogram generated from a sample obtained from the subject to a thermogram generated from a sample associated with a known status.
In some embodiments, proteins, nucleic acids, phospholipids, small organic molecules, and/or other components of a sample unique to a particular condition of interest (biomarkers) are of interest. The presence of and the expression level of specific biomarkers in a sample can be referred to as the biomarker profile of the sample. The biomarker profile of a sample obtained from a subject having a condition differs from the biomarker profile of a normal subject, i.e., condition-free subject. As such, information about a subject of unknown status (having condition vs. normal/lacking condition) can be obtained by comparing a thermogram generated from a sample obtained from the subject to a thermogram generated from a sample associated with a known status.
Such thermograms have many advantages, for example: they are easily obtained on unlabeled, underivitized, unfractionated biological samples; they consume only modest amounts of sample; they are obtained relatively quickly; they are based on rigorous, fundamental physical properties of proteins within the sample; they are quantitative, and reflect the exact protein composition of the sample; the procedures for obtaining thermograms are amenable to automated, high-throughput screening; and they provide a new window for viewing components of a biological sample based on thermal stability rather than on molecular weight and charge as is the case for electrophoresis and mass spectrometry.
The methods of the presently-disclosed subject matter make use of signature thermograms and standard thermograms. As used herein, the term signature thermogram refers to a thermogram generated using a particular sample of interest, or fraction of a sample of interest. The sample of interest is often a sample obtained from a particular subject.
In some embodiments, a method is provided for diagnosing or monitoring a condition of interest in a subject. In such embodiments, the signature thermogram can be a thermogram generated using a sample, or fraction thereof, obtained from the subject being diagnosed or monitored. In some embodiments, a method is provided for assessing a treatment program for a subject. In such embodiments, the signature thermogram can be a thermogram generated using a sample, or fraction thereof, obtained from the subject being whose treatment program is being assessed.
In some embodiments, a method of identifying biomarkers useful for diagnosing a condition of interest in a subject is provided. In such embodiments, the signature thermogram can be a thermogram particularly associated with the condition of interest, e.g., a sample or fraction of a sample from a subject known to have the condition of interest. In some embodiments, a method of screening a composition for use in treating a condition of interest in a subject is provided. In such embodiments, the signature thermogram can be a thermogram generated using a sample or fraction of a smaple obtained from the subject receiving the composition. In some embodiments, a method of screening a composition for plasma-protein interactions is provided. In such embodiments, the signature thermogram can be a thermogram generated using a plasma sample that has been contacted with the composition of interest, or a fraction thereof.
In some embodiments, it can be desirable to obtain multiple signature thermograms. In such embodiments, the multiple signature thermograms are generated using samples of interest, or fractions thereof, that are related in a particular manner. In such embodiments, samples of interest can be collected from the same subject (i.e., samples related in that they are obtained from the same subject) at different time points during the course of the treatment program.
As used herein, the term standard thermogram refers to a thermogram that is used as a reference to which a signature thermogram can be compared. A standard thermogram can be generated using a standard or control sample. A standard thermogram can be an average of multiple thermograms generated using multiple standard samples. For example, twenty (or another number of) standard samples can be obtained and a thermogram can be generated from each sample. The twenty generated thermograms could then be averaged to generate a standard thermogram.
In some embodiments, it can be desirable to provide a negative standard thermogram and/or a positive standard thermogram to which a signature thermogram can be compared. A negative standard thermogram is generated using a negative standard sample, or a fraction thereof. For example, a negative standard thermogram can be generated using a sample known to be associated with an absence of a condition of interest, e.g., a sample obtained from a subject known not to have a condition of interest. A positive standard thermogram is generated using a positive standard sample. For example, a positive standard thermogram can be generated using a sample known to be associated with a presence of a condition of interest, e.g., a sample obtained from a subject known to have a condition of interest.
A standard thermogram can be generated at a time point before, at a time point concurrent with or close to, or at a time point after the generation of a signature thermogram to which it will be compared. In some embodiments, it can be desirable to have a standard thermogram prepared to compare with various future-generated signature thermograms. In some embodiments, it can be desirable to provide a kit including one or more standard thermograms and instructions for generating signature thermograms for comparing with the one or more standard thermograms.
As noted herein, a thermogram can be obtained for a sample, or a fraction thereof. As used herein, the term “fraction” is used to describe a portion of a sample of interest, which is obtained by fractionating the sample. A sample can be divided into different portions, or fractions, using a variety of methods, i.e., fractionation methods, that will be known to those of ordinary skill in the art. For example, gel filtration can be used to fractionate a sample. Gel filtration chromatography separates protein mixtures on the basis of size and shape. Since plasma proteins, for example, vary greatly in the molecular weights and hydrodynamic shapes, gel filtration chromatography can fractionate a plasma sample into distinct component. Differential scanning calorimetry (DSC) of fractions emerging from a gel filtration column will reveal the thermograms of the protein components of those fractions.
As will be recognized by those skilled in the art, gel filtration is but one method by which a sample of interest can be fractionated. Additional fractionation methods include, but are not limited to the following: gel electrophoresis, chromatography, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. Depending on the desired results, gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, and/or high pressure liquid chromatography (HPLC) can be desirable fractionation techniques. Other techniques that can bee desirable include, but are not limited to mass spectroscopy (MS) techniques, such as MS conducted using high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, or matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS.
It is understood that fractionation can refer to separation by size, mass, shape, charge, thermal stability, or other physical quality of the sample components. The particular methods or techniques chosen will depend on the particular fluid to be fractionated. Suitable methods will be apparent to those skilled in the art.
By fractionating a sample and observing the thermograms of each fraction, it is possible to identify the component(s) in a sample whose thermogram(s) is (are) altered, e.g., a signature thermogram altered for a sample from a subjects having a condition of interest, as compared to standard thermogram. Since these components may be different because of substances bound thereto, the combination of fractionation and obtaining thermograms for each fraction provides a means of pinpointing the sample components to which potential biomarkers are bound.
When a sample is fractionated, and thermograms are obtained for multiple fractions of the sample, the thermograms can be described as a “series” of thermograms.
When a test sample is fractionated, and a control sample is similarly fractionated for purposes of comparison, certain fractions of the test sample and certain fractions of the control sample can be identified as “siblings.” For example, as will be understood by those skilled in the art, if a control sample and a test sample are fractionated by gel filtration chromatography, and fractions emerge from the gel filtration column at a fixed flow rate (e.g., 0.05 to 0.2 mL/min) and fractions of a fixed volume (e.g., 50-200 μL) are collected, then the first fraction of the control sample and the first fraction of the test sample can be said to be siblings, the second fraction of the control sample and the second fraction of the test sample can be said to be siblings, etc. As used herein, a standard thermogram that is a “sibling” of a signature thermogram is generated from a fraction of a control sample that is a “sibling” of a fraction of a test sample.
When comparing thermograms in accordance with methods of the presently-disclosed subject matter, they can be good simulations of one another or poor simulations of one another. When comparing thermograms, when a first thermogram is not a good simulation of a second thermogram, then it is a poor simulation of the second thermogram. A first thermogram is a good simulation of a second thermogram when it has substantial similarity to the second thermogram. In some embodiments, it is evident whether a first thermogram has substantial similarity to the second thermogram by inspection of the thermograms superimposed on one another, e.g., a signature thermogram superimposed on graphs of the standard(s). For example,
One of ordinary skill in the art can use his or her knowledge to make appropriate determinations of whether a substantial similarity can be found in particular situations. In some embodiments, substantial similarity can be found when each of the peaks of the first thermogram occur at about the same temperatures as each of the peaks of the second thermogram. In some embodiments, substantial similarity can be found when the peaks of the first thermogram occur at temperatures within one standard deviation of the peaks of the second thermogram. In some embodiments, substantial similarity can be found when the peaks of the first thermogram occur at temperatures within two standard deviations of the peaks of the second thermogram. In some embodiments, substantial similarity can be found when each of the peaks of the signature thermogram yield about the same heat capacity as the peaks of the standard thermogram. In some embodiments, substantial similarity can be found when the heat capacity of the peaks of the signature thermogram is within one standard deviation of the heat capacity of the peaks of the standard thermogram. In some embodiments, substantial similarity can be found when the heat capacity of the peaks of the signature thermogram is within two standard deviation of the heat capacity of the peaks of the standard thermogram.
