Biology subjects

Merico, D.

Publications and source records attributed to Merico, D..

4 recordsLinked to original sources

bioRxiv2018

Complete Disruption of Autism-Susceptibility Genes by Gene-Editing Predominantly Reduces Functional Connectivity of Isogenic Human Neurons

Autism Spectrum Disorder is phenotypically and genetically heterogeneous, but genomic analyses have identified candidate susceptibility genes. We present a CRISPR gene editing strategy to insert a protein tag and premature termination sites creating an induced pluripotent stem cell (iPSC) knockout resource for functional studies of 10 ASD-relevant genes (AFF2/FMR2, ANOS1, ASTN2, ATRX, CACNA1C, CHD8, DLGAP2, KCNQ2, SCN2A, TENM1). Neurogenin 2 (NEUROG2)-directed differentiation of iPSCs allowed production of cortical excitatory neurons, and mutant proteins were not detectable. RNAseq revealed convergence of several neuronal networks. Using both patch-clamp and multi-electrode array approaches, the electrophysiological deficits measured were distinct for different mutations. However, they culminated in a consistent reduction in synaptic activity, including reduced spontaneous excitatory post-synaptic current frequencies in AFF2/FMR2-, ASTN2-, ATRX-, KCNQ2- and SCN2A-null neurons. Despite ASD susceptibility genes belonging to different gene ontologies, isogenic stem cell resources can reveal common functional phenotypes, such as reduced functional connectivity.

neuroscience↗

bioRxiv2018

Matching drug transcriptional signatures to rare losses disrupting synaptic gene networks identifies known and novel candidate drugs for schizophrenia

Schizophrenia is a complex neuropsychiatric disorder. The etiology is not fully understood, but genetics plays an important role. Pathway analysis of genetic variants have suggested a central role for neuronal synaptic processes. Currently available antipsychotic medications successfully control positive symptoms (hallucinations and delusions) largely by inhibiting the dopamine D2 receptors; however, these drugs have more limited impact on negative symptoms (social withdrawal, flat affections, anhedonia) and cognitive deterioration. Drug development efforts have focused on a wide range of neurotransmitter systems and other agents, with conflicting or inconclusive results. New drug development paradigms are needed. A recent analysis, using common variant association results to match drugs based on their transcriptional perturbation signature, found drugs enriched in known antipsychotics plus novel candidates.\n\nWe followed a similar approach, but started our analysis from a synaptic gene network implicated by rare copy number loss variants. We found that a significant number of antipsychotics (p-value = 0.0002) and other psychoactive drugs (p-value = 0.0004) upregulate synaptic network genes. Based on global gene expression similarity, active drugs formed two main clusters: one with many known antipsychotics and antidepressants, the other with various drug categories including two nootropics. We specifically recommend further examination of nootropics with limited side effects (meclofenoxate, piracetam and vinpocetine) for combination therapy with antipsychotics to improve cognitive performance. Detailed experimental follow-up is required to further evaluate other candidate drugs lacking an official nervous system indication, although, for at least a few of these, psychoactive effects have been reported in the literature.

bioinformatics↗

bioRxiv2018

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding.

bioinformatics↗

bioRxiv2017

Pathway enrichment analysis of -omics data

Pathway enrichment analysis helps gain mechanistic insight into large gene lists typically resulting from genome scale (-omics) experiments. It identifies biological pathways that are enriched in the gene list more than expected by chance. We explain pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome sequencing experiments. The protocol comprises three major steps: define a gene list from genome scale data, determine statistically enriched pathways, and visualize and interpret the results. We focus on differentially expressed genes and mutated cancer genes, however the described principles can be applied to diverse -omics data. The protocol is designed for biologists with no prior bioinformatics training and uses freely available software including g:Profiler, GSEA, Cytoscape and Enrichment Map.

bioinformatics↗