<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martínez-Nava, Gabriela Angélica</style></author><author><style face="normal" font="default" size="100%">Altamirano-Molina, Efren</style></author><author><style face="normal" font="default" size="100%">Vázquez-Mellado, Janitzia</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lozada-Pérez, Carlos</style></author><author><style face="normal" font="default" size="100%">Herrera-López, Brígida</style></author><author><style face="normal" font="default" size="100%">Martínez-Gómez, Laura Edith</style></author><author><style face="normal" font="default" size="100%">Martínez-Armenta, Carlos</style></author><author><style face="normal" font="default" size="100%">Guido-Gómora, Dafne Lissete</style></author><author><style face="normal" font="default" size="100%">Valle-Gutiérrez, Sarahí</style></author><author><style face="normal" font="default" size="100%">Suarez-Ahedo, Carlos</style></author><author><style face="normal" font="default" size="100%">Camacho-Rea, María Del Carmen</style></author><author><style face="normal" font="default" size="100%">Martínez-García, Mireya</style></author><author><style face="normal" font="default" size="100%">Gutiérrez-Esparza, Guadalupe</style></author><author><style face="normal" font="default" size="100%">Amezcua-Guerra, Luis M</style></author><author><style face="normal" font="default" size="100%">Zamudio-Cuevas, Yessica</style></author><author><style face="normal" font="default" size="100%">Martínez-Flores, Karina</style></author><author><style face="normal" font="default" size="100%">Fernández-Torres, Javier</style></author><author><style face="normal" font="default" size="100%">Burguete-García, Ana I</style></author><author><style face="normal" font="default" size="100%">Orbe-Orihuela, Yaneth Citlalli</style></author><author><style face="normal" font="default" size="100%">Lagunas-Martínez, Alfredo</style></author><author><style face="normal" font="default" size="100%">Méndez-Salazar, Eder Orlando</style></author><author><style face="normal" font="default" size="100%">Francisco-Balderas, Adriana</style></author><author><style face="normal" font="default" size="100%">Palacios-González, Berenice</style></author><author><style face="normal" font="default" size="100%">Pineda, Carlos</style></author><author><style face="normal" font="default" size="100%">López-Reyes, Alberto</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Amino Acids</style></keyword><keyword><style  face="normal" font="default" size="100%">Bacteria</style></keyword><keyword><style  face="normal" font="default" size="100%">Case-Control Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Feces</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gastrointestinal Microbiome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Gout</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Hyperuricemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyruvic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Mar 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">9981</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Several pathologies with metabolic origin, such as hyperuricemia and gout, have been associated with the gut microbiota taxonomic profile. However, there is no evidence of which bacterial genes are being expressed in the gut microbiome, and of their potential effects on hyperuricemia and gout. We sequenced the RNA of 26 fecal samples from 10 healthy normouricemic controls, 10 with asymptomatic hyperuricemia (AH), and six gout patients. The coding sequences were mapped to KEGG orthologues (KO). We compared the expression levels using generalized linear models and validated the expression of four KO in a larger sample by qRT-PCR. A distinct genetic expression pattern was identified among groups. AH individuals and gout patients showed an over-expression of KOs mainly related to pyruvate metabolism (Log2foldchange &gt; 23, p-adj ≤ 3.56 × 10), the pentose pathway (Log2foldchange &gt; 24, p-adj &lt; 1.10 × 10) and purine metabolism (Log2foldchange &gt; 22, p-adj &lt; 1.25 × 10). AH subjects had lower expression of KO related to glycine metabolism (Log2foldchange=-18, p-adj &lt; 1.72 × 10) than controls. Gout patients had lower expression (Log2foldchange=-22.42, p-adj &lt; 3.31 × 10) of a KO involved in phenylalanine biosynthesis, in comparison to controls and AH subjects. The over-expression seen for the KO related to pyruvate metabolism and the pentose pathway in gout patients´ microbiome was validated. There is a differential gene expression pattern in the gut microbiome of normouricemic individuals, AH subjects and gout patients. These differences are mainly located in metabolic pathways involved in acetate precursors and bioavailability of amino acids.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fernández-Palacios, Pablo</style></author><author><style face="normal" font="default" size="100%">Galán-Sánchez, Fátima</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Jurado-Tarifa, Estefanía</style></author><author><style face="normal" font="default" size="100%">Arroyo, Federico</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Chaves, J Alberto</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Rodriguez-Iglesias, Manuel A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genotypic characterization and antimicrobial susceptibility of human  isolates in Southern Spain.