Biology subjects

Urrutia, R. A.

Publications and source records attributed to Urrutia, R. A..

10 recordsLinked to original sources

bioRxiv2025

A Multi-State Structural Genomics Approach Enables Large-Scale, Mechanistic, and Context-Specific Classification of ABCC6 Genetic Variants Implicated in Calcification Diseases

PurposeGenetic variation in ATP Binding Cassette Subfamily C Member 6 (ABCC6) can cause both pseudoxanthoma elasticum (PXE) and generalized arterial calcification of infancy (GACI). Despite both diseases being rare, there are already 930 distinct missense variants in ABCC6 reported, 87% of which are of uncertain clinical significance (VUS). New approaches are needed to interpret and classify these VUS mechanistically. MethodsWe developed 3D protein models of ABCC6 in three functionally relevant conformations to calculate the structural effects of variants and identify 3D mutational hotspots. With this and additional functional information, we categorized variants in a mechanistic ontology based on which critical functions of ABCC6 they impact. We then compared PXE and GACI -associated variants. ResultsWe identified two three-dimensional hotspots of pathogenic variants and six specific functions of ABCC6 which variants impact. From this, we propose a mechanism for pathogenicity for 41% of VUS according to their impacted function, 30 of which could be reclassified as Likely Pathogenic from our non-clinical data. Finally, we found slight differences between PXE and GACI-associated variants. ConclusionThe mechanistic information we present will guide future research to better address calcification disorders and understand genetic variants. Further, our VUS reclassification will improve the diagnosis of ABCC6-driven diseases, shortening diagnostic odysseys. We believe that computational structural genomics approaches will soon take prominence in genomics data interpretation. HighlightsVariant 3D hotspot detection and effect clustering, GACI and PXE Variant Comparison, consistent ability to (re)categorize known pathogenic variants, and population genetics differences.

genetics↗

bioRxiv2025

An Integrative Multitiered Computational Analysis for Better Understanding the Structure and Function of 85 Miniproteins

BackgroundMiniproteins, defined as polypeptides containing fewer than 50 amino acids, have recently elicited significant interest due to an emerging understanding of their diverse roles in fundamental biological processes. In addition, miniprotein dysregulation underlies human diseases and is a significant focus for biotechnology and drug development. Notably, the human genome project revealed the existence of many novel miniproteins, most of which remain uncharacterized. This study reports an approach for analyzing and scoring previously uncharacterized miniproteins by integrating knowledge from classic sequence-based bioinformatics, computational biophysics, and system biology annotations. Our results demonstrate that these approaches provide novel information on the structure-function relationship of these molecules with a particular focus on their biomedical relevance. MethodsWe identified 85 human miniproteins using a simple multi-tier approach. First, we performed a sequence-based analysis of these proteins using several algorithms to identify regions of structural and functional importance. Protein-protein interactions and gene ontology annotations were used to analyze miniprotein function. Then, we predicted miniprotein three-dimensional structures using AI-based methods and peptide modeling to determine their relative yields for these understudied polymers. Subsequently, we used several computational biophysics methods and structure-based calculations to annotate and evaluate results from both algorithms. ResultsWe find several relations between predicted structure and functional properties to assign these proteins to several groups with similar properties. Sequence-based analysis leads us to identify motifs and residues that link structure-to-function for most of these proteins. We suggest novel miniprotein functions, such as thymosin beta proteins regulating the shelterin complex through TERF1 and POT1 interactions, FAM86JP and FAM66E participating in endocytic processes, and BAGE1 influencing chromatin remodeling through interaction with nuclear proteins. Further, known functions of miniproteins, such as STRIT1, STMP1, and SLN, were supported. Finally, structure-based scoring led us to build 3D models that provided complementary information to ontologies. We identify that structural propensity is not strictly dependent on polymer length. In fact, in this dataset, peptide-based algorithms may have advantages over AI-based algorithms for certain groups of miniproteins. ConclusionThis analytic approach and resulting identification and annotation of miniproteins adds much to what is currently known about miniproteins. Our determination of novel properties of miniproteins bears significant mechanistic and biomedical relevance. We propose novel functions of miniproteins, which expands our understanding of their potential roles in cellular processes. And, we practically identify which sequence and structure-based tools provide the most information, aiding future studies of miniproteins.

bioinformatics↗

bioRxiv2024

Advancing Variant Phenotyping in Myelodysplastic Syndromes via Computational Genomics of Mitochondrial Enzyme Complexes

