Early-life stress and the gut microbiome: A comprehensive population-based investigation

Early-life stress (ELS) has been robustly associated with a range of poor mental and physical health outcomes. Recent studies implicate the gut microbiome in stress-related mental, cardio-metabolic and immune health problems, but research on humans is scarce and thus far often based on small, selected samples, often using retrospective reports of ELS. We examined associations between ELS and the human gut microbiome in a large, population-based study of children. ELS was measured prospectively from birth to 10 years of age in 2,004 children from the Generation R Study. We studied overall ELS, as well as unique effects of five different ELS domains, including life events, contextual risk, parental risk, interpersonal risk, and direct victimization. Stool microbiome was assessed using 16S rRNA sequencing at age 10 years and data were analyzed at multiple levels (i.e. α- and β-diversity indices, individual genera and predicted functional pathways). In addition, we explored potential mediators of ELS-microbiome associations, including diet at age 8 and body mass index at 10 years. While no associations were observed between overall ELS (composite score of five domains) and the microbiome after multiple testing correction, contextual risk - a specific ELS domain related to socio-economic stress, including risk factors such as financial difficulties and low maternal education - was significantly associated with microbiome variability. This ELS domain was associated with lower α-diversity, with β-diversity, and with predicted functional pathways involved, amongst others, in tryptophan biosynthesis. These associations were in part mediated by overall diet quality, a pro-inflammatory diet, fiber intake, and body mass index (BMI). These results suggest that stress related to socio-economic adversity - but not overall early life stress - is associated with a less diverse microbiome in the general population, and that this association may in part be explained by poorer diet and higher BMI. Future research is needed to test causality and to establish whether modifiable factors such as diet could be used to mitigate the negative effects of socio-economic adversity on the microbiome and related health consequences.

Association of blood cell-based inflammatory markers with gut microbiota and cancer incidence in the Rotterdam study

The immune response-gut microbiota interaction is implicated in various human diseases, including cancer. Identifying the link between the gut microbiota and systemic inflammatory markers and their association with cancer will be important for our understanding of cancer etiology. The current study was performed on 8090 participants from the population-based Rotterdam study. We found a significant association (false discovery rate [FDR] ≤0.05) between lymphocytes and three gut microbial taxa, namely the family Streptococcaceae, genus Streptococcus, and order Lactobacillales. In addition, we identified 95 gut microbial taxa that were associated with inflammatory markers (p < 0.05). Analyzing the cancer data, we observed a significant association between higher systemic immune-inflammation index (SII) levels at baseline (hazard ratio (HR): 1.65 [95% confidence interval (CI); 1.10-2.46, p ≤ 0.05]) and a higher count of lymphocytes (HR: 1.38 [95% CI: 1.15-1.65, p ≤ 0.05]) and granulocytes (HR: 1.69 [95% CI: 1.40-2.03, p ≤ 0.05]) with increased risk of lung cancer after adjusting for age, sex, body mass index (BMI), and study cohort. This association was lost for SII and lymphocytes after additional adjustment for smoking (SII = HR:1.46 [95% CI: 0.96-2.22, p = 0.07] and lymphocytes = HR: 1.19 [95% CI: 0.97-1.46, p = 0.08]). In the stratified analysis, higher count of lymphocyte and granulocytes at baseline were associated with an increased risk of lung cancer in smokers after adjusting for age, sex, BMI, and study cohort (HR: 1.33 [95% CI: 1.09-1.62, p ≤0.05] and HR: 1.57 [95% CI: 1.28-1.92, p ≤0.05], respectively). Our study revealed a positive association between gut microbiota, higher SII levels, and higher lymphocyte and granulocyte counts, with an increased risk of developing lung cancer.

Glaucoma Patients Have a Lower Abundance of Butyrate-Producing Taxa in the Gut

Purpose

Glaucoma is an eye disease that is the most common cause of irreversible blindness worldwide. It has been suggested that gut microbiota can produce reactive oxygen species and pro-inflammatory cytokines that may travel from the gastric mucosa to distal sites, for example, the optic nerve head or trabecular meshwork. There is evidence for a gut-eye axis, as microbial dysbiosis has been associated with retinal diseases. We investigated the microbial composition in patients with glaucoma and healthy controls. Moreover, we analyzed the association of the gut microbiome with intraocular pressure (IOP; risk factor of glaucoma) and vertical cup-to-disc ratio (VCDR; quantifying glaucoma severity).

Methods

The discovery analyses included participants of the Rotterdam Study and the Erasmus Glaucoma Cohort. A total of 225 patients with glaucoma and 1247 age- and sex-matched participants without glaucoma were included in our analyses. Stool samples were used to generate 16S rRNA gene profiles. We assessed associations with 233 genera and species. We used data from the TwinsUK and the Study of Health in Pomerania (SHIP) to replicate our findings.

Results

Several butyrate-producing taxa (e.g. Butyrivibrio, Caproiciproducens, Clostridium sensu stricto 1, Coprococcus 1, Ruminococcaceae UCG 007, and Shuttleworthia) were less abundant in people with glaucoma compared to healthy controls. The same taxa were also associated with lower IOP and smaller VCDR. The replication analyses confirmed the findings from the discovery analyses.

Conclusions

Large human studies exploring the link between the gut microbiome and glaucoma are lacking. Our results suggest that microbial dysbiosis plays a role in the pathophysiology of glaucoma.

Midgut neuroendocrine tumor patients have a depleted gut microbiome with a discriminative signature

RATIONALE

When compared to other types of cancer, the prevalence of midgut neuroendocrine tumors (NET) has disproportionally increased over the past decades. To date, there has been very little progress in discovering (epi)genetic drivers and treatment options for these tumors. Recent microbiome research has revealed that enteroendocrine cells communicate with the intestinal microbiome and has provided novel treatment targets for various other cancer types. Hence, our aim was to analyze the role of the gut microbiome in midgut NET patients.

METHODS

Fecal samples, prospectively collected from patients and control subjects, were analyzed with next generation 16S sequencing. Patients with neuroendocrine carcinomas and recent antibiotics use were excluded. Relevant variables were extracted from questionnaires and electronic health records. Microbial composition was compared between patients and controls as well as between groups within the patient cohort.

RESULTS

87 midgut NET patients and 95 controls were included. Midgut NET patients had a less rich and diverse gut microbiome than controls (p < 0.001). Moreover, we identified 31 differentially abundant species and a gut microbial signature consisting of 17 species that was predictive of midgut NET presence with an area under the receiver operating characteristic curve of 0.863. Gut microbial composition was not directly associated with the presence of the carcinoid syndrome, tumor grade or multifocality. Nonetheless, we did observe a potential link between microbial diversity and the presence of carcinoid syndrome symptoms within the subset of patients with elevated 5-hydroxyindolacetic acid levels.

CONCLUSION

Midgut NET patients have an altered gut microbiome which suggests a role in NET development and could provide novel targets for microbiome-based diagnostics and therapeutics.