In some embodiments, substantial similarity can be determined by application of published statistical procedures, for example, quantile-quantile plots (Lodder and Hieftje (1988)) can be used and/or a two-way Kolmogorov-Smirnov test can be used (Young (1977)). Briefly, for these tests, the thermogram must be converted to a quantile distribution.
To construct a quantile-quantile plot, the quantile values derived from one thermogram is plotted against the quantile values derived from a second thermogram.
The same quantile values used to construct the quantile-quantile plot can be used to conduct a two-way Kolmogorov-Smirnov test, as implemented in standard statistical software packages and as is available online on service web sites (See, e.g., http://www.physics.csbsju.edu/stats/KS-test.html). The Kolmogorov-Smirnov test is designed to test the null hypothesis that two quantile distributions are not statistically different. The test returns a P-value for the confidence level with which the null hypothesis can be rejected. In this regard, in some embodiments, if the null hypothesis that the two quantile distributions are not statistically different (are good simulations) is rejected, it can be determined that the first thermogram is not substantially similar to the second thermogram. In some embodiments, the P-value is less than or equal to 0.5, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, or 0.001.
By way of an example, when using the quantile values of
In some embodiments of the presently-disclosed subject matter, a method of identifying biomarkers useful for diagnosis of a condition of interest in a subject is provided. The method of the presently-disclosed subject matter, which includes fractionation of samples, and generation of thermograms therefrom, provides a unique process for identifying useful biomarkers. For example, analysis of a fractionated sample by the presently-disclosed method can provide information about the relative concentration of all major component proteins, and other components, present in the sample; determination of the presence of binding interactions with the major component proteins, and other components, of the sample; and information about binding component(s) and evaluation of their binding constant(s). The presently-disclosed methods of identifying biomarkers incorporates the unique perspective that biomarkers can be detected by their influence on the thermodynamic stability of proteins in plasma and other biological samples.
DSC thermograms, as generated in accordance with the presently-disclosed subject matter, are highly sensitive to binding interactions, which is a unique and attractive feature of the technology. When a ligand recognizes and binds to a site on a protein in a sample, it stabilizes that protein with respect to thermal or chemical denaturation. Relative to the unligated protein, upon binding, the melting temperature is elevated or the concentration of denaturant required to unfold the protein is increased. Conversely, if a ligand were to selectively recognize some feature of the denatured protein, the melting temperature would be lowered. For DSC measurements of proteins, detailed and specific protocols for the analysis of ligand-induced shifts in denaturation thermograms have been published, and include closed form equations that may be applied to extract reliable binding constants for the ligand-protein interaction. The magnitude of melting temperature shifts for proteins in the presence of ligands can be dramatic (easily tens of degrees) and depends on the magnitude of the equilibrium binding constant and binding enthalpy.
The sensitivity of DSC thermogram technology to binding interactions is useful as both studies support the notion that, in the presence of a condition of interest, e.g., diseased states, low molecular weight proteins or circulating nucleic acids unique to the condition of interest (and therefore indicative of its presence), increase in concentration in biological samples, e.g., plasma, serum, CSF, etc. Such biomarkers form complexes with the more abundant proteins in the plasma (e.g., albumin and immunoglobulins) and can alter the denaturation thermogram profiles for the proteins which they bind. This binding produces characteristic changes in signature thermograms. Since the presently-disclosed subject matter is sensitive to such binding interactions in ways that current electrophoresis and mass spectrometry assay techniques are not, entirely new aspects of the plasma proteome can be divulged. Changes in signature thermograms resulting from binding of small proteins to a larger receptor are far more dramatic than changes in either mass or charge. Consequences of significant interactions with the major proteins are observed by alteration of the melting curve of proteins to which the biomarkers bind. A number of more standard biochemical characterization techniques can then be employed to identify and characterize biomarkers discovered in accordance with the presently-disclosed subject matter. For example, potential biomarkers in a sample can be isolated by separation techniques known to those of ordinary skill in the art, and further characterized by mass spectrometry.
In some embodiment, the method of identifying useful biomarkers includes: providing a test sample associated with the condition of interest; fractionating the test sample to obtain fractions of the test sample; generating a signature thermogram for each fraction of the test sample; comparing a signature thermogram to a sibling standard thermogram; and identifying as biomarkers any components of the sample that result in the signature thermogram being a poor simulation of the sibling standard thermogram.
In some embodiment of the presently-disclosed subject matter a system is provided for identifying useful biomarkers, which includes includes: means for fractionating a test sample to obtain fractions of the test sample; means for generating a signature thermogram for each fraction of the test sample; and means for comparing a signature thermogram to a sibling standard thermogram such that biomarkers can be identified.
In some embodiments, the standard thermogram is generated using a sample including a candidate biomarker. For example, control sample can be obtained and contacted with a candidate biomarker, such as a known protein, nucleic acid, phospholipid, small organic molecule, or another compound that can be unique to a particular condition of interest. In this regard, a candidate biomarker can be identified as an actual biomarker when the signature thermogram of a fraction of the test sample is a good simulation of the standard thermogram of the candidate biomarker.
In some embodiments, the method of identifying useful biomarkers further includes providing a control sample associated with an absence of the condition of interest; fractionating the control sample to obtain sibling fractions of the control sample; generating a sibling standard thermogram for each sibling fraction of the control sample; comparing a signature thermogram to a sibling standard thermogram for a sibling fraction of the control sample.
As will be understood by those skilled in the art, the biological sample provided for use in accordance with the presently-disclosed subject matter can be any appropriate biological sample that is suspected of containing a biomarker, such as a body fluid. Appropriate body fluids include, but are not limited to ascites fluid, blood, cerebral spinal fluid, serum, peritoneal fluid, plasma, saliva, senovial fluid ocular fluid, and urine. As will be understood by those skilled in the art, in some cases it can be desirable to select the type of sample being collected based on the selected condition of interest. For example, in some embodiments when the condition of interest is ALS, it can be desirable to obtain a cerebral spinal fluid sample. As is well understood by those skilled in the art, a control sample should be selected such that variables are limited. In this regard, for example, if the test sample is a plasma sample, it is preferred that the control sample is also a plasma sample. Similarly, if the test sample is a CSF sample, it is preferred that the control sample is also a CSF sample, etc.
In some embodiments, an obtained sample can be prepared in the following manner. A blood sample is drawn from the subject and plasma or serum is isolated from the blood using known methods. A small volume of about 100 μL, of plasma or serum is dialyzed at about 4° C. against a standard buffer (e.g., 10 mM potassium phosphate, 150 mM NaCl, 0.38% (w/v) sodium citrate, pH 7.5 for plasma; 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 for serum). Dialyzed plasma or serum is filtered to remove particulates and then diluted about 25-fold into the standard buffer.
All samples can be prepared by essentially the same techniques, although buffer components and concentrations can vary with particular sample types. In any case, samples are equilibrated with a suitable buffer in order to compare with the buffer baseline of the reference. This can be achieved by dialysis or other suitable buffer exchange methods.”
