Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning

Abstract

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition.Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

“…S3, Geva-Zatorsky et al, 2017). Bacteroides species, including B. ovatus and B. thetaiotaomicron, have previously been found to be negatively associated with host age (Galkin et al, 2020). Recent work has demonstrated that the receptor binding domain of SARS-CoV-2, unlike previous SARS-like coronaviruses, must be pre-activated by the proprotein convertase furin to effectively bind ACE2 (Walls et al, 2020, Hoffman 2020.…”

An accurate aging clock developed from the largest dataset of microbial and human gene expression reveals molecular mechanisms of aging

“…The discovery cohort was used to tune model hyperparameters using cross validation, then a final model was trained on the full discovery cohort. The final model was applied to the validation cohort (drawn from the same population but not used in training), cohorts matched to two validation datasets (CV and HC) analyzed in Galkin et al (2020), and additional cohorts described in Cohort Comparisons below.…”

“…We note that some discrepancy between predicted and actual age is expected in a useful biological age candidate. If age was perfectly predicted, it would indicate either that the aspects of health captured by the biomarker decline in lockstep with chronological age, or that the biomarker is statistically associated with properties that vary systematically with age but are irrelevant to health.We present an in-depth comparison of our microbiome model work with the gut metagenomic aging clock reported byGalkin et al (2020).Galkin et al report MAE of 10.60 and R 2 of 0.21 (vs our 9.49 and .42) in a dataset with a baseline MAE of 13.03 (vs our 12.98). In a secondary validation exercise, their model obtains MAE of 6.81 and R 2 of 0.134 when applied to a separate dataset (HC) with a lower baseline MAE of 9.27 .…”

“…Biological age refers to biological markers of the aging process, and may be accelerated or slowed in some individuals relative to their chronological age. Recent research has proposed computational aging clocks based on various biomarkers including metabolites, blood cell count and other routine lab tests (Earls et al, 2019;Momoshina, Kochetov et al 2018), DNA methylation (Fraga & Esteller, 2007;Horvath & Raj 2018;Bell et al 2019), gene expression in tissue (Momoshina, Volosnikova et al 2018) or blood (Harries et al, 2011;Lin et al, 2019), taxonomic composition of the gut microbiome (Galkin et al, 2020), among others. Aging clocks propose to use a signal derived from these biomarkers as a health-related metric for aging.…”

A neural network-based framework to understand the Type 2 Diabetes (T2D)-related alteration of the human gut microbiome

“…Deep learning-based methods, which typically use the neural network (NN) with multiple hidden layers, show promising performance in recent studies as they can identify novel patterns that would have been ignored by other methods in a given complex dataset (Angermueller, Pärnamaa et al 2016, Morton, Aksenov et al 2019, Galkin, Mamoshina et al 2020). Recently, these methods have been applied in the field of biology, such as predicting special genes/protein functions (Kulmanov, Khan et al 2017, Arango-Argoty, Garner et al 2018, identifying medical diagnoses (Kermany, Goldbaum et al 2018), drug discovery (Chen, Engkvist et al 2018), etc.…”