Biology subjects

Schmidt, F.

Publications and source records attributed to Schmidt, F..

4 recordsLinked to original sources

bioRxiv2017

Temporal epigenomic profiling identifies AHR as dynamic super-enhancer controlled regulator of mesenchymal multipotency

Temporal data on gene expression and context-specific open chromatin states can improve identification of key transcription factors (TFs) and the gene regulatory networks (GRNs) controlling cellular differentiation. However, their integration remains challenging. Here, we delineate a general approach for data-driven and unbiased identification of key TFs and dynamic GRNs, called EPIC-DREM. We generated time-series transcriptomic and epigenomic profiles during differentiation of mouse multipotent bone marrow stromal cells (MSCs) towards adipocytes and osteoblasts. Using our novel approach we constructed time-resolved GRNs for both lineages. To prioritize the identified shared regulators, we mapped dynamic super-enhancers in both lineages and associated them to target genes with correlated expression profiles. We identified aryl hydrocarbon receptor (AHR) and Glis family zinc finger 1 (GLIS1) as mesenchymal key TFs controlled by dynamic MSC-specific super-enhancers that become repressed in both lineages. AHR and GLIS1 control differentiation-induced genes and we propose they function as guardians of mesenchymal multipotency.

bioinformatics↗

bioRxiv2017

Direct in vivo mapping of functional suppressors in glioblastoma genome

Glioblastoma (GBM) is one of the deadliest cancers, with limited effective treatments and single-digit five-year survival 1-7. A causative understanding of genetic factors that regulate GBM formation is of central importance 8-19. However, a global, quantitative and functional understanding of gliomagenesis in the native brain environment has been lacking due to multiple challenges. Here, we developed an adeno-associated virus (AAV) mediated autochthonous CRISPR screen and directly mapped functional suppressors in the GBM genome. Stereotaxic delivery of an AAV library targeting significantly mutated genes into fully immunocompetent conditional Cas9 mice robustly led to gliomagenesis, resulting in tumors that recapitulate features of human GBM. Targeted capture sequencing revealed deep mutational profiles with diverse patterns across mice, uncovering in vivo roles of previously uncharacterized factors in GBM such as immune regulator B2m, zinc finger protein Zc3h13, transcription repressor Cic, epigenetic regulators Mll2/3 and Arid1b, alongside canonical tumor suppressors Nf1 and Pten. Comparative cancer genomics showed that the mutation frequencies across all genes tested in mice significantly correlate with those in human from two independent patient cohorts. Co-mutation analysis identified frequently co-occurring driver combinations, which were validated using AAV minipools, such as Mll2, B2m-Nf1, Mll3-Nf1 and Zc3h13-Rb1. Distinct from Nf1-oncotype tumors, Rb1-oncotype tumors exhibit undifferentiated histopathology phenotype and aberrant activation of developmental reprogramming signatures such as Homeobox gene clusters. The secondary addition of Zc3h13 or Pten mutations drastically altered the gene expression profiles of Rb1 mutants and rendered them more resistant to the GBM chemotherapeutic temozolomide. Our study provides a systematic functional landscape of GBM suppressors directly in vivo, opening new paths for high-throughput molecular mapping and cancer phenotyping.

systems biology↗

bioRxiv2017

Natural Colonization of Laboratory Mice with Staphylococcus aureus Primes a Systemic Immune Response

BackgroundWhether mice are an appropriate model for S. aureus infection and vaccination studies is a matter of debate, because they are not considered as natural hosts of S. aureus. Sparked by an outbreak of S. aureus infections in laboratory mice, we investigated whether laboratory mice are commonly colonized with S. aureus and whether this might impact on infection experiments.\n\nMethodsWe characterized 99 S. aureus isolates from laboratory mice (spa typing, virulence gene PCR), and quantified murine antibodies using FlexMap technology.\n\nResultsSpecific-pathogen-free mice from various vendors were frequently colonized with S. aureus (0-21%). S. aureus was readily transmitted from murine parents to offspring, which became persistently colonized. Most murine isolates belonged to the lineage CC88 (54%). Murine strains showed features of host adaptation, such as absence of hlb-converting phages and superantigen genes, as well as enhanced coagulation of murine plasma. Importantly, S. aureus colonization induced a systemic IgG response specific for numerous S. aureus proteins, including several vaccine candidates.\n\nConclusionLaboratory mice are natural hosts of S. aureus and, therefore, provide better infection models than previously assumed. Pre-exposure to S. aureus is a possible confounder in S. aureus infection and vaccination studies.

immunology↗

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↗