Genetic mapping across autoimmune diseases reveals shared associations and mechanisms.

Autoimmune and inflammatory diseases are polygenic disorders of the immune system. Many genomic loci harbor risk alleles for several diseases, but the limited resolution of genetic mapping prevents determining whether the same allele is responsible, indicating a shared underlying mechanism. Here, using a collection of 129,058 cases and controls across 6 diseases, we show that ~40% of overlapping associations are due to the same allele. We improve fine-mapping resolution for shared alleles twofold by combining cases and controls across diseases, allowing us to identify more expression quantitative trait loci driven by the shared alleles. The patterns indicate widespread sharing of pathogenic mechanisms but not a single global autoimmune mechanism. Our approach can be applied to any set of traits and is particularly valuable as sample collections become depleted.

Transmission and dynamics of mother-infant gut viruses during pregnancy and early life.

Early development of the gut ecosystem is crucial for lifelong health. While infant gut bacterial communities have been studied extensively, the infant gut virome remains under-explored. To study the development of the infant gut virome over time and the factors that shape it, we longitudinally assess the composition of gut viruses and their bacterial hosts in 30 women during and after pregnancy and in their 32 infants during their first year of life. Using shotgun metagenomic sequencing applied to dsDNA extracted from Virus-Like Particles (VLPs) and bacteria, we generate 205 VLP metaviromes and 322 total metagenomes. With this data, we show that while the maternal gut virome composition remains stable during late pregnancy and after birth, the infant gut virome is dynamic in the first year of life. Notably, infant gut viromes contain a higher abundance of active temperate phages compared to maternal gut viromes, which decreases over the first year of life. Moreover, we show that the feeding mode and place of delivery influence the gut virome composition of infants. Lastly, we provide evidence of co-transmission of viral and bacterial strains from mothers to infants, demonstrating that infants acquire some of their virome from their mother's gut.

Choice of DNA extraction method affects stool microbiome recovery and subsequent phenotypic association analyses.

The lack of standardization in the methods of DNA extraction from fecal samples represents the major source of experimental variation in the microbiome research field. In this study, we aimed to compare the metagenomic profiles and microbiome-phenotype associations obtained by applying two commercially available DNA extraction kits: the AllPrep DNA/RNA Mini Kit (APK) and the QIAamp Fast DNA Stool Mini Kit (FSK). Using metagenomic sequencing data available from 745 paired fecal samples from two independent population cohorts, Lifelines-DEEP (LLD, n = 292) and the 500 Functional Genomics project (500FG, n = 453), we confirmed significant differences in DNA yield and the recovered microbial communities between protocols, with the APK method resulting in a higher DNA concentration and microbial diversity. Further, we observed a massive difference in bacterial relative abundances at species-level between the APK and the FSK protocols, with > 75% of species differentially abundant between protocols in both cohorts. Specifically, comparison with a standard mock community revealed that the APK method provided higher accuracy in the recovery of microbial relative abundances, with the absence of a bead-beating step in the FSK protocol causing an underrepresentation of gram-positive bacteria. This heterogeneity in the recovered microbial composition led to remarkable differences in the association with anthropometric and lifestyle phenotypes. The results of this study further reinforce that the choice of DNA extraction method impacts the metagenomic profile of human gut microbiota and highlight the importance of harmonizing protocols in microbiome studies.

Host genetic regulation of human gut microbial structural variation.

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

Impact of occupational pesticide exposure on the human gut microbiome.

