bioRxiv Science⌕ Search

Biology subjects

Valencia, A.

Publications and source records attributed to Valencia, A..

At least 37 records · Page 2Linked to original sources

Frustraevo: A Web Server To Localize And Quantify The Conservation Of Local Energetic Frustration In Protein Families

According to the Principle of Minimal Frustration, folded proteins can have only a minimal number of strong energetic conflicts in their native states. However, not all interactions are energetically optimized for folding but some remain in energetic conflict, i.e. they are highly frustrated. This remaining local energetic frustration has been shown to be statistically correlated with distinct functional aspects such as protein-protein interaction sites, allosterism and catalysis. Fuelled by the recent breakthroughs in efficient protein structure prediction that have made available good quality models for most proteins, we have developed a strategy to calculate local energetic frustration within large protein families and quantify its conservation over evolutionary time. Based on this evolutionary information we can identify how stability and functional constraints have appeared at the common ancestor of the family and have been maintained over the course of evolution. Here, we present FrustraEvo, a web server tool to calculate and quantify the conservation of local energetic frustration in protein families. The webserver is freely available at URL: https://frustraevo.qb.fcen.uba.ar

bioinformatics↗

p53 rapidly restructures 3D chromatin organization to trigger a transcriptional response

Activation of the p53 tumor suppressor triggers a transcriptional program to control cellular response to stress. However, the molecular mechanisms by which p53 controls gene transcription are not completely understood. Here, using a multi-omics integration framework, we uncover the critical role of spatio-temporal genome architecture in this process. We demonstrate that p53 drives direct and indirect changes in genome compartments, topologically associating domains and DNA loops within minutes of its activation, which escort the p53 transcriptional program along time. Focused on p53-bound enhancers, we report a core transcriptional program of 340 genes directly regulated by p53 over distance, most of these not previously identified. Finally, we showcase that p53 controls transcription of distal genes through newly formed and pre-existing enhancer-promoter loops in a cohesin dependent manner. Taken together, our findings demonstrate a previously unappreciated architectural role of p53 as regulator at distinct topological layers and provide a reliable set of new p53 direct target genes that may help future designs of p53-based cancer therapies.

genomics↗

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

VLP-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice

BackgroundNeoantigens are patient- and tumor-specific peptides that arise from somatic mutations. They stand as promising targets for personalized therapeutic cancer vaccines. The identification process for neoantigens has evolved with the use of next-generation sequencing technologies and bioinformatic tools in tumor genomics. However, in silico strategies for selecting immunogenic neoantigens still have very low accuracy rates, since they mainly focus on predicting peptide binding to Major Histocompatibility Complex (MHC) molecules, which is key but not the sole determinant for immunogenicity. MethodsWe developed a novel neoantigen selection pipeline based on existing software combined with a novel prediction method, the Neoantigen Optimization Algorithm (NOAH), which takes into account structural features of the peptide/MHC-I interaction in its prediction strategy. Moreover, to maximize neoantigens therapeutic potential, neoantigen-based vaccines should be manufactured in an optimal delivery platform that elicits robust de novo immune responses and bypasses central and peripheral tolerance. ResultsWe generated a highly immunogenic vaccine platform based on engineered HIV-1 Gag-based Virus-Like Particles (VLPs) expressing a high copy number of each in silico selected neoantigen. We tested different neoantigen-loaded VLPs (neoVLPs) in a B16-F10 melanoma mouse model to evaluate their capability to generate new immunogenic specificities. NeoVLPs were used in in vivo immunogenicity and tumor challenge experiments. ConclusionsNeoVLPs can promote the generation of de novo antitumor-specific immune responses, resulting in a delay in tumor growth. Vaccination with the neoVLP platform is a robust alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy. WHAT IS ALREADY KNOWN ON THIS TOPICIdentification of highly immunogenic neoantigens is still challenging, currently available pipelines base their prediction on MHC-I binding affinity. Moreover, neoantigen-based vaccine delivery needs to be improved to increase the potency of anti-tumor immune response. WHAT THIS STUDY ADDSNOAH is a novel pipeline for the identification and selection of neoantigens that combines binding affinity and structural features of the peptide/MHC-I interaction. Preclinical studies show highly immunogenic vaccine platform based on HIV-1 Gag based VLPs (neoVLPs) generates antitumor-specific immune responses, delaying tumor growth. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYThe combination of NOAH and neoVLP platform represents an alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy.

