Biology subjects

Molina-Mora, J. A.

Publications and source records attributed to Molina-Mora, J. A..

5 recordsLinked to original sources

bioRxiv2022

Overview of the SARS-CoV-2 genotypes circulating in Latin America during 2021

Latin America is one of the regions in which the COVID-19 pandemic has had a stronger impact, with more than 72 million reported infections and 1.6 million deaths until June 2022. Since this region is ecologically diverse and is affected by enormous social inequalities, efforts to identify genomic patterns of the circulating SARS-CoV-2 genotypes are necessary for the suitable management of the pandemic. To contribute to the genomic surveillance of the SARS-CoV-2 in Latin America, we extended the number of SARS-CoV-2 genomes available from the region by sequencing and analyzing the viral genome from COVID-19 patients from seven countries (Argentina, Brazil, Costa Rica, Colombia, Mexico, Bolivia and Peru). Subsequently, we analyzed the genomes circulating mainly during 2021 including records from GISAID database from Latin America. A total of 1534 genome sequences were generated from seven countries, demonstrating the laboratory and bioinformatics capabilities for genomic surveillance of pathogens that have been developed locally. For Latin America, patterns regarding several variants associated with multiple re-introductions, a relatively low percentage of sequenced samples, as well as an increment in the mutation frequency since the beginning of the pandemic, are in line with worldwide data. Besides, some variants of concern (VOC) and variants of interest (VOI) such as Gamma, Mu and Lambda, and at least 83 other lineages have predominated locally with a country-specific enrichments. This work has contributed to the understanding of the dynamics of the pandemic in Latin America as part of the local and international efforts to achieve timely genomic surveillance of SARS-CoV-2.

microbiology↗

bioRxiv2021

Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica

Emerging mutations and genotypes of the SARS-CoV-2 virus, responsible for the COVID-19 pandemic, have been reported globally. In Costa Rica during the year 2020, a predominant genotype carrying the mutation T1117I in the spike (S:T1117I) was previously identified. To investigate the possible effects of this mutation on the function of the spike, i.e. the biology of the virus, different bioinformatic pipelines based on phylogeny, natural selection and co-evolutionary models, molecular docking and epitopes prediction were implemented. Results of the phylogeny of sequences carrying the S:T1117I worldwide showed a polyphyletic group, with the emergency of local lineages. In Costa Rica, the mutation is found in the lineage B.1.1.389 and it is suggested to be a product of positive/adaptive selection. Different changes in the function of the spike protein and more stable interaction with a ligand (nelfinavir drug) were found. Only one epitope out 742 in the spike was affected by the mutation, with some different properties, but suggesting scarce changes in the immune response and no influence on the vaccine effectiveness. Jointly, these results suggest a partial benefit of the mutation for the spread of the virus with this genotype during the year 2020 in Costa Rica, although possibly not strong enough with the introduction of new lineages during early 2021 which became predominant later. In addition, the bioinformatics pipeline offers an integrative and exhaustive in silico strategy to eventually study other mutations of interest for the SARS-CoV-2 virus and other pathogens. HighlightsO_LIIn Costa Rica during the year 2020, a predominant SARS-CoV-2 genotype carrying the mutation T1117I in the spike (S:T1117I) was identified. C_LIO_LIThe S:T1117I was assessed for possible effects of this mutation on the function of the spike with a in silico approach. C_LIO_LIPhylogeny revealed that sequences carrying the S:T1117I worldwide define a polyphyletic group, with the emergency of local lineages, including the lineage B.1.1.389 in Costa Rica. C_LIO_LIA positive/adaptive selection was identified for S:T1117I, with different changes in the function of the spike protein, more stable interaction with ligands and scarce changes in the immune response. C_LIO_LIThe bioinformatics pipeline can be eventually used to study other mutations of the SARS-CoV-2 virus and other pathogens. C_LI

bioinformatics↗

bioRxiv2021

High efficacy of therapeutic equine hyperimmune antibodies against SARS CoV-2 variants of concern

SARS-CoV-2 variants of concern (VoC) show reduced neutralization by vaccine-induced and therapeutic monoclonal antibodies. We tested therapeutic equine polyclonal antibodies (pAbs) against four VoC (alpha, beta, epsilon and gamma). We show that equine pAbs efficiently neutralize VoC, suggesting they are an effective, broad coverage, low-cost and a scalable COVID-19 treatment.

microbiology↗

bioRxiv2020

Common Marmoset Gut Microbiome Profiles in Health and Intestinal Disease

Chronic gastrointestinal (GI) diseases are the most common diseases in captive marmosets. The gut microbiome of healthy (n=91), inflammatory bowel disease (IBD) (n=59), and duodenal ulcer/stricture (n=23) captive marmosets was characterized. Healthy marmosets exhibited a "humanized," Bacteroidetes-dominant microbiome. Despite standardized conditions, cohorts subdivided into Prevotella- and Bacteroides-dominant groups based on marmoset source. IBD was highest in a Prevotella-dominant cohort while strictures were highest in a Bacteroides-dominant cohort. Stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. Stricture tissue presented upregulation of lipid metabolism genes and increased abundance of C. perfringens, a causative agent of GI diseases and intestinal strictures in humans. IBD was associated with a lower Bacteroides:P. copri ratio within each source. Consistent with Prevotella-linked diseases, pro-inflammatory genes were upregulated. This report highlights the humanization of the captive marmoset microbiome and its potential as a "humanized" animal model of C. perfringens-induced enteritis/strictures and P. copri-associated IBD.

microbiology↗

bioRxiv2020

A first Pseudomonas aeruginosa perturbome: Identification of core genes related to multiple perturbations by a machine learning approach

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

bioinformatics↗