An accurate aging clock developed from large-scale gut microbiome and human gene expression data.

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

The salivary metatranscriptome as an accurate diagnostic indicator of oral cancer.

Despite advances in cancer treatment, the 5-year mortality rate for oral cancers (OC) is 40%, mainly due to the lack of early diagnostics. To advance early diagnostics for high-risk and average-risk populations, we developed and evaluated machine-learning (ML) classifiers using metatranscriptomic data from saliva samples (n = 433) collected from oral premalignant disorders (OPMD), OC patients (n = 71) and normal controls (n = 171). Our diagnostic classifiers yielded a receiver operating characteristics (ROC) area under the curve (AUC) up to 0.9, sensitivity up to 83% (92.3% for stage 1 cancer) and specificity up to 97.9%. Our metatranscriptomic signature incorporates both taxonomic and functional microbiome features, and reveals a number of taxa and functional pathways associated with OC. We demonstrate the potential clinical utility of an AI/ML model for diagnosing OC early, opening a new era of non-invasive diagnostics, enabling early intervention and improved patient outcomes.

Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer's disease.

BACKGROUND: Given multiple studies of brain microRNA (miRNA) in relation to Alzheimer's disease (AD) with few consistent results and the heterogeneity of this disease, the objective of this study was to explore their mechanism by evaluating their relation to different elements of Alzheimer's disease pathology, confounding factors and mRNA expression data from the same subjects in the same brain region. METHODS: We report analyses of expression profiling of miRNA (n = 700 subjects) and lincRNA (n = 540 subjects) from the dorsolateral prefrontal cortex of individuals participating in two longitudinal cohort studies of aging. RESULTS: We confirm the association of two well-established miRNA (miR-132, miR-129) with pathologic AD in our dataset and then further characterize this association in terms of its component neuritic ß-amyloid plaques and neurofibrillary tangle pathologies. Additionally, we identify one new miRNA (miR-99) and four lincRNA that are associated with these traits. Many other previously reported associations of microRNA with AD are associated with the confounders quantified in our longitudinal cohort. Finally, by performing analyses integrating both miRNA and RNA sequence data from the same individuals (525 samples), we characterize the impact of AD associated miRNA on human brain expression: we show that the effects of miR-132 and miR-129-5b converge on certain genes such as EP300 and find a role for miR200 and its target genes in AD using an integrated miRNA/mRNA analysis. CONCLUSIONS: Overall, miRNAs play a modest role in human AD, but we observe robust evidence that a small number of miRNAs are responsible for specific alterations in the cortical transcriptome that are associated with AD.

Short hydrogen bonds in proteins.

Short hydrogen bonds are present in many chemical and biological systems. It is well known that these short hydrogen bonds are found in the active site of enzymes and aid enzyme catalysis. This study aims to systematically characterize all short hydrogen bonds from a nonredundant dataset of protein structures. The study has revealed that short hydrogen bonds are commonly found in proteins and are widely present in different regions of the protein chain, such as the backbone or side chain, and in different secondary structural regions such as helices, strands and turns. The frequency of occurrence of donors and acceptors from the charged side chains as well as from the neutral backbone atoms is equally high. This suggests that short hydrogen bonds in proteins occur either due to increased strength or due to geometrical constraints and this has been illustrated from several examples.