The prepared sample is then fractionated using a desired technique. For example, in some embodiments, a fractionation technique is used that results in fractions including different size classes of proteins. In some embodiments, fractionating is conducted using gel filtration, gel electrophoresis, chromatographic fractionation, separation columns, immunoaffinity, centrifugation, mass spectroscopy, bioinformatic fractionation, or combinations thereof. In some embodiments, the fractionating is conduced using a chromatographic fractionation technique selected from: gel filtration chromatography, liquid chromatography (LC), LC-mass spectroscopy (LC-MS), affinity chromatography, and high pressure liquid chromatography (HPLC). In some embodiments, the fractionating is conduced using a mass spectroscopy technique selected from: high-resolution LC-MS/MS, surface-enhanced laser desorption/ionization-time-of-flight (SELDI-TOF) MS, and matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS.
The test sample and/or control sample fractions can then be used to generate one or more signature thermograms containing a protein composition pattern, or other component/biomarker composition pattern.
With reference to
In some embodiments, a particular fraction will be of interest, and only one signature thermogram is generated. In some embodiments when a particular test fraction is of interest, and only one sibling standard thermogram is generated for purposes of comparing with the signature thermogram of interest. In some embodiments, a subset of fractions are of interest, and multiple signature thermograms are generated. In some embodiments when a particular subset of test fractions are of interest, and a subset of sibling control fractions are used to generate sibling standard thermograms for purposes of comparing with the signature thermograms of interest. In some embodiments, all of the fractions are of interest, and a complete series of signature thermograms are generated. In some embodiments when all of the test fractions are of interest, a complete series of sibling standard thermograms are generated for purposes of comparing with the series of signature thermograms.
Each fraction of interest is run on a differential scanning calorimeter (DSC) to obtain a thermogram for the sample. A differential scanning calorimeter (DSC) can be obtained from MicroCal, LLC (Northampton, Mass.), for example, the MicroCal, LLC VP Capillary Differential Scanning Calorimeter can be used. Any differential scanning calorimeter (DSC) with the requisite sensitivity, temperature range, scanning rate, and baseline stability could be used in accordance with the methods of the presently-disclosed subject matter. Examples of other instruments that would be suitable include: Calorimetric Sciences Corporation's N-DSCII Differential Scanning Calorimeter, TA Instruments Incorporated's Q2000 Differential Scanning Calorimeter, and Perkin Elmer Corporation's Diamond DSC Differential Scanning Calorimeter. Newly designed instruments that might become available with the requisite sensitivity, temperature range, scanning rate, and baseline stability could also be used to practice the methods of the presently-disclosed subject matter. For example, Energetic Genomics Corporation's 96-well differential scanning calorimeter that is under development, and for which a prototype instrument is available to the inventors, could be used to practice the methods of the presently-disclosed subject matter. Standard software and protocols can be used to obtain a thermogram for the sample with the selected DSC.
Using the MicroCal, LLC DSC as an example, a sample volume of approximately 0.4 mL is needed for liquid-handling for proper filling of the sample cell, although the effective cell volume is only approximately 0.133 mL. Each DSC run takes about 1-2 hours to complete. Total protein concentrations of the diluted sample can be determined by standard colorimetric, spectrophotometric, or refractometric methods. These concentrations can be used to normalize experimental thermograms to a g/L protein concentration scale. This normalized thermogram shows the “Excess Specific Heat Capacity” as a function of temperature for a plasma/serum sample (See, e.g., the dashed line thermogram of
When the desired signature and/or standard thermograms are obtained, they can be compared and assessments about the samples can be made. For example, if a signature thermogram of a first fraction of a test sample associated with a condition of interest is a poor simulation of sibling negative standard thermogram of a first fraction of a control sample, it is indicative of a unique component associated with the condition of interest being present in the first fraction of the test sample. This unique component, for example, could be interacting with a particular plasma protein found in that fraction, e.g., an albumin fraction. The fraction identified as containing the unique component can be further examined to identify the unique component, which can be useful as a biomarker for the condition of interest. For example, in some embodiments the fraction can be analyzed using mass spectrometry to identify the unique component of the fraction.
In some embodiments, test samples and negative control samples are fractionated, as described herein, and thermograms are generated for the various resulting fractions. The surface defined by the two dimensions of elution volume and temperature reveals the unique components of the test samples, and fractions thereof, relative to the negative control samples, and sibling fractions thereof.
In some embodiments, it can be useful samples and/or fractions thereof with a denaturant (e.g., urea, guanidine HCL) and to obtain thermograms as a function of added denaturant concentration. Such a method can uncover additional distinctions between components of samples, and fractions thereof, associated with a condition of interest, and negative control samples, and fractions thereof, associated with a lack of a condition of interest. In some embodiments, unfractionated plasma test samples and negative control samples are subjected to various concentrations of denaturant, and thermograms are generated. The surface defined by two dimensions of denaturant concentration and termperture reveals the unique components of the test samples relative to the negative control samples. Binding interactions stabilize native conformations and can be elevated at the concentration of denaturant required for unfolding.
In some embodiments of the presently-disclosed subject matter, a method of diagnosing or monitoring a condition of interest in a subject is provided. In some embodiments, a method of diagnosing or monitoring a condition of interest in a subject includes, providing a test sample obtained from the subject, fractionating the test sample to obtain fractions of the test sample; generating a signature thermogram for at least one fraction of the test sample; comparing at least one signature thermogram with a standard thermogram; and identifying the subject as having the condition of interest or lacking the condition of interest. In some embodiments, the method further includes providing a control sample; fractionating the control sample to obtain sibling fractions of the control sample; generating a sibling standard thermogram for at least one sibling fraction of the control sample; comparing the signature thermogram with the at least one sibling standard thermogram; and identifying the subject as having the condition of interest or lacking the condition of interest.
In some embodiments of the presently-disclosed subject matter, a system for diagnosing or monitoring a condition of interest in a subject is provided, which includes means for fractionating a test sample to obtain fractions of the test sample; means for generating a signature thermogram for at least one fraction of the test sample; and means for comparing at least one signature thermogram with a standard thermogram, such that it can be determined whether the signature thermogram is a good simulation or a poor simulation of the standard thermogram.
As will be understood by those skilled in the art, the sample obtained from the subject can be any appropriate biological sample, such as a body fluid. Appropriate body fluids include, but are not limited to ascites fluid, blood, cerebral spinal fluid, serum, peritoneal fluid, plasma, saliva, senovial fluid, ocular fluid, and urine. As will be understood by those skilled in the art, in some cases it can be desirable to select the type of sample being collected based on the selected condition of interest. For example, in some embodiments when the condition of interest is ALS, it can be desirable to obtain a cerebral spinal fluid sample.
As will be understood by those skilled in the art, and as noted hereinabove, when a control sample is provided, it can be a positive control sample or a negative control sample. For example, a positive control sample can be a biological sample that is obtained from a subject known to have the condition of interest, while a negative control sample can be a biological sample that is obtained form a subject known to be normal or free of the condition of interest. As is well-understood by those skilled in the art, a control sample should be selected such that variables are limited. In this regard, for example, if the test sample is a plasma sample, it is preferred that the control sample is also a plasma sample. Similarly, if the test sample is a CSF sample, it is preferred that the control sample is also a CSF sample, etc.
The obtained sample can be prepared as described herein, and also fractionated as described herein.
