Biology subjects

Lopez Garcia de Lomana, A.

Publications and source records attributed to Lopez Garcia de Lomana, A..

7 recordsLinked to original sources

bioRxiv2026

ATG7 function promotes pancreatic cancer progression independently of autophagy

Autophagy is a cellular degradation process that recycles dysfunctional components to maintain cellular homeostasis. Beyond this canonical role, autophagy-related proteins, such as the essential autophagy initiation protein ATG7, are increasingly recognized to have autophagy independent functions in diverse biological processes and disease contexts, including cancer progression and metastasis. However, the mechanisms underlying these autophagy independent functions remain unclear. Previously, we identified a short isoform, ATG7(2), that lacks canonical autophagy activity. To understand the unique role of ATG7(2), we analysed clinical data from publicly available databases and found that high ATG7(2) expression is associated with poor prognosis in pancreatic adenocarcinoma (PAAD). Using CRISPR/Cas9 in PAAD cells, we selectively knocked out the canonical isoform ATG7(1) or total ATG7. While total knock-out of ATG7 slowed proliferation and migration of PAAD cells, high levels of ATG7(2) were found to enhance both processes. In addition, RNA sequencing linked ATG7(2) with immune signalling, extracellular matrix organization and cell-cell interactions. Critically, ATG7(2) inhibition in a murine xenograft model substantially reduces tumour growth and overall progression in vivo, establishing functional relevance in a physiological tumour context. Together, these results suggest that ATG7(2) has a role in regulating immune signalling in PAAD cells, contributes to migration and proliferation in an autophagy-independent manner, and suggests ATG7(2) as a potential therapeutic target for the treatment of PAAD.

cancer biology↗

bioRxiv2026

Physics-Informed Neural Networks for Parameter Recovery in the Repressilator Oscillatory Model

Parameter estimation in nonlinear biological dynamical systems is a difficult inverse problem because the governing equations are often stiff or oscillatory, the data are sparse and noisy, and the objective landscape is non-convex. Physics-informed neural networks (PINNs) offer an alternative to purely simulation-based calibration by representing state trajectories with neural networks while penalizing violations of the governing equations. This paper studies the empirical reliability of PINNs for recovering the parameters of the repressilator, a synthetic genetic oscillator formed by three cyclically repressive genes. We use synthetic time-series generated from the standard ordinary differential equation model and train inverse PINNs to estimate the production parameter {beta} and the Hill coefficient n. The study varies observation noise, partial observation of repressors, sampling density, sensitivity to initial parameter guesses, and the difference between stable and oscillatory regimes. The results show that PINNs can reconstruct trajectories accurately when the model structure is correct and the three repressors are observed, but parameter recovery is more fragile than trajectory fitting. Noise, sparse sampling, unobserved variables, and unfavorable initial guesses increase the risk of biased estimates. The stable regime is easier to reconstruct, whereas the oscillatory regime provides richer information but also exposes optimization sensitivity. These findings support PINNs as a useful reverse-engineering tool for small gene-regulatory ODE models, while highlighting the need for repeated runs, uncertainty reporting, and experimental designs that improve identifiability.

bioinformatics↗

bioRxiv2026

Integrating trajectory inference and gene regulatory network analysis to resolve transcriptional programs of T cell state transitions in the tumor microenvironment

Reconstructing dynamic immune cell state transitions from single-cell transcriptomic data requires coordinated analytical strategies that capture both phenotypic progression and underlying regulatory programs. This protocol describes a step-by-step computational workflow for analyzing human tumor-infiltrating T cells using the sequential application of dimensionality reduction, pseudotime trajectory inference, regulon activity analysis, and transcription factor-transcription factor network reconstruction. The workflow outlines data preprocessing and quality control, trajectory rooting and parameter selection, branch-specific differential analysis, and the integration of regulon inference to contextualize transcriptional programs along inferred trajectories. Regulon-based TF-TF network reconstruction is used as a downstream interpretive layer to identify regulatory modules associated with distinct cell-state transitions. Publicly available at GitHub repository https://github.com/rogercasalsfr/immuno-trajectory-grn-integrative-workflow, this protocol emphasizes practical considerations including parameter sensitivity, trajectory robustness, and consistency between phenotypic and regulatory outputs. The protocol supports reproducible analysis and interpretation of immune cell dynamics in human tumor microenvironment studies using single-cell RNA sequencing data.

