Biology subjects

Rodriguez Martinez, M.

Publications and source records attributed to Rodriguez Martinez, M..

7 recordsLinked to original sources

bioRxiv2026

Cross-Cohort Optimal Transport Maps Macrophage Plasticity and Competing Routes to Inflammation and Fibrosis in Human Atherosclerotic Plaques

Single-cell transcriptomics has revealed extensive macrophage heterogeneity in atherosclerotic plaques, but how macrophages move between states, and whether transition mechanisms depend on cellular origin, remain unclear. Here we develop a computational framework that reconstructs directed cell-state transition networks from cross-sectional single-cell RNA-sequencing data by combining optimal transport with RNA velocity and systematic cross-cohort validation. Applying this approach to seven human atherosclerotic plaque cohorts, we generate an integrated atlas of 81,633 monocytes and macrophages and identify 15 statistically significant pairwise transitions, of which 11 directed transitions organize into three biological axes: monocyte fate diversification, inflammatory reactivation, and fibrotic remodeling. The strongest transition links scavenging macrophages to inflammatory macrophages, suggesting that plaque inflammation is driven predominantly by reactivation of tissue-adapted macrophages rather than by direct differentiation of newly recruited monocytes. By tracking gene expression changes along the OT target-association gradient, we find that macrophage plasticity follows an origin-dependent spectrum. Tissue-resident macrophages, in particular scavenging macrophages, acquire inflammatory programs while preserving and reinforcing their resident scavenging identity, a mechanism we term transcriptional layering, whereas monocyte-derived transitions proceed through selective loss of source-identity modules. Despite these distinct routes, transitions converging on the same fate activate shared destination-specific regulatory circuits, with inflammatory and fibrotic programs governed by mutually antagonistic transcription factor networks. These findings identify inflammatory reactivation of scavenging macrophages as a dominant transition axis in human atherosclerosis and suggest that macrophage origin constrains how disease-associated programs are acquired. More broadly, this framework provides a general strategy for quantifying cell-state transitions and dissecting plasticity mechanisms in chronic inflammatory disease.

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bioRxiv2026

IMMREP25: Unseen Peptides

T cell receptors (TCRs) can bind to peptides presented by MHC molecules (pMHC) as a first step to trigger a T cell response. Reliable approaches to predict TCR:pMHC binding would have broad applications in clinical diagnostics, therapeutics, and the fundamental understanding of molecular interactions. IMMREP is a community organized series of prediction contests that asks participants to predict TCR:pMHC binding on unpublished datasets. Previous iterations in 2022 and 2023 showed multiple approaches can predict TCR-pMHC binding with significant accuracy (median AUC_0.1[≥]0.7) for peptides where experimental data is available ("seen" peptides). In contrast, models did not outperform random guessing for peptides that have no such data available ("unseen" peptides). Here we report on the results of IMMREP25, which focused solely on unseen peptides in order to evaluate the cutting edge of the field. We received 126 named submissions predicting the specificity of 1,000 TCRs against twenty unseen peptides restricted by one of two MHC molecules (HLA-A*02:01 and HLA-B*40:01). The best performing methods showed a macro-AUC_0.1 of 0.60, significantly better than random, demonstrating significant advances in the field. The top performing methods incorporated structural modeling into their approach, indicating that especially for unseen peptides, a structural understanding aids in the prediction of TCR:pMHC interactions. The results from this benchmark highlight the significant challenges remaining for TCR:pMHC predictions and will inform future method development.

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bioRxiv2026

Benchmarking generative scaffold design methods for peptide engineering in TCR-MHC complexes

De novo peptide design at T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) interfaces is a central challenge in computational immunology, with direct implications for vaccine development, cancer immunotherapy, and autoimmune disease. Despite rapid advances in generative protein modeling, there is currently no systematic benchmark evaluating these methods in the highly constrained and immunologically relevant setting of peptide-MHC presentation and TCR recognition. Here, we present two complementary contributions. First, we introduce a multi-stage computational pipeline for peptide design in predefined TCR-pMHC contexts, integrating generative modeling with sequence optimization and structure-based filtering. Second, we establish a benchmark for evaluating generative peptide design methods in TCR-pMHC complexes. Using a curated dataset of high-quality crystal structures deposited after the AlphaFold3 training cutoff, we assess state-of-the-art generative approaches for peptide backbone generation, sequence design, and the enrichment of near-native solutions. We explicitly examine whether different backbone generation strategies respect the geometric constraints of the MHC binding groove and recover native-like peptide conformations. Our results reveal substantial method-dependent differences: some generative strategies fail systematically in the groove-bound peptide setting, whereas others generate physically plausible backbones with varying accuracy and conformational diversity. We further show that enforcing anchor constraints strongly influences peptide conformations at non-anchor positions, highlighting a trade-off between structural accuracy and conformational sampling. To enable fair and reproducible comparison, we introduce a standardized, multi-stage scoring protocol that integrates MHC binding prediction, physics-based energy evaluation, and independent structure prediction confidence metrics to enrich near-native designs from large candidate pools. Together, this work establishes the first comprehensive pipeline and benchmark for generative peptide design at TCR-pMHC interfaces and provides practical guidelines for developing peptide design workflows and evaluating generative models in immunologically constrained protein design settings.

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bioRxiv2022

Quantifying B-cell Clonal Diversity In Repertoire Data

The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B-cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We find that different methods may lead to different clonal definitions, which in turn can affect the quantification of clonal diversity in repertoire data. Interestingly, we find the Shannon entropy to be overall the most robust diversity index in regard to different clonal identification. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter read length. We make our implementation freely available as a Python library cdiversity.

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bioRxiv2022

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

Many different solutions to predicting the cognate epitope target of a T-cell receptor (TCR) have been proposed. However several questions on the advantages and disadvantages of these different approaches remain unresolved, as most methods have only been evaluated within the context of their initial publications and data sets. Here, we report the findings of the first public TCR-epitope prediction benchmark performed on 23 prediction models in the context of the ImmRep 2022 TCR-epitope specificity workshop. This benchmark revealed that the use of paired-chain alpha-beta, as well as CDR1/2 or V/J information, when available, improves classification obtained with CDR3 data, independent of the underlying approach. In addition, we found that straight-forward distance-based approaches can achieve a respectable performance when compared to more complex machine-learning models. Finally, we highlight the need for a truly independent follow-up benchmark and provide recommendations for the design of such a next benchmark.

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bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

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bioRxiv2019

A stochastic hybrid model for DNA replication incorporating protein mobility dynamics

DNA replication, the basis of genetic information maintenance, is a remarkably robust yet highly stochastic process. We present a computational model that incorporates experimental genome structures and protein mobility dynamics to mechanistically describe the stochastic foundations of DNA replication. Analysis of about 300,000 in silico profiles for fission yeast indicates that the number of firing factors is rate-limiting and dominates completion time. Incorporating probabilistic activation and binding, a full-genome duplication was achieved with at least 300 firing factors, with the only assumption that factors get recycled upon replication fork collision. Spatial patterns of replication timing were reproduced only when firing factors were explicitly activated proximally to the spindle pole body. Independent in vivo experiments corroborate that the spindle pole body acts as a replication activator, driving origin firing. Our model provides a framework to realistically simulate full-genome DNA replication and investigate the effects of nuclear architecture.

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