Mitochondria are essential cellular organelles that play critical roles in hematological disorders. Recurrent mutations in mitochondrial DNA (mtDNA) have been identified in patients with myelodysplastic syndromes (MDS) and serve as significant prognostic indicators for their outcomes following allogeneic hematopoietic stem-cell transplantation (allo-HCT). However, the biological mechanisms of mtDNA mutations remain unclear. The current study utilizes computational structural genomics to improve our understanding of pathogenic variants in mitochondria-encoded genes. This emerging genomics discipline employs structural models, molecular mechanic calculations, and accelerated molecular dynamic simulations to analyze gene products, focusing on their structures and motions that determine their function. We applied this methodology to perform deep variant phenotyping of entire mitochondria-encoded protein complexes associated with the pathobiology of MDS and their prognosis after HCT. Our results demonstrate that this approach significantly outperforms conventional analytical methods, providing enhanced and more accurate information to support the potential pathogenicity of these variants and better infer their dysfunctional mechanisms. We conclude that the adoption and further expansion of computational structural genomics approaches, as applied to the mitochondrial genome, have the potential to significantly increase our understanding of molecular mechanisms underlying the disease. Our study lays a foundation for translating mitochondrial biology into clinical applications, which is of significant mechanistic and biomedical relevance and should be considered in modern biomedical research.

genomics↗

bioRxiv2024

EHMT2 Inactivation in Pancreatic Epithelial Cells Shapes the Transcriptional Landscape and Inflammation Response of the Whole Pancreas

The Euchromatic Histone Methyl Transferase Protein 2 (EHMT2), also known as G9a, deposits transcriptionally repressive chromatin marks that play pivotal roles in the maturation and homeostasis of multiple organs. Recently, we have shown that EHMT2 inactivation alters growth and immune gene expression networks, antagonizing KRAS-mediated pancreatic cancer initiation and promotion. Here, we elucidate the essential role of EHMT2 in maintaining a transcriptional landscape that protects organs from inflammation. Comparative RNA-seq studies between normal postnatal and young adult pancreatic tissue from EHMT2 conditional knockout animals (EHMT2fl/fl) targeted to the exocrine pancreatic epithelial cells (Pdx1-Cre and P48Cre/+), reveal alterations in gene expression networks in the whole organ related to injury-inflammation-repair, suggesting an increased predisposition to damage. Thus, we induced an inflammation repair response in the EHMT2fl/flpancreas and used a data science-based approach to integrate RNA-seq-derived pathways and networks, deconvolution digital cytology, and spatial transcriptomics. We also analyzed the tissue response to damage at the morphological, biochemical, and molecular pathology levels. The EHMT2fl/fl pancreas displays an enhanced injury-inflammation-repair response, offering insights into fundamental molecular and cellular mechanisms involved in this process. More importantly, these data show that conditional EHMT2 inactivation in exocrine cells reprograms the local environment to recruit mesenchymal and immunological cells needed to mount an increased inflammatory response. Mechanistically, this response is an enhanced injury-inflammation-repair reaction with a small contribution of specific EHMT2-regulated transcripts. Thus, this new knowledge extends the mechanisms underlying the role of the EHMT2-mediated pathway in suppressing pancreatic cancer initiation and modulating inflammatory pancreatic diseases.

cancer biology↗

bioRxiv2023

A Multi-Layered Computational Structural Genomics Approach Enhances Domain-Specific Interpretation of Kleefstra Syndrome Variants in EHMT1

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretation of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within this domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism of variant dysfunction.

genomics↗

bioRxiv2022

Microglia influence neurofilament deposition in ALS iPSC-derived motor neurons

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease in which upper and lower motor neuron loss is the primary phenotype, leading to muscle weakness and wasting, respiratory failure, and death. Although a portion of ALS cases are linked to one of over 50 unique genes, the vast majority of cases are sporadic in nature. However, the mechanisms underlying the motor neuron loss in either familial or sporadic ALS are not entirely clear. Here we used induced pluripotent stem cells derived from a set of identical twin brothers discordant for ALS to assess the role of astrocytes and microglia on the expression and accumulation of neurofilament proteins in motor neurons. We found that motor neurons derived from the affected twin exhibited increased transcript levels of all three neurofilament isoforms and increased expression of phosphorylated neurofilament puncta. We further found that treatment of the motor neurons with astrocyte conditioned medium and microglial conditioned medium significantly impacted neurofilament deposition. Together, these data suggest that glial-secreted factors can alter neurofilament pathology in ALS iPSC-derived motor neurons.

