Biology subjects

Valencia, A.

Publications and source records attributed to Valencia, A..

9 recordsLinked to original sources

bioRxiv2026

PhysiCelldFBA: Linking single-cell genome-scale metabolism to spatially explicit multicellular dynamics

Genome-scale metabolic models can predict how individual cells allocate resources and respond to their environment, yet few frameworks link single-cell metabolism to the spatial organisation of multicellular systems. Here we introduce PhysiCelldFBA, an extension of the PhysiCell agent-based framework that couples genome-scale dynamic flux balance analysis to off-lattice multicellular simulations. Each simulated cell carries its own metabolic model, allowing local environmental conditions to shape metabolism while metabolic activity feeds back on the surrounding environment, cellular behaviour, and spatial organisation. We first validate this coupling by showing that glucose consumption, CO2 production, and biomass accumulation remain mass-balanced in a closed E. coli system, with simulated biomass agreeing with analytical predictions to within 1%. We then demonstrate how metabolic phenotypes emerge from this coupling across microbial and mammalian systems. Spatial nutrient gradients generate metabolic stratification and acetate cross-feeding in growing E. coli colonies; diffusion-limited metabolism produces proliferative, hypoxic, and necrotic zones across a broad panel of metabolites in a tumour-like tissue; distinct, organism-specific metabolic networks give rise to syntrophic cross-feeding and spatial niche formation in a two-species consortium; and metabolic state couples energy availability to transitions between cellular motility and growth. Across these examples, metabolic stratification, cross-feeding, and phenotypic adaptation emerge from local metabolic optimisation and environmental feedback rather than being explicitly prescribed. PhysiCelldFBA therefore provides a general framework for simulating genome-scale metabolism at single-cell resolution and linking intracellular metabolic state to cellular behaviour and emergent organisation across scales.

systems biology↗

bioRxiv2018

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis. Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. These shared transcriptomic alterations could help explain epidemiological observations suggesting direct and inverse comorbid associations between ASD and particular cancer types.

systems biology↗

bioRxiv2018

Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles

Comorbidity is an impactful medical problem that is attracting increasing attention in healthcare and biomedical research. However, little is known about the molecular processes leading to the development of a specific disease in patients affected by other conditions. We present a disease interaction network inferred from similarities in patients molecular profiles, which significantly recapitulates epidemiologically documented comorbidities, providing the basis for their interpretation at a molecular level. Furthermore, expanding on the analysis of subgroups of patients with similar molecular profiles, our approach discovers comorbidity relations not previously described, implicates distinct genes in such relations, and identifies drugs whose side effects are potentially associated to the observed comorbidities.

bioinformatics↗

bioRxiv2018

Pathway and network analysis of more than 2,500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

cancer biology↗

bioRxiv2018

Epigenomic and functional dynamics of human bone marrow myeloid differentiation to mature blood neutrophils

Neutrophils are short-lived blood cells that play a critical role in host defense against infections. To better comprehend neutrophil functions and their regulation, we provide a complete epigenetic and functional overview of their differentiation stages from bone marrow-residing progenitors to mature circulating cells. Integration of epigenetic and transcriptome dynamics reveals an enforced regulation of differentiation, through cellular functions such as: release of proteases, respiratory burst, cell cycle regulation and apoptosis. We observe an early establishment of the cytotoxic capability, whilst the signaling components that activate antimicrobial mechanisms are transcribed at later stages, outside the bone marrow, thus preventing toxic effects in the bone marrow niche. Altogether, these data reveal how the developmental dynamics of the epigenetic landscape orchestrate the daily production of large number of neutrophils required for innate host defense and provide a comprehensive overview of the epigenomes of differentiating human neutrophils.\n\nKey pointsO_LIDynamic acetylation enforces human neutrophil progenitor differentiation.\nC_LI\n\nO_LINeutrophils cytotoxic capability is established early at the (pro)myelocyte stage.\nC_LI\n\nO_LICoordinated signaling component expression prevents unwanted toxic effects to the bone marrow niche.\nC_LI

immunology↗

bioRxiv2017

Lessons Learned: Recommendations for Establishing Critical Periodic Scientific Benchmarking

