{"data":{"event":{"id":"99e58de5-9e88-42a2-a1ca-4de2e181530a","slug":"interpretable-machine-learning-reveals-complementary-age-related-signatu-a54018d17c","title":"\n\nInterpretable Machine Learning Reveals Complementary Age-Related Signatures in the Oral and Gut Microbiome \n\n","short_summary":"Whether combining microbiome data from multiple body sites improves prediction, and whether different sites carry complementary or redundant information, are distinct questions that most studies conflate into a single accuracy metric. This work makes two contributions, one methodological and one biological, using paired stool and oral cavity microbiome samples from 44 subjects across two age groups, healthy adults and newborns (Ferretti et al., 2018). Methodologically, we show that a subject-matched fusion design combined with SHAP-based (SHapley Additive exPlanations) site attribution can det","full_description":"Whether combining microbiome data from multiple body sites improves prediction, and whether different sites carry complementary or redundant information, are distinct questions that most studies conflate into a single accuracy metric. This work makes two contributions, one methodological and one biological, using paired stool and oral cavity microbiome samples from 44 subjects across two age groups, healthy adults and newborns (Ferretti et al., 2018). Methodologically, we show that a subject-matched fusion design combined with SHAP-based (SHapley Additive exPlanations) site attribution can detect complementary information between body sites even when no measurable accuracy gain results. This is a pattern that conventional model comparison would misread as a null result. Gut (stool) composition alone achieved near-perfect classification (area under the receiver operating characteristic curve, AUC = 1.00), and combined stool-oral models never exceeded this ceiling. A null baseline, bootstrap confidence intervals, and preprocessing sensitivity checks confirmed that this ceiling reflects genuine biological signal rather than an artifact. Despite the flat accuracy curve, SHAP analysis of the fused model showed that oral cavity features carried more total feature importance than stool features (58.1% versus 41.9%), indicating that the model draws on real, non-redundant information from both sites. Biologically, the taxa driving this pattern include Malassezia restricta, Staphylococcus epidermidis, and Prevotella melaninogenica. These taxa behave in a manner consistent with their established roles as early colonizers of the neonatal gut, skin, and oral cavity, once their model-specific behavior is verified directly against abundance data rather than inferred from the literature alone. An independent, substantially larger paired-cohort study using a different analytical method reports a compatible pattern. Together, these results support a model of oral-gut microbiome maturation as two distinct, complementary processes, and demonstrate that detecting this kind of relationship requires examining a model's internal reasoning rather than its accuracy alone.\n\nUpdate (2026-09-19): Learning Another Language May Be One of the Best Ways to Keep Your Brain Healthy. Article URL: https://theconversation.com/learning-another-language-may-be-one-of-the-best-ways-to-keep-your-brain-healthy-as-you-age-291951\nComments URL: https://news.ycombinator.com/item?id=49766821\nPoints: 30\n# Comments: 21\n\nUpdate (2026-09-21): \n\nSerum metabolomics reveals signatures associated with physical resilience trajectories from middle to older age \n\n. Lifecourse physical resilience is defined by the ability to maintain abilities across multiple domains of physical performance. While the importance of physical resilience in functional independence and mobility disability is clear, studies investigating metabolomic signatures of physical resilience are lacking. Here, we performed untargeted metabolomics on serum samples from a community-based cohort of 237 individuals followed over 28 years, and applied spectral data mining tools to map identified metabolites to health phenotypes from public repositories. We identified metabolites across multiple chemical classes, including acylcarnitines, glutamine conjugates, and phosphocholines, that were differentially associated with physical resilience status. Notably, medium-chain acylcarnitines negatively associated with physical resilience were more frequently observed in disease phenotypes than in healthy individuals. Kynurenine, a tryptophan metabolite linked to age-related functional decline, increased more steeply with age in individuals with low\n\nUpdate (2026-09-24): \n\nSystematic discovery of protein kinase-like domains reveals diverse evolutionary strategies in the human oral microbiome \n\n. The protein kinase-like (PKL) superfamily regulates metabolism, biofilm formation, host interactions, and antimicrobial resistance in bacteria, although much of its diversity remains unknown. We used a structure-based pipeline that combined protein structure prediction with structural similarity searches to search 5.1 million protein sequences in the Human Oral Microbiome Database (HOMD). Together with 30 families that had been previously characterized, we identified 20 PKL families new to this genome collection: 15 entirely novel and 5 previously reported as preliminary findings. Our taxonomic analysis revealed that the families were distributed either across multiple bacterial phyla or restricted to a single species; hits spanning domains suggest either ancient origins or horizontal transfer. Three families were examined in detail and show different evolutionary pathways: SEAE1 from Segetibacter aerophilus, which is a putative lipid kinase; POGI1 from Porphyromonas gingivalis, a putative ethanolamine kinase with the PKL fold that is\n\nUpdate (2026-09-24): \n\nFluoxetine Hydrochloride Treatment Influences Site-Specific ADAR Editing and Transcriptome Regulation in Arid1b +/- Mice \n\n. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by repetitive behavior and impaired social interactions. Recent reports from human postmortem brain samples of ASD show transcriptome-wide dysregulations, including changes in differential gene expression and alternative splicing. An important mechanism regulating transcriptome function is the editing of double-stranded RNA by adenosine deaminases acting on RNA (ADARs), which play regulatory roles in neurodevelopment and innate immunity. We explore the ADAR editing changes associated with the use of Selective Serotonin Reuptake Inhibitors (SSRIs) in Arid1b haploinsufficient mouse models. Our results show dynamic changes in site-specific editing rates in autistic mice. We further demonstrate the transcriptome-wide effects of differential ADAR editing in terms of changes in miRNA-mRNA interactions. Moreover, our findings highlight a site-specific change in editing rates for evolutionarily conserved sites in mammals and ASD associated risk genes, such as Gria2.\n\nUpdate (2026-09-24): Anthropic's Gene Editing Discovery Isn't Yet a Breakthrough, Scientists Say - bloomberg.com. 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While the importance of physical resilience in functional independence and mobility disability is clear, studies investigating metabolomic signatures of physical resilience are lacking. 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We used a structure-based pipeline that combined protein structure prediction with structural similarity searches to search 5.1 million protein sequences in the Human Oral Microbiome Database (HOMD). 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