US20120094859A1

US20120094859A1 - Methods for detection of depressive disorders

Info

Publication number: US20120094859A1
Application number: US13/276,565
Authority: US
Inventors: Eva Redei; Brian Andrus
Original assignee: Northwestern University
Current assignee: Northwestern University
Priority date: 2010-10-19
Filing date: 2011-10-19
Publication date: 2012-04-19
Also published as: US20180237859A1; US9951387B2; US9469875B2; US20160122824A1; US20170037473A1

Abstract

The present invention relates generally to the detection or diagnosis of depressive disorders, and provides methods and compositions useful for this purpose. In particular, the present invention provides biomarkers for the detection or diagnosis of major depressive disorder, and methods of use thereof.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/394,449, filed Oct. 19, 2010, which is incorporated by reference in its entirety.

FIELD OF THE INVENTION

BACKGROUND OF THE INVENTION

Depressive disorders (e.g. Major depressive disorder (MDD)) are the leading cause of disability in the United States when measured as total time lost to disability, affecting more than 18 million people annually in the USA alone. Depressive disorders, the most common of affective illnesses, include a large set of illnesses ranging from seasonal depressive disorder to chronic depression. There are currently no known available biological markers for depression; diagnosis is made by physicians or psychologists based on structured interviews with the patients. Depressive disorders are among only a few major illnesses that remain reliant upon subjective diagnoses. This contributes to under recognition, trivialization and stigmatization of these disabling illnesses.
Pre-adult onset of MDD, which occurs most often during adolescence, occurs in approximately 40% of patients with MDD. This sub-group has a poor prognosis, with high levels of adult affective disorder, substance use disorders, physical illness, and social maladjustment. This dysfunction includes problematic parenting behaviors in both women and men, with negative consequences for offspring. Thus, the public health benefit of treating adolescent MDD affects not only teens, but subsequent generations as well. In addition, the disorder is the major psychiatric risk factor for teen suicide, with rates in this group being more than 20 times greater than in the general adolescent population.
Treatments for adolescent MDD exist but response rates vary, medication side effects are unpredictable, and adolescents have lower response rates than adults. Exacerbating the problem is that 40% of youths with clinically significant levels of depression never come to the attention of a medical or mental health care provider. Many of these issues with treatment and identification are due, at least in part, to a diagnostic process that relies primarily on patient self-report. While symptom report is critical to the diagnostic process, it is subject to recall bias, the vagaries of culture-, gender-, education-influenced interpretations by the patients, and in the case of parent-report, the parent's own psychological state. Symptoms and signs, therefore, do not always discriminate between youths with and without MDD. The current diagnostic practice results in some youths who need treatment but are not getting it, and some who get treatment but may not need it. MDD treatments (e.g. antidepressants and psychotherapy) carry risks of adverse effects, and the economic cost of inappropriate treatment is high, as the effects of antidepressant medication on the developing adolescent brain are not completely understood. Conversely, the individual, societal, and economic costs of not treating a youth who truly does have MDD can also be quite high, and include suicide, hospitalization, and/or protracted impairment. The costs of misclassification for research studies are also significant in wasted dollars, time, and incorrect results.

SUMMARY OF THE INVENTION

In some embodiments, the present invention provides a method for assessing depressive disorders in a subject, comprising: (a) providing a sample from a subject; (b) characterizing the levels of gene expression of one or more genes selected from the genes listed in Tables 1 and 4; and (c) identifying risk of depressive disorders in the subject based on the levels of gene expression and/or protein expression. In some embodiments, the subject is a human subject. In some embodiments, the human subject is suspected of suffering from depressive disorder.
In some embodiments, the subject is suspected of suffering from MDD. In some embodiments, assessing depressive disorders comprises: detecting, quantifying, diagnosing, indicating, or determining the presence, risk, severity, and/or type of depressive disorder.
In some embodiments, the subject is an adolescent. In some embodiments, the genes comprise one or more of the genes listed in Table 1. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 1 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity). In some embodiments, the genes comprise one or more of the genes listed in Table 4. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 4 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity). In some embodiments, the genes comprise one or more of ADCY3, ATP2C1, CD59, FAM46A, FMR1, GNAQ, IGSF4A/CADM1, MAF, MARCKS, NAGA, PTPRM, TLR7, and ZNF291/SCAPER.
In some embodiments, the subject is an adult. In some embodiments, the genes comprise one or more of the genes listed in Table 1. In some embodiments, the genes comprise one or more of FAM46A, MARCKS, ATP2C1, NAGA, TLR7, ADCY3, ASAH1, CD59, FOS, IGSF4A/CADM1, ZNF291/SCAPER, ATP11C, MAF, GNAQ, FMR1, and PTPRM. In some embodiments, the genes comprise one or more of FAM46A, CD59, IGSF4A/CADM1, NAGA and TLR7.
In some embodiments, characterizing the levels of gene expression comprises detecting the amount of mRNA. In some embodiments, detecting the amount of mRNA comprises exposing a sample to nucleic acid probes complementary to the mRNA. In some embodiments, nucleic acid probes are covalently linked to a solid surface. In some embodiments, detecting the amount of mRNA in a sample comprises use of a detection technique selected from the group consisting of microarray analysis, reverse transcriptase PCR, quantitative reverse transcriptase PCR, and hybridization analysis.
In some embodiments, characterizing the levels of gene expression comprises detecting the amount of protein (e.g. in a sample). In some embodiments, detecting the amount of protein comprises using antibodies, antibody fragments, or other protein binding agents.
In some embodiments, the present invention provides kits and/or panels for detecting depressive disorders in subjects, comprising reagents for detecting two or more genes listed in Tables 1 and/or 4, or proteins encoded thereby. In some embodiments, the subject is a human subject. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 1 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity). In some embodiments, the genes comprise one or more of the genes listed in Table 4. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 4 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity). In some embodiments, the genes comprise one or more of ADCY3, ATP2C1, CD59, FAM46A, FMR1, GNAQ, IGSF4A/CADM1, MAF, MARCKS, NAGA, PTPRM, TLR7, and ZNF291/SCAPER. In some embodiments, the subject is an adult. In some embodiments, the genes comprise one or more of the genes listed in Table 1. In some embodiments, the genes comprise one or more of FAM46A, MARCKS, ATP2C1, NAGA, TLR7, ADCY3, ASAH1, CD59, FOS, IGSF4A/CADM1, ZNF291/SCAPER, ATP11C, MAF, GNAQ, FMR1, and PTPRM. In some embodiments, the genes comprise one or more of FAM46A, CD59, IGSF4A/CADM1, NAGA and TLR7.
In some embodiments, the present invention provides methods for assessing chronic stress in a subject, comprising: (a) characterizing the levels of gene expression of one or more genes in a sample from a subject, wherein said one or more genes are selected from the genes listed in Table 2; and (b) identifying risk of chronic stress in said subject based on said levels of gene expression. In some embodiments, the subject is a human subject. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 2 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity).
In some embodiments, the present invention provides kits or panels for detecting chronic stress in a subject comprising reagents for detecting two or more genes listed in Table 2, or proteins encoded thereby. In some embodiments, the subject is a human subject. In some embodiments, the subject is a human subject. In some embodiments, the genes comprise a variant of one or more of the genes listed in Table 2 (e.g. >50% identity, >60% identity, >70% identity, >80% identity, >90% identity, >95% identity, >98% identity, >99% identity).

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows plots validating adolescent biomarker genes by quantitative RT-PCR; RNA was prepared from the blood of adolescents with MDD and ND controls; the amounts of specific mRNA were normalized to 18s rRNA expression.

FIG. 2 shows plots validating adult biomarker genes by quantitative RT-PCR; RNA was prepared from the blood of adult subjects with MDD, and age- and race-matched controls; the amounts of specific mRNA were normalized to 18s rRNA expression.

