Metabolomic derangements are associated with mortality in critically ill adult patients.

OBJECTIVE: To identify metabolomic biomarkers predictive of Intensive Care Unit (ICU) mortality in adults. RATIONALE: Comprehensive metabolomic profiling of plasma at ICU admission to identify biomarkers associated with mortality has recently become feasible. METHODS: We performed metabolomic profiling of plasma from 90 ICU subjects enrolled in the BWH Registry of Critical Illness (RoCI). We tested individual metabolites and a Bayesian Network of metabolites for association with 28-day mortality, using logistic regression in R, and the CGBayesNets Package in MATLAB. Both individual metabolites and the network were tested for replication in an independent cohort of 149 adults enrolled in the Community Acquired Pneumonia and Sepsis Outcome Diagnostics (CAPSOD) study. RESULTS: We tested variable metabolites for association with 28-day mortality. In RoCI, nearly one third of metabolites differed among ICU survivors versus those who died by day 28 (N = 57 metabolites, p<.05). Associations with 28-day mortality replicated for 31 of these metabolites (with p<.05) in the CAPSOD population. Replicating metabolites included lipids (N = 14), amino acids or amino acid breakdown products (N = 12), carbohydrates (N = 1), nucleotides (N = 3), and 1 peptide. Among 31 replicated metabolites, 25 were higher in subjects who progressed to die; all 6 metabolites that are lower in those who die are lipids. We used Bayesian modeling to form a metabolomic network of 7 metabolites associated with death (gamma-glutamylphenylalanine, gamma-glutamyltyrosine, 1-arachidonoylGPC(20:4), taurochenodeoxycholate, 3-(4-hydroxyphenyl) lactate, sucrose, kynurenine). This network achieved a 91% AUC predicting 28-day mortality in RoCI, and 74% of the AUC in CAPSOD (p<.001 in both populations). CONCLUSION: Both individual metabolites and a metabolomic network were associated with 28-day mortality in two independent cohorts. Metabolomic profiling represents a valuable new approach for identifying novel biomarkers in critically ill patients.

Carrier testing for severe childhood recessive diseases by next-generation sequencing.

Of 7028 disorders with suspected Mendelian inheritance, 1139 are recessive and have an established molecular basis. Although individually uncommon, Mendelian diseases collectively account for ~20% of infant mortality and ~10% of pediatric hospitalizations. Preconception screening, together with genetic counseling of carriers, has resulted in remarkable declines in the incidence of several severe recessive diseases including Tay-Sachs disease and cystic fibrosis. However, extension of preconception screening to most severe disease genes has hitherto been impractical. Here, we report a preconception carrier screen for 448 severe recessive childhood diseases. Rather than costly, complete sequencing of the human genome, 7717 regions from 437 target genes were enriched by hybrid capture or microdroplet polymerase chain reaction, sequenced by next-generation sequencing (NGS) to a depth of up to 2.7 gigabases, and assessed with stringent bioinformatic filters. At a resultant 160x average target coverage, 93% of nucleotides had at least 20x coverage, and mutation detection/genotyping had ~95% sensitivity and ~100% specificity for substitution, insertion/deletion, splicing, and gross deletion mutations and single-nucleotide polymorphisms. In 104 unrelated DNA samples, the average genomic carrier burden for severe pediatric recessive mutations was 2.8 and ranged from 0 to 7. The distribution of mutations among sequenced samples appeared random. Twenty-seven percent of mutations cited in the literature were found to be common polymorphisms or misannotated, underscoring the need for better mutation databases as part of a comprehensive carrier testing strategy. Given the magnitude of carrier burden and the lower cost of testing compared to treating these conditions, carrier screening by NGS made available to the general population may be an economical way to reduce the incidence of and ameliorate suffering associated with severe recessive childhood disorders.

Genomic convergence analysis of schizophrenia: mRNA sequencing reveals altered synaptic vesicular transport in post-mortem cerebellum.

Schizophrenia (SCZ) is a common, disabling mental illness with high heritability but complex, poorly understood genetic etiology. As the first phase of a genomic convergence analysis of SCZ, we generated 16.7 billion nucleotides of short read, shotgun sequences of cDNA from post-mortem cerebellar cortices of 14 patients and six, matched controls. A rigorous analysis pipeline was developed for analysis of digital gene expression studies. Sequences aligned to approximately 33,200 transcripts in each sample, with average coverage of 450 reads per gene. Following adjustments for confounding clinical, sample and experimental sources of variation, 215 genes differed significantly in expression between cases and controls. Golgi apparatus, vesicular transport, membrane association, Zinc binding and regulation of transcription were over-represented among differentially expressed genes. Twenty three genes with altered expression and involvement in presynaptic vesicular transport, Golgi function and GABAergic neurotransmission define a unifying molecular hypothesis for dysfunction in cerebellar cortex in SCZ.