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.

Environmental factors shaping the gut microbiome in a Dutch population.

The gut microbiome is associated with diverse diseases1-3, 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.

Correction to: The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1.

Following publication of the original article [1], the authors.

The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic complex disease of the gastrointestinal tract. Patients with IBD can experience a wide range of symptoms, but the pathophysiological mechanisms that cause these individual differences in clinical presentation remain largely unknown. In consequence, IBD is currently classified into subtypes using clinical characteristics. If we are to develop a more targeted treatment approach, molecular subtypes of IBD need to be discovered that can be used as new drug targets. To achieve this, we need multiple layers of molecular data generated from the same IBD patients. CONSTRUCTION AND CONTENT: We initiated the 1000IBD project ( https://1000ibd.org ) to prospectively follow more than 1000 IBD patients from the Northern provinces of the Netherlands. For these patients, we have collected a uniquely large number of phenotypes and generated multi-omics profiles. To date, 1215 participants have been enrolled in the project and enrolment is on-going. Phenotype data collected for these participants includes information on dietary and environmental factors, drug responses and adverse drug events. Genome information has been generated using genotyping (ImmunoChip, Global Screening Array and HumanExomeChip) and sequencing (whole exome sequencing and targeted resequencing of IBD susceptibility loci), transcriptome information generated using RNA-sequencing of intestinal biopsies and microbiome information generated using both sequencing of the 16S rRNA gene and whole genome shotgun metagenomic sequencing. UTILITY AND DISCUSSION: All molecular data generated within the 1000IBD project will be shared on the European Genome-Phenome Archive ( https://ega-archive.org , accession no: EGAS00001002702). The first data release, detailed in this announcement and released simultaneously with this publication, will contain basic phenotypes for 1215 participants, genotypes of 314 participants and gut microbiome data from stool samples (315 participants) and biopsies (107 participants) generated by tag sequencing the 16S gene. Future releases will comprise many more additional phenotypes and -omics data layers. 1000IBD data can be used by other researchers as a replication cohort, a dataset to test new software tools, or a dataset for applying new statistical models. CONCLUSIONS: We report on the establishment and future development of the 1000IBD project: the first comprehensive multi-omics dataset aimed at discovering IBD biomarker profiles and treatment targets.

Novel Algorithms for Improved Sensitivity in Non-Invasive Prenatal Testing.

Non-invasive prenatal testing (NIPT) of cell-free DNA in maternal plasma, which is a mixture of maternal DNA and a low percentage of fetal DNA, can detect fetal aneuploidies using massively parallel sequencing. Because of the low percentage of fetal DNA, methods with high sensitivity and precision are required. However, sequencing variation lowers sensitivity and hampers detection of trisomy samples. Therefore, we have developed three algorithms to improve sensitivity and specificity: the chi-squared-based variation reduction (χ2VR), the regression-based Z-score (RBZ) and the Match QC score. The χ2VR reduces variability in sequence read counts per chromosome between samples, the RBZ allows for more precise trisomy prediction, and the Match QC score shows if the control group used is representative for a specific sample. We compared the performance of χ2VR to that of existing variation reduction algorithms (peak and GC correction) and that of RBZ to trisomy prediction algorithms (standard Z-score, normalized chromosome value and median-absolute-deviation-based Z-score). χ2VR and the RBZ both reduce variability more than existing methods, and thereby increase the sensitivity of the NIPT analysis. We found the optimal combination of algorithms was to use both GC correction and χ2VR for pre-processing and to use RBZ as the trisomy prediction method.

Factors that influence the volatile organic compound content in human breath.

BACKGROUND: Thousands of endogenous and exogenous volatile organic compounds (VOCs) are excreted in each breath. Inflammatory and deviant metabolic processes affect the level of endogeneous VOCs, which can serve as specific biomarkers for clinical diagnosis and disease monitoring. Important issues that still need to be tackled are related to potential confounding factors like gender and age and endogenous and exogenous factors, like f.i. smoking. METHODS: The aim of this study was to systematically access the effect of endogenous and exogenous factors on VOC composition of exhaled breath. In the current study breath samples from 1417 adult participants from the LifeLines cohort, a general population cohort in the Netherlands, were collected and the total content of VOCs was measured using gas chromatography-time-of-flight-mass spectrometry. Breath samples were collected in Groningen and transferred to carbon tubes immediately. These samples were then shipped to Maastricht and measured in batches. VOCs profiles were correlated to 14 relevant characteristics of all participants including age, BMI, smoking and blood cell counts and metabolic parameters as well as to 16 classes of medications. RESULTS: VOCs profiles were shown to be significantly influenced by smoking behavior and to a lesser extent by age, BMI and gender. These factors need to be controlled for in breath analysis studies. We found no evidence whatsoever in this 1417 subjects' cohort that white blood cell counts, cholesterol or triglycerides levels have an influence on the VOC profile. Thus they may not have to be controlled for in exhaled breath studies. CONCLUSION: The large cohort of volunteers used here represents a unique opportunity to gauge the factors influencing VOCs profiles in a general population i.e. the most clinically relevant population. Classical clinical parameters and smoking habits clearly influence breath content and should therefore be accounted for in future clinical studies involving breath analysis.

Exome sequencing in a family segregating for celiac disease.

Celiac disease is a multifactorial disorder caused by an unknown number of genetic factors interacting with an environmental factor. Hence, most patients are singletons and large families segregating with celiac disease are rare. We report on a three-generation family with six patients in which the inheritance pattern is consistent with an autosomal dominant model. To date, 27 loci explain up to 40% of the heritable disease risk. We hypothesized that part of the missing heritability is because of low frequency or rare variants. Such causal variants could be more prominent in multigeneration families where private mutations might co-segregate with the disease. They can be identified by linkage analysis combined with whole exome sequencing. We found three linkage regions on 4q32.3-4q33, 8q24.13-8q24.21 and 10q23.1-10q23.32 that segregate with celiac disease in this family. We performed exome sequencing on two affected individuals to investigate the positional candidate regions and the remaining exome for causal nonsense variants. We identified 12 nonsense mutations with a low frequency (minor allele frequency <10%) present in both individuals, but none mapped to the linkage regions. Two variants in the CSAG1 and KRT37 genes were present in all six affected individuals. Two nonsense variants in the MADD and GBGT1 genes were also present in 5 of 6 and 4 of 6 individuals, respectively; future studies should determine if any of these nonsense variants is causally related to celiac disease.