The dependence of life scientists on software has steadily grown in recent years. For many tasks, researchers have to decide which of the available bioinformatics software are more suitable for their specific needs. Additionally researchers should be able to objectively select the software that provides the highest accuracy, the best efficiency and the highest level of reproducibility when integrated in their research projects.\n\nCritical benchmarking of bioinformatics methods, tools and web services is therefore an essential community service, as well as a critical component of reproducibility efforts. Unbiased and objective evaluations are challenging to set up and can only be effective when built and implemented around community driven efforts, as demonstrated by the many ongoing community challenges in bioinformatics that followed the success of CASP. Community challenges bring the combined benefits of intense collaboration, transparency and standard harmonization. Only open systems for the continuous evaluation of methods offer a perfect complement to community challenges, offering to larger communities of users that could extend far beyond the community of developers, a window to the developments status that they can use for their specific projects. We understand by continuous evaluation systems as those services which are always available and periodically update their data and/or metrics according to a predefined schedule keeping in mind that the performance has to be always seen in terms of each research domain.\n\nWe argue here that technology is now mature to bring community driven benchmarking efforts to a higher level that should allow effective interoperability of benchmarks across related methods. New technological developments allow overcoming the limitations of the first experiences on online benchmarking e.g. EVA. We therefore describe OpenEBench, a novel infra-structure designed to establish a continuous automated benchmarking system for bioinformatics methods, tools and web services.\n\nOpenEBench is being developed so as to cater for the needs of the bioinformatics community, especially software developers who need an objective and quantitative way to inform their decisions as well as the larger community of end-users, in their search for unbiased and up-to-date evaluation of bioinformatics methods. As such OpenEBench should soon become a central place for bioinformatics software developers, community-driven benchmarking initiatives, researchers using bioinformatics methods, and funders interested in the result of methods evaluation.

bioinformatics↗

bioRxiv2017

Widespread population variability of intron size in evolutionary old genes: implications for gene expression variability

Introns can be extraordinarily large and they account for the majority of the DNA sequence in human genes. However, little is known about their population patterns of structural variation and their functional implication. By combining the most extensive maps of CNVs in human populations, we have found that intronic losses are the most frequent copy number variants (CNVs) in protein-coding genes in human, with more than 12,986 intronic deletions, affecting 4,147 genes (including 1,154 essential genes and 1,638 disease-related genes). These intronic length variation results in dozens of genes showing extreme population variability in size, with 40 genes with 10 or more different sizes and up to 150 allelic sizes. Intronic losses are frequent in evolutionarily ancient genes that are highly conserved at the protein sequence level. This result contrasts with losses overlapping exons, which are observed less often than expected by chance and almost exclusively affect primate-specific genes. An integrated analysis of CNVs and RNA-seq data showed that intronic loss can be associated with significant differences in gene expression levels in the population (CNV-eQTLs). These intronic CNV-eQTLs regions are enriched for intronic enhancers and can be associated with expression differences of other genes showing long distance intron-promoter 3D interactions. Our data suggests that intronic structural variation of protein-coding genes can exert an important role in maintaining the variability of gene expression and splicing in human populations.\n\nAuthor SummaryMost human genes have introns that have to be removed after a gene is transcribed from DNA to RNA because they not encode information to translate RNA into proteins. As mutations in introns do not affect protein sequences, they are usually ignored when looking for normal or pathogenic genomic variation. However, introns comprise about half of the human non-coding genome and they can have important regulatory roles. We show that deletions of intronic regions appear more frequent than previously expected in the healthy population, with a significant proportion of genes with evolutionary ancient and essential functions carrying them. This finding was very surprising, as ancient genes tend to have high conservation of their coding sequence. However, we show that deletions of their non-coding intronic sequence can produce considerable changes in their locus length. We found that a significant number of these intronic deletions are associated with under- or over-expression of the affected genes or distant genes interacting in 3D. Our data suggests that the frequent gene length variation in protein-coding genes resulting from intronic CNVs might influence their regulation in different individuals.

genomics↗

bioRxiv2016

Genome-wide Analysis of Differential Transcriptional and Epigenetic Variability Across Human Immune Cell Types

BackgroundA healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.\n\nResultsWe applied a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16- monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naive T cells, from the same 125 healthy individuals. We discovered substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression were found to be implicated in key immune pathways and to associate with cellular properties and environmental exposure. We also observed increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites were enriched at dynamic chromatin regions and active enhancers.\n\nConclusionsOur data highlight the importance of transcriptional and epigenetic variability for the neutrophils key role as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.

molecular biology↗

bioRxiv2016

Searching for the chromatin determinants of human hematopoiesis

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify forty-two human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet

genomics↗