immunology↗

3Dmapper: A Command Line Tool For BioBank-scale Mapping Of Variants To Protein Structures

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a standalone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. 3Dmapper is freely available on GitHub at https://github.com/vicruiser/3Dmapper.

bioinformatics↗

Novel Spike-stabilized trimers with improved production protect K18-hACE2 mice and golden Syrian hamsters from the highly pathogenic SARS-CoV-2 Beta variant.

Most COVID-19 vaccines are based on the SARS-CoV-2 Spike glycoprotein (S) or their subunits. However, the S shows some structural instability that limits its immunogenicity and production, hampering the development of recombinant S-based vaccines. The introduction of the K986P and V987P (S-2P) mutations increases the production of the recombinant S trimer and, more importantly, its immunogenicity, suggesting that these two parameters are related. However, S-2P still shows some molecular instability and it is produced with low yield. Thus, S-2P production can be further optimized. Here we described a novel set of mutations identified by molecular modelling and located in the S2 region of the Spike that increase S-2P production up to five-fold. Besides their immunogenicity, the efficacy of two representative S-2P-based mutants, S-29 and S-21, protecting from a heterologous SARS-CoV-2 Beta variant challenge was assayed in K18-hACE2 mice (an animal model of severe SARS-CoV-2 disease) and golden Syrian hamsters (GSH) (a moderate disease model). S-21 induced higher level of WH1 and Delta variants neutralizing antibodies than S-2P in K18-hACE2 mice three days after challenge. Viral load in nasal turbinate and oropharyngeal samples were reduced in S-21 and S-29 vaccinated mice. Despite that, only the S-29 protein protected 100% of K18-hACE2 mice from severe disease. When GSH were analyzed, all immunized animals were protected from disease development irrespectively of the immunogen they received. Therefore, the higher yield of S-29, as well as its improved immunogenicity and efficacy protecting from the highly pathogenic SARS-CoV-2 Beta variant, pinpoint the S-29 spike mutant as an alternative to the S-2P protein for future SARS-CoV-2 vaccine development. Authors summaryThe rapid development of SARS-CoV-2 vaccines have been pivotal in the control of the COVID-19 pandemic worldwide. Most of these vaccines include the S glycoprotein as the main immunogen since this protein, and particularly its receptor binding domain (RBD), is the major target of neutralizing antibodies. SARS-CoV-2 have been evolving from the beginning of the pandemic and several variants with increased transmissibility, pathogenicity or resistance to infection- or vaccine-induced immunity have emerged. Different strategies have been adopted to improve vaccine protection including additional booster doses or the adaptation of the S immunogens to the novel SARS-CoV-2 variants. As a complementary strategy we have identified a combination of non-proline mutations that increase S production by 5-fold (S-29 protein). Despite the sequence of this novel S-29 immunogen is based on the ancestral SARS-CoV-2 WH1 variant, it effectively protects animal model from the highly pathogenic and neutralization resistant SARS-CoV-2 Beta variant. Thus, we describe a novel set of mutations that can increase the production and efficacy of S-based COVID-19 vaccines.

immunology↗

The Evolution of Local Energetic Frustration in Protein Families

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We have analyzed these signals in very well studied cases such as PDZ, SH3, and {beta} globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We have applied our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as emergent pathogens.

