@article {719, title = {Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.}, journal = {Mol Med}, volume = {27}, year = {2021}, month = {2021 05 24}, pages = {50}, abstract = {

OBJECTIVE: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.

METHODS: Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.

RESULTS: We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.

CONCLUSION: Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.

}, keywords = {Biodiversity, Computational Biology, Dysbiosis, Gastrointestinal Microbiome, Gout, Humans, Metagenome, metagenomics, Protein Interaction Mapping, Protein Interaction Maps, Uric Acid}, issn = {1528-3658}, doi = {10.1186/s10020-021-00311-5}, author = {M{\'e}ndez-Salazar, Eder Orlando and V{\'a}zquez-Mellado, Janitzia and Casimiro-Soriguer, Carlos S and Dopazo, Joaquin and Cubuk, Cankut and Zamudio-Cuevas, Yessica and Francisco-Balderas, Adriana and Mart{\'\i}nez-Flores, Karina and Fern{\'a}ndez-Torres, Javier and Lozada-P{\'e}rez, Carlos and Pineda, Carlos and S{\'a}nchez-Gonz{\'a}lez, Austreberto and Silveira, Luis H and Burguete-Garc{\'\i}a, Ana I and Orbe-Orihuela, Citlalli and Lagunas-Mart{\'\i}nez, Alfredo and Vazquez-Gomez, Alonso and L{\'o}pez-Reyes, Alberto and Palacios-Gonz{\'a}lez, Berenice and Mart{\'\i}nez-Nava, Gabriela Ang{\'e}lica} } @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} } @article {1005, title = {Genomics and transcriptomics in drug discovery.}, journal = {Drug discovery today}, volume = {19}, number = {2}, year = {2014}, month = {2013 Jun 14}, pages = {126-32}, abstract = {The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery.}, keywords = {adverse effects, Drug discovery, drug repositioning, metagenomics, modeling, network analysis, pathway analysis}, issn = {1878-5832}, doi = {10.1016/j.drudis.2013.06.003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23773860}, author = {Dopazo, Joaquin} }