Biology subjects

Pinero, J.

Publications and source records attributed to Pinero, J..

12 recordsLinked to original sources

bioRxiv2026

DISGENET: Accelerating Data-Driven Discovery in Disease Genomics and Therapeutic Development

Precision medicine and therapeutic development rely on a comprehensive understanding of genotype-phenotype relationships, yet this information remains fragmented across diverse sources. DISGENET, established over 15 years ago, addresses this challenge by systematically integrating gene-disease, variant-disease, and disease-disease associations from authoritative databases and the literature. This major upgrade expands coverage with chemical and pharmacological annotations and integrates biobank and clinical data. An advanced natural language processing (NLP) pipeline captures emerging evidence with full provenance and contextual details, key to streamlining data-driven insights. DISGENET supports diverse users through multiple tools, including an intuitive web interface, a REST API, an R package, a Cytoscape app, and an AI assistant for natural language queries. Quarterly updates ensure data currency, while a sustainable freemium model provides free academic access and supports ongoing development. DISGENET aims to accelerate data-driven discoveries and advance precision medicine and drug development. The platform is accessible at https://www.disgenet.com. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/697749v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1cf5b9aorg.highwire.dtl.DTLVardef@87004aorg.highwire.dtl.DTLVardef@1243b2borg.highwire.dtl.DTLVardef@1a8adab_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISGENET accelerates precision medicine via comprehensive genotype-phenotype data C_LIO_LIAccess to gene, variant, disease, and drug data in a single platform C_LIO_LIUp-to-date evidence with provenance and full context C_LIO_LISuite of tools to serve diverse research and clinical communities C_LIO_LISustainable freemium model ensures ongoing platform innovation. C_LI

bioinformatics↗

bioRxiv2024

Nucleus Pulposus Cell Network Modelling in the Intervertebral Disc

Intervertebral disc degeneration (IDD) arises from an intricate imbalance between the anabolic and catabolic processes governing the extracellular matrix (ECM) within the disc. Biochemical processes are complex, redundant and feedback-looped, thus improved integration of knowledge is needed. To address this, a literature-based regulatory network model (RNM) for nucleus pulposus cells (NPC) is proposed, representing the normal state of the intervertebral disc (IVD) cells, in which proteins are represented by nodes that interact with each other through activation and/or inhibition edges. This model includes 32 different proteins and 150 edges by incorporating critical biochemical interactions in IVD regulation, tested in vivo or in vitro in human and animal NPC, alongside non-tissue-specific protein-protein interactions. We used the network to calculate the dynamic regulation of each node through a semi-quantitative method. The basal steady state successfully represented the activity of a normal NPC, and the model was assessed through the published literature, by replicating two independent experimental studies in human normal NPC. Pro-catabolic or pro-anabolic shifts of the network activated by nodal perturbations could be predicted. Sensitivity analysis underscored the significant influence of transforming growth factor beta (TGF-{beta}) and interleukin-1 receptor antagonist (IL-1Ra) on the regulation of structural proteins and degrading enzymes within the system. Given the ongoing challenge of elucidating the mechanisms that drive ECM degradation in IDD, this unique IVD RNM holds promise as a tool for exploring and predicting IDD progression, shedding light on IVD phenotypes and guiding experimental research efforts.

systems biology↗

bioRxiv2024

PREDICTING GENE DISEASE ASSOCIATIONS WITH KNOWLEDGE GRAPH EMBEDDINGS FOR DISEASES WITH CURTAILED INFORMATION

Knowledge graph embeddings (KGE) are a powerful technique used in the biological domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and in particular the limitations for diseases with reduced information on gene-disease associations. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGEs by implementing state-of-the-art methods, and two novel algorithms: DLemb and BioKG2Vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that our novel approaches outperform existing algorithms in both scenarios. Our results indicate that data preprocessing and integration influence the quality of the predictions and that the embeddings efficiently encodes biological information when compared to a null model. Finally, we employed KGE to predict genes associated with Intervertebral disc degeneration (IDD) and showed that functions relevant to the disease are enriched in the genes prioritized from the model GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/575314v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1ba98b7org.highwire.dtl.DTLVardef@1801d6borg.highwire.dtl.DTLVardef@b87a7org.highwire.dtl.DTLVardef@f70cf8_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Genopyc: a python library for investigating the genomic basis of complex diseases

MotivationUnderstanding the genetic basis of complex diseases is a paramount challenge in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relation-ships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of the genetics underlying complex dis-eases. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic da-tasets with ease. ResultsIn this study, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate variants associated to intervertebral disc degeneration (IDD) enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, fostering advancements in the un-derstanding of complex diseases and thus paving the way for more targeted therapeutic interventions. Availability: Genopyc is available at pip (https://pypi.org/project/genopyc/) and the source code of Genopyc is available at https://github.com/freh-g/genopyc Contactfrancesco.gualdi01@estudiant.upf.edu Supplementary informationsupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Behavioural and transcriptomic characterization of the comorbidity between Alzheimer s disease and Major Depression

Major Depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimers disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. In this regard, we investigated the cognitive and emotional responses in 3- and 6-month-old in APP/PSEN1-Tg mutant mice, before {beta}-amyloid plaques were detected. Then, we studied the deregulation of genes and pathways in prefrontal cortex, striatum, hippocampus and amygdala, using transcriptomic and functional data analysis. The results demonstrated that depressive-like and anxiety-like behaviours, as well as memory impairments are already present at 3-month-old together with the deregulation of several genes and gene sets, including components of the circadian rhythms, electronic transport chain and neurotransmission. Finally, DisGeNET GSEA provides translational support for common depregulated gene sets related to MD and AD. Altogether, the results demonstrate that MD could be an early manifestation of AD.