DETAILED DESCRIPTION OF THE INVENTION

Depressive disorders (e.g. MDD) are the leading cause of disability in the United States when measured as total time lost to disability, affecting more than 18 million people annually in the USA alone. Depressive disorders, the most common of affective illnesses, include a large set of illnesses from seasonal depressive disorder to chronic depression. There are currently no known biological markers for depression; diagnosis is made by physicians or psychologists based on structured interviews with the patients. Biomarkers can improve definitive diagnosis, treatment, and potentially prevention of depression.
In some embodiments, the present invention provides biological markers indicative of and/or diagnostic of a depressive disorder. In some embodiments, biological markers are indicative of and/or diagnostic of MDD. In some embodiments, biological markers are blood biomarkers. In some embodiments, the present invention provides one or more biomarkers, or a panel of biological markers, that can be identified from tissue or blood or other sample types. In some embodiments, these biological markers show increased or decreased levels of gene-specific RNA in subjects with current depressive symptoms (e.g. MDD symptoms) compared to those of controls. In some embodiments, these biological markers show increased or decreased levels of protein expressed from these genes in subjects with current depressive symptoms (e.g. MDD symptoms) compared to those of controls. In some embodiments, the present invention assesses one or more genes from the genes listed in Tables 1 and 4. In some embodiments the present invention provides a panel of reagents for detecting genes or encoded proteins comprising one or more gene from Tables 1 and 4. In some embodiments the present invention provides a panel of reagents for detecting genes or encoded proteins consisting of one or more gene from Tables 1 and 4. In some embodiments, a panel comprises one or more reagents for detecting genes or encoded proteins from Tables 1 and/or 4 and one or more additional genes. In some embodiments, the present invention provides a set of genes whose mRNA levels differ in the blood of subjects showing higher and lower level of depressive behavior (e.g. MDD). In some embodiments, the present invention provides a set of genes whose protein levels differ in the blood of subjects showing higher and lower level of depressive behavior (e.g. MDD). In some embodiments, the present invention provides biological markers that are common between those expressed in the blood and those expressed in the brain regions of animals, showing higher and lower level of depressive behavior. In some embodiments of the present invention, one or more such genes are used to diagnose or suggest a risk of depression from human sample (e.g., blood sample). In some embodiments, the presence of a gene or panel of genes that correlates with depression (e.g. is indicative of depression, is diagnostic of depression) allows a treating physician to take any number of courses of action, including, but not limited to, further diagnostic assessment, selection of appropriate treatment (e.g., pharmaceutical, nutritional, counseling, and the like), increased or decreased monitoring, etc. In some embodiments, changes in expression of a gene or panel of genes that correlates with depression (e.g. is indicative of depression, is diagnostic of depression) allows a treating physician to take any number of courses of action, including, but not limited to, further diagnostic assessment, selection of appropriate treatment (e.g., pharmaceutical, nutritional, counseling, and the like), increased or decreased monitoring, etc.
In some embodiments the present invention provides a method for detecting or assessing the risk of depressive disorders (e.g. MDD or chronic stress) in a subject. In some embodiments the present invention provides a method for diagnosing depressive disorders (e.g. MDD) in a subject. In some embodiments the present invention provides a method for detecting or assessing the risk of depressive disorders in a subject, comprising the steps of: providing a sample from a subject (e.g., a blood sample), identifying the level of gene expression of one or more genes, wherein said one or more genes is selected from the genes listed in Tables 1, 2 and/or 4, and identifying the presence or absence or a risk of depressive disorders (e.g. MDD) in said subject based on said level of gene expression. In some embodiments the present invention provides methods for characterizing the level of gene expression of a panel of genes comprising detecting the amount of mRNA of a panel of genes of one or more of the genes listed in Tables 1, 2 and/or 4. In some embodiments, the panel comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 . . . 30 . . . 40, etc. genes. In some embodiments the present invention provides methods comprising the step of exposing a sample to nucleic acid probes complementary to said mRNA of a panel of genes from the genes listed in Tables 1, 2 and/or 4. In some embodiments the methods employ a nucleic acid detection technique comprising one or more of microarray analysis, reverse transcriptase PCR, quantitative reverse transcriptase PCR, and hybridization analysis. In some embodiments the present invention provides a method for detecting depressive disorders (e.g MDD) in a human subject.
In some embodiments the present invention provides methods for characterizing the level of gene expression of a panel of genes comprising detecting the amount of protein (e.g. in the blood) expressed by a panel of genes of one or more of the genes listed in Tables 1, 2 and/or 4. In some embodiments the present invention provides methods detecting changes in the amount of protein (e.g. in the blood) expressed by a panel of genes of one or more of the genes listed in Tables 1, 2 and/or 4. In some embodiments, the panel comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 . . . 30 . . . 40, etc. genes. In some embodiments the present invention provides methods comprising the step of exposing a sample to antibodies for the proteins expressed by a panel of genes from the genes listed in Tables 1, 2 and/or 4. In some embodiments, detecting a change in the expression of one or more of the genes listed in Tables 1 and/or 3 comprises exposing a sample to containing the one or more biomarkers to antibodies specific to the biomarkers and detecting the binding of the antibodies to the biomarkers. In some embodiments the present invention provides a method for detecting depressive disorders (e.g. MDD) in a human subject.
In some embodiments, the present invention provides biomarkers for depressive disorders (e.g. MDD) in adolescents (e.g. biomarkers of Table 4). In some embodiments, altered expression of one or more biomarkers of adolescent depression (e.g. MDD) is indicative and/or diagnostic for adolescent depression (e.g. MDD). In some embodiments, increased expression of one or more biomarkers of adolescent depression (e.g. MDD) is indicative and/or diagnostic for adolescent depression (e.g. MDD). In some embodiments, decreased expression of one or more biomarkers of adolescent depression (e.g. MDD) is indicative and/or diagnostic for adolescent depression (e.g. MDD). In some embodiments, altered (e.g. increased and/or decreased) expression of one or more biomarkers (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13) selected from of ADCY3, ATP2C1, CD59, FAM46A, FMR1, GNAQ, IGSF4A/CADM1, MAF, MARCKS, NAGA, PTPRM, TLR7, and ZNF291/SCAPER is indicative and/or diagnostic for adolescent depression (e.g. MDD). In some embodiments, altered (e.g. increased and/or decreased) expression of one or more biomarkers (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13) selected from of ADCY3, ATP2C1, CD59, FAM46A, FMR1, GNAQ, IGSF4A/CADM1, MAF, MARCKS, NAGA, PTPRM, TLR7, and ZNF291/SCAPER demonstrates an increased risk adolescent depression (e.g. MDD).
In some embodiments, the present invention provides biomarkers for depressive disorders (e.g. MDD) in adults (e.g. biomarkers of Table 1). In some embodiments, altered expression of one or more biomarkers of adult depression (e.g. MDD) is indicative of and/or diagnostic for adult depression (e.g. MDD). In some embodiments, altered expression of one or more biomarkers of adult depression (e.g. MDD) is indicative of increased risk of adult depression (e.g. MDD). In some embodiments, increased expression of one or more biomarkers of adult depression (e.g. MDD) is indicative and/or diagnostic for adult depression (e.g. MDD). In some embodiments, decreased expression of one or more biomarkers of adult depression (e.g. MDD) is indicative and/or diagnostic for adult depression (e.g. MDD). In some embodiments, altered (e.g. increased and/or decreased) expression of one or more biomarkers (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13) selected from of FAM46A, MARCKS, ATP2C1, NAGA, TLR7, ADCY3, ASAH1, CD59, FOS, IGSF4A/CADM1, ZNF291/SCAPER, ATP11C, MAF, GNAQ, FMR1, and PTPRM is indicative and/or diagnostic for adult depression (e.g. MDD). In some embodiments, altered (e.g. increased and/or decreased) expression of one or more biomarkers (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13) selected from of FAM46A, MARCKS, ATP2C1, NAGA, TLR7, ADCY3, ASAH1, CD59, FOS, IGSF4A/CADM1, ZNF291/SCAPER, ATP11C, MAF, GNAQ, FMR1, and PTPRM is indicative of an increased risk of adult depression (e.g. MDD). In some embodiments, altered expression of one or both of FAM46A and TLR7 is indicative and/or diagnostic for adult depression (e.g. MDD). In some embodiments, altered expression of one or more of FAM46A CD59, IGSF4A/CADM1, NAGA and TLR7 is indicative of an increased risk of adult depression (e.g. MDD).
