title: Identifying blood biomarkers for mood disorders using convergent functional genomics.
abstract: There are to date no objective clinical laboratory blood tests for mood disorders. The current reliance on patient self-report of symptom severity and on the clinicians' impression is a rate-limiting step in effective treatment and new drug development. We propose, and provide proof of principle for, an approach to help identify blood biomarkers for mood state. We measured whole-genome gene expression differences in blood samples from subjects with bipolar disorder that had low mood vs those that had high mood at the time of the blood draw, and separately, changes in gene expression in brain and blood of a mouse pharmacogenomic model. We then integrated our human blood gene expression data with animal model gene expression data, human genetic linkage/association data and human postmortem brain data, an approach called convergent functional genomics, as a Bayesian strategy for cross-validating and prioritizing findings. Topping our list of candidate blood biomarker genes we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6 and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of 10 top candidate biomarkers (five for high mood and five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state.
authors.affiliation: Laboratory of Neurophenomics, Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202-4887, USA.
authors.forename: H
authors.initials: H
authors.lastname: Le-Niculescu
authors.forename: S M
authors.initials: SM
authors.lastname: Kurian
authors.forename: N
authors.initials: N
authors.lastname: Yehyawi
authors.forename: C
authors.initials: C
authors.lastname: Dike
authors.forename: S D
authors.initials: SD
authors.lastname: Patel
authors.forename: H J
authors.initials: HJ
authors.lastname: Edenberg
authors.forename: M T
authors.initials: MT
authors.lastname: Tsuang
authors.forename: D R
authors.initials: DR
authors.lastname: Salomon
authors.forename: J I
authors.initials: JI
authors.lastname: Nurnberger
authors.forename: A B
authors.initials: AB
authors.lastname: Niculescu
mesh_terms: D000328:Adult; D000368:Aged; D000818:Animals; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood*; D001921:Brain / Q000378:metabolism; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D015870:Gene Expression / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D006801:Humans; D036341:Intercellular Signaling Peptides and Proteins / Q000097:blood / Q000235:genetics; D008297:Male; D051379:Mice; D008875:Middle Aged; D019964:Mood Disorders / Q000097:blood* / Q000145:classification / Q000235:genetics* / Q000473:pathology; D009186:Myelin Sheath / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011180:Postmortem Changes; D011237:Predictive Value of Tests; D012016:Reference Values; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000235:genetics; D055815:Young Adult
chemical_list: D015415:Biomarkers; D036341:Intercellular Signaling Peptides and Proteins