The test sample and/or control sample fractions can then be used to generate one or more signature thermograms containing a protein composition pattern, or other component/biomarker composition pattern. In some embodiments, a particular fraction will be of interest, and only one signature thermogram is generated. In some embodiments when a particular test fraction is of interest, and only one sibling standard thermogram is generated for purposes of comparing with the signature thermogram of interest. In some embodiments, a subset of fractions are of interest, and multiple signature thermograms are generated. In some embodiments when a particular subset of test fractions are of interest, and a subset of sibling control fractions are used to generate sibling standard thermograms for purposes of comparing with the signature thermograms of interest. In some embodiments, all of the fractions are of interest, and a complete series of signature thermograms are generated. In some embodiments when all of the test fractions are of interest, a complete series of sibling standard thermograms are generated for purposes of comparing with the series of signature thermograms.
Each fraction of interest is run on a differential scanning calorimeter (DSC) to obtain a thermogram for the sample, as described herein.
Once at least one signature thermogram is generated, it can be compared to a standard thermogram or another signature thermogram. To minimize uncontrolled variables, the test sample used to generate the signature thermogram should be prepared in the same manner as the control sample used to generate the standard thermogram, or the sample used to generate another signature thermogram. Similarly, the calorimeter, software, and protocols used to generate the thermograms that are to be compared should be substantially the same.
The standard thermogram can be a negative standard thermogram, in that it is associated with an absence of the condition of interest. The negative standard thermogram can be generated using a sample obtained from a subject who is “normal,” i.e., condition-free. In some cases the sample can have been obtained from the subject being diagnosed or monitored at a time when that subject was known to be condition-free. The standard thermogram can also be a positive standard thermogram, in that it is associated with a presence of the condition of interest. The positive standard thermogram can be generated using a sample obtained from a subject who has the condition of interest. In some cases the sample can have been obtained from the subject being diagnosed or monitored at a time when that subject was known have the condition. In some embodiments, the signature thermogram can be compared to both a negative standard thermogram and a positive standard thermogram.
In some embodiments, the subject can be identified as having the condition of interest when the signature thermogram of a fraction of the test sample is compared to a negative standard thermogram, and is found to be a poor simulation of the negative standard thermogram.
In some embodiments, the subject can be identified as having the condition of interest when the signature thermogram of a fraction of the test sample is compared to a positive standard thermogram, and is found to be a good simulation of the positive standard thermogram.
In some embodiments, the subject can be identified as having the condition of interest when the signature thermogram is compared to a positive standard thermogram and a negative standard thermogram, and is found to be a good simulation of the positive standard thermogram and a poor simulation of the negative standard thermogram
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as having the condition of interest when at least one of the signature thermograms of a fraction of the test sample is compared to a negative standard thermogram, and is found to be a poor simulation of the negative standard thermogram.
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as having the condition of interest when at least one of the signature thermograms of a fraction of the test sample is compared to a positive standard thermogram, and is found to be a good simulation of the positive standard thermogram.
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as having the condition of interest when at least one of the signature thermograms is compared to a positive standard thermogram and a negative standard thermogram, and is found to be a good simulation of the positive standard thermogram and a poor simulation of the negative standard thermogram.
In some embodiments, the subject can be identified as lacking the condition of interest when the signature thermogram of a fraction of the test sample is compared to a negative standard thermogram and is found to be a good simulation of the negative standard thermogram.
In some embodiments, the subject can also be identified as lacking the condition of interest when the signature thermogram of a fraction of the test sample is compared to a positive standard thermogram and is found to be a poor simulation of the positive standard thermogram.
In some embodiments, the subject can also be identified as lacking the condition of interest when the signature thermogram of a fraction of the test sample is compared to a negative standard thermogram and a positive standard thermogram, and is found to be a good simulation of the negative standard thermogram and a poor simulation of the positive standard thermogram.
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as lacking the condition of interest when all of the signature thermograms of the fraction of the test sample are compared to sibling negative standard thermograms, and are found to be good simulations of the negative standard thermograms.
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as having the condition of interest when all of the signature thermograms of the fractions of the test sample are compared to sibling positive standard thermograms, and is found to be a poor simulation of the positive standard thermograms.
In some embodiments, in which multiple signature thermograms and standard thermorgrams are generated, the subject can be identified as having the condition of interest when all of the signature thermograms are compared to sibling positive standard thermograms and sibling negative standard thermogram, and are found to be poor simulations of the positive standard thermograms and a good simulations of the negative standard thermograms.
In some embodiments, the subject can be identified as having a condition, albeit unidentified for the time being, when the signature thermogram is found to be a poor simulation of the negative standard thermogram. Upon such a finding, the signature thermogram can then be compared to positive standard thermograms associated with conditions of interest in order to make a diagnosis.
In some embodiments, the signature thermogram can be compared to multiple positive standard thermograms, e.g., a database including multiple positive standard thermograms, each positive standard thermogram being associated with a particular condition of interest. As will be recognized by those of ordinary skill in the art, where standard thermograms are used that are previously generated, e.g., standard thermograms found in a database, care should be taken to minimize variables. As such, it is preferred that the standard thermogram that is selected for comparison be one that was generated using a fraction of a sample of the same type, fractionated by the same method, and being of a “sibling” fraction as the test fraction used to generate the signature thermogram. The positive standard thermogram obtained under the parameters that most resembles those under which the signature thermogram was obtained can be selected. The subject can be identified as having the condition associated with the positive standard thermogram that most resembles the signature thermogram. In some embodiments, the method can be useful to distinguish between two conditions having initial symptoms that are difficult to distinguish; for example, in some embodiments, the method can be used to distinguish multiple sclerosis and ALS in a subject.
As will be understood by those of ordinary skill in the art, it can sometimes be desirable to obtain multiple samples from the subject at various time points, in order to monitor the condition of interest. For example, in some embodiments, a second sample can be obtained from the subject at a time point after the first sample is obtained. The second sample can be fractionated and a signature thermogram can be generated from one or more fractions of the second sample. The first signature thermogram, or series of first signature thermograms, can be compared to the second signature thermogram, or series of second signature thermograms. The condition of interest can be identified as changed when the second sibling signature thermogram is a poor simulation of the first signature thermogram. The condition of interest can be identified as unchanged when the second sibling signature thermogram is a good simulation of the first signature thermogram.
As such, in some embodiments, the method of diagnosing or monitoring a condition of interest further includes providing a second test sample obtained from the subject at a time point after the test sample is obtained; fractionating the second test sample to obtain fractions of the second test sample; generating a signature thermogram for each fraction of the second test sample; comparing a signature thermogram of the second test sample with a standard thermogram, and/or a signature thermogram of the first test sample; and identifying the subject as having the condition of interest or lacking the condition of interest, and/or having a change or having no change in status associated with the condition of interest.
In some embodiments, the method of diagnosing or monitoring a condition of interest further includes providing a second test sample obtained from the subject at a time point after the test sample is obtained; fractionating the second test sample to obtain fractions of the second test sample; generating signature thermograms for each fraction of the second test sample; comparing a signature thermogram of the second test sample with a sibling standard thermogram of the control sample, and/or a sibling signature thermogram of the first test sample; and identifying the subject as having the condition of interest or lacking the condition of interest; or having a change or having no change in status associated with the condition of interest.
In some embodiments, the second signature thermogram (including second sibling signature thermogram, or second series of signature thermograms) can also be compared to a negative standard thermogram (including sibling negative standard thermogram, or a series of negative standard thermograms). If the second signature thermogram is a good simulation of the negative standard thermogram, for a subject that had previously been identified as having a particular condition, the subject can be identified as having improved to the point of lacking the condition. In some embodiments, the second signature thermogram can also be compared to various positive standard thermograms (including sibling positive standard thermograms, or a series of positive standard thermograms) associated with different stages of a particular condition. In this regard, it can be determined whether the condition is progressing, i.e., becoming more severe, or regressing, i.e., improving.