The rising use of pesticides in modern agriculture has led to a shift in disease burden in which exposure to these chemicals plays an increasingly important role. The human gut microbiome, which is partially responsible for the biotransformation of xenobiotics, is also known to promote biotransformation of environmental pollutants. Understanding the effects of occupational pesticide exposure on the gut microbiome can thus provide valuable insights into the mechanisms underlying the impact of pesticide exposure on health. Here we investigate the impact of occupational pesticide exposure on human gut microbiome composition in 7198 participants from the Dutch Microbiome Project of the Lifelines Study. We used job-exposure matrices in combination with occupational codes to retrieve categorical and cumulative estimates of occupational exposures to general pesticides, herbicides, insecticides and fungicides. Approximately 4% of our cohort was occupationally exposed to at least one class of pesticides, with predominant exposure to multiple pesticide classes. Most participants reported long-term employment, suggesting a cumulative profile of exposure. We demonstrate that contact with insecticides, fungicides and a general "all pesticides" class was consistently associated with changes in the gut microbiome, showing significant associations with decreased alpha diversity and a differing beta diversity. We also report changes in the abundance of 39 different bacterial taxa upon exposure to the different pesticide classes included in this study. Together, the extent of statistically relevant associations between gut microbial changes and pesticide exposure in our findings highlights the impact of these compounds on the human gut microbiome.

Impaired resolution of blood transcriptomes through tuberculosis treatment with diabetes comorbidity.

BACKGROUND: People with diabetes are more likely to develop tuberculosis (TB) and to have poor TB-treatment outcomes than those without. We previously showed that blood transcriptomes in people with TB-diabetes (TB-DM) co-morbidity have excessive inflammatory and reduced interferon responses at diagnosis. It is unknown whether this persists through treatment and contributes to the adverse outcomes. METHODS: Pulmonary TB patients recruited in South Africa, Indonesia and Romania were classified as having TB-DM, TB with prediabetes, TB-related hyperglycaemia or TB-only, based on glycated haemoglobin concentration at TB diagnosis and after 6 months of TB treatment. Gene expression in blood at diagnosis and intervals throughout treatment was measured by unbiased RNA-Seq and targeted Multiplex Ligation-dependent Probe Amplification. Transcriptomic data were analysed by longitudinal mixed-model regression to identify whether genes were differentially expressed between clinical groups through time. Predictive models of TB-treatment response across groups were developed and cross-tested. RESULTS: Gene expression differed between TB and TB-DM patients at diagnosis and was modulated by TB treatment in all clinical groups but to different extents, such that differences remained in TB-DM relative to TB-only throughout. Expression of some genes increased through TB treatment, whereas others decreased: some were persistently more highly expressed in TB-DM and others in TB-only patients. Genes involved in innate immune responses, anti-microbial immunity and inflammation were significantly upregulated in people with TB-DM throughout treatment. The overall pattern of change was similar across clinical groups irrespective of diabetes status, permitting models predictive of TB treatment to be developed. CONCLUSIONS: Exacerbated transcriptome changes in TB-DM take longer to resolve during TB treatment, meaning they remain different from those in uncomplicated TB after treatment completion. This may indicate a prolonged inflammatory response in TB-DM, requiring prolonged treatment or host-directed therapy for complete cure. Development of transcriptome-based biomarker signatures of TB-treatment response should include people with diabetes for use across populations.

Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire.

Phage-displayed immunoprecipitation sequencing (PhIP-seq) has enabled high-throughput profiling of human antibody repertoires. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is lacking. In this study, we investigated the effects of genetic, environmental, and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We detected individual-specificity, temporal consistency, and co-housing similarities in antibody repertoires. Genetic analyses showed the involvement of the HLA, IGHV, and FUT2 gene regions in antibody-bound peptide reactivity. Furthermore, we uncovered associations between phenotypic factors (including age, cell counts, sex, smoking behavior, and allergies, among others) and particular antibody-bound peptides. Our results indicate that human antibody epitope repertoires are shaped by both genetics and environmental exposures and highlight specific signatures of distinct phenotypes and genotypes.

Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures.

