02053nas a2200181 4500008004100000022001400041245013200055210006900187260001600256300000800272490000700280520135300287100003301640700002001673700002401693700002001717856013401737 2022 eng d a2045-232200aTowards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.0 aTowards a metagenomics machine learning interpretable model for  c2022 Jan 10 a4500 v123 a

Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.

1 aCasimiro-Soriguer, Carlos, S1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://www.clinbioinfosspa.es/content/towards-metagenomics-machine-learning-interpretable-model-understanding-transition-adenoma03601nas 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-functional