bioinformatics↗

Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network analysis of protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. We explore this in more detail for one of the proteins not previously associated with the NMJ, USH2A. Loss of the zebrafish USH2A ortholog revealed some effects on early movement and gross NMJ morphology. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

bioinformatics↗

Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolution

Long-range interactions between regulatory elements and promoters are key in gene transcriptional control; however, their study requires large amounts of starting material, which is not compatible with clinical scenarios nor the study of rare cell populations. Here we introduce low input capture Hi-C (liCHi-C) as a cost-effective, flexible method to map and robustly compare promoter interactomes at high resolution. As proof of its broad applicability, we implement liCHi-C to study normal and malignant human hematopoietic hierarchy in clinical samples. We demonstrate that the dynamic promoter architecture identifies developmental trajectories and orchestrates transcriptional transitions during cell-state commitment. Moreover, liCHi-C enables the identification of new disease-relevant cell types, genes and pathways potentially deregulated by non-coding alterations at distal regulatory elements. Finally, we show that liCHi-C can be harnessed to uncover genome-wide structural variants, resolve their breakpoints and infer their pathogenic effects. Collectively, our optimized liCHi-C method expands the study of 3D chromatin organization to unique, low-abundance cell populations, and offers an opportunity to uncover novel factors and regulatory networks involved in disease pathogenesis.

genomics↗

Reconstructing protein interactions at enhancer-promoter regions in prostate cancer

Here we introduce Promoter-ENhancer-GUided Interaction Networks (PENGUIN), a method to uncover protein-protein interaction (PPI) networks at enhancer-promoter contacts. By integrating H3K27ac-HiChIP data and tissue-specific PPI information, PENGUIN enables cluster enhancers-promoter PPI networks (EPINs) and pinpoint actionable factors. Validating PENGUIN in cancer (LNCaP) and benign (LHSAR) prostate cell lines, we observed distinct CTCF-enriched clusters, which identifies diverse chromatin conformations. In LNCaP, we found an EPIN cluster enriched with oncogenes and prostate cancer-associated SNPs. We uncovered a total of 208 SNPs in LNCaP EPINs and used CRISPR/Cas9 knockout and RNAi screens to confirm their relevance. PENGUINs application in prostate cancer demonstrates its potential for studying human diseases. The approach allows exploration in different cell types and combinations of GWAS data, offering promising avenues for future investigations. In conclusion, PENGUIN provides valuable insights into the interplay between enhancer-promoter interactions and PPI networks, facilitating the identification of relevant genes and potential intervention targets.

cancer biology↗

Genome-wide CRISPR screens of T cell exhaustion identify chromatin remodeling factors that limit T cell persistence

T cell exhaustion limits anti-tumor immunity, but the molecular determinants of this process remain poorly understood. Using a chronic antigen stimulation assay, we performed genome-wide CRISPR/Cas9 screens to systematically discover genetic regulators of T cell exhaustion, which identified an enrichment of epigenetic factors. In vivo CRISPR screens in murine and human tumor models demonstrated that perturbation of several epigenetic regulators, including members of the INO80 and BAF chromatin remodeling complexes, improved T cell persistence in tumors. In vivo paired CRISPR perturbation and single-cell RNA sequencing revealed distinct transcriptional roles of each complex and that depletion of canonical BAF complex members, including Arid1a, resulted in the maintenance of an effector program and downregulation of terminal exhaustion-related genes in tumor-infiltrating T cells. Finally, Arid1a-depletion limited the global acquisition of chromatin accessibility associated with T cell exhaustion and led to improved anti-tumor immunity after adoptive cell therapy. In summary, we provide a comprehensive atlas of the genetic regulators of T cell exhaustion and demonstrate that modulation of the epigenetic state of T cell exhaustion can improve T cell responses in cancer immunotherapy.

immunology↗

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former version and expands its functionalities such as using user-defined models and cells specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license with several repositories of accompanying interoperable tools. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).

