Biology subjects

Rosenstiel, P.

Publications and source records attributed to Rosenstiel, P..

5 recordsLinked to original sources

bioRxiv2018

A multi-parent recombinant inbred line population of Caenorhabditis elegans enhances mapping resolution and identification of novel QTLs for complex life-history traits

Local populations of the bacterivorous nematode Caenorhabditis elegans can be genetically almost as diverse as global populations. To investigate the effect of local genetic variation on heritable traits, we developed a new recombinant inbred line (RIL) population derived from four wild isolates. The wild isolates were collected from two closely located sites in France: Orsay and Santeuil. By crossing these four genetically diverse parental isolates a population of 200 RILs was constructed. RNA-seq was used to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the four genotypes with an average spacing of 11 kb, possibly doubling the mapping resolution relative to currently available RIL panels. The SNPs were used to construct a genetic map to facilitate QTL analysis. Life history traits, such as lifespan, stress resistance, developmental speed and population growth were measured in different environments. For most traits substantial variation was found, and multiple QTLs could be detected, including novel QTLs not found in previous QTL analysis, for example for lifespan or pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping. Taken together, we show that RNA-seq can be used for genotyping, that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates permits analysis of local adaptation and life history trade-offs.

genetics↗

bioRxiv2018

Segregational drift and the interplay between plasmid copy number and evolvability

The ubiquity of plasmids in all prokaryotic phyla and habitats and their ability to transfer between cells marks them as prominent constituents of prokaryotic genomes. Many plasmids are found in their host cell in multiple copies. This leads to an increased mutational supply of plasmid-encoded genes and genetically heterogeneous plasmid genomes. Nonetheless, the segregation of plasmid copies into daughter cells during cell division is considered to occur in the absence of selection on the plasmid alleles. We investigate the implications of random genetic drift of multicopy plasmids during cell division - termed here segregational drift - to plasmid evolution. Performing experimental evolution of low- and high-copy non-mobile plasmids in Escherichia coli, we find that the evolutionary rate of multicopy plasmids does not reflect the increased mutational supply expected according to their copy number. In addition, simulated evolution of multicopy plasmid alleles demonstrates that segregational drift leads to increased loss frequency and extended fixation time of plasmid mutations in comparison to haploid chromosomes. Furthermore, an examination of the experimentally evolved hosts reveals a significant impact of the plasmid type on the host chromosome evolution. Our study demonstrates that segregational drift of multicopy plasmids interferes with the retention and fixation of novel plasmid variants. Depending on the selection pressure on newly emerging variants, plasmid genomes may evolve slower than haploid chromosomes, regardless of their higher mutational supply. We suggest that plasmid copy number is an important determinant of plasmid evolvability due to the manifestation of segregational drift.

microbiology↗

bioRxiv2018

Integrative analysis of single cell expression data reveals distinct regulatory states in bidirectional promoters

BackgroundBidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single cell technologies are not yet established, in the context of BPs.\n\nResultsWe performed integrative analyses of novel human single cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles.\n\nConclusionsOur results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

genomics↗

bioRxiv2016

normR: Regime enrichment calling for ChIP-seq data

ChIP-seq probes genome-wide localization of DNA-associated proteins. To mitigate technical biases ChIP-seq read densities are normalized to read densities obtained by a control. Our statistical framework \"normR\" achieves a sensitive normalization by accounting for the effect of putative protein-bound regions on the overall read statistics. Here, we demonstrate normRs suitability in three studies: (i) calling enrichment for high (H3K4me3) and low (H3K36me3) signal-to-ratio data; (ii) identifying two previously undescribed H3K27me3 and H3K9me3 heterochromatic regimes of broad and peak enrichment; and (iii) calling differential H3K4me3 or H3K27me3-enrichment between HepG2 hepatocarcinoma cells and primary human Hepatocytes. normR is readily available on http://bioconductor.org/packages/normr

bioinformatics↗

bioRxiv2016

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

bioinformatics↗