Distinct factors associated with short-term and long-term weight loss induced by low-fat or low-carbohydrate diet intervention.

To understand what determines the success of short- and long-term weight loss, we conduct a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we find that dietary adherence and diet quality, not just caloric restriction, are important for short-term weight loss in both diets. Interestingly, we observe minimal dietary differences between those who succeeded in long-term weight loss and those who did not. Instead, proteomic and gut microbiota signatures significantly differ between these two groups at baseline. Moreover, the baseline respiratory quotient may suggest a specific diet for better weight-loss outcomes. Overall, the identification of these dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, provides a roadmap for developing individualized weight-loss strategies.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration.

Identifying the transcription factors (TFs) responsible for observed changes in gene expression is an important step in understanding gene regulatory networks. ChIP-X Enrichment Analysis 3 (ChEA3) is a transcription factor enrichment analysis tool that ranks TFs associated with user-submitted gene sets. The ChEA3 background database contains a collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate a composite rank that improves the prediction of the correct upstream TF compared to ranks produced by individual libraries. We compare ChEA3 with existing TF prediction tools and show that ChEA3 performs better. By integrating the ChEA3 libraries, we illuminate general transcription factor properties such as whether the TF behaves as an activator or a repressor. The ChEA3 web-server is available from https://amp.pharm.mssm.edu/ChEA3.