Biology subjects

Cabeli, V.

Publications and source records attributed to Cabeli, V..

9 recordsLinked to original sources

bioRxiv2026

The Pentose Phosphate Pathway Regulates Myelo-Lymphoid Lineage Specification

Following infection, hematopoietic stem and progenitor cells (HSPCs) support immunity by increasing the rate of innate immune cell production but the metabolic cues that guide this process are unknown. To address this question, we combined in situ RNA barcoding and metabolomics approaches to perform metabolic state-fate mapping in vivo. This approach revealed a subset of myeloid-biased HSPCs that express a distinct set of metabolic enzymes and transporters as well as the surface marker CD62L. Metabolically, CD62Lhigh HSPCs have differential activity of the pentose phosphate pathway (PPP), OXPHOS and translation, as well as differential levels of S-adenosylmethionine (SAM) cycle metabolites associated with epigenetic modifications. Inhibition of the PPP skews HSPC lineage fate decisions by disrupting myeloid associated enhancers, while simultaneously increasing enhancer activity for the master B-lymphoid regulator Ikaros. In vivo, overexpression of glucose-6-phosphate dehydrogenase, a rate limiting enzyme of the PPP, skewed HSPC output from B-lymphocytes. In summary, our data shows that HSPCs undergo significant metabolic changes to facilitate the bioenergetic and epigenetic demands of myeloid versus lymphoid lineage specification. We highlight a key role for the pentose phosphate pathway which modulates myeloid rather than lymphoid commitment by shaping the HSPC enhancer landscape, providing proof of principle that HSPC metabolism can be targeted to modulate immune system dynamics.

immunology↗

bioRxiv2026

BOTANIC-1: a series of long-context plant genomic foundation models in the agentic era

The development of climate-resilient crops would be greatly accelerated by models able to reason directly over plant genomic sequences and to pinpoint trait-associated regions or loci. Anticipating the impact of DNA base changes (variants) remains challenging, and understanding regulatory mechanisms is still an active area of research. Through self-supervised training on unannotated genomic data, genomic language models (gLMs) can learn DNA syntax and grammar that go beyond current annotations, thus complementing standard bioinformatics analyses that rely on rules established by decades of genomics research. Here we present our agent-powered Model Factory and its first outputs: the Botanic1 family of gLMs designed for plant research, which operates reliably on sequences from hundreds of base pairs up to 128 kbp. These models outperform all generalist and plant-specific gLMs (as well as specialised baselines) on one of the largest sets of plant genomics evaluation tasks reported to date, at a much smaller budget than concurrent models. Mechanistic interpretability analysis identifies features associated with biologically meaningful sequence properties including coding region boundaries and splice site motifs, demonstrating that these models are a source of biological insight beyond their benchmark performance. Finally, because a gLM only becomes practically useful when embedded in a broader workflow, we integrate Botanic1 as a specialised genomic layer callable by a generalist large language model (LLM) agent, illustrating how such hybrid systems could accelerate plant biology research. To support the plant genomics research community, we release the four Botanic1 models, their pre-training corpus and the trained sparse autoencoder for research use at https://huggingface.co/spaces/living-models/botanic1-report.

plant biology↗

bioRxiv2026

BOTANIC-0: a series of foundation models for plant genomic data

Genomic language models (gLMs) have emerged as a powerful paradigm for learning regulatory biology directly from DNA sequence. Here, we introduce Botanic0{paragraph}, a family of plant genomic foundation models spanning 100M to 1B parameters and pretrained on 43 phylogenetically diverse plant genomes. The Botanic0-S, Botanic0-M, and Botanic0-L models form the first generation of a long-term research initiative, dedicated to advancing crop improvement research, genotype-to-phenotype modeling, and sequence-based genome editing. The architecture, pre-training pipeline and pre-training dataset of Botanic0 follow the seminal work of [1]. Across a broad suite of genomic and genetic prediction tasks, including regulatory element annotation, gene expression inference, and variant effect prediction, Botanic0 models achieve performance competitive with state-of-the-art foundation models, both in zero-shot settings and after fine-tuning. Scaling analyses reveal consistent improvements in predictive power with increased model capacity, highlighting the benefits of large-model pretraining for plant genomics. This work establishes our ability to train foundation models at scale, and lays the foundation for the next generations of models to come. To support reproducible research and community benchmarking, we release all Botanic0 models at https://huggingface.co/living-models/models.

plant biology↗

bioRxiv2026

Comprehensive benchmarking of batch integration methods for spatial transcriptomics using a large-scale cancer atlas

