Distinct cellular immune responses in children en route to type 1 diabetes with different first-appearing autoantibodies.

Previous studies have revealed heterogeneity in the progression to clinical type 1 diabetes in children who develop islet-specific antibodies either to insulin (IAA) or glutamic acid decarboxylase (GADA) as the first autoantibodies. Here, we test the hypothesis that children who later develop clinical disease have different early immune responses, depending on the type of the first autoantibody to appear (GADA-first or IAA-first). We use mass cytometry for deep immune profiling of peripheral blood mononuclear cell samples longitudinally collected from children who later progressed to clinical disease (IAA-first, GADA-first, ≥2 autoantibodies first groups) and matched for age, sex, and HLA controls who did not, as part of the Type 1 Diabetes Prediction and Prevention study. We identify differences in immune cell composition of children who later develop disease depending on the type of autoantibodies that appear first. Notably, we observe an increase in CD161 expression in natural killer cells of children with ≥2 autoantibodies and validate this in an independent cohort. The results highlight the importance of endotype-specific analyses and are likely to contribute to our understanding of pathogenic mechanisms underlying type 1 diabetes development.

Differences in Gut Microbiota Profiles and Microbiota Steroid Hormone Biosynthesis in Men with and Without Prostate Cancer.

Background: Although prostate cancer (PCa) is the most common cancer in men in Western countries, there is significant variability in geographical incidence. This might result from genetic factors, discrepancies in screening policies, or differences in lifestyle. Gut microbiota has recently been associated with cancer progression, but its role in PCa is unclear. Objective: Characterization of the gut microbiota and its functions associated with PCa. Design setting and participants: In a prospective multicenter clinical trial (NCT02241122), the gut microbiota profiles of 181 men with a clinical suspicion of PCa were assessed utilizing 16S rRNA sequencing. Outcome measurements and statistical analysis: Sequences were assigned to operational taxonomic units, differential abundance analysis, and α- and ß-diversities, and predictive functional analyses were performed. Plasma steroid hormone levels corresponding to the predicted microbiota steroid hormone biosynthesis profiles were investigated. Results and limitations: Of 364 patients, 181 were analyzed, 60% of whom were diagnosed with PCa. Microbiota composition and diversity were significantly different in PCa, partially affected by Prevotella 9, the most abundant genus of the cohort, and significantly higher in PCa patients. Predictive functional analyses revealed higher 5-α-reductase, copper absorption, and retinol metabolism in the PCa-associated microbiome. Plasma testosterone was associated negatively with the predicted microbial 5-α-reductase level. Conclusions: Gut microbiota of the PCa patients differed significantly compared with benign individuals. Microbial 5-α-reductase, copper absorption, and retinol metabolism are potential mechanisms of action. These findings support the observed association of lifestyle, geography, and PCa incidence. Patient summary: In this report, we found that several microbes and potential functions of the gut microbiota are altered in prostate cancer compared with benign cases. These findings suggest that gut microbiota could be the link between environmental factors and prostate cancer.

An Infancy-Onset 20-Year Dietary Counselling Intervention and Gut Microbiota Composition in Adulthood.

The randomized controlled Special Turku Coronary Risk Factor Intervention Project (STRIP) has completed a 20-year infancy-onset dietary counselling intervention to reduce exposure to atherosclerotic cardiovascular disease risk factors via promotion of a heart-healthy diet. The counselling on, e.g., low intake of saturated fat and cholesterol and promotion of fruit, vegetable, and whole-grain consumption has affected the dietary characteristics of the intervention participants. By leveraging this unique cohort, we further investigated whether this long-term dietary intervention affected the gut microbiota bacterial profile six years after the intervention ceased. Our sub-study comprised 357 individuals aged 26 years (intervention n = 174, control n = 183), whose gut microbiota were profiled using 16S rRNA amplicon sequencing. We observed no differences in microbiota profiles between the intervention and control groups. However, out of the 77 detected microbial genera, the Veillonella genus was more abundant in the intervention group compared to the controls (log2 fold-change 1.58, p < 0.001) after adjusting for multiple comparison. In addition, an association between the study group and overall gut microbiota profile was found only in males. The subtle differences in gut microbiota abundances observed in this unique intervention setting suggest that long-term dietary counselling reflecting dietary guidelines may be associated with alterations in gut microbiota.

Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples.

Mass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.

PhosPiR: an automated phosphoproteomic pipeline in R.

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

Metagenomics analysis of gut microbiota in response to diet intervention and gestational diabetes in overweight and obese women: a randomised, double-blind, placebo-controlled clinical trial.

OBJECTIVE: Gut microbiota and diet are known to contribute to human metabolism. We investigated whether the metagenomic gut microbiota composition and function changes over pregnancy are related to gestational diabetes mellitus (GDM) and can be modified by dietary supplements, fish oil and/or probiotics. DESIGN: The gut microbiota of 270 overweight/obese women participating in a mother-infant clinical study were analysed with metagenomics approach in early (mean gestational weeks 13.9) and late (gestational weeks 35.2) pregnancy. GDM was diagnosed with a 2 hour 75 g oral glucose tolerance test. RESULTS: Unlike women with GDM, women without GDM manifested changes in relative abundance of bacterial species over the pregnancy, particularly those receiving the fish oil + probiotics combination. The specific bacterial species or function did not predict the onset of GDM nor did it differ according to GDM status, except for the higher abundance of Ruminococcus obeum in late pregnancy in the combination group in women with GDM compared with women without GDM. In the combination group, weak decreases over the pregnancy were observed in basic bacterial housekeeping functions. CONCLUSIONS: The specific gut microbiota species do not contribute to GDM in overweight/obese women. Nevertheless, the GDM status may disturb maternal gut microbiota flexibility and thus limit the capacity of women with GDM to respond to diet, as evidenced by alterations in gut microbiota observed only in women without GDM. These findings may be important when considering the metabolic complications during pregnancy, but further studies with larger populations are called for to verify the findings.

A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota.

Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-L-arabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides- and Prevotella-derived CAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence.

Data-Independent Acquisition Mass Spectrometry in Metaproteomics of Gut Microbiota-Implementation and Computational Analysis.

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratory-assembled microbial mixtures as well as human fecal samples.

Gut microbiota composition is associated with temperament traits in infants.

BACKGROUND: One of the key behavioral phenotypes in infancy are different temperament traits, and certain early life temperament traits have been shown to precede later mental health problems. Differences in the gut microbiota composition (GMC) have been suggested to link with neurodevelopment. For example, toddler temperament traits have been found to associate with differences in GMC; however, studies in infants are lacking although infancy is a rapid period of neurodevelopment as well as GM development. Thus, we aimed to investigate association between infant GMC and temperament. METHODS: The study population (n = 301, 53% boys) was drawn from the FinnBrain Birth Cohort Study. Stool samples were collected from the 2.5-month-old infants and sequenced with 16S Illumina MiSeq platform. GMC taxonomic composition (at Genus and OTU level), observed sample clusters, diversity and richness were investigated in relation to the maternal reports of Infant Behavior Questionnaire -Revised (IBQ-R) at the age of 6 months. RESULTS: Three sample clusters (Bifidobacterium/Enterobacteriaceae, Bacteroides, V. Dispar) based on GMC were identified, of which Bifidobacterium/Enterobacteriaceae-cluster presented with higher scores on the IBQ-R main dimension regulation and its subscale duration of orienting compared to Bacteroides-cluster. The clusters associated with temperament in a sex-dependent manner. The IBQ-R main dimension surgency (positive emotionality) was associated positively both with genus Bifidobacterium and Streptococcus. Alpha diversity had a negative association with negative emotionality and fear reactivity. CONCLUSION: This is the first study demonstrating associations, but not causal connections, between GMC and temperament in young infants in a prospective design.

A Data Analysis Protocol for Quantitative Data-Independent Acquisition Proteomics.

