Terms of use This work is brought to you by the University of Southern Denmark through the SDU Research Portal. Unless otherwise specified it has been shared according to the terms for self-archiving. If no other license is stated, these terms apply: • You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access version Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer Authors

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

BackgroundRecent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.ObjectiveTo explore the faecal and salivary microbiota as potential diagnostic biomarkers.MethodsWe applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase.ResultsFaecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.ConclusionTaken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers. However, computational tools tailored to such analyses are still scarce. Here, we present the SIAMCAT R package, a versatile and user-friendly toolbox for comparative metagenome analyses using machine learning (ML), statistical tests, and visualization. Based on a large meta-analysis of gut microbiome studies, we optimized the choice of ML algorithms and preprocessing routines for default workflow settings. Furthermore, we illustrate common pitfalls leading to overfitting and show how SIAMCAT safeguards against these to make statistically rigorous ML workflows broadly accessible. SIAMCAT is available from siamcat.embl.de and Bioconductor.

The frequency of amyotrophic lateral sclerosis (ALS) mutations has been extensively investigated in several populations; however, a systematic analysis in Turkish cases has not been reported so far. In this study, we screened 477 ALS patients for mutations, including 116 familial ALS patients from 82 families and 361 sporadic ALS (sALS) cases. Patients were genotyped for C9orf72 (18.3%), SOD1 (12.2%), FUS (5%), TARDBP (3.7%), and UBQLN2 (2.4%) gene mutations, which together account for approximately 40% of familial ALS in Turkey. No SOD1 mutations were detected in sALS patients; however, C9orf72 (3.1%) and UBQLN2 (0.6%) explained 3.7% of sALS in the population. Exome sequencing revealed mutations in OPTN, SPG11, DJ1, PLEKHG5, SYNE1, TRPM7, and SQSTM1 genes, many of them novel. The spectrum of mutations reflect both the distinct genetic background and the heterogeneous nature of the Turkish ALS population.

Short-chain fatty acids (SCFAs, mainly acetate, propionate, and butyrate), which are primarily derived from the gut microbiome, may exert anti-inflammatory and immunomodulatory effects, and regulate energy homeostasis. It has been suggested that weight loss may affect SCFA metabolism, but a systematic review of intervention studies is lacking. We aimed to systematically assess the effects of dietary, physical activity–based, and surgical weight-loss interventions among overweight [body mass index (BMI) 25–29.9 kg/m2)] or obese (BMI ≥30 kg/m2) adults (≥18 y) on concentrations of acetate, propionate, butyrate, and total SCFAs in blood, urine, or feces. We conducted a systematic literature search in PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) up to April 30, 2018 for randomized and nonrandomized weight-loss trials among overweight or obese adults, in which the concentrations of individual and total SCFAs were assessed. A total of 9 studies consisting of 2 randomized parallel-arm trials, 4 crossover trials, and 3 nonrandomized clinical or surgical trials were included. In the majority of studies, changes in fecal SCFA concentrations were assessed, whereas changes in serum SCFAs were reported from 1 trial. Individual and total SCFA concentrations either remained unchanged or decreased significantly following weight loss. Three of the dietary interventions that resulted in decreased SCFA concentrations were low (≤5% of energy) in total carbohydrates. Most of the studies had a high risk of bias. Decreases in SCFA concentrations may accompany weight loss induced by bariatric surgery or dietary restriction among overweight or obese adults, particularly when carbohydrate intake is reduced. However, findings were inconsistent and based on studies with high to unclear risk of bias, and small sample sizes. Because measurements of fecal SCFAs may not be ideal due to limited sample standardization, well-powered trials with repeated blood measurements of SCFAs are required. This review was registered at PROSPERO as CRD42018088716.

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