Gut microbiota analysis in pediatric-onset multiple sclerosis compared to pediatric monophasic demyelinating syndromes and pediatric controls

BACKGROUND AND PURPOSE

Gut microbiota dysbiosis may lead to proinflammatory conditions contributing to multiple sclerosis (MS) etiology. Pediatric-onset MS patients are close to biological disease onset and less exposed to confounders. Therefore, this study investigated gut microbiota composition and functional pathways in pediatric-onset MS, compared to monophasic acquired demyelinating syndromes (mADS) and healthy controls (HCs).

METHODS

Pediatric participants were selected from the Dutch national prospective cohort study including ADS patients and HCs <18 years old. Amplicon sequence variants (ASVs) were generated from sequencing the V3/4 regions of the 16S rRNA gene. Functional MetaCyc microbial pathways were predicted based on Enzyme Commission numbers. Gut microbiota composition (alpha/beta diversity and individual microbe abundance at ASV to phylum level) and predicted functional pathways were tested using nonparametric tests, permutational multivariate analysis of variance, and linear regression.

RESULTS

Twenty-six pediatric-onset MS (24 with disease-modifying therapy [DMT]), 25 mADS, and 24 HC subjects were included. Alpha/beta diversity, abundance of individual resident microbes, and microbial functional features were not different between these participant groups. Body mass index (BMI) showed significant differences, with obese children having a lower alpha diversity (Chao1 Index p = 0.015, Shannon/Simpson Diversity Index p = 0.014/p = 0.023), divergent beta diversity (R2  = 3.7%, p = 0.013), and higher abundance of numerous individual resident microbes and functional microbial pathways.

CONCLUSIONS

Previous results of gut microbiota composition and predicted functional features could not be validated in this Dutch pediatric-onset MS cohort using a more sensitive 16S pipeline, although it was limited by sample size and DMT use. Notably, several other host-related factors were found to associate with gut microbiota variation, especially BMI.

Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals

Importance

Metabolomics reflect the net effect of genetic and environmental influences and thus provide a comprehensive approach to evaluating the pathogenesis of complex diseases, such as depression.

Objective

To identify the metabolic signatures of major depressive disorder (MDD), elucidate the direction of associations using mendelian randomization, and evaluate the interplay of the human gut microbiome and metabolome in the development of MDD.

Design, Setting and Participants

This cohort study used data from participants in the UK Biobank cohort (n = 500 000; aged 37 to 73 years; recruited from 2006 to 2010) whose blood was profiled for metabolomics. Replication was sought in the PREDICT and BBMRI-NL studies. Publicly available summary statistics from a 2019 genome-wide association study of depression were used for the mendelian randomization (individuals with MDD = 59 851; control individuals = 113 154). Summary statistics for the metabolites were obtained from OpenGWAS in MRbase (n = 118 000). To evaluate the interplay of the metabolome and the gut microbiome in the pathogenesis of depression, metabolic signatures of the gut microbiome were obtained from a 2019 study performed in Dutch cohorts. Data were analyzed from March to December 2021.

Main Outcomes and Measures

Outcomes were lifetime and recurrent MDD, with 249 metabolites profiled with nuclear magnetic resonance spectroscopy with the Nightingale platform.

Results

The study included 6811 individuals with lifetime MDD compared with 51 446 control individuals and 4370 individuals with recurrent MDD compared with 62 508 control individuals. Individuals with lifetime MDD were younger (median [IQR] age, 56 [49-62] years vs 58 [51-64] years) and more often female (4447 [65%] vs 2364 [35%]) than control individuals. Metabolic signatures of MDD consisted of 124 metabolites spanning the energy and lipid metabolism pathways. Novel findings included 49 metabolites, including those involved in the tricarboxylic acid cycle (ie, citrate and pyruvate). Citrate was significantly decreased (β [SE], -0.07 [0.02]; FDR = 4 × 10-04) and pyruvate was significantly increased (β [SE], 0.04 [0.02]; FDR = 0.02) in individuals with MDD. Changes observed in these metabolites, particularly lipoproteins, were consistent with the differential composition of gut microbiota belonging to the order Clostridiales and the phyla Proteobacteria/Pseudomonadota and Bacteroidetes/Bacteroidota. Mendelian randomization suggested that fatty acids and intermediate and very large density lipoproteins changed in association with the disease process but high-density lipoproteins and the metabolites in the tricarboxylic acid cycle did not.

Conclusions and Relevance

The study findings showed that energy metabolism was disturbed in individuals with MDD and that the interplay of the gut microbiome and blood metabolome may play a role in lipid metabolism in individuals with MDD.

Advanced Glycation End Products (AGEs) in Diet and Skin in Relation to Stool Microbiota: The Rotterdam Study

BACKGROUND

Advanced glycation end products (AGEs) are involved in age-related diseases, but the interaction of gut microbiota with dietary AGEs (dAGEs) and tissue AGEs in the population is unknown.

OBJECTIVE

Our objective was to investigate the association of dietary and tissue AGEs with gut microbiota in the population-based Rotterdam Study, using skin AGEs as a marker for tissue accumulation and stool microbiota as a surrogate for gut microbiota.

DESIGN

Dietary intake of three AGEs (dAGEs), namely carboxymethyl-lysine (CML), N-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MGH1), and carboxyethyl-lysine (CEL), was quantified at baseline from food frequency questionnaires. Following up after a median of 5.7 years, skin AGEs were measured using skin autofluorescence (SAF), and stool microbiota samples were sequenced (16S rRNA) to measure microbial composition (including alpha-diversity, beta-dissimilarity, and taxonomic abundances) as well as predict microbial metabolic pathways. Associations of both dAGEs and SAF with microbial measures were investigated using multiple linear regression models in 1052 and 718 participants, respectively.

RESULTS

dAGEs and SAF were not associated with either the alpha-diversity or beta-dissimilarity of the stool microbiota. After multiple-testing correction, dAGEs were not associated with any of the 188 genera tested, but were nominally inversely associated with the abundance of Barnesiella, Colidextribacter, Oscillospiraceae UCG-005, and Terrisporobacter, in addition to being positively associated with Coprococcus, Dorea, and Blautia. A higher abundance of Lactobacillus was associated with a higher SAF, along with several nominally significantly associated genera. dAGEs and SAF were nominally associated with several microbial pathways, but none were statistically significant after multiple-testing correction.

CONCLUSIONS

Our findings did not solidify a link between habitual dAGEs, skin AGEs, and overall stool microbiota composition. Nominally significant associations with several genera and functional pathways suggested a potential interaction between gut microbiota and AGE metabolism, but validation is required. Future studies are warranted, to investigate whether gut microbiota modifies the potential impact of dAGEs on health.

