Biology subjects

Moreau, Y.

Publications and source records attributed to Moreau, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

When batch correction corrupts gene expression: uncovering distortions in correlation structures

Batch correction is essential for integrating datasets and enabling population-level insights into health and disease. Embedding-based approaches are among the most widely used solutions, but here we highlight a critical, overlooked limitation: these methods can distort feature-to-feature (e.g., gene-gene) relationships, potentially undermining downstream analyses. We investigate this issue and introduce a novel metric to quantify it.

bioinformatics↗

bioRxiv2026

LGTM: Gaussian Process Modulated Neural Topic Modeling for Longitudinal Microbiome

Longitudinal microbiome data are key to understanding the dynamics of microbial communities and their relationships with the host and environment. However, analysis of such data is challenging due to high dimensionality, compositionality, irregular sampling and temporal dependencies on external covariates. Existing analytical approaches typically address only subsets of these challenges, limiting their ability to yield biologically interpretable insights. We introduce LGTM, a probabilistic modeling framework that combines flexible non-linear longitudinal modeling with interpretable topic-based representations of the microbiome. LGTM simultaneously identifies microbial co-abundance patterns ("topics") and models how their proportions change over time and in relation to host and environmental covariates. Using multiple longitudinal human gut microbiome datasets, we demonstrate that LGTM identifies diverse microbial topics whose major patterns are reproducible across runs, while achieving competitive performance in imputation and forecasting tasks. A key strength of the framework is its interpretability: LGTM yields microbial topics with biologically interpretable taxonomic compositions and directly quantifies associations between covariates and microbial dynamics. LGTM is available at https://github.com/yuanx749/lgtm.

microbiology↗

bioRxiv2026

DESPOT: Direction-Enhanced Scoring POTentials

Knowledge-based potentials (KBPs) remain among the most reliable and interpretable scoring functions for protein-ligand interactions, yet most share two structural limitations. They assume that the space around each protein atom is isotropic, and their interaction-conditioned reference state cannot represent regions of space that are preferentially left empty. We introduce DESPOT (Direction-Enhanced Scoring POTentials), an all-atom anisotropic KBP that overcomes both. DESPOT classifies atoms into isotropic, axially symmetric, and fully anisotropic symmetry classes from their hybridization and bonding environment, and discretizes the surrounding interaction space using the according symmetry. By adopting a positionally averaged reference state and using a void ligand atom type, it learns, for every point around a protein atom, the probability that the point is occupied by a given ligand atom type or preferentially left empty - a ligand-independent description that naturally encodes steric exclusion. This occupancy-conditioned potential captures the precise, atom-level placement of ligand atoms; we pair it with a complementary geometry-conditioned, residue-level formulation (DESPOT-screen, in the spirit of KORP-PL) and combine the two inverse-Boltzmann scores into a consensus score, DESPOT-combo. Derived from 110,943 curated, energy-minimized complexes drawn from the CROWN database and evaluated on the CASF-2016 benchmark, DESPOT achieves competitive scoring power (Pearson r = 0.61), while DESPOT-combo attains best-in-class docking power (89.5% top-1 success); all anisotropic DESPOT variants significantly outperform isotropic KBPs and established empirical scoring functions in virtual screening. Anisotropy is decisive for rejecting geometrically implausible poses, and uniting the atom-level precision of DESPOT with the implicit flexibility tolerance of the residue-level score yields the most consistent performance across tasks. Because the same occupancy-conditioned potentials can be evaluated over an empty grid, DESPOT generates molecular interaction fields as well, unifying pose scoring with direction-aware binding-site characterization within a single interpretable model.

bioinformatics↗

bioRxiv2026

CROWN: Curated Repository Of Well-resolved Noncovalent interactions

The development of machine-learning models for protein-ligand interactions is constrained by the quality and diversity of the available structural data. Existing resources force researchers into a trade-off: carefully curated collections such as PDBBind and HiQBind offer high structural reliability but cover only a narrow slice of the Protein Data Bank (PDB), whereas large-scale resources such as PLINDER provide broad coverage with minimal quality control. We present CROWN (Curated Repository Of Well-resolved Non-covalent interactions), a machine-learning-ready dataset that reconciles scale and rigor through a fully automated preprocessing pipeline. Starting from the PDB database, CROWN applies a series of interleaved quality filters and processing stages that address crystallographic resolution, ligand identity, pocket completeness, structural repair, interaction quality, and protonation at physiological pH. The pipeline finishes with a constrained energy-minimization step built on custom flat-bottomed restraints - a step absent from all existing protein-ligand datasets - that balances crystallographic evidence against the relaxation of intramolecular strain. By reconciling the heterogeneous refinement practices of different depositions without distorting the experimentally observed binding geometry, this step yields a structurally uniform collection of 178,263 complexes, representing a roughly four-fold increase in protein diversity over PDBBind and HiQBind. Rather than organizing the data around sparsely available, bias-prone binding affinities, CROWN adopts a geometry-centric design philosophy that treats the three-dimensional arrangement of atoms at the binding interface as a self-consistent source of information. To demonstrate its value as a training resource, we trained two knowledge-based scoring functions on CROWN and benchmarked them on CASF-2016: relative to HiQBind-trained counterparts, CROWN-trained models showed markedly improved ranking power (mean Spearman correlation rising from 0.509 to 0.637) and docking power (top-1 near-native pose recovery of 0.785 versus 0.724). Because CROWN imposes no requirement for affinity labels, it can in principle support any model that learns from or is evaluated against protein-ligand complex structures. We anticipate that it will serve as a broadly useful resource for tasks such as the training of binder generation, protein design or protein folding models conditioned on bound ligands, the development of scoring functions or benchmarking of interaction-prediction methods.

bioinformatics↗

bioRxiv2025

How an arthropod-borne virus manages host switches: exploration of host-specific mutations and gene formula in bluetongue virus

Arthropod-borne viruses, also known as arboviruses, cause disease worldwide. These viruses alternate between vertebrates and arthropods, facing distinct selection pressures in each host type. Surprisingly, whether specific mechanisms facilitate host alternation remains unclear. Here, we use bluetongue virus (BTV) as a model to investigate two phenomena potentially facilitating host alternation: adaptive mutations and genome formula variation (GFV). First, we searched for mutations indicative of host adaptation in cattle, sheep, and midges infected during an epizooty. We found a limited number of parallel mutations. This result suggests that adaptive mutations are not a main mechanism facilitating host alternation in BTV. Second, we investigated a link of GFV with a proxy of viral fitness, the speed of virus dissemination in midges. We analyzed the genome formula (GF) in experimentally-infected Culicoides imicola midges, either with an infection disseminated into the head or not. GFs differed between dissemination levels, suggesting a link between GF and the speed of virus dissemination. Finally, we investigated the mechanisms underlying GF generation. Currently, two scenarios have been proposed: random generation followed by selection, or directed generation through molecular mechanisms. To limit the influence of selection, we analyzed the GF after a single replication cycle in cell culture. GF values converged towards specific values among replicates. Moreover, GFs differed between arthropod and vertebrate cells. Thus, GF generation is not purely stochastic and is probably influenced by host-specific mechanisms. Overall, our findings highlight GFV as a potential mechanism facilitating BTV alternation across hosts, but not classical mutation-driven evolution. Author SummaryArthropod-borne viruses are important public health problems in the world. These viruses follow a complex cycle involving alternation between arthropods and vertebrates. Surprisingly, we know little on the mechanisms that facilitate alternation between the two host types. Knowledge on such mechanisms could allow the development of antiviral tools. Here, using Bluetongue virus as a model, we investigated two potential mechanisms facilitating alternation: adaptive mutations and genome formula variation (GFV). We found limited evidence for host-specific mutations in natural populations. We observed a potential link between GFV and viral fitness. Moreover, our results suggest that molecular mechanisms generate GFV in a host-specific way from the first round of cell infection.

microbiology↗

bioRxiv2025

PRISM-G: an interpretable privacy scoring method for assessing risk in synthetic human genome data

Synthetic genomic data promises broader access, but unresolved privacy risks persist. In Europe, these risks increasingly hinder cross-border use of national genomic resources due to limitations in trust and legal interoperability rather than scientific demand. At the same time, privacy risk is not uniformly distributed: leakage driven by relatedness structure and rare-variant uniqueness can disproportionately affect underrepresented or vulnerable populations when shared data are misused or linked with external resources, making transparent, domain-aware measurement of privacy exposure central to responsible governance. We introduce PRISM-G, a model-agnostic framework that quantifies privacy exposure in synthetic genome data cross three complementary components: proximity to real genomes in genetic-coordinate space, replay of familial or population-structure patterns, and trait-linked exposure via rare variants and membership-inference signals. These components are normalized and combined through a risk-averse aggregation into a single 0-100 PRISM-G score. We evaluated PRISM-G on synthetic cohorts generated by a generative adversarial network (GAN), a restricted Boltzmann machine (RBM), and a logic-based SAT-solver (Genomator). Our results show that privacy vulnerabilities concentrate along different axes across models and marker densities, demonstrating that a single similarity-based metric is sufficient to characterize genomic privacy risk.