Spatial transcriptomics (ST) enables spatially-resolved gene expression measurement, providing insights into tissue architecture and disease biology. However, batch effects from sequencing protocols, sample processing, and other technical factors can confound biological signals. Although batch correction has been extensively studied in single-cell transcriptomics, spatial integration methods lack rigorous benchmarking on large real-world datasets. This study benchmarks 11 representation-learning methods across three categories--linear, graph-based and probabilistic methods using Owkins MOSAIC Window dataset, a large-scale spatial transcriptomics atlas of human cancers. Methods are evaluated across three criteria: batch correction, biological conservation, and spatial conservation. We also propose a new integration metric to assess robustness of representations to domain shifts and generalizability to unseen samples. Probabilistic methods (scVIVA, scVI) outperform linear and graph-based approaches in batch correction and biological conservation. On the other hand Graph-based methods excelled at spatial conservation but underperformed in batch integration. Out-of-distribution evaluation reveals that sophisticated methods show reduced peformance on unseen samples while linear methods maintain robust generalization, highlighting trade-offs between integration quality and generalizability that should guide method selection for real-world applications.

cancer biology↗

bioRxiv2024

Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients.

Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of YAP1 and TEAD-family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications. Therefore, identifying patients with a dysregulated Hippo pathway is key to enhancing treatment impact. Although recent studies have derived RNAseq-based signatures, there remains a need for a reproducible and cost-effective method to measure the pathway activation. In recent years, deep learning applied to histology slides have emerged as an effective way to predict molecular information from a data modality available in clinical routine. Here, we trained models to predict YAP1/TEAD activity from H&E-stained histology slides in multiple cancers. The robustness of our approach was assessed in seven independent validation cohorts. Finally, we showed that histological markers of disease aggressiveness were associated with dysfunctional Hippo signaling.

bioinformatics↗

bioRxiv2024

CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

Live-cell microscopy routinely provides massive amount of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell-cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer associated fibroblasts directly inhibit cancer cell apoptosis, independently from anti-cancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.

systems biology↗

bioRxiv2024

Robust Evaluation of Deep Learning-based Representation Methods for Survival and Gene Essentiality Prediction on Bulk RNA-seq Data

Deep learning (DL) has shown potential to provide powerful representations of bulk RNA-seq data in cancer research. However, there is no consensus regarding the impact of design choices of DL approaches on the performance of the learned representation, including the model architecture, the training methodology and the various hyperparameters. To address this problem, we evaluate the performance of various design choices of DL representation learning methods using TCGA and DepMap pan-cancer datasets, and assess their predictive power for survival and gene essentiality predictions. We demonstrate that non DL-based baseline methods achieve comparable or superior performance compared to more complex models on survival predictions tasks. DL representation methods, however, are the most efficient to predict the gene essentiality of cell lines. We show that auto-encoders (AE) are consistently improved by techniques such as masking and multi-head training. Our results suggest that the impact of DL representations and of pre-training are highly task- and architecture-dependent, highlighting the need for adopting rigorous evaluation guidelines. These guidelines for robust evaluation are implemented in a pipeline made available to the research community.

bioinformatics↗

bioRxiv2023

Metabolically Primed Multipotent Hematopoietic Progenitors Fuel Innate Immunity

Following infection, hematopoietic stem and progenitor cells (HSPCs) support immunity by increasing the rate of innate immune cell production but the metabolic cues that guide this process are unknown. To address this question, we developed MetaFate, a method to trace the metabolic expression state and developmental fate of single cells in vivo. Using MetaFate we identified a gene expression program of metabolic enzymes and transporters that confers differences in myeloid differentiation potential in a subset of HSPCs that express CD62L. Using single-cell metabolic profiling, we confirmed that CD62Lhigh myeloid-biased HSPCs have an increased dependency on oxidative phosphorylation and glucose metabolism. Importantly, metabolism actively regulates immune-cell production, with overexpression of the glucose-6-phosphate dehydrogenase enzyme of the pentose phosphate pathway skewing MPP output from B-lymphocytes towards the myeloid lineages, and expansion of CD62Lhigh HSPCs occurring to support emergency myelopoiesis. Collectively, our data reveal the metabolic cues that instruct innate immune cell development, highlighting a key role for the pentose phosphate pathway. More broadly, our results show that HSPC metabolism can be manipulated to alter the cellular composition of the immune system.

immunology↗

bioRxiv2022

PyDESeq2: a python package for bulk RNA-seq differential expression analysis

SummaryWe present PyDESeq2, a python implementation of the DESeq2 workflow for differential expression analysis on bulk RNA-seq data. This implementation achieves better precision, allows speed improvements on large datasets, as shown in experiments on TCGA data, and can be more easily interfaced with modern python-based data science tools. Availability and ImplementationPyDESeq2 is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/owkin/PyDESeq2. Contactboris.muzellec@owkin.com

bioinformatics↗