Biology subjects

Bitbol, A.-F.

Publications and source records attributed to Bitbol, A.-F..

At least 19 recordsLinked to original sources

bioRxiv2026

Hydrodynamic flow in the gut hinders the fixation of beneficial mutants but accelerates their invasion

Gut bacteria experience a spatially structured environment with hydrodynamic flow. Using a branching-process approach, we show that beneficial mutants arising randomly upon division have a reduced fixation probability compared to the well-mixed case. The suppression factor is set by the spatial heterogeneity of mutant fate across the gut. In contrast, the invasion of mutant lineages that avoid early stochastic extinction is accelerated in the gut compared to a matched well-mixed case. Stronger advective flow decreases fixation probabilities while accelerating mutant invasion.

biophysics↗

bioRxiv2026

Competition against the intestinal microbiota selects for pathoadaptive traits in ESBL E. coli

The intestinal tract is a reservoir for Extended-Spectrum {beta}-Lactamase (ESBL)-producing Escherichia coli. Asymptomatic gut colonization by these pathobionts represents a major risk for extraintestinal infections. Despite clinical relevance, the genetic basis of gut colonization by ESBL E. coli remains poorly understood. Here, we determined how the microbiota shapes the fitness landscape of diverse ESBL E. coli strains, defining the functional requirements for intestinal colonization. In microbiota-depleted hosts, colonization relies mostly on metabolic functions. In contrast, in mice harbouring a microbiota, pathoadaptive functions associated with adhesion and biofilm formation are dominant determinants of E. coli fitness, together with accessory virulence functions. Consistent with these observations, experimental evolution in mice reveals convergent adaptation of ESBL E. coli to the presence of a complex microbiota through enhanced adhesion. These findings establish the microbiota as a major ecological driver of pathoadaptation in antibiotic-resistant pathobionts.

microbiology↗

bioRxiv2026

Predicting host-pathogen interactions using a proteome-scale language model

ProteomeLM (Malbranke et al., 2025) is a proteome-scale language model trained on proteomes spanning the tree of life to reconstruct masked protein embeddings from proteome context within each species. Its attention coefficients capture protein-protein interactions without supervision. Here, we show that this capability extends to cross-species host-pathogen interactions (HPI) across ten human pathogen taxa spanning viruses and bacteria, and can be further improved with lightweight fine-tuning. We introduce ProteomeLM-HPI, a parameter-efficient adaptation via LoRA, trained on concatenated host-pathogen proteomes to reconstruct masked pathogen embeddings from host context. ProteomeLM-HPI involves two key design choices: asymmetric masking (pathogen-heavy masking) and blocked self-attention. Systematic ablations show that both choices contribute. To assess generalization, we introduce a strict cross-species benchmark enforcing pathogen-level hold-out and 40% sequence-identity filtering. On this benchmark, Proteome-HPI improves AUC on 9 out of 10 unseen pathogens.

bioinformatics↗

bioRxiv2026

Promotion of cooperation in deme-structured populations with growth-merging dynamics

The spatial structure of populations may promote the emergence and maintenance of cooperation. Cooperation in the prisoner's dilemma is favored under specific update rules in evolutionary graph theory models with one individual per node of a graph, but this effect vanishes in models with well-mixed demes connected by migrations under soft selection (each deme contributing independently of its productivity). In contrast, experiments and models involving cycles of growth, merging and dilution have shown that spatial structure can favor cooperation. Here, we reconcile these findings by studying deme-structured populations under growth-merging-dilution dynamics, corresponding to a clique (fully connected graph) under hard selection (each deme contributing in proportion to its productivity). We obtain analytical conditions for the cooperator fraction to increase during deterministic logistic growth, and to increase on average under dilution-growth-merging cycles, in the weak selection regime, where fitness differences between genotypes are small. Furthermore, we analytically express the fixation probability of cooperators under weak selection, yielding a criterion for cooperators to have a higher fixation probability than neutral mutants. Finally, numerical simulations show that stochastic growth further promotes cooperation. Overall, hard selection is essential for cooperation to be promoted in deme-structured populations.

