@article {762, title = {Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.}, journal = {Viruses}, volume = {14}, year = {2022}, month = {2022 Aug 27}, abstract = {

OBJECTIVES: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain.

METHODS: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis.

RESULTS: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins.

CONCLUSIONS: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

}, keywords = {COVID-19, Genome, Viral, Humans, mutation, Pandemics, Phylogeny, SARS-CoV-2}, issn = {1999-4915}, doi = {10.3390/v14091893}, author = {Loucera, Carlos and Perez-Florido, Javier and Casimiro-Soriguer, Carlos S and Ortuno, Francisco M and Carmona, Rosario and Bostelmann, Gerrit and Mart{\'\i}nez-Gonz{\'a}lez, L Javier and Mu{\~n}oyerro-Mu{\~n}iz, Dolores and Villegas, Rom{\'a}n and Rodr{\'\i}guez-Ba{\~n}o, Jes{\'u}s and Romero-G{\'o}mez, Manuel and Lorusso, Nicola and Garcia-Le{\'o}n, Javier and Navarro-Mar{\'\i}, Jose M and Camacho-Martinez, Pedro and Merino-Diaz, Laura and Salazar, Adolfo de and Vi{\~n}uela, Laura and Lepe, Jose A and Garc{\'\i}a, Federico and Dopazo, Joaquin} } @article {672, title = {Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.}, journal = {Biol Direct}, volume = {14}, year = {2019}, month = {2019 08 20}, pages = {15}, abstract = {

BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles.

RESULTS: Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification.

CONCLUSIONS: We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances.

REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.

}, keywords = {biomarkers, Cities, Drug Resistance, Microbial, Machine Learning, Metabolome, Metagenome, metagenomics, Microbiota}, issn = {1745-6150}, doi = {10.1186/s13062-019-0246-9}, author = {Casimiro-Soriguer, Carlos S and Loucera, Carlos and Perez Florido, Javier and L{\'o}pez-L{\'o}pez, Daniel and Dopazo, Joaquin} }