The gut microbiome and child mental health: A population-based study

The link between the gut microbiome and the brain has gained increasing scientific and public interest for its potential to explain psychiatric risk. While differences in gut microbiome composition have been associated with several mental health problems, evidence to date has been largely based on animal models and human studies with modest sample sizes. In this cross-sectional study in 1,784 ten-year-old children from the multi-ethnic, population-based Generation R Study, we aimed to characterize associations of the gut microbiome with child mental health problems. Gut microbiome was assessed from stool samples using 16S rRNA sequencing. We focused on overall psychiatric symptoms as well as with specific domains of emotional and behavioral problems, assessed via the maternally rated Child Behavior Checklist. While we observed lower gut microbiome diversity in relation to higher overall and specific mental health problems, associations were not significant. Likewise, we did not identify any taxonomic feature associated with mental health problems after multiple testing correction, although suggestive findings indicated depletion of genera previously associated with psychiatric disorders, including Hungatella, Anaerotruncus and Oscillospiraceae. The identified compositional abundance differences were found to be similar across all mental health problems. Finally, we did not find significant enrichment for specific microbial functions in relation to mental health problems. In conclusion, based on the largest sample examined to date, we do not find clear evidence of associations between gut microbiome diversity, taxonomies or functions and mental health problems in the general pediatric population. In future, the use of longitudinal designs with repeated measurements of microbiome and psychiatric outcomes will be critical to identify whether and when associations between the gut microbiome and mental health emerge across development and into adulthood.

Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's disease

Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD.

Whole-exome sequencing of 14 389 individuals from the ESP and CHARGE consortia identifies novel rare variation associated with hemostatic factors

Plasma levels of fibrinogen, coagulation factors VII and VIII and von Willebrand factor (vWF) are four intermediate phenotypes that are heritable and have been associated with the risk of clinical thrombotic events. To identify rare and low-frequency variants associated with these hemostatic factors, we conducted whole-exome sequencing in 10 860 individuals of European ancestry (EA) and 3529 African Americans (AAs) from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and the National Heart, Lung and Blood Institute's Exome Sequencing Project. Gene-based tests demonstrated significant associations with rare variation (minor allele frequency < 5%) in fibrinogen gamma chain (FGG) (with fibrinogen, P = 9.1 × 10-13), coagulation factor VII (F7) (with factor VII, P = 1.3 × 10-72; seven novel variants) and VWF (with factor VIII and vWF; P = 3.2 × 10-14; one novel variant). These eight novel rare variant associations were independent of the known common variants at these loci and tended to have much larger effect sizes. In addition, one of the rare novel variants in F7 was significantly associated with an increased risk of venous thromboembolism in AAs (Ile200Ser; rs141219108; P = 4.2 × 10-5). After restricting gene-based analyses to only loss-of-function variants, a novel significant association was detected and replicated between factor VIII levels and a stop-gain mutation exclusive to AAs (rs3211938) in CD36 molecule (CD36). This variant has previously been linked to dyslipidemia but not with the levels of a hemostatic factor. These efforts represent the largest integration of whole-exome sequence data from two national projects to identify genetic variation associated with plasma hemostatic factors.

The diagnostic value of nasal microbiota and clinical parameters in a multi-parametric prediction model to differentiate bacterial versus viral infections in lower respiratory tract infections

Background

The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI.

Results

Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the ‘bacterial’ patients and 82% of the ‘viral’ patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus).

Conclusions

We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.

Gut Microbiota Composition Is Related to AD Pathology

Introduction

Several studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).

Materials and Methods

We included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.

Results

The machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.

Conclusions

Gut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity: A Microbiome-Wide Analysis From Population Studies

IMPORTANCE

Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear.

OBJECTIVE

To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study.

EXPOSURES

The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), β diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition.

MAIN OUTCOMES AND MEASURES

Associations among α diversity, β diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020.

RESULTS

There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The β diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R2 = 0.004, P = .001 in the Rotterdam Study and R2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (β = -0.08; 95% CI, -0.12 to -0.03: P < .001), Christensenellaceae R7 group (β = -0.07; 95% CI, -0.12 to -0.03; P < .001), Marvinbryantia (β = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG005 (β = -0.09; 95% CI, -0.13 to -0.05; P < .001), Ruminococcaceae UCG008 (β = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG010 (β = -0.08; 95% CI, -0.12 to -0.04; P < .001), or Ruminococcaceae NK4A214 group (β = -0.09; 95% CI, -0.13 to -0.05; P < .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P < .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P < .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P < .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P < .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P < .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, higher microbiome α diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.

Prevalence of and risk factors for extended-spectrum beta-lactamase genes carriership in a population-based cohort of middle-aged and elderly

INTRODUCTION

Increasing resistance to beta-lactam antibiotics is an alarming development worldwide. Fecal carriership of TEM, SHV, CTX-M and CMY was studied in a community-dwelling population of middle-aged and elderly individuals.

PATIENTS AND METHODS

Feces was obtained from individuals of the Rotterdam Study. Carriership of the TEM, SHV, CTX-M and CMY genes was determined using real-time polymerase chain reaction (qPCR). Possible associations were investigated between carriership of these genes and several risk factors, such as the use of antimicrobial drugs, diabetes mellitus, protein pump inhibitor (PPI) use, travelling, the composition of the gut microbiota, and intake of certain foods.

RESULTS

The most prevalent gene was TEM (53.0%), followed by SHV (18.4%), CTX-M (5.4%) and CMY (3.6%). Use of penicillins with extended spectrum was associated with TEM carriership, whereas use of macrolides and lincosamides was associated with TEM and SHV carriership. Interestingly, use of PPIs was associated with a higher prevalence of carriership of TEM, SHV and CMY (TEM: odds ratio [OR] 1.34; 95% confidence interval [CI] 1.05-1.77; SHV: OR 2.17; 95%CI 1.55-2.87; CMY: OR 2.26; 95%CI 1.23-4.11). Furthermore, associations were found between the richness and composition of the gut microbiota and TEM and SHV carriership.

CONCLUSIONS

The prevalence of carriership of TEM was substantial, but the prevalence of carriership of the extended-spectrum β-lactamase gene, CTX-M and the AmpC β-lactamase gene, CMY was relatively low in this community-dwelling, population-based cohort. The composition of the microbiota might play a role in the retention of resistance genes, but future studies are necessary to further elucidate this relationship.

Composition of cutaneous bacterial microbiome in seborrheic dermatitis patients: A cross-sectional study

Background

Seborrheic dermatitis (SD) is a chronic inflammatory skin disease with a multifactorial aetiology. Malassezia yeasts have been associated with the disease but the role of bacterial composition in SD has not been thoroughly investigated.

Objectives

To profile the bacterial microbiome of SD patients and compare this with the microbiome of individuals with no inflammatory skin disease (controls).

Methods

This was a cross sectional study embedded in a population-based study. Skin swabs were taken from naso-labial fold from patients with seborrheic dermatitis (lesional skin: n = 22; non-lesional skin SD: n = 75) and controls (n = 465). Sample collection began in 2016 at the research facility and is still ongoing. Shannon and Chao1 α- diversity metrics were calculated per group. Associations between the microbiome composition of cases and controls was calculated using multivariate statistics (permANOVA) and univariate statistics.

