Patent Application
20180171408
The presently-disclosed subject matter relates to the characterization of gastro-intestinal (GI) diseases in a subject, including diagnosis of GI diseases and exclusion of a diagnosis of GI diseases.
Inflammatory bowel diseases (IBD), Crohn's disease (CD), Celiac's disease (CeD), and ulcerative colitis (UC) are chronic relapsing remitting inflammatory conditions affecting the gastrointestinal tract, primarily the small intestine and colon [1]. CD is most frequently diagnosed in patients in their 20s and UC in their 30s; however, the diagnosis can be made at any age [2]. IBD diagnosis is often straightforward, as disease can be seen by endoscopy or imaging modalities. However, diagnosis can be difficult as patients may experience symptoms consistent with IBD but ultimately have other diagnoses including functional gastrointestinal disorders such as irritable bowel syndrome (IBS) [3-6]. Patients with IBS can have symptoms very similar to those with IBD. IBD can be limited to difficult to evaluate areas of the GI tract such as isolated small bowel disease. Also, within IBD, differentiating between CD and UC can be difficult, especially within patients with severe inflammatory activity, often termed indeterminate colitis [7]. When the clinical presentation is severe and an operation including colectomy is indicated, differentiating CD and UC is imperative, as ileal pouch-anal anastomosis (IPAA) is generally contraindicated in CD due to high morbidity [8].
Developing biomarkers that can be easily obtained and allow for the correct diagnosis early into evaluation can avoid costly interventions that expose patients to multiple unnecessary procedures. Blood markers for both IBD and IBS have been sought for decades. For IBD, perinuclear antineutrophil cytoplasmic antibody (p-ANCA) and anti-Saccharomyces cerevisiae antibody (ASCA) have been reported to be markers for UC and CD, respectively. However, p-ANCA is also detected in 10-40% of patients with CD and ASCA is detected in 6-14% of patients with UC [1]. Other markers increased in subjects with CD include antibodies to (a) Escherichia coli outer membrane porin C (Omp-C), (b) protein from Pseudomonas fluorescens [9] and (c) flagellin c-BIR1 (anti-CBIR1) [10], but these markers remain insensitive. In patients with indeterminate colitis, those with one or more positive antibodies, including ANCA, ASCA, 12 (antibody to Pseudomonas fluorescens), and Omp-C, have significantly higher post-operative complications [11]. Other inflammatory biomarkers such as C-reactive protein, fecal calprotectin, and fecal lactoferrin differentiate IBD from other gastrointestinal disorders such as IBS [5], but tests do not differentiate among various types of inflammatory colitides [12].
Therefore, improved tests that can effectively, efficiently, and noninvasively characterize GI diseases are needed, including tests to diagnose GI diseases and/or to exclude a diagnosis of a GI disease.
The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are used, and the accompanying drawings of which:
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.
The presently-disclosed subject matter includes methods, devices, and kits useful for characterizing an auto-immune disease in a subject and, more particularly, for characterizing gastro-intestinal (GI) diseases in a subject. In some embodiments, the method involves providing a biological sample from the subject; determining expression values of at least two genes in the biological sample; calculating one or more ratios of the expression values of the at least two genes; and comparing each ratios to a reference, wherein the GI disease(s) is characterized based on a difference in the ratios of the expression values of the at least two genes in the biological sample from the subject as compared to the references. In some embodiments, the biological sample is blood obtained from the subject or another biological sample containing a cell obtained from the subject, e.g., a subject suspected of having a GI disease. The method can be used, in some embodiments, to diagnose the subject with a GI disease. In some embodiments, the method can be used to exclude the subject from a diagnosis of GI disease.
The method can be used, in some embodiments, to diagnose the subject with a GI disease that is either an inflammatory bowel disease (IBD) or inflammatory bowel syndrome (IBS). In some embodiments, the method can be used to exclude the subject from a diagnosis of an IBD. In some embodiments, the method can be used to exclude the subject from a diagnosis of an IBD and to diagnose the subject with IBS. In some embodiments, the method can be used to exclude the subject from a diagnosis of IBS. In some embodiments, the method can be used to exclude the subject from a diagnosis of IBS and to diagnose the subject with an IBD.
The method can be used, in some embodiments, to diagnose the subject with a GI disease that is either Crohn's disease (CD) or ulcerative colitis (UC). In some embodiments, the subject is one who has received a diagnosis of IBD. In some embodiments, the method can be used to exclude the subject from a diagnosis of CD. In some embodiments, the method can be used to exclude the subject from a diagnosis of CD and to diagnose the subject with UC. In some embodiments, the method can be used to exclude the subject from a diagnosis of UC. In some embodiments, the method can be used to exclude the subject from a diagnosis of UC and to diagnose the subject with CD.
Methods of the presently-disclosed methods include determining expression values of genes in biological samples. As such, nucleic acid molecules or nucleotides are relevant to the disclosed subject matter. Nucleotides or genes, the expression of which is desired to be determined for characterizing an auto-immune disease, include, but are not limited to those identified in Table A, the isolated nucleic acid molecules of any one of SEQ ID NOs: 1-47, fragments of the isolated nucleic acid molecules of any one of SEQ ID NOs: 1-47 where detection of such fragments are indicative of expression of an associated gene, e.g., as identified in Table A, complementary nucleic acid molecules, isolated nucleic acid molecules capable of hybridizing to any one of the SEQ ID NOs: 1-47 under conditions disclosed herein, and corresponding RNA and/or DNA molecules.
As used herein, “nucleic acid” and “nucleic acid molecule” refer to any of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), oligonucleotides, fragments generated by the polymerase chain reaction (PCR), and fragments generated by any of ligation, scission, endonuclease action, and exonuclease action. The term “isolated”, when used in the context of an isolated DNA molecule or an isolated polypeptide, is a DNA molecule or polypeptide that, by the hand of man, exists apart from its native environment and is therefore not a product of nature.
Unless otherwise indicated, a particular nucleotide sequence also implicitly encompasses complementary sequences, subsequences, elongated sequences, as well as the sequence explicitly indicated. The terms “nucleic acid molecule” or “nucleotide sequence” can also be used in place of “gene”, “cDNA”, or “mRNA”. Nucleic acids can be derived from any source, including any organism. In one embodiment, a nucleic acid is derived from a biological sample isolated from a subject.
