NLRP12 attenuates colon inflammation by maintaining colonic microbial diversity and promoting protective commensal bacterial growth

Inflammatory bowel diseases involve the dynamic interplay of host genetics, microbiome and inflammatory response. Here, we report that NLRP12, a negative regulator of innate immunity, is reduced in human ulcerative colitis by comparing monozygotic twins and other patient cohorts. In parallel, Nlrp12-deficiency in mice caused increased colonic basal inflammation, leading to a less-diverse microbiome, loss of protective gut commensal strains (Lachnospiraceae) and increased colitogenic strains (Erysipelotrichaceae). Dysbiosis and colitis susceptibility associated with Nlrp12-deficency were reversed equally by treatment with antibodies targeting inflammatory cytokines or by administration of beneficial commensal Lachnospiraceae isolates. Fecal transplants from specific pathogen free reared mice into germ-free Nlrp12-deficient mice showed that NLRP12 and the microbiome each contribute to immune signaling that culminates in colon inflammation. These findings reveal a feed-forward loop where NLRP12 promotes specific commensals that can reverse gut inflammation, while cytokine blockade during NLRP12-deficiency can reverse dysbiosis

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Associations of total legume, pulse, and soy consumption with incident type 2 diabetes: federated meta-analysis of 27 studies from diverse world regions

Background The consumption of legumes is promoted as part of a healthy diet in many countries but associations of total and types of legume consumption with type 2 diabetes (T2D) are not well established. Analyses across diverse populations are lacking despite the availability of unpublished legume consumption data in prospective cohort studies. Objective To examine the prospective associations of total and types of legume intake with the risk of incident T2D. Methods Meta-analyses of associations between total legume, pulse, and soy consumption and T2D were conducted using a federated approach without physical data-pooling. Prospective cohorts were included if legume exposure and T2D outcome data were available and the cohort investigators agreed to participate. We estimated incidence rate ratios (IRRs) and CIs of associations using individual participant data including ≤42,473 incident cases among 807,785 adults without diabetes in 27 cohorts across the Americas, Eastern Mediterranean, Europe, and Western Pacific. Random-effects meta-analysis was used to combine effect estimates and estimate heterogeneity. Results Median total legume intake ranged from 0–140 g/d across cohorts. We observed a weak positive association between total legume consumption and T2D (IRR = 1.02, 95% CI: 1.01 to 1.04) per 20 g/d higher intake, with moderately high heterogeneity (I2 = 74%). Analysis by region showed no evidence of associations in the Americas, Eastern Mediterranean, and Western Pacific. The positive association in Europe (IRR = 1.05, 95% CI: 1.01 to 1.10, I2 = 82%) was mainly driven by studies from Germany, UK, and Sweden. No evidence of associations was observed for the consumption of pulses or soy. Conclusions These findings suggest no evidence of an association of legume intakes with T2D in several world regions. The positive association observed in some European studies warrants further investigation relating to overall dietary contexts in which legumes are consumed, including accompanying foods which may be positively associated with T2D.</p

Use of the second-generation antipsychotic, risperidone, and secondary weight gain are associated with an altered gut microbiota in children

The atypical antipsychotic risperidone (RSP) is often associated with weight gain and cardiometabolic side effects. The mechanisms for these adverse events are poorly understood and, undoubtedly, multifactorial in etiology. In light of growing evidence implicating the gut microbiome in the host's energy regulation and in xenobiotic metabolism, we hypothesized that RSP treatment would be associated with changes in the gut microbiome in children and adolescents. Thus, the impact of chronic (>12 months) and short-term use of RSP on the gut microbiome of pediatric psychiatrically ill male participants was examined in a cross-sectional and prospective (up to 10 months) design, respectively. Chronic treatment with RSP was associated with an increase in body mass index (BMI) and a significantly lower ratio of Bacteroidetes:Firmicutes as compared with antipsychotic-naïve psychiatric controls (ratio=0.15 vs 1.24, respectively; P<0.05). Furthermore, a longitudinal observation, beginning shortly after onset of RSP treatment, revealed a gradual decrease in the Bacteroidetes:Firmicutes ratio over the ensuing months of treatment, in association with BMI gain. Lastly, metagenomic analyses were performed based on extrapolation from 16S ribosomal RNA data using the software package, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). Those data indicate that gut microbiota dominating the RSP-treated participants are enriched for pathways that have been implicated in weight gain, such as short-chain fatty acid production

Consistent metagenomic biomarker detection via robust PCA

BACKGROUND: Recent developments of high throughput sequencing technologies allow the characterization of the microbial communities inhabiting our world. Various metagenomic studies have suggested using microbial taxa as potential biomarkers for certain diseases. In practice, the number of available samples varies from experiment to experiment. Therefore, a robust biomarker detection algorithm is needed to provide a set of potential markers irrespective of the number of available samples. Consistent performance is essential to derive solid biological conclusions and to transfer these findings into clinical applications. Surprisingly, the consistency of a metagenomic biomarker detection algorithm with respect to the variation in the experiment size has not been addressed by the current state-of-art algorithms. RESULTS: We propose a consistency-classification framework that enables the assessment of consistency and classification performance of a biomarker discovery algorithm. This evaluation protocol is based on random resampling to mimic the variation in the experiment size. Moreover, we model the metagenomic data matrix as a superposition of two matrices. The first matrix is a low-rank matrix that models the abundance levels of the irrelevant bacteria. The second matrix is a sparse matrix that captures the abundance levels of the bacteria that are differentially abundant between different phenotypes. Then, we propose a novel Robust Principal Component Analysis (RPCA) based biomarker discovery algorithm to recover the sparse matrix. RPCA belongs to the class of multivariate feature selection methods which treat the features collectively rather than individually. This provides the proposed algorithm with an inherent ability to handle the complex microbial interactions. Comprehensive comparisons of RPCA with the state-of-the-art algorithms on two realistic datasets are conducted. Results show that RPCA consistently outperforms the other algorithms in terms of classification accuracy and reproducibility performance. CONCLUSIONS: The RPCA-based biomarker detection algorithm provides a high reproducibility performance irrespective of the complexity of the dataset or the number of selected biomarkers. Also, RPCA selects biomarkers with quite high discriminative accuracy. Thus, RPCA is a consistent and accurate tool for selecting taxanomical biomarkers for different microbial populations. REVIEWERS: This article was reviewed by Masanori Arita and Zoltan Gaspari. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-017-0175-4) contains supplementary material, which is available to authorized users