evolutionary biology↗

bioRxiv2025

ProteomeLM: A proteome-scale language model allowing fast prediction of protein-protein interactions and gene essentiality across taxa

Language models trained on biological sequences are advancing inference tasks from the scale of single proteins to that of genomic neighborhoods. Here, we introduce ProteomeLM, a transformer-based language model that uniquely operates on entire proteomes from species spanning the tree of life. ProteomeLM is trained to reconstruct masked protein embeddings using the whole proteomic context, yielding contextualized protein representations that reflect proteome-scale functional constraints. Notably, ProteomeLMs attention coefficients encode protein-protein interactions (PPI), despite being trained without interaction labels. Furthermore, it enables interactome-wide PPI screening that is substantially more accurate, and orders of magnitude faster, than amino-acid coevolution-based methods. We further develop ProteomeLM-PPI, a supervised model that combines ProteomeLM embeddings and attention coefficients to achieve state-of-the-art PPI prediction across benchmarks and species. Finally, we introduce ProteomeLM-Ess, a supervised gene essentiality predictor that generalizes across diverse taxa. Our results demonstrate the potential of proteome-scale language models for addressing function and interactions at the organism level. Significance statementPredicting protein interactions and functions is a key challenge in biology. Although deep learning-based language models are advancing the analysis of individual protein sequences and of genomic neighborhoods, they struggle to capture properties involving all the proteins expressed in a cell, such as protein-protein interactions (PPI) and gene essentiality. We present ProteomeLM, a language model that reasons on entire proteomes across diverse species. ProteomeLM captures PPI without supervision, and enables more accurate and faster screening of entire interactomes than current sequence-based approaches. ProteomeLM also delivers state-of-the-art supervised PPI prediction, and improves supervised prediction of gene essentiality compared to protein language models. These results demonstrate the potential of proteome-scale language models to reveal system-level organization and functional relationships.

bioinformatics↗

bioRxiv2025

Environment heterogeneity creates fast amplifiers of natural selection in graph-structured populations

Complex spatial structure, with partially isolated subpopulations, and environment heterogeneity, such as gradients in nutrients, oxygen, and drugs, both shape the evolution of natural populations. We investigate the impact of environment heterogeneity on mutant fixation in spatially structured populations with demes on the nodes of a graph. When migrations between demes are frequent, we find that environment heterogeneity can amplify natural selection and simultaneously accelerate mutant fixation and extinction, thereby fostering the quick fixation of beneficial mutants. We demonstrate this effect in the star graph, and more strongly in the line graph. We show that amplification requires mutants to have a stronger fitness advantage in demes with stronger migration outflow, and that this condition allows amplification in more general graphs. As a baseline, we consider circulation graphs, where migration inflow and outflow are equal in each deme. In this case, environment heterogeneity has no impact to first order, but increases the fixation probability of beneficial mutants to second order. Finally, when migrations between demes are rare, we show that environment heterogeneity can also foster amplification of selection, by allowing demes with sufficient mutant advantage to become refugia for mutants.

evolutionary biology↗

bioRxiv2025

Out-of-equilibrium noise facilitates inference from protein sequence data

Homologous proteins have similar three-dimensional structures and biological functions that shape their sequences. The resulting coevolution-driven correlations underlie methods from Potts models to AlphaFold, which infer protein structure and function from sequences. Using a minimal model, we show that fluctuating selection strength and the onset of new selection pressures improve coevolutionbased inference of structural contacts. Our conclusions extend to realistic synthetic data and to the inference of interaction partners. Out-of-equilibrium noise arising from ubiquitous variations in natural selection thus enhances, rather than hinders, the success of inference from protein sequences.