The terms “complementary” and “complementary sequences”, as used herein, refer to two nucleotide sequences that comprise antiparallel nucleotide sequences capable of pairing with one another upon formation of hydrogen bonds between base pairs. As used herein, the term “complementary sequences” means nucleotide sequences which are substantially complementary, as can be assessed by the same nucleotide comparison set forth herein, or is defined as being capable of hybridizing to the nucleic acid segment in question under conditions such as those described herein. In one embodiment, a complementary sequence is at least 80% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 85% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 90% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 95% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 98% complementary to the nucleotide sequence with which is it capable of pairing. In another embodiment, a complementary sequence is at least 99% complementary to the nucleotide sequence with which is it capable of pairing. In still another embodiment, a complementary sequence is at 100% complementary to the nucleotide sequence with which is it capable of pairing. A particular example of a complementary nucleic acid segment is an antisense oligonucleotide.
“Stringent hybridization conditions” in the context of nucleic acid hybridization experiments are both sequence- and environment-dependent. Longer sequences hybridize specifically at higher temperatures. Generally, highly stringent hybridization and wash conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. The Tm is the temperature (under defined ionic strength and pH) at which 50% of the target sequence hybridizes to a perfectly matched probe. Very stringent conditions are selected to be equal to the Tm for a particular probe. Typically, under “stringent conditions” a probe hybridizes specifically to its target sequence, but to no other sequences. An extensive guide to the hybridization of nucleic acids is found in Tijssen 1993, which is incorporated herein by this reference. In general, a signal to noise ratio of 2-fold (or higher) than that observed for a negative control probe in a same hybridization assay indicates detection of specific or substantial hybridization.
It is understood that in order to determine a gene expression level by hybridization, a full-length cDNA need not be employed. To determine the expression level of a gene represented by one of SEQ ID NOs: 1-47, any representative fragment or subsequence of the sequences set forth in SEQ ID NOs: 1-47 can be employed in conjunction with the hybridization conditions disclosed herein. As a result, a nucleic acid sequence used to assay a gene expression level can comprise sequences corresponding to the open reading frame (or a portion thereof), the 5′ untranslated region, and/or the 3′ untranslated region. It is understood that any nucleic acid sequence that allows the expression level of a reference gene to be specifically determined can be employed with the methods and compositions of the presently disclosed subject matter.
As used herein, the terms “corresponding to” and “representing”, “represented by” and grammatical derivatives thereof, when used in the context of a nucleic acid sequence corresponding to or representing a gene, refers to a nucleic acid sequence that results from transcription, reverse transcription, or replication from a particular genetic locus, gene, or gene product (for example, an mRNA). In other words, a partial cDNA, or full-length cDNA corresponding to a particular reference gene is a nucleic acid sequence that one of ordinary skill in the art would recognize as being a product of either transcription or replication of that reference gene (for example, a product produced by transcription of the reference gene). One of ordinary skill in the art would understand that the partial cDNA, or full- length cDNA itself is produced by in vitro manipulation to convert the mRNA into a cDNA, for example by reverse transcription of an isolated RNA molecule that was transcribed from the reference gene. One of ordinary skill in the art will also understand that the product of a reverse transcription is a double-stranded DNA molecule, and that a given strand of that double-stranded molecule can embody either the coding strand or the non-coding strand of the gene. The sequences presented in the Sequence Listing are single-stranded, however, and it is to be understood that the presently claimed subject matter is intended to encompass the genes represented by the sequences presented in SEQ ID NOs: 1-47, including the specific sequences set forth as well as the reverse/complement of each of these sequences.
The term “gene expression” generally refers to the cellular processes by which a biologically active polypeptide is produced from a DNA sequence. Generally, gene expression comprises the processes of transcription and translation, along with those modifications that normally occur in the cell to modify the newly translated protein to an active form and to direct it to its proper subcellular or extracellular location.
The terms “gene expression level” and “expression level” as used herein refer to an amount of gene-specific RNA or polypeptide that is present in a biological sample. When used in relation to an RNA molecule, the term “abundance” can be used interchangeably with the terms “gene expression level” and “expression level”.
Determination of expression levels of genes of interest can be achieved using any technique known the skilled artisan. For example, in some embodiments, RNA can be purified from the biological sample, converted to the more-stable complementary DNA (cDNA), before the gene expression products of genes of interest are detected. As will be recognized by the skilled artisan, where amplification of the sample is desired, polymerase chain reaction amplification can be employed. Determining the expression levels can be achieved, for example, using reverse transcription-polymerase chain reaction (RT-PCR), microarray analysis, or other techniques known to the skilled artisan.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, or 47 genes represented by SEQ ID NOs: 1-47. In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to ABR, ACTB, ACTR1A, EXT2, KRAS, LLGL2, NRAS, PGK1, and POU6F1.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to ACTR1A, CD55, HRAS, IL11RA, JUN, PGK1, POU6F1, TAF11, TBP, and TP53.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to ABR, CD55, CTSS, GAPDH, HLA-DRA, HRAS, JUN, OAS1, ORC1L, and TBP.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of the genes corresponding to ANAPC1, CDH1, EXT2, GAPDH, GNB5, NRAS, ORC1L, POU6F1, TBP, and TP53.
In some embodiments, determining the expression levels of genes in the biological sample includes determining the expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
As used herein, a “ratio” or “expression ratio” is the expression value of a first biomarker (numerator) divided by the expression value of a second biomarker (denominator), e.g., Gene A/Gene B. As such, once the expression levels of at least two genes are determined, a ratio can be calculated. Ratios can be calculated using expression levels of genes in a biological sample obtained from a subject. In some embodiments, a reference can be a ratio calculated using expression levels of genes from another source. As such, the term “subject ratio” can used herein to refer to a ratio calculated using expression values of a gene pair in a biological sample obtained from a subject, while the term “reference ratio” can be used to refer to a ratio of the same biomarker pair in a reference sample, which serves as a reference to which the subject ratio is compared.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios of expression levels of genes corresponding to those set forth in Table A, wherein each ratio is calculated by dividing the expression level of a first gene in Table A by the expression level of a second gene in Table A.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, or 89 ratios set forth in Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 1 (IBD vs. CTRL) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 ratios set forth in Column 1 (IBD vs. Control) of Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 2 (IBS v. Control) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, or 19 ratios set forth in Column 2 (IBS v. Control) of Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 3 (IBD vs. IBS) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 ratios set forth in Column 3 (IBD vs. IBS) of Table B.