</style></title><secondary-title><style face="normal" font="default" size="100%">Microbiol Spectr</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Microbiol Spectr</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Anti-Bacterial Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Campylobacter Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">Campylobacter jejuni</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Child, Preschool</style></keyword><keyword><style  face="normal" font="default" size="100%">Ciprofloxacin</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Erythromycin</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Microbial Sensitivity Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Tetracycline</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Oct 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">e0102824</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt; is the main cause of bacterial gastroenteritis and a public health problem worldwide. Little information is available on the genotypic characteristics of human  in Spain. This study is based on an analysis of the resistome, virulome, and phylogenetic relationship, antibiogram prediction, and antimicrobial susceptibility of 114 human isolates of  from a tertiary hospital in southern Spain from October 2020 to June 2023. The isolates were sequenced using Illumina technology, and a bioinformatic analysis was subsequently performed. The susceptibility of  isolates to ciprofloxacin, tetracycline, and erythromycin was also tested. The resistance rates for each antibiotic were 90.3% for ciprofloxacin, 66.7% for tetracycline, and 0.88% for erythromycin. The fluoroquinolone resistance rate obtained is well above the European average (69.1%). CC-21 ( = 23), ST-572 ( = 13), and ST-6532 ( = 13) were the most prevalent clonal complexes (CCs) and sequence types (STs). In the virulome, the , and  genes were detected in all the isolates. A prevalence of 20.1% was obtained for the genes  and , which are related to the pathogenesis of Guillain-Barré syndrome (GBS). The prevalence of the main antimicrobial resistance markers detected were CmeABC (92.1%), RE-cmeABC (7.9%), the T86I substitution in  (88.9%),  (72.6%) (65.8%), and  (17.1%). High antibiogram prediction rates (&gt;97%) were obtained, except for in the case of the erythromycin-resistant phenotype. This study contributes significantly to the knowledge of  genomics for the prevention, treatment, and control of infections caused by this pathogen.IMPORTANCEDespite being the pathogen with the greatest number of gastroenteritis cases worldwide,  remains a poorly studied microorganism. A sustained increase in fluoroquinolone resistance in human isolates is a problem when treating  infections. The development of whole genome sequencing (WGS) techniques has allowed us to better understand the genotypic characteristics of this pathogen and relate them to antibiotic resistance phenotypes. These techniques complement the data obtained from the phenotypic analysis of  isolates. The zoonotic transmission of  through the consumption of contaminated poultry supports approaching the study of this pathogen through &quot;One Health&quot; approach. In addition, due to the limited information on the genomic characteristics of  in Spain, this study provides important data and allows us to compare the results with those obtained in other countries.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Robles, Enrique A</style></author><author><style face="normal" font="default" size="100%">Lara, María</style></author><author><style face="normal" font="default" size="100%">Aguado, Andrea</style></author><author><style face="normal" font="default" size="100%">Rodríguez Iglesias, Manuel A</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Pérez-Alegre, Mónica</style></author><author><style face="normal" font="default" size="100%">Andújar, Eloísa</style></author><author><style face="normal" font="default" size="100%">Jiménez, Victoria E</style></author><author><style face="normal" font="default" size="100%">Camino, Lola P</style></author><author><style face="normal" font="default" size="100%">Loruso, Nicola</style></author><author><style face="normal" font="default" size="100%">Ameyugo, Ulises</style></author><author><style face="normal" font="default" size="100%">Vazquez, Isabel María</style></author><author><style face="normal" font="default" size="100%">Lozano, Carlota M</style></author><author><style face="normal" font="default" size="100%">Chaves, J Alberto</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The integrated genomic surveillance system of Andalusia (SIEGA) provides a One Health regional resource connected with the clinic.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Resistance, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">One Health</style></keyword><keyword><style  face="normal" font="default" size="100%">Virulence Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Whole Genome Sequencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Aug 19</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">19200</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The One Health approach, recognizing the interconnectedness of human, animal, and environmental health, has gained significance amid emerging zoonotic diseases and antibiotic resistance concerns. This paper aims to demonstrate the utility of a collaborative tool, the SIEGA, for monitoring infectious diseases across domains, fostering a comprehensive understanding of disease