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Vienne, M.

Publications and source records attributed to Vienne, M..

3 recordsLinked to original sources

Genome-wide CRISPR/Cas9 screen reveals factors that influence the susceptibility of tumor cells to NK cell-mediated killing

BackgroundNatural killer (NK) cells exhibit potent cytotoxic activity against various cancer cell types. Over the past five decades, numerous methodologies have been employed to elucidate the intricate molecular mechanisms underlying NK cell-mediated tumor control. While significant progress has been made in elucidating the interactions between NK cells and tumor cells, the regulatory factors governing NK cell-mediated tumor cell destruction are not yet fully understood. This includes the diverse array of tumor ligands recognized by NK cells and the mechanisms that NK cells employ to eliminate tumor cells. MethodsIn this study, we employed a genome-wide CRISPR/Cas9 screening approach in conjunction with functional cytotoxicity assays to delineate the proteins modulating the susceptibility of colon adenocarcinoma HCT-116 cells to NK cell-mediated cytotoxicity. ResultsAnalysis of guide RNA (gRNA) distribution in HCT-116 cells that survived co-incubation with NK cells identified ICAM-1 as a pivotal player in the NKp44-mediated immune synapse, with NKp44 serving as an activating receptor crucial for the elimination of HCT-116 tumor cells by NK cells. Furthermore, disruption of genes involved in the apoptosis or IFN-{gamma} signaling pathways conferred resistance to NK cell attack. We further dissected that NK cell-derived IFN-{gamma} promotes mitochondrial apoptosis in vitro and exerts control over B16-F10 lung metastases in vivo. ConclusionMonitoring ICAM-1 levels on the surface of tumor cells or modulating its expression should be considered in the context of NK cell-based therapy. Additionally, considering the diffusion properties of IFN-{gamma}, our findings highlight the potential of leveraging NK cell-derived IFN-{gamma} to enhance direct tumor cell killing and facilitate bystander effects via cytokine diffusion, warranting further investigation. WHAT IS ALREADY KNOWN ON THIS TOPICNK cells play a crucial role in identifying and eliminating various cancer cell types. However, the mechanisms that regulate NK cell-mediated destruction of tumor cells are not yet fully understood. This involves the array of tumor ligands that NK cells recognize and the processes they utilize to carry out tumor cell elimination. WHAT THIS STUDY ADDSOur research emphasizes the critical role of ICAM-1 in NKp44-mediated destruction of HCT-116 tumor cells. Additionally, we found that interfering with genes related to apoptosis or IFN-{gamma} signaling pathways increased resistance to NK cell attack. We showed that IFN-{gamma} produced by NK cells induces mitochondrial apoptosis in vitro and helps regulate B16-F10 lung metastases in vivo. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYGiven the ability of IFN-{gamma} to diffuse, our findings suggest that NK cell-derived IFN-{gamma} can be harnessed to directly kill tumor cells and trigger bystander effects through cytokine spread. This approach holds promise for further exploration. Additionally, assessing or manipulating ICAM-1 levels on tumor cell surfaces could enhance the effectiveness of NK cell-based therapies.

immunology↗

metagWGS, a comprehensive workflow to analyze metagenomic data using Illumina or PacBio HiFi reads

BackgroundTo study communities of micro-organisms taxonomically and functionally, metagenomic analyses are now often used. If there is no reference gene catalogue, a de novo approach is required. Because genomes are easier to interpret than contigs, the recovery of metagenome-assembled genomes (MAGs) by binning of contigs from metagenomic data has recently become a common task for microbial studies. However, during this process, there is a significant loss of information between the assembly and the binning of contigs. This is why it is important to produce taxonomic and functional matrices for all contigs and not just those included in correct bins. In addition, Pacbio HiFi reads (long and of good quality) are now a possible, albeit more expensive, alternative to short Illumina reads. We therefore developed a workflow that is easy to install with dependencies fixed using singularity images and easy to use on a computing cluster, that is capable of analyzing either short or long reads, and that should allow analysis at the contig and/or bin level, depending on the users choice. Following is a presentation of metagWGS, a fully automated workflow for metagenomic data analysis. It uses a new tool for refining bins (called Binette) that we will demonstrate is more efficient than competing tools. MethodsmetagWGS is a Nextflow workflow distributed with two singularity images and complete documentation to facilitate its installation and use. Because the main original features of metagWGS concern binning (short and long reads) and the analysis of HiFi reads, we compared metagWGS with the MAG construction workflow proposed by PacBio to a public dataset used by Pacbio to promote its workflow. ResultsmetagWGS differs from existing workflows by (i) offering flexible approaches for the assembly; (ii) supporting short reads (Illumina) or PacBio HiFi reads; (iii) combining multiple binning algorithms with a new bin refinement tool, referred to as "Binette", to achieve high-quality genome bins; and (iv) providing taxonomic and functional annotation for all genes, all contigs built and bins. metagWGS produces more medium (708) and high-quality (255) bins on 11 public metagenomic samples from human gut data than the Pacbio HiFi dedicated workflow, referred to as the HiFi-MAGS-pipeline (659 medium quality bins and 231 high quality bins), primarily due to the better performance of Binette.

bioinformatics↗

A ready-to-use logistic Verhulst model implemented in R shiny to estimate growth parameters of microorganisms

In microbiology, the estimation of the growth rate of microorganisms is a critical parameter to describe a new strain or characterize optimal growth conditions. Traditionally, this parameter is estimated by selecting subjectively the exponential phase of the growth, and then determining the slope of this curve section, by linear regression. However, for some experiments, the number of points to describe the growth can be very limited, and consequently such linear model will not fit, or the parameters estimation can much lower and strongly variable. In this paper, we propose a tools to estimate growth parameters using a logistic Verhulst model that take into account the entire growth curve for the estimation of the growth rate. The efficiency of such model is compared to the linear model. Finally, the novelty of our work is to propose a "Shiny-web application", online, without any programming or modelling skills, to allow estimating growth parameters including growth rate, maximum population, and beginning of the exponential phase, as well as an estimation of their variability. The final results can be displayed in the form of a scatter plot representing the model, its efficiency and the estimated parameters are downloadable.

microbiology↗