Biology subjects

Bessiere, C.

Publications and source records attributed to Bessiere, C..

9 recordsLinked to original sources

bioRxiv2026

Tumor microenvironment signature associated with morphology in systemic ALK-positive anaplastic large cell lymphoma

Peripheral T-cell lymphomas (PTCL) are heterogeneous entities whose tumoral microenvironment (TME) may influence disease phenotype and outcome. To dissect their immune and stromal composition, we used two complementary algorithms, CIBERSORTx and MCP-counter, on Affymetrix data from 255 patients whole-tissue biopsies, including 78 systemic anaplastic large cell lymphomas (ALCL). Clustering based on inferred cell proportions revealed a clear separation between ALCL and other PTCL subtypes, including angioimmunoblastic T-cell lymphoma (AITL) and PTCL-not otherwise specified (PTCL-NOS). ALCL-enriched clusters were characterised by granulocytes and macrophages lineages, mast cells, NK cells, memory CD4+ T-cells, as well as fibroblast and endothelial signatures, whereas as expected, AITL cluster was enriched for T-follicular helper cells, B-cells and macrophages. Finally, we focused on 66 ALCL cases with relapse risk and morphological subtype clinical annotations. Distinct TME features enriched in M1 macrophages and monocytes were associated with adverse outcomes and non-common morphological variants. Transcriptomic analyses of mononuclear phagocytes across Affymetrix and RNAseq datasets confirmed distinct clustering according to cell morphology. These analyses identified potential biomarkers associated with uncommon variants and absent from ALK+ ALCL cell lines, confirming their TME origin. Macrophage- and monocyte-related signatures emerged as key contributors in ALK+ ALCL patients heterogeneity, linking TME patterns to tumor morphology and prognosis. These signatures may serve as biomarkers for patient risk stratification and guide the development of targeted therapies.

cancer biology↗

bioRxiv2026

CircZBTB46, a promising therapeutic target in crizotinib resistant ALK-positive T lymphomas

Circular RNAs (circRNAs) are increasingly recognized as functional non-coding transcripts with oncogenic potential. Here, a comprehensive analysis of circRNA expression in primary ALK(+) anaplastic large-cell lymphoma (ALK(+) ALCL) is presented. Integrated transcriptomic profiling revealed that aberrant expression of circZBTB46 and of its linear host transcript, normally restricted to dendritic cells, is exclusive to ALK(+) lymphoma cells and driven by the oncogenic NPM1-ALK/STAT3 axis. Functional studies showed that circZBTB46, unlike its protein-coding counterpart, promotes resistance to the ALK inhibitor crizotinib. Silencing circZBTB46 restored crizotinib sensitivity in resistant ALCL cells both in vitro and in vivo. Transcriptomic analyses identified PIP5K1C as a downstream effector regulated through a competitive endogenous RNA mechanism in which circZBTB46 acts as a sponge to miR-25-3p, alleviating its repression of PIP5K1C. These findings uncover a previously unrecognized mechanism of drug resistance in ALK(+) ALCL and establish circZBTB46 as a promising therapeutic target.

cancer biology↗

bioRxiv2025

REINDEER2: practical abundance index at scale

Recent advances in biological sequence indexing have enabled the efficient querying of sequence presence across massive genomic data repositories. While presence queries have become tractable at petabyte scale, retrieving quantitative information such as sequence abundances remains a significant algorithmic challenge. Existing abundance-aware indexes are mostly static, difficult to scale, and often trade off completeness, precision, or updatability. We describe a novel discrete abundance index designed for scalability, dynamic updates, and tunable precision. We combine an inverted index with probabilistic and exact structures to support fast, memory-efficient construction and precise high-throughput queries across thousands of RNA datasets. Our experiments demonstrate that our method REINDEER2 achieves one to two orders of magnitude speedup in construction compared to existing methods, while maintaining comparable or better memory use. Despite using approximate structures for scalability, REINDEER2 achieves sub-1% error on abundance recovery and correlates strongly with reference quantifiers like Kallisto. It also supports sequence-level queries in seconds over thousands of datasets. Code and experiments github.com/Yohan-HernandezCourbevoie/REINDEER2

bioinformatics↗

bioRxiv2024

Exploring a large cancer cell line RNA-sequencing dataset with k-mers

Analyzing the immense diversity of RNA isoforms in large RNA-seq repositories requires laborious data processing using specialized tools. Indexing techniques based on k-mers have previously been effective at searching for RNA sequences across thousands of RNA-seq libraries but falling short of enabling direct RNA quantification. We show here that RNAs queried in the form of k-mer sets can be quantified in seconds, with a precision akin to that of conventional RNA quantification methods. We showcase several applications by exploring an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples. Non-reference RNA sequences such as RNAs harboring driver mutations and fusions, splicing isoforms or RNAs derived from repetitive elements, can be retrieved with high accuracy. Moreover, we show that k-mer indexing offers a powerful means to reveal variant RNAs induced by specific gene alterations, for instance in splicing factors. A web server allows public queries in CCLE and other indexes: https://transipedia.fr. Code is provided to allow users to set up their own server from any RNA-seq dataset.