Results

We found an increased α-diversity between SD lesional cases versus controls (Shannon diversity: Kruskal-Wallis rank sum: Chi-squared: 19.06; global p-value = 7.7x10^-5). Multivariate statistical analysis showed significant associations in microbiome composition when comparing lesional SD skin to controls (p-value = 0.03;R² = 0.1%). Seven out of 13 amplicon sequence variants (ASVs) that were significantly different between controls and lesional cases were members of the genus Staphylococcus, most of which showed increased composition in lesional cases, and were closely related to S. capitis S. caprae and S. epidermidis.

Conclusion

Microbiome composition differs in patients with seborrheic dermatitis and individuals without diseases. Differences were mainly found in the genus Staphylococcus.

A population-based study on associations of stool microbiota with atopic diseases in school-age children

BACKGROUND

Infants with less diverse gut microbiota seem to have higher risks of atopic diseases in early life, but any associations at school age are unclear.

OBJECTIVES

This study sought to examine the associations of diversity, relative abundance, and functional pathways of stool microbiota with atopic diseases in school-age children.

METHODS

We performed a cross-sectional study within an existing population-based prospective cohort among 1440 children 10 years of age. On stool samples, 16S ribosomal RNA gene sequencing was performed, and taxonomic and functional tables were produced. Physician-diagnosed eczema, allergy, and asthma were measured by questionnaires, allergic sensitization by skin prick tests, and lung function by spirometry.

RESULTS

The α-diversity of stool microbiota was associated with a decreased risk of eczema (odds ratio [OR], 0.98; 95% CI, 0.97, 1.00), and β-diversity was associated with physician-diagnosed inhalant allergy (R² = 0.001; P = .047). Lachnospiraceae, Ruminococcaceae_UCG-005, and Christensenellaceae_R-7_group species were associated with decreased risks of eczema, inhalant allergic sensitization, and physician-diagnosed inhalant allergy (OR range, 0.88-0.94; 95% CI range, 0.79-0.96 to 0.88-0.98), while Agathobacter species were associated with an increased risk of physician-diagnosed inhalant allergy (OR, 1.23; 95% CI, 1.08-1.42). Functional pathways related to heme and terpenoid biosynthesis were associated with decreased risks of physician-diagnosed inhalant allergy and asthma (OR range, 0.89-0.86; 95% CI range, 0.80-0.99 to 0.73-1.02). No associations of stool microbiota with lung function were observed.

CONCLUSIONS

The diversity, relative abundance and functional pathways of stool microbiota were most consistently associated with physician-diagnosed inhalant allergy in school-age children and less consistently with other atopic diseases.

Microbiomics, Metabolomics, Predicted Metagenomics, and Hepatic Steatosis in a Population-Based Study of 1,355 Adults

BACKGROUND AND AIMS

Previous small studies have appraised the gut microbiome (GM) in steatosis, but large-scale studies are lacking. We studied the association of the GM diversity and composition, plasma metabolites, predicted functional metagenomics, and steatosis.

APPROACH AND RESULTS

This is a cross-sectional analysis of the prospective population-based Rotterdam Study. We used 16S ribosomal RNA gene sequencing and determined taxonomy using the SILVA reference database. Alpha diversity and beta diversity were calculated using the Shannon diversity index and Bray-Curtis dissimilarities. Differences were tested across steatosis using permutational multivariate analysis of variance. Hepatic steatosis was diagnosed by ultrasonography. We subsequently selected genera using regularized regression. The functional metagenome was predicted based on the GM using Kyoto Encyclopedia of Genes and Genomes pathways. Serum metabolomics were assessed using high-throughput proton nuclear magnetic resonance. All analyses were adjusted for age, sex, body mass index, alcohol, diet, and proton-pump inhibitors. We included 1,355 participants, of whom 472 had steatosis. Alpha diversity was lower in steatosis (P = 1.1∙10^-9 ), and beta diversity varied across steatosis strata (P = 0.001). Lasso selected 37 genera of which three remained significantly associated after adjustment (Coprococcus3: β = -65; Ruminococcus Gauvreauiigroup: β = 62; and Ruminococcus Gnavusgroup: β = 45, Q-value = 0.037). Predicted metagenome analyses revealed that pathways of secondary bile-acid synthesis and biotin metabolism were present, and D-alanine metabolism was absent in steatosis. Metabolic profiles showed positive associations for aromatic and branched chain amino acids and glycoprotein acetyls with steatosis and R. Gnavusgroup, whereas these metabolites were inversely associated with alpha diversity and Coprococcus3.

CONCLUSIONS

We confirmed, on a large-scale, the lower microbial diversity and association of Coprococcus and Ruminococcus Gnavus with steatosis. We additionally showed that steatosis and alpha diversity share opposite metabolic profiles.

Large-scale association analyses identify host factors influencing human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 × 10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 × 10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 × 10-10 < P < 5 × 10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.

Long-term effects of antimicrobial drugs on the composition of the human gut microbiota

INTRODUCTION

Antimicrobial drugs are known to have effects on the human gut microbiota. We studied the long-term temporal relationship between several antimicrobial drug groups and the composition of the human gut microbiota determined in feces samples.

METHODS

Feces samples were obtained from a community-dwelling cohort of middle-aged and elderly individuals (Rotterdam Study). Bacterial DNA was isolated and sequenced using V3/V4 16 S ribosomal RNA sequencing (Illumina MiSeq). The time between the last prescription of several antimicrobial drug groups and the day of sampling was categorized into 0-12, 12-24, 24-48 and >48 months. The effects of the antimicrobial drug groups on the Shannon alpha-diversity (diversity), the Bray-Curtis beta-diversity (community structure), the Firmicutes/Bacteroidetes (F/B) ratio and individual genera were determined.

RESULTS

We studied the gut microbiota of 1413 individuals (57.5% female, median age 62.6 years). The alpha-diversity was significantly lower up to 4 years after prescriptions of macrolides and lincosamides. It was also lower in the first year after the use of beta-lactams. The community structure (beta-diversity) of the microbiota was significantly different up to 4 years for macrolides and lincosamides, the first year for beta-lactams and at least the first year for quinolones. For the F/B ratio, drugs with a high anaerobic activity shifted the ratio toward Firmicutes in the first year whereas other antimicrobial drugs shifted the ratio toward Bacteroidetes.

CONCLUSION

Use of antimicrobial drugs is associated with a shift in the composition of the gut microbiota.These effects differ in strength and duration, depending on the antimicrobial drug group used. These findings should be considered when prescribing antimicrobial drugs.

Biological Consequences of Psychological Distress in Caregivers of Children with Autism Spectrum Disorder and its Potential Relevance to Other Chronic Diseases Including Cancer

Purpose of Review

Caregivers of children with a chronic illness are a neglected group in medical research and patient care, and are frequently confronted with chronic psychological distress. The biological consequences of this chronic distress are unclear but highly relevant, as these caregivers have a lifelong task in caring for their child. In this review, the authors specifically describe caregiver distress related to autism spectrum disorder (ASD), but the review may be relevant to other chronic diseases, including cancer.

