Biology subjects

Guyon, L.

Publications and source records attributed to Guyon, L..

9 recordsLinked to original sources

bioRxiv2025

Benchmarking microRNA Target Prediction Algorithms Using Single-Cell Co-Sequencing Data

(1) BackgroundMicroRNAs (miRNAs) are small non-coding RNAs that play pivotal roles in the post-transcriptional regulation of gene expression, influencing a wide range of physiological and pathological processes. Accurately identifying miRNA targets is crucial for understanding miRNA modes of action. To this aim, a plethora of algorithms have been developed to predict miRNA targets, each employing distinct methodologies and relying on different features. The limited overlap among target predictions generated by various algorithms underscores the necessity for comprehensive and independent benchmarks to evaluate their performance. (2) MethodsWe selected seven algorithms among the most popular ones to perform a benchmark with an original approach using recently published datasets of miRNA-mRNA co-sequencing at the single-cell level. We used Gene Set Enrichment Analysis to assess algorithms capabilities to predict sets of targets statistically anti-correlated in expression with miRNAs. We worked with both co-sequencing datasets of human and mouse single-cells. (3) ResultsOur benchmark shows high performances for mirDIP, which corresponds to the consensus result of 24 different algorithms, for human miRNAs. In human cell lines, Diana microT, TargetScan, miRmap, and miRDB also provide excellent results, while RNA22 and miRWalk exhibited poorer results. In mouse primary cells, Diana microT leads, closely followed by miRmap. Intriguingly, RNA22 performs better in mouse primary cells than in human cell lines. Moreover, our benchmark highlights the benefit of reducing targets to the experimentally validated ones. Finally, we demonstrated that performance varies depending on the number of targets used, with TargetScan performing better than mirDIP when considering only a few dozen targets.

bioinformatics↗

bioRxiv2025

Avirulent Pseudomonas aeruginosa T3SS-negative strains belonging to Clade 5 produce variable quantities of secondary metabolites

Pseudomonas species are ubiquitous in the environment and serve as valuable source of enzymes and secondary metabolites for industrial applications. P. aeruginosa secretes metalloproteases such as elastase LasB and produces bioactive small molecules, including pyocyanin, rhamnolipids, and pyoverdine, with potential biotechnological applications. However, the interest in P. aeruginosa for industrial use has been limited due to the virulence-associated Type III Secretion System (T3SS), a key factor in host-pathogen interactions. In this study, we genotypically and phenotypically characterized a collection of P. aeruginosa strains naturally lacking T3SS-encoding genes. Phylogenetic analysis revealed that these strains belong to two distinct clades. Several strains exhibited low or no cytotoxicity on epithelial cell lines and were avirulent in the Galleria infection model. The level of LasB and the three metabolites -- pyocyanin, rhamnolipids, and pyoverdine -- varied independently of virulence profiles. Notably, we identified avirulent strains capable of producing at least two secondary metabolites, including monorhamnolipids, highlighting their potential for biotechnological applications.

microbiology↗

bioRxiv2025

Historic Genomes Uncover Demographic Shifts and Kinship Structures in Post-Roman Central Europe

Many European towns and villages trace their origins to Early Medieval foundations. In former Roman territories, their emergence has traditionally been linked to mass migrations from outside the Roman Empire. However, recent studies have emphasised local continuity with some individual-level mobility. We generated and analysed 248 historic genomes from Late Roman (3rd and 4th century CE) and Early Medieval (5th-8th century CE) burial sites in southern Germany, comparing them to over 2,500 contemporary and Iron Age genomes in addition to 1,344 modern-day genomes from Germany, Italy and Great-Britain. Despite small inferred Early Medieval period community sizes, genetic diversity exceeded that of modern German cities. In the Altheim graveyard, established in the 5th century by a group of Northern European descent, we inferred a demographic shift in the 6th century with the integration of newcomers with ancestry typical of a nearby Roman military camp, likely as a result of the collapse of Roman state structures. We reconstructed multigenerational pedigrees and, using a novel approach to infer ancestry of unsampled relatives, inferred immediate intermarriage between incoming and local groups, with a distinct tendency for men from former Roman background marrying women of northern descent. Burial proximity correlates strongly with kinship, in some cases spanning six generations. These communities were organized around small family units, exhibited loosely patrilineal or bilateral descent patterns, practiced reproductive monogamy, and avoided close-kin marriages. Such practices reflect broader transformations in family structures that began during the Late Roman period, were transferred to small agrarian societies in the Early Medieval period, and continued to shape European societies. By the 7th century, ongoing admixture had shaped genetic diversity patterns into those resembling Central Europe today.

genetics↗

bioRxiv2023

Plakins are involved in the maintenance of epithelial polarity

As epithelia are the interface between the organism and the external environment, they are often subject to damage and must be frequently renewed. However, maintaining epithelial integrity during this renewal is challenging, and loss of cell polarity is a potent inducer of tumorigenesis. In this study, we used transcriptomic data from breast cancer cells at different stages of tumor development to identify molecular changes associated with the early stages of tumor transformation. We correlated these protein expression profiles with either cell polarity defects or cell progression along the epithelial-to-mesenchymal transition (EMT). We identified plakins, namely epiplakin (EPPK1), desmoplakin (DSP) and periplakin (PPL), that were downregulated in cells that had lost their epithelial polarity and also downregulated in cells that had progressed through EMT. We further tested them experimentally by knocking down their expression in a non-tumorigenic epithelial breast cell line (MCF10A). We demonstrated their causal role in the loss of polarity, as revealed by the misorientation of the nuclear centrosome vector. We also found that vimentin, a marker of EMT, was overexpressed in plakin knocked-down cells, suggesting that plakins may have both a structural and a regulatory role in maintaining the epithelial state.