Data-independent acquisition (DIA) mode of mass spectrometry, such as the SWATH-MS technology, enables accurate and consistent measurement of proteins, which is crucial for comparative proteomics studies. However, there is lack of free and easy to implement data analysis protocols that can handle the different data processing steps from raw spectrum files to peptide intensity matrix and its downstream analysis. Here, we provide a data analysis protocol, named diatools, covering all these steps from spectral library building to differential expression analysis of DIA proteomics data. The data analysis tools used in this protocol are open source and the protocol is distributed at Docker Hub as a complete software environment that supports Linux, Windows, and macOS operating systems.

Chronic nonbacterial prostate inflammation in a rat model is associated with changes of gut microbiota that can be modified with a galactoglucomannan-rich hemicellulose extract in the diet.

OBJECTIVES: To investigate dietary effects on the gut microbiota composition in a rat model of nonbacterial chronic prostate inflammation (CPI). MATERIALS AND METHODS: Nonbacterial CPI was induced in the Wistar rat strain with subcutaneous testosterone and 17ß-oestradiol (E2 ) hormone pellets for 18 weeks. Rats with placebo pellets served as healthy controls. Rats with CPI were stratified into two groups, which drank either plain tap water (control group) or tap water supplemented with 2% galactoglucomannan-rich hemicellulose extract (GGM group) from Norway spruce (Picea abies) for 5 weeks. Faecal samples were collected at the end of the study, total DNA was extracted, and the bacterial composition was analysed by 16S rRNA gene sequencing. In addition, faecal samples were assayed for short-chain fatty acid (SCFA) concentrations using gas chromatography. Lipopolysaccharide-binding protein (LBP) was measured in serum samples, as an indirect indicator for bacterial lipopolysaccharide (LPS) load in blood. RESULTS: The microbial biodiversity was significantly different between the treatment groups. In the rats with CPI, there was a significant increase in gut microbial populations Rikenellaceae, Odoribacter, Clostridiaceae, Allobaculum and Peptococcaceae compared with healthy rats. Conversely, levels of Bacteroides uniformis, Lactobacillus and Lachnospiraceae were decreased in rats with CPI. SCFA butyric-, valeric- and caproic-acid concentrations were also decreased in the faecal samples of the rats with CPI. In contrast, acetic acid concentrations and serum LBP were significantly elevated in CPI rats compared with healthy ones. Amongst rats with CPI, treatment with the GGM extract significantly reduced the abundance of Odoribacter and Clostridiaceae levels, and increased the B. uniformis levels compared with CPI rats drinking tap water only. In addition, GGM significantly increased the levels of butyric acid and caproic acid, and reduced the levels of LBP in serum. CONCLUSIONS: Hormone-induced nonbacterial CPI in rats is associated with specific changes in gut microbiota and secondary changes in SCFAs and LPS due to gut microbiota alteration. Our results further suggest that fermentable compounds may have a beneficial effect on CPI.

Six-Week Endurance Exercise Alters Gut Metagenome That Is not Reflected in Systemic Metabolism in Over-weight Women.

Recent studies suggest that exercise alters the gut microbiome. We determined whether six-weeks endurance exercise, without changing diet, affected the gut metagenome and systemic metabolites of overweight women. Previously sedentary overweight women (n = 19) underwent a six-weeks endurance exercise intervention, but two were excluded due to antibiotic therapy. The gut microbiota composition and functions were analyzed by 16S rRNA gene amplicon sequencing and metagenomics. Body composition was analyzed with DXA X-ray densitometer and serum metabolomics with NMR metabolomics. Total energy and energy-yielding nutrient intakes were analyzed from food records using Micro-Nutrica software. Serum clinical variables were determined with KONELAB instrument. Soluble Vascular Adhesion Protein 1 (VAP-1) was measured with ELISA and its' enzymatic activity as produced hydrogen peroxide. The exercise intervention was effective, as maximal power and maximum rate of oxygen consumption increased while android fat mass decreased. No changes in diet were observed. Metagenomic analysis revealed taxonomic shifts including an increase in Akkermansia and a decrease in Proteobacteria. These changes were independent of age, weight, fat % as well as energy and fiber intake. Training slightly increased Jaccard distance of genus level ß-diversity. Training did not alter the enriched metagenomic pathways, which, according to Bray Curtis dissimilarity analysis, may have been due to that only half of the subjects' microbiomes responded considerably to exercise. Nevertheless, tranining decreased the abundance of several genes including those related to fructose and amino acid metabolism. These metagenomic changes, however, were not translated into major systemic metabolic changes as only two metabolites, phospholipids and cholesterol in large VLDL particles, decreased after exercise. Training also decreased the amine oxidase activity of pro-inflammatory VAP-1, whereas no changes in CRP were detected. All clinical blood variables were within normal range, yet exercise slightly increased glucose and decreased LDL and HDL. In conclusion, exercise training modified the gut microbiome without greatly affecting systemic metabolites or body composition. Based on our data and existing literature, we propose that especially Akkermansia and Proteobacteria are exercise-responsive taxa. Our results warrant the need for further studies in larger cohorts to determine whether exercise types other than endurance exercise also modify the gut metagenome.

