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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]

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.