In some embodiments the present invention relates to novel gene expression profiles that correlate with depressive disorders (e.g. MDD or chronic stress), and uses thereof. In some embodiments the present invention relates to novel gene expression profiles that are indicative of depressive disorders (e.g. MDD). In some embodiments the present invention relates to novel gene expression profiles that are diagnostic of depressive disorders (e.g. MDD). In some embodiments, a panel of two or more genes is analyzed (e.g. 2 genes . . . 5 genes . . . 10 genes . . . 20 genes . . . 50 genes . . . 100 genes . . . 200 genes . . . 500 genes, etc.). In some embodiments, the panel has a number of different detection reagents (e.g., oligonucleotide probes) that have specificity for genes associated with depression and identified herein (e.g. Table 1, 2 and/or 4). It is contemplated that in some embodiments samples are prepared from blood RNA samples of patients with depressive disorders and control samples, and the prepared samples are applied to the panel. It is contemplated that the differential hybridization of the patient samples relative to the control samples provides an expression signature of depressive disorders. In some embodiments, gene expression from a test sample is compared with a prior sample from the same patient to monitor changes over time. In some embodiments, gene expression from a test sample is compared with a sample from the patient under a different treatment regimen (e.g., pharmaceutical therapy) to test or monitor the effect of the therapy. In some embodiments, gene expression from a test sample is compared to gene expression from a negative control sample (e.g., a subject known to not have depression). In some embodiments, gene expression levels from a test sample are compared to predetermined threshold levels identified (e.g., based on population averages for patients with similar age, gender, metabolism, etc.) as “normal” for individuals without depression. In some embodiments, an increase or decrease of greater than 1.2-fold (e.g., 1.5-fold, 2-fold, 3-fold, 5-fold, 10-fold, or higher) compared to “normal” levels or any increase over a normal level or threshold level is identified as at risk for depression.
A variety of known biological assays may be used to assess expression levels of depression markers. For example, in some embodiments, a microarray is used. Different kinds of biological assays are called microarrays including, but not limited to: DNA microarrays (e.g., cDNA microarrays and oligonucleotide microarrays); protein microarrays; tissue microarrays; transfection or cell microarrays; chemical compound microarrays; and, antibody microarrays. A DNA microarray, commonly known as gene chip, DNA chip, or biochip, is typically a collection of microscopic DNA spots attached to a solid surface (e.g., glass, plastic or silicon chip) forming an array for the purpose of expression profiling or monitoring expression levels for thousands of genes simultaneously. The affixed DNA segments are known as probes, thousands of which can be used in a single DNA microarray. Microarrays can be used to identify disease genes by comparing gene expression in disease and normal cells. Microarrays can be fabricated using a variety of technologies, including but not limiting: printing with fine-pointed pins onto glass slides; photolithography using pre-made masks; photolithography using dynamic micromirror devices; ink-jet printing; or, electrochemistry on microelectrode arrays.
Southern and Northern blotting is used to detect specific DNA or RNA sequences, respectively. DNA or RNA extracted from a sample is fragmented, electrophoretically separated on a matrix gel, and transferred to a membrane filter. The filter bound DNA or RNA is subject to hybridization with a labeled probe complementary to the sequence of interest. Hybridized probe bound to the filter is detected. A variant of the procedure is the reverse Northern blot, in which the substrate nucleic acid that is affixed to the membrane is a collection of isolated DNA fragments and the probe is RNA extracted from a tissue and labeled.
Genomic DNA and mRNA may be amplified prior to or simultaneous with detection. Illustrative non-limiting examples of nucleic acid amplification techniques include, but are not limited to, polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), transcription-mediated amplification (TMA), ligase chain reaction (LCR), strand displacement amplification (SDA), and nucleic acid sequence based amplification (NASBA). Those of ordinary skill in the art will recognize that certain amplification techniques (e.g., PCR) require that RNA be reversed transcribed to DNA prior to amplification (e.g., RT-PCR), whereas other amplification techniques directly amplify RNA (e.g., TMA and NASBA).
The polymerase chain reaction (U.S. Pat. Nos. 4,683,195, 4,683,202, 4,800,159 and 4,965,188, each of which is herein incorporated by reference in its entirety), commonly referred to as PCR, uses multiple cycles of denaturation, annealing of primer pairs to opposite strands, and primer extension to exponentially increase copy numbers of a target nucleic acid sequence. In a variation called RT-PCR, reverse transcriptase (RT) is used to make a complementary DNA (cDNA) from mRNA, and the cDNA is then amplified by PCR to produce multiple copies of DNA. For other various permutations of PCR see, e.g., U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159; Mullis et al., Meth. Enzymol. 155: 335 (1987); and, Murakawa et al., DNA 7: 287 (1988), each of which is herein incorporated by reference in its entirety.
Transcription mediated amplification (U.S. Pat. Nos. 5,480,784 and 5,399,491, each of which is herein incorporated by reference in its entirety), commonly referred to as TMA, synthesizes multiple copies of a target nucleic acid sequence autocatalytically under conditions of substantially constant temperature, ionic strength, and pH in which multiple RNA copies of the target sequence autocatalytically generate additional copies. See, e.g., U.S. Pat. Nos. 5,399,491 and 5,824,518, each of which is herein incorporated by reference in its entirety. In a variation described in U.S. Publ. No. 20060046265 (herein incorporated by reference in its entirety), TMA optionally incorporates the use of blocking moieties, terminating moieties, and other modifying moieties to improve TMA process sensitivity and accuracy.
The ligase chain reaction (Weiss, R., Science 254: 1292 (1991), herein incorporated by reference in its entirety), commonly referred to as LCR, uses two sets of complementary DNA oligonucleotides that hybridize to adjacent regions of the target nucleic acid. The DNA oligonucleotides are covalently linked by a DNA ligase in repeated cycles of thermal denaturation, hybridization and ligation to produce a detectable double-stranded ligated oligonucleotide product.
Strand displacement amplification (Walker, G. et al., Proc. Natl. Acad. Sci. USA 89: 392-396 (1992); U.S. Pat. Nos. 5,270,184 and 5,455,166, each of which is herein incorporated by reference in its entirety), commonly referred to as SDA, uses cycles of annealing pairs of primer sequences to opposite strands of a target sequence, primer extension in the presence of a dNTPαS to produce a duplex hemiphosphorothioated primer extension product, endonuclease-mediated nicking of a hemimodified restriction endonuclease recognition site, and polymerase-mediated primer extension from the 3′ end of the nick to displace an existing strand and produce a strand for the next round of primer annealing, nicking and strand displacement, resulting in geometric amplification of product. Thermophilic SDA (tSDA) uses thermophilic endonucleases and polymerases at higher temperatures in essentially the same method (EP Pat. No. 0 684 315).
Other amplification methods include, for example: nucleic acid sequence based amplification (U.S. Pat. No. 5,130,238, herein incorporated by reference in its entirety), commonly referred to as NASBA; one that uses an RNA replicase to amplify the probe molecule itself (Lizardi et al., BioTechnol. 6: 1197 (1988), herein incorporated by reference in its entirety), commonly referred to as Qβ replicase; a transcription based amplification method (Kwoh et al., Proc. Natl. Acad. Sci. USA 86:1173 (1989)); and, self-sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci. USA 87: 1874 (1990), each of which is herein incorporated by reference in its entirety). For further discussion of known amplification methods see Persing, David H., “In Vitro Nucleic Acid Amplification Techniques” in Diagnostic Medical Microbiology: Principles and Applications (Persing et al., Eds.), pp. 51-87 (American Society for Microbiology, Washington, D.C. (1993)).
Non-amplified or amplified nucleic acids can be detected by any conventional means. For example, in some embodiments, nucleic acids are detected by hybridization with a detectably labeled probe and measurement of the resulting hybrids. Illustrative non-limiting examples of detection methods are described below.
One illustrative detection method, the Hybridization Protection Assay (HPA) involves hybridizing a chemiluminescent oligonucleotide probe (e.g., an acridinium ester-labeled (AE) probe) to the target sequence, selectively hydrolyzing the chemiluminescent label present on unhybridized probe, and measuring the chemiluminescence produced from the remaining probe in a luminometer. See, e.g., U.S. Pat. No. 5,283,174 and Norman C. Nelson et al., Nonisotopic Probing, Blotting, and Sequencing, ch. 17 (Larry J. Kricka ed., 2d ed. 1995, each of which is herein incorporated by reference in its entirety).