In embodiments of the presently-disclosed subject matter, the method involves calculating one or more ratios set forth in Column 4 (CD vs. UC) of Table B. In some embodiments, the method includes calculating 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ratios set forth in Column 4 (CD vs. UC) of Table B.
Various references are appropriate for use in connection with the presently-disclosed subject matter, with non-limiting examples described herein. In some embodiments, the reference comprises a reference ratio calculated using of the expression level of two genes in a biological sample taken from one or more individuals, which two genes are the same two genes used to calculate the subject ratio. The expression levels of genes in biological samples from one or more individuals can be a expression levels from a reference group or comparator group.
In some embodiments, a “comparator group” or “reference group” includes individuals having a common characterization, for example, healthy control individuals, individuals who have been diagnosed with a condition often confused with an auto-immune disease of interest in the context of clinical diagnosis, individuals who have been diagnosed with an auto-immune disease of interest, or individuals who have another common characterization of interest. Expression values of biomarkers obtained from biological samples of individuals in a comparator group can be used to calculate reference ratios. Data associated with one or more comparator groups can be stored, for example, in a database that can be accessed when practicing a method in accordance with the presently-disclosed subject matter.
With reference to Table B, for example, ratios-of-interest are provided for use with a healthy control comparator group (CTRL, column 1 and column 2) or a comparator group of individuals having IBS or IBD (column 3), or having CD or UC (column 4). Examples of comparator groups relevant to characterization of a GI disease include, but are not limited to: healthy control (CTRL), irritable bowel syndrome (IBS), inflammatory bowel diseases (IBD), Crohn's disease (CD), Celiac's disease (CeD), and ulcerative colitis (UC). Because a comparator group can include data from multiple individuals, as will be recognized by one of ordinary skill in the art, it is expected that the expression values of biomarkers in biological samples obtained from different individuals in the same comparator group might differ. As such, identification of a reference ratio for a particular gene pair can be made with reference to a “threshold reference ratio” for the gene pair within the comparator group. In some embodiments, for example, the threshold expression ratio could be a median, an average, a value based on statistical analysis of the distribution of ratios of expression levels of the gene pair within the comparator group, or another threshold value, e.g., top value in the group, second highest value in the group, third highest value in the group, etc.
In some embodiments, the reference comprises a reference ratio calculated using a standard sample containing standard biomarker amounts, which can be analyzed in the same manner or even concurrently with the biological sample. In some embodiments, the reference comprises ratio values, such as standard threshold values. Such values can be published in a format useful for the practitioner, such as in a list, table, database, or incorporated into a software or system for use in connection with the presently-disclosed subject matter. Such values can in some cases be based, for example, on information obtained from a comparator group.
Ratios of interest, or ratios of gene pairs that are useful for characterizing GI diseases, have the ability to distinguish groups, e.g., IBD group and health control group, IBS group and health control group, IBD group and IBS group, CD group and UC group. Table B includes examples of ratios of interest for IBD vs. healthy control (CTRL), IBS vs. healthy control, IBD vs. IBS, and CD vs. UC. In this regard, an auto-immune disease can be characterized based on a difference in the ratios of the expression values of at least two genes in a biological sample from the subject as compared to a reference ratio.
In some embodiments, it can be useful to compare one or more subject ratios to one or more first reference ratios, e.g., from a first comparator group, and also to compare the one or more subject ratios to one or more second reference ratios, e.g., from a second comparator group. Such a multi-tiered approach can improve the efficacy of the characterization of GI diseases, as will be explained further in the Examples section.
Characterizing can include providing a diagnosis, prognosis, and/or theragnosis of an auto-immune disease in a subject.
“Making a diagnosis” or “diagnosing,” as used herein, are further inclusive of making a prognosis, which can provide for predicting a clinical outcome (with or without medical treatment), selecting an appropriate treatment (or whether treatment would be effective), or monitoring a potential auto-immune disease, based on calculated ratios of expression levels of genes. Diagnostic testing that involves treatment, such as treatment monitoring or decision making can be referred to as “theranosis.” Further, in some embodiments of the presently disclosed subject matter, multiple determinations of ratios of expression levels of genes over time can be made to facilitate diagnosis (including prognosis), evaluating treatment efficacy, and/or progression of a potential auto-immune disease or auto-immune disease. A temporal change in one or more ratios can be used to predict a clinical outcome, monitor the progression of the condition, and/or efficacy of administered therapies. In such an embodiment for example, one could observe a change in a particular ratio in a biological sample over time during the progression of a condition and/or during the course of a therapy.
The presently disclosed subject matter further provides in some embodiments a method for theranostic testing, such as evaluating progression of a condition and/or treatment efficacy in a subject. In some embodiments, the method comprises providing a series of biological samples over a time period from the subject; determining expression values of at least two genes in each of the biological samples; calculating one or more ratios of the expression values of the at least two genes for each of the biological samples; and determining any measurable change in the ratios in each of the biological samples from the series to thereby evaluate progression of the condition and/or treatment efficacy.
Any changes in the ratios, and changes in the ratios relative to references, over the time period can be used to make a diagnosis, predict clinical outcome, determine whether to initiate or continue the therapy, and whether a current therapy is effectively.
The phrase “determining the prognosis” as used herein refers to methods by which the skilled artisan can predict the course or outcome of a condition in a subject. The term “prognosis” can refer to the ability to predict the course or outcome of a condition with up to 100% accuracy, or predict that a given course or outcome is more or less likely to occur based on the ratios of expression values of genes of interest. The term “prognosis” can also refer to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a subject when compared to individuals in a comparator group. For example, in individuals exhibiting subject ratios-of-interest that are higher than reference ratio-of-interest, the chance of a given outcome (e.g., a GI disease diagnosis) may be very high. In certain embodiments, a prognosis is about a 5% chance of a given expected outcome, about a 7% chance, about a 10% chance, about a 12% chance, about a 15% chance, about a 20% chance, about a 25% chance, about a 30% chance, about a 40% chance, about a 50% chance, about a 60% chance, about a 75% chance, about a 90% chance, or about a 95% chance.