bioinformatics↗

bioRxiv2025

geneRNIB: a living benchmark for gene regulatory network inference

Gene regulatory networks (GRNs) underpin cellular identity and function, playing a key role in health and disease. GRN inference has received substantial attention, motivating systematic benchmarking. Despite various benchmarking efforts, existing studies remain limited in the number of methods, datasets, and metrics, fail to capture the context-specific nature of regulatory interactions across biological conditions, and are constrained by the absence of a reliable ground truth. Here, we introduce geneRNIB, a comprehensive GRN inference benchmarking framework built on three key principles: continuous integration, context-specific evaluation, and holistic assessment in the absence of a true reference network. geneRNIB enables the seamless incorporation of new algorithms, datasets, and evaluation metrics to reflect ongoing developments. In the current version, we systematically integrated and assessed 12 GRN inference methods, spanning single- and multiomics approaches across 11 datasets including thousands of perturbation scenarios. We introduced complementary metrics specifically designed to assess context-specific inference. Our findings indicate that simple models with fewer assumptions often outperform more complex pipelines across several perturbation-informed and predictive metrics. Notably, gene expression-based algorithms yielded better results than more advanced multimodal approaches. In addition, we identify several potential factors that influence the performance of GRN inference and offer actionable guidelines for the future development of the method. By addressing these critical limitations in existing benchmarks, geneRNIB advances GRN inference research and fosters progress toward personalized medicine.

bioinformatics↗

bioRxiv2024

The Specification Game: Rethinking the Evaluation of DrugResponse Prediction for Precision Oncology

Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug-response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to generalize over the immense space of cancer cells and chemical compounds. This paper shows that the commonly used evaluation strategies for DRP methods learn solutions that optimize an unintended DRP score and fail to predict the proper drug-response activity ("specification gaming"). This problem hinders the advancement of the DRP field, and here we propose a new validation paradigm composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) and three train-test Splitting Strategies to ensure a realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment. We thus propose also an alternative prediction label for DRP which is safer from this perspective.

bioinformatics↗

bioRxiv2024

Alleviating cell-free DNA sequencing biases with optimal transport

Cell-free DNA (cfDNA) is a rich source of biomarkers for various (patho)physiological conditions. Recent developments have used Machine Learning on large cfDNA data sets to enhance the detection of cancers and immunological diseases. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders that influence such data sets and lead to domain shifts (i.e., shifts in data distribution as those confounders vary across time or space). Here, we present a domain adaptation method that builds on the concept of optimal transport, and explicitly corrects for the effect of such preanalytical variables. Our approach can be used to merge cohorts representative of the same population but separated by technical biases. Moreover, we also demonstrate that it improves cancer detection via Machine Learning by alleviating the sources of variation that are not of biological origin. Our method also improves over the widely used GC-content bias correction, both in terms of bias removal and cancer signal isolation. These results open perspectives for the downstream analysis of larger data sets through the integration of cohorts produced by different sequencing pipelines or collected in different centers. Notably, the approach is rather general with the potential for application to many other genomic data analysis problems.

bioinformatics↗

bioRxiv2022

Mycobacterium tuberculosis disease associates with higher HIV-1-specific antibody responses

