The role of immuno-metabolic depression features in the effects of light therapy in patients with depression and type 2 diabetes mellitus: A randomized controlled trial

OBJECTIVE

Immuno-metabolic depression (IMD) is proposed to be a form of depression encompassing atypical, energy-related symptoms (AES), low-grade inflammation and metabolic dysregulations. Light therapy may alleviate AES by modulating inflammatory and metabolic pathways. We investigated whether light therapy improves clinical and biological IMD features and whether effects of light therapy on AES or depressive symptom severity are moderated by baseline IMD features. Associations between changes in symptoms and biomarkers were explored.

METHODS

In secondary analyses, clinical trial data was used from 77 individuals with depression and type 2 diabetes mellitus (T2DM) randomized to four weeks of light therapy or placebo. AES severity and depressive symptom severity were based on the Inventory of Depressive Symptomatology. Biomarkers included 73 metabolites (Nightingale) summarized in three principal components and CRP, IL-6, TNF-α, INF-γ. Linear regression analyses were performed.

RESULTS

Light therapy had no effect on AES severity, inflammatory markers and metabolite principle components versus placebo. None of these baseline features moderated the effects of light therapy on AES severity. Only a principle component reflecting metabolites implicated in glucose homeostasis moderated the effects of light therapy on depressive symptom severity (βinteraction = 0.65, P = 0.001, FDR = 0.003). Changes in AES were not associated with changes in biomarkers.

CONCLUSION

Findings do not support the efficacy of light therapy in reducing IMD features in patients with depression and T2DM. We find limited evidence that light therapy is a more beneficial depression treatment among those with more IMD features. Changes in clinical and biological IMD features did not align over four-weeks' time.

TRIAL REGISTRATION

The Netherlands Trial Register (NTR) NTR4942.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials

BACKGROUND

Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

AIMS

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

METHOD

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

RESULTS

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

CONCLUSIONS

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Acylcarnitines metabolism in depression: association with diagnostic status, depression severity and symptom profile in the NESDA cohort

Background

Acylcarnitines (ACs) are involved in bioenergetics processes that may play a role in the pathophysiology of depression. Studies linking AC levels to depression are few and provide mixed findings. We examined the association of circulating ACs levels with Major Depressive Disorder (MDD) diagnosis, overall depression severity and specific symptom profiles.

Methods

The sample from the Netherlands Study of Depression and Anxiety included participants with current (n=1035) or remitted (n=739) MDD and healthy controls (n=800). Plasma levels of four ACs (short-chain: acetylcarnitine C2 and propionylcarnitine C3; medium-chain: octanoylcarnitine C8 and decanoylcarnitine C10) were measured. Overall depression severity as well as atypical/energy-related (AES), anhedonic and melancholic symptom profiles were derived from the Inventory of Depressive Symptomatology.

Results

As compared to healthy controls, subjects with current or remitted MDD presented similarly lower mean C2 levels (Cohen's d=0.2, p≤1e-4). Higher overall depression severity was significantly associated with higher C3 levels (ß=0.06, SE=0.02, p=1.21e-3). No associations were found for C8 and C10. Focusing on symptom profiles, only higher AES scores were linked to lower C2 (ß=-0.05, SE=0.02, p=1.85e-2) and higher C3 (ß=0.08, SE=0.02, p=3.41e-5) levels. Results were confirmed in analyses pooling data with an additional internal replication sample from the same subjects measured at 6-year follow-up (totaling 4195 observations).

Conclusions

Small alterations in levels of short-chain acylcarnitine levels were related to the presence and severity of depression, especially for symptoms reflecting altered energy homeostasis. Cellular metabolic dysfunctions may represent a key pathway in depression pathophysiology potentially accessible through AC metabolism.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up

BACKGROUND

The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level.

METHODS

Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82).

RESULTS

Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%).

CONCLUSIONS

This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.

The Metabolome-Wide Signature of Major Depressive Disorder

Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

Background

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remain underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

Methods

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N=651) and the Netherlands Twin Register (NTR) (N=665). Follow-up comprised four BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated using latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. The sources of genetic and environmental variation underlying the protein abundances were quantified using twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) using mixed-effect models and correlation networks.

Results

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 6 and 4 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with many metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

Conclusions

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

Managing the Risk of COVID-19 Using Model Based Predictors: The Case of North Dakota

OBJECTIVE

North Dakota (ND) had the highest coronavirus disease 2019 (COVID-19) case and mortality rate in the United States for nearly 2 mo. This study aims to compare 3 metrics ND used to guide public health action across its 53 counties.

METHODS

Daily COVID-19 case and death totals in North Dakota were evaluated using data from the COVID-tracker website provided by the North Department of Health (NDDoH). It was reported as: active cases per 10,000, tests administered per 10,000, and test positivity rate (the North Dakota health metric). The COVID-19 Response press conferences provided data for the Governor's metric. The Harvard model used daily new cases per 100,000. A chi-squared test was used to compare differences in these 3 metrics on July 1, August 26, September 23, and November 13, 2020.

RESULTS

On July 1, no significant difference between the metrics was found. By September 23, Harvard's health metric indicated critical risk while ND's health metric was moderate risk, and the Governor's metric was still low risk.

CONCLUSIONS

ND's and the Governor's metric underrepresented the risk of the COVID-19 outbreak in North Dakota. The Harvard metric reflected North Dakota's increasing risk; it should be considered as a national standard in future pandemics.

PUBLIC HEALTH IMPLICATIONS

Model-based predictors could guide policy-makers to effectively control spread of infectious disease; proactive models could reduce risk of disease as it progresses in vulnerable communities.

Metabolomics signatures of depression: the role of symptom profiles

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an "atypical/energy-related" symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An 'AES profile' score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10^-12), isoleucine (β = 0.13, p = 1.45*10^-10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10^-9) and saturated fatty acid levels (β = 0.09, p = 3.68*10^-10), and lower high-density lipoproteins cholesterol (β = -0.07, p = 1.14*10^-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature-commonly linked to cardiometabolic disorders-associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression's detrimental impact on health.

Low-noise φ-OTDR employing nonlinear optical preamplification for distributed acoustic sensing

A phase sensitive optical time domain reflectometry sensing scheme employing a two-stage nonlinear optical preamplification system is proposed to reduce the blind spot effect in Rayleigh scattering by improving the optical power distribution and to enhance the resolution at locations of low-backscatter intensity measurements, providing a higher signal-to-noise ratio for distributed acoustic measurements; the developed system has been tested in-laboratory and on in-field monitoring of a survey well in Cottessen, The Netherlands. The characterization shows strain noise levels below 1 nɛ for a 10 kHz sampling rate.

Dissection of depression heterogeneity using proteomic clusters

BACKGROUND

The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms.

METHODS

The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA).

RESULTS

Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster.

CONCLUSIONS

Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Genome-wide analysis identifies genetic effects on reproductive success and ongoing natural selection at the FADS locus

Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success.

High variation across E. coli hybrid isolates identified in metabolism-related biological pathways co-expressed with virulent genes

Virulent genes present in Escherichia coli (E. coli) can cause significant human diseases. These enteropathogenic E. coli (EPEC) and enterotoxigenic E. coli (ETEC) isolates with virulent genes show different expression levels when grown under diverse laboratory conditions. In this research, we have performed differential gene expression analysis using publicly available RNA-seq data on three pathogenic E. coli hybrid isolates in an attempt to characterize the variation in gene interactions that are altered by the presence or absence of virulent factors within the genome. Almost 26.7% of the common genes across these strains were found to be differentially expressed. Out of the 88 differentially expressed genes with virulent factors identified from PATRIC, nine were common in all these strains. A combination of Weighted Gene Co-Expression Network Analysis and Gene Ontology Enrichment Analysis reveals significant differences in gene co-expression involving virulent genes common among the three investigated strains. The co-expression pattern is observed to be especially variable among biological pathways involving metabolism-related genes. This suggests a potential difference in resource allocation or energy generation across the three isolates based on genomic variation.

Abstract B005: Polycyclic aromatic hydrocarbons and pancreatic cancer: An analysis of blood biomarker PheT as an exposure identifier

Exposure to polycyclic aromatic hydrocarbons (PAHs), a byproduct of incomplete combustion, and its effects on the development of cancer needs further evaluation. Recent studies have analyzed the relationship between PAHs and tobacco or dietary intake in the form of processed foods and smoked/well-done meats. This study aims to assess the association of a blood biomarker and metabolite of PAHs, r-1-,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene (PheT), selected metabolism SNPs, and pancreatic cancer. Demographics, food-frequency data, SNPs, treatment history and levels of PheT in plasma were collected from 400 participants (202 cases and 198 control) and evaluated based on pancreatic adenocarcinoma diagnosis. A two-stage analysis was completed to identify 1) SNPs that were significantly associated with pancreatic cancer and 2) Dietary intake and PheT variables significantly associated with pancreatic cancer. A univariate analysis for variables with OR≠1 and p-value < 0.05 suggests significant relationship with pancreatic cancer and were carried forward to a multiple regression model for either food items or SNPs. Backward stepwise selection was used to eliminate non-significant variables. A collection of 5 SNPs were observed to form several of the most significant (adjusted p-value <0.05) interaction pairs across different SNP models. These were rs566979 (gene: CAT), rs28540420 (gene: PTGER4), rs12029406 (gene: none), rs730368 (gene: PTGER4), and rs3181077 (gene: CCR1). A final multiple regression model combined the significant food items and SNPs. Final significant factors associated with pancreatic cancer after backward selection included: Type 2 Diabetes [7.66 (3.46, 17.76)], corrected percent recovered version of the PAH (cPheT) [1.03 (1.02, 1.05)], very well done red meat [0.90 (0.84, 0.96)], recessive(rs12203582) [3.58 (1.50, 8.83)], recessive(rs56679) [0.25 (0.06, 0.80)], overdominant(rs3784605) [3.42 (1.81, 6.66)], overdominant(rs721430) [0.42 (0.21, 0.83)]. Of note by design, the level of smoking did not differ between our cases and controls. This study provides evidence that a metabolite PheT could be a biomarker of pancreatic cancer independent of dietary intake, smoking, and select metabolism SNPs.

Citation Format: Sierra Nguyen, Heather Carlson, Andrea Yoder, William R. Bamlet, Ann L. Oberg, Gloria M. Peterson, Steven Hecht, Rick Jansen. Polycyclic aromatic hydrocarbons and pancreatic cancer: An analysis of blood biomarker PheT as an exposure identifier [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B005.

Impact of long-term exposure to PM2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response

BACKGROUND

Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts.

METHODS

In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO₂, NO_x, particulate matter (PM_2.5, PM_2.5abs, PM₁₀, PM_coarse)_, and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID.

RESULTS

In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM_2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM_2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors.

CONCLUSION

Our results indicate that PM_2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Identifying the Common Genetic Basis of Antidepressant Response

Background

Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.

Methods

Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.

Results

Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.

Conclusions

This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues

Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.

Identification of Early-Onset Metastasis in SF3B1 Mutated Uveal Melanoma

Simple Summary

This study describes clinical and genetic characteristics of the largest aggregated cohort of Splicing Factor 3 Subunit B1 (SF3B1)-mutated Uveal Melanoma (UM) in the literature (n = 146). Missense mutations in the spliceosome gene SF3B1 result in an altered splice site recognition and aberrant mRNA transcripts. The SF3B1-mutated UM show early- and late-onset of metastatic disease for which, currently, no distinguishing biomarkers exist. Using a cutoff of 60 months for stratification, we found that a largest basal tumor diameter was more prevalent in the early-onset metastatic disease group. Furthermore, using differential gene expression and the detection of aberrant transcripts, we found that the expression of alpha/beta-Hydrolase domain containing 6 (ABHD6) is associated with early-onset metastatic SF3B1 and aberrant transcripts that are associated with early-onset SF3B1-mutated UM. Our results provide more accurate prognostication and targets for future functional studies in an effort to elucidate pathogenesis of SF3B1-mutated UM.

Abstract

Approximately 25% of all uveal melanoma (UM) contain driver mutations in the gene encoding the spliceosome factor SF3B1, and whilst patients with such SF3B1 mutations generally have an intermediate risk on developing metastatic disease, a third of these patients develop early metastasis within 5 years after diagnosis. We therefore investigated whether clinical and/or genetic variables could be indicative of short progression-free survival (PFS < 60 months) or long PFS (PFS ≥ 60 months) for SF3B1-mutated (SF3B1^mut) UM patients. We collected 146 SF3B1^mut UM from our Rotterdam Ocular Melanoma Studygroup (ROMS) database and external published datasets. After stratification of all SF3B1^mut UM using short PFS vs. long PFS, only largest tumor diameter (LTD) was significantly larger (mean: 17.7 mm (±2.8 SD) in the short PFS SF3B1^mut group vs. the long PFS group (mean: 14.7 (±3.7 SD, p = 0.001). Combined ROMS and The Cancer Genome Atlas (TCGA) transcriptomic data were evaluated, and we identified SF3B1^mut-specific canonical transcripts (e.g., a low expression of ABHD6 indicative for early-onset metastatic disease) or distinct expression of SF3B1^mut UM aberrant transcripts, indicative of early- or late-onset or no metastatic SF3B1^mut UM.

A highly virulent variant of HIV-1 circulating in the Netherlands

We discovered a highly virulent variant of subtype-B HIV-1 in the Netherlands. One hundred nine individuals with this variant had a 0.54 to 0.74 log₁₀ increase (i.e., a ~3.5-fold to 5.5-fold increase) in viral load compared with, and exhibited CD4 cell decline twice as fast as, 6604 individuals with other subtype-B strains. Without treatment, advanced HIV-CD4 cell counts below 350 cells per cubic millimeter, with long-term clinical consequences-is expected to be reached, on average, 9 months after diagnosis for individuals in their thirties with this variant. Age, sex, suspected mode of transmission, and place of birth for the aforementioned 109 individuals were typical for HIV-positive people in the Netherlands, which suggests that the increased virulence is attributable to the viral strain. Genetic sequence analysis suggests that this variant arose in the 1990s from de novo mutation, not recombination, with increased transmissibility and an unfamiliar molecular mechanism of virulence.

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening

Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from malignant and nonmalignant cells, where EV contents are evaluated as a collective attribute rather than single factors. Circular dichroism, Fourier transform infrared spectroscopy, fluorescence staining assays, and a de novo workflow combining proteomics, bioinformatics, and protein folding simulations were employed to validate the collective attribute at both cellular and EV levels. Based on the BR of the tumorous EVs, we integrated immunoprecipitation and fluorescence labeling targeting the circulating tumor-derived EVs in serum and developed the process into a clinical assay, named EvIPThT. The assay can distinguish patients with and without malignant disease in a pilot cohort, with weak correlations to prognosis biomarkers, suggesting the potential for a cancer screening panel with existing prognostic biomarkers to improve overall performance.

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

Metabolomic profiles discriminating anxiety from depression

OBJECTIVE

Depression has been associated with metabolomic alterations. Depressive and anxiety disorders are often comorbid diagnoses and are suggested to share etiology. We investigated whether differential metabolomic alterations are present between anxiety and depressive disorders and which clinical characteristics of these disorders are related to metabolomic alterations.

METHODS

Data were from the Netherlands Study of Depression and Anxiety (NESDA), including individuals with current comorbid anxiety and depressive disorders (N = 531), only a current depression (N = 304), only a current anxiety disorder (N = 548), remitted depressive and/or anxiety disorders (N = 897), and healthy controls (N = 634). Forty metabolites from a proton nuclear magnetic resonance lipid-based metabolomics panel were analyzed. First, we examined differences in metabolites between disorder groups and healthy controls. Next, we assessed whether depression or anxiety clinical characteristics (severity and symptom duration) were associated with metabolites.

RESULTS

As compared to healthy controls, seven metabolomic alterations were found in the group with only depression, reflecting an inflammatory (glycoprotein acetyls; Cohen's d = 0.12, p = 0.002) and atherogenic-lipoprotein-related (e.g., apolipoprotein B: Cohen's d = 0.08, p = 0.03, and VLDL cholesterol: Cohen's d = 0.08, p = 0.04) profile. The comorbid group showed an attenuated but similar pattern of deviations. No metabolomic alterations were found in the group with only anxiety disorders. The majority of metabolites associated with depression diagnosis were also associated with depression severity; no associations were found with anxiety severity or disease duration.

CONCLUSION

While substantial clinical overlap exists between depressive and anxiety disorders, this study suggests that altered inflammatory and atherogenic-lipoprotein-related metabolomic profiles are uniquely associated with depression rather than anxiety disorders.

WeFaceNano: a user-friendly pipeline for complete ONT sequence assembly and detection of antibiotic resistance in multi-plasmid bacterial isolates

Background

Bacterial plasmids often carry antibiotic resistance genes and are a significant factor in the spread of antibiotic resistance. The ability to completely assemble plasmid sequences would facilitate the localization of antibiotic resistance genes, the identification of genes that promote plasmid transmission and the accurate tracking of plasmid mobility. However, the complete assembly of plasmid sequences using the currently most widely used sequencing platform (Illumina-based sequencing) is restricted due to the generation of short sequence lengths. The long-read Oxford Nanopore Technologies (ONT) sequencing platform overcomes this limitation. Still, the assembly of plasmid sequence data remains challenging due to software incompatibility with long-reads and the error rate generated using ONT sequencing. Bioinformatics pipelines have been developed for ONT-generated sequencing but require computational skills that frequently are beyond the abilities of scientific researchers. To overcome this challenge, the authors developed ‘WeFaceNano’, a user-friendly Web interFace for rapid assembly and analysis of plasmid DNA sequences generated using the ONT platform. WeFaceNano includes: a read statistics report; two assemblers (Miniasm and Flye); BLAST searching; the detection of antibiotic resistance- and replicon genes and several plasmid visualizations. A user-friendly interface displays the main features of WeFaceNano and gives access to the analysis tools.

Results

Publicly available ONT sequence data of 21 plasmids were used to validate WeFaceNano, with plasmid assemblages and anti-microbial resistance gene detection being concordant with the published results. Interestingly, the “Flye” assembler with “meta” settings generated the most complete plasmids.

Conclusions

WeFaceNano is a user-friendly open-source software pipeline suitable for accurate plasmid assembly and the detection of anti-microbial resistance genes in (clinical) samples where multiple plasmids can be present.

DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10-7; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.

Effects of dietary interventions on depressive symptom profiles: results from the MooDFOOD depression prevention study

BACKGROUND

Dietary interventions did not prevent depression onset nor reduced depressive symptoms in a large multi-center randomized controlled depression prevention study (MooDFOOD) involving overweight adults with subsyndromal depressive symptoms. We conducted follow-up analyses to investigate whether dietary interventions differ in their effects on depressive symptom profiles (mood/cognition; somatic; atypical, energy-related).

METHODS

Baseline, 3-, 6-, and 12-month follow-up data from MooDFOOD were used (n = 933). Participants received (1) placebo supplements, (2) food-related behavioral activation (F-BA) therapy with placebo supplements, (3) multi-nutrient supplements (omega-3 fatty acids and a multi-vitamin), or (4) F-BA therapy with multi-nutrient supplements. Depressive symptom profiles were based on the Inventory of Depressive Symptomatology.

RESULTS

F-BA therapy was significantly associated with decreased severity of the somatic (B = -0.03, p = 0.014, d = -0.10) and energy-related (B = -0.08, p = 0.001, d = -0.13), but not with the mood/cognition symptom profile, whereas multi-nutrient supplementation was significantly associated with increased severity of the mood/cognition (B = 0.05, p = 0.022, d = 0.09) and the energy-related (B = 0.07, p = 0.002, d = 0.12) but not with the somatic symptom profile.

CONCLUSIONS

Differentiating depressive symptom profiles indicated that food-related behavioral interventions are most beneficial to alleviate somatic symptoms and symptoms of the atypical, energy-related profile linked to an immuno-metabolic form of depression, although effect sizes were small. Multi-nutrient supplements are not indicated to reduce depressive symptom profiles. These findings show that attention to clinical heterogeneity in depression is of importance when studying dietary interventions.

Waveguide-based absorption measurement system for visible wavelength applications

We present a miniaturized waveguide-based absorption measurement system operating at a wavelength of 635 nm, based on a silicon nitride integrated photonic platform, suitable for lab-on-chip applications. We experimentally demonstrate a high correlation between the bulk dye concentration and the measured absorption loss levels in the waveguides. We explain a photonic design process for choosing the ideal waveguide to minimize the coefficient of variation on the analyte concentration. The approach is designed for camera readout, allowing multiple readouts and easy integration for lab-on chip cartridge approach.

Germline and Somatic Genetic Variants in the p53 Pathway Interact to Affect Cancer Risk, Progression, and Drug Response

Insights into oncogenesis derived from cancer susceptibility loci (SNP) hold the potential to facilitate better cancer management and treatment through precision oncology. However, therapeutic insights have thus far been limited by our current lack of understanding regarding both interactions of these loci with somatic cancer driver mutations and their influence on tumorigenesis. For example, although both germline and somatic genetic variation to the p53 tumor suppressor pathway are known to promote tumorigenesis, little is known about the extent to which such variants cooperate to alter pathway activity. Here we hypothesize that cancer risk-associated germline variants interact with somatic TP53 mutational status to modify cancer risk, progression, and response to therapy. Focusing on a cancer risk SNP (rs78378222) with a well-documented ability to directly influence p53 activity as well as integration of germline datasets relating to cancer susceptibility with tumor data capturing somatically-acquired genetic variation provided supportive evidence for this hypothesis. Integration of germline and somatic genetic data enabled identification of a novel entry point for therapeutic manipulation of p53 activities. A cluster of cancer risk SNPs resulted in increased expression of prosurvival p53 target gene KITLG and attenuation of p53-mediated responses to genotoxic therapies, which were reversed by pharmacologic inhibition of the prosurvival c-KIT signal. Together, our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel combinatorial therapies. SIGNIFICANCE: These results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and present novel therapeutic targets.

An integrative study of five biological clocks in somatic and mental health

Biological clocks have been developed at different molecular levels and were found to be more advanced in the presence of somatic illness and mental disorders. However, it is unclear whether different biological clocks reflect similar aging processes and determinants. In ~3000 subjects, we examined whether five biological clocks (telomere length, epigenetic, transcriptomic, proteomic, and metabolomic clocks) were interrelated and associated to somatic and mental health determinants. Correlations between biological aging indicators were small (all r < 0.2), indicating little overlap. The most consistent associations of advanced biological aging were found for male sex, higher body mass index (BMI), metabolic syndrome, smoking, and depression. As compared to the individual clocks, a composite index of all five clocks showed most pronounced associations with health determinants. The large effect sizes of the composite index and the low correlation between biological aging indicators suggest that one's biological age is best reflected by combining aging measures from multiple cellular levels.

Associations between depressive symptom profiles and immunometabolic characteristics in individuals with depression and their siblings

OBJECTIVES

The present study examined associations between immunometabolic characteristics (IMCs) and depressive symptom profiles (DSPs) in probands with lifetime diagnoses of depression and/or anxiety disorders and their siblings.

METHODS

Data were from the Netherlands Study of Depression and Anxiety, comprising 256 probands with lifetime diagnoses of depression and/or anxiety and their 380 siblings. Measured IMCs included blood pressure, waist circumference, and levels of glucose, triglycerides, HDL cholesterol, CRP, TNF-α and IL-6. DSPs included mood, cognitive, somatic and atypical-like profiles. We cross-sectionally examined whether DSPs were associated with IMCs within probands and within siblings, and whether DSPs were associated with IMCs between probands and siblings.

RESULTS

Within probands and within siblings, higher BMI and waist circumference were associated with higher somatic and atypical-like profiles. Other IMCs (IL-6, glucose and HDL cholesterol) were significantly related to DSPs either within probands or within siblings. DSPs and IMCs were not associated between probands and siblings.

CONCLUSIONS

The results suggest that there is a familial component for each trait, but no common familial factors for the association between DSPs and IMCs. Alternative mechanisms, such as direct causal effects or non-shared environmental risk factors, may better fit these results.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

BACKGROUND

DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data.

RESULTS

By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation.

CONCLUSION

We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.

A methylation study of long-term depression risk

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10^-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

BACKGROUND

A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies.

RESULTS

We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n = ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes.

CONCLUSIONS

Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

High Treatment Uptake in Human Immunodeficiency Virus/Hepatitis C Virus-Coinfected Patients After Unrestricted Access to Direct-Acting Antivirals in the Netherlands

Background

The Netherlands has provided unrestricted access to direct-acting antivirals (DAAs) since November 2015. We analyzed the nationwide hepatitis C virus (HCV) treatment uptake among patients coinfected with human immunodeficiency virus (HIV) and HCV.

Methods

Data were obtained from the ATHENA HIV observational cohort in which >98% of HIV-infected patients ever registered since 1998 are included. Patients were included if they ever had 1 positive HCV RNA result, did not have spontaneous clearance, and were known to still be in care. Treatment uptake and outcome were assessed. When patients were treated more than once, data were included from only the most recent treatment episode. Data were updated until February 2017. In addition, each treatment center was queried in April 2017 for a data update on DAA treatment and achieved sustained virological response.

Results

Of 23574 HIV-infected patients ever linked to care, 1471 HCV-coinfected patients (69% men who have sex with men, 15% persons who [formerly] injected drugs, and 15% with another HIV transmission route) fulfilled the inclusion criteria. Of these, 87% (1284 of 1471) had ever initiated HCV treatment between 2000 and 2017, 76% (1124 of 1471) had their HCV infection cured; DAA treatment results were pending in 6% (92 of 1471). Among men who have sex with men, 83% (844 of 1022) had their HCV infection cured, and DAA treatment results were pending in 6% (66 of 1022). Overall, 187 patients had never initiated treatment, DAAs had failed in 14, and a pegylated interferon-alfa-based regimen had failed in 54.

Conclusions

Fifteen months after unrestricted DAA availability the majority of HIV/HCV-coinfected patients in the Netherlands have their HCV infection cured (76%) or are awaiting DAA treatment results (6%). This rapid treatment scale-up may contribute to future HCV elimination among these patients.

