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Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D. [ABSTRACT FROM AUTHOR]

Motivation Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging. Results We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Availability and implementation The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract Candida species are the most commonly isolated opportunistic fungal pathogens in humans. These species exhibit very different infection strategies, since they acquired pathogenesis independently throughout evolution. Candida albicans causes most of the diagnosed infections, closely followed by Candida glabrata. C. albicans is well studied, and many genes have been shown to be important for infection and colonization of the host. It is, however, less clear how C. glabrata infects the host. With the help of a fungal RNA enrichment technology, we here investigated for the first time the transcriptomic profile of C. glabrata during urinary tract infection (UTI) in mice in order to determine which pathways and genes are regulated in the host compared to in vitro conditions. In the UTI model, bladders and kidneys are major target organs and therefore fungal transcriptomes were addressed in these organs. Our results showed that, next to adhesins and proteases, which seem to have a specific expression profile that may be linked to the niche in which the fungus grows, nitrogen metabolism and regulation play a vital role during C. glabrata UTI. Genes involved in nitrogen metabolism were upregulated and among them we show that DUR1,2 (urea amidolyase) and GAP1 (amino acid permease) are important for virulence. Furthermore, we confirmed the importance of the glyoxylate cycle in the host and identified MLS1 (malate synthase) as an important gene necessary for C. glabrata virulence. In conclusion, our study shows with the support of in vivo transcriptomics how C. glabrata adapts to host conditions. [ABSTRACT FROM AUTHOR]

As part of the human microbiota, the fungus Candida albicans colonizes the oral cavity and other mucosal surfaces of the human body. Commensalism is tightly controlled by complex interactions of the fungus and the host to preclude fungal elimination but also fungal overgrowth and invasion, which can result in disease. As such, defects in antifungal T cell immunity render individuals susceptible to oral thrush due to interrupted immunosurveillance of the oral mucosa. The factors that promote commensalism and ensure persistence of C. albicans in a fully immunocompetent host remain less clear. Using an experimental model of C. albicans oral colonization in mice we explored fungal determinants of commensalism in the oral cavity. Transcript profiling of the oral isolate 101 in the murine tongue tissue revealed a characteristic metabolic profile tailored to the nutrient poor conditions in the stratum corneum of the epithelium where the fungus resides. Metabolic adaptation of isolate 101 was also reflected in enhanced nutrient acquisition when grown on oral mucosa substrates. Persistent colonization of the oral mucosa by C. albicans also correlated inversely with the capacity of the fungus to induce epithelial cell damage and to elicit an inflammatory response. Here we show that these immune evasive properties of isolate 101 are explained by a strong attenuation of a number of virulence genes, including those linked to filamentation. De-repression of the hyphal program by deletion or conditional repression of NRG1 abolished the commensal behaviour of isolate 101, thereby establishing a central role of this factor in the commensal lifestyle of C. albicans in the oral niche of the host., Competing Interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests:Simon Altmeier is now an employee of Novartis. His findings presented in this paper are solely his views and are independent from Novartis work. The other authors have no competing interest to declare.

Candida glabrata is an important fungal pathogen which develops rapid antifungal resistance in treated patients. It is known that azole treatments lead to antifungal resistance in this fungal species and that multidrug efflux transporters are involved in this process. Specific mutations in the transcriptional regulator PDR1 result in upregulation of the transporters. In addition, we showed that the PDR1 mutations can contribute to enhance virulence in animal models. In this study, we were interested to compare genomes of two specific C. glabrata -related isolates, one of which was azole susceptible (DSY562) while the other was azole resistant (DSY565). DSY565 contained a PDR1 mutation (L280F) and was isolated after a time-lapse of 50 d of azole therapy. We expected that genome comparisons between both isolates could reveal additional mutations reflecting host adaptation or even additional resistance mechanisms. The PacBio technology used here yielded 14 major contigs (sizes 0.18-1.6 Mb) and mitochondrial genomes from both DSY562 and DSY565 isolates that were highly similar to each other. Comparisons of the clinical genomes with the published CBS138 genome indicated important genome rearrangements, but not between the clinical strains. Among the unique features, several retrotransposons were identified in the genomes of the investigated clinical isolates. DSY562 and DSY565 each contained a large set of adhesin-like genes (101 and 107, respectively), which exceed by far the number of reported adhesins (63) in the CBS138 genome. Comparison between DSY562 and DSY565 yielded 17 nonsynonymous SNPs (among which the was the expected PDR1 mutation) as well as small size indels in coding regions (11) but mainly in adhesin-like genes. The genomes contained a DNA mismatch repair allele of MSH2 known to be involved in the so-called hyper-mutator phenotype of this yeast species and the number of accumulated mutations between both clinical isolates is consistent with the presence of a MSH2 defect. In conclusion, this study is the first to compare genomes of C. glabrata sequential clinical isolates using the PacBio technology as an approach. The genomes of these isolates taken in the same patient at two different time points exhibited limited variations, even if submitted to the host pressure., (Copyright © 2017 Vale-Silva et al.)

We introduce a graph-theoretic model for predicting the supersecondary structure of transmembrane β-barrel proteins--a particular class of proteins that performs diverse important functions but it is difficult to determine their structure with experimental methods. This ab initio model resolves the protein folding problem based on pseudo-energy minimization with the aid of a simple probabilistic filter. It also allows for determining structures whose barrel follows a given permutation on the arrangement of β-strands, and allows for rapidly discriminating the transmembrane β-barrels from other kinds of proteins. The model is fairly accurate, robust and can be run very efficiently on PC-like computers, thus proving useful for genome screening.