Mycobacterium tuberculosis (Mtb) can enhance immune responses against unrelated pathogens. Although Mtb is the most common co-infection in people living with HIV (PWH), there has been no examination of its impact on HIV-1 immune responses. Plasma neutralization and antibody dependent cellular cytotoxicity (ADCC) was compared among PWH and Mtb disease (PWH/Active Mtb) and PWH/No Mtb both prior to and after antiretroviral treatment (ART) and completion of Mtb therapy. We assessed HIV-1 sequences, total antibody quantities and isotypes, and plasma cytokine levels to ascertain mechanisms that affect humoral responses. HIV-1 neutralizing antibodies (nAbs) were broader and more potent in PWH/Active Mtb as compared to PWH/No Mtb, and nAbs increased among PWH who developed Mtb after ART initiation. ADCC was also higher in the PWH who had Mtb disease after starting ART. PWH/Active Mtb as compared to PWH/No Mtb had unique HIV-1 envelope sequence motifs associated with neutralization resistance further implying differences in humoral selection. The Mtb-linked antibody augmentation associated with elevated plasma cytokine levels important for B cells and antibody production, namely interleukin-6, a proliferation-inducing ligand (APRIL), and B-cell activating factor (BAFF). Increased plasma virus levels, greater HIV-1 envelope diversity, higher levels of all antibodies, and cross-reactive responses did not explain the enhanced HIV-1 humoral responses in those with Mtb. Mtb disease enhances HIV-1 humoral responses likely by perturbing pathways important for antibody production in lymphoid tissue that has both pathogens. These findings have implications for using antibody-based therapies and inducing optimal HIV-1 antibody responses. Author SummaryMycobacterium tuberculosis (Mtb) is the most common infection among people with HIV (PWH) in the world. Mtb infection can enhance immune responses against unrelated pathogens. Previous studies have not examined the impact of Mtb disease on HIV antibodies in PWH. This information has importance for future strategies aimed at enhancing HIV antibody responses in naive individuals or PWH. We show that HIV neutralizing antibodies and antibody-dependent cellular cytotoxicity are broader and more potent in PWH in the presence as compared to the absence of Mtb disease. PWH and Mtb disease as compared to those without Mtb also harbor unique HIV envelope sequences, which further indicates that there is differential antibody selection pressure. The Mtb linked HIV antibody enhancement associated with specific mediators important for B cell and antibody development. Importantly, the Mtb mediated HIV antibody augmentation was not due to cross-reactivity, a generalized increase in all antibodies, or a higher level, more diverse, or longer duration of antigen exposure. We speculate that more potent HIV antibodies arise in lymphatic tissue that harbors both Mtb and HIV. Our findings have implications for both future uses of HIV antibodies as prophylaxis or treatment and strategies aimed inducing better HIV antibody responses.

microbiology↗

bioRxiv2022

WiNGS: Widely integrated NGS platform for federated genome analysis

Next-generation sequencing (NGS) has been increasingly used in a wide range of research communities and in routine clinical practice and leads to an ever increasing amount of sequencing data. Sequencing data comes with, several challenges such as sharing, storing, integrating, analyzing, and interpretion. The management of the expanding amount of data is challenging and, especially for human omics data, privacy protection is crucial. Unraveling the causes of rare diseases is critically dependent on data sharing, but progress is hampered by regulations and privacy concerns. To overcome the concerns associated with centralized human genomic data storage, we developed a federated analysis platform, referred to as Widely Integrated NGS (WiNGS). The presented approach enables datasharing and combined data-analysis of omics data across a consortium without a centralized data store. Moreover, the platform incorporates extensive variant interpretation tools from genotype to phenotype for the diagnosis of rare developmental disorders.

bioinformatics↗

bioRxiv2022

Vertical HIV-1 transmission in the setting of maternal broad and potent antibody responses

Despite the worldwide availability of antiretroviral therapy (ART), approximately 150,000 pediatric HIV infections continue to occur annually. ART can dramatically reduce HIV mother-to-child transmission (MTCT), but inconsistent drug access and adherence, as well as primary maternal HIV infection during pregnancy and lactation are major barriers to eliminating vertical HIV transmission. Thus, immunologic strategies to prevent MTCT, such as an HIV vaccine, will be required to attain an HIV-free generation. A primary goal of HIV vaccine research has been to elicit broadly neutralizing antibodies (bnAbs) given the ability of passive bnAb immunization to protect against sensitive strains, yet we previously observed that HIV-transmitting mothers have more plasma neutralization breadth than non-transmitting mothers. Additionally, we have identified infant transmitted/founder (T/F) viruses that escape maternal bnAb responses. In this study, we examine a cohort of postpartum HIV-transmitting women with neutralization breadth to determine if certain maternal bnAb specificities drive the selection of infant T/F viruses. Using HIV pseudoviruses that are resistant to neutralizing antibodies targeting common bnAb epitopes, we mapped the plasma bnAb specificities of this cohort. Significantly more transmitting women with plasma bnAb activity had a mappable plasma bnAb specificity (six of seven, or 85.7%), compared to that of non-transmitting women with plasma bnAb activity (seven of twenty-one, or 33.3%, p=0.029 by 2-sided Fisher exact test). Our study suggests that having multispecific broad activity and/or uncommon epitope-specific bnAbs in plasma may be associated with protection against the vertical HIV transmission in the setting of maternal bnAb responses. ImportanceAs mother to child transmission (MTCT) of HIV plays a major part in the persistence of the HIV/AIDS epidemic and bnAb-based passive and active vaccines are a primary strategy for HIV prevention, research in this field is of great importance. While previous MTCT research has investigated the neutralizing antibody activity of HIV-infected women, this is to our knowledge the largest study identifying differences in bnAb specificity of maternal plasma between transmitting and non-transmitting women. Here, we show that among HIV-infected women with broad and potent neutralization activity, more postpartum-transmitting women had a mappable plasma broadly neutralizing antibody (bnAb) specificity, compared to that of non-transmitting women, suggesting that the non-transmitting women more often have multi-specific bnAb responses or bnAb responses that target uncommon epitopes. Such responses may be required for protection against vertical HIV transmission in the setting of maternal bnAb responses.