dynamics and risk factors, highlighting the pivotal role of One Health surveillance systems. Raw whole-genome sequencing is processed through different species-specific open software that additionally reports the presence of genes associated to anti-microbial resistances and virulence. The SIEGA application is a Laboratory Information Management System, that allows customizing reports, detect transmission chains, and promptly alert on alarming genetic similarities. The SIEGA initiative has successfully accumulated a comprehensive collection of more than 1900 bacterial genomes, including Salmonella enterica, Listeria monocytogenes, Campylobacter jejuni, Escherichia coli, Yersinia enterocolitica and Legionella pneumophila, showcasing its potential in monitoring pathogen transmission, resistance patterns, and virulence factors. SIEGA enables customizable reports and prompt detection of transmission chains, highlighting its contribution to enhancing vigilance and response capabilities. Here we show the potential of genomics in One Health surveillance when supported by an appropriate bioinformatic tool. By facilitating precise disease control strategies and antimicrobial resistance management, SIEGA enhances global health security and reduces the burden of infectious diseases. The integration of health data from humans, animals, and the environment, coupled with advanced genomics, underscores the importance of a holistic One Health approach in mitigating health threats.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Bostelmann, Gerrit</style></author><author><style face="normal" font="default" size="100%">Martínez-González, L Javier</style></author><author><style face="normal" font="default" size="100%">Muñoyerro-Muñiz, Dolores</style></author><author><style face="normal" font="default" size="100%">Villegas, Román</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Baño, Jesús</style></author><author><style face="normal" font="default" size="100%">Romero-Gómez, Manuel</style></author><author><style face="normal" font="default" size="100%">Lorusso, Nicola</style></author><author><style face="normal" font="default" size="100%">Garcia-León, Javier</style></author><author><style face="normal" font="default" size="100%">Navarro-Marí, Jose M</style></author><author><style face="normal" font="default" size="100%">Camacho-Martinez, Pedro</style></author><author><style face="normal" font="default" size="100%">Merino-Diaz, Laura</style></author><author><style face="normal" font="default" size="100%">Salazar, Adolfo de</style></author><author><style face="normal" font="default" size="100%">Viñuela, Laura</style></author><author><style face="normal" font="default" size="100%">Lepe, Jose A</style></author><author><style face="normal" font="default" size="100%">García, Federico</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival.</style></title><secondary-title><style face="normal" font="default" size="100%">Viruses</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Viruses</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pandemics</style></keyword><keyword><style  face="normal" font="default" size="100%">Phylogeny</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Aug 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Méndez-Salazar, Eder Orlando</style></author><author><style face="normal" font="default" size="100%">Vázquez-Mellado, Janitzia</style></author><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Zamudio-Cuevas, Yessica</style></author><author><style face="normal" font="default" size="100%">Francisco-Balderas, Adriana</style></author><author><style face="normal" font="default" size="100%">Martínez-Flores, Karina</style></author><author><style face="normal" font="default" size="100%">Fernández-Torres, Javier</style></author><author><style face="normal" font="default" size="100%">Lozada-Pérez, Carlos</style></author><author><style face="normal" font="default" size="100%">Pineda, Carlos</style></author><author><style face="normal" font="default" size="100%">Sánchez-González, Austreberto</style></author><author><style face="normal" font="default" size="100%">Silveira, Luis H</style></author><author><style face="normal" font="default" size="100%">Burguete-García, Ana I</style></author><author><style face="normal" font="default" size="100%">Orbe-Orihuela, Citlalli</style></author><author><style face="normal" font="default" size="100%">Lagunas-Martínez, Alfredo</style></author><author><style face="normal" font="default" size="100%">Vazquez-Gomez, Alonso</style></author><author><style face="normal" font="default" size="100%">López-Reyes, Alberto</style></author><author><style face="normal" font="default" size="100%">Palacios-González, Berenice</style></author><author><style face="normal" font="default" size="100%">Martínez-Nava, Gabriela Angélica</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Dysbiosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gastrointestinal Microbiome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gout</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metagenome</style></keyword><keyword><style  face="normal" font="default" size="100%">metagenomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Maps</style></keyword><keyword><style  face="normal" font="default" size="100%">Uric Acid</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 05 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34030623?dopt=Abstract</style></custom1></record></records></xml>