Recent Findings

Epidemiological evidence illustrates the increased mortality risk in caregivers of children with ASD although some individual factors appear to diminish these risks. Biological studies demonstrate that caregiver distress can lead to dysregulation of the hypothalamic-pituitary-adrenal-axis, a pro-inflammatory state of the immune and central nervous system, and gut microbiome imbalance.

Summary

Caregivers of children with a chronic illness like ASD deserve more health-related attention with respect to their psychological and physical well-being. Such attention would benefit individual caregivers, as well as their children, as both are highly interconnected. Structural psychological and physical screening of caregivers can be considered.

Dietary Patterns and Gut Microbiome Composition in a Large Population-Based Cohort

Objectives

Effects of diet on health and disease risk may be mediated by changes in gut microbiome composition. Our aim was to examine associations between intake of food groups and overall diet quality with gut microbiome composition in a large population-based cohort.

Methods

We analyzed data of 1130 participants (median age 57 years) from the Rotterdam Study, a population-based cohort study in the Netherlands. We measured dietary intake using a 389-item FFQ, and assessed adherence to dietary guidelines for 14 food groups and combined into a diet quality score. We assessed gut microbiome composition using 16S rRNA gene sequencing. Data were available for 11 phyla, 19 classes, 25 orders, 44 families, and 184 genera. Alpha diversity was quantified by Shannon index and Richness, and beta diversity was qualified by Bray-Curtis distance. We used linear models to examine associations with Shannon index and Richness, Adonis function to examine variations of Bray-Curtis distance, and Multivariate Association with Linear Models to examine associations with gut microbial communities. Models were adjusted for technical covariates, energy intake, age, sex, physical activity, education, smoking and BMI.

Results

After adjustment, higher diet quality was associated with more alpha diversity and explained part of the variation in beta diversity (P &lt; 0.001). Overall diet quality was associated with relative abundance of four families (Erysipelotrichaceae, Ruminococcaceae, Lachnospiraceae, Christensenellaceae), and 15 genera (Torques group, RuminococcaceaeUCG002, RuminococcaceaeUCG003, RuminococcaceaeUCG005, RuminococcaceaeUCG010, Xylanophilum group, Blautia, RuminococcaceaeNK4A214 group, Eligens group, Coprococcus3, Senegalimassilia, Lachnospira, Halliigroup, ChristensenellaceaeR7 group, Ventriosum group) (adjusted p: q &lt; 0.05). Results were not explained by any single food group. Higher intake of fruits, vegetables, whole grains, and tea, and lower intake of red meat and alcohol were all related to microbiome composition. Replication analyses are ongoing.

Conclusions

Better overall diet quality may improve overall gut microbial diversity. Furthermore, diet quality may influence abundance of certain gut microbial communities, several of which have previously been linked to lower risk of metabolic and inflammatory diseases.

Funding Sources

N/A.

Exome Sequencing Analysis Identifies Rare Variants in ATM and RPL8 That Are Associated With Shorter Telomere Length

Telomeres are important for maintaining genomic stability. Telomere length has been associated with aging, disease, and mortality and is highly heritable (∼82%). In this study, we aimed to identify rare genetic variants associated with telomere length using whole-exome sequence data. We studied 1,303 participants of the Erasmus Rucphen Family (ERF) study, 1,259 of the Rotterdam Study (RS), and 674 of the British Heart Foundation Family Heart Study (BHF-FHS). We conducted two analyses, first we analyzed the family-based ERF study and used the RS and BHF-FHS for replication. Second, we combined the summary data of the three studies in a meta-analysis. Telomere length was measured by quantitative polymerase chain reaction in blood. We identified nine rare variants significantly associated with telomere length (p-value < 1.42 × 10^–7, minor allele frequency of 0.2–0.5%) in the ERF study. Eight of these variants (in C11orf65, ACAT1, NPAT, ATM, KDELC2, and EXPH5) were located on chromosome 11q22.3 that contains ATM, a gene involved in telomere maintenance. Although we were unable to replicate the variants in the RS and BHF-FHS (p-value ≥ 0.21), segregation analysis showed that all variants segregate with shorter telomere length in a family. In the meta-analysis of all studies, a nominally significant association with LTL was observed with a rare variant in RPL8 (p-value = 1.48 × 10⁻⁶), which has previously been associated with age. Additionally, a novel rare variant in the known RTEL1 locus showed suggestive evidence for association (p-value = 1.18 × 10^–4) with LTL. To conclude, we identified novel rare variants associated with telomere length. Larger samples size are needed to confirm these findings and to identify additional variants.

Exercise with food withdrawal at thermoneutrality impacts fuel use, the microbiome, AMPK phosphorylation, muscle fibers, and thyroid hormone levels in rats

Exercise under fasting conditions induces a switch to lipid metabolism, eliciting beneficial metabolic effects. Knowledge of signaling responses underlying metabolic adjustments in such conditions may help to identify therapeutic strategies. Therefore, we studied the effect of mild exercise on rats submitted to food withdrawal at thermoneutrality (28°C) for 3 days. Animals were housed at thermoneutrality rather than the standard housing temperature (22°C) to avoid beta-adrenergic signaling responses that themselves affect metabolism and well-being. Quantitative analysis of multi-organ mRNA levels, myofibers, and serum metabolites shows that this protocol (a) boosts fat oxidation in muscle and liver, (b) reduces lipogenesis and increases gluconeogenesis in liver, (c) increases serum acylcarnitines (especially C4 OH) and ketone bodies and the use of the latter as fuel in muscle, (d) increases Type I myofibers, and (e) is associated with an increased thyroid hormone uptake and metabolism in muscle. In addition, stool microbiome DNA analysis revealed that food withdrawal dramatically alters the presence of bacterial genera associated with ketone metabolism. Taken together, this protocol induces a drastic switch toward increased lipid and ketone metabolism compared to exercise or food withdrawal alone, which may prove beneficial and may involve local thyroid hormones, which may be regarded as exercise mimetics.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Relationship between gut microbiota and circulating metabolites in population-based cohorts

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials

Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composition between these three epithelial materials provides challenges for their differentiation and identification when using previously proposed human cell biomarkers, while their microbiota composition largely differs. By using validated 16S rRNA gene massively parallel sequencing data from the Human Microbiome Project of 1636 skin, oral and vaginal samples, 50 taxonomy-independent deep learning networks were trained to classify these three tissues. Validation testing was performed in de-novo generated high-throughput 16S rRNA gene sequencing data using the Ion Torrent^™ Personal Genome Machine from 110 test samples: 56 hand skin, 31 saliva and 23 vaginal secretion specimens. Body-site classification accuracy of these test samples was very high as indicated by AUC values of 0.99 for skin, 0.99 for oral, and 1 for vaginal secretion. Misclassifications were limited to 3 (5%) skin samples. Additional forensic validation testing was performed in mock casework samples by de-novo high-throughput sequencing of 19 freshly-prepared samples and 22 samples aged for 1 up to 7.6 years. All of the 19 fresh and 20 (91%) of the 22 aged mock casework samples were correctly tissue-type classified. Moreover, comparing the microbiome results with outcomes from previous human mRNA-based tissue identification testing in the same 16 aged mock casework samples reveals that our microbiome approach performs better in 12 (75%), similarly in 2 (12.5%), and less good in 2 (12.5%) of the samples. Our results demonstrate that this new microbiome approach allows for accurate tissue-type classification of three human epithelial materials of skin, oral and vaginal origin, which is highly relevant for future forensic investigations.