immunology↗

bioRxiv2019

Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs.

Drug repositioning aims to find new indications for existing drugs, in order to reduce drug development cost and time. Currently, numerous successful stories of drug repositioning have been reported and many drugs are already available on the market. Although many of those cases are products of serendipitous findings, repositioning opportunities can be uncovered systematically by following either a disease-centric approach, as a result of a close relation between an old and new indication, a target-centric one, which links a known target and its established drug to a new indication, or a drug-centric approach, which connects a known drug to a new target and its associated indication. The three approaches differ in their complexity, potential, and limits, and most important the necessary starting information and computational power. Which one is predominant in current drug repositioning and what does this imply for future developments? To address these questions, we systematically evaluated over 100 drugs, 200 targets structures and over 300 indications from the Drug Repositioning Database. Each of the analysed cases has been classified based on one of the three repositioning approaches, showing that the majority, more than 60%, falls within the disease-centric definition, around 30% within the target-centric, and only less than 10% within the drug-centric. We therefore concluded that so far repositioning has mainly been disease and target repositioning and not, as drug repositioning, as expected by definition. We discuss the reasons and suggest directions to exploit the full potential of techniques useful for drug-centric in order to sustain future rationale repositioning pipelines.

bioinformatics↗

bioRxiv2019

Structure-based drug repositioning reveals ibrutinib as VEGFR2 inhibitor

Many drugs are promiscuous and bind to multiple targets. On the one hand, these targets may be linked to unwanted side effects, but on the other, they may achieve a combined desired effect (polypharmacology) or represent multiple diseases (drug repositioning). With the growth of 3D structures of drug-target complexes, it is today possible to study drug promiscuity at the structural level and to screen vast amounts of drug-target interactions to predict side effects, polypharmacological potential, and repositioning opportunities. Here, we pursue such an approach to identify drugs inactivating B-cells, whose dysregulation can function as a driver of autoimmune diseases. Screening over 500 kinases, we identified 22, whose knock out impeded the activation of B-cells. Among these 22 is the gene KDR, whose gene product VEGFR2 is a prominent cancer target with anti-VEGFR2 drugs on the market for over a decade. The main result of this paper is that structure-based drug repositioning for the identified kinase targets identified the cancer drug ibrutinib as micromolar VEGFR2 inhibitor with a very high therapeutic index in B-cell inactivation. These findings prove that ibrutinib is not only acting on the Brutons tyrosine kinase BTK, against which it was designed. Instead, it may be a polypharmacological drug, which additionally targets angiogenesis via inhibition of VEGFR2. Therefore ibrutinib carries potential to treat other VEGFR2 associated disease. Structure-based drug repositioning explains ibrutinibs anti VEGFR2 action through a specific pattern of interactions shared between BTK and VEGFR2. Overall, structure-based drug repositioning was able to predict these findings at a fraction of the time and cost of a conventional screen.

bioinformatics↗

bioRxiv2019

How to maintain a high virulence: evolution of a killer in hosts of various susceptibilities.

