Adina Weinberger

TL;DR: A machine-learning algorithm is devised that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in an 800-person cohort and shows that it accurately predicts personalized postprandial glycemic response to real-life meals, and a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postpr andial responses and consistent alterations to gut microbiota configuration.

TL;DR: Genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment are examined, and it is demonstrated that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition.

TL;DR: It is demonstrated that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota, thereby calling for a reassessment of massive NAS usage.

TL;DR: A comprehensive analysis of the tumor microbiome was undertaken, studying 1526 tumors and their adjacent normal tissues across seven cancer types, finding that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome.

TL;DR: A method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters is devised and applied to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences.