Biology subjects

Hurtado, M.

Publications and source records attributed to Hurtado, M..

12 recordsLinked to original sources

bioRxiv2026

A multiscale pharmacokinetic-Boolean modelling framework for ibrutinib resistance in a Chronic Lymphocytic Leukaemia case study

Ibrutinib, a covalent Bruton's tyrosine kinase (BTK) inhibitor, achieves durable remissions in chronic lymphocytic leukaemia (CLL), and yet a substantial proportion of patients relapse. A fundamental question is how the quantitative dynamics of drug exposure relate to the regulatory programmes and cell fate decisions that characterise response and relapse. Addressing it requires a multiscale framework that bridges the quantitative pharmacology of drug exposure with the molecular mechanisms encoded in the gene regulatory network, a coupling that has remained largely unexplored in the context of targeted therapy resistance. Here we present a multi-scale computational framework that addresses this question by coupling continuous patient-specific pharmacokinetic profiles with discrete Boolean dynamics, bridging two modelling paradigms that have so far operated in isolation. Applied to a longitudinal single-cell dataset from one CLL patient treated with ibrutinib across three clinical timepoints (pre-treatment, response, relapse), the framework integrates gene regulatory network inference, cell fate trajectory analysis, and a stochastic Boolean model, whose dynamics is driven by the patient-specific monthly drug exposure. AP-1 and integrated stress response regulons were specific to relapse, and the coupled model reproduced the clinical course. Its perturbation screens identified co-inhibition of BTK with BCL2L1 or NFKB1 as synergistic, in line with the rationale for dual targeting of BTK and the BCL2 family in treatment-naive CLL. These results show how the framework turns single-patient data into mechanistic, testable hypotheses on resistance and combination therapy, which can be evaluated in larger cohorts and in experimental models.

systems biology↗

bioRxiv2026

CellTFusion: a transcriptional regulatory network framework for the identification of functional multicellular states from bulk RNA-seq data

Bulk RNA-seq remains the most accessible transcriptomic platform for tumor microenvironment (TME) characterization, yet existing computational approaches treat cell type abundance, pathway activity, and transcription factor (TF) activity as independent sources of information, missing the coordinated regulatory programs that define functional multicellular states. Here we introduce CellTFusion, a framework that integrates cell type deconvolution with transcriptional regulatory network analysis from bulk RNA-seq data to identify functional multicellular groups. CellTFusion produces a mixture representation of the TME in which each patient is described as a weighted combination of states, each capturing a distinct coordinated hallmark program. Applied to melanoma and bladder cancer cohorts, CellTFusion identified recurrent TME programs with opposing associations with immunotherapy responses that only emerged through joint multivariate modeling, and demonstrated superior cross-cohort transferability compared to established TME characterization tools.

bioinformatics↗

bioRxiv2026

A new pipeline for cross-validation fold-aware machine learning prediction of clinical outcomes addresses hidden data-leakage in omics based 'predictors'.

MotivationMachine learning (ML) approaches are increasingly applied to high-dimensional biological data in which features are often dataset-dependent. In many omics workflows, features are computed using information derived from the entire dataset, such as correlations between variables, clustering structures, or enrichment scores. We refer to these as global dataset features, defined as features whose computation depends on properties of the full dataset. In such cases, standard validation strategies can fail, especially when evaluating on independent datasets, due to information leakage that leads to overly optimistic performance estimates. ResultsTo address this challenge, we present pipeML, a flexible and modular machine learning framework designed to support leakage-free model training through custom cross-validation (CV) fold construction. pipeML enables users to recompute global dataset features independently within each CV fold, ensuring strict separation between training and test data, while preserving compatibility with a wide range of ML algorithms for both classification and survival tasks. Using real-world biological datasets, we demonstrate that pipeML enables leakage-free model evaluation when global dataset features are used. We argue that overestimation of model performance during CV can lead to overoptimistic expectations for validation on independent datasets. By explicitly addressing data leakage and offering a transparent, modular workflow, pipeML provides a robust solution for developing and validating ML models in complex biological settings. AvailabilityThe pipeML R package as well as a tutorial are available at https://github.com/VeraPancaldiLab/pipeML Contactvera.pancaldi@inserm.fr or marcelo.hurtado@inserm.fr Supplementary informationAvailable at Bioinformatics online.

bioinformatics↗

bioRxiv2025

multideconv - Integrative pipeline for cell type deconvolution from bulk RNAseq using first and second generation methods