Gut Microbiota Composition in Mid-Pregnancy Is Associated with Gestational Weight Gain but Not Prepregnancy Body Mass Index.

BACKGROUND: Pregnancy is a time of numerous hormonal, metabolic, and immunological changes for both the mother and the fetus. Furthermore, maternal gut microbiota composition (GMC) is altered during pregnancy. One major factor affecting GMC in pregnant and nonpregnant populations is obesity. The aim was to analyze associations between maternal overweight/obesity, as well as gestational weight gain (GWG) and GMC. Moreover, the modifying effect of depression and anxiety symptom scores on weight and GMC were investigated. METHODS: Study included 46 women from the FinnBrain Birth Cohort study, of which 36 were normal weight, and 11 overweight or obese according to their prepregnancy body mass index (BMI). Stool samples were collected in gestational week 24, and the GMC was sequenced with Illumina MiSeq approach. Hierarchical clustering was executed to illuminate group formation according to the GMC. The population was divided according to Firmicutes and Bacteroidetes dominance. Symptoms of depression, general anxiety, and pregnancy-related anxiety were measured by using standardized questionnaires. RESULTS: Excessive GWG was associated with distinct GMC in mid-pregnancy as measured by hierarchical clustering and grouping according to Firmicutes or Bacteroidetes dominance, with Bacteroidetes being prominent and Firmicutes being less prominent in the GMC among those with increased GWG. Reduced alpha diversity was observed among the Bacteroidetes-dominated subjects. There were no zero-order effects between the abundances of bacterial genera or phyla, alpha or beta diversity, and prepregnancy BMI or GWG. CONCLUSION: Bacteroidetes-dominated GMC in mid-pregnancy is associated with increased GWG and reduced alpha diversity.

Early fecal microbiota composition in children who later develop celiac disease and associated autoimmunity.

OBJECTIVES: Several studies have reported that the intestinal microbiota composition of celiac disease (CD) patients differs from healthy individuals. The possible role of gut microbiota in the pathogenesis of the disease is, however, not known. Here, we aimed to assess the possible differences in early fecal microbiota composition between children that later developed CD and healthy controls matched for age, sex and HLA risk genotype. MATERIALS AND METHODS: We used 16S rRNA gene sequencing to examine the fecal microbiota of 27 children with high genetic risk of developing CD. Nine of these children developed the disease by the age of 4 years. Stool samples were collected at the age of 9 and 12 months, before any of the children had developed CD. The fecal microbiota composition of children who later developed the disease was compared with the microbiota of the children who did not have CD or associated autoantibodies at the age of 4 years. Delivery mode, early nutrition, and use of antibiotics were taken into account in the analyses. RESULTS: No statistically significant differences in the fecal microbiota composition were found between children who later developed CD (n = 9) and the control children without disease or associated autoantibodies (n = 18). CONCLUSIONS: Based on our results, the fecal microbiota composition at the age of 9 and 12 months is not associated with the development of CD. Our results, however, do not exclude the possibility of duodenal microbiota changes or a later microbiota-related trigger for the disease.

