GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.

Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html)

Consensus clustering applied to multi-omics disease subtyping

Background: Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. Conclusion: We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics

Divergent wiring of repressive and active chromatin interactions between mouse embryonic and trophoblast lineages.

The establishment of the embryonic and trophoblast lineages is a developmental decision underpinned by dramatic differences in the epigenetic landscape of the two compartments. However, it remains unknown how epigenetic information and transcription factor networks map to the 3D arrangement of the genome, which in turn may mediate transcriptional divergence between the two cell lineages. Here, we perform promoter capture Hi-C experiments in mouse trophoblast (TSC) and embryonic (ESC) stem cells to understand how chromatin conformation relates to cell-specific transcriptional programmes. We find that key TSC genes that are kept repressed in ESCs exhibit interactions between H3K27me3-marked regions in ESCs that depend on Polycomb repressive complex 1. Interactions that are prominent in TSCs are enriched for enhancer-gene contacts involving key TSC transcription factors, as well as TET1, which helps to maintain the expression of TSC-relevant genes. Our work shows that the first developmental cell fate decision results in distinct chromatin conformation patterns establishing lineage-specific contexts involving both repressive and active interactions

Modulation of the ATM/autophagy pathway by a G-quadruplex ligand tips the balance between senescence and apoptosis in cancer cells

G-quadruplex ligands exert their antiproliferative effects through telomere-dependent and telomere-independent mechanisms, but the inter-relationships among autophagy, cell growth arrest and cell death induced by these ligands remain largely unexplored. Here, we demonstrate that the G-quadruplex ligand 20A causes growth arrest of cancer cells in culture and in a HeLa cell xenografted mouse model. This response is associated with the induction of senescence and apoptosis. Transcriptomic analysis of 20A treated cells reveals a significant functional enrichment of biological pathways related to growth arrest, DNA damage response and the lysosomal pathway. 20A elicits global DNA damage but not telomeric damage and activates the ATM and autophagy pathways. Loss of ATM following 20A treatment inhibits both autophagy and senescence and sensitizes cells to death. Moreover, disruption of autophagy by deletion of two essential autophagy genes ATG5 and ATG7 leads to failure of CHK1 activation by 20A and subsequently increased cell death. Our results, therefore, identify the activation of ATM by 20A as a critical player in the balance between senescence and apoptosis and autophagy as one of the key mediators of such regulation. Thus, targeting the ATM/autophagy pathway might be a promising strategy to achieve the maximal anticancer effect of this compound

Inhibition of DDR1 enhances in vivo chemosensitivity in KRAS-mutant lung adenocarcinoma

Platinum-based chemotherapy in combination with immune-checkpoint inhibitors is the current standard of care for patients with advanced lung adenocarcinoma (LUAD). However, tumor progression evolves in most cases. Therefore, predictive bioma ricers are needed for better patient stratification and for the identification of new therapeutic strategies, including enhancing the efficacy of chemotoxic agents. Here, we hypothesized that discoidin domain receptor 1 (DDR1) may be both a predictive factor for chemoresistance in patients with LUAD and a potential target positively selected in resistant cells. By using biopsies from patients with LUAD, KRAS-mutant LUAD cell lines, and in vivo genetically engineered KRAS-driven mouse models, we evaluated the role of DDR1 in the context of chemotherapy treatment. We found that DORT is upregulated during chemotherapy both in vitro and in viva. Moreover, analysis of a cohort of patients with LUAD suggested that high DOR1 levels in pretreatment biopsies correlated with poor response to chemotherapy. Additionally, we showed that combining DORI inhibition with chemotherapy prompted a synergistic therapeutic effect and enhanced cell death of KRAS-mutant tumors in vivo. Collectively, this study suggests a potential role for DDR1 as both a predictive and prognostic biomarker, potentially improving the chemotherapy response of patients with LUAD

Deciphering the complex role of thrombospondin-1 in glioblastoma development

We undertook a systematic study focused on the matricellular protein Thrombospondin-1 (THBS1) to uncover molecular mechanisms underlying the role of THBS1 in glioblastoma (GBM) development. THBS1 was found to be increased with glioma grades. Mechanistically, we show that the TGFβ canonical pathway transcriptionally regulates THBS1, through SMAD3 binding to the THBS1 gene promoter. THBS1 silencing inhibits tumour cell invasion and growth, alone and in combination with anti-angiogenic therapy. Specific inhibition of the THBS1/CD47 interaction using an antagonist peptide decreases cell invasion. This is confirmed by CD47 knock-down experiments. RNA sequencing of patient-derived xenograft tissue from laser capture micro-dissected peripheral and central tumour areas demonstrates that THBS1 is one of the gene with the highest connectivity at the tumour borders. All in all, these data show that TGFβ1 induces THBS1 expression via Smad3 which contributes to the invasive behaviour during GBM expansion. Furthermore, tumour cell-bound CD47 is implicated in this process

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements