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 < 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 < 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.