The skilled artisan will understand that associating a prognostic indicator with a predisposition to an adverse outcome can be performed using statistical analysis. For example, subject ratios that are higher than reference ratios in some embodiments can signal that a subject is more likely to suffer from an auto-immune disease than subjects with ratios that are substantially equal to reference ratios, as determined by a level of statistical significance. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983, incorporated herein by reference in its entirety. Exemplary confidence intervals of the present subject matter are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while exemplary p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001. When performing multiple statistical tests, p values can be corrected for multiple comparisons using techniques known in the art.
Further with respect to the methods of the presently disclosed subject matter, a preferred subject is a vertebrate subject. A preferred vertebrate is warm-blooded; a preferred warm-blooded vertebrate is a mammal. A mammal is most preferably a human. As used herein, the term “subject” includes 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 diagnosis 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; 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; 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 presently-disclosed subject matter further includes kits and devices useful for detecting and/or determining expression levels of at least two genes in a biological sample.
The kits of the presently-disclosed subject matter can include primer pairs for determining expression levels of at least two genes, which can be useful for calculating ratios as disclosed herein. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the kit includes primer pairs for determining expression levels of at least two genes corresponding to those set forth in Table A. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A.
In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ABR, ACTB, ACTR1A, EXT2, KRAS, LLGL2, NRAS, PGK1, and POU6F1. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ACTR1A, CD55, HRAS, IL11RA, JUN, PGK1, POU6F1, TAF11, TBP, and TP53. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ABR, CD55, CTSS, GAPDH, HLA-DRA, HRAS, JUN, OAS1, ORC1L, and TBP. In some embodiments, the kit includes primer pairs for determining expression levels of the genes corresponding to ANAPC1, CDH1, EXT2, GAPDH, GNB5, NRAS, ORC1L, POU6F1, TBP, and TP53. In some embodiments, the kit includes primer pairs for determining expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
The devices of the presently-disclosed subject matter can include a probe for selectively binding each of at least two gene expression products to detect at least two genes, which can be useful for determining expression levels of the genes and for calculating ratios as disclosed herein. Such probes can selectively bind the gene products, for example, by hybridization of the probe and a nucleotide gene product. In some embodiments, the device includes probes for detecting each of at least two genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes represented by SEQ ID NOs: 1-47. In some embodiments, the device includes probes for detecting each of at least two genes corresponding to those set forth in Table A. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, or 48 genes corresponding to those set forth in Table A.
In some embodiments, the device includes probes for detecting each of the genes corresponding to ABR, ACTB, ACTR1A, EXT2, KRAS, LLGL2, NRAS, PGK1, and POU6F1. In some embodiments, the device includes probes for detecting each of the genes corresponding to ACTR1A, CD55, HRAS, IL11RA, JUN, PGK1, POU6F1, TAF11, TBP, and TP53. In some embodiments, the device includes probes for detecting each of the genes corresponding to ABR, CD55, CTSS, GAPDH, HLA-DRA, HRAS, JUN, OAS1, ORC1L, and TBP. In some embodiments, the device includes probes for detecting each of the genes corresponding to ANAPC1, CDH1, EXT2, GAPDH, GNB5, NRAS, ORC1L, POU6F1, TBP, and TP53. In some embodiments, the device includes probes for detecting each of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, or 34 genes corresponding to those set forth in Table B.
Some of the gene sequences disclosed herein are cross-referenced to GENBANK® accession numbers. The sequences cross-referenced in the GENBANK® database are expressly incorporated by reference as are equivalent and related sequences present in GENBANK® or other public databases. Also expressly incorporated herein by reference are all annotations present in the GENBANK® database associated with the sequences disclosed herein. Unless otherwise indicated or apparent, the references to the GENBANK® database are references to the most recent version of the database, as of the filing date of this Application.
While the terms used herein are believed to be well understood by one of ordinary skill in the art, definitions are set forth 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.
As used herein, ranges can be expressed as from “about” one particular value, and/or to “about” another particular value. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.
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.
Inflammatory bowel diseases, ulcerative colitis and Crohn's disease are considered to be of autoimmune origin, but the etiology of irritable bowel syndrome remains elusive. Furthermore, classifying patients into irritable bowel syndrome and inflammatory bowel diseases can be difficult without invasive testing and holds important treatment implications. Our aim was to assess the ability of gene expression profiling in blood to differentiate among these subject groups.
It is generally thought that different profiles of biomarkers could provide useful information to guide clinical decision-making; from diagnosis to choice of optimal therapies and in some cases these biomarker profiles are being implemented in clinical practice [3,12-24]. Searches for optimal biomarker profiles can be achieved using clustering methods e.g., heirarchical clustering, K-means clustering, which depend upon the general ability to find common features across a sample population or forms of linear discriminate analysis, which depend upon the ability to find linear combinations of features that have the ability to separate two or more classes. The former method is a common method to analyze large numbers of features, such as microarray data whereas the latter is a more common method for analysis of smaller numbers of features. Both methods are suitable for further analyses using machine learning methods such as support vector machines, logistic regression, principal components analysis or prediction analysis for microarrays. Using a form of linear discriminant analysis, we have attempted to employ mRNA transcript profiles to distinguish between subjects with multiple sclerosis and other comparator groups [25,26]. Our results clearly demonstrate that mRNA transcript profiling has the capacity to distinguish between MS, even early in the disease process, and homogeneous comparator groups, such as healthy subjects (CTRL), or subjects with clinically related diseases such as neuromyelitis optica or transverse myelitis. Thus, these binary comparisons can produce a test of exclusion of multiple sclerosis. Here, we applied this approach to IBD and IBS. Our results demonstrate that distinct mRNA profiles accurately discriminate IBD from CTRL, IBS from CTRL, IBD from IBS, and CD from UC with high degrees of sensitivity and specificity. We propose these approaches may provide useful guides for clinical decision-making.
Methods
Transcript levels of a total of 45 genes in blood were determined by quantitative real-time polymerase chain reaction (RT-PCR). We applied three separate analytic approaches; one utilized a scoring system derived from combinations of ratios of expression levels of two genes and two different support vector machines.
Human Subjects
Blood samples collected in PAXgene tubes were obtained from CTRL, IBS, CeD, CD or UC subjects. Diagnosis of IBD, both CD and UC, was made by colonoscopy or sigmoidoscopy and tissue biopsy to localize inflammation to all layers of the intestinal wall (CD) or only the inner lining layer (UC). Diagnosis of IBS was made by the absence of pathologic damage in the colon after examination by colonoscopy or sigmoidoscopy. Inclusion criteria were diagnosis by a gastro-intestinal specialist using these methods. Age, race and gender were not statistically different among the different study groups. Time of blood draw, for example, morning/afternoon clinics, was also not statistically significant among the different study groups. Relevant institutional review board approval was obtained from all participating sites.
MRNA Transcript Determination
Total RNA was purified using Qiagen's RNA isolation kits using standard protocols and was reverse-transcribed using poly-A primers using Superscript III (Invitrogen, Carlsbad, Calif., USA). A TaqMan Low Density Array (TLDA) was designed to analyze expression levels of 44 target genes and of four housekeeping genes in 300 ng cDNA. The gene probes on the TLDA plate were: ABR, ACTB, ACTR1A, ADAMTSL4, ANAPC1, APOBEC3F, ASL, B2M, BRCA1, CD55, CDH1, CDKN1B, CHEK2, CSF3R, CTSS, EPHX2, EXT2, FOS, FOSL1, GAPDH, GATA3, GNB5-1, GNB5-1, GSTM4, HLA-DRA, HRAS, IFI27, IL11RA, JUN, KRAS, LEPREL4, LLGL2, NRAS, OAS1, ORC1L, PGK1, PMAIP1, POU6F1, RANGAP1, SC65, SPIB, TAF11, TBP, TGFBR2, TP53-1 TP53-2, TXK. GNB5-1 and -2 and TP53-1 and -2 interrogate different exon-intron junctions. [26]. Inclusion of the specific gene targets was based upon the following criteria: (a) previous studies demonstrating differential expression among control and multiple autoimmune diseases, (b) protein products possess known inflammatory functions, (c) expression levels change in response to pro-inflammatory stimuli (cytokines), and/or (d) protein products have known roles in cell cycle progression and/or apoptosis. Patient diagnosis was blinded for all experimental procedures. Relative expression levels were determined directly from the observed threshold cycle (CT). Linear expression levels were determined using the formula, 240-CT.
Ratioscore and Support Vector Machine (SVM) Methods
Principal Component Analysis (PCA) was applied directly to the normalized gene expression data using MATLAB's Bioinformatics Toolkit (The MathWorks, Inc.) and other techniques to identify a lower dimensional space of gene expressions that could be used to classify controls from cases. The results were disappointing and we concluded that looking at ratios of the gene expression data may be a more productive approach. The computational algorithm and permutation testing strategy employed to identify discriminatory combinations of ratios to create the ratioscore (our terminology) have been previously described [26]. For completeness, we summarize the algorithm used in the Ratioscore Method below. Let D denote the set of gene-expression levels associated with the disease group and C denote the set of gene-expression levels associated with the control group. The algorithm searches for the “best” set of gene ratios that partitions D and C:
The algorithm allows one to identify the smallest number of ratios that partitions the case and control groups.
Two support vector machines (SVM) were independently created and trained using ratios identified by the Ratioscore Method. The first SVM was coded in Mathematica (Wolfram Research, Inc.) and the second SVM employed LS-SVMLab software (http://www.esat.kuleuven.be/sista/lssvmab). We decided to use the two independently developed SVM since the choice of kernels, optimization algorithms, and the training algorithms can produce differing results. There was little difference in the performance of the two machines when classifying the different case-control combinations. To confirm the results of the Ratioscore Method and the SVM approaches, logistic regression was employed to separate to the case and control sets using the gene ratios. Its performance was in line with the other two approaches and hence, we have chosen not to report these results.
Statistical Analysis
The Welch's-corrected T-test not assuming equal variances was employed to calculate p-values in two-way comparisons. Fisher's exact test was employed to calculate p-values in 2 by 2 comparisons. The Bonferroni's method was employed to correct for multiple testing [27].
Results
All methods discriminated different subject cohorts, irritable bowel syndrome from control, inflammatory bowel disease from control, irritable bowel syndrome from inflammatory bowel disease, and ulcerative colitis from Crohn's disease, with high degrees of sensitivity and specificity.
Gene-Expression Patterns in Distinct Gastrointestinal Diseases
CTRL, IBD (CD and UC), IBS subjects were recruited from multiple sites within the United States. Demographic characteristics of the different gastrointestinal disease cohorts were not statistically different from the CTRL cohort (Table 1). We measured expression patterns of a common set of genes assayed using a common platform in CTRL and subjects with different gastrointestinal conditions, CD and UC, IBS, and CeD. Genes for analysis were selected from prior microarray studies [20,26]. Gene transcript levels were determined by quantitative RT-PCR and normalized to GAPDH transcript levels. We employed a heatmap to depict those genes differentially expressed in individual subject cohorts relative to the CTRL cohort, p-value<0.05 (after Bonferroni correction for multiple testing; see
†C, Caucasian; AA, African American; As, Asian; H, Hispanic
Discrimination of IBD or IBS from CTRL Based Upon Gene-Expression Ratios
Initially, we employed standard methods of microarray analyses including unsupervised heirarchical clustering, supervised heirarchical clustering, and principal components analysis using the TIGR microarray software Multiexperiment Viewer to segregate patient groups. After normalization to GAPDH, gene expression data from IBD samples or IBS samples and CTRL samples were analyzed using unsupervised and supervised heirarchical clustering using all genes or only those genes whose expression was statistically significant using the supervised T-test. We found that unsupervised heirarchical clustering segregated 72% of IBD samples in one major branch and 28% of IBD samples in the second major branch. Similarly, 36% of CTRL samples were segregated into the branch with most of the IBD samples while 64% of CTRL samples were segregated into the alternate branch. Comparison of IBS and CTRL using unsupervised heirarchical clustering also did not produce the desired level of discrimination between case and control cohorts. Supervised heirarchical clustering and principal components analysis produced a similar low level of overall accuracy.
