Environmental factors shaping the gut microbiome in a Dutch population

The gut microbiome is associated with diverse diseases^1-3, but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.

Role of gut microbiota in chronic low-grade inflammation as potential driver for atherosclerotic cardiovascular disease: a systematic review of human studies

A hallmark of obesity is chronic low-grade inflammation, which plays a major role in the process of atherosclerotic cardiovascular disease (ACVD). Gut microbiota is one of the factors influencing systemic immune responses, and profound changes have been found in its composition and metabolic function in individuals with obesity. This systematic review assesses the association between the gut microbiota and markers of low-grade inflammation in humans. We identified 14 studies which were mostly observational and relatively small (n = 10 to 471). The way in which the microbiome is analysed differed extensively between these studies. Lower gut microbial diversity was associated with higher white blood cell counts and high sensitivity C-reactive protein (hsCRP) levels. The abundance of Bifidobacterium, Faecalibacterium, Ruminococcus and Prevotella were inversely related to different markers of low-grade inflammation such as hsCRP and interleukin (IL)-6. In addition, this review speculates on possible mechanisms through which the gut microbiota can affect low-grade inflammation and thereby ACVD. We discuss the associations between the microbiome and the inflammasome, the innate immune system, bile acids, gut permeability, the endocannabinoid system and TMAO. These data reinforce the importance of human research into the gut microbiota as potential diagnostic and therapeutic strategy to prevent ACVD.

Lytic bacteriophage PM16 specific for Proteus mirabilis: a novel member of the genus Phikmvvirus

Lytic Proteus phage PM16, isolated from human faeces, is a novel virus that is specific for Proteus mirabilis cells. Bacteriophage PM16 is characterized by high stability, a short latency period, large burst size and the occurrence of low phage resistance. Phage PM16 was classified as a member of the genus Phikmvvirus on the basis of genome organization, gene synteny, and protein sequences similarities. Within the genus Phikmvvirus, phage PM16 is grouped with Vibrio phage VP93, Pantoea phage LIMElight, Acinetobacter phage Petty, Enterobacter phage phiKDA1, and KP34-like bacteriophages. An investigation of the phage-cell interaction demonstrated that phage PM16 attached to the cell surface, not to the bacterial flagella. The study of P. mirabilis mutant cells obtained during the phage-resistant bacterial cell assay that were resistant to phage PM16 re-infection revealed a non-swarming phenotype, changes in membrane characteristics, and the absence of flagella. Presumably, the resistance of non-swarming P. mirabilis cells to phage PM16 re-infection is determined by changes in membrane macromolecular composition and is associated with the absence of flagella and a non-swarming phenotype.

A combination of fecal calprotectin and human beta-defensin 2 facilitates diagnosis and monitoring of inflammatory bowel disease

Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) show a large overlap in clinical presentation, which presents diagnostic challenges. As a consequence, invasive and burdensome endoscopies are often used to distinguish between IBD and IBS. Here, we aimed to develop a noninvasive fecal test that can distinguish between IBD and IBS and reduce the number of endoscopies.We used shotgun metagenomic sequencing to analyze the composition and function of gut microbiota of 169 IBS patients, 447 IBD patients and 1044 population controls and measured fecal Calprotectin (FCal), human beta defensin 2 (HBD2), and chromogranin A (CgA) in these samples. These measurements were used to construct training sets (75% of data) for logistic regression and machine learning models to differentiate IBS from IBD and inactive from active IBD. The results were replicated on test sets (remaining 25% of the data) and microbiome data obtained using 16S sequencing.Fecal HBD2 showed high sensitivity and specificity for differentiating between IBD and IBS (sensitivity = 0.89, specificity = 0.76), while the inclusion of microbiome data with biomarkers (HBD2 and FCal) showed a potential for improvement in predictive power (optimal sensitivity = 0.87, specificity = 0.93). Shotgun sequencing-based models produced comparable results using 16S-sequencing data. HBD2 and FCal were found to have predictive power for IBD disease activity (AUC ≈ 0.7).HBD2 is a novel biomarker for IBD in patients with gastro-intestinal complaints, especially when used in combination with FCal and potentially in combination with gut microbiome data.

Gut feelings: the relations between depression, anxiety, psychotropic drugs and the gut microbiome

The gut microbiome is involved in the bi-directional relationship of the gut - brain axis. As most studies of this relationship are small and do not account for use of psychotropic drugs (PTDs), we explored the relations of the gut microbiome with several internalizing disorders, while adjusting for PTDs and other relevant medications, in 7,656 Lifelines participants from the Northern Netherlands (5,522 controls and 491 participants with at least one internalizing disorder). Disorders included dysthymia, major depressive disorder (MDD), any depressive disorder (AnyDep: dysthymia or MDD), generalized anxiety disorder (GAD) and any anxiety disorder (AnyAnx: GAD, social phobia and panic disorder). Compared to controls, 17 species were associated with depressive disorders and 3 were associated with anxiety disorders. Around 90% of these associations remained significant (FDR <0.05) after adjustment for PTD use, suggesting that the disorders, not PTD use, drove these associations. Negative associations were observed for the butyrate-producing bacteria Ruminococcus bromii in participants with AnyDep and for Bifidobacterium bifidum in AnyAnx participants, along with many others. Tryptophan and glutamate synthesis modules and the 3,4-Dihydroxyphenylacetic acid synthesis module (related to dopamine metabolism) were negatively associated with MDD and/or dysthymia. After additional adjustment for functional gastrointestinal disorders and irritable bowel syndrome, these relations remained either statistically (FDR <0.05) or nominally (P < 0.05) significant. Overall, multiple bacterial species and functional modules were associated with internalizing disorders, including gut - brain relevant components, while associations to PTD use were moderate. These findings suggest that internalizing disorders rather than PTDs are associated with gut microbiome differences relative to controls.