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Malabat, C.

Publications and source records attributed to Malabat, C..

5 recordsLinked to original sources

Spatially-resolved metabolomic identifies colibactin-specific principles of reprogrammed lipid metabolism to promote cancer progression.

Intratumoral bacteria locally contribute to cellular and molecular tumor heterogeneity that support cancer stemness through poorly understood mechanisms. This study aims to explore how Colibactin-producing Escherichia coli (CoPEC) flexibly alters the tumor microenvironment in right-sided colorectal cancer (CRC). Metabolomic and transcriptomic spatial profiling uncovered that CoPEC colonization establishes a high-glycerophospholipid microenvironment within the tumor that is conducive to exhaustion of infiltrated CD8+ T cell and has a lowered prognostic value in right-sided CRC. Mechanistically, the accumulation of lipid droplets in infected cancer cells relied on the production of colibactin as a measure to limit genotoxic stress and supply with sufficient energy for sustaining cell survival and lowering tumor immunogenicity. Specifically, a heightened phosphatidylcholine remodeling of CoPEC-infected cancer cells by the enzyme of the Lands cycle coincided with a lowered accumulation of proapoptotic ceramide and lysophosphatidylcholine. Consequently, a reduced infiltration of CD8+ T lymphocytes that produce the cytotoxic cytokines IFN-{gamma} was found where invading bacteria have been geolocated. By contrast, such an immunosuppressive dysmetabolic process was not observed when human colon cancer cells were infected with the mutant strain that did not produce colibactin (11G5{delta}ClbQ). This work revealed an unexpected property of CoPEC on lipid overload within tumors that could locally provide an inflammatory environment leading to immunosuppressive mechanisms and tumor expansion. This may pave the way for improving chemoresistance and subsequently outcome of CRC patients who are colonized by CoPEC.

cancer biology↗

A virus-encoded microRNA contributes to evade innate immune response during SARS-CoV-2 infection

SARS-CoV-2 infection results in impaired interferon response in severe COVID-19 patients. However, how SARS-CoV-2 interferes with host immune response is incompletely understood. Here, we sequenced small RNAs from SARS-CoV-2-infected human cells and identified a micro-RNA (miRNA) encoded in a recently evolved region of the viral genome. We show that the virus-encoded miRNA produces two miRNA isoforms in infected cells by the enzyme Dicer and they are loaded into Argonaute proteins. Moreover, the predominant miRNA isoform targets the 3UTR of interferon-stimulated genes and represses their expression in a miRNA-like fashion. Finally, the two viral miRNA isoforms were detected in nasopharyngeal swabs from COVID-19 patients. We propose that SARS-CoV-2 employs a virus-encoded miRNA to hijack the host miRNA machinery and evade the interferon-mediated immune response.

molecular biology↗

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with mutants related with RNA metabolism and measured growth rates of about 700 000 Saccharomyces cerevisiae double mutant strains. The screens used the gene deletion collection in addition to a set of 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1 471 genes and 117 biological processes. To validate specific results, we tested and confirmed a link between an inositol polyphosphate hydrolase complex and mRNA translation initiation. Altogether, the results and the newly developed analysis strategy should represent a useful resource for discovery of gene function in yeast.

genetics↗

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical representation of the detected signatures. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some case, programming skills. Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide among the largest panels of interactive visualizations as compared to the other options that are currently available. SHAMAN is specifically designed for non-expert users who may benefit from using an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two datasets (a mock community sequencing and published 16S metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis.

bioinformatics↗

Antisense transcriptional interference mediates condition-specific gene repression in budding yeast

Pervasive transcription generates many unstable non-coding transcripts in budding yeast. The transcription of such noncoding RNAs, in particular antisense RNAs (asRNAs), has been shown in a few examples to repress the expression of the associated mRNAs. Yet, such mechanism is not known to commonly contribute to the regulation of a given class of genes. Using a mutant context that stabilised pervasive transcripts, we observed that the least expressed mRNAs during the exponential phase were associated with high levels of asRNAs. These asRNAs also overlapped their corresponding gene promoters with a much higher frequency than average. Interrupting antisense transcription of a subset of genes corresponding to quiescence-enriched mRNAs restored their expression. The underlying mechanism acts in cis and involves several chromatin modifiers. Our results convey that transcription interference represses up to 30% of the 590 least expressed genes, which includes 163 genes with quiescence-enriched mRNAs. We also found that pervasive transcripts constitute a higher fraction of the transcriptome in quiescence relative to the exponential phase, consistent with gene expression itself playing an important role to suppress pervasive transcription. Accordingly, the HIS1 asRNA, normally only present in quiescence, is expressed in exponential phase upon HIS1 mRNA transcription interruption.

genomics↗