For these reasons, we turned to a type of linear discriminant analysis classifier
(Ratioscore Method) that we employed previously to discriminate subjects with multiple sclerosis from different control cohorts. We employed a search algorithm to identify those ratios of gene-expression levels in which the greatest number of subjects in the test group possessed a ratio value greater than the highest ratio value in the comparator group. We employed a second algorithm to perform permutation testing of one subject group to identify the optimum set of discriminatory ratios. CeD was excluded from this analysis due to the low number of cases in this cohort. Examination of expression levels of ratios of genes rather than individual genes offered the following advantages. First, ratios normalized for differences in mRNA or cDNA template quantity and quality among different samples. Second, ratios obviated the need for inclusion of a housekeeping genes in the analysis and the assumption that expression levels of housekeeping genes did not vary among different subject populations. Third, comparisons of ratios or combinations of ratios may more accurately identify cellular phenotypes that may contribute to disease. For example, a ratio containing one gene in the numerator that is over-expressed in the case cohort relative to the control cohort and one gene in the denominator that is under-expressed in the case cohort relative to the control cohort should produce a greater ratio value difference between individuals in the two cohorts than a single expression value. Fourth, ANAPC1, RANGAP1, and LEPREL4 genes encode unique proteins and each participates in mitosis [28-33]. Thus, a defect in expression of any one of these genes could produce a common cellular phenotype; a defect in mitosis, and for example, one subject with a given disease may exhibit a deficiency in expression of ANAPC1 while a second individual with the same disease may exhibit a deficiency in expression of RANGAP1 and a third with the same disease may exhibit a defect in LEPREL4 expression levels. Any of these defects has the potential to produce a common cellular phenotype. Our approach makes it possible to capture each subject as positive for a given disease. We refer to this as the Ratioscore Method.
We applied this approach to determine how accurately it would distinguish subjects with IBD or IBS from CTRL. First, we identified ratios capable of discriminating IBD subjects from CTRL. Second, we applied a re-sampling permutation testing strategy to identify ratios that consistently displayed high discriminatory power. Third, we identified the smallest number of ratios producing the greatest discrimination between two comparator groups. The single ratio with the greatest discriminatory power was PGK1/POU6F1 (
We continued our analysis to determine how well IBS and CTRL cohorts were differentiated. Interestingly, the optimum ratio that distinguished the IBD cohort from the CTRL cohort, PGK1/POU6F 1, was also the optimum ratio that distinguished the IBS cohort from the CTRL cohort (
IBS-IBD Discrimination Based Upon the Ratioscore Method
Next, we assessed our ability to distinguish IBS and IBD cohorts. The optimum ratio we identified was HRAS/TBP, p-value<0.0001 (
UC-CD Discrimination Disease Based Upon the Ratioscore Method
Finally, we determined if our approach accurately discriminated between the two inflammatory bowel diseases, UC and CD. The optimum ratio was POU6F1/ANAPC1, p-value=0.003 (
Disease Discrimination Based Upon the SVM Method
Support Vector Machines (SVM) were also employed to classify the data into two distinct groups. The inputs for the SVM were the same ratios used to calculate the ratioscores. For example, when separating IBS patients from CTRL subjects, the same 19 ratios of normalized gene-expression ratios employed to compute the ratioscore were used as input to the SVM. In the SVM calculations, we chose the radial basis kernel (RBK) to perform the kernel trick. This kernel contains a fitting parameter β. We also used the “soft margin” approach to the fitting of the hyper-surface that separates the two groups (cases and controls). This introduced a second fitting parameter C. Programs written in Mathematica (Wolfram Research, Inc.) were created and random training subsets of the two groups were chosen to find the parameters, β and C. Each training subset consisted of 60% of the total dataset. The values of the two fitting parameters that produced the smallest number of incorrect cases and controls were used to define the SVM. This SVM analysis also accurately discriminated the different subject groups: (i) IBD and CTRL, (ii) IBS and CTRL, (iii) IBD and IBS, and (iv) CD and UC (Table 2).
†TP = true positive, FN = false negative, TN = true negative, FP = false positive
A second SVM was also employed using LS-SVMLab software (http://www.esat.kuleuven.ac.be/sista/lssvmlab) to validate the SVM created with Mathematica. The procedure for training the SVM followed the following algorithm:
Numbers of correct and incorrect classifications were tabulated for total sets (training and validation), training sets and validation sets (Table 3). Overall accuracy in the training sets was greater than overall accuracy of the validation sets. The different training sessions did not produce much variation in the overall accuracy of the corresponding validation sets. Using the above algorithm, two different kernels, a polynomial kernel and Radial Basis Function (RBF) kernel, were used to create different machines. Overall, the SVM with the RBF kernels performed somewhat better than the polynomial kernels.
This second SVM was used to discriminate between the different subject groups, IBD and CTRL, IBS and CTRL, IBD and IBS, and CD and UC producing levels of sensitivity and specificity comparable to the Ratioscore Method or the first SVM method (Table 4). We determined receiver operating characteristic (ROC) curves from data produced by the second SVM method. The area-under-the-curve (AUC) for each comparison exceeded 0.96 (
The IBD:CTRL comparison produced the greatest overall accuracy (AUC of 0.997). Thus, a tiered approach, using either ratioscore or SVM analysis, can be employed to segregate between IBD and IBS, first, followed by segregation between CD and UC if a subject is IBD positive. This approach produced high levels of sensitivity and specificity at both tiers of the analysis (
In the above discussion, two support vector machines were independently created and trained using the ratios identified by the Ratioscore Method. There was little difference in the performance of the two machines when used to classify the different case-control combinations. One advantage of the SVM-based approach is that it can be used to classify more than two groups. As an example of classification into three groups, we considered data for UC (N=40), CD (N=46), and CTRL (N=113). Using gene ratios determined by comparing CTRL (controls) to UC+CD (cases), the SVM identified 99.8% of CTRL, 72.5% of UC, and 56.5% of the CD. Hence, the performance of the tertiary classification was not as accurate as the binary classifications. However, the tertiary classification was improved by using a different set of gene ratios, e.g., the union of the set from CTRL vs. CD, CTRL vs. UC, and CD vs. UC. In this case, the SVM identified 99.1% of CTRL, 100% of UC, and 84.8% of CD. One factor that may contribute to this increased accuracy is that the number of gene ratios used in the training of the SVM was increased from 23 ratios to 49 thus introducing additional parameters into the SVM structure.