Gut Microbiota Analysis Results Are Highly Dependent on the 16S rRNA Gene Target Region, Whereas the Impact of DNA Extraction Is Minor.

Next-generation sequencing (NGS) is currently the method of choice for analyzing gut microbiota composition. As gut microbiota composition is a potential future target for clinical diagnostics, it is of utmost importance to enhance and optimize the NGS analysis procedures. Here, we have analyzed the impact of DNA extraction and selected 16S rDNA primers on the gut microbiota NGS results. Bacterial DNA from frozen stool specimens was extracted with 5 commercially available DNA extraction kits. Special attention was paid to the semiautomated DNA extraction methods that could expedite the analysis procedure, thus being especially suitable for clinical settings. The microbial composition was analyzed with 2 distinct protocols: 1 targeting the V3-V4 and the other targeting the V4-V5 area of the bacterial 16S rRNA gene. The overall effect of DNA extraction on the gut microbiota 16S rDNA profile was relatively small, whereas the 16S rRNA gene target region had an immense impact on the results. Furthermore, semiautomated DNA extraction methods clearly appeared suitable for NGS procedures, proposing that application of these methods could importantly reduce hands-on time and human errors without compromising the validity of results.

Neuropeptide Y Overexpressing Female and Male Mice Show Divergent Metabolic but Not Gut Microbial Responses to Prenatal Metformin Exposure.

BACKGROUND: Prenatal metformin exposure has been shown to improve the metabolic outcome in the offspring of high fat diet fed dams. However, if this is evident also in a genetic model of obesity and whether gut microbiota has a role, is not known. METHODS: The metabolic effects of prenatal metformin exposure were investigated in a genetic model of obesity, mice overexpressing neuropeptide Y in the sympathetic nervous system and in brain noradrenergic neurons (OE-NPYDßH). Metformin was given for 18 days to the mated female mice. Body weight, body composition, glucose tolerance and serum parameters of the offspring were investigated on regular diet from weaning and sequentially on western diet (at the age of 5-7 months). Gut microbiota composition was analysed by 16S rRNA sequencing at 10-11 weeks. RESULTS: In the male offspring, metformin exposure inhibited weight gain. Moreover, weight of white fat depots and serum insulin and lipids tended to be lower at 7 months. In contrast, in the female offspring, metformin exposure impaired glucose tolerance at 3 months, and subsequently increased body weight gain, fat mass and serum cholesterol. In the gut microbiota, a decline in Erysipelotrichaceae and Odoribacter was detected in the metformin exposed offspring. Furthermore, the abundance of Sutterella tended to be decreased and Parabacteroides increased. Gut microbiota composition of the metformin exposed male offspring correlated to their metabolic phenotype. CONCLUSION: Prenatal metformin exposure caused divergent metabolic phenotypes in the female and male offspring. Nevertheless, gut microbiota of metformin exposed offspring was similarly modified in both genders.

Gut Microbiota Richness and Composition and Dietary Intake of Overweight Pregnant Women Are Related to Serum Zonulin Concentration, a Marker for Intestinal Permeability.

