The gut microbiota play a critical role in maintaining a healthy human body and their dysregulation is associated with various diseases. In this study, we investigated the influence of the gut microbiome diversity on chronic lymphocytic leukemia (CLL) development. Stool sample analysis of 59 CLL patients revealed individual and heterogeneous microbiome compositions, but allowed for grouping of patients according to their microbiome diversity. Interestingly, CLL patients with a lower microbiome diversity and an enrichment of bacteria linked to poor health suffered from a more advanced or aggressive form of CLL. In the Eμ-TCL1 mouse model of CLL, we observed a faster course of disease when mice were housed in high hygiene conditions. Shotgun DNA sequencing of fecal samples showed that this was associated with a lower microbiome diversity which was dominated by Mucispirillum and Parabacteroides genera in comparison to mice kept under lower hygiene conditions. In conclusion, we applied taxonomic microbiome analyses to demonstrate a link between the gut microbiome diversity and the clinical course of CLL in humans, as well as the development of CLL in mice. Our novel data serve as a basis for further investigations to decipher the pathological and mechanistic role of intestinal microbiota in CLL development.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts.

We report a droplet microfluidic method to target and sort individual cells directly from complex microbiome samples and to prepare these cells for bulk whole-genome sequencing without cultivation. We characterize this approach by recovering bacteria spiked into human stool samples at a ratio as low as 1:250 and by successfully enriching endogenous to the level required for de novo assembly of high-quality genomes. Although microbiome strains are increasingly demanded for biomedical applications, a vast majority of species and strains are uncultivated and without reference genomes. We address this shortcoming by encapsulating complex microbiome samples directly into microfluidic droplets and amplifying a target-specific genomic fragment using a custom molecular TaqMan probe. We separate those positive droplets by droplet sorting, selectively enriching single target strain cells. Finally, we present a protocol to purify the genomic DNA while specifically removing amplicons and cell debris for high-quality genome sequencing.

The gut microbiota has been suggested to play a significant role in the development of overweight and obesity. However, the effects of calorie restriction on gut microbiota of overweight and obese adults, especially over longer durations, are largely unexplored.

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 .

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.

Association studies have linked microbiome alterations with many human diseases. However, they have not always reported consistent results, thereby necessitating cross-study comparisons. Here, a meta-analysis of eight geographically and technically diverse fecal shotgun metagenomic studies of colorectal cancer (CRC, n = 768), which was controlled for several confounders, identified a core set of 29 species significantly enriched in CRC metagenomes (false discovery rate (FDR) < 1 × 10). CRC signatures derived from single studies maintained their accuracy in other studies. By training on multiple studies, we improved detection accuracy and disease specificity for CRC. Functional analysis of CRC metagenomes revealed enriched protein and mucin catabolism genes and depleted carbohydrate degradation genes. Moreover, we inferred elevated production of secondary bile acids from CRC metagenomes, suggesting a metabolic link between cancer-associated gut microbes and a fat- and meat-rich diet. Through extensive validations, this meta-analysis firmly establishes globally generalizable, predictive taxonomic and functional microbiome CRC signatures as a basis for future diagnostics.