The number of computational methods for cell type deconvolution from bulk RNA-seq data has been increasing in the last years, but their high feature complexity and variability of results across methods and signatures limit their utility and effectiveness for patient stratification. Applying multiple combinations of deconvolution methods and signatures often results in hundreds of redundant or contradictory cell type features describing the composition of complex tumour samples. Benchmarking efforts are inherently limited by the lack of bias-free ground truth, often yielding inconsistent results or no consensus. To address these limitations, we present multideconv, an R package that reduces dimensionality and eliminates redundancy in deconvolution results, through unsupervised filtering and iterative correlation analyses. Built on top of existing frameworks, multideconv harmonizes outputs across methods to identify robust cell type proportion estimates and mitigate signature-driven heterogeneity. We benchmarked multideconv against two existing methods that provide similar functions and found it to yield more accurate estimations of cell type proportions based on virtual bulk reconstruction from single-cell expression datasets. We also increase computational efficiency by providing a meta-cell aggregation of the single-cell datasets, showing it preserves the samples complexity. Despite our focus on tumour samples in the context of immuno-oncology, the tool is flexible and can be adapted to infer mixed sample composition from bulk RNAseq datasets. The multideconv R package and tutorials are available at https://github.com/VeraPancaldiLab/multideconv. The code to reproduce the analysis and figures is available on github at https://github.com/VeraPancaldiLab/multideconv_paper. Contact: marcelo.hurtado@inserm.fr or vera.pancaldi@inserm.fr

bioinformatics↗

bioRxiv2025

Time-series RNA-Seq and data-driven network inference unveil dynamics of cell activation, survival and crosstalk in Chronic Lymphocytic Leukaemia in vitro models

How do cancer cells respond to their environment, and what are the key regulators behind their behaviour? While immune cell reprogramming in the tumour microenvironment (TME) has been extensively studied, the dynamic regulatory changes within cancer cells in response to interactions with immune cells remain poorly understood. In Chronic Lymphocytic Leukaemia (CLL), this knowledge gap limits our ability to fully grasp the disease progression and to design effective, personalised interventions. To tackle this, we combine time-series transcriptomics with data-driven gene regulatory network (GRN) inference to uncover the temporal regulatory mechanisms driving CLL cell behaviour within a reconstituted in vitro TME. Using cultures of peripheral blood from CLL patients or of purified patient-derived CLL cells, we profile gene expression across five time points spanning 14 days under these experimental conditions. By inferring GRNs from transcription factor activity, we capture patient-specific and temporally resolved regulatory interactions that highlight how immune signals shape cancer cell phenotypic changes. Our network analysis reveals distinct gene modules associated with critical processes such as cytokine signalling, metabolic reprogramming and differentiation, hallmarks of immune-cancer cell interaction. Intriguingly, we found that while the presence of immune cells in the environment significantly alters CLL cell activation, their survival trajectories are predominantly governed by intrinsic features. This study not only offers mechanistic insights into how immune cell presence influences CLL cell fate but also presents a robust computational framework for integrating time-series transcriptomics with GRN inference, which can then be used to study the long-term behaviour of the CLL cells through dynamical modelling.

systems biology↗

bioRxiv2022

Plant spatial aggregation modulates the interplay between plant competition and pollinator attraction with contrasting outcomes of plant fitness

Ecosystem functions such as seed production are the result of a complex interplay between competitive plant-plant interactions and mutualistic pollinator-plant interactions. In this interplay, spatial plant aggregation could work in two different directions: it could increase intra- and interspecific competition, thus reducing seed production; but it could also attract pollinators increasing plant fitness. To shed light on how plant spatial arrangement modulates this balance, we conducted a field study in a Mediterranean annual grassland with three focal plant species with different phenology (Chamaemelum fuscatum (early phenology), Leontodon maroccanus (middle phenology) and Pulicaria paludosa (late phenology)) and a diverse guild of pollinators (flies, bees, beetles, and butterflies). All three species showed spatial aggregation of conspecific individuals. Additionally, we found that the two mechanisms were working simultaneously: crowded neighborhoods reduced individual seed production via plant-plant competition, but they also made individual plants more attractive for some pollinator guilds, increasing visitation rates and plant fitness. The balance between these two forces varied depending on the focal species and the spatial scale considered. Therefore, our results indicate that mutualistic interactions not always effectively compensate for competitive interactions in situations of spatial aggregation of flowering plants, at least in our study system. We highlight the importance of explicitly considering the spatial structure at different spatial scales of multitrophic interactions to better understand individual plant fitness and community dynamics.

ecology↗

bioRxiv2022

DEGoldS: a workflow to assess the accuracy of differential expression analysis pipelines through gold-standard construction

RNA sequencing (RNA-seq) is a high throughput sequencing method that has become one the most employed tools in transcriptomics. The implementation of optimal bioinformatic analyses required in RNA-seq experiments may be complicated due to the large amounts of data generated by the sequencing platforms, along with the intrinsic nature of these data types. In the last years many programs and pipelines have been developed for differential expression (DE) analyses, but their effectiveness can be reduced when working with non-model species lacking public genomic resources. Moreover, there is not a universal recipe for all the experiments and datasets and the modification of standard RNA-seq bioinformatic pipelines through parameter tuning and the use of alternative software may have a strong impact in the outcome of DE analysis. Therefore, although the selection of the most accurate DE pipeline configuration and the evaluation of how these changes could affect the final DE results in RNA-seq experiments is mandatory to reduce bias, the lack of gold-standard datasets with known expression patterns hampers its implementation. In the present manuscript we present DEGoldS, a workflow consisting on sequential Bash and R scripts to construct gold-standards for simulation-based benchmarking of user selected pipelines for DE analysis and the computation of the accuracy of the pipelines. We validated the workflow with a case study consisting on real RNA-seq libraries of radiata pine, an important forest tree species with no publicly available reference genome. The results showed that slight pipeline modifications produced remarkable differences in the outcome of DE analysis.