Differences in Systemic IgA Reactivity and Circulating Th Subsets in Healthy Volunteers With Specific Microbiota Enterotypes

Changes in the intestinal microbiota have been associated with the development of immune-mediated diseases in humans. Additionally, the introduction of defined bacterial species into the mouse intestinal microbiota has been shown to impact on the adaptive immune response. However, how much impact the intestinal microbiota composition actually has on regulating adaptive immunity remains poorly understood. Therefore, we studied aspects of the adaptive immunity in healthy adults possessing distinct intestinal microbiota profiles. The intestinal microbiota composition was determined via Illumina sequencing of bacterial 16S rRNA genes extracted from the feces of 35 individuals. Blood B-cell and T-cell subsets from the same individuals were studied using flow cytometry. Finally, the binding of fecal and plasma Immunoglobulin A (IgA) to intestinal bacteria (associated with health and disease) Bacteroides fragilis, Prevotella copri, Bifidobacterium longum, Clostridium difficile, and Escherichia coli was analyzed using ELISA. Unsupervised clustering of microbiota composition revealed the presence of three clusters within the cohort. Cluster 1 and 2 were similar to previously-described enterotypes with a predominance of Bacteroides in Cluster 1 and Prevotella in Cluster 2. The bacterial diversity (Shannon index) and bacterial richness of Cluster 3 was significantly higher than observed in Clusters 1 and 2, with the Ruminococacceae tending to predominate. Within circulating B- and T-cell subsets, only Th subsets were significantly different between groups of distinct intestinal microbiota. Individuals of Cluster 3 have significantly fewer Th17 and Th22 circulating cells, while Th17.1 cell numbers were increased in individuals of Cluster 1. IgA reactivity to intestinal bacteria was higher in plasma than feces, and individuals of Cluster 1 had significant higher plasma IgA reactivity against B. longum than individuals of Cluster 2. In conclusion, we identified three distinct fecal microbiota clusters, of which two clusters resembled previously-described "enterotypes". Global T-cell and B-cell immunity seemed unaffected, however, circulating Th subsets and plasma IgA reactivity were significantly different between Clusters. Hence, the impact of intestinal bacteria composition on human B cells, T cells and IgA reactivity appears limited in genetically-diverse and environmentally-exposed humans, but can skew antibody reactivity and Th cell subsets.

The effect of antimicrobial drug use on the composition of the genitourinary microbiota in an elderly population

BACKGROUND

The urinary tract is inhabited by a diversity of microorganisms, known as the genitourinary microbiota. Here, we investigated the association between the use of antimicrobial drugs and the composition of the genitourinary microbiota.

RESULTS

Clean-catch urinary samples were collected from 27 participants of the Rotterdam Study. Bacterial DNA was extracted and the 16S ribosomal RNA gene variable regions V3 and V4 were analyzed using Illumina sequencing. 23 of the 27 participants were included in the analysis. The population consisted of 10 men and 13 women with a mean age of 75 ± 3 years. The time between the last prescription of an antimicrobial drug and sampling was determined and categorized. The use of antimicrobial drugs prior to urine sampling was associated with statistically significant differences in the beta-diversity of the genitourinary microbiota. No association was found between antimicrobial drug use and the alpha-diversity of the genitourinary microbiota. Operational Taxonomic Units (OTUs) that were lowest in participants who used antimicrobial drug belonged to Lactobacillus and Finegoldia. In contrast, an OTU belonging to the genus Parabacteroides had higher abundances. Also, an OTU belonging to the species E.coli was higher in the participants who used antimicrobial drugs.

CONCLUSION

Prior use of antimicrobial drugs is associated with a different composition of the genitourinary microbiota. Our results might indicate a persisting effect of antimicrobial drugs on the composition of the microbiota, but reverse causality cannot be ruled out. Future studies are needed to differentiate between two possibilities. Genitourinary dysbiosis could be the result of antimicrobial drug use or genitourinary dysbiosis could be a risk factor for urinary tract infections resulting in increased use of antimicrobial drugs. This may have important implications for treatment and prevention of (recurrent) UTIs.

Whole-Genome Linkage Scan Combined With Exome Sequencing Identifies Novel Candidate Genes for Carotid Intima-Media Thickness

Carotid intima-media thickness (cIMT) is an established heritable marker for subclinical atherosclerosis. In this study, we aim to identify rare variants with large effects driving differences in cIMT by performing genome-wide linkage analysis of individuals in the extremes of cIMT trait distribution (>90th percentile) in a large family-based study from a genetically isolated population in the Netherlands. Linked regions were subsequently explored by fine-mapping using exome sequencing. We observed significant evidence of linkage on chromosomes 2p16.3 [rs1017418, heterogeneity LOD (HLOD) = 3.35], 19q13.43 (rs3499, HLOD = 9.09), 20p13 (rs1434789, HLOD = 4.10), and 21q22.12 (rs2834949, HLOD = 3.59). Fine-mapping using exome sequencing data identified a non-coding variant (rs62165235) in PNPT1 gene under the linkage peak at chromosome 2 that is likely to have a regulatory function. The variant was associated with quantitative cIMT in the family-based study population (effect = 0.27, p-value = 0.013). Furthermore, we identified several genes under the linkage peak at chromosome 21 highly expressed in tissues relevant for atherosclerosis. To conclude, our linkage analysis identified four genomic regions significantly linked to cIMT. Further analyses are needed to demonstrate involvement of identified candidate genes in development of atherosclerosis.

Rare gene deletions in genetic generalized and Rolandic epilepsies

Genetic Generalized Epilepsy (GGE) and benign epilepsy with centro-temporal spikes or Rolandic Epilepsy (RE) are common forms of genetic epilepsies. Rare copy number variants have been recognized as important risk factors in brain disorders. We performed a systematic survey of rare deletions affecting protein-coding genes derived from exome data of patients with common forms of genetic epilepsies. We analysed exomes from 390 European patients (196 GGE and 194 RE) and 572 population controls to identify low-frequency genic deletions. We found that 75 (32 GGE and 43 RE) patients out of 390, i.e. ~19%, carried rare genic deletions. In particular, large deletions (>400 kb) represent a higher burden in both GGE and RE syndromes as compared to controls. The detected low-frequency deletions (1) share genes with brain-expressed exons that are under negative selection, (2) overlap with known autism and epilepsy-associated candidate genes, (3) are enriched for CNV intolerant genes recorded by the Exome Aggregation Consortium (ExAC) and (4) coincide with likely disruptive de novo mutations from the NPdenovo database. Employing several knowledge databases, we discuss the most prominent epilepsy candidate genes and their protein-protein networks for GGE and RE.