Another illustrative detection method provides for quantitative evaluation of the amplification process in real-time. Evaluation of an amplification process in “real-time”, involves determining the amount of amplicon in the reaction mixture either continuously or periodically during the amplification reaction, and using the determined values to calculate the amount of target sequence initially present in the sample. A variety of methods for determining the amount of initial target sequence present in a sample based on real-time amplification are well known in the art. These include methods disclosed in U.S. Pat. Nos. 6,303,305 and 6,541,205, each of which is herein incorporated by reference in its entirety. Another method for determining the quantity of target sequence initially present in a sample, but which is not based on a real-time amplification, is disclosed in U.S. Pat. No. 5,710,029, herein incorporated by reference in its entirety.
Amplification products may be detected in real-time through the use of various self-hybridizing probes, most of which have a stem-loop structure. Such self-hybridizing probes are labeled so that they emit differently detectable signals, depending on whether the probes are in a self-hybridized state or an altered state through hybridization to a target sequence. By way of non-limiting example, “molecular torches” are a type of self-hybridizing probe that includes distinct regions of self-complementarity (referred to as “the target binding domain” and “the target closing domain”) which are connected by a joining region (e.g., non-nucleotide linker) and which hybridize to each other under predetermined hybridization assay conditions. In a preferred embodiment, molecular torches contain single-stranded base regions in the target binding domain that are from 1 to about 20 bases in length and are accessible for hybridization to a target sequence present in an amplification reaction under strand displacement conditions. Under strand displacement conditions, hybridization of the two complementary regions, which may be fully or partially complementary, of the molecular torch is favored, except in the presence of the target sequence, which will bind to the single-stranded region present in the target binding domain and displace all or a portion of the target closing domain. The target binding domain and the target closing domain of a molecular torch include a detectable label or a pair of interacting labels (e.g., luminescent/quencher) positioned so that a different signal is produced when the molecular torch is self-hybridized than when the molecular torch is hybridized to the target sequence, thereby permitting detection of probe:target duplexes in a test sample in the presence of unhybridized molecular torches. Molecular torches and a variety of types of interacting label pairs are disclosed in U.S. Pat. No. 6,534,274, herein incorporated by reference in its entirety.
Another example of a detection probe having self-complementarity is a “molecular beacon.” Molecular beacons include nucleic acid molecules having a target complementary sequence, an affinity pair (or nucleic acid arms) holding the probe in a closed conformation in the absence of a target sequence present in an amplification reaction, and a label pair that interacts when the probe is in a closed conformation. Hybridization of the target sequence and the target complementary sequence separates the members of the affinity pair, thereby shifting the probe to an open conformation. The shift to the open conformation is detectable due to reduced interaction of the label pair, which may be, for example, a fluorophore and a quencher (e.g., DABCYL and EDANS). Molecular beacons are disclosed in U.S. Pat. Nos. 5,925,517 and 6,150,097, herein incorporated by reference in its entirety.
Other self-hybridizing probes are well known to those of ordinary skill in the art. By way of non-limiting example, probe binding pairs having interacting labels, such as those disclosed in U.S. Pat. No. 5,928,862 (herein incorporated by reference in its entirety) might be adapted for use in the present invention. Probe systems used to detect single nucleotide polymorphisms (SNPs) might also be utilized in the present invention. Additional detection systems include “molecular switches,” as disclosed in U.S. Publ. No. 20050042638, herein incorporated by reference in its entirety. Other probes, such as those comprising intercalating dyes and/or fluorochromes, are also useful for detection of amplification products in the present invention. See, e.g., U.S. Pat. No. 5,814,447 (herein incorporated by reference in its entirety).
As used herein, the term “sample” is used in its broadest sense. In one sense it can refer to biological samples obtained from animals (including humans) and encompass fluids, solids, tissues, and gases. Biological samples include blood products (e.g., plasma and serum), saliva, urine, and the like. These examples are not to be construed as limiting the sample types applicable to the present invention.
In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of expression a panel of genes) into data of predictive value for a clinician. The clinician can access the predictive data using any suitable means. Thus, in some preferred embodiments, the present invention provides the further benefit that the clinician, who is not likely to be trained in genetics or molecular biology, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.
The present invention contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information provides, medical personal, and subjects. For example, in some embodiments of the present invention, a sample (e.g., a biopsy or a blood or serum sample) is obtained from a subject and submitted to a profiling service (e.g., clinical lab at a medical facility, genomic profiling business, etc.), located in any part of the world (e.g., in a country different than the country where the subject resides or where the information is ultimately used) to generate raw data. Where the sample comprises a tissue or other biological sample, the subject may visit a medical center to have the sample obtained and sent to the profiling center, or subjects may'collect the sample themselves (e.g., a urine sample) and directly send it to a profiling center. Where the sample comprises previously determined biological information, the information may be directly sent to the profiling service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic communication systems). Once received by the profiling service, the sample is processed and a profile is produced (i.e., expression data), specific for the diagnostic or prognostic information desired for the subject.
The profile data is then prepared in a format suitable for interpretation by a treating clinician. For example, rather than providing raw expression data, the prepared format may represent a diagnosis or risk assessment (e.g., likelihood of depression being present) for the subject, along with recommendations for particular treatment options. The data may be displayed to the clinician by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the clinician (e.g., at the point of care) or displayed to the clinician on a computer monitor.
In some embodiments, the information is first analyzed at the point of care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and/or to convert the raw data to information useful for a clinician or patient. The central processing facility provides the advantage of privacy (all data is stored in a central facility with uniform security protocols), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the clinician, the subject, or researchers.
In some embodiments, the subject is able to directly access the data using the electronic communication system. The subject may chose further intervention or counseling based on the results. In some embodiments, the data is used for research use. For example, the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease.
Compositions for use in the diagnostic methods of the present invention include, but are not limited to, probes, amplification oligonucleotides, and antibodies. Particularly preferred compositions detect the level of expression of a panel of genes. Systems and kits are provided that are useful, necessary, and/or sufficient for detecting expression of one or more genes.
Any of these compositions, alone or in combination with other compositions of the present invention, may be provided in the form of a kit. For example, the single labeled probe and pair of amplification oligonucleotides may be provided in a kit for the amplification and detection and/or quantification of a panel of genes selected from a group comprising the genes listed in Tables 1, 2 and/or 4. The kit may include any and all components necessary or sufficient for assays including, but not limited to, the reagents themselves, buffers, control reagents (e.g., tissue samples, positive and negative control sample, etc.), solid supports, labels, written and/or pictorial instructions and product information, inhibitors, labeling and/or detection reagents, package environmental controls (e.g., ice, desiccants, etc.), and the like. In some embodiments, the kits provide a sub-set of the required components, wherein it is expected that the user will supply the remaining components. In some embodiments, the kits comprise two or more separate containers wherein each container houses a subset of the components to be delivered.
In some embodiments, the present invention provides therapies for diseases characterized by altered expression of disease markers identified using the methods of the present invention. In particular, the present invention provides methods and compositions for monitoring the effects of a candidate therapy and for selecting therapies for patients.

