Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes

Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases

Trials

BACKGROUND: The aim of this open-label, randomized controlled trial conducted in four African countries (Madagascar, Niger, Central African Republic, and Senegal) is to compare three strategies of renutrition for moderate acute malnutrition (MAM) in children based on modulation of the gut microbiota with enriched flours alone, enriched flours with prebiotics or enriched flours coupled with antibiotic treatment. METHODS: To be included, children aged between 6 months and 2 years are preselected based on mid-upper-arm circumference (MUAC) and are included based on a weight-for-height Z-score (WHZ) between - 3 and - 2 standard deviations (SD). As per current protocols, children receive renutrition treatment for 12 weeks and are assessed weekly to determine improvement. The primary endpoint is recovery, defined by a WHZ >/= - 1.5 SD after 12 weeks of treatment. Data collected include clinical and socioeconomic characteristics, side effects, compliance and tolerance to interventions. Metagenomic analysis of gut microbiota is conducted at inclusion, 3 months, and 6 months. The cognitive development of children is evaluated in Senegal using only the Developmental Milestones Checklist II (DMC II) questionnaire at inclusion and at 3, 6, and 9 months. The data will be correlated with renutrition efficacy and metagenomic data. DISCUSSION: This study will provide new insights for the treatment of MAM, as well as original data on the modulation of gut microbiota during the renutrition process to support (or not) the microbiota hypothesis of malnutrition. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT03474276 Last update 28 May 2018

Opposing roles for Set2 and yFACT in regulating TBP binding at promoters

Previous work links histone methylation by Set2 with transcriptional elongation. yFACT (Spt16–Pob3 and Nhp6) reorganizes nucleosomes and functions in both transcriptional initiation and elongation. We show that growth defects caused by spt16 or pob3 mutations can be suppressed by deleting SET2, suggesting that Set2 and yFACT have opposing roles. Set2 methylates K36 of histone H3, and K36 substitutions also suppress yFACT mutations. In contrast, set1 enhances yFACT mutations. Methylation at H3 K4 by Set1 is required for set2 to suppress yFACT defects. We did not detect an elongation defect at an 8 kb ORF in yFACT mutants. Instead, pob3 mutants displayed reduced binding of both pol II and TBP to the GAL1 promoter. Importantly, both GAL1 transcription and promoter binding of pol II and TBP are significantly restored in the pob3 set2 double mutant. Defects caused by an spt16 mutation are enhanced by either TBP or TFIIA mutants. These synthetic defects are suppressed by set2, demonstrating that yFACT and Set2 oppose one another during transcriptional initiation at a step involving DNA binding by TBP and TFIIA