biophysics↗

bioRxiv2025

RAG-ESM: Improving pretrained protein language models via sequence retrieval

AO_SCPLOWBSTRACTC_SCPLOWProtein language models are significantly advancing the modeling of sequence-function relationships. However, most of them are not directly informed of homology and evolutionary relationships between protein sequences. Here, we propose a method to make them homology-aware. We introduce RAG-ESM, a retrieval-augmented framework that allows to condition pretrained ESM2 protein language models on homologous sequences, using a minimal number of additional cross-attention parameters and minimal computational cost. We show that RAG-ESM models outperform larger ESM2 models for masked amino acid prediction. We find that sequence alignment capabilities spontaneously emerge in specific cross-attention heads of RAG-ESM. By using a discrete diffusion objective for training, and by conditioning on homologs during inference, RAG-ESM reaches state-of-the-art performance for conditional protein sequence generation and motif scaffolding, among sequence-based models. Our method thus possesses strong potential for scalable, efficient and controlled protein engineering.

bioinformatics↗

bioRxiv2025

Rubisco is slow across the tree of life

Rubisco is the main gateway through which inorganic carbon enters the biosphere, catalyzing the vast majority of carbon fixation on Earth. This pivotal enzyme has long been observed to be kinetically constrained. Yet, this impression is based on kinetic measurements heavily focused on eukaryotic rubiscos, a rather conserved group of low genetic diversity. Moreover, the fastest rubiscos that we know of so far were found among the sparsely sampled prokaryotes. Could there be yet faster rubiscos among the uncharted regions of rubiscos phylogenetic diversity? Here, we perform a characterization of more than 250 rubiscos from a wide range of bacteria and archaea, thereby doubling the coverage of the diversity of this key enzyme. We assess the distribution of the carboxylation rates at saturating levels of CO2, and establish that rubisco is a relatively slow enzyme across the tree of life, never exceeding {approx}30 reactions per second at 30{degrees}C. We show that relatively faster subclades share similar evolutionary contexts, involving micro-oxygenic environments or a CO2 concentrating mechanism. Leveraging a simple machine learning model trained on this dataset, we predict the carboxylation rate for all {approx}68,000 sequenced rubisco variants found in nature to date. This study provides the largest and most diverse dataset of natural variants for an enzyme and their associated rates, establishing a solid benchmark for future efforts to predict catalytic rates from sequence data. SignificanceDiscovering a fast carboxylating rubisco has been a long-standing challenge in the scientific community, given its potential impact on sustainable food and fuel production. Yet, only a small fraction of rubiscos natural diversity has been kinetically characterized. Here, we present a large-scale kinetic survey covering the entire spectrum of rubiscos diversity found in nature. Focusing on genetic clusters with above-average rates, we show that rubiscos catalytic rate does not exceed {approx}30 reactions per second at 30{degrees}C. Supported by a machine-learning predictive model, we extend this finding to all sequenced natural variants. Our study provides the most comprehensive kinetic dataset for a single enzyme to date.

systems biology↗

bioRxiv2024

Impact of spatial structure on early and long-term adaptation in rugged fitness landscapes

We study how rugged fitness landscapes are explored by spatially structured populations with demes on the nodes of a graph, connected by migrations. In the weak mutation and rare migration regime, we find that, in most landscapes, migration asymmetries associated with some suppression of natural selection allow the population to reach higher fitness peaks first. In this sense, suppression of selection can make early adaptation more efficient. However, the time it takes to reach the first fitness peak is then increased. We also find that suppression of selection tends to enhance finite-size effects. Finite structures can adapt more efficiently than very large ones, especially in high-dimensional fitness landscapes. We extend our study to frequent migrations, suggesting that our conclusions hold in this regime. We then investigate the impact of spatial structure with rare migrations on long-term evolution by studying the steady state of the population with weak mutation, and introducing an associated steady-state effective population size. We find that suppression of selection is associated to small steady-state effective population sizes, and thus to small average steady-state fitnesses.

evolutionary biology↗

bioRxiv2024

DiffPaSS - High-performance differentiable pairing of protein sequences using soft scores

Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amino acid usage due to the need to maintain complementary interaction interfaces. Thus, the problem of finding interacting pairs can be formulated as searching for a pairing of sequences that maximizes a sequence similarity or a coevolution score. Several methods have been developed to address this problem, applying different approximate optimization methods to different scores. We introduce DiffPaSS, a differentiable framework for flexible, fast, and hyperparameter-free optimization for pairing interacting biological sequences, which can be applied to a wide variety of scores. We apply it to a benchmark prokaryotic dataset, using mutual information and neighbor graph alignment scores. DiffPaSS outperforms existing algorithms for optimizing the same scores. We demonstrate the usefulness of our paired alignments for the prediction of protein complex structure. DiffPaSS does not require sequences to be aligned, and we also apply it to non-aligned sequences from T cell receptors.

bioinformatics↗

bioRxiv2024

Spatial structure facilitates evolutionary rescue by cost-free drug resistance

Bacterial populations often have complex spatial structures, which can impact their evolution. Here, we study how spatial structure affects the evolution of antibiotic resistance in a bacterial population. We consider a minimal model of spatially structured populations where all demes (i.e., subpopulations) are identical and connected to each other by identical migration rates. We show that spatial structure can facilitate the survival of a bacterial population to antibiotic treatment, starting from a sensitive inoculum. Specifically, the bacterial population can be rescued if antibiotic resistant mutants appear and are present when drug is added, and spatial structure can impact the fate of these mutants and the probability that they are present. Indeed, the probability of fixation of neutral or deleterious mutations providing drug resistance is increased in smaller populations. This promotes local fixation of resistant mutants in the structured population, which facilitates evolutionary rescue by drug resistance in the rare mutation regime. Once the population is rescued by resistance, migrations allow resistant mutants to spread in all demes. Our main result that spatial structure facilitates evolutionary rescue by antibiotic resistance extends to more complex spatial structures, and to the case where there are resistant mutants in the inoculum. Author SummaryAntibiotic resistance is a major challenge, since bacteria tend to adapt to the drugs they are subjected to. Understanding what conditions facilitate or hinder the appearance and spread of resistance in a bacterial population is thus of strong interest. Most natural microbial populations have complex spatial structures. This includes host-associated microbiota, such as the gut microbiota. Here, we show that spatial structure can facilitate the survival of a bacterial population to antibiotic treatment, by promoting the presence of resistant bacteria. Indeed, neutral or deleterious mutants giving resistance can take over small populations more easily than large ones, thanks to the increased importance of fluctuations in small populations. Resistant mutants can then spread to the whole structured population. Thus, population spatial structure can be a source of antibiotic treatment failure. This effect of spatial structure is generic and does not require environment heterogeneity.

biophysics↗

bioRxiv2024

Intra- and inter-chain contacts determine TCR specificity: applying protein co-evolution methods to TCRαβ pairing

The six complementarity determining regions (CDRs) of the T cell receptor (TCR) form multiple contacts with cognate peptide and major histocompatibility complex, thus determining antigen specificity. However, the contacts between the CDRs themselves are less understood. We perform a systematic study of all available TCR structures, and identify consistent patterns of intra- and inter-chain CDR contacts. We further show that the sequences of paired TCR and TCR{beta} are not independent within sets of antigen-specific TCRs, for most epitopes. We quantify this sequence restriction using a mutual information framework. Co-evolution models can achieve some de novo prediction of TCR/TCR{beta} pairing, without using a training set of known pairs. The conserved pattern of CDR amino acid contacts, and the mutual sequence constraints between antigen-specific sets of T cell receptor and {beta} chains could play an important role in shaping the antigen-specific T cell repertoire.