BACKGROUND: Increased intestinal permeability may precede adverse metabolic conditions. The extent to which the composition of the gut microbiota and diet contribute to intestinal permeability during pregnancy is unknown. OBJECTIVE: The aim was to investigate whether the gut microbiota and diet differ according to serum zonulin concentration, a marker of intestinal permeability, in overweight pregnant women. METHODS: This cross-sectional study included 100 overweight women [mean age: 29 y; median body mass index (in kg/m(2)): 30] in early pregnancy (<17 wk of gestation; median: 13 wk). Serum zonulin (primary outcome) was determined by using ELISA, gut microbiota by 16S ribosomal RNA sequencing, and dietary intake of macro- and micronutrients from 3-d food diaries. The Mann-Whitney U test was used for pairwise comparisons and linear regression and Spearman's nonparametric correlations for relations between serum zonulin and other outcome variables. RESULTS: Women were divided into "low" (<46.4 ng/mL) and "high" (≥46.4 ng/mL) serum zonulin groups on the basis of the median concentration of zonulin (46.4 ng/mL). The richness of the gut microbiota (Chao 1, observed species and phylogenetic diversity) was higher in the low zonulin group than in the high zonulin group (P = 0.01). The abundances of Bacteroidaceae and Veillonellaceae, Bacteroides and Blautia, and Blautia sp. were lower and of Faecalibacterium and Faecalibacterium prausnitzii higher (P < 0.05) in the low zonulin group than in the high zonulin group. Dietary quantitative intakes of n-3 (ω-3) polyunsaturated fatty acids (PUFAs), fiber, and a range of vitamins and minerals were higher (P < 0.05) in women in the low zonulin group than those in the high zonulin group. CONCLUSIONS: The richness and composition of the gut microbiota and the intake of n-3 PUFAs, fiber, and a range of vitamins and minerals in overweight pregnant women are associated with serum zonulin concentration. Modification of the gut microbiota and diet may beneficially affect intestinal permeability, leading to improved metabolic health of both the mother and fetus. This trial was registered at clinicaltrials.gov as NCT01922791.

Toward more realistic drug-target interaction predictions.

A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluated under overly simplified settings that do not reflect the real-life problem in practical applications. Using quantitative drug-target bioactivity assays for kinase inhibitors, as well as a popular benchmarking data set of binary drug-target interactions for enzyme, ion channel, nuclear receptor and G protein-coupled receptor targets, we illustrate here the effects of four factors that may lead to dramatic differences in the prediction results: (i) problem formulation (standard binary classification or more realistic regression formulation), (ii) evaluation data set (drug and target families in the application use case), (iii) evaluation procedure (simple or nested cross-validation) and (iv) experimental setting (whether training and test sets share common drugs and targets, only drugs or targets or neither). Each of these factors should be taken into consideration to avoid reporting overoptimistic drug-target interaction prediction results. We also suggest guidelines on how to make the supervised drug-target interaction prediction studies more realistic in terms of such model formulations and evaluation setups that better address the inherent complexity of the prediction task in the practical applications, as well as novel benchmarking data sets that capture the continuous nature of the drug-target interactions for kinase inhibitors.

Fermentable fibres condition colon microbiota and promote diabetogenesis in NOD mice.

AIMS/HYPOTHESIS: Gut microbiota (GM) and diet both appear to be important in the pathogenesis of type 1 diabetes. Fermentable fibres (FFs), of which there is an ample supply in natural, diabetes-promoting diets, are used by GM as a source of energy. Our aim was to determine whether FFs modify GM and diabetes incidence in the NOD mouse. METHODS: Female NOD mice were weaned to a semisynthetic diet and the effects of FF supplementation on diabetes incidence and insulitis were evaluated. Real-time quantitative PCR was employed to determine the effects imposed to gene transcripts in the colon and lymph nodes. Changes to GM were analysed by next-generation sequencing. RESULTS: NOD mice fed semisynthetic diets free from FFs were largely protected from diabetes while semisynthetic diets supplemented with the FFs pectin and xylan (PX) resulted in higher diabetes incidence. Semisynthetic diet free from FFs altered GM composition significantly; addition of PX changed the composition of the GM towards that found in natural-diet-fed mice and increased production of FF-derived short-chain fatty acid metabolites in the colon. The highly diabetogenic natural diet was associated with expression of proinflammatory and stress-related genes in the colon, while the semisynthetic diet free from FFs promoted Il4, Il22, Tgfß and Foxp3 transcripts in the colon and/or pancreatic lymph node. PX in the same diet counteracted these effects and promoted stress-related IL-18 activation in gut epithelial cells. 16S RNA sequencing revealed each diet to give rise to its particular GM composition, with different Firmicutes to Bacteroidetes ratios, and enrichment of mucin-degrading Ruminococcaceae following diabetes-protective FF-free diet. CONCLUSIONS/INTERPRETATION: FFs condition microbiota, affect colon homeostasis and are important components of natural, diabetes-promoting diets in NOD mice.