Biology subjects

Paytuvi-Gallart, A.

Publications and source records attributed to Paytuvi-Gallart, A..

3 recordsLinked to original sources

bioRxiv2021

Nasopharyngeal Microbiota as an early severity biomarker in COVID-19 hospitalised patients: a retrospective cohort study in a Mediterranean area.

BackgroundThere is mounting evidence suggesting that the microbiome composition could be different in COVID-19 patients. However, the relationship between microbiota and COVID-19 severity progression is still being assessed. This study aimed to analyse the diversity and taxonomic composition of the nasopharyngeal microbiota, to determine its association with COVID-19 clinical outcome. Methods and FindingsSamples came from a retrospective cohort of adult patients with COVID-19, hospitalised in a tertiary centre. To study the nasopharyngeal microbiota, we utilized 16S rRNA sequencing. Raw sequences were processed by QIIME2. The associations between the microbiota, invasive mechanical ventilation (IMV), and all-cause mortality were analysed by multiple logistic regression (OR; 95%CI), adjusted for age, gender, and comorbidity. 177 patients were included: median age 68.0 years, 57.6% males, 59.3% had a Charlson comorbidity index [≥]3, and 89.2% with pneumonia. The microbiota diversity indexes were lower in patients with a fatal outcome, and this association persisted after adjustment for the main confounders; whereas the {beta} diversity analysis showed a significant clustering, grouping the patients with a fatal outcome. After multivariate adjustment, the presence of Selenomonas spp., Filifactor spp., Actinobacillus spp., or Chroococcidiopsis spp., was associated with a reduced risk of IMV (adjusted OR 0.06[95%CI 0.01-0.0.47], p = 0.007). ConclusionsThe microbiota diversity and taxonomic composition are related to COVID-19 severity. Higher diversity and the presence of certain genera in the nasopharyngeal microbiota seem to be early biomarkers of a favourable clinical evolution in hospitalised patients with moderate to severe SARS-CoV-2 infections.

microbiology↗

bioRxiv2020

A NEW BIOINFORMATIC TOOL TO INTERPRET METAGENOMICS /METATRANSCRIPTOMICS RESULTS BASED ON THE GEOMETRY OF THE CLUSTERING NETWORK AND ITS DIFFERENTIALLY GENE ONTOLOGIES (GANGO)

High-throughput experimental techniques, such as metagenomics or metatranscriptomics, produce large amounts of data, which interpretation and conversion into understandable knowledge can be challenging and out of reach. We present GANGO, a new algorithm based on the ecological concept of consortium (groups biologically connected) and by using clustering network analysis, gene ontologies and powerful hypothesis test allows the identification and interpretation of complex ecological networks, allowing the identification of the relationship between taxa/genes, the number of groups, their relations and their functionalities using the annotated genes of an organism in a database (e.g. UniProt or Ensembl). Three examples of the use of GANGO are shown: a simulated mixture of fungi and bacteria, alterations in soil fungi communities after a diesel-oil spill and genomic changes in Saccharomyces cerevisae due to abiotic stress.

bioinformatics↗

bioRxiv2019

GAIA: an integrated metagenomics suite

Identifying the biological diversity of a microbial population is of fundamental importance due to its implications in industrial processes, environmental studies and clinical applications. Today, there is still an outstanding need to develop new, easy-to-use bioinformatics tools to analyze both amplicon and shotgun metagenomics, including both prokaryotic and eukaryotic organisms, with the highest accuracy and the lowest running time. With the aim of overcoming this need, we introduce GAIA, an online software solution that has been designed to provide users with the maximum information whether it be 16S, 18S, ITS, or shotgun analysis. GAIA is able to obtain a comprehensive and detailed overview at any taxonomic level of microbiomes of different origins: human (e.g. stomach or skin), agricultural and environmental (e.g. land, water or organic waste). By using recently published benchmark datasets from shotgun and 16S experiments we compared GAIA against several available pipelines. Our results show that for shotgun metagenomics, GAIA obtained the highest F-measures at species level above all tested pipelines (CLARK, Kraken, LMAT, BlastMegan, DiamondMegan and NBC). For 16S metagenomics, GAIA also obtained excellent F-measures comparable to QIIME at family level. The overall objective of GAIA is to provide both the academic and industrial sectors with an integrated metagenomics suite that will allow to perform metagenomics data analysis easily, quickly and affordably with the highest accuracy.

bioinformatics↗