Discussion
IBS and IBD can exhibit overlapping clinical symptoms making diagnosis difficult without invasive procedures [4,12,34]. Therapy and medication for IBS and IBD are vastly different and incorrect diagnosis and treatment plans have significant consequences. Differentiation between UC and CD can also be difficult, having important implications when considering medical and operative treatment options. For example, ASCA and p-ANCA have clinical utility in diagnosing IBD. ASCA IgA is found in 35-50% of patients with CD but <1% of patients with UC. ASCA IgG is found in 50-80% of patients with CD but only 20% of patients with UC. In contrast, atypical p-ANCA is found in 70% of UC patients but only 20% of CD patients [19]. Here, we describe a relatively non-invasive procedure capable of accurately discriminating between (a) IBS and IBD, and (b) the two forms of IBD, UC and CD, using three independent methods based upon transcript levels in blood of a discrete set of genes. Each method employs the same input, which are multiple ratios of expression levels of two genes. The analytic methods, ratioscore, two SVM methods, and logistic regression, produce similar levels of overall accuracy determined by ROC curves which exceed 95%. We have summarized the overall process of going from the raw samples to classification in
In contrast, biomarkers for IBS are non-existent and diagnosis largely depends upon the absence of pathological findings in the colon. Previously identified experimental biomarkers to distinguish UC and CD clearly do not perform with the same degree of accuracy as experimental approaches described here. Thus, we propose these gene expression ratio tests using the Ratioscore Method, SVM, or logistic regression for analysis represent simple non-invasive tests that could accurately classify patients to IBS or IBD categories and IBD patients to UC or CD categories even without colonoscopy or sigmoidoscopy and tissue biopsy.
UC and CD are chronic inflammatory autoimmune diseases. Using various strategies, numerous studies have identified unique gene-expression signatures in blood or peripheral blood mononuclear cells (PBMC) associated with different autoimmune diseases [22]. Some are unique to a single autoimmune disease, some discriminate between two autoimmune diseases and some are shared among multiple autoimmune diseases. Thus perhaps it is not too surprising that we could employ a similar strategy to identify gene-expression signatures capable of discriminating the two forms of IBD, UC and CD, or IBD from CTRL or IBD from IBS.
Somewhat surprising is that IBS can be readily distinguished from CTRL. IBS is a disorder whose etiology and pathogenic mechanisms are incompletely understood [4]. Our results clearly demonstrate that IBS possesses an underlying gene-expression signature. One possibility is that IBS possesses an unrecognized mucosal pathology sensed by the immune system and expressed by changes in transcript levels of specific genes. Another possibility is that IBS generates expression of cytokines, chemokines, adhesion molecules, neurotransmitters or other mediators read by the immune system. In support of this notion, over-expression of PGK1 is associated with IBS, CeD, CD, and UC and PGK1 is known to be induced by hypoxia and may be induced by other forms of stress, inflammation or generalized mucosal irritation [35]. Further, ABR, ACTR1A, EXT2, HRAS, and KRAS are over-expressed in both IBS and CeD but not CD and UC. In contrast, APOBEC3F, ASL and SPIB are under-expressed in CD and UC, but not IBS and CeD. Thus, the IBS gene-expression signature is more similar to the CeD gene-expression signature and the UC signature is more similar to the CD signature. It is uncertain if this suggests that IBS may bear additional relationships to CeD. An improved understanding of mechanisms producing differences in levels of specific gene transcripts in IBS may further our understanding of the pathogenesis of IBS.
Limitations to our study include selection of patients with pre-existing diagnoses of IBS and IBD, as this may not completely represent patients in the general population in whom these tests may be performed. However, in other studies we have shown that subjects with clinically isolated syndrome, a precursor of multiple sclerosis, who progress to a diagnosis of multiple sclerosis score positive in ratioscore- or SVM-based analyses, similar to those described here. This may suggest that subjects with initial clinical symptoms associated with IBD or IBS, CD or UC, may be discriminated by this approach. Future longitudinal approaches are planned to evaluate utility of these tests. Additional methods, such as analysis of gene-expression ratios in multi-dimensional space rather than binary space may improve the diagnostic capabilities of these tests. We employed three independent approaches to evaluate the ability of gene-expression ratios to discriminate subjects with gastro-intestinal diseases with overlapping clinical symptoms and each produced high degrees of specificity and sensitivity. Thus, these minimally invasive tests may assist in excluding or establishing a diagnosis of IBS or IBD, CD or UC.
Throughout this document, various references are mentioned. All such references are incorporated herein by reference, including the references set forth in the following list:
It will be understood that various details of the presently disclosed subject matter can be changed without departing from the scope of the subject matter disclosed herein. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.
The following are complementary DNA (cDNA) sequences of genes-of-interest identified in Table A. The portion of the sequences bolded and underlined are Applied BioSystems context sequences, the region of that can be amplified in some embodiments of the presently-disclosed subject matter. ABI assay numbers for the sequences are provided in Table A.