Inflammatory bowel diseases (IBDs), e.g., Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases. A comprehensive overview of an IBD-specific antibody epitope repertoire is, however, lacking. Using high-throughput phage-display immunoprecipitation sequencing (PhIP-Seq), we identified antibodies against 344,000 antimicrobial, immune, and food antigens in 497 individuals with IBD compared with 1,326 controls. IBD was characterized by 373 differentially abundant antibody responses (202 overrepresented and 171 underrepresented), with 17% shared by both IBDs, 55% unique to CD, and 28% unique to UC. Antibody reactivities against bacterial flagellins dominated in CD and were associated with ileal involvement, fibrostenotic disease, and anti-Saccharomyces cerevisiae antibody positivity, but not with fecal microbiome composition. Antibody epitope repertoires accurately discriminated CD from controls (area under the curve [AUC] = 0.89), and similar discrimination was achieved when using only ten antibodies (AUC = 0.87). Individuals with IBD thus show a distinct antibody repertoire against selected peptides, allowing clinical stratification and discovery of immunological targets.

iPSC-derived organ-on-a-chip models for personalized human genetics and pharmacogenomics studies.

Genome-wide association studies (GWAS) have now correlated hundreds of genetic variants with complex genetic diseases and drug efficacy. Functional characterization of these factors remains challenging, particularly because of the lack of human model systems. Molecular and nanotechnological advances, in particular the ability to generate patient-specific PSC lines, differentiate them into diverse cell types, and seed and combine them on microfluidic chips, have led to the establishment of organ-on-a-chip (OoC) platforms that recapitulate organ biology. OoC technology thus provides unique personalized platforms for studying the effects of host genetics and environmental factors on organ physiology. In this review we describe the technology and provide examples of how OoCs may be used for disease modeling and pharmacogenetic research.

Phenotypic and Genetic Factors Associated with Absence of Cardiomyopathy Symptoms in PLN:c.40_42delAGA Carriers.

The c.40_42delAGA variant in the phospholamban gene (PLN) has been associated with dilated and arrhythmogenic cardiomyopathy, with up to 70% of carriers experiencing a major cardiac event by age 70. However, there are carriers who remain asymptomatic at older ages. To understand the mechanisms behind this incomplete penetrance, we evaluated potential phenotypic and genetic modifiers in 74 PLN:c.40_42delAGA carriers identified in 36,339 participants of the Lifelines population cohort. Asymptomatic carriers (N = 48) showed shorter QRS duration (- 5.73 ms, q value = 0.001) compared to asymptomatic non-carriers, an effect we could replicate in two different independent cohorts. Furthermore, symptomatic carriers showed a higher correlation (rPearson = 0.17) between polygenic predisposition to higher QRS (PGSQRS) and QRS (p value = 1.98 × 10-8), suggesting that the effect of the genetic variation on cardiac rhythm might be increased in symptomatic carriers. Our results allow for improved clinical interpretation for asymptomatic carriers, while our approach could guide future studies on genetic diseases with incomplete penetrance.

Systematic analysis of relationships between plasma branched-chain amino acid concentrations and cardiometabolic parameters: an association and Mendelian randomization study.

BACKGROUND: Branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) are essential amino acids that are associated with an increased risk of cardiometabolic diseases (CMD). However, there are still only limited insights into potential direct associations between BCAAs and a wide range of CMD parameters, especially those remaining after correcting for covariates and underlying causal relationships. METHODS: To shed light on these relationships, we systematically characterized the associations between plasma BCAA concentrations and a large panel of 537 CMD parameters (including atherosclerosis-related parameters, fat distribution, plasma cytokine concentrations and cell counts, circulating concentrations of cardiovascular-related proteins and plasma metabolites) in 1400 individuals from the Dutch population cohort LifeLines DEEP and 294 overweight individuals from the 300OB cohort. After correcting for age, sex, and BMI, we assessed associations between individual BCAAs and CMD parameters. We further assessed the underlying causality using Mendelian randomization. RESULTS: A total of 838 significant associations were detected for 409 CMD parameters. BCAAs showed both common and specific associations, with the most specific associations being detected for isoleucine. Further, we found that obesity status substantially affected the strength and direction of associations for valine, which cannot be corrected for using BMI as a covariate. Subsequent univariable Mendelian randomization (UVMR), after removing BMI-associated SNPs, identified seven significant causal relationships from four CMD traits to BCAA levels, mostly for diabetes-related parameters. However, no causal effects of BCAAs on CMD parameters were supported. CONCLUSIONS: Our cross-sectional association study reports a large number of associations between BCAAs and CMD parameters. Our results highlight some specific associations for isoleucine, as well as obesity-specific effects for valine. MR-based causality analysis suggests that altered BCAA levels can be a consequence of diabetes and alteration in lipid metabolism. We found no MR evidence to support a causal role for BCAAs in CMD. These findings provide evidence to (re)evaluate the clinical importance of individual BCAAs in CMD diagnosis, prevention, and treatment.

Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome.

The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual's genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of Eubacterium rectale in decreasing plasma levels of hydrogen sulfite-a toxin that affects cardiovascular function. Lastly, based on analysis of the plasma metabolome of 311 individuals at two time points separated by 4 years, we observed a positive correlation between the stability of metabolite levels and the amount of variance in the levels of that metabolite that could be explained in our analysis. Altogether, characterization of factors that explain inter-individual variation in the plasma metabolome can help design approaches for modulating diet or the gut microbiome to shape a healthy metabolome.

Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation.

Organ transplantation is a life-saving treatment for patients with end-stage disease, but survival rates after transplantation vary considerably. There is now increasing evidence that the gut microbiome is linked to the survival of patients undergoing hematopoietic cell transplant, yet little is known about the role of the gut microbiome in solid organ transplantation. We analyzed 1370 fecal samples from 415 liver and 672 renal transplant recipients using shotgun metagenomic sequencing to assess microbial taxonomy, metabolic pathways, antibiotic resistance genes, and virulence factors. To quantify taxonomic and metabolic dysbiosis, we also analyzed 1183 age-, sex-, and body mass index-matched controls from the same population. In addition, a subset of 78 renal transplant recipients was followed longitudinally from pretransplantation to 24 months after transplantation. Our data showed that both liver and kidney transplant recipients suffered from gut dysbiosis, including lower microbial diversity, increased abundance of unhealthy microbial species, decreased abundance of important metabolic pathways, and increased prevalence and diversity of antibiotic resistance genes and virulence factors. These changes were found to persist up to 20 years after transplantation. Last, we demonstrated that the use of immunosuppressive drugs was associated with the observed dysbiosis and that the extent of dysbiosis was associated with increased mortality after transplantation. This study represents a step toward potential microbiome-targeted interventions that might influence the outcomes of recipients of solid organ transplantation.

Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations.

Copy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (p < 3.0 × 10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 56% compared with published quality filtering methods or scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls, and demonstrated its utility by improving the statistical power for downstream association analyses.

Transcriptional profiles predict treatment outcome in patients with tuberculosis and diabetes at diagnosis and at two weeks after initiation of anti-tuberculosis treatment.