The presently-disclosed subject matter also includes a method of assessing efficacy of a treatment program for a subject having a condition of interest, or being at risk for developing the condition of interest. As used herein, a treatment program includes a plan for treating a subject or providing treatment to a subject. As used herein, the terms treatment or treating relate to any treatment of a condition of interest, including but not limited to prophylactic treatment and therapeutic treatment. As such, the terms treatment or treating include, but are not limited to: preventing the development of a condition of interest; inhibiting the progression of a condition of interest; arresting or preventing the development of a condition of interest; reducing the severity of a condition of interest; ameliorating or relieving symptoms associated with a condition of interest; and causing a regression of the condition of interest or one or more of the symptoms associated with the condition of interest. As will be understood by those of ordinary skill in the art, a treatment program can differ depending on the condition of interest and the subject being treated. A treating physician can select a particular treatment program based on the condition of interest, and the particular subject being treated. Depending on the situation, a treatment program could include, for example, administering a treatment composition or a series of treatment compositions, administering a radiation treatment, prescribing an altered diet, prescribing a particular exercise regimen, prescribing low activity or rest, a combination thereof, etc.
In some embodiments, a method of assessing a treatment program for a subject includes the following: providing a first sample obtained from the subject at a first time point of interest, e.g., prior to the initiation of the treatment program; fractionating the first test sample to obtain a first series of fractions; generating a first series of signature thermograms for the first series of fractions of the first test sample; providing a second test sample obtained from the subject at a second time point of interest, e.g., after the initiation of the treatment program; fractionating the second test sample to obtain a second series of fractions; generating a second series of signature thermograms for the second series of fractions of the second test sample; comparing the first series of signature thermograms to the second series of signature thermograms; and identifying the presence or absence of a change in the condition of interest.
The first sample can be obtained from the subject before initiation of the treatment program, or at another time point of interest that will service as a base-line by which the treatment program will be assessed. The first sample is fractionated, as described herein, and the fractions are used to generate a first series of signature thermograms. It is contemplated that in some embodiments, a particular fraction or subset of fractions will be of interest, and it is possible that only the particular fraction or subset of fractions will be used to generate signature thermograms. In other embodiments, it will be desirable to obtain a complete series of first signature thermograms.
In some embodiments, the subject has a condition of interest when the first sample is collected. In some embodiments, the subject does not have a condition of interest, but there is otherwise a reason for receiving a treatment program, as will be understood by those of ordinary skill in the art. For example, a subject lacking a condition of interest, but having a risk for obtaining the condition of interest could receive a treatment program, the efficacy of which can be assessed using the method of the presently-disclosed subject matter.
The second sample is obtained from the subject at a second time point of interest, e.g., following the initiation of the treatment program. The treatment program can include, for example, administration of a treatment composition and the second sample can be obtained after the subject has been receiving the treatment composition for a day, week, month, or other time period of interest. For another example, the treatment program can include providing radiation treatment and the second sample can be obtained after the subject has been receiving the radiation treatment for a specific period of time. In any event, the second sample is generally obtained at a time point of interest after the treatment program has been initiated. Additional samples can be obtained at different time points of interest to generate a time course describing the effect of the treatment program on the subject.
The second sample is fractionated and used to generate a sibling series of second signature thermograms associated with the treatment program of the subject. It is contemplated that in some embodiments, a particular fraction or subset of fractions will be of interest, and it is possible that only the particular fraction or subset of fractions will be used to generate signature thermograms. In other embodiments, it will be desirable to obtain a complete series of second signature thermograms. Generally, it will be desirable to second signature thermograms that are siblings of each generated first signature thermogram, such that the sibling second signature thermograms can be compared to each of the first signature thermograms.
The signature thermograms are generated by running the samples on a differential scanning calorimeter (DSC), as described herein. Once the signature thermograms are generated, they can be compared to one another. To minimize uncontrolled variables, the sample used to generate the first signature thermogram (or series of first signature thermograms) should be prepared in the same manner and be of the same type as the sample used to generate the second signature thermogram (or series of second signature thermograms). Similarly, the calorimeter, software, and protocols used to generate each of the thermogram should be substantially the same.
When the signature thermograms are compared, the treatment program can be identified as having not changed the condition of the subject (i.e., indicating absence of a change in status, or maintaining the status of the subject) when each second signature thermgram of the fractions of the second sample is a good simulation of the sibling first signature thermograms of the sibling fractions of the first sample.
When the signature thermograms are compared, the treatment program can be identified as affecting a change in the condition of the subject (i.e., indicating a change in status of the subject) when at least one second signature thermogram of the fractions of the second sample is a poor simulation of the sibling first signature thermogram of the sibling fraction of the first sample.
As will be understood by those of ordinary skill in the art, depending on the goal of the treatment program, an absence or a presence of a change can be indicative of an effective or an ineffective treatment program. As such, the determination of whether the presence or absence of a change is indicative of an effective treatment program will differ depending on the goal of the treatment program.
In some embodiments, when there is an absence of a change, the treatment program can be identified as an effective treatment program. In some embodiments, when there is an absence of a change, the treatment program can be identified as an ineffective treatment program. For example, if a prophylactic treatment program is administered to a subject lacking a condition of interest, with a goal of preventing an onset of the condition of interest, an absence of a change in the condition of the subject can be indicative of an effective (successful) treatment program. For another example, if a therapeutic treatment program is administered to a subject having a condition of interest, an absence of a change in the condition of the subject can be indicative of an effective treatment program if the goal is to prevent progression of the condition, or an ineffective treatment program if the goal is to cause a regression of the condition.
In some embodiments, when there is a presence of a change, the treatment program can be identified as an effective treatment program. In some embodiments, when there is a presence of a change, the treatment program can be identified as an ineffective treatment program. For example, in some embodiments, a prophylactic treatment program is administered to a subject who initially lacked a condition of interest; in such embodiments, a change in the condition can be indicative of an ineffective treatment program.
In some embodiments, it is apparent by inspecting the thermograms whether a change is indicative of an effective or an ineffective treatment program, e.g., change indicative of a regression of a condition, or a progression of a condition, as will be understood by those of ordinary skill in the art. In some embodiments, it can be desirable to additionally compare the signature thermogram to one or more standard thermograms. For example, in some embodiments a treatment program is administered to a subject who initially had a condition of interest; in such embodiments, a change in the condition can be indicative of either a regression or a progression of the condition. In such cases, as will be understood by those of ordinary skill in the art, it can be useful to additionally compare the second signature thermogram to one or more standard thermograms. For example, if the second signature thermogram is a good simulation of a negative standard thermogram, then the change can be indicative of a regression. In some embodiments, it can be useful to compare the second signature thermogram to a multiple positive standard thermograms, each associated with a particular stage of the condition of interest. Such comparisons can also provide information about whether a change in the condition is indicative of a progression or a regression of the condition.
The presently-disclosed subject matter also includes a method of screening for a composition useful for treating a condition of interest. In some embodiments, the method includes: interacting a sample associated with the condition of interest with a candidate composition; fractionating the sample to obtain a series of fractions; generating a series of signature thermograms for the series of fractions; comparing the series of signature thermograms to sibling standard thermograms; and determining the utility of the candidate composition.