Pathogens should evolve to avirulence. However, while baculoviruses can be transmitted through direct contact, their main route of infection goes through the death and liquefaction of their caterpillar hosts and highly virulent strains still seem to be advantaged through infection cycles. Furthermore, one of them, Autographa californica multiple nucleopolyhedrovirus, is so generalist that it can infect more than 100 different hosts.\n\nTo understand and characterize the evolutionary potential of this virus and how it is maintained while killing some of its hosts in less than a week, we performed an experimental evolution starting from an almost natural isolate of AcMNPV, known for its generalist infection capacity. We made it evolve on 4 hosts of different susceptibilities for 10 cycles and followed hosts survival each day. We finally evaluated whether the generalist capacity was maintained after evolving on one specific host species and tested an epidemiological model through simulations to understand how.\n\nFinally, on very highly susceptible hosts, transmission-virulence trade-offs seem to disappear and the virus can maximize transmission and virulence. When less adapted to its host, the pathogens virulence has not been modified along cycles but the yield was increased, apparently through an increased transmission probability and an increased latent period between exposition and infection.

evolutionary biology↗

bioRxiv2019

HiVA: an integrative wet- and dry-lab platform for haplotype and copy number analysis of single-cell genomes

Haplotyping is imperative for comprehensive analysis of genomes, imputation of genetic variants and interpretation of error-prone single-cell genomic data. Here we present a novel sequencing-based approach for whole-genome SNP typing of single cells, and determine genome-wide haplotypes, the copy number of those haplotypes as well as the parental and segregational origin of chromosomal aberrations from sequencing- and array-based SNP landscapes of single cells. The analytical workflow is made available as an interactive web application HiVA (https://hiva.esat.kuleuven.be).

genomics↗

bioRxiv2019

HIV-1 co-receptor usage and variable loop contact impacts V3 loop bnAb susceptibility

In clinical trials, HIV-1 broadly neutralizing antibodies (bnAbs) effectively lower plasma viremia and delay virus reemergence after antiretroviral treatment is stopped among infected individuals that have undetectable virus levels. Presence of less neutralization susceptible strains prior to treatment, however, decreases the efficacy of these antibody-based treatments. The HIV-1 envelope glycoprotein harbors extensive genetic variation, and thus, neutralization sensitivity often cannot be predicted by sequence analysis alone. Sequence-based prediction methods are needed because phenotypic-based assays are labor intensive and not sensitive. Based on the finding that phenotypically confirmed CXCR4- as compared to exclusive CCR5-utilizing strains are less neutralization sensitive, especially to variable loop 1 and 2 (V1-V2) and V3 loop bnAbs, we show that an algorithm that predicts receptor usage identifies envelopes with decreased V3 loop bnAb susceptibility. Homology modeling suggests that the primary V3 loop bnAb epitope is equally accessible among CCR5- and CXCR4-using strains although variants that exclusively use CXCR4 have V3 loop protrusions that interfere with CCR5 receptor interactions. On the other hand, homology modeling also shows that envelope V1 loop orientation interferes with V3 loop directed bnAb binding, and this accounts for decreased neutralization sensitivity in some but not all cases. Thus, there are likely different structural reasons for the co-receptor usage restriction and the differential bnAb susceptibility. Algorithms that use sequence data to predict receptor usage and antibody-envelope homology models can be used to identify variants with decreased sensitivity to V3 loop and potentially other bnAbs.\n\nAUTHOR SUMMARYHIV-1 broadly neutralizing antibody (bnAb) therapies are effective, but the pre-existence of less susceptible variants may lead to therapeutic failure. Sequence-based methods are needed to predict pre-treatment variants neutralization sensitivity. HIV-1 strains that use the CXCR4 as compared to the CCR5 receptor are less neutralization susceptible, especially to V1-V2 and V3 loop bnAbs. A sequence-based algorithm that predicts receptor usage can identify envelope variants with decreased V3 loop bnAb susceptibility. While the inability to utilize the CCR5 receptor maps to a predicted protrusion in the envelope V3 loop, this viral determinant does not directly influence V3 loop bnAb sensitivity. Furthermore, homology modeling predicted contact between the envelope V1 loop and an antibody also impact V3 loop bnAb susceptibility in some but not all cases. An algorithm that predicts receptor usage and homology modeling can be used to predict sensitivity to bnAbs that target the V3 loop and potentially other envelope domains. These sequence-based methods will be useful as HIV-1 bnAbs enter the clinical arena.

microbiology↗

bioRxiv2019

Predicting disease-causing variant combinations

Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (bi-locus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bi-locus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bi-locus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important new step towards the genetic understanding of rare diseases, paving the way to new clinical knowledge and improved patient care.

bioinformatics↗