neuroscience↗

bioRxiv2020

Synthetic Turing patterns in engineered microbial consortia

Multicellular entities are characterized by exquisite spatial patterns, intimately related to the functions they perform. Oftentimes these patterns emerge as periodic structures with a well-defined characteristic scale. A candidate mechanism to explain their origins was early introduced by Alan Turing through the interaction and diffusion of two so called morphogens. Unfortunately, most available evidence for Turing patterns in biology is usually obscured by the tangled nature of regulatory phenomena, making difficult to validate Turing’s proposal in developmental processes. Here we follow a different approach, by designing synthetic genetic circuits in engineered E. coli strains that implement the essential activator-inhibitor motif (AIM) using a two-cell consortium. The two diffusible compartments are one cell type (activator, small-diffusion component) and a small signal molecule (a homoserine lactone, acting as the fast-diffusing inhibitor). Using both experimental results, we show that the AIM is capable of generating diffusion-induced instabilities leading to regular spatial patterns. The artificial construction taken here can help validate developmental theories and identify universal properties underpinning biological pattern formation. The implications of the work for the area of synthetic developmental biology are outlined.Competing Interest StatementThe authors have declared no competing interest.View Full Text

synthetic biology↗

bioRxiv2018

Habitat loss-induced tipping points in metapopulations with facilitation

Habitat loss is known to pervade extinction thresholds in metapopulations. Such thresholds result from a loss of stability that can eventually lead to collapse. Several models have been developed to understand the nature of these transitions and how are they affected by the locality of interactions, fluctuations, or external drivers. Most models consider the impact of grazing or aridity as a control parameter that can trigger sudden shifts, once critical values are reached. Others explore instead the role played by habitat loss and fragmentation. Here we consider a minimal model incorporating facilitation along with habitat destruction, with the aim of understanding how local cooperation and habitat loss interact with each other. An explicit mathematical model is derived, along with a spatially explicit simulation model. It is found that a catastrophic shift is expected for increasing levels of habitat loss, but the breakpoint dynamics becomes continuous when dispersal is local. Under these conditions, spatial patchiness is found and the qualitative change from discontinuous to continuous results from a universal behaviour found in a broad class of nonlinear ecological systems (Weissmann and Shnerb, 2014; Martin et al. PNAS (2015) E1828-E1836). Our results suggest that species exhibiting facilitation and displaying short-range dispersal will be markedly more capable of dealing with habitat destruction, also avoiding catastrophic tipping points.

ecology↗

bioRxiv2018

Statistical physics of liquid brains

Liquid neural networks (or \"liquid brains\") are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (\"solid brains\"), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to external signals.

systems biology↗

bioRxiv2018

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

The current wealth of genomic variation data identified at the nucleotide level has provided us with the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that amino acid variation of a molecules sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Here we present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data content is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website at www.ebi.ac.uk/intact/resources/datasets#mutationDs and is being enhanced with every monthly release.

bioinformatics↗

bioRxiv2018

Targeting comorbid diseases via network endopharmacology

The traditional drug discovery paradigm has shaped around the idea of \"one target, one disease\". Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to \"endopharmacology\" (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimers disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

systems biology↗

bioRxiv2017

Nonequilibrium entropic bounds for Darwinian replicators

Life evolved on our planet by means of a combination of Darwinian selection and innovations leading to higher levels of complexity. The emergence and selection of replicating entities is a central problem in prebiotic evolution. Theoretical models have shown how populations of different types of replicating entities exclude or coexist with other classes of replicators. Models are typically kinetic, based on standard replicator equations. On the other hand, the presence of thermodynamical constrains for these systems remain an open question. This is largely due to the lack of a general theory of out of statistical methods for systems far from equilibrium. Nonetheless, a first approach to this problem has been put forward in a series of novel developements in non-equilibrium physics, under the rubric of the extended second law of thermodynamics. The work presented here is twofold: firstly, we review this theoretical framework and provide a brief description of the three fundamental replicator types in prebiotic evolution: parabolic, malthusian and hyperbolic. Finally, we employ these previously mentioned techinques to explore how replicators are constrained by thermodynamics.

biophysics↗

bioRxiv2017

The Paradox Of Constant Oceanic Plastic Debris: Evidence For Evolved Microbial Biodegradation?

Although the presence of vast amounts of plastic in the open ocean has generated great concern due to its potential ecological consequences, recent studies reveal that its measured abundance is much smaller than expected. Regional and global studies indicate that the difference between expected and actual estimates is enormous, suggesting that a large part of the plastic has been degraded by either physical and biotic processes. A paradoxical observation is the lack of a trend in plastic accumulation found in the North Atlantic Subtropical Gyre, despite the rapid increase in plastic production and disposal. In this paper we show, using mathematical and computer models, that this observation could be explained by the nonlinear coupling between plastic (as a resource) and an evolved set of organisms (the consumers) capable of degrading it. The result is derived using two different resource-consumer mathematical approaches as well as a spatially-dependent plastic-microbial model incorporating a minimal hydrodynamical coupling with a two-dimensional fluid. The potential consequences of the evolution of marine plastic garbage and its removal are outlined.

ecology↗