With regard to the step of interacting the sample associated with the condition of interest with a candidate composition, in some embodiment, the candidate composition can be administered to an infected subject. The subject can be any appropriate test subject, for example, a mouse, a rat, a rabbit, or another appropriate test subject. In some embodiments, the candidate treatment composition can be administered to a subject that is a model for a condition of interest, e.g., mouse model for a particular condition. The candidate composition can be administered by any appropriate method, depending on the characteristics of the composition being screened. A sample, e.g., body fluid sample, can then be obtained from the test subject for use in generating the signature thermogram. In some embodiments, the step of interacting a sample associated with the condition of interest with a candidate treatment composition includes administering the candidate treatment composition to cells in culture, which cells have been infected with or are otherwise associated with the condition of interest. A sample can then be extracted from the cells for use in obtaining fractions and generating the series of signature thermograms. The fractionating can be conducting using techniques identified herein. The signature thermograms can be generated using a differential scanning calorimeter (DSC).
Once the signature thermogram (or series of signature thermorgrams) is generated, it can be compared to a sibling standard thermogram (or series of sibling standard thermograms). To minimize uncontrolled variables, the sample used to generate the signature thermogram should be prepared in the same manner and obtained from the same species as the sample used to generate the standard thermogram. Similarly, the calorimeter, software, and protocols used to generate the signature thermogram should be substantially the same as those used to generate the standard thermogram.
The standard thermogram can be a negative standard thermogram, in that it is associated with an absence of the condition of interest. The negative standard thermogram can be generated using a sample fraction(s) associated with an absence of the condition of interest, e.g., fractions of a sample obtained from a subject who is “normal,” or condition-free. In some embodiments, the negative standard sample can be obtained from a subject administered the candidate treatment composition, in which case it is obtained prior to the infection of the subject and prior to administration of the candidate treatment composition.
The standard thermogram can also be a positive standard thermogram, in that it is associated with a presence of the condition of interest. In some embodiments, the positive standard thermogram can be generated using a sample fraction(s) obtained from a subject who has the condition of interest. In some embodiments, the positive standard sample can be obtained from the subject administered the candidate treatment composition, in which case it is obtained after the subject is infected and prior to administration of the candidate treatment composition.
In some embodiments, the signature thermogram is a good simulation of the sibling negative standard thermogram associated with an absence of the condition of interest, and the candidate treatment composition can be identified as being useful.
In some embodiments, the signature thermogram is a good simulation of the sibling positive standard thermogram associated with a presence of the condition of interest. It can then be determined whether the candidate treatment composition is either useful for preventing a progression of the condition, or is ineffective if the goal is to cause a regression of the condition.
In some embodiments, the signature thermogram is a poor simulation of the sibling negative standard thermogram and/or a poor simulation of the sibling positive standard thermogram. It can then be determined whether the candidate treatment composition is either useful for causing a regression of the condition, useful for preventing a progression of the condition, or is ineffective, i.e., not treatment affected, or causes a progression of the condition.
In order to make the determination of whether the candidate treatment composition is useful for causing a regression of the condition, useful for preventing a progression of the condition, or is ineffective, it can be desirable to obtain multiple samples collected over time, for use in generating a multiple of signature thermograms (or multiple series of signature thermograms). The multiple of signature thermograms generated from samples collected over time and fractionated can be compared to identify any changes. In some embodiments, it is apparent by inspecting multiple sibling signature thermograms whether a change is indicative of an effective or an ineffective treatment program. For example, if the multiple sibling of signature thermograms of sibling fractions of samples collected over time display a trend towards a good simulation of the sibling negative standard thermogram, then it can be determined that the candidate treatment composition causes a regression of the condition. For another example, if the multiple sibling of signature thermograms of sibling fractions of samples collected over time display no change, then it can be determined that the candidate treatment composition prevents a progression of the condition. For another example, if the multiple sibling of signature thermograms of sibling fractions of samples collected over time display a trend towards a good simulation of the sibling positive standard thermogram, then it can be determined that the candidate treatment composition neither causes a regression of the condition nor prevents a progression of the condition, i.e., ineffective.
In some embodiments, it can be desirable to additionally compare the signature thermogram (or series of signature thermograms) to one or more sibling standard thermograms. In some embodiments, the signature thermogram can be compared to one or more sibling positive standard thermograms associated with different stages of a condition of interest. For example, if the condition of interest is cervical cancer, standard thermograms associated with moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer can be provided. The signature thermograms can be used to determine whether the candidate treatment composition affects a regression of the cervical cancer from stage IVB cervical cancer, to early stage cervical cancer, to moderate cervical dysplasia; a progression from moderate cervical dysplasia, to early stage cervical cancer, to stage IVB cervical cancer; or no change. In some embodiment where the condition of interest is a brain cancer, standard thermograms associated with grade 1 astrocytoma, grade 2 astrocytoma, grade 3 astrocytoma, and grade 4 astrocytoma (also referred to as glyoblastoma mutiforme) can be provided. The signature thermograms can be used to determine whether the candidate treatment composition affects a regression in the brain cancer, a progression in the brain cancer, or no change.
In some embodiments, the candidate treatment composition can be administered to a test subject before the test subject has been infected with the condition of interest. The subject can then be infected, samples obtained, samples fractionated, and thermograms generated. The thermograms can be compared to determine the ability of the candidate treatment composition to prevent or inhibit an onset or progression of a condition of interest.
The presently-disclosed subject matter further includes a method of screening a composition, e.g. candidate drug or treatment, for protein interactions, to identify and/or monitor the capacity of the composition to interact with protein. In some embodiments, the method includes: interacting the composition with a first plasma sample; fractionating the first plasma sample to obtain a first series of fractions; generating a first series of signature thermograms for the first series of fractions; comparing the first series of signature thermograms to sibling negative standard thermograms associated with an absence of plasma protein interactions; and/or a sibling second series of signature thermogram generated using a second series of fractions from a second plasma sample not interacted with the composition; and identifying the composition as lacking substantial plasma protein interactions when the first series of signature thermograms are good simulations of the sibling negative standard thermograms, and/or the sibling second series of signature thermograms.
In some embodiments, the thermogram containing a protein composition pattern associated with an absence of protein interactions can be a negative standard thermogram. In some embodiments, the thermogram containing a protein composition pattern associated with an absence of protein interactions can be a second signature thermogram generated using a second sample not interacted with the composition.
In some embodiments, the sample is a plasma sample or a serum sample. In such embodiments, the method can be used to identify and/or monitor capacity of composition, e.g., candidate drug, to bind serum albumin and/or other serum or plasma protein interactions. During drug development and efficacy studies, it can be desirable to identify and monitor interactions between a compound of interest (e.g., drug candidate) and components of plasma. For example, it will be appreciated by those of ordinary skill in the art that it can be desirable to identify and/or monitor a compound of interest for binding to serum albumin.
The presently-disclosed subject matter is further illustrated by the following specific but non-limiting examples. The following examples may include compilations of data that are representative of data gathered at various times during the course of development and experimentation related to the present invention.
The average normal thermogram in
Normal plasma thermogram is the weighted sum of the denaturation of individual plasma proteins. Applicants hypothesized that the thermograms seen in
This hypothesis was tersted. With reference to
Referring now to
The data presented in
Thermograms of HSA-depleted serum.
Distinctive thermograms for samples associated with a condition of interest. Plasma samples for subjects suffering from various conditions were obtained from BBI Diagnostics (West Bridgewater, Mass.). For comparison, plasma samples from 15 normal subjects were studied. Thermograms were obtained and compared as described herein, and the results are shown in
The thermogram for Lyme disease (
Origin of the altered thermograms. What causes the dramatic alterations in thermograms seen in FIG. 10? One possibility is that the concentrations of the major proteins in plasma are changed. This possibility was tested by experiments, and it was found that such is not the case.
