GC-MS based metabolomics identification of possible novel biomarkers for schizophrenia in peripheral blood mononuclear cells

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

Genomics and metabolomics of muscular mass in a community-based sample of UK females

The contribution of specific molecular-genetic factors to muscle mass variation and sarcopenia remains largely unknown. To identify endogenous molecules and specific genetic factors associated with appendicular lean mass (APLM) in the general population, cross-sectional data from the TwinsUK Adult Twin Registry were used. Non-targeted mass spec-based metabolomic profiling was performed on plasma of 3953 females (mostly dizygotic and monozygotic twins). APLM was measured using dual-energy X-ray absorptiometry (DXA) and genotyping was genome-wide (GWAS). Specific metabolites were used as intermediate phenotypes in the identification of single-nucleotide polymorphisms associated with APLM using GWAS. In all, 162 metabolites were found significantly correlated with APLM, and explained 17.4% of its variation. However, the top three of them (unidentified substance X12063, urate, and mannose) explained 11.1% (P ≤ 9.25 × 10(-26)) so each was subjected to GWAS. Each metabolite showed highly significant (P ≤ 9.28 × 10(-46)) associations with genetic variants in the corresponding genomic regions. Mendelian randomization using these SNPs found no evidence for a direct causal effect of these metabolites on APLM. However, using a new software platform for bivariate analysis we showed that shared genetic factors contribute significantly (P ≤ 4.31 × 10(-43)) to variance in both the metabolites and APLM--independent of the effect of the associated SNPs. There are several metabolites, having a clear pattern of genetic inheritance, which are highly significantly associated with APLM and may provide a cheap and readily accessible biomarker of muscle mass. However, the mechanism by which the genetic factor influences muscle mass remains to be discovered.

Biomarkers for Psychiatry: The Journey from Fantasy to Fact, a Report of the 2013 CINP Think Tank

BACKGROUND

A think tank sponsored by the Collegium Internationale Neuropsychopharmacologium (CINP) debated the status and prospects of biological markers for psychiatric disorders, focusing on schizophrenia and major depressive disorder.

METHODS

Discussions covered markers defining and predicting specific disorders or domains of dysfunction, as well as predicting and monitoring medication efficacy. Deliberations included clinically useful and viable biomarkers, why suitable markers are not available, and the need for tightly-controlled sample collection.

RESULTS

Different types of biomarkers, appropriate sensitivity, specificity, and broad-based exploitability were discussed. Whilst a number of candidates are in the discovery phases, all will require replication in larger, real-life cohorts. Clinical cost-effectiveness also needs to be established.

CONCLUSIONS

Since a single measure is unlikely to suffice, multi-modal strategies look more promising, although they bring greater technical and implementation complexities. Identifying reproducible, robust biomarkers will probably require pre-competitive consortia to provide the resources needed to identify, validate, and develop the relevant clinical tests.