bioinformatics↗

bioRxiv2025

Transcriptional reprogramming of tumor-infiltrating T cells during PD-1 blockade revealed through gene regulatory network and trajectory inference in squamous cell carcinoma

Understanding the tumor microenvironment is crucial for optimizing anti-cancer immune responses. At single-cell resolution, trajectory inference methods can reconstruct the dynamic transitions between cell states during differentiation. Immune checkpoint blockade (ICB) therapies, such as PD-1/PD-L1 inhibitors, are used across multiple cancers, including non-melanoma skin cancers (NMSCs), yet the transcriptional mechanisms that shape T cell responses in this context remain unclear. Here, we analyzed a publicly available squamous cell carcinoma (SCC) single-cell RNA-seq dataset comprising 25,581 tumor-infiltrating T-cell profiles to map differentiation trajectories before and after anti-PD-1 therapy. In CD8+ T cells, therapy enhanced the transition from memory to activated states, prominently involving IL-12-associated pathways, and revealed a distinct memory-to-exhaustion trajectory driven by EOMES and TCF7 regulatory activity. Gene regulatory network inference further revealed therapy-induced transcriptional rewiring distinguishing precursor exhausted (Tpex) from terminally exhausted (Tex) states. CD4+ T cell populations also underwent substantial reshaping, with trajectory and functional analyses highlighting therapy-driven programs that enhanced CXCL13+ Tfh responses while generating fewer but more transcriptionally active Tregs. Together, these findings reveal a dual remodeling of helper and cytotoxic T cell compartments upon PD-1 blockade, define key transcriptional regulators controlling cell-state transitions, and identify potential molecular targets and biomarkers to predict and enhance treatment response.

immunology↗

bioRxiv2024

Gene Regulatory Network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) manifests from heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation to glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation is inherently resistant to temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and thereby drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how topology of transcription factor interactions drives distinct trajectories of cell state transitions of susceptible and resistant PD-GSCs in response to cytotoxic drug treatment. By experimentally testing TRN simulation-based predictions, we show that drug treatment drives surviving cells of a PD-GSC along a trajectory of intermediate states, akin to a bottleneck in gene expression space, exposing vulnerability to potentiated killing by sequential addition of siRNA or a second drug targeting transcriptional programs governing non-genetic plasticity of a PD-GSC. Thus, our findings demonstrate an approach to uncover and use TRN topology of a PD-GSC to rationally predict combinatorial and sequential treatments that block treatment escape and acquired resistance in GBM.

systems biology↗

bioRxiv2021

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multimodal single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multimodal data with single cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that regulate subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches in a concurrent, neo-adjuvant, or recurrent setting. SummaryInference of mechanistic drivers of therapy-induced evolution of glioblastoma at single cell resolution using RNA-seq and ATAC-seq from patient samples and model systems undergoing standard-of-care treatment informs strategy for identification of tumor evolutionary trajectories and possible cell state-directed therapeutics.

systems biology↗

bioRxiv2020

Genetic program activity delineates risk, relapse, and therapy responsiveness in Multiple Myeloma

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to their previous therapies. Although many therapies exist with diverse mechanisms of action, it is not yet clear how the differences in MM biology across patients impacts the likelihood of success for existing therapies and those in the pipeline. Therefore, we not only need the ability to predict which patients are at high risk for disease progression, but also a means to understand the mechanisms underlying their risk. We hypothesized that knowledge of the biological networks that give rise to MM, specifically the transcriptional regulatory network (TRN) and the mechanisms by which mutations impact gene regulation, would enable improved predictions of disease progression and actionable insights for treatment. Here we present a method to infer TRNs from multi-omics data and apply it to the generation of a MM TRN that links chromosomal abnormalities and somatic mutations to downstream effects on gene expression via perturbation of transcriptional regulators. We find that 141 genetic programs underlie the disease and that the activity profile of these programs fall into one of 25 distinct transcriptional states. These transcriptional signatures prove to be more predictive of outcomes than do mutations and reveal plausible mechanisms for relapse, including the establishment of an immuno-suppressive microenvironment. Moreover, we observe subtype-specific vulnerabilities to interventions with existing drugs and motivate the development of new targeted therapies that appear especially promising for relapsed refractory MM.

systems biology↗