BACKGROUND: Globally, the tuberculosis (TB) treatment success rate is approximately 85%, with treatment failure, relapse and death occurring in a significant proportion of pulmonary TB patients. Treatment success is lower among people with diabetes mellitus (DM). Predicting treatment outcome early after diagnosis, especially in TB-DM patients, would allow early treatment adaptation for individuals and may improve global TB control. METHODS: Samples were collected in a longitudinal cohort study of adult TB patients from South Africa (n  =  94) and Indonesia (n  =  81), who had concomitant DM (n  =  59), intermediate hyperglycaemia (n  =  79) or normal glycaemia/no DM (n  =  37). Treatment outcome was monitored, and patients were categorized as having a good (cured) or poor (failed, recurrence, died) outcome during treatment and 12 months follow-up. Whole blood transcriptional profiles before, during and at the end of TB treatment were characterized using unbiased RNA-Seq and targeted gene dcRT-MLPA. FINDINGS: We report differences in whole blood transcriptome profiles, which were observed before initiation of treatment and throughout treatment, between patients with a good versus poor TB treatment outcome. An eight-gene and a 22-gene blood transcriptional signature distinguished patients with a good TB treatment outcome from patients with a poor TB treatment outcome at diagnosis (AUC = 0·815) or two weeks (AUC = 0·834) after initiation of TB treatment, respectively. High accuracy was obtained by cross-validating this signature in an external cohort (AUC = 0·749). INTERPRETATION: These findings suggest that transcriptional profiles can be used as a prognostic biomarker for treatment failure and success, even in patients with concomitant DM. FUNDING: The research leading to these results, as part of the TANDEM Consortium, received funding from the European Community's Seventh Framework Programme (FP7/2007-2013 Grant Agreement No. 305279) and the Netherlands Organization for Scientific Research (NWO-TOP Grant Agreement No. 91214038). The research leading to the results presented in the Indian validation cohort was supported by Research Council of Norway Global Health and Vaccination Research (GLOBVAC) projects: RCN 179342, 192534, and 248042, the University of Bergen (Norway).

Genetic, parental and lifestyle factors influence telomere length.

The average length of telomere repeats (TL) declines with age and is considered to be a marker of biological ageing. Here, we measured TL in six blood cell types from 1046 individuals using the clinically validated Flow-FISH method. We identified remarkable cell-type-specific variations in TL. Host genetics, environmental, parental and intrinsic factors such as sex, parental age, and smoking are associated to variations in TL. By analysing the genome-wide methylation patterns, we identified that the association of maternal, but not paternal, age to TL is mediated by epigenetics. Single-cell RNA-sequencing data for 62 participants revealed differential gene expression in T-cells. Genes negatively associated with TL were enriched for pathways related to translation and nonsense-mediated decay. Altogether, this study addresses cell-type-specific differences in telomere biology and its relation to cell-type-specific gene expression and highlights how perinatal factors play a role in determining TL, on top of genetics and lifestyle.

A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes.

Background: The large inter-individual variability in immune-cell composition and function determines immune responses in general and susceptibility o immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown. Methods: Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on >200 immune-cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls. Results: Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTLs) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation. Conclusions: This study provides new insights into the genetic factors that affect immunological responses in T1D. Funding: This work was supported by an ERC starting grant (no. 948207) and a Radboud University Medical Centre Hypatia grant (2018) to YL and an ERC advanced grant (no. 833247) and a Spinoza grant of the Netherlands Association for Scientific Research to MGN CT received funding from the Perspectief Biomarker Development Center Research Programme, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). AJ was funded by a grant from the European Foundation for the Study of Diabetes (EFSD/AZ Macrovascular Programme 2015). XC was supported by the China Scholarship Council (201706040081).

Every year around the world, over 100,000 people are diagnosed with type 1 diabetes. This disease develops when the immune system mistakenly destroys the cells that produce a hormone called insulin, leaving affected individuals unable to regulate their blood sugar levels. Type 1 diabetes patients must rely on regular injections of manufactured insulin to survive. The composition and activity of the human immune system is under genetic control, and people with certain changes in their genes are more susceptible than others to develop type 1 diabetes. Previous studies have identified around 60 locations in the human DNA (known as loci) associated with the condition, but it remains unclear how these loci influence the immune system and whether diabetes will emerge. Chu, Janssen, Koenen et al. explored how variations in genetic information can influence the composition of the immune system, and the type of molecules it releases to perform its role. To do so, blood samples from 243 individuals of Dutch descent with type 1 diabetes were collected, and genetic associations were investigated. The results revealed that a major type of immune actors known as T cells are under the control of genetic factors associated with type 1 diabetes susceptibility. For instance, a specific type of T cells showed shared genetic control with type 1 diabetes. In addition, 15 loci were identified that influenced immune responses in the patients. Among those, 12 have never been reported to be involved in immune responses in healthy people, implying that these regions might only regulate the immune system of individuals with type 1 diabetes and other similar disorders. Finally, Chu, Janssen, Koenen et al. propose 11 genes within the identified loci as potential targets for new diabetes medication. These results represent an important resource for researchers exploring the genetic and immune basis of type 1 diabetes, and they could open new avenues for drug development.

