Biology subjects

Benjamin A Logsdon

Publications and source records attributed to Benjamin A Logsdon.

3 recordsLinked to original sources

bioRxiv2016

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↗

bioRxiv2016

Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer

BackgroundDiscovering patient subtypes and molecular drivers of a subtype are difficult and driving problems underlying most modern disease expression studies collected across patient populations. Expression patterns conserved across multiple expression datasets from independent disease studies are likely to represent important molecular events underlying the disease.\n\nMethodsWe present the INSPIRE (INferring Shared modules from multiPle gene expREssion datasets) method to infer highly coherent and robust modules of co-expressed genes and the dependencies among the modules from multiple expression datasets. Focusing on inferring modules and their dependencies conserved across multiple expression datasets is important for several reasons. First, using multiple datasets will increase the power to detect robust and relevant patterns (modules and dependencies among modules). Second, INSPIRE enables the use of multiple datasets that contain different sets of genes due to, e.g., the difference in microarray platforms. Many methods designed for expression data analysis cannot integrate multiple datasets with variable discrepancy to infer a single combined model, whereas INSPIRE can naturally model the dependencies among the modules even when a large proportion of genes are not observed on a certain platform.\n\nResultsWe evaluated INSPIRE on synthetically generated datasets with known underlying network structure among modules, and gene expression datasets from multiple ovarian cancer studies. We show that the model learned by INSPIRE can explain unseen data better and can reveal prior knowledge on gene functions more accurately than alternative methods. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to the identification of a new marker and potential molecular driver of tumor-associated stroma - HOPX. We also demonstrate that the HOPXmodule strongly overlaps with the genes defining the mesenchymal patient subtype identified in The Cancer Genome Atlas (TCGA) ovarian cancer data. We provide evidence for a previously unknown molecular basis of tumor resectability efficacy involving tumor-associated mesenchymal stem cells represented by HOPX.\n\nConclusionsINSPIRE extracts a low-dimensional description from multiple gene expression data, which consists of modules and their dependencies. The discovery of a new tumor-associated stroma marker, HOPX, and its module suggests a previously unknown mechanism underlying tumor-associated stroma.

Systems Biology↗

bioRxiv2016

Identifying Network Perturbation in Cancer

We present a computational framework, called DISCERN (DIfferential SparsE Regulatory Network), to identify informative topological changes in gene-regulator dependence networks inferred on the basis of mRNA expression datasets within distinct biological states. DISCERN takes two expression datasets as input: an expression dataset of diseased tissues from patients with a disease of interest and another expression dataset from matching normal tissues. DISCERN estimates the extent to which each gene is perturbed - having distinct regulator connectivity in the inferred gene-regulatory dependencies between the disease and normal conditions. This approach has distinct advantages over existing methods. First, DISCERN infers conditional dependencies between candidate regulators and genes, where conditional dependence relationships discriminate the evidence for direct interactions from indirect interactions more precisely than pairwise correlation. Second, DISCERN uses a new likelihood-based scoring function to alleviate concerns about accuracy of the specific edges inferred in a particular network. DISCERN identifies perturbed genes more accurately in synthetic data than existing methods to identify perturbed genes between distinct states. In expression datasets from patients with acute myeloid leukemia (AML), breast cancer and lung cancer, genes with high DISCERN scores in each cancer are enriched for known tumor drivers, genes associated with the biological processes known to be important in the disease, and genes associated with patient prognosis, in the respective cancer. Finally, we show that DISCERN can uncover potential mechanisms underlying network perturbation by explaining observed epigenomic activity patterns in cancer and normal tissue types more accurately than alternative methods, based on the available epigenomic from the ENCODE project.

Genomics↗