Combinatorial Cis-regulation in Saccharomyces Species.

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

Selective Spectrum Antibiotic Modulation of the Gut Microbiome in Obesity and Diabetes Rodent Models.

The gastrointestinal tract microbiome has been suggested as a potential therapeutic target for metabolic diseases such as obesity and Type 2 diabetes mellitus (T2DM). However, the relationship between changes in microbial communities and metabolic disease-phenotypes are still poorly understood. In this study, we used antibiotics with markedly different antibacterial spectra to modulate the gut microbiome in a diet-induced obesity mouse model and then measured relevant biochemical, hormonal and phenotypic biomarkers of obesity and T2DM. Mice fed a high-fat diet were treated with either ceftazidime (a primarily anti-Gram negative bacteria antibiotic) or vancomycin (mainly anti-Gram positive bacteria activity) in an escalating three-dose regimen. We also dosed animals with a well-known prebiotic weight-loss supplement, 10% oligofructose saccharide (10% OFS). Vancomycin treated mice showed little weight change and no improvement in glycemic control while ceftazidime and 10% OFS treatments induced significant weight loss. However, only ceftazidime showed significant, dose dependent improvement in key metabolic variables including glucose, insulin, protein tyrosine tyrosine (PYY) and glucagon-like peptide-1 (GLP-1). Subsequently, we confirmed the positive hyperglycemic control effects of ceftazidime in the Zucker diabetic fatty (ZDF) rat model. Metagenomic DNA sequencing of bacterial 16S rRNA gene regions V1-V3 showed that the microbiomes of ceftazidime dosed mice and rats were enriched for the phylum Firmicutes while 10% OFS treated mice had a greater abundance of Bacteroidetes. We show that specific changes in microbial community composition are associated with obesity and glycemic control phenotypes. More broadly, our study suggests that in vivo modulation of the microbiome warrants further investigation as a potential therapeutic strategy for metabolic diseases.

Bacterial resistance to leucyl-tRNA synthetase inhibitor GSK2251052 develops during treatment of complicated urinary tract infections.

GSK2251052, a novel leucyl-tRNA synthetase (LeuRS) inhibitor, was in development for the treatment of infections caused by multidrug-resistant Gram-negative pathogens. In a phase II study (study LRS114688) evaluating the efficacy of GSK2251052 in complicated urinary tract infections, resistance developed very rapidly in 3 of 14 subjects enrolled, with ≥32-fold increases in the GSK2251052 MIC of the infecting pathogen being detected. A fourth subject did not exhibit the development of resistance in the baseline pathogen but posttherapy did present with a different pathogen resistant to GSK2251052. Whole-genome DNA sequencing of Escherichia coli isolates collected longitudinally from two study LRS114688 subjects confirmed that GSK2251052 resistance was due to specific mutations, selected on the first day of therapy, in the LeuRS editing domain. Phylogenetic analysis strongly suggested that resistant Escherichia coli isolates resulted from clonal expansion of baseline susceptible strains. This resistance development likely resulted from the confluence of multiple factors, of which only some can be assessed preclinically. Our study shows the challenges of developing antibiotics and the importance of clinical studies to evaluate their effect on disease pathogenesis. (These studies have been registered at ClinicalTrials.gov under registration no. NCT01381549 for the study of complicated urinary tract infections and registration no. NCT01381562 for the study of complicated intra-abdominal infections.).

ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species.

Saccharomyces cerevisiae is a primary model for studies of transcriptional control, and the specificities of most yeast transcription factors (TFs) have been determined by multiple methods. However, it is unclear which position weight matrices (PWMs) are most useful; for the roughly 200 TFs in yeast, there are over 1200 PWMs in the literature. To address this issue, we created ScerTF, a comprehensive database of 1226 motifs from 11 different sources. We identified a single matrix for each TF that best predicts in vivo data by benchmarking matrices against chromatin immunoprecipitation and TF deletion experiments. We also used in vivo data to optimize thresholds for identifying regulatory sites with each matrix. To correct for biases from different methods, we developed a strategy to combine matrices. These aligned matrices outperform the best available matrix for several TFs. We used the matrices to predict co-occurring regulatory elements in the genome and identified many known TF combinations. In addition, we predict new combinations and provide evidence of combinatorial regulation from gene expression data. The database is available through a web interface at http://ural.wustl.edu/ScerTF. The site allows users to search the database with a regulatory site or matrix to identify the TFs most likely to bind the input sequence.