bioRxiv ScienceSearch

Biology subjects

Andrew Chess

Publications and source records attributed to Andrew Chess.

2 recordsLinked to original sources

Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia

Over 100 genetic loci harbor schizophrenia associated variants, yet how these common variants confer risk is uncertain. The CommonMind Consortium has sequenced dorsolateral prefrontal cortex RNA from schizophrenia cases (n=258) and control subjects (n=279), creating the largest publicly available resource to date of gene expression and its genetic regulation; [~]5 times larger than the latest release of GTEx. Using this resource, we find that [~]20% of the schizophrenia risk loci have common variants that could explain regulation of brain gene expression. In five loci, these variants modulate expression of a single gene: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Experimentally altered expression of three of them, FURIN, TSNARE1, and CNTN4, perturbs the proliferation and apoptotic index of neural progenitors and leads to neuroanatomical deficits in zebrafish. Furthermore, shRNA mediated knock-down of FURIN1 in neural progenitor cells derived from human induced pluripotent stem cells produces abnormal neural migration. Although 4.2% of genes (N = 693) display significant differential expression between cases and controls, 44% show some evidence for differential expression. All fold changes are [≤] 1.33, and an independent cohort yields similar differential expression for these 693 genes (r = 0.58). These findings are consistent with schizophrenia being highly polygenic, as has been reported in investigations of common and rare genetic variation. Co-expression analyses identify a gene module that shows enrichment for genetic associations and is thus relevant for schizophrenia. Taken together, these results pave the way for mechanistic interpretations of genetic liability for schizophrenia and other brain diseases.

Genomics

Single molecule-level detection and long read-based phasing of epigenetic variations in bacterial methylomes

Comprehensive genome-wide analyses of bacterial DNA methylation have not been possible until the recent advent of single molecule, real-time (SMRT) sequencing. This technology enables the direct detection of N6-methyladenine (6mA) and 4-methylcytosine (4mC) at single nucleotide resolution on a genome-wide scale. The distributions of these two major types of DNA methylation, along with 5-methylcytosine (5mC), comprise the bacterial methylome, some rare exceptions notwithstanding. SMRT sequencing has already revealed marked diversity in bacterial methylomes as well as the existence of heterogeneity of methylation in cells in single bacterial colonies, where such epigenetic variation can enable bacterial populations to rapidly adapt to changing conditions. However, current methods for studying bacterial methylomes using SMRT sequencing mainly rely on population-level summaries that do not provide the single-cell resolution necessary for dissecting the epigenetic heterogeneity in bacterial populations. Here, we present a novel SMRT sequencing-based framework, consisting of two complementary methods, for single molecule-level detection of DNA methylation and assessment of methyltransferase activity through single molecule-level long read-based epigenetic phasing. Using seven bacterial strains and integrating data from SMRT and Illumina sequencing, we show that our method yields significantly improved resolution compared to existing population-level methods, and reveals several distinct types of epigenetic heterogeneity. Our approach enables new investigations of the complex architecture and dynamics of bacterial methylomes and provides a powerful new tool for the study of bacterial epigenetic control.

Microbiology