neuroscience↗

bioRxiv2021

Metabolomic profiling of pancreatic adenocarcinoma reveals fundamental clinical features

In this study, we perform the metabolomics profiling of 77 PDAC patient-derived tumor xenografts (PDTX) to investigate the relationship of metabolic profiles with overall survival (OS) in PDAC patients, tumor phenotypes and resistance to five anticancer drugs (gemcitabine, oxaliplatin, docetaxel, SN-38 and 5-Fluorouracil). We identified a metabolic signature that was able to predict the clinical outcome of PDAC patients (p < 0.001, HR=2.68 [95% CI: 1.5-4.9]). The correlation analysis showed that this metabolomic signature was significantly correlated with the PDAC molecular gradient (PAMG) (R = 0.44 and p < 0.001) indicating significant association to the transcriptomic phenotypes of tumors. Resistance score established, based on growth rate inhibition metrics using 35 PDTX-derived primary cells, allowed to identify several metabolites related to drug resistance which was globally accompanied by accumulation of several diacy-phospholipids and decrease in lysophospholipids. Interestingly, targeting glycerophospholipid synthesis improved sensitivity to the three tested cytotoxic drugs indicating that interfering with metabolism could be a promising therapeutic strategy to overcome the challenging resistance of PDAC. Short abstractTargeting metabolism of cancer cells gives a precious opportunity to overcome challenges related to the high mortality and chemoresistance in PDAC. Metabolic profiling of PDAC patient-derived tumor xenografts used in this study allowed highlighting the strong link between metabolism and both clinical outcome of the patients and chemoresistance. Metabolic signature was able to discriminate between good and bad prognosis groups of patients based on their level of key metabolites. Identification of key metabolic markers associated to chemoresistance allowed to improve sensitivity to anticancer drugs. These results provide new insights to help to predict patient survival and elaborate new combinatory therapies against chemoresistance in PDAC patients attesting of the important clinical value of this work.

cancer biology↗

bioRxiv2020

A GATA6-centered gene regulatory network involving HNFs and ΔNp63 controls plasticity and immune escape in pancreatic cancer

ObjectiveMolecular taxonomy of tumors is the foundation of personalized medicine and is becoming of paramount importance for therapeutic purposes. Four transcriptomics-based classification systems of pancreatic ductal adenocarcinoma (PDAC) exist, which consistently identified a subtype of highly aggressive PDACs with basal-like features, including {Delta}Np63 expression and loss of the epithelial master regulator GATA6. We investigated the precise molecular events driving PDAC progression and the emergence of the basal program. DesignWe combined the analysis of patient-derived transcriptomics datasets and tissue samples with mechanistic experiments using a novel dual-recombinase mouse model for Gata6 deletion at late stages of KRasG12D-driven pancreatic tumorigenesis (Gata6LateKO). ResultsThis comprehensive human-to-mouse approach allowed us to show that GATA6 loss is necessary, but not sufficient, for the expression of a basal program in patients and in mice. The concomitant loss of HNF1A and HNF4A, likely through epigenetic silencing, is required for the full phenotype switch. Moreover, Gata6 deletion in mice dramatically increased the metastatic rate, with a propensity for lung metastases. Through RNA-Seq analysis of primary cells isolated from mouse tumors, we show that Gata6 inhibits tumor cell plasticity and immune evasion, suggesting that it works as a barrier for acquiring the fully developed basal and metastatic phenotype. ConclusionsOur work provides both a mechanistic molecular link between the basal phenotype and metastasis and a valuable preclinical tool to investigate the most aggressive subtype of PDAC. These data, therefore, are important for understanding the pathobiological features underlying the heterogeneity of pancreatic cancer in both mice and human. What is already known about this subject?O_LIMultiple transcriptomics-based studies have identified a basal-like subtype of pancreatic ductal adenocarcinoma (PDAC) with especially poor prognosis. C_LIO_LILoss of GATA6 in PDAC cells is associated with altered differentiation, including ectopic expression of basal markers such as KRT14. C_LIO_LIAberrant expression of the {Delta}Np63 transcription factor can drive the expression of the basal transcriptional program. C_LI What are the new findings?O_LILoss of GATA6 expression is necessary but not sufficient for the expression of {Delta}Np63 and the basal phenotype. C_LIO_LIConcomitant silencing of HNF4A and HNF1A, possibly through epigenetic mechanisms, is required for the full-blown phenotype. C_LIO_LIGata6 deletion in established murine tumors favors the basal and metastatic phenotype, with a lung tropism, in a next-generation model of KRasG12D-driven PDAC. C_LIO_LILoss of GATA6 expression is associated with features of immune escape in mouse and human PDAC cells. C_LI How might it impact on clinical practice in the foreseeable future?O_LIThe combined analysis of GATA6, HNFs, and TP63 expression in patient-derived samples will provide a more precise classification of PDAC. C_LIO_LIRestoration of the classical PDAC phenotype may not only reduce metastatic potential but also increase immune recognition of tumor cells. C_LI