EXPERIMENTAL DATA

Example 1

Identification of MDD biomarkers

Experiments were conducted during the development of embodiments of the invention to identify biomarkers of MDD through genome-wide expression analysis in the blood and relevant brain regions of animal models of MDD developed from a well-accepted genetic animal model of depression, the Wistar Kyoto (WKY) rat strain. Two sub-strains were developed from the WKY strain that show opposite behavior in behavioral tests used to measure depression. The WKY More Immobile (WMI) line is more depressed while the WKY Less Immobile (WLI) line is less depressed. Since these sub-strains were developed from an inbred line, their genetic variability is very small, thereby indicating that expression differences in their brain and blood are related to the behavioral differences between them.
AFFYMETRIX microarray profiling of gene expression patterns, using the Rat 230v2 AFFYMETRIX GENECHIP arrays, were carried out in the frontal cortex, amygdale, hippocampus, and striatum in both WMI and WLI males. Microarray analysis was repeated from the same brain regions using different generation animals. Microarray profiling of gene expression patterns was also carried out in the blood of WMI and WLI males. Blood microarray analysis was repeated from the 20^THgeneration WMI-WLIs using ILLUMINA SENTRIX Rat (Ref-12) Expression BEADCHIP, version 1.0, release 1. Based on the analyses of these microarray data, a biomarker list was created using the data of blood and brain expression (Table 1).

TABLE 1

Biomarkers for endogenous MDD

Gene		Gene
Symbol	Gene name (number of transcripts)	Symbol	Gene name (number of transcripts)

FAM46A	family with sequence similarity 46,	NRP1	neuropilin 1 (18)
	member A (7)	PDE6D	phosphodiesterase 6D, cGMP-specific, rod, delta (6)
MARCKS	myristoylated alanine-rich protein kinase	PPP1R3B	protein phosphatase 1, regulatory subunit 3B (1)
	C substrate(1)	PRPF18	PRP18 pre-mRNA processing factor 18 homolog (5)
ATP2C1	ATPase Ca++ transporting, type 2C,	RNASEL	ribonuclease L (2)
	member 1 (6)	SEMA4A	semaphorin 4A (16)
NAGA	N-acetyl-galactosaminidase, alpha (5)	SERPINB1	serpin peptidase inhibitor, clade B, member 1 (5)
TLR7	toll-like receptor 7 (2)	SLFN12	schlafen family member 12 (8)
ADCY3	adenylate cyclase 3 (15)	SMPDL3A	sphingomyelin phosphodiesterase, acid-like 3A (2)
ANXA4	annexin A4 (13)	SNX10	sorting nexin 10 (8)
APP	amyloid beta(A4) precursor protein (20)	SOAT1	sterol O-acyltransferase 1 (2)
ASAH1	N-acylsphingosine amidohydrolase 1 (4)	SYNJ1	synaptojanin 1 (19)
ATP6AP2	ATPase, H+ transporting, lysosomal accessory	TCF7L2	transcription factor 7-like 2 (16)
	protein 2 (7)	ZNF291/	S-phase cyclin A-associated protein (2)
BCAT1	branched chain aminotransferase 1, cytosolic	SCAPER
	(2)	FUCA2	fucosidase, alpha-L-2, plasma (6)
CAST	calpastatin (13)	ATP11C	ATPase, class VI, type 11C (11)
CD59	CD59 molecule, complement regulatory	MAF	v-maf oncogene homolog (2)
	protein (7)	GNAQ	guanine nucleotide binding protein alpha
CEBPA	CCAAT/enhancer binding protein, alpha (1)		stimulating activity polypeptide 1 (2)
CITED2	Cbp/p300-interacting transactivator, with	MAST4	microtubule associated serine/threonine kinase
	Glu/Asp-rich carboxy-terminal domain, 2 (2)		family member 4 (30)
FOS	FBJ murine osteosarcoma viral oncogene	FMR1	fragile X mental retardation 1 (13)
	homolog (1)	PTPRM	protein tyrosine phosphatase, receptor type,
IDH1	isocitrate dehydrogenase 1 (NADP+), soluble		mu polypeptide (4)
	(13)
IGSF4A/	cell adhesion molecule 1 (3)
CADM1
IL13RA1	interleukin 13 receptor, alpha 1 (4)
KLF4	Kruppel-like factor 4 (8)
LRRC40	leucine rich repeat containing 40 (1)

Example 2

Biomarkers of Chronic Stress States

During development of embodiments of the present invention, chronic-stress experiments were conducted utilizing four phylogenetically, physiologically and behaviorally different strains of rats to identify chronic stress-related peripheral biomarkers. Genetic polymorphisms between the strains ranged from 25.9% to 66%, representing a substantial inter-strain variation that is aimed to mimic individual variation among the human subjects. Blood transcripts with the largest fold changes in response to chronic stress across all four strains were identified as chronic stress markers.
For the isolation of the chronic stress markers, whole blood were collected into PAXgene Blood RNA tubes from non-stressed and chronically stressed Fisher-344 (F344), Brown Norway (BN-SS), Lewis (Lew), and Wistar Kyoto (WKY) male rats. Blood microarray analyses were carried out using the Illumina Sentrix® Rat (Ref-12) Expression BeadChip, version 1.0, release 1. Transcripts were selected with significant, differences between chronic stress and control and with an absolute fold change of greater than 1.2. 117 genes in the blood were identified that were differentially expressed between the CRS and no stress (NS) conditions and had human orthologues (See Table 2).