Integrated fecal microbiome-metabolome signatures reflect stress and serotonin metabolism in irritable bowel syndrome.

To gain insight into the complex microbiome-gut-brain axis in irritable bowel syndrome (IBS), several modalities of biological and clinical data must be combined. We aimed to identify profiles of fecal microbiota and metabolites associated with IBS and to delineate specific phenotypes of IBS that represent potential pathophysiological mechanisms. Fecal metabolites were measured using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gut microbiome using shotgun metagenomic sequencing (MGS) in a combined dataset of 142 IBS patients and 120 healthy controls (HCs) with extensive clinical, biological and phenotype information. Data were analyzed using support vector classification and regression and kernel t-SNE. Microbiome and metabolome profiles could distinguish IBS and HC with an area-under-the-receiver-operator-curve of 77.3% and 79.5%, respectively, but this could be improved by combining microbiota and metabolites to 83.6%. No significant differences in predictive ability of the microbiome-metabolome data were observed between the three classical, stool pattern-based, IBS subtypes. However, unsupervised clustering showed distinct subsets of IBS patients based on fecal microbiome-metabolome data. These clusters could be related plasma levels of serotonin and its metabolite 5-hydroxyindoleacetate, effects of psychological stress on gastrointestinal (GI) symptoms, onset of IBS after stressful events, medical history of previous abdominal surgery, dietary caloric intake and IBS symptom duration. Furthermore, pathways in metabolic reaction networks were integrated with microbiota data, that reflect the host-microbiome interactions in IBS. The identified microbiome-metabolome signatures for IBS, associated with altered serotonin metabolism and unfavorable stress response related to GI symptoms, support the microbiota-gut-brain link in the pathogenesis of IBS.

Single-Cell RNA Sequencing of Peripheral Blood Mononuclear Cells From Pediatric Coeliac Disease Patients Suggests Potential Pre-Seroconversion Markers.

Celiac Disease (CeD) is a complex immune disorder involving villous atrophy in the small intestine that is triggered by gluten intake. Current CeD diagnosis is based on late-stage pathophysiological parameters such as detection of specific antibodies in blood and histochemical detection of villus atrophy and lymphocyte infiltration in intestinal biopsies. To date, no early onset biomarkers are available that would help prevent widespread villous atrophy and severe symptoms and co-morbidities. To search for novel CeD biomarkers, we used single-cell RNA sequencing (scRNAseq) to investigate PBMC samples from 11 children before and after seroconversion for CeD and 10 control individuals matched for age, sex and HLA-genotype. We generated scRNAseq profiles of 9559 cells and identified the expected major cellular lineages. Cell proportions remained stable across the different timepoints and health conditions, but we observed differences in gene expression profiles in specific cell types when comparing patient samples before and after disease development and comparing patients with controls. Based on the time when transcripts were differentially expressed, we could classify the deregulated genes as biomarkers for active CeD or as potential pre-diagnostic markers. Pathway analysis showed that active CeD biomarkers display a transcriptional profile associated with antigen activation in CD4+ T cells, whereas NK cells express a subset of biomarker genes even before CeD diagnosis. Intersection of biomarker genes with CeD-associated genetic risk loci pinpointed genetic factors that might play a role in CeD onset. Investigation of potential cellular interaction pathways of PBMC cell subpopulations highlighted the importance of TNF pathways in CeD. Altogether, our results pinpoint genes and pathways that are altered prior to and during CeD onset, thereby identifying novel potential biomarkers for CeD diagnosis in blood.