Biology subjects

Subirana-Granes, M.

Publications and source records attributed to Subirana-Granes, M..

6 recordsLinked to original sources

bioRxiv2026

Common cis-eQTLs do not explain chromosome 21 expression deviations from the ploidy expectation in Down syndrome

Down syndrome (DS) results from trisomy 21 (T21), the triplication of human chromosome 21 (HSA21). On average, genes on HSA21 are overexpressed at the expected 1.5-fold increase. A small subset of HSA21 genes is expressed closer to euploid levels. Could local genetic regulatory variation explain deviations from the expected dosage? Using the largest DS-specific transcriptomic dataset to date from the Human Trisome Project (HTP) and GTEx expression quantitative trait loci (eQTLs), we tested whether local genetic regulatory variation contributes to gene-specific buffering or amplification of HSA21 dosage effects in DS. We compared the bulk RNA-seq profiles of protein-coding genes, lncRNAs, and pseudogenes from whole-blood samples of 274 participants with T21 and 80 euploid (D21) participants. We filtered out genes with low read counts or high genomic repeat content and applied ploidy correction to the differential expression analysis. We found 13 genes that differed significantly from the expected fold change. We cross-referenced GTEx whole-blood eQTLs (cis window 1 {+/-} Mb) with the 13 deviating genes. A common cis-eQTL was detected for 8 of the 10 testable genes. However, 59 of 99 HSA21 genes that had the expected fold change had significant cis-eQTLs. Additionally, T21 participants carry the eQTL alleles at similar frequencies to GTEx donors. Therefore, cis-eQTLs explain differences between people with T21, not the difference between T21 and D21. Overall, common cis-regulatory variation acts on HSA21 genes in T21 as it does in euploid blood, but it does not explain which genes deviate from the ploidy expectation.

systems biology↗

bioRxiv2026

An AI-Powered Trisomy 21 Research Assistant

Down syndrome, caused by trisomy 21, increases the risk of diverse co-occurring conditions. With more than 34,000 related publications indexed in PubMed as of early 2026, keeping pace with this expanding literature is challenging. While general-purpose large language models are widely used for information retrieval, they often rely on broad training data rather than specific evidence. Retrieval-augmented generation (RAG) improves rigor and reliability of responses by linking model outputs to source texts. In research, source texts are peer-reviewed articles. Standard implementations treat all manuscript sections equally, allowing background text to rank as highly as experimental results. To focus model outputs on experimentally supported responses, we developed the T21 Research Assistant, a section-aware RAG system that prioritizes Results sections to ground responses in primary experimental evidence. The system draws exclusively from 1,789 open-access Down syndrome publications from PubMed Central, including 327 NIH INCLUDE-funded studies, and uses a multistage pipeline for query validation, retrieval, reranking, synthesis, and citation verification. Built on NVIDIA Nemotron models, it generates structured, cited responses. Evaluation using expert-curated questions demonstrated strong performance, achieving a BERTScore F1 of 0.712 and recall of 0.758, comparable to or exceeding leading proprietary and open-source models. T21 Research Assistant is available at: https://bioinformatics.cuanschutz.edu/t21-res-assi/

bioinformatics↗

bioRxiv2025

PLIERv2: bigger, better and faster

Gene expression analysis has long been fundamental for elucidating molecular pathways and gene-disease relationships, but traditional single-gene approaches cannot capture the coordinated regulatory networks underlying complex phenotypes; although unsupervised matrix factorization methods (e.g., PCA, NMF) reveal coexpression patterns, they lack the ability to incorporate prior biological knowledge and often struggle with interpretability and technical noise correction. Semi-supervised strategies such as PLIER have improved interpretability by integrating pathway annotations during latent variable extraction, yet the original PLIER implementation is prohibitively slow and memory-intensive, making it impractical for modern large-scale resources like ARCHS4 or recount3. Here, we introduce CLAMP, which overcomes these constraints through a two-phase algorithmic design (an unsupervised "CLAMPbase" initialization followed by a "CLAMPfull" regression that incorporates priors via glmnet), rigorous internal cross-validation to tune regularization parameters for each latent variable, and efficient on-disk data handling using memory-mapped matrices from the bigstatsr package. Benchmarking on GTEx, recount2, and ARCHS4 demonstrates that CLAMP achieves 7x-41x speedups over PLIER, succeeds in modeling hundreds of thousands of samples that PLIER cannot handle, and maintains or improves biological specificity of latent variables as shown by tissue-alignment and pathway enrichment analyses. By filling the gap in scalable, biologically informed latent variable extraction, CLAMP enables comprehensive analysis of modern transcriptomic compendia and paves the way for deeper insights into gene regulatory networks and downstream applications in translational genomics.

bioinformatics↗

bioRxiv2025

Mechanistic insights into Down syndrome comorbidities via convergent RNA-seq and TWAS signals

Down syndrome (DS) is caused by trisomy of chromosome 21 and is associated with diverse clinical manifestations, yet the molecular pathways linking chromosome-21 dosage effects to DS comorbidities remain poorly defined. Here we address this gap by applying a network-based, integrative framework that combines whole-blood transcriptomic data with gene-trait associations to uncover mechanistic insights into DS-associated conditions. First, we performed matrix factorization using PLIER on Human Trisome Project (HTP) RNA-Seq profiles from 304 trisomy-21 (T21) and 95 euploid (D21) individuals, deriving 156 biologically interpretable gene modules. We then identified 92 modules whose activity differed significantly between T21 and D21 and annotated these with prior-knowledge and KEGG pathways. To connect modules to clinical traits, we integrated PrediXcan-derived TWAS results from the UK Biobank, revealing 25 T21-specific modules with significant gene-trait associations (FDR < 0.1), including modules linked to cardiovascular, hematological, immune, metabolic, and neurological phenotypes relevant to DS. Using HTP clinical records as a replication cohort, 13 of these modules reliably predicted comorbidity status (AUC > 0.65, mAPS > 0.65). Most notably module 37, an interferon-stimulated gene network, whose elevated expression robustly distinguished DS individuals with pulmonary hypertension (AUC = 0.76, mAPS = 0.73). Overall, our study demonstrates that integrating blood-derived gene modules with population-scale genetic data uncovers coherent molecular signatures underlying DS comorbidities, identifies candidate biomarkers and therapeutic targets (e.g., ISG15, IFITs, MX1), and highlights the power of combining transcriptomic and genetic evidence to elucidate complex disease mechanisms.

bioinformatics↗

bioRxiv2024

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumours arising from the pancreatic {beta}-cells. While retaining the ability to produce insulin, insulinomas feature aberrant proliferation and altered hormone secretion resulting in failure to maintain glucose homeostasis. With the aim of uncovering the role of noncoding regulatory regions and their aberrations to the development of these tumors, we coupled epigenetic and gene expression profiling with whole-genome sequencing. As a result, we mapped H3K27ac sites in the tumoral tissue and unraveled overlapping somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development and epigenetic modifying genes, including key components of the polycomb complex. Chromatin remodeling is apparent as insulinoma-selective regions are mostly clustered in regulatory domains, shared across patients and containing a specific set of regulatory sequences dominated by the binding motif of the transcription factor SOX17. Moreover, a large fraction of these regions are H3K27me3-repressed in unaffected {beta}-cells, suggesting that tumoral transition is coupled with derepression of {beta}-cell polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements and transcription factors that alter {beta}-cell function and fate in their progression to pancreatic neuroendocrine tumors and a framework to identify coding and noncoding driver mutations.

genomics↗

bioRxiv2019

The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides new insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic {beta} cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the {beta}-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the {beta}-cell transcriptome, proteome and 3D chromatin structure. Our data indicates that the {beta} cell response to cytokines is mediated by the induction of novel regulatory regions as well as the activation of primed regulatory elements pre-bound by islet-specific transcription factors. We found that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human {beta} cells. Our study illustrates how {beta} cells respond to a pro-inflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

genomics↗