TABLE 2

Chronic stress blood markers

Gene symbol	Gene name	Gene symbol	Gene name

ADD2	adducin 2	KIAA1539	KIAA1539
ADIPOR1	adiponectin receptor 1	LEPROTL1	leptin receptor overlap transcript-
ARF5	ADP-ribosylation factor 5		like 1
AHSP	alpha hemoglobin stabilizing	LAPTM5	lysosomal-associated protein
	protein (ERAF)		transmembrane 5
ANK1	ankyrin 1, erythroid	MTVR2	mammary tumor virus receptor 2
APOL3	apolipoprotein L, 3	MXD1	max dimerization protein 1
ARIH1	ariadne ubiquitin-conjugating	MEMO1	mediator of cell motility 1
	enzyme E2 binding protein	MIIP	migration and invasion inhibitory
	homolog 1		protein
AMFR	autocrine motility factor	MAP2K3	mitogen activated protein kinase
	receptor		kinase 3
BOLA3	bolA homolog 3	NAT9	N-acetyltransferase 9
CHP	calcium binding protein p22	NBEAL2-PS1	neurobeachin-like 2, pseudogene
CA2	carbonic anhydrase 2		1
CA1	carbonic anhydrase 1	NXT1	NTF2-related export protein 1
CSNK1G2	casein kinase 1, gamma 2	NOP56	nucleolar protein 56
CAT	catalase	PCYT2	phosphate cytidylyltransferase 2,
CTSB	cathepsin B		ethanolamine
CD3D	CD3 antigen delta polypeptide	PLCG2	phospholipase C, gamma 2
CD37	CD37 antigen	PARP10	poly (ADP-ribose) polymerase
CD82	CD82 antigen		family, member 10
CREG1	cellular repressor of E1A-	KTCD20	potassium channel
	stimulated genes		tetramerisation domain
CDR2	cerebellar dergeneration-related 2	PAQR9	containing 20
C7orf70	chromosome 7 open reading		progestin and adipoQ receptor
	frame 70		family member IX
C2	complement component 2	PSME1	protease 28 subunit, alpha
CXXC1	CXXC finger 1	PSMB10	proteasome subunit, beta type 10
CMAS	cytidine monophospho-N-	PSMB3	proteasome subunit, beta type 3
	acetylneuraminic acid	PSMB8	proteasome subunit, beta type 8
	synthetase	PPP1R10	protein phosphatase 1, subunit 10
DDX24	DEAD box polypeptide 24	PTP4A3	protein tyrosine phosphatase 4a3
DENND5A	DENN/MADD domain	RAB10	member RAS oncogene family
	containing 5A	RTP4	receptor (chemosensory)
DGKA	diacylglycerol kinase, alpha		transporter protein 4
DNAJB6	DnaJ homolog, subfamily B,	MAF1	Repressor of RNA polymerase
	member 6		III transcription MAF1 homolog
DYRK3	dual-specificity tyrosine--	SELO	selenoprotein O
	phosphorylation regulated	1-Sep	septin 1
	kinase 3	SERBP1	SERPINE1 mRNA binding
DYNC1H1	dynein, cytoplasmic, heavy		protein 1
	chain 1	SHARPIN	shank-interacting protein
DYNLL1	dynein, cytoplasmic, light	SMAP2	small ArfGAP2
	chain 1	SLC16A10	solute carrier family 16, member
EMB	embigin		10
EPB42	erythrocyte membrane protein	SLC4A1	solute carrier family 4, member 1
	band 4.2	SPTAN1	spectrin, alpha, non-erythrocytic
FBXO7	F-box only protein 7		1
FAM117A	family with sequence	SYK	spleen tyrosine kinase
	similarity 117, member A	ST3GAL2	ST3 beta-galactoside alpha-2,3-
FAM125A	family with sequence		sialyltransferase 2
	similarity 125, member A	STRADB	STE20-related kinase adaptor
FECH	ferrochelatase		beta
FLNA	Filamin A	STRA8	stimulated by retinoic acid gene
FMNL1	formin-like 1		8
FN3K	fructosamine-3-kinase	TSPAN8	tetraspanin 8
FUS	fusion in malignant	TREX1	3-5 exonuclease TREX1
	liposarcoma)	ATP5G1	ATP synthase, H+ transporting,
FYN	fyn proto-oncogene		mitochondrial F0 complex,
GATA1	GATA binding protein 1		subunit c, isoform 1
GCLM	glutamate cysteine ligase,	TMCC2	transmembrane and coiled-coil
	modifier subunit		domain family 2
ERICH1	glutamate-rich 1	TMEM183A	transmembrane protein 183A
GLRX5	glutaredoxin 5	TRIM10	tripartite motif-containing 10
GPX4	glutathione peroxidase 4	TMOD1	tropomodulin 1
GLTP	Glycolipid transfer protein	TINAGL1	tubulointerstitial nephritis
GLG1	golgi apparatus protein 1		antigen-like 1
GGA3	golgi associated, gamma	TIE1	tyrosine kinase receptor 1
	adaptin ear containing, ARF	UBAC1	ubiquitin associated domain
	binding protein 3		containing 1
GCH1	GTP cyclohydrolase 1	USP8	ubiquitin specific protease 8
HPS1	Hermansky-Pudlak syndrome	UBE2C	ubiquitin-conjugating enzyme
	1 homolog		E2C
SUV420H2	histone-lysine N-	UBE2O	ubiquitin-conjugating enzyme
	methyltransferase		E2O
HAGH	hydroxyacyl glutathione	UBE2R2	ubiquitin-conjugating enzyme
	hydrolase		E2R 2
INSL3	insulin-like 3	UROD	uroporphyrinogen decarboxylase
IRF3	interferon regulatory factor 3	VAMP3	vesicle-associated membrane
IFRD2	interferon-related		protein 3
	developmental regulator 2	VKORC1L1	vitamin K epoxide reductase
ISG12	interferon, alpha-inducible		complex, subunit 1-like 1
	protein 27-like	WDR26	WD repeat domain 26
ISCU	iron-sulfur cluster scaffold	WDR45	WD repeat domain 45
	homolog	ZAP70	zeta-chain associated protein
			kinase 70 kDa

Example 3

Identification of Adolescent MDD Biomarkers

Experiments were conducted during the development of embodiments of the invention to identify biomarkers for MDD in adolescent human subjects.
Males and females, 15-18 years of age, were recruited through other research studies at the Research Institute at Nationwide Children's Hospital (RINCH), the Nationwide Children's Hospital (NCH) Adolescent Medicine Clinic, NCH Behavioral Medicine Clinic, and community flyers (Table 3). Subjects were recruited into either an MDD or No Disorder (ND) group, based on results from a youth and a parent full, standardized, structured psychiatric interview. Exclusion criterion for the MDD group included: younger or older than 15-18 years; major medical illness; antidepressant use; bipolar disorder; psychosis; pregnancy; mental retardation. Additional exclusion criterion for the No Disorder (ND) group was any lifetime psychiatric disorder.

TABLE 3

Adolescent Project Sample Characteristics

Age

Race

Diagnosis	N	Gender	Mean	Range	White	Black

MDD	1	M	15	15		X
	3	F	17	17	X (3)
ND	2	M	16.5	15-18	X	X
	3	F	15.5	15-16	X (2)	X

Consent/assent or youth consent (18 year-olds) were obtained. 15 cc of blood was drawn into 2 PaxGene tubes at 4 PM from each youth. Youth and parent were then interviewed separately about the youth's medical and psychiatric history. Psychiatric diagnoses were made with the Computerized Diagnostic Interview Schedule for Children (C-DISC)-Youth and Parent Versions, a valid and reliable interview that we have used in over 300 youths; it assesses every Axis I disorder and takes 2-3 hours to administer. Diagnoses of MDD were counted from either parent or youth report, although in all but one subject, symptoms of depression were reported by both informants.
Blood samples with only numbers as identification, collected in PAXgene tubes, were processed for RNA using the PAXgene Blood RNA Kit (Qiagen Inc.). RNA yields from 2.5 ml human whole blood were >14 μg with an average 260/280 ratio of >1.7. cDNA was prepared using random primers and the TaqMan RT reagents (ABI). RNA quantification was performed using SYBR Green based real time PCR technology with the ABI Prism 7300 (Applied Biosystems, Foster City, Calif.). 18s rRNA was used as an internal control. Specific primers were designed to amplify the human homolog of the rat microarray probe region using the PRIMER EXPRESS Software (version 3.0, PE Applied Biosystems). Melting curve analyses and no template control reactions were performed to confirm appropriate target amplification.
Statistically significant differences were found between the two groups on 3 of the comparisons (SEE FIG. 1) and 13 of the effect sizes were in the large range (Table 4). These data indicate that a larger sample would yield statistically significant differences in more of the biomarkers (e.g. the majority).

TABLE 4

Quantitative RT-PCR pilot results from blood RNA: adolescent candidate
biomarkers for MDD.

