03601nas a2200505 4500008004100000022001400041245013900055210006900194260001500263300000700278490000700285520188200292653001702174653002602191653001402217653003202231653000902263653001102272653001502283653001702298653003202315653002902347653001402376100003502390700003102425700003302456700002002489700001802509700002802527700003202555700002902587700003002616700002602646700001902672700003602691700002202727700002902749700002802778700003102806700002602837700002602863700003302889700004002922856013302962 2021 eng d a1528-365800aTaxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.0 aTaxonomic variations in the gut microbiome of gout patients with c2021 05 24 a500 v273 a

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.

10aBiodiversity10aComputational Biology10aDysbiosis10aGastrointestinal Microbiome10aGout10aHumans10aMetagenome10ametagenomics10aProtein Interaction Mapping10aProtein Interaction Maps10aUric Acid1 aMéndez-Salazar, Eder, Orlando1 aVázquez-Mellado, Janitzia1 aCasimiro-Soriguer, Carlos, S1 aDopazo, Joaquin1 aCubuk, Cankut1 aZamudio-Cuevas, Yessica1 aFrancisco-Balderas, Adriana1 aMartínez-Flores, Karina1 aFernández-Torres, Javier1 aLozada-Pérez, Carlos1 aPineda, Carlos1 aSánchez-González, Austreberto1 aSilveira, Luis, H1 aBurguete-García, Ana, I1 aOrbe-Orihuela, Citlalli1 aLagunas-Martínez, Alfredo1 aVazquez-Gomez, Alonso1 aLópez-Reyes, Alberto1 aPalacios-González, Berenice1 aMartínez-Nava, Gabriela, Angélica uhttps://www.clinbioinfosspa.es/content/taxonomic-variations-gut-microbiome-gout-patients-and-without-tophi-might-have-functional02126nas a2200289 4500008004100000022001400041245015000055210006900205260001500274300000700289490000700296520113800303653001501441653001101456653003101467653002101498653001501519653001501534653001701549653001501566100003301581700002001614700002701634700002601661700002001687856012901707 2019 eng d a1745-615000aAntibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.0 aAntibiotic resistance and metabolic profiles as functional bioma c2019 08 20 a150 v143 a

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.

10abiomarkers10aCities10aDrug Resistance, Microbial10aMachine Learning10aMetabolome10aMetagenome10ametagenomics10aMicrobiota1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aFlorido, Javier, Perez1 aLópez-López, Daniel1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/antibiotic-resistance-and-metabolic-profiles-functional-biomarkers-accurately-predict