cancer biology↗

bioRxiv2019

Integration of Multi-level Molecular Scoring for the Interpretation of RAS-Family Genetic Variation

Protein-coding genetic variants are the first considered in most studies and Precision Medicine workflows, but their interpretation is primarily driven by DNA sequence-based analytical tools and annotations. Thus, more specific and mechanistic interpretations should be attainable by integrating DNA-based scores with scores from the protein 3D structure. However, reliable and reproducible standardization of methods that use 3D structure for genomic variation is still lacking. Further, we believe that the current paradigm of aiming to directly predict the pathogenicity of variants skips the critical step of inferring, with precision, molecular mechanisms of dysfunction. Thus, we report herein the development and evaluation of single and composite 3D structure-based scores and their integration with protein and DNA sequence-based scores to better understand not only if a genomic variant alters a protein, but how. We believe this is a critical step for understanding mechanistic changes due to genomic variants, designing functional validation tests, and for improving disease classifications. We applied this approach to the RAS gene family encoding seven distinct proteins and their 935 unique missense variants present somatically in cancer, in rare diseases (termed RASopathies), and in the currently healthy adult population. This knowledge shows that protein structure-based scores are distinct from information available from genomic annotation, that they are useful for interpreting genomic variants, and they should be taken into consideration in future guidelines for genomic data interpretation.\n\nSignificance StatementGenetic information from patients is a powerful data type for understanding individual differences in disease risk and treatment, but most of the genetic variation we observe has no mechanistic interpretation. This lack of interpretation limits the use of genomics data in clinical care. Standard methods for genomics data interpretation take advantage of annotations available for the human reference genome, but they do not consider the 3D protein molecule. We believe that changes to the 3D molecule must be considered, to augment current practice and lead to more precise interpretation. In this work, we present our initial process for systematic multi-level molecular scores, including 3D, to interrogate 935 RAS-family variants that are relevant in both cancer and rare diseases.

genetics↗

bioRxiv2018

Inactivation of NUPR1 promotes cell death by coupling ER-stress responses with necrosis

Genetic inhibition of NUPR1 induces tumor growth arrest. Inactivation of NUPR1 expression in pancreatic cancer cells results in lower ATP production, higher consumption of glucose with a significant switch from OXPHOS to glycolysis followed by necrotic cell death. Importantly, induction of necrosis is independent of the caspase activity. We demonstrated that NUPR1 inactivation triggers a massive release of Ca2+ from the endoplasmic reticulum (ER) to the cytosol and a strong increase in ROS production by mitochondria with a concomitant relocalization of mitochondria to the vicinity of the ER. In addition, transcriptomic analysis of NUPR1-deficient cells shows the induction of an ER stress which is associated to a decrease in the expression of some ER stress response-associated genes. Indeed, during ER stress induced by the treatment with thapsigargin, brefeldin A or tunicamycin, an increase in the mitochondrial malfunction with higher induction of necrosis was observed in NUPR1-defficent cells. Finally, activation of NUPR1 during acute pancreatitis protects acinar cells of necrosis in mice. Altogether, these data enable us to describe a model in which inactivation of NUPR1 in pancreatic cancer cells results in an ER stress that induces a mitochondrial malfunction, a deficient ATP production and, as consequence, the cell death by necrosis.\n\nHighlightsNUPR1 expression promotes pancreatic cancer development and progression\n\nNUPR1-depletion is a promising therapeutic strategy to be used for treating cancers\n\nNUPR1-depletion induces ER stress, mitochondrial malfunction and a significant switch from OXPHOS to glycolysis followed by necrotic cell death\n\nInactivation of NUPR1 antagonizes cell growth by coupling a defective ER-stress response and a caspase-independent necrosis.

cell biology↗