Observational multi-centre, prospective study to characterize novel pathogen-and host-related factors in hospitalized patients with lower respiratory tract infections and/or sepsis - the "TAILORED-Treatment" study

Background

The emergence and spread of antibiotic resistant micro-organisms is a global concern, which is largely attributable to inaccurate prescribing of antibiotics to patients presenting with non-bacterial infections. The use of ‘omics’ technologies for discovery of novel infection related biomarkers combined with novel treatment algorithms offers possibilities for rapidly distinguishing between bacterial and viral infections. This distinction can be particularly important for patients suffering from lower respiratory tract infections (LRTI) and/or sepsis as they represent a significant burden to healthcare systems. Here we present the study details of the TAILORED-Treatment study, an observational, prospective, multi-centre study aiming to generate a multi-parametric model, combining host and pathogen data, for distinguishing between bacterial and viral aetiologies in children and adults with LRTI and/or sepsis.

Methods

A total number of 1200 paediatric and adult patients aged 1 month and older with LRTI and/or sepsis or a non-infectious disease are recruited from Emergency Departments and hospital wards of seven Dutch and Israeli medical centres. A panel of three experienced physicians adjudicate a reference standard diagnosis for all patients (i.e., bacterial or viral infection) using all available clinical and laboratory information, including a 28-day follow-up assessment. Nasal swabs and blood samples are collected for multi-omics investigations including host RNA and protein biomarkers, nasal microbiota profiling, host genomic profiling and bacterial proteomics. Simplified data is entered into a custom-built database in order to develop a multi-parametric model and diagnostic tools for differentiating between bacterial and viral infections. The predictions from the model will be compared with the consensus diagnosis in order to determine its accuracy.

Discussion

The TAILORED-Treatment study will provide new insights into the interplay between the host and micro-organisms. New host- or pathogen-related biomarkers will be used to generate a multi-parametric model for distinguishing between bacterial and viral infections. This model will be helpful to better guide antimicrobial therapy for patients with LRTI and sepsis. This study has the potential to improve patient care, reduce unnecessary antibiotic prescribing and will contribute positively to institutional, national and international healthcare economics.

Trial Registration

NCT02025699. Registration Date: January, 1, 2014.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3300-9) contains supplementary material, which is available to authorized users.

Meta-analysis of human genome-microbiome association studies: the MiBioGen consortium initiative

BACKGROUND

In recent years, human microbiota, especially gut microbiota, have emerged as an important yet complex trait influencing human metabolism, immunology, and diseases. Many studies are investigating the forces underlying the observed variation, including the human genetic variants that shape human microbiota. Several preliminary genome-wide association studies (GWAS) have been completed, but more are necessary to achieve a fuller picture.

RESULTS

Here, we announce the MiBioGen consortium initiative, which has assembled 18 population-level cohorts and some 19,000 participants. Its aim is to generate new knowledge for the rapidly developing field of microbiota research. Each cohort has surveyed the gut microbiome via 16S rRNA sequencing and genotyped their participants with full-genome SNP arrays. We have standardized the analytical pipelines for both the microbiota phenotypes and genotypes, and all the data have been processed using identical approaches. Our analysis of microbiome composition shows that we can reduce the potential artifacts introduced by technical differences in generating microbiota data. We are now in the process of benchmarking the association tests and performing meta-analyses of genome-wide associations. All pipeline and summary statistics results will be shared using public data repositories.

CONCLUSION

We present the largest consortium to date devoted to microbiota-GWAS. We have adapted our analytical pipelines to suit multi-cohort analyses and expect to gain insight into host-microbiota cross-talk at the genome-wide level. And, as an open consortium, we invite more cohorts to join us (by contacting one of the corresponding authors) and to follow the analytical pipeline we have developed.

Whole-Exome Sequencing in Age-Related Macular Degeneration Identifies Rare Variants in COL8A1, a Component of Bruch's Membrane

PURPOSE

Genome-wide association studies and targeted sequencing studies of candidate genes have identified common and rare variants that are associated with age-related macular degeneration (AMD). Whole-exome sequencing (WES) studies allow a more comprehensive analysis of rare coding variants across all genes of the genome and will contribute to a better understanding of the underlying disease mechanisms. To date, the number of WES studies in AMD case-control cohorts remains scarce and sample sizes are limited. To scrutinize the role of rare protein-altering variants in AMD cause, we performed the largest WES study in AMD to date in a large European cohort consisting of 1125 AMD patients and 1361 control participants.

DESIGN

Genome-wide case-control association study of WES data.

PARTICIPANTS

One thousand one hundred twenty-five AMD patients and 1361 control participants.

METHODS

A single variant association test of WES data was performed to detect variants that are associated individually with AMD. The cumulative effect of multiple rare variants with 1 gene was analyzed using a gene-based CMC burden test. Immunohistochemistry was performed to determine the localization of the Col8a1 protein in mouse eyes.

MAIN OUTCOME MEASURES

Genetic variants associated with AMD.

RESULTS

We detected significantly more rare protein-altering variants in the COL8A1 gene in patients (22/2250 alleles [1.0%]) than in control participants (11/2722 alleles [0.4%]; P = 7.07×10^-5). The association of rare variants in the COL8A1 gene is independent of the common intergenic variant (rs140647181) near the COL8A1 gene previously associated with AMD. We demonstrated that the Col8a1 protein localizes at Bruch's membrane.

CONCLUSIONS

This study supported a role for protein-altering variants in the COL8A1 gene in AMD pathogenesis. We demonstrated the presence of Col8a1 in Bruch's membrane, further supporting the role of COL8A1 variants in AMD pathogenesis. Protein-altering variants in COL8A1 may alter the integrity of Bruch's membrane, contributing to the accumulation of drusen and the development of AMD.

Microbiome measurement: Possibilities and pitfalls

Microbiome research is an emerging field in medical sciences. Several studies have made headways in understanding the influence of microbes on our health and disease states. Further progress in mapping microbiome populations across different body sites and understanding the underlying mechanisms of microbiome-host interactions depends critically on study design, collection protocols, analytical genetic techniques, and reference databases. In particular, a shift has appeared going from small sample collections to large-scale population studies (with extensive phenotypic information including disease status) which calls for some adaptions. In this review we will focus on gut microbiome profiling using the 16S ribosomal RNA approach in the setting of large-scale population studies, and discuss some novel developments.

