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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]

Extracellular vesicles (EV) are membranous particles secreted by all cells and found in body fluids. Established EV contents include a variety of RNA species, proteins, lipids and metabolites that are considered to reflect the physiological status of their parental cells. However, to date, little is known about cell‐type enriched EV cargo in complex EV mixtures, especially in urine. To test whether EV secretion from distinct human kidney cells in culture differ and can recapitulate findings in normal urine, we comprehensively analysed EV components, (particularly miRNAs, long RNAs and protein) from conditionally immortalised human kidney cell lines (podocyte, glomerular endothelial, mesangial and proximal tubular cells) and compared to EV secreted in human urine. EV from cell culture media derived from immortalised kidney cells were isolated by hydrostatic filtration dialysis (HFD) and characterised by electron microscopy (EM), nanoparticle tracking analysis (NTA) and Western blotting (WB). RNA was isolated from EV and subjected to miRNA and RNA sequencing and proteins were profiled by tandem mass tag proteomics. Representative sets of EV miRNAs, RNAs and proteins were detected in each cell type and compared to human urinary EV isolates (uEV), EV cargo database, kidney biopsy bulk RNA sequencing and proteomics, and single‐cell transcriptomics. This revealed that a high proportion of the in vitro EV signatures were also found in in vivo datasets. Thus, highlighting the robustness of our in vitro model and showing that this approach enables the dissection of cell type specific EV cargo in biofluids and the potential identification of cell‐type specific EV biomarkers of kidney disease. [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. Author summary: The oral microbiota represents an important part of the human microbiota and includes several hundreds to several thousands of bacterial and fungal species. One of the most prominent fungus colonizing the oral cavity is the yeast Candida albicans. While the presence of C. albicans usually remains unnoticed, the fungus can under certain circumstances cause lesions on the lining of the mouth referred to as oral thrush or contribute to other common oral diseases such as caries. Maintaining C. albicans commensalism in the oral mucosa is therefore of utmost importance for oral health and overall wellbeing. While overt fungal growth and disease is limited by immunosurveillance mechanisms during homeostasis, C. albicans strives to survive and evades elimination from the host. Here, we show that while commensalism in the oral cavity is characterized by a restricted fungal virulence and hyphal program, enforcing filamentation in a commensal isolate is sufficient for driving pathogenicity and fungus-induced inflammation in the oral mucosa thwarting persistent colonization. Our results further support a critical role for specialized nutrient acquisition allowing the fungus to thrive in the nutrient poor environment of the squamous epithelium. Together, this work revealed key determinants of C. albicans commensalism in the oral niche. [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]

Controlled immune activation in response to commensal microbes is critical for the maintenance of stable colonization and prevention of microbial overgrowth on epithelial surfaces. Our understanding of the host mechanisms that regulate bacterial commensalism has increased substantially, however, much less data exist regarding host responses to members of the fungal microbiota on colonized surfaces. Using a murine model of oropharyngeal candidiasis, we have recently shown that differences in immune activation in response to diverse natural isolates of Candida albicans are associated with different outcomes of the host-fungal interaction. Here we applied a genome-wide transcriptomic approach to show that rapid induction of a strong inflammatory response characterized by neutrophil-associated genes upon C. albicans colonization inversely correlated with the ability of the fungus to persist in the oral mucosa. Surprisingly, persistent fungal isolates showed no signs of a compensatory regulatory immune response. By combining RNA-seq data, genetic mouse models, and co-infection experiments, we show that attenuation of the inflammatory response at the onset of infection with a persistent isolate is not a consequence of enhanced immunosuppression. Importantly, depletion of regulatory T cells or deletion of the immunoregulatory cytokine IL-10 did not alter host-protective type 17 immunity nor did it impair fungal survival in the oral mucosa, indicating that persistence of C. albicans in the oral mucosa is not a consequence of suppressed antifungal immunity. [ABSTRACT FROM AUTHOR]

Background: Transmembrane β-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane β-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane β-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. Results: We present a novel graph-theoretic model for classification and structure prediction of transmembrane bbarrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize β-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane β-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudoenergy model is close to the actual physical model. We test our classification approach and show that it is able to reject a-helical bundles with 100% accuracy and β-barrel lipocalins with 97% accuracy. Conclusions: We show that it is possible to design models for classification and structure prediction for transmembrane β-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening. [ABSTRACT FROM AUTHOR]

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. [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]