The most likely explanation for the shifts in the thermograms in
In order to test the hypothesis that shifted thermograms result from interactions, the following study was performed. Bromocresol green is a small organic molecule that binds to Site I of human serum albumin (HSA) with a binding constant of 7×105 M−1 (Peters (1996)). The consequences of such binding on plasma thermograms was studied by spiking a normal plasma sample with 30 micromolar bromocresol green. That concentration corresponds to roughly 1 equivalent of the compound per HSA protein molecule.
With reference to
The results of another study are shown in
The shifts in denaturation transition curves that accompany ligand binding to protein are well understood, and have been explained by a number of specific statistical mechanical and thermodynamic models (Brandts (1990) and Schellman (1958)). The effects of binding on the magnitude and exact shape of a melting transition curve depends precisely on the ligand binding affinity, enthalpy, and stoichiometry. Complex multiphasic transition curves can result from partial saturation. Peptide biomarkers in plasma could produce a myriad of thermogram shapes, depending on the exact proteins (and protein binding sites) that they occupy, and their affinity. The interactions of multiple unique biomarkers with different plasma proteins could produce unique, characteristic thermograms that reflect the underlying complexity of the interactions. While calorimetry may not sense signals arising from the denaturation of the biomarkers themselves, it is uniquely sensitive to interactions of these biomarkers with the more abundant plasma proteins.
Distinctive thermograms for samples associated with additional conditions of interest. Plasma samples were obtained from subjects diagnosed with cervical cancer (samples obtained from a gynecological cancer tissue bank maintained at the University of Louisville). Thermograms were generated using the cervical cancer samples. The samples were associated with either moderate cervical dysplasia (CIN II), early stage cervical cancer, or stage IVB cervical cancer. With reference to
Aliquots of these identical samples were also analyzed by the FDA approved serum protein electrophoresis assay. Densitometric scans of the stained gels are shown for comparison in
For the cervical cancers, thermograms were generated for several samples from the gynecological tissue bank. Samples from four normals, four CIN II cervical dysplasia, and four diagnosed cervical cancers were studied. These results are plotted in
Using methods described herein, thermograms are obtained using plasma samples from normal subjects and from subjects diagnosed with a variety of cancers in order to explore and discover the range of patterns resulting from these diseases. Deidentified plasma samples are obtained from a tissue bank maintained at the University of Louisville. This resource maintains “discard” pieces of benign, premalignant, and malignant gynecological tissues for each patient donor, along with pre- and post-operative blood and urine samples, and ascites fluid (when possible). Plasma is prepared from blood samples by standard methods and was stored at −80° C.
With reference to
With reference to
Using methods described herein, thermograms are obtained using plasma samples from normal subjects and from subjects diagnosed with a variety of conditions. With reference to
With reference to
With reference to
The results of the studies described herein indicate that the methods of the presently-disclosed subject matter are extremely sensitive to binding interactions between proteins. Changes in low-abundance “biomarkers” of conditions of interest that cannot be detected by known methods such as mass spectroscopy or 2-dimensional electrophoresis can be detected with sensitivity using the methods of the presently-disclosed subject matter.
The methods of the presently-disclosed subject are sensitive not only to changes in protein compositions in a noninteracting mixture, but also to interactions resulting from increased concentrations of smaller components (e.g., “biomarkers”) that would themselves not be directly observed. In either case, reproducible signature changes in thermograms relative to normal samples are seen.
Normal thermograms and thermograms for specific conditions of interest are reproducible and distinct. A thermogram for a specific condition of interest is different than a normal thermogram, and is also different than thermograms for other conditions of interest, i.e., they are poor simulations of one another. Each condition of interest has a distinctive and characteristic thermogram. Indeed, in some embodiments, different stages of a condition of interest have distinctive and characteristic thermograms. Therefore, the methods of the presently-disclosed subject matter have beneficial clinical utility and research utility. Benefits of the methods include, the sensitivity, simplicity, non-invasive sample collection, ability to work with low-volume samples, ease of sample preparation, and the capacity for high-throughput.
Materials and Methods
Pure protein samples. Human serum albumin (HSA) (lot # 113K7601), immunoglobulin G (IGG) (lot # 415781/1), immunoglobulin A (IGA) (lot # 105K3777), α1-acid glycoprotein (AAG) (lot # 073K7607), α1-antitrypsin (AAT) (lot # 033K7603), fibrinogen (FIB) (lot # 083K7604), transferrin (TRF) (lot # 123K14511), haptoglobin (HPT) (lot # 055K1664) and immunoglobulin M (IGM) (lot # 016K4876) were purchased from Sigma-Aldrich Chemical Co. (St. Louis, Mo.). α1-Antichymotrypsin (ACT) (lot # B58700), complement C3 (C3) (lot # D33204), complement C4 (C4) (lot # D34721), ceruloplasmin (CER) (lot # B70322), α2-macroglobulin (A2M) (lot # B73605) and prealbumin (PRE) (lot # B68296) were purchased from Calbiochem. C-reactive protein (CRP) (lot # 32F0305FP) was purchased from Life Diagnostics.
Manufactured mixtures. By using available purified plasma proteins, solution mixtures of any desired composition can be made and thermograms for these preparations can be obtained. Such is done, in order to match experimental thermograms of normal and diseased plasma/serum samples. This approach allows for an exploration of the effects of individual components on thermogram shape.
Standard reference serum. A serum reference material (sample # 16910) was purchased from Sigma-Aldrich Chemical Co. (St. Louis, Mo.). A standardized human serum sample can be provided with a certificate of analysis that includes certified values for the concentrations (g/L) of the 15 most abundant proteins, along with the uncertainty in the concentration determination. Concentrations of each sample are determined on the same sample independently by multiple different laboratories. Each sample is provided as a lyophilized portion under nitrogen, and a strict standardized protocol for reconstitution of the material is provided. Thermograms obtained for such materials are useful for multicomponent analysis, since the protein concentrations that are being sought by the numerical analyses procedure are precisely known for the experimental sample. The goodness of fits can thus be rigorously evaluated.
Plasma samples. Normal plasma samples (lot # JA053759, JA053761, JA053763, JA053764, JA053765, JA053766, JC014372, JM034968, JM034969, JM034970, JM034971) were purchased from Innovative Research (Southfield, Mich.) and were also obtained from the Gynecological Cancer Repository of the James Graham Brown Cancer Center. Plasma from subjects suffering from Lyme disease (lot # BM146897, BM140032, BM140031, BM140028), systemic lupus erythematosis (lot # BM142168, BM142160) and rheumatoid arthritis (lot # BM204810, BM205222, BM203373, BM202803, BM200182) were purchased from BBI Diagnostics (West Bridgewater, Mass.).
Sample preparation. IGM, C3, C4 and CRP were purchased as solutions in buffer, lyophilized to dryness and then re-constituted in a smaller volume of ultrapure water (18.2 MS2-cm) to yield a concentration suitable for DSC. PRE, A2M, CER, ACT were purchased as a powder lyophilized from buffer and were reconstituted with ultrapure water. HSA, IGG, IGA, AAG, AAT, FIB, TRF and HPT were reconstituted with 10 mM potassium phosphate, 150 mM NaCl, pH 7.5. Reference serum was reconstituted according to the guidelines. Pure proteins and reference serum were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 to ensure complete solvent exchange. Pure proteins were diluted with dialysate to a concentration suitable for DSC. Reference serum was diluted 25-fold with the dialysate. Plasma samples (100 μL) were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, 0.38% (w/v) sodium citrate, pH 7.5 to ensure complete solvent exchange then diluted 25-fold with the same buffer. All samples (0.45 micron, cellulose acetate or polyethersulfone) and buffers (0.22 micron, polyethersulfone) were filtered before use. Pure protein concentrations were quantitated spectrophotometrically using the following extinction coefficients (ε280; L-1·g-1·cm-1): HSA, 0.53; IGG, 1.38; IGA, 1.32; AAG, 0.89; AAT, 0.53; FIB, 1.55; TRF, 1.12; HPT, 1.2; IGM, 1.18; ACT, 0.62; C3, 0.97; C4, 0.92; CER, 1.49; A2M, 0.893; PRE, 1.41; CRP, 1.95.