Excessive burden of lysosomal storage disorder gene variants in Parkinson's disease

Mutations in the glucocerebrosidase gene (GBA), which cause Gaucher disease, are also potent risk factors for Parkinson's disease. We examined whether a genetic burden of variants in other lysosomal storage disorder genes is more broadly associated with Parkinson's disease susceptibility. The sequence kernel association test was used to interrogate variant burden among 54 lysosomal storage disorder genes, leveraging whole exome sequencing data from 1156 Parkinson's disease cases and 1679 control subjects. We discovered a significant burden of rare, likely damaging lysosomal storage disorder gene variants in association with Parkinson's disease risk. The association signal was robust to the exclusion of GBA, and consistent results were obtained in two independent replication cohorts, including 436 cases and 169 controls with whole exome sequencing and an additional 6713 cases and 5964 controls with exome-wide genotyping. In secondary analyses designed to highlight the specific genes driving the aggregate signal, we confirmed associations at the GBA and SMPD1 loci and newly implicate CTSD, SLC17A5, and ASAH1 as candidate Parkinson's disease susceptibility genes. In our discovery cohort, the majority of Parkinson's disease cases (56%) have at least one putative damaging variant in a lysosomal storage disorder gene, and 21% carry multiple alleles. Our results highlight several promising new susceptibility loci and reinforce the importance of lysosomal mechanisms in Parkinson's disease pathogenesis. We suggest that multiple genetic hits may act in combination to degrade lysosomal function, enhancing Parkinson's disease susceptibility.

Exome-Wide Meta-Analysis Identifies Rare 3'-UTR Variant in ERCC1/CD3EAP Associated with Symptoms of Sleep Apnea

Obstructive sleep apnea (OSA) is a common sleep breathing disorder associated with an increased risk of cardiovascular and cerebrovascular diseases and mortality. Although OSA is fairly heritable (~40%), there have been only few studies looking into the genetics of OSA. In the present study, we aimed to identify genetic variants associated with symptoms of sleep apnea by performing a whole-exome sequence meta-analysis of symptoms of sleep apnea in 1,475 individuals of European descent. We identified 17 rare genetic variants with at least suggestive evidence of significance. Replication in an independent dataset confirmed the association of a rare genetic variant (rs2229918; minor allele frequency = 0.3%) with symptoms of sleep apnea (p-value_meta = 6.98 × 10⁻⁹, β_meta = 0.99). Rs2229918 overlaps with the 3′ untranslated regions of ERCC1 and CD3EAP genes on chromosome 19q13. Both genes are expressed in tissues in the neck area, such as the tongue, muscles, cartilage and the trachea. Further, CD3EAP is localized in the nucleus and mitochondria and involved in the tumor necrosis factor-alpha/nuclear factor kappa B signaling pathway. Our results and biological functions of CD3EAP/ERCC1 genes suggest that the 19q13 locus is interesting for further OSA research.

Population-specific genetic variation in large sequencing data sets: why more data is still better

We have generated a next-generation whole-exome sequencing data set of 2628 participants of the population-based Rotterdam Study cohort, comprising 669 737 single-nucleotide variants and 24 019 short insertions and deletions. Because of broad and deep longitudinal phenotyping of the Rotterdam Study, this data set permits extensive interpretation of genetic variants on a range of clinically relevant outcomes, and is accessible as a control data set. We show that next-generation sequencing data sets yield a large degree of population-specific variants, which are not captured by other available large sequencing efforts, being ExAC, ESP, 1000G, UK10K, GoNL and DECODE.

Nonsynonymous Variation in NKPD1 Increases Depressive Symptoms in European Populations

BACKGROUND

Despite high heritability, little success was achieved in mapping genetic determinants of depression-related traits by means of genome-wide association studies.

METHODS

To identify genes associated with depressive symptomology, we performed a gene-based association analysis of nonsynonymous variation captured using exome-sequencing and exome-chip genotyping in a genetically isolated population from the Netherlands (n = 1999). Finally, we reproduced our significant findings in an independent population-based cohort (n = 1604).

RESULTS

We detected significant association of depressive symptoms with a gene NKPD1 (p = 3.7 × 10^-08). Nonsynonymous variants in the gene explained 0.9% of sex- and age-adjusted variance of depressive symptoms in the discovery study, which is translated into 3.8% of the total estimated heritability (h² = 0.24). Significant association of depressive symptoms with NKPD1 was also observed (n = 1604; p = 1.5 × 10^-03) in the independent replication sample despite little overlap with the discovery cohort in the set of nonsynonymous genetic variants observed in the NKPD1 gene. Meta-analysis of the discovery and replication studies improved the association signal (p = 1.0 × 10^-09).

CONCLUSIONS

Our study suggests that nonsynonymous variation in the gene NKPD1 affects depressive symptoms in the general population. NKPD1 is predicted to be involved in the de novo synthesis of sphingolipids, which have been implicated in the pathogenesis of depression.

Exome-sequencing in a large population-based study reveals a rare Asn396Ser variant in the LIPG gene associated with depressive symptoms

Despite a substantial genetic component, efforts to identify common genetic variation underlying depression have largely been unsuccessful. In the current study we aimed to identify rare genetic variants that might have large effects on depression in the general population. Using high-coverage exome-sequencing, we studied the exonic variants in 1265 individuals from the Rotterdam study (RS), who were assessed for depressive symptoms. We identified a missense Asn396Ser mutation (rs77960347) in the endothelial lipase (LIPG) gene, occurring with an allele frequency of 1% in the general population, which was significantly associated with depressive symptoms (P-value=5.2 × 10^-08, β=7.2). Replication in three independent data sets (N=3612) confirmed the association of Asn396Ser (P-value=7.1 × 10^-03, β=2.55) with depressive symptoms. LIPG is predicted to have enzymatic function in steroid biosynthesis, cholesterol biosynthesis and thyroid hormone metabolic processes. The Asn396Ser variant is predicted to have a damaging effect on the function of LIPG. Within the discovery population, carriers also showed an increased burden of white matter lesions (P-value=3.3 × 10^-02) and a higher risk of Alzheimer's disease (odds ratio=2.01; P-value=2.8 × 10^-02) compared with the non-carriers. Together, these findings implicate the Asn396Ser variant of LIPG in the pathogenesis of depressive symptoms in the general population.

Quantifying prion disease penetrance using large population control cohorts

More than 100,000 genetic variants are reported to cause Mendelian disease in humans, but the penetrance-the probability that a carrier of the purported disease-causing genotype will indeed develop the disease-is generally unknown. We assess the impact of variants in the prion protein gene (PRNP) on the risk of prion disease by analyzing 16,025 prion disease cases, 60,706 population control exomes, and 531,575 individuals genotyped by 23andMe Inc. We show that missense variants in PRNP previously reported to be pathogenic are at least 30 times more common in the population than expected on the basis of genetic prion disease prevalence. Although some of this excess can be attributed to benign variants falsely assigned as pathogenic, other variants have genuine effects on disease susceptibility but confer lifetime risks ranging from <0.1 to ~100%. We also show that truncating variants in PRNP have position-dependent effects, with true loss-of-function alleles found in healthy older individuals, a finding that supports the safety of therapeutic suppression of prion protein expression.