Disparities in Secondary Prevention of Atherosclerotic Heart Disease After Coronary Artery Bypass Grafting in Northern Plains American Indians

Introduction: Cardiovascular disease has become the leading cause of death in American Indians (AIs). For patients with severe disease requiring coronary artery bypass grafting (CABG), AIs have been demonstrated to present with increased risk factors. Guideline-directed medical therapy after CABG effectively reduces mortality and recurrent ischemic events in all patients and is especially important in high-risk populations such as AIs. Methods: Isolated CABG patients between 2012 and 2017 were studied and 74 AI patients were identified. Propensity matching was performed and the resulting 148 patients were followed for a year after surgery. Guideline-directed medical therapy (GDMT) for secondary prevention of atherosclerotic disease after CABG was detailed in all patients. Results: GDMT was similar between groups (85% AI vs. 89% non-AI; p=NS), and the incidence of prescribed antiplatelet medications, beta-blockers, and statins was similar. AIs were more likely to receive insulin therapy (p=0.002) and opioids (p=0.03) at discharge, while non-AIs were more likely to receive anti-arrhythmic medications (p=0.002). One year after discharge, GDMT trended lower in AIs (75% AI vs. 85% non-AI; p=0.2) and AIs were less likely to be on a statin 1 year after surgery (81% AI vs. 93% non-AI; p=0.04). Opioid use trended higher after 1 year in AIs (28% AI vs. 18% non-AI; p=NS) and fewer AI patients participated in cardiac rehabilitation (CR) after CABG. Conclusions: Disparities in GDMT for secondary prevention of coronary artery disease after CABG exist, with fewer AI patients receiving statins and undergoing CR 1 year after surgery. Increased use of opioids in AIs is troubling and deserves further investigation. Improved adherence to GDMT would be expected to improve long-term outcomes after CABG in this high risk population.

A characterization of cis- and trans-heritability of RNA-Seq-based gene expression

Insights into individual differences in gene expression and its heritability (h²) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h²_total, composed of cis-heritability (h²_cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h²_res, the residual variance explained by all other genome-wide variants). Mean h²_total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h² = 0.14, p = 6.15 × 10^-258). Mean h²_cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p < 10^-308) and with estimates from earlier RNA-Seq-based studies. Mean h²_res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p < 1.89 × 10^-3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10^-15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h² estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Involvement of inflammatory gene expression pathways in depressed patients with hyperphagia

The pathophysiology of major depressive disorder (MDD) is highly heterogeneous. Previous evidence at the DNA level as well as on the serum protein level suggests that the role of inflammation in MDD pathology is stronger in patients with hyperphagia during an active episode. Which inflammatory pathways differ in MDD patients with hyperphagia inflammatory pathways in terms of gene expression is unknown. We analyzed whole-blood gene expression profiles of 881 current MDD cases and 331 controls from the Netherlands Study of Depression and Anxiety (NESDA). The MDD patients were stratified according to patients with hyperphagia (characterized by increased appetite and/or weight, N = 246) or hypophagia (characterized by decreased appetite and/or weight, N = 342). Using results of differential gene expression analysis between controls and the MDD subgroups, enrichment of curated inflammatory pathways was estimated. The majority of the pathways were significantly (FDR < 0.1) enriched in the expression profiles of MDD cases with hyperphagia, including top pathways related to factors responsible for the onset of inflammatory response ('caspase', 'GATA3', 'NFAT', and 'inflammasomes' pathways). Only two pathways ('adaptive immune system' and 'IL-8- and CXCR2-mediated signaling') were enriched in the MDD with hypophagia subgroup, these were also enriched in the total current MDD group and the group with hyperphagia. This confirms the importance of inflammation in MDD pathology of patients with hyperphagia, and suggests that distinguishing more uniform MDD phenotypes can help in finding their pathophysiological basis.

Global longitudinal strain to predict left ventricular dysfunction in asymptomatic patients with severe mitral valve regurgitation: literature review

The optimal treatment strategy for asymptomatic patients with severe mitral valve regurgitation (MR) and preserved left ventricular (LV) function is challenging. This manuscript reviews the available literature on the value of left ventricular global longitudinal strain (LV-GLS) in predicting LV dysfunction after mitral valve surgery in these patients and discusses its current place in the treatