Biology subjects

Behdenna, A.

Publications and source records attributed to Behdenna, A..

7 recordsLinked to original sources

bioRxiv2026

CMS4-focused multi-omic integration enhances antigen target identification in colorectal cancer

Colorectal cancer (CRC) remains a major cause of cancer mortality, with limited options for poor-prognosis subtypes such as CMS4. Antigen-targeted therapies show promise but tend to fail due to inadequate target selection and insufficient patient stratification. Effective prioritization requires large harmonized data capturing CRC heterogeneity - a resource that is currently lacking. To address this need, we built a harmonized multi-omic CRC knowledge base and applied a scalable discovery pipeline to identify antigen targets specifically associated with CMS4 biology and with strong translational potential. We constructed a harmonized CRC atlas by integrating 79 transcriptomics datasets (5,033 tumors, 161 normal samples) using proprietary AI-powered data scouting, integration, and curation technologies. Consensus Molecular Subtypes (CMS) were inferred to capture CMS4-specific expression patterns and this atlas was then combined with 3 bulk RNA-seq reference datasets, 2 single-cell atlases, and 8 protein annotation databases to form a unified multi-omic CRC knowledge base of unmatched scale. From this integrated system, we identified genes differentially expressed in CMS4 patients encoding druggable cell-surface proteins, which we then prioritized using a weighted efficacy- and safety-based scoring model. We identified 236 CMS4-enriched candidates, including 124 not detectable at the CRC-wide level, demonstrating the added resolution gained through subtype stratification. Recovery of known investigational CRC (LGR5, MET, TACSTD2) and CMS4-associated targets of clinical emerging interest (PDGFRB, ALK5/TGFBR1, FAP) support the biological and methodological validity of our approach. Benchmarking against thresholds from FDA-approved pan-cancer targets and terminated trials identified 32 candidates with comparable or superior therapeutic profiles. Among these, 11 were enriched for CMS4-defining pathways, including epithelial-mesenchymal transition, angiogenesis, and stromal invasion, and 5 showed strong profile similarity to established CRC and CMS4 benchmarks. After extensive data exploration, particularly promising candidates were shortlisted for further validation. This work shows that CMS4-focused molecular stratification, when combined with an unprecedentedly large harmonized multi-omic knowledge base, yields a refined set of antigen candidates with enhanced specificity, safety, and biological relevance. The prioritized targets illustrate the power of subtype-resolved discovery to uncover clinically actionable insights. Our pipelines modular design can extend to other tumor contexts, offering a robust foundation for accelerating targeted therapy development.

cancer biology↗

bioRxiv2024

Bulk Transcriptomic Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python

We introduce InMoose, an open-source Python environment aimed at omic data analysis. We illustrate its capabilities for bulk transcriptomic data analysis. Due to its wide adoption, Python has grown as a de facto standard in fields increasingly important for bioinformatic pipelines, such as data science, machine learning, or artificial intelligence (AI). As a general-purpose language, Python is also recognized for its versatility and scalability. InMoose aims at bringing state-of-the-art tools, historically written in R, to the Python ecosystem. Our intent is to provide a drop-in replacement for R tools, so our approach focuses on the faithfulness to the original tools outcomes. The first development phase has focused on bulk transcriptomic data, with current capabilities encompassing data simulation, batch effect correction, and differential analysis and meta-analysis.

bioinformatics↗

bioRxiv2024

Differential Expression Analysis with InMoose, the Integrated Multi-Omic Open-Source Environment in Python

We present the differential expression features of InMoose, a Python implementation of R tools limma, edgeR, and DESeq2. We experimentally show that InMoose stands as a drop-in replacement for those tools, with nearly identical results. This ensures reproducibility when interfacing both languages in bioinformatic pipelines. InMoose is an open source software released under the GPL3 license, available at www.github.com/epigenelabs/inmoose and https://inmoose.readthedocs.io.

bioinformatics↗

bioRxiv2024

Dynamics of influenza transmission in vampire bats revealed by longitudinal monitoring and a large-scale anthropogenic perturbation

Interrupting pathogen transmission between species is a priority strategy to mitigate zoonotic threats. However, avoiding counterproductive interventions requires knowing animal reservoirs of infection and the dynamics of transmission within them, neither of which are easily ascertained from the cross-sectional surveys which currently dominate investigations into newly discovered viruses. We used biobanked sera and metagenomic data to reconstruct the transmission of recently discovered bat-associated influenza virus (BIV) over 12 years in three zones of Peru. Mechanistic models fit under a Bayesian framework, which enabled joint inference from serological and molecular data, showed that common vampire bats maintain BIV independently of the currently assumed fruit bat reservoir through immune waning and seasonal transmission pulses. A large-scale vampire bat cull targeting rabies incidentally halved BIV transmission, confirming vampire bats as maintenance hosts. Our results show how combining field studies, perturbation responses and multi-data type models can elucidate pathogen dynamics in nature and reveal pathogen-dependent effects of interventions.

ecology↗

bioRxiv2020

A minimal yet flexible likelihood framework to assess correlated evolution

An evolutionary process is reflected in the sequence of changes of any trait (e.g. morphological, molecular) through time. Yet, a better understanding of evolution would be procured by characterizing correlated evolution, or when two or more evolutionary processes interact. Many previously developed parametric methods often require significant computing time as they rely on the estimation of many parameters. Here we propose a minimal likelihood framework modelling the joint evolution of two traits on a known phylogenetic tree. The type and strength of correlated evolution is characterized by few parameters tuning mutation rates of each trait and interdependencies between these rates. The framework can be applied to study any discrete trait or character ranging from nucleotide substitution to gain or loss of a biological function. More specifically, it can be used to 1) test for independence between two evolutionary processes, 2) identify the type of interaction between them and 3) estimate parameter values of the most likely model of interaction. In its current implementation, the method takes as input a phylogenetic tree together with mapped discrete evolutionary events on it and then maximizes the likelihood for one or several chosen scenarios. The strengths and limits of the method, as well as its relative power when compared to a few other methods, are assessed using both simulations and data from 16S rRNA sequences in a sample of 54 {gamma}-enterobacteria. We show that even with datasets of fewer than 100 species, the method performs well in parameter estimation and in the selection of evolutionary scenario.

evolutionary biology↗

bioRxiv2020

Predicting the presence and titer of rabies virus neutralizing antibodies from low-volume serum samples in low-containment facilities

Serology is a core component of the surveillance and management of viral zoonoses. Virus neutralization tests are a gold standard serological diagnostic, but requirements for large volumes of serum and high biosafety containment can limit widespread use. Here, focusing on Rabies lyssavirus, a globally important zoonosis, we developed a pseudotype micro-neutralization rapid fluorescent focus inhibition test (pmRFFIT) that overcomes these limitations. Specifically, we adapted an existing micro-neutralization test to use a green fluorescent protein-tagged murine leukemia virus pseudotype in lieu of pathogenic rabies virus, reducing the need for specialized reagents for antigen detection and enabling use in low-containment laboratories. We further used statistical analysis to generate rapid, quantitative predictions of the probability and titer of rabies virus neutralizing antibodies from microscopic imaging of neutralization outcomes. Using 47 serum samples from domestic dogs with neutralizing antibody titers estimated using the fluorescent antibody virus neutralization test (FAVN), pmRFFIT showed moderate sensitivity (78.79%) and high specificity (84.62%). Despite small conflicts, titer predictions were correlated across tests repeated on different dates both for dog samples (r = 0.93), and for a second dataset of sera from wild common vampire bats (r = 0.72, N = 41), indicating repeatability. Our test uses a starting volume of 3.5 L of serum, estimates titers from a single dilution of serum rather than requiring multiple dilutions and end point titration, and may be adapted to target neutralizing antibodies against alternative lyssavirus species. The pmRFFIT enables high-throughput detection of rabies virus neutralizing antibodies in low-biocontainment settings and is suited to studies in wild or captive animals where large serum volumes cannot be obtained.

immunology↗

bioRxiv2020

pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods

BackgroundVariability in datasets is not only the product of biological processes: they are also the product of technical biases. ComBat and ComBat-Seq are among the most widely used tools for correcting those technical biases, called batch effects, in, respectively, microarray and RNA-Seq expression data. ResultsIn this technical note, we present a new Python implementation of ComBat and ComBat-Seq. While the mathematical framework is strictly the same, we show here that our implementations: (i) have similar results in terms of batch effects correction; (ii) are as fast or faster than the original implementations in R and; (iii) offer new tools for the bioinformatics community to participate in its development. pyComBat is implemented in the Python language and is distributed under GPL-3.0 (https://www.gnu.org/licenses/gpl-3.0.en.html) license as a module of the inmoose package. Source code is available at https://github.com/epigenelabs/inmoose and Python package at https://pypi.org/project/inmoose. ConclusionsWe present a new Python implementation of state-of-the-art tools ComBat and ComBat-Seq for the correction of batch effects in microarray and RNA-Seq data. This new implementation, based on the same mathematical frameworks as ComBat and ComBat-Seq, offers similar power for batch effect correction, at reduced computational cost. Contactakpeli@epigenelabs.com

bioinformatics↗