cell biology↗

bioRxiv2023

COL7A1 expression improves prognosis prediction for patients with clear cell renal cell carcinoma atop of stage

Clear cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate, and treatment options that come with high costs and potential side effects correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. We used Kaplan-Meier curves and Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as Gene Set Enrichment Analysis to investigate genes that are co-expressed with COL7A1. COL7A1 expression was used to stratify patients into four groups of expression, where the 5-year survival probability of each group was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In-vitro knockdown of COL7A1 expression significantly impacted ccRCC cells ability to migrate and proliferate. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.

cancer biology↗

bioRxiv2022

Optimal microRNA sequencing depth to predict cancer patient survival with random forest and Cox models

(1) Backgroundtumor profiling enables patient survival prediction. The two essential parameters to be calibrated when designing a study based on tumor profiles from a cohort are the sequencing depth of RNA-seq technology and the number of patients. This calibration is carried out under cost constraints, and a compromise has to be found. In the context of survival data, the goal of this work is to benchmark the impact of the number of patients and of the sequencing depth of miRNA-seq and mRNA-seq on the predictive capabilities for both the Cox model with elastic net penalty and random survival forest. (2) Resultswe first show that the Cox model and random survival forest provide comparable prediction capabilities, with significant differences for some cancers. Second, we demonstrate that miRNA and/or mRNA data improve prediction over clinical data alone. mRNA-seq data leads to slightly better prediction than miRNA-seq, with the notable exception of lung adenocarcinoma for which the tumor miRNA profile shows higher predictive power. Third, we demonstrate that the sequencing depth of RNA-seq data can be reduced for most of the investigated cancers without degrading the prediction abilities, allowing the creation of independent validation sets at lower cost. Finally, we show that the number of patients in the training dataset can be reduced for the Cox model and random survival forest, allowing the use of different models on different patient subgroups. (3) AvailabilityR script is available at https://github.com/remyJardillier/Survival_seq_depth

bioinformatics↗

bioRxiv2020

Benchmark of lasso-like penalties in the Cox model for TCGA datasets reveal improved performance with pre-filtering and wide differences between cancers

MotivationPrediction of patient survival from tumor molecular omics data is a key step toward personalized medicine. With this aim, the databases available are growing, with the collection of various omics characterizations of patient tumors, together with their associated clinical outcomes for weeks to years of follow-up. Cox models with variable selection used with RNA profiling datasets are popular for identification of prognostic biomarkers and for clinical predictions. However, these models are confronted with the curse of dimensionality, as the number p of covariates (genes) can greatly exceed the number n of patients. To tackle this problem, variance-based pre-filtering and penalization methods are popular for dimension reduction. In the present paper, we study the impact of a pre-filtering step based on gene variability, and we evaluate the performance of the lasso penalization of the Cox model and four variants (i.e., elastic net, adaptive elastic net, ridge, univariate Cox) in terms of prediction, selection and stability. ResultsFirst, we show that the prediction capacity with the Cox penalties method is cancer dependent. Second, we develop a methodology to fix a threshold to filter out genes with low variability without losing prediction capacity. Third, we show that it is best not to use the Cox model to select prognostic biomarkers, as its false discovery proportion is always [≥] 50%. Finally, to predict overall survival, we can suggest the use of the ridge penalty, or the elastic net if a more parsimonious model is needed, after the pre-filtering step. AvailabilityWe provide the R script generated to reproduce all of the figures presented in this article. Supplementary informationSupplementary Figures and R scripts are available.

bioinformatics↗

bioRxiv2019

miRViz: a novel webserver application to visualize and interpret microRNA datasets

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the regulation of major pathways in eukaryotic cells through repression of their target genes at the post-transcriptional level1. While high-throughput approaches are broadly used to decipher the biological relevance of miRNAs, extraction of significant information from large miRNA datasets remains challenging. For example, sequencing technologies can quantify the relative expression of up to thousands of mature miRNAs under various experimental conditions. However, in such datasets, small subsets of miRNAs can often show significant differential expression, and deciding which one(s) should be further analyzed can prove difficult. Thus, the current challenge resides in objective analysis, interpretation and visualization of these large datasets, for which specifically suited methods are lacking.

genomics↗

bioRxiv2019

Biophysical properties of intermediate states of EMT outperform both epithelial and mesenchymal states.

Potential metastatic cells can dissociate from a primary breast tumor by undergoing an epithelial-to-mesenchymal transmission (EMT). Recent work has revealed that cells in intermediate states of EMT acquire an augmented capacity for tumor-cell dissemination. These states have been characterized by molecular markers, but the structural features and the cellular mechanisms that underlie the acquisition of their invasive properties are still unknown. Using human mammary epithelial cells, we generated cells in intermediate states of EMT through the induction of a single EMT-inducing transcription factor, ZEB1, and cells in a mesenchymal state by stimulation with TGF{beta}. In stereotypic and spatially-defined culture conditions, the architecture, internal organization and mechanical properties of cells in the epithelial, intermediate and mesenchymal state were measured and compared. We found that the lack of intercellular cohesiveness in epithelial and mesenchymal cells can be detected early by microtubule destabilization and the repositioning of the centrosome from the cell-cell junction to the cell center. Consistent with their high migration velocities, cells in intermediate states produced low contractile forces compared with epithelial and mesenchymal cells. The high contractile forces in mesenchymal cells powered a retrograde flow pushing the nucleus away from cell adhesion to the extracellular matrix. Therefore, cells in intermediate state had structural and mechanical properties that were distinct but not necessarily intermediate between epithelial and mesenchymal cells. Based on these observations, we found that a panel of triple-negative breast cancer lines had intermediate rather than mesenchymal characteristics suggesting that the structural and mechanical properties of the intermediate state are important for understanding tumor-cell dissemination.

cell biology↗