bioinformatics↗

bioRxiv2024

KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction

SummaryKaMRaT is a program for processing large k-mer count tables extracted from high throughput sequencing data. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into longer contigs and selecting k-mers based on their presence in certain samples. KaMRaT s main application is the reference-free analysis of multi-sample and multi-condition datasets from RNA-seq, as well as ChiP-seq or ribo-seq experiments. KaMRaT enables the identification of condition-specific or differential sequences, irrespective of any gene or transcript annotation. Implementation and availabilityKaMRaT is implemented in C++. Source code and documentation are available via https://github.com/Transipedia/KaMRaT. Container images are available via https://hub.docker.com/r/xuehl/kamrat.

bioinformatics↗

bioRxiv2021

Kmerator Suite: design of specific k-mer signatures andautomatic metadata discovery in large RNA-Seq datasets.

The huge body of publicly available RNA-seq libraries is a treasure of functional information allowing to quantify the expression of known or novel transcripts in tissues. However, transcript quantification commonly relies on alignment methods requiring a lot of computational resources and processing time, which does not scale easily to large datasets. K-mer decomposition constitutes a new way to process RNA-seq data for the identification of transcriptional signatures, as k-mers can be used to quantify accurately gene expression in a less resource-consuming way. We present the Kmerator Suite, a set of three tools designed to extract specific k-mer signatures, quantify these k-mers into RNA-seq datasets and quickly visualize large datasets characteristics. The core tool, Kmerator, produces specific k-mers for 97% of human genes, enabling the measure of gene expression with high accuracy in simulated datasets. KmerExploR, a direct application of Kmerator, uses a set of predictor genes specific k-mers to infer metadata including library protocol, sample features or contaminations from RNA-seq datasets. KmerExploR results are visualised through a user-friendly interface. Moreover, we demonstrate that the Kmerator Suite can be used for advanced queries targeting known or new biomarkers such as mutations, gene fusions or long non coding-RNAs for human health applications.

bioinformatics↗

bioRxiv2019

Sequence-level instructions direct transcription at polyT short tandem repeats.

BackgroundUsing the Cap Analysis of Gene Expression technology, the FANTOM5 consortium provided one of the most comprehensive maps of Transcription Start Sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers.\n\nResultsHere, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at short tandem repeats (STRs) corresponding to homopolymers of thymidines (T). Additional analyse confirm that these CAGEs are truly associated with transcriptionally active chromatin marks. Furthermore, we train a sequence-based deep learning model able to predict CAGE signal at T STRs with high accuracy (~81%) Extracting features learned by this model reveals that transcription at T STRs is mostly directed by STR length but also instructions lying in the downstream sequence. Excitingly, our model also predicts that genetic variants linked to human diseases affect this STR-associated transcription.\n\nConclusionsTogether, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism. We also provide a new metric that can be considered in future studies of STR-related complex traits.

genomics↗

bioRxiv2018

Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae

Nitrogen composition of the grape must has an impact on yeast growth and fermentation kinetics as well as on the organoleptic properties of the final product. In some technological processes, such as white wine/rose winemaking, the yeast-assimilable nitrogen content is sometimes insufficient to cover yeast requirements, which can lead to slow or sluggish fermentations. Growth is nevertheless quickly restored upon relief from nutrient starvation, e.g. through the addition of ammonium nitrogen, allowing fermentation completion. The aim of this study was to determine how nitrogen repletion affected the transcriptional response of a Saccharomyces cerevisiae wine yeast strain, in particular within the first hour after nitrogen addition. We found almost 4800 genes induced or repressed, sometimes within minutes after nutrient changes. Some of these responses to nitrogen depended on the TOR pathway, which controls positively ribosomal protein genes, amino acid and purine biosynthesis or amino acid permease genes and negatively stress-response genes, and genes related to the retrograde response (RTG) specific to the tricarboxylic acid (TCA) cycle and nitrogen catabolite repression (NCR). Some unexpected transcriptional responses concerned all the glycolytic genes, the starch and glucose metabolism and citrate cycle-related genes that were down-regulated, as well as genes from the lipid metabolism.

microbiology↗

bioRxiv2017

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains

Gene expression is orchestrated by distinct regulatory regions (e.g. promoters, enhancers, UTRs) to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks (e.g. ChIP-seq, methylation, DNase hypersensitivity), with the ultimate goal of identifying driving genomic regions and mutations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, their biological interpretation can be misleading. Finally these methods are not designed to capture potential regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. We develop here a method for predicting mRNA levels based solely on sequence features collected from distinct regulatory regions, which is as accurate as methods based on experimental data. Our approach confirms the importance of nucleotide composition in predicting gene expression and ranks regulatory regions according to their contribution. It also unveils strong influence of gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

bioinformatics↗