immunology↗

bioRxiv2024

ProtMamba: a homology-aware but alignment-free protein state space model

MotivationProtein language models are enabling advances in elucidating the sequence-to-function mapping, and have important applications in protein design. Models based on multiple sequence alignments efficiently capture the evolutionary information in homologous protein sequences, but multiple sequence alignment construction is imperfect. ResultsWe present ProtMamba, a homology-aware but alignment-free protein language model based on the Mamba architecture. In contrast with attention-based models, ProtMamba efficiently handles very long context, comprising hundreds of protein sequences. It is also computationally efficient. We train ProtMamba on a large dataset of concatenated homologous sequences, using two GPUs. We combine autoregressive modeling and masked language modeling through a fill-in-the-middle training objective. This makes the model adapted to various protein design applications. We demonstrate ProtMambas usefulness for sequence generation, motif inpainting, fitness prediction, and modeling intrinsically disordered regions. For homolog-conditioned sequence generation, ProtMamba outperforms state-of-the-art models. ProtMambas competitive performance, despite its relatively small size, sheds light on the importance of long-context conditioning. AvailabilityA Python implementation of ProtMamba is freely available in our GitHub repository: https://github.com/Bitbol-Lab/ProtMamba-ssm.

bioinformatics↗

bioRxiv2024

Impact of phylogeny on the inference of functional sectors from protein sequence data

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information. Author SummaryProteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences.

bioinformatics↗

bioRxiv2024

Mutant fate in spatially structured populations on graphs: connecting models to experiments

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population. Author SummaryPredicting how mutations spread through a population and eventually take over is important for understanding evolution. Complex spatial structures are ubiquitous in natural microbial populations, and can impact the fate of mutants. Theoretical models have been developed to describe this effect. They predict that some spatial structures have mutant fixation probabilities that differ from those of well-mixed populations. Experiments are beginning to probe these effects in the laboratory. However, there is a disconnect between models and experiments, because they consider different conditions. In this work, we connect them through a new model that closely matches experimental conditions. We analyze recent experiments and propose new ones that should allow testing the effects of complex population spatial structures on mutant fate.

biophysics↗

bioRxiv2023

Interactions between pili affect the outcome of bacterial competition driven by the type VI secretion system

The bacterial type VI secretion system (T6SS) is a widespread, kin-discriminatory weapon capable of shaping microbial communities. Due to the systems dependency on contact, cellular interactions can lead to either competition or kin protection. Cell-to-cell contact is often accomplished via surface-exposed type IV pili (T4P). In Vibrio cholerae, these T4P facilitate specific interactions when the bacteria colonize natural chitinous surfaces. However, it has remained unclear whether and, if so, how these interactions affect the bacteriums T6SS-mediated killing. In this study, we demonstrate that pilus-mediated interactions can be harnessed to reduce the population of V. cholerae under liquid growth conditions in a T6SS-dependent manner. We also show that the naturally occurring diversity of pili determines the likelihood of cell-to-cell contact and, consequently, the extent of T6SS-mediated competition. To determine the factors that enable or hinder the T6SSs targeted reduction of competitors carrying pili, we developed a physics-grounded computational model for autoaggregation. Collectively, our research demonstrates that T4P involved in cell-to-cell contact can impose a selective burden when V. cholerae encounters non-kin cells that possess an active T6SS. Additionally, our study underscores the significance of T4P diversity in protecting closely related individuals from T6SS attacks through autoaggregation and spatial segregation.

microbiology↗

bioRxiv2023

Evolution of cooperation in deme-structured populations on graphs

Understanding how cooperation can evolve in populations despite its cost to individual cooperators is an important challenge. Models of spatially structured populations with one individual per node of a graph have shown that cooperation, modeled via the prisoners dilemma, can be favored by natural selection. These results depend on microscopic update rules, which determine how birth, death and migration on the graph are coupled. Recently, we developed coarse-grained models of spatially structured populations on graphs, where each node comprises a well-mixed deme, and where migration is independent from division and death, thus bypassing the need for update rules. Here, we study the evolution of cooperation in these models in the rare migration regime, within the prisoners dilemma. We find that cooperation is not favored by natural selection in these coarsegrained models on graphs where overall deme fitness does not directly impact migration from a deme. This is due to a separation of scales, whereby cooperation occurs at a local level within demes, while spatial structure matters between demes.

biophysics↗