OBJECTIVE: To investigate deep and comprehensive analysis of gut microbial communities and biological parameters after prebiotic administration in obese and diabetic mice. RESEARCH DESIGN AND METHODS: Genetic (ob/ob) or diet-induced obese and diabetic mice were chronically fed with prebiotic-enriched diet or with a control diet. Extensive gut microbiota analyses, including quantitative PCR, pyrosequencing of the 16S rRNA, and phylogenetic microarrays, were performed in ob/ob mice. The impact of gut microbiota modulation on leptin sensitivity was investigated in diet-induced leptin-resistant mice. Metabolic parameters, gene expression, glucose homeostasis, and enteroendocrine-related L-cell function were documented in both models. RESULTS: In ob/ob mice, prebiotic feeding decreased Firmicutes and increased Bacteroidetes phyla, but also changed 102 distinct taxa, 16 of which displayed a >10-fold change in abundance. In addition, prebiotics improved glucose tolerance, increased L-cell number and associated parameters (intestinal proglucagon mRNA expression and plasma glucagon-like peptide-1 levels), and reduced fat-mass development, oxidative stress, and low-grade inflammation. In high fat–fed mice, prebiotic treatment improved leptin sensitivity as well as metabolic parameters. CONCLUSIONS: We conclude that specific gut microbiota modulation improves glucose homeostasis, leptin sensitivity, and target enteroendocrine cell activity in obese and diabetic mice. By profiling the gut microbiota, we identified a catalog of putative bacterial targets that may affect host metabolism in obesity and diabetes.

The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.

Novel high-throughput DNA sequencing technologies allow researchers to characterize a bacterial genome during a single experiment and at a moderate cost. However, the increase in sequencing throughput that is allowed by using such platforms is obtained at the expense of individual sequence read length, which must be assembled into longer contigs to be exploitable. This study focuses on the Illumina sequencing platform that produces millions of very short sequences that are 35 bases in length. We propose a de novo assembler software that is dedicated to process such data. Based on a classical overlap graph representation and on the detection of potentially spurious reads, our software generates a set of accurate contigs of several kilobases that cover most of the bacterial genome. The assembly results were validated by comparing data sets that were obtained experimentally for Staphylococcus aureus strain MW2 and Helicobacter acinonychis strain Sheeba with that of their published genomes acquired by conventional sequencing of 1.5- to 3.0-kb fragments. We also provide indications that the broad coverage achieved by high-throughput sequencing might allow for the detection of clonal polymorphisms in the set of DNA molecules being sequenced.

Carriage of antimicrobial-resistant pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) places patients at high risk of infection.^1,2 Early identification of patients colonized with MRSA and subsequent prevention of patient-to-patient spread through infection control measures are believed to be potent interventions to control MRSA.³ In addition, rapid MRSA screening of surgical patients may decrease MRSA infections by facilitating appropriate selection of antibiotic agents for preoperative prophylaxis.⁴

We evaluated the robustness of loop-mediated isothermal amplification (LAMP) of DNA for bacterial diagnostic applications. Salmonella enterica serova

Bacterial identification relies primarily on culture-based methodologies requiring 24 h for isolation and an additional 24 to 48 h for species identification. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is an emerging technology newly applied to the problem of bacterial species identification. We evaluated two MALDI-TOF MS systems with 720 consecutively isolated bacterial colonies under routine clinical laboratory conditions. Isolates were analyzed in parallel on both devices, using the manufacturers' default recommendations. We compared MS with conventional biochemical test system identifications. Discordant results were resolved with “gold standard” 16S rRNA gene sequencing. The first MS system (Bruker) gave high-confidence identifications for 680 isolates, of which 674 (99.1%) were correct; the second MS system (Shimadzu) gave high-confidence identifications for 639 isolates, of which 635 (99.4%) were correct. Had MS been used for initial testing and biochemical identification used only in the absence of high-confidence MS identifications, the laboratory would have saved approximately US$5 per isolate in marginal costs and reduced average turnaround time by more than an 8-h shift, with no loss in accuracy. Our data suggest that implementation of MS as a first test strategy for one-step species identification would improve timeliness and reduce isolate identification costs in clinical bacteriology laboratories now.