Biology subjects

Kreimer, A.

Publications and source records attributed to Kreimer, A..

3 recordsLinked to original sources

bioRxiv2018

Massively parallel characterization of regulatory dynamics during neural induction

The molecular components governing neural induction remain largely unknown. Here, we applied a suite of genomic and computational tools to comprehensively identify these components. We performed RNA-seq, ChIP-seq (H3K27ac, H3K27me3) and ATAC-seq on human embryonic stem cells (hESCs) at seven early neural differentiation time points (0-72 hours) and identified thousands of induced genes and regulatory regions. We analyzed the function of ~2,500 selected regions using massively parallel reporter assays at all time points. We found numerous temporal enhancers that correlated with similarly timed epigenetic marks and gene expression. Development of a prioritization method that incorporated all genomic data identified key transcription factors (TFs) involved in neural induction. Individual overexpression of eleven TFs and several combinations in hESCs found novel neural induction regulators. Combined, our results provide a comprehensive map of genes and functional regulatory elements involved in neural induction and identify master regulator TFs that are instrumental for this process.\n\nOne Sentence SummaryUsing numerous genomic assays and computational tools we characterized the dynamic changes that take place during neural induction.

genomics↗

bioRxiv2017

Meta-analysis of massive parallel reporter assay enables functional regulatory elements prediction

Deciphering the potential of non-coding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA-sequences and their variants in a single experiment. With increasing number of publically available MPRA datasets, one can now develop data-driven models which, given a DNA-sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA datasets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across datasets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest.

genomics↗

bioRxiv2017

Use antibiotics in cell culture with caution: genome-wide identification ofantibiotic-induced changes in gene expression and regulation

Standard cell culture guidelines often use media supplemented with antibiotics to prevent cell contamination. However, relatively little is known about the effect of antibiotic use in cell culture on gene expression and the extent to which this treatment could confound results. To comprehensively characterize the effect of antibiotic treatment on gene expression, we performed RNA-seq and ChIP-seq for H3K27ac on HepG2 cells, a human liver cell line commonly used for pharmacokinetic, metabolism and genomic studies, cultured in media supplemented with penicillin-streptomycin (PenStrep) or vehicle control. We identified 205 PenStrep-responsive genes, including transcription factors such as ATF3 that are likely to alter the regulation of other genes. Pathway analyses found a significant enrichment for \"xenobiotic metabolism signaling\" and \"PXR/RXR activation\" pathways. Our H3K27ac ChIP-seq identified 9,514 peaks that are PenStrep responsive. These peaks were enriched near genes that function in cell differentiation, tRNA modification, nuclease activity and protein dephosphorylation. Our results suggest that PenStrep treatment can significantly alter gene expression and regulation in a common liver cell type such as HepG2, advocating that antibiotic treatment should be taken into account when carrying out genetic, genomic or other biological assays in cultured cells.

genomics↗