Biology subjects

Llorens, C.

Publications and source records attributed to Llorens, C..

8 recordsLinked to original sources

bioRxiv2024

MITF Targets in Gastrointestinal Stromal Tumors: Implication in Autophagy and Extracellular Vesicle Secretion

Previous studies have identified Microphthalmia-associated Transcription Factor (MITF) involvement in regulating Gastrointestinal Stromal Tumors (GIST) growth and cell cycle progression. This study uses Chromatin Immunoprecipitation combined with high-throughput sequencing (ChIP-seq) and RNA sequencing to explore MITF-modulated genes in GIST. Our findings reveal that MITF regulates genes involved in lysosome biogenesis, vesicle generation, autophagy, and mTOR signaling pathways. Comparative transcriptome analysis following MITF silencing in GIST cells shows differential enrichment in mTOR signaling, impacting tumor growth and autophagy. In the context of cancer, the interplay between autophagy and extracellular vesicle release can influence tumor progression and metastasis. We examined MITFs role in autophagy and extracellular vesicle (EV) production in GIST, finding that MITF overexpression increases autophagy, as shown by elevated LC3II levels while silencing MITF disrupts autophagosome and autolysosome formation. Despite no significant changes in EV size or number, MITF silencing notably reduces KIT expression in EV content. KIT secretion in EVs has been linked to GIST metastasis, suggesting that MITF is a crucial target for managing tumor growth and metastasis in GIST.

cancer biology↗

bioRxiv2022

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian-Network Approach

In aquaculture systems, microbiomes of farmed fishes may contain thousands of bacterial taxa that establish complex networks of interactions among each other and among the host and the environment. Gut microbiomes in many fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to: i) infer information how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and ii) predict how the diversity and functional profile of that microbiome would change under other experimental variables. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of bayesian network scenarios created by SAMBA using data from: a) a microbial synthetic experiment; and b) the pan-microbiome of the gilthead sea bream (Sparus aurata) under different experimental feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems and can be used for modelling microbiome-host network relationships in any vertebrate organism, including humans, in any system and/or ecosystem.

bioinformatics↗

bioRxiv2022

SARS-CoV-2 Point Mutation and Deletion Spectra, and Their Association with Different Disease Outcome

Mutant spectra of RNA viruses are important to understand viral pathogenesis, and response to selective pressures. There is a need to characterize the complexity of mutant spectra in coronaviruses sampled from infected patients. In particular, the possible relationship between SARS-CoV-2 mutant spectrum complexity and disease associations has not been established. In the present study, we report an ultra-deep sequencing (UDS) analysis of the mutant spectrum of amplicons from the nsp12 (polymerase)- and spike (S)-coding regions of thirty nasopharyngeal isolates (diagnostic samples) of SARS-CoV-2 of the first COVID-19 pandemic wave (Madrid, Spain, April 2020) classified according to the severity of ensuing COVID-19. Low frequency mutations and deletions, counted relative to the consensus sequence of the corresponding isolate, were overwhelmingly abundant. We show that the average number of different point mutations, mutations per haplotype and several diversity indices was significantly higher in SARS-CoV-2 isolated from patients who developed mild disease than in those associated with moderate or severe disease (exitus). No such bias was observed with RNA deletions. Location of amino acid substitutions in the three dimensional structures of nsp12 (polymerase) and S suggest significant structural or functional effects. Thus, patients who develop mild symptoms may be a richer source of genetic variants of SARS-CoV-2 than patients with moderate or severe COVID-19. IMPORTANCEThe study shows that mutant spectra of SARS-CoV-2 from diagnostic samples differ in point mutation abundance and complexity, and that significantly larger values were observed in virus from patients who developed mild COVID-19 symptoms. Mutant spectrum complexity is not a uniform trait among isolates. The nature and location of low frequency amino acid substitutions present in mutant spectra anticipate great potential for phenotypic diversification of SARS-CoV-2.

microbiology↗

bioRxiv2020

Functional characterization of SARS-CoV-2 infection suggests a complex inflammatory response and metabolic alterations

Covid-19, caused by the SARS-CoV-2 virus, has reached the category of a worldwide pandemic. Even though intensive efforts, no effective treatments or a vaccine are available. Molecular characterization of the transcriptional response in Covid-19 patients could be helpful to identify therapeutic targets. In this study, RNAseq data from peripheral blood mononuclear cell samples from Covid-19 patients and healthy controls was analyzed from a functional point of view using probabilistic graphical models. Two networks were built: one based on genes differentially expressed between healthy and infected individuals and another one based on the 2,000 most variable genes in terms of expression in order to make a functional characterization. In the network based on differentially expressed genes, two inflammatory response nodes with different tendencies were identified, one related to cytokines and chemokines, and another one related to bacterial infections. In addition, differences in metabolism, which were studied in depth using Flux Balance Analysis, were identified. SARS-CoV2-infection caused alterations in glutamate, methionine and cysteine, and tetrahydrobiopterin metabolism. In the network based on 2,000 most variable genes, also two inflammatory nodes with different tendencies between healthy individuals and patients were identified. Similar to the other network, one was related to cytokines and chemokines. However, the other one, lower in Covid-19 patients, was related to allergic processes and self-regulation of the immune response. Also, we identified a decrease in T cell node activity and an increase in cell division node activity. In the current absence of treatments for these patients, functional characterization of the transcriptional response to SARS-CoV-2 infection could be helpful to define targetable processes. Therefore, these results may be relevant to propose new treatments.

microbiology↗

bioRxiv2020

Simulating the Influence of Conjugative Plasmids Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Plasmids harboring antibiotic resistance genes differ in their kinetic values as plasmid conjugation rate, segregation rate by incompatibility with related plasmids, rate of stochastic loss during replication, cost reducing the host-cell fitness, and frequency of compensatory mutations to reduce plasmid cost, depending on the cell mutation frequency. How variation in these values influence the success of a plasmid and their resistance genes in complex ecosystems, as the microbiota? Genes are located in plasmids, plasmids in cells, cells in populations. These populations are embedded in ensembles of species in different human hosts, are able to exchange between them bacterial ensembles during cross-infection and are located in the hospital or the community setting, under various levels of antibiotic exposure. Simulations using new membrane computing methods help predict the influence of plasmid kinetic values on such multilevel complex system. In our simulation, conjugation frequency needed to be at least 10-3 to clearly influence the dominance of a strain with a resistant plasmid. Host strains able to stably maintain two copies of similar plasmids harboring different resistances, coexistence of these resistances can occur in the population. Plasmid loss rates of 10-4 or 10-5 or plasmid fitness costs [≥]0.06 favor the plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost, only at high mutation frequencies (10-3-10-5). Membrane computing helps set a multilevel landscape to study the effect of changes in plasmid kinetic values on the success of resistant organisms in complex ecosystems.

microbiology↗

bioRxiv2019

Comprehensive characterization of the mutational landscape in localized anal squamous cell carcinoma

PurposeAnal squamous cell carcinoma (ASCC) is a rare neoplasm. Chemo-radiotherapy is the standard of care, with no therapeutic advances achieved over the past three decades. Thus, a deeper molecular characterization of this disease is still necessary.\n\nMethodsWe analyzed 46 paraffin-embedded tumor samples from patients diagnosed with primary ASCC by exome sequencing. A bioinformatics approach focused in the identification of high impact genetic variants, which may act as drivers of oncogenesis, was performed. The relation between genetics variant and prognosis was also studied.\n\nResultsThe list of high impact genetic variants was unique for each patient. However, the pathways in which these genes are involved are well-known hallmarks of cancer, such as angiogenesis or immune pathways. Additionally, we determined that genetic variants in BRCA2, ZNF750, FAM208B, ZNF599 and ZC3H13 genes are related with poor disease-free survival in ASCC.\n\nConclusionHigh impact genetic variants in ASCC affect pathways involved in cancer development, and may play a role in the etiology of the disease. Variants in BRCA2, ZNF750, FAM208B, ZNF599 and ZC3H13 genes seem to be related with prognosis in ASCC. Sequencing of ASCC clinical samples appears an encouraging tool for the molecular portrait of this disease.

cancer biology↗

bioRxiv2019

Genome Sequencing and Transcriptome Analysis Reveal Recent Species-specific Gene Duplications in the Plastic Gilthead Sea Bream

Gilthead sea bream is an economically important fish species that is remarkably well-adapted to farming and changing environments. Understanding the genomic basis of this plasticity will serve to orientate domestication and selective breeding towards more robust and efficient fish. To address this goal, a draft genome assembly was reconstructed combining short- and long-read high-throughput sequencing with genetic linkage maps. The assembled unmasked genome spans 1.24 Gb of an expected 1.59 Gb genome size with 932 scaffolds ([~]732 Mb) anchored to 24 chromosomes that are available as a karyotype browser at www.nutrigroup-iats.org/seabreambrowser. Homology-based functional annotation, supported by RNA-seq transcripts, identified 55,423 actively transcribed genes corresponding to 21,275 unique descriptions with more than 55% of duplicated genes. The mobilome accounts for the 75% of the full genome size and it is mostly constituted by introns (599 Mb), whereas the rest is represented by low complexity repeats, RNA retrotransposons, DNA transposons and non-coding RNAs. This mobilome also contains a large number of chimeric/composite genes (i. e. loci presenting fragments or exons mostly surrounded by LINEs and Tc1/mariner DNA transposons), whose analysis revealed an enrichment in immune-related functions and processes. Analysis of synteny and gene phylogenies uncovered a high rate of species-specific duplications, resulting from recent independent duplications rather than from genome polyploidization (2.024 duplications per gene; 0.385 excluding gene expansions). These species-specific duplications were enriched in gene families functionally related to genome transposition, immune response and sensory responses. Additionally, transcriptional analysis of liver, skeletal muscle, intestine, gills and spleen supported a high number of functionally specialized paralogs under tissue-exclusive regulation. Altogether, these findings suggest a role of recent large-scale gene duplications coupled to tissue expression diversification in the evolution of gilthead sea bream genome during its successful adaptation to a changing and pathogen-rich environment. This issue also underscores a role of evolutionary routes for rapid increase of the gene repertoire in teleost fish that are independent of polyploidization. Since gilthead sea bream has a well-recognized plasticity, the current study will advance our understanding of fish biology and how organisms of this taxon interact with the environment.

genomics↗

bioRxiv2018

Multi-Hierarchical Dynamics of Antimicrobial Resistance Simulated in a Nested Membrane Computing Model

Membrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested \"membrane-surrounded entities\" able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

microbiology↗