	Control	Depressed	Effect size		Control	Depressed	Effect size
Gene	(mcan +/− SD)	(mean +/− SD)	(Cohen's d)	Gene	(mean +/- SD)	(mean +/− SD)	(Cohen's d)

ADCY3	0.822 +/−	0.653 +/−	0.909##	IGSF4A*	4.849 +/−	2.121+/−	2.109##
	0.230	0.127			1.566	0.944
ATP2C1	0.634 +/−	0.842 +/−	−0.660##	MAF*	1.626 +/−	0.948 +/−	1.901##
	0.404	0.186			0.438	0247
CD59	0.879 +/−	0.779 +/−	0.680##	MARCKS	0.853 +/−	1.212 +/−	−0.935##
	0.196	0.061			0.476	0.262
EDG2	0.853 +/−	0.842 +/−	0.030	NAGA	0.839 +/−	0.558 +/−	1.189##
	0.250	0.429			0.321	0.090
FAM46A	1.212 +/−	0.970 +/−	1.152##	PDE6D	0.878 +/−	0.803 +/−	0.413
	0.278	0.103			0.221	0.129
FGFR1	1.348 +/−	1.085 +/−	0.470	PTPRM	1.274 +/−	0.966+/−	0.716##
	0.755	0.229			0.458	0.401
FMR1	1.350 +/−	1.090 +/−	1.320##	SLC6A4	0.866 +/−	0.982 +/−	−0.294
	0.020	0.189			0.381	0.407
FOS	0.799 +/−	1.001 +/−	−0.392	TLR7	0.775 +/−	0.148 +/−	1.054##
	0.399	0.698			0.303	0.740
GNAQ	1.075 +/−	1.272 +/−	−0.776##	ZNF291*	1.080 +/−	0.780 +/−	1.617##
	0.258	0.249			0.228	0.129

*p < 0.05;
##Large effect sizes

Experiments were conducted in which blood samples were analyzed in participants with MDD or No Disorder using the same diagnostic criteria as above. In this set of adolescents, Chronic Stressor Exposure was also assessed using the Childhood Trauma Questionnaire (CTQ). Correlations between the chronic stress model genes, 15 genes selected from Table 2 (highlighted in gray in Table 2) with an absolute fold change between 1.28-1.95, and the stressor/stress scale scores of chronic stress, the CTQ Total Score, (See Table 5).

TABLE 5

Spearman correlation between blood transcript abundance of
some chronic stress blood markers and stressor/stress scale scores

		KIAA
CDR2	CMAS	1539	PSME1	PTP4A3	FECH	IRF3

CTQ	rho	.473	.525	.513	.605	.531	.439	.499
Total	P (2-tailed)	.064	.037	.042	.013	.034	.089	.049
Score	N	16	16	16	16	16	16	16
Emotional	rho	.271	.230	.463	.555	.433	.253	.466
Abuse	P (2-tailed)	.310	.391	.071	.026	.094	.345	.069
	N	16	16	16	16	16	16	16
Physical	rho	−.026	.168	.145	.129	−.050	.016	−.010
Abuse	P (2-tailed)	.924	.535	.592	.635	.853	.952	.971
	N	16	16	16	16	16	16	16
Sexual	rho	.203	.162	.216	.045	.076	.173	.039
Abuse	P (2-tailed)	.451	.549	.422	.867	.781	.522	.886
	N	16	16	16	16	16	16	16
Emotional	rho	.498	.571	.461	.585	.496	.468	.538
Neglect	P (2-tailed)	.050	.021	.073	.017	.051	.067	.032
	N	16	16	16	16	16	16	16
Physical	rho	.494	.521	.392	.480	.383	.460	.472
Neglect	P (2-tailed)	.052	.038	.133	.060	.143	.073	.065
	N	16	16	16	16	16	16	16

Effect sizes were calculated between subjects with No disorder and MDD (See Table 6).

TABLE 6

Effect sizes for the MDD vs. No Disorder comparison
for both sets of blood biomarkers

Mean		Fold	Effect
DCT^a	SD	Change^b	Size

“Endogenous
Depression”
marker
ATP11C	No Disorder	14.9232	.74081	1	−0.8361
	MDD	15.1088	.45897	0.87928
CD59	No Disorder	13.9868	.59346	1	−0.7811
	MDD	14.3945	.43889	0.75382
FAM46A	No Disorder	15.6206	.62560	1	−0.0036
	MDD	15.6184	.60388	1.00153
IGSF4A	No Disorder	16.5055	2.63519	1	−0.5909
	MDD	17.7116	1.17864	0.43344
MAF	No Disorder	17.7071	.88667	1	−0.6169
	MDD	18.1609	.54407	0.73012
MARKS	No Disorder	13.2903	1.42679	1	−0.3974
	MDD	13.7691	.93121	0.71757
NAGA	No Disorder	13.9092	.83357	1	−0.2849
	MDD	14.1327	.73240	0.85649
RAPH1	No Disorder	15.7073	2.56428	1	−0.6208
	MDD	16.9376	1.02885	0.42623
TLR7	No Disorder	15.4338	.85478	1	−0.0035
	MDD	15.4311	.66038	1.00187
UBE3A	No Disorder.	15.1981	1.65462	1	−0.2701
	MDD	15.5834	1.12891	0.76562
ZNF291	No Disorder	16.1619	1.15379	1	−0.3600
	MDD	16.4937	.60660	0.79454
“Chronic
Stress”
marker
AHSP	No Disorder	12.9605	.89321	1	−0.4637
	MDD	12.5939	.67266	1.28931
AMFR	No Disorder	13.5744	1.68521	1	−0.5673
	MDD	14.2731	.44021	0.61613
CAT	No Disorder	14.6880	1.08626	1	−0.5376
	MDD	15.1444	.47227	0.72880
CDR2	No Disorder	14.8752	.65338	1	−0.8657
	MDD	15.3368	.36033	0.72618
CMAS	No Disorder	15.5308	2.43662	1	−0.766
	MDD	16.9309	.72694	0.37890
DGKA	No Disorder	11.2385	1.41685	1	−0.0716
	MDD	11.1497	1.03203	1.06349
EMB	No Disorder	12.7808	1.05224	1	−0.1918
	MDD	12.9464	.59428	0.89156
FECH	No Disorder	13.4058	1.15795	1	−0.4100
	MDD	13.7887	.63539	0.76689
GCLM1	No Disorder	16.2397	.91531	1	−0.1939
	MDD	16.3997	.71448	0.89503
GGA3	No Disorder	15.3662	.88044	1	−0.2513
	MDD	15.5736	.78498	0.86610
IRF3	No Disorder	13.0719	.95953	1	−0.3423
	MDD	13.3472	.61042	0.82628
KIAA1539	No Disorder	13.4514	1.32581	1	−0.0142
	MDD	13.4686	1.07637	0.98815
PSME1	No Disorder	11.1975	.53582	1	−0.5231
	MDD	11.4749	.52421	0.82508
PTP4A3	No Disorder	14.9475	.94725	1	−0.7703
	MDD	15.5894	.68885	0.64087
SLC4A1	No Disorder	13.1906	1.29940	1	−0.0676
	MDD	13.2694	1.01411	0.94684

^aDCT = CTtarget gene-CThousekeeping gene; [Target]/[Housekeeping] = 2^-DCT;
^bFold Change = 2^{-DCT(MDD)-DCT(ND}

Additional analyses also shown that some of the potential blood markers can distinguish patients with MDD only from patients with MDD and Anxiety. Effects sizes for this comparison are shown in Table 7.

TABLE 7

Comparison between MDD only and MDD with Anxiety

Diagnoses, Youth or			Effect
Parent	Mean	SD	Size

Endogenous
Depression
markers
ATP11C	MDD Only	15.2596	.23454	0.49
	MDD + Anxiety Disorder	15.0147	.55075
CD59	MDD Only	14.6551	.09019	0.94
	MDD + Anxiety Disorder	14.2498	.49290
FAM46A	MDD Only	15.3099	.51630	−0.78
	MDD + Anxiety Disorder	15.7898	.60560
IGSF4A	MDD Only	18.4211	.68470	0.95
	MDD + Anxiety Disorder	17.3174	1.23838
MAF	MDD Only	18.1239	.58738	−0.10
	MDD + Anxiety Disorder	18.1815	.55424
MARKS	MDD Only	13.5260	.57322	−0.37
	MDD + Anxiety Disorder	13.9042	1.08966
NAGA	MDD Only	13.6951	.53240	−0.94
	MDD + Anxiety Disorder	14.3758	.73741
RAPH1	MDD Only	17.1122	.82167	0.25
	MDD + Anxiety Disorder	16.8285	1.18044
TLR7	MDD Only	15.0418	.56444	−0.93
	MDD + Anxiety Disorder	15.6474	.63404
UBE3A	MDD Only	15.8918	.32713	0.41
	MDD + Anxiety Disorder	15.3906	1.41887
ZNF291	MDD Only	16.1685	.59014	−0.82
	MDD + Anxiety Disorder	16.6744	.56655
Chronic
Stress
markers
AHSP	MDD Only	12.3232	.52023	−0.59
	MDD + Anxiety Disorder	12.7443	.72712
AMFR	MDD Only	13.9849	.35561	−1.06
	MDD + Anxiety Disorder	14.4332	.41345
CAT	MDD Only	14.9740	.60841	−0.55
	MDD + Anxiety Disorder	15.2509	.37033
CDR2	MDD Only	15.5524	.20734	0.99
	MDD + Anxiety Disorder	15.2020	.37955
CMAS	MDD Only	17.3159	.28832	0.85
	MDD + Anxiety Disorder	16.6903	.82848
DGKA	MDD Only	10.3071	.68101	−1.47
	MDD + Anxiety Disorder	11.6179	.89919
EMB	MDD Only	13.0919	.25565	0.36
	MDD + Anxiety Disorder	12.8555	.73724
FECH	MDD Only	13.7521	.83616	−0.08
	MDD + Anxiety Disorder	13.8091	.55241
GCLM1	MDD Only	16.7181	.24730	0.69
	MDD + Anxiety Disorder	16.2007	.85000
GGA3	MDD Only	15.2748	.63735	−0.58
	MDD + Anxiety Disorder	15.7604	.84886
IRF3	MDD Only	13.0075	.60782	−0.86
	MDD + Anxiety Disorder	13.5359	.55548
KIAA1539	MDD Only	12.8294	.88024	−0.99
	MDD + Anxiety Disorder	13.8681	1.03397
PSME1	MDD Only	11.6564	.40741	0.52
	MDD + Anxiety Disorder	11.3615	.58138
PTP4A3	MDD Only	15.7955	.62373	0.45
	MDD + Anxiety Disorder	15.4607	.73612
SLC4A1	MDD Only	12.6948	1.26028	−0.88
	MDD + Anxiety Disorder	13.5886	.74571

Example 4

Identification of Adult MDD Biomarkers

Experiments were conducted during the development of embodiments of the invention to identify biomarkers for MDD in adult human subjects. Demographic data and depression scores using the PHQ-9 were obtained for adult subjects (Table 8).
TABLE 8

Age PHQ-9

No. Gender Mean Range Mean Range

Patient 2 M 48 40-56 20.5 14-27

4 F 40 25-56 17 12-22

Control 2 M 50.5 33-68 2 0-4

4 F 39 24-57 1.5 0-5

Blood samples were collected in PAXGENE tubes, and processed for RNA using the PAXGENE blood RNA Kit (QIAGEN). RNA yields from 2.5 ml human whole blood were >14 μg with an average 260/280 ratio of >1.7. cDNA was prepared using random primers and the TaqMan RT reagents (ABI). RNA quantification was performed using SYBR Green based real time PCR technology with the ABI Prism 7300 (Applied Biosystems, Foster City, Calif.). 18s rRNA was used as internal control. There were significant differences in the expression of FAM46A and TLR7 between patients with MDD and controls (SEE FIG. 2). FAM46A (Family with sequence similarity 46, member A) is a gene with unknown function that encodes a 437-amino acid protein with a calculated molecular mass of 49.2 kD. Database analysis indicates that FAM46A is conserved from yeast to human.
Experiments were conducted employing subjects with the characteristics described in Table 9.

TABLE 9

						HAM-score after
					HAM-score before	treatment
	No.	Gender	Age, Mean	Age Range	treatment (mean +/− SD)	(mean +/− SD)

MDD	24	F	48.7	23-83	23.3 +/− 3.6	12.4 + /− 7.7
	10	M	50.3	34-79	22.5 +/− 4.4	14.8 + /− 3.5
Controls	24	F	48.4	23-84	4.2 +/− 2.4	ND
	10	M	53.6	34-79	4.1 +/− 2.0	ND

Race: Females, 16.6% African American and 8% Hispanic. Males all Caucasian

Abundance of the following transcripts was significantly different between subjects with MDD before cognitive behavioral therapy and their matched controls: ADCY3 (p=0.0028), ASAH1 (0.0263), FAM46A (p=0.0039), IGSF4A/CADM1 (0.001), MARCKS (v0.0008), NAGA (p=0.0312), TLR7 (p=0.0019). HAM scores correlated significantly (Spearman correlation) with transcript levels of ATP11C (p=0.0089) and FAM46A (p=0.0045). Additional significant results were obtained for CD59, CDR2, CMAS, PSME1 and DGKA.
All publications and patents provided herein incorporated by reference in their entireties. Various modifications and variations of the described compositions and methods of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the relevant fields are intended to be within the scope of the present invention.

Claims

1. A method for assessing depressive disorders in a subject, comprising:

a) characterizing the levels of gene expression of one or more genes in a sample from a subject, wherein said one or more genes are selected from the genes listed in Tables 1 and 4; and

b) identifying risk of depressive disorders in said subject based on said levels of gene expression.

2. The method of claim 1, wherein said subject comprises a human subject.

3. The method of claim 2, wherein said human subject is suspected of suffering from depressive disorder.

4. The method of claim 3, wherein said subject is an adolescent.

5. The method of claim 4, wherein said genes comprise one or more of the genes listed in Table 1.

6. The method of claim 5, wherein said genes comprise one or more of the genes listed in Table 4.

7. The method of claim 6, wherein said genes comprise one or more of ADCY3, ATP2C1, CD59, FAM46A, FMR1, GNAQ, IGSF4A/CADM1, MAF, MARCKS, NAGA, PTPRM, TLR7, and ZNF291/SCAPER.

8. The method of claim 3, wherein said subject is an adult.

9. The method of claim 8, wherein said genes comprise one or more of the genes listed in Table 1.

10. The method of claim 9, wherein said genes comprise one or more of FAM46A, MARCKS, ATP2C1, NAGA, TLR7, ADCY3, ASAH1, CD59, FOS, IGSF4A/CADM1, ZNF291/SCAPERSCAPER, ATP11C, MAF, GNAQ, FMR1, and PTPRM.

11. The method of claim 10, wherein said genes comprise one or more of FAM46A CD59, IGSF4A/CADM1, NAGA and TLR7.

12. The method of claim 1, wherein said characterizing the levels of gene expression comprises detecting the amount of mRNA.

13. The method of claim 12, wherein said detecting the amount of mRNA comprises exposing said sample to nucleic acid probes complementary to said mRNA.

14. The method of claim 13, wherein said nucleic acid probes are covalently linked to a solid surface.

15. The method of claim 1, wherein said characterizing the levels of gene expression comprises detecting the amount of expressed protein.

16. The method of claim 12, wherein said detecting the amount of mRNA in said sample comprises use of a detection technique selected from the group consisting of microarray analysis, reverse transcriptase PCR, quantitative reverse transcriptase PCR, and hybridization analysis.

17. The method of claim 1, wherein said depressive disorder comprises MDD.

18. A kit or panel for detecting depressive disorders in a subject comprising reagents for detecting two or more genes listed in Tables 1 and/or 4, or proteins encoded thereby.

19. A method for assessing chronic stress in a subject, comprising:

a) characterizing the levels of gene expression of one or more genes in a sample from a subject, wherein said one or more genes are selected from the genes listed in Table 2; and

b) identifying risk of chronic stress in said subject based on said levels of gene expression.

20. A kit or panel for detecting chronic stress in a subject comprising reagents for detecting two or more genes listed in Table 2, or proteins encoded thereby.