Revealing General Patterns of Microbiomes That Transcend Systems: Potential and Challenges of Deep Transfer Learning.

A growing body of research has established that the microbiome can mediate the dynamics and functional capacities of diverse biological systems. Yet, we understand little about what governs the response of these microbial communities to host or environmental changes. Most efforts to model microbiomes focus on defining the relationships between the microbiome, host, and environmental features within a specified study system and therefore fail to capture those that may be evident across multiple systems. In parallel with these developments in microbiome research, computer scientists have developed a variety of machine learning tools that can identify subtle, but informative, patterns from complex data. Here, we recommend using deep transfer learning to resolve microbiome patterns that transcend study systems. By leveraging diverse public data sets in an unsupervised way, such models can learn contextual relationships between features and build on those patterns to perform subsequent tasks (e.g., classification) within specific biological contexts.

Variation in Methylmercury Metabolism and Elimination in Humans: Physiological Pharmacokinetic Modeling Highlights the Role of Gut Biotransformation, Skeletal Muscle, and Hair.

The biological half-life (t1/2) of methylmercury (MeHg) shows considerable individual variability (t1/2 < 30 to > 120 days), highlighting the importance of mechanisms controlling MeHg metabolism and elimination. Building on a prior physiologically based pharmacokinetic (PBPK) model, we elucidate parameters that have the greatest influence on variability of MeHg t1/2 in the human body. Employing a dataset of parameters for mean organ volumes and blood flow rates appropriate for man and woman (25-35 years) and child (4 - 6 years), we demonstrate model fitness by simulating data from our prior controlled study of MeHg elimination in people. Model predictions give MeHg t1/2 of 46.9, 38.9, and 31.5 days and steady-state blood MeHg of 2.6, 2.6, and 2.3 µg/l in man, woman, and child, respectively, subsequent to a weekly dose of 0.7 µg/kg body weight. The major routes of elimination are biotransformation to inorganic Hg in the gut lumen (73% in adults, 61% in child) and loss of MeHg via excretion within growing hair (13% in adults, 24% in child). Local and global sensitivity analyses of model parameters reveal that variation in biotransformation rate in the gut lumen, and rates of transport between gut lumen and gut tissue, have the greatest influence on MeHg t1/2. Volume and partition coefficients for skeletal muscle (SM) and gut tissue also show significant sensitivity affecting model output of MeHg t1/2. Our results emphasize the role of gut microbiota in MeHg biotransformation, transport kinetics at the level of the gut, and SM mass as moderators of MeHg kinetics in the human body.