Type 2 diabetes mellitus (T2D) is a growing health problem, but little is known about its early disease stages, its effects on biological processes or the transition to clinical T2D. To understand the earliest stages of T2D better, we obtained samples from 106 healthy individuals and individuals with prediabetes over approximately four years and performed deep profiling of transcriptomes, metabolomes, cytokines, and proteomes, as well as changes in the microbiome. This rich longitudinal data set revealed many insights: first, healthy profiles are distinct among individuals while displaying diverse patterns of intra- and/or inter-personal variability. Second, extensive host and microbial changes occur during respiratory viral infections and immunization, and immunization triggers potentially protective responses that are distinct from responses to respiratory viral infections. Moreover, during respiratory viral infections, insulin-resistant participants respond differently than insulin-sensitive participants. Third, global co-association analyses among the thousands of profiled molecules reveal specific host–microbe interactions that differ between insulin-resistant and insulin-sensitive individuals. Last, we identified early personal molecular signatures in one individual that preceded the onset of T2D, including the inflammation markers interleukin-1 receptor agonist (IL-1RA) and high-sensitivity C-reactive protein (CRP) paired with xenobiotic-induced immune signalling. Our study reveals insights into pathways and responses that differ between glucose-dysregulated and healthy individuals during health and disease and provides an open-access data resource to enable further research into healthy, prediabetic and T2D states.

BackgroundChanges in diet and exercise can alter the gut microbiome of humans and mice; however, few studies to date have assessed the microbiomes of highly fit athletes. In this pilot study, we used metagenomic whole genome shotgun (mWGS) and metatranscriptomic (RNA-Seq) sequencing to show what organisms are both present and active in the gut microbiomes of both professional and amateur level competitive cyclists and to determine if any significant differences exist between these two groups.ResultsUsing mWGS sequencing data, we showed that the gut microbiomes of 33 cyclists split into three taxonomic clusters, characterized by either high Prevotella, high Bacteroides or a mix of many genera including Bacteroides, Prevotella, Eubacterium, Ruminococcus, and Akkermansia. While no significant correlations could be found between taxonomic cluster and being either a professional or amateur level cyclist, high abundance of the genus Prevotella (≥2.5%) was significantly correlated with time reported exercising during an average week. Increased abundance of Prevotella was correlated with a number of amino acid and carbohydrate metabolism pathways, including branched chain amino acid metabolism. Further analysis of the metatranscriptome revealed significant taxonomic differences when compared to the metagenome. There was increased abundance of Methanobrevibacter smithii transcripts in a number of professional cyclists in comparison to amateur cyclists and this archaeon had upregulation of genes involved in the production of methane. Furthermore, when methane metabolism was upregulated, there was similar upregulation of energy and carbohydrate metabolism pathways.ConclusionsThese results provide a framework for common constituents of the gut community in individuals who follow an exercise-rich lifestyle. These data also suggest how certain organisms such as M. smithii may beneficially influence the metabolic efficiency of the gut community in professional cyclists due to synergistic metabolic cross-feeding events.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0320-4) contains supplementary material, which is available to authorized users.

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Environmental microbes have been associated with both protective and adverse health effects in children and adults. Epidemiological studies often rely on broad biomarkers of microbial exposure (i.e. endotoxin, 1→3, Beta-D glucan), but fail to identify the taxonomic composition of the microbial community. Our aim was to characterize the bacterial and fungal microbiome in different types of environmental samples collected in studies of human health effects. We determined the composition of microbial communities present in home, school and outdoor air samples by amplifying and sequencing regions of rRNA genes from bacteria (16S) and fungi (18S and ITS). Samples for this pilot study included indoor settled dust (from both a Boston area birth cohort study on Home Allergens and Asthma (HAA)(n=12) and a study of school exposures and asthma symptoms (SICAS) (n=1)), as well as fine and coarse concentrated outdoor ambient particulate (CAP) samples (n=9). Sequencing of amplified 16S, 18S, and ITS regions was performed on the Roche-454 Life Sciences Titanium pyrosequencing platform. Indoor dust samples were dominated by gram-positive bacteria (Firmicutes and Actinobacteria); the most abundant bacterial genera were those related to human flora (Streptococcus, Staphylococcus, Corynebacterium and Lactobacillus). Outdoor CAPs were dominated by gram-negative Proteobacteria from water and soil sources, in particular the genera Acidovorax, and Brevundimonas (which were present at very low levels or entirely absent in indoor dust). Phylum-level fungal distributions identified by 18S or ITS regions showed very similar findings: a predominance of Ascomycota in indoor dust and Basidiomycota in outdoor CAPs. ITS sequencing of fungal genera in indoor dust showed significant proportions of Aureobasidium and Leptosphaerulina along with some contribution from Cryptococcus, Epicoccum, Aspergillus and the human commensal Malassezia. ITS sequencing detected an additional 70 fungal genera in indoor dust not observed by culture. Microbiome sequencing is feasible for different types of archived environmental samples (indoor dust, and low biomass air particulate samples), and offers the potential to study how whole communities of microbes (including unculturable taxa) influence human health.

ABSTRACT:The need for much more useful molecular dynamics simulations of nanosized system requires precise and unambiguous methods to determine force field parameters avoiding fitting procedures in favor of first principles ones. We use a procedure FUERZA to calculate force constant parameters for glycine oligopeptides using as an input the Hessian tensor from an ab initio calculation. For a molecular system having n atoms, The FUERZA procedure reduces the 3n ϫ 3n problem to 3n 3 ϫ 3 matrices representing atom-atom interactions. The procedure reproduces quite well most of the frequencies and as expected, it overestimates somehow stretching frequencies of bonds with terminal atoms such as hydrogens but it yields precise results for all other frequencies. A harmonic force field is reported for glycine oligopeptides.

We study the electrical characteristics of a group of glycine oligopeptides (1-, 3-, 6-, and 9-mers) molecules attached to gold nanoelectrodes using a combined density functional theorysGreen's function approach. Our procedure considers the applied voltage through the molecule and contacts, recalculates self-consistently the molecular orbitals, Hamiltonian and overlap matrices at each applied voltage, and uses these results to estimate the current-voltage characteristics such that the chemistry of the molecule is fully considered when including the effect of the nanoelectrodes. Our results show that oligoglycines can be used to tailor specific properties for the fabrication of molecular devices and the characteristics may be strongly affected by the few contact atoms addressing the molecule. Oligoglycines show transport behaviors that go from ohmic conductance to Schottky barriers as the length of the oligomers and conformation of the electrode atoms vary.

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Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.