Homo sapiens active BCR-related gene (ABR), transcript variant 3, mRNA
CTCCTGGGGTGGAAGCAGGGAAA
GGCCTGGAGATGAGGAAGCTGGTTCTCTCGGGGTTCTTGGCCAGCGA
Homo sapiens actin, beta (ACTB), mRNA
Homo sapiens ARP1 actin-related protein 1 homolog A, centractin alpha
CTGTTA
CCTATCCATAAACCCCCAAAAGGATGAGACGCTAGAGACAGAGAAAGCTCAGTACTACCTGCCT
Homo sapiens ADAMTS-like 4 (ADAMTSL4), transcript variant 1, mRNA
Homo sapiens anaphase promoting complex subunit 1 (ANAPC1), mRNA
CAACCGGTATCATCTCCAGGCTCT
CCGGCACCTCTATGTGCTGGCCGCGGAGCCCAGGCTTCTAGTGCCT
Homo sapiens apolipoprotein B mRNA editing enzyme, catalytic polypeptide-
Homo sapiens argininosuccinate lyase (ASL), transcript variant 1, mRNA
Homo sapiens beta-2-microglobulin (B2M), mRNA
G
CAGCATCATGGAGGTTTGAAGATGCCGCATTTGGATTGGATGAATTCCAAATTCTGCTTGCTTGCTTTT
Homo sapiens breast cancer 1, early onset (BRCA1), transcript variant
CCAGAAGAATTTATGCTCGTGT
ACAAGTTTGCCAGAAAACACCACATCACTTTAACTAATCTAATTACTG
Homo sapiens CD55 molecule, decay accelerating factor for complement
Homo sapiens cadherin 1, type 1, E-cadherin (epithelial)(CDH1), mRNA
Homo sapiens cyclin-dependent kinase inhibitor 1B (p27, Kip1)(CDKN1B),
Homo sapiens checkpoint kinase 2 (CHEK2), transcript variant 3, mRNA
G
AGAGCTGTTTGACAAAGTGGTGGGGAATAAACGCCTGAAAGAAGCTACCTGCAAGCTCTATTTTTACCA
Homo sapiens colony stimulating factor 3 receptor (granulocyte)(CSF3R),
Homo sapiens epoxide hydrolase 2, cytoplasmic (EPHX2), mRNA
Homo sapiens exostosin 2 (EXT2), transcript variant 2, mRNA
Homo sapiens FBJ murine osteosarcoma viral oncogene homolog (FOS), mRNA
TCTGCACGGACCTG
GCCGTCTCCAGTGCCAACTTCATTCCCACGGTCACTGCCATCTCGACCAGTCCGGA
Homo sapiens FOS-like antigen 1 (FOSL1), mRNA
T
CCCTCTAATGAGACTGACCATATTGTGCTTCACAGTAGAGCCAGCTTGGGGCCACCAAAGCTGCCCACT
Homo sapiens forkhead box N3 (FOXN3), transcript variant 1, mRNA
Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), mRNA
Homo sapiens glyceraldehyde-3-phosphate dehydrogenase (GAPDH), mRNA
Homo sapiens GATA binding protein 3 (GATA3), transcript variant 1, mRNA
Homo sapiens guanine nucleotide binding protein (G protein), beta 5
Homo sapiens glutathione S-transferase mu 4 (GSTM4), transcript variant
Homo sapiens major histocompatibility complex, class II, DR alpha
Homo sapiens v-Ha-ras Harvey rat sarcoma viral oncogene homolog (HRAS),
CCCGGCAGGGAGTGGAGGATG
CCTTCTACACGTTGGTGCGTGAGATCCGGCAGCACAAGCTGCGGAAGCT
Homo sapiens interferon, alpha-inducible protein 27 (TF127), transcript
CTGGCCAGGATTGCTACAGTT
GTGATTGGAGGAGTTGTGGCCATGGCGGCTGTGCCCATGGTGCTCAGTG
Homo sapiens interleukin 11 receptor, alpha (IL11RA), transcript variant
CCTGACCC
ACCCCAGGGCCTGCGGGTAGAGTCAGTACCAGGTTACCCCCGACGCCTGCGAGCCAGCTGGA
Homo sapiens jun proto-oncogene (JUN), mRNA
Homo sapiens v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS),
GGGTGTTGAT
GATGCCTTCTATACATTAGTTCGAGAAATTCGAAAACATAAAGAAAAGATGAGCAAAGAT
Homo sapiens leprecan-like 4 (LEPREL4), mRNA
Homo sapiens lethal giant larvae homolog 2 (Drosophila) (LLGL2),
CG
CCACCGGCGTGTGTTCGAGATGGTGGAGGCACTGCAGGAGCACCCTCGAGACCCCAACCAGATCCTGA
Homo sapiens neuroblastoma RAS viral (v-ras) oncogene homolog (NRAS),
ACAGGGTGTTGA
AGATGCTTTTTACACACTGGTAAGAGAAATACGCCAGTACCGAATGAAAAAACTCAAC
Homo sapiens 2′-5′-oligoadenylate synthetase 1, 40/46 kDa (OAS1),
Homo sapiens origin recognition complex, subunit 1 (ORC1), transcript
Homo sapiens phosphoglycerate kinase 1 (PGK1), mRNA
Homo sapiens phorbol-12-myristate-13-acetate-induced protein 1 (PMATP1),
GGAAGTCGA
GTGTGCTACTCAACTCAGGAGATTTGGAGACAAACTGAACTTCCGGCAGAAACTTCTGAAT
Homo sapiens POU class 6 homeobox 1 (POU6F1), transcript variant 2, non-
TGCAGCCCATCCA
GCCCACACCAACCGTGCCCCAGCCTGCTGTGGTCATTGCCAGCCCAGCTCCAGCCGC
Homo sapiens Ran GTPase activating protein 1 (RANGAP1), mRNA
CCCTGATCTCACTAG
GGGAAGGACTCATCACAGCTGGGGCTCAGCTGGTGGAGCTGGACTTAAGCGACAA
Homo sapiens Spi-B transcription factor (Spi-1/PU.1 related) (SPTB),
TGTCTGTACCCAG
ATGGCGTCTTCTATGACCTGGACAGCTGCAAGCATTCCAGCTACCCTGATTCAGAGG
Homo sapiens TAF11 RNA polymerase II, TATA box binding protein (TBP)-
Homo sapiens TATA box binding protein (TBP), transcript variant 2, mRNA
Homo sapiens transforming growth factor, beta receptor II (70/80 kDa)
Homo sapiens tumor protein p53 (TP53), transcript variant 4, mRNA
Homo sapiens tumor protein p53 (TP53), transcript variant 2, mRNA
ACTTGCACGTACTCCCCTG
CCCTCAACAAGATGTTTTGCCAACTGGCCAAGACCTGCCCTGTGCAGCTGT
Homo sapiens TXK tyrosine kinase (TXK), mRNA
AGTGTATGCCAGGATATATGTGAAGGAATGGAATATCTGGAGAGGAATGGCTATATTCATAG
GGATTTGG
CGGCAAGGAATTGTTTG
GTCAGTTCAACATGCATAGTAAAAATTTCAGACTTTGGAATGACAAGGTACGT
This application claims priority from U.S. patent application Ser. No. 14/089,102 filed Nov. 25, 2013, which claims priority from U.S. Provisional Application No. 61/731,265 filed Nov. 29, 2012, the entire disclosures of which are incorporated herein by this reference.
This invention was made with government support under AI53984 and A1044924 awarded by the National Institutes of Health. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 61731265 | Nov 2012 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 14089102 | Nov 2013 | US |
| Child | 15835268 | US |
