Biology subjects

Madrigal, A.

Publications and source records attributed to Madrigal, A..

8 recordsLinked to original sources

bioRxiv2026

TCF4 Mediates PHF6 Transcriptional Regulation of Neural Stem Cells in the Developing Brain, a Mechanism Disrupted in Börjeson Forssman Lehmann Syndrome (BFLS)

Transcriptional hierarchies govern neural stem cell (NSC) fate decisions during neurodevelopment, and disruption of these regulatory networks underlies neurodevelopmental disorders. Borjeson-Forssman-Lehmann syndrome (BFLS) is an X-linked intellectual disability (XLID) caused by mutations in a chromatin-associated transcriptional regulator, the plant homeodomain zinc finger protein 6 (PHF6). Although BFLS mouse models harboring PHF6 patient mutations recapitulate neurogenic and cognitive deficits, the molecular mechanisms linking PHF6 dysfunction to these phenotypes remain poorly understood. Here, we identify transcription factor 4 (TCF4), a basic helix-loop-helix transcription factor implicated in neurodevelopmental and psychiatric disorders, as a critical downstream effector of PHF6 in NSC fate regulation. Tcf4 expression is reduced in BFLS mouse models harbouring R342X and C99F-m mutations and in a conditional Phf6 knockout driven by Nestin-Cre. Mechanistically, PHF6 binds the Tcf4 gene regulatory element upstream of its transcriptional start site (TSS) and promotes Tcf4 expression. Tcf4 depletion in embryonic NSCs enhances self-renewal and stemness, increasing neurosphere formation and expression of stem cell markers, Nestin and Sox2. Conversely, TCF4 restoration rescues NSC defects caused by PHF6 loss of function in BFLS and Phf6/Nestin-Cre mice. This regulatory relationship persists in adult NSCs where TCF4 similarly restricts stem cell expansion. Importantly, restoration of TCF4 expression in embryonic brain ameliorates behavioral and cognitive deficits in BFLS mice. Together, these findings establish a PHF6-Tcf4 transcriptional pathway that restricts NSC self-renewal and links disrupted transcriptional control of NSC fate to the neurodevelopmental and behavioral abnormalities of BFLS.

neuroscience↗

bioRxiv2026

Lysyl oxidase drives ccRCC progression by coordinating HIF-2α transcription program with tumor microenvironment

Clear cell renal cell carcinoma (ccRCC) is driven by persistent HIF-2 transcription program initiated by VHL loss, yet molecular mediators sustaining this program are poorly defined. Using single-cell transcriptomics, we identified lysyl oxidase (LOX) as a driver of ccRCC progression, selectively enriched in a hypoxia/epithelial-mesenchymal transition (EMT) gene program associated with poor outcome. While LOX oxidizes and stabilizes HIF-2 by antagonizing HUWE1-mediated ubiquitination and degradation, thereby sustaining HIF-2-driven transcription in cancer cells, it also remodels extracellular matrix (ECM) and promotes angiogenesis in the tumor microenvironment (TME). Genetic or pharmacological inhibition of LOX destabilizes HIF-2, disrupts ECM, inhibits angiogenesis, and suppresses tumor initiation, growth, and metastasis in vivo. LOX inhibition enhances anti-angiogenic therapy response and remains effective in belzutifan-resistant HIF-2 G323E-mutant tumors. Nuclear LOX protein correlates with nuclear HIF-2 in high-grade patient tumors. Together, LOX coordinates HIF-2 transcription program with TME and is a therapeutic target in ccRCC.

cancer biology↗

bioRxiv2026

A novel risk score model based on seven protein-coding genes and three pseudogenes predicts overall survival in acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common hematologic malignancy in adults and is associated with poor clinical outcomes. Accurate prognostic stratification remains essential for improving patient management and identifying potential therapeutic targets. Here, we analyzed RNA-seq data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) cohorts to identify genes associated with overall survival (OS) in patients with AML. Using penalized regression modeling and integrative survival analyses, we identified a prognostic signature composed of seven protein-coding genes (ACOT7, SLC35E4, SELPLG, CCND3, RRAS, ITGAX, and COMTD1) and three processed pseudogenes (FDPSP2, UBE2V1P13, and AL158214.1) consistently associated with OS across independent AML cohorts. Based on these genes, we developed a novel risk score model that stratified AML patients into low- and high-risk groups with significantly different survival outcomes. The model demonstrated reproducible prognostic performance in TARGET and TCGA cohorts. In addition, several genes included in the prognostic signature were significantly dysregulated in AML compared with healthy samples. Functional enrichment analyses further revealed that high-risk AML was associated with suppression of translational and RNA-processing pathways, together with cohort-specific enrichment of metabolic and immune-related programs. Overall, this study presents a novel prognostic risk score integrating protein-coding genes and pseudogenes that robustly predicts OS in AML, providing a framework for improved prognostic stratification and potential biological insights into AML progression.