DSC protocol. An automated capillary Differential Scanning Calorimeter (DSC) (MicroCal, LLC, Northampton, Mass.) was used for the studies described herein. Samples and dialysate were stored in 96-well plates at 5° C. until being loaded into the calorimeter using the robotic attachment. Scans were recorded from 20-110° C. at 1° C./min using the mid feedback mode, a filtering period of 2 s and with a pre-scan thermostat of 15 min. Data were analyzed using Origin 7.0. Sample scans were first corrected for the instrument baseline by subtracting an appropriate buffer scan. Nonzero baselines were then corrected by applying a linear baseline fit. Scans were finally normalized for the gram concentration of protein. For the pure protein samples, protein concentrations were determined spectrophotometrically as outlined herein. Total protein concentrations of the reference serum and plasma samples were measured by the bicinchoninic acid method (Pierce, Rockford, Ill.). Thermograms were plotted as Excess Specific Heat Capacity (cal/° C.g) versus temperature.
Clinical Laboratory Testing. Both total protein and the concentration of the individual major serum proteins are measured, for example, immunoglobulins G, A and M, transferrin, haptoglobin, prealbumin, complement factors C3 and C4, ceruloplasmin, apolipoproteins A1 and B, α1-antitrypsin, α1-acid glycoprotein, and C-reactive protein. In addition, serum (or plasma) protein electrophoresis is performed on each sample. All of these assays are performed by FDA approved, standard clinical laboratory procedures. The concentrations of the specific serum proteins and the SPE patterns are correlated with the thermograms determined by the methods described herein.
Lipoproteins (HDL, LDL, VLDL, and chylomicrons) are more complex than the other serum proteins. They contain not only the apolipoproteins, but also cholesterol and triglyceride, as well as other minor components. The lipoproteins are likely to cause a significant signal in the thermogram patterns. Therefore, cholesterol and triglyceride of the samples are also measured. Cholesterol and triglyceride is measured on the Vitros by enzymatic methods.
C-reactive protein (CRP) is normally present at a low concentration, which is unlikely to contribute to the thermogram pattern. However, during the acute phase reaction, which is common among sick patients, the concentration of CRP can be high enough to be detectable by the methods described herein.
Clinical assay methods. Protein electrophoresis was performed on agarose gels using the SPIFE 3000 and scanned with the QUICKSCAN 2000 (Helena Laboratories, Beaumont, Tex.). Total protein was measured by the biuret method on the Ortho Vitros 950 (Vitros) (Ortho-Clinical Diagnostic, Rochester, N.Y.) chemistry analyzer. Albumin was measured on the Vitros by the bromocresol green dye binding assay or by an immunoturbidometric assay on the Cobas Integra 800 (Integra) (Roche, Indianapolis, Ind.). Albumin concentrations were also determined from the fraction percent on the protein electrophoresis assay along with the total protein concentration. Specific serum proteins (IGG, IGA, TRF, HPT, IGM, C3, C4, PRE, CRP) were measured by immunoturbidimetry on the Integra.
Column depletion experiments. Reference serum was depleted of HSA using the SwellGel Blue™ albumin removal kit with some minor modifications to the manufacturer's protocol (Pierce, Rockford, Ill.). The serum sample was diluted 10-fold into 10 mM potassium phosphate, pH 7.5 in order to achieve salt conditions and albumin concentrations required for good column binding. Diluted serum (200 μL) was applied to a column containing 2 SwellGel™ discs. An HSA-depleted fraction was obtained following the standard protocol. A single 200 μL volume of the supplied binding/wash buffer was used to obtain a wash fraction. Finally, an eluted HSA fraction was obtained from a single 200 μL addition of the supplied elution buffer. In order to obtain a greater volume of each fraction for subsequent experiments, multiple columns were run using an identical protocol and each of the fractions pooled. Fractions for DSC analysis were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 and diluted as necessary with dialysate. DSC scans were performed on an N-DSC II instrument (Calorimetry Sciences Corporation, Provo, Utah) from 20-110° C. at 1° C./min with a pre-scan equilibration time of 10 min. Data were analyzed using Origin 7.0.
Fractionation and Generation of Thermograms from Sample Fractions.
Plasma from a healthy normal subject was fractionated using Suprdex 75 (GE Healthcare). Fractions were taken at different points along the elution profile and subjected to DSC. The thermograms of each fraction (
Methods as disclosed herein, including fractionation and generation of thermograms, provide a novel multidimensional signature of a sample, and allows identification of the component most responsible for shifts in the thermogram for plasma, e.g., as in a subject having a condition of interest.
With reference to
Protocol. Undiluted plasma or serum (300-500 μL) were fractionated using gel filtration chromatography (Superdex 75 10/300 GL or Superdex 200 10/300 GL; manufacturers instructions and Superdex-related materials are incorporated herein by this reference). The columns were prepared for use following standard procedures and finally rinsed with a standard phosphate buffer (for plasma: 10 mM potassium phosphate, 150 mM sodium chloride, 0.38% sodium citrate, pH 7.5; for serum: 10 mM potassium phosphate, 150 mM sodium chloride, 0.38% sodium citrate, pH 7.5) before use. Fractions (50-200 μL) were collected overnight at a flow rate of 0.05 to 0.2 mL/min. The total protein concentration of each fraction was estimated spectrophotometrically. Fractions were selected for DSC analysis based on the elution profile and the protein concentration. Based on the estimated protein concentration and the volume requirements for DSC analysis, the selected fractions were diluted (with gel filtration running buffer) or pooled as necessary before loading into 96 well plates for DSC analysis. A final total protein concentration was determined colorimetrically for each DSC sample (BCA assay; Pierce Biotechnology Inc., Rockford, Ill.) in a microplate format (Tecan U.S., Research Triangle Park, N.C.). The colorimetric total protein concentrations were subsequently used to normalize the DSC thermograms. DSC thermograms were collected and analyzed according to the DSC protocol using the gel filtration running buffer as the reference buffer.
Throughout this document, various references are mentioned. All such references are incorporated herein by reference, including the references set forth in the following list:
This application claims priority from U.S. Provisional Application Ser. No. 61/097,433 filed on Sep. 16, 2008, and is a continuation-in-part of commonly assigned and co-pending U.S. patent application Ser. No. 11/972,921 filed on Jan. 22, 2008. U.S. patent application Ser. No. 11/972,921 claims priority from U.S. Provisional Patent Application Ser. Nos. 60/978,252 filed Oct. 8, 2007, and 60/884,730 filed Jan. 12, 2007. The entire disclosures of U.S. Patent Application Ser. Nos. 61/097,433; 11/972,921; 60/978,252; and 60/884,730 are incorporated herein by this reference.
Subject matter described herein was made with government support under Grant Number R44 CA103437 awarded by the National Cancer Institute. The United States government has certain rights in the described subject matter.
| Number | Date | Country | |
|---|---|---|---|
| 61097433 | Sep 2008 | US | |
| 60978252 | Oct 2007 | US | |
| 60884730 | Jan 2007 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11972921 | Jan 2008 | US |
| Child | 12561081 | US |