bioinformatics↗

bioRxiv2021

The non-random structure of multi-trophic ecological interactions maximizes species coexistence within ecologically realistic constraints

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength, and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here we model community feasibility domains, a theoretically-informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators, and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

ecology↗

bioRxiv2021

Disentangling signatures of selection before and after European colonization in Latin Americans

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. While classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ~4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico and Peru. Our approach replicates previous reports of selection in the HLA region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly-used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

genomics↗

bioRxiv2021

Community level individual-based plant-pollinator networks unveil pollen flow dynamics and plant reproductive success

Interaction networks are a widely used tool to understand the dynamics of plant-pollinator ecological communities. However, while most mutualistic networks have been defined at the species level, ecological processes such as pollination take place at different scales, including the individual or patch levels. Yet, current approaches studying fine-grain sub-specific plant-pollinator networks only account for interactions among nodes belonging to a single plant species due to the conceptual and mathematical limitations of modeling simultaneously several plant species each composed of several nodes. Here, we introduce a multilayer diffusion network framework that allows modeling simple diffusion processes between nodes pertaining to the same or different layers (i.e. species). It is designed to depict from the network structure the potential conspecific and heterospecific pollen flows among plant individuals or patches. This potential pollen flow is modeled as a transport-like system, in which pollen grain movements are represented as random-walkers that diffuse on an ensemble of bipartite layers of conspecific plants and their shared pollinators. We exemplify this physical conceptualization using a dataset of nine fine-grain sub-specific plant-pollinator networks from a Mediterranean grassland of annual plants, where plant nodes represent groups of conspecifics within patches of 1m2. The diffusion networks show pollinators effectively connecting sets of patches of the same and different plant species, forming a modular structure. Interestingly, different properties of the network structure, such as the conspecific pollen arrival probability and the number of conspecific subgraphs in which plants are embedded, are critical for the seed production of different plant species. We provide a simple but robust set of metrics to calculate potential pollen flow and scale down network ecology to functioning properties at the individual or patch level, where most ecological processes take place, hence moving forward the description and interpretation of species-rich communities across scales.

ecology↗

bioRxiv2020

Prediction Of Eye, Hair And Skin Color In Admixed Populations Of Latin America

We report an evaluation of prediction accuracy for eye, hair and skin pigmentation based on genomic and phenotypic data for over 6,500 admixed Latin Americans (the CANDELA dataset). We examined the impact on prediction accuracy of three main factors: (i) The methods of prediction, including classical statistical methods and machine learning approaches, (ii) The inclusion of non-genetic predictors, continental genetic ancestry and pigmentation SNPs in the prediction models, and (iii) Compared two sets of pigmentation SNPs: the commonly-used HIrisPlex-S set (developed in Europeans) and novel SNP sets we defined here based on genome-wide association results in the CANDELA sample. We find that Random Forest or regression are globally the best performing methods. Although continental genetic ancestry has substantial power for prediction of pigmentation in Latin Americans, the inclusion of pigmentation SNPs increases prediction accuracy considerably, particularly for skin color. For hair and eye color, HIrisPlex-S has a similar performance to the CANDELA-specific prediction SNP sets. However, for skin pigmentation the performance of HIrisPlex-S is markedly lower than the SNP set defined here, including predictions in an independent dataset of Native American data. These results reflect the relatively high variation in hair and eye color among Europeans for whom HIrisPlex-S was developed, whereas their variation in skin pigmentation is comparatively lower. Furthermore, we show that the dataset used in the training of prediction models strongly impacts on the portability of these models across Europeans and Native Americans.

genetics↗

bioRxiv2018

Latin Americans show wide-spread Converso ancestry and the imprint of local Native ancestry on physical appearance

Historical records and genetic analyses indicate that Latin Americans trace their ancestry mainly to the admixture of Native Americans, Europeans and Sub-Saharan Africans1. Using novel haplotype-based methods here we infer the sub-populations involved in admixture for over 6,500 Latin Americans and evaluate the impact of sub-continental ancestry on the physical appearance of these individuals. We find that pre-Columbian Native genetic structure is mirrored in Latin Americans and that sources of non-Native ancestry, and admixture timings, match documented migratory flows. We also detect South/East Mediterranean ancestry across Latin America, probably stemming from the clandestine colonial migration of Christian converts of non-European origin (Conversos). Furthermore, we find that Central Andean ancestry impacts on variation of facial features in Latin Americans, particularly nose morphology, possibly relating to environmental adaptation during the evolution of Native Americans.

genetics↗