systems biology↗

Optimizing dosage-specific treatments in a multi-scale model of a tumor growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell-cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool to study biology at a very different time and spatial scales, as they can integrate different processes that take place at the molecular, cellular and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells spatial geometry, as well as, population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

systems biology↗

DIDO3 acts at the interface of RNAPII transcription and chromatin structure regulation

While chromatin structure influences gene expression, how chromatin readers exert transcriptional control is still not fully understood. DIDO3, an isoform of the Death Inducer Obliterator protein, has been implicated in cell differentiation and cancer, yet its precise genomic functions are unclear. Here we integrated ChIP-seq, RNA-seq and Hi-C analyses in mouse embryonic stem cells to reveal that DIDO3 predominantly binds to RNA polymerase II at topological domain boundaries and chromatin loops. We demonstrate that DIDO3 interacts with the architectural protein CTCF and that the N-terminal region of DIDO3 (DIDO31-528) also co-localizes with the histone marks H3K4me3 and H3K36me3. DIDO3 influences the expression of genes encoding chromatin remodeling components, as well as those involved in transcriptional dynamics and three-dimensional chromatin organization. These findings suggest that DIDO3 is associated with both transcriptional regulation and chromatin organization, with potential implications in terms of cell differentiation and tumorigenesis.

bioinformatics↗

A structural biology community assessment of AlphaFold 2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

biophysics↗

SARS-CoV-2 B.1.351 (beta) variant shows enhanced infectivity in K18-hACE2 transgenic mice and expanded tropism to wildtype mice compared to B.1 variant

SARS-CoV-2 variants display enhanced transmissibility and/or immune evasion and can be generated in humans or animals, like minks, thus generating new reservoirs. The continuous surveillance of animal susceptibility to new variants is necessary to predict pandemic evolution. In this study we demonstrate that, compared to the B.1 SARS-CoV-2 variant, K18-hACE2 transgenic mice challenged with the B.1.351 variant displayed a faster progression of infection. Furthermore, we also report that B.1.351 can establish infection in wildtype mice, while B.1 cannot. B.1.351-challenged wildtype mice showed a milder infection than transgenic mice, confirmed by detectable viral loads in oropharyngeal swabs and tissues, lung pathology, immunohistochemistry and serology. In silico models supported these findings by demonstrating that the Spike mutations in B.1.351 resulted in increased affinity for both human and murine ACE2 receptors. Overall, this study highlights the plasticity of SARS-CoV-2 animal susceptibility landscape, which may contribute to viral persistence and expansion.

microbiology↗

The structural coverage of the human proteome before and after AlphaFold

The protein structure field is experiencing a revolution. From the increased throughput of techniques to determine experimental structures, to developments such as cryo-EM that allow us to find the structures of large protein complexes or, more recently, the development of artificial intelligence tools, such as AlphaFold, that can predict with high accuracy the folding of proteins for which the availability of homology templates is limited. Here we quantify the effect of the recently released AlphaFold database of protein structural models in our knowledge on human proteins. Our results indicate that our current baseline for structural coverage of 48%, considering experimentally-derived or template-based homology models, elevates up to 76% when including AlphaFold predictions. At the same time the fraction of dark proteome is reduced from 26% to just 10% when AlphaFold models are considered. Furthermore, although the coverage of disease-associated genes and mutations was near complete before AlphaFold release (69% of Clinvar pathogenic mutations and 88% of oncogenic mutations), AlphaFold models still provide an additional coverage of 3% to 13% of these critically important sets of biomedical genes and mutations. Finally, we show how the contribution of AlphaFold models to the structural coverage of non-human organisms, including important pathogenic bacteria, is significantly larger than that of the human proteome. Overall, our results show that the sequence-structure gap of human proteins has almost disappeared, an outstanding success of direct consequences for the knowledge on the human genome and the derived medical applications.

bioinformatics↗

Patient-specific Boolean models of signaling networks guide personalized treatments

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalized Boolean models and illustrate how they can be used for precision oncology.

systems biology↗