Cancer Biology↗

bioRxiv2026

Recursive mutational robustness in cancer through intra- and inter-genic compensation

Mutational robustness is a fundamental property of biological systems allowing phenotypic stability against genotypic perturbation. At the gene level, this is primarily attributed to inter-genic functional redundancy driven by paralogs. Here, we show that intra-genic functional redundancy, driven by alternatively spliced isoforms, is another prevalent source of mutational robustness. Integrating isoform-resolved pan-cancer genomics and transcriptomics data across tumors and cancer cell lines, we show that alternative isoforms often bypass deleterious somatic mutations. Mutation-skipping isoforms exhibit compensatory upregulation indicative of robustness against deleterious effects, especially in highly expressed genes. We found that intra-genic robustness is strongly associated with perturbation tolerance, is highly context-specific and, notably, can be complemented and augmented recursively by inter-genic robustness. Non-tumour-suppressor genes generally show higher robustness than tumour suppressor genes, and intra-genic compensation of their mutations is associated with worse disease outcome in most cancer types. Furthermore, intra-genic compensation by mutation-skipping isoforms associates with differential isoform usage among proximal interactors, suggesting restorative protein interaction rewiring at isoform-level. Mechanistically, self-transcriptional adaptation (self-TA) following nonsense-mediated decay of perturbed isoforms provides an explanation for the observed up-regulation of mutation-skipping isoforms. Overall, the proposed recursive mutational robustness framework opens new avenues for deeper mechanistic investigation into cancer evolution and identification of selective vulnerabilities in cancer cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/727768v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1b0f79aorg.highwire.dtl.DTLVardef@14fa261org.highwire.dtl.DTLVardef@12da16aorg.highwire.dtl.DTLVardef@c03dee_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data

Analysis of single cells across multiple samples and/or conditions encompasses a series of interrelated tasks, which range from normalization and inter-sample harmonization to identification of cell state shifts associated with experimental conditions. Other downstream analyses are further needed to annotate cell states, extract pathway-level activity metrics, and/or nominate gene regulatory drivers of cell-to-cell variability or cell state shifts. Existing methods address these analytical requirements sequentially, lacking a cohesive framework to unify them. Moreover, these analyses are currently confined to specific modalities where the biological quantity of interest gives rise to a singular measurement. However, other modalities require joint consideration of dual measurements; for example, modeling the latent space of alternative splicing involves joint analysis of exon inclusion and exclusion reads. Here, we introduce a generative model, called GEDI, to identify latent space variations in multi-sample, multi-condition single cell datasets and attribute them to sample-level covariates. GEDI enables cross-sample cell state mapping on par with the state-of-the-art integration methods, cluster-free differential gene expression analysis along the continuum of cell states in the form of transcriptomic vector fields, and machine learning-based prediction of sample characteristics from single-cell data. By incorporating gene-level prior knowledge, it can further project pathway and regulatory network activities onto the cellular state space, enabling the computation of the gradient fields of transcription factor activities and their association with the transcriptomic vector fields of sample covariates. Finally, we demonstrate that GEDI surpasses the gene-centric approach by extending all these concepts to the study of alternative cassette exon splicing and mRNA stability landscapes in single cells.

bioinformatics↗

bioRxiv2021

Transcriptional Control of Brain Tumour Stem Cell Fate by a Carbohydrate Binding Protein

Brain tumour stem cells (BTSCs) and intratumoural heterogeneity represent major challenges in glioblastoma therapy. Here, we report that the LGALS1 gene, encoding the carbohydrate binding protein, galectin1, is a key regulator of BTSCs and glioblastoma resistance to therapy. Genetic deletion of LGALS1 alters BTSC gene expression profiles and results in downregulation of gene sets associated with mesenchymal subtype of glioblastoma. Using a combination of pharmacological and genetic approaches, we establish that inhibition of LGALS1 signalling in BTSCs impairs self-renewal, suppresses tumourigenesis, prolongs lifespan, and improves glioblastoma response to ionizing radiation in preclinical animal models. Mechanistically, we show that LGALS1 is a direct transcriptional target of STAT3 with its expression robustly regulated by the ligand OSM. Importantly, we establish that galectin1 forms a complex with the transcription factor HOXA5 to reprogram BTSC transcriptional landscape. Our data unravel an oncogenic signalling pathway by which galectin1/HOXA5 complex maintains BTSCs and promotes glioblastoma.

cancer biology↗

bioRxiv2020

COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types.

Common genetic polymorphisms associated with severity of COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Here, we assessed the effects of 679 COVID-19-risk variants on gene expression in a wide-range of immune cell types. Severe COVID-19-risk variants were significantly associated with the expression of 11 protein-coding genes, and overlapped with either target gene promoter or cis-regulatory regions that interact with target promoters in the cell types where their effects are most prominent. For example, we identified that the association between variants in the 3p21.31 risk locus and the expression of CCR2 in classical monocytes is likely mediated through an active cis-regulatory region that interacted with CCR2 promoter specifically in monocytes. The expression of several other genes showed prominent genotype-dependent effects in non-classical monocytes, NK cells, B cells, or specific T cell subtypes, highlighting the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

immunology↗

bioRxiv2020

CD4+ follicular helper-like T cells are key players in anti-tumor immunity

To determine the nature of CD4+ T cells that provide help for generating robust anti-tumor CD8+ cytotoxic T cell (CTL) responses, we profiled the transcriptomes of patient-matched CD4+ and CD8+ T cells present in the tumor micro-environment (TME) and analyzed them jointly using integrated weighted gene correlation network analysis. We found the follicular helper T cell (TFH) program in CD4+ T cells was strongly associated with proliferation and tissue-residency in CD8+ CTLs. Single-cell analysis demonstrated the presence of TFH-like cells and features linked to cytotoxic function and their provision of CD8+ T cell help. Tumor-infiltrating TFH-like cells expressed PD-1 and were enriched in tumors following checkpoint blockade, suggesting that they may respond to anti-PD-1 therapy. Adoptive transfer or induction of TFH cells in mouse models resulted in augmented CD8+ CTL responses and impairment of tumor growth, indicating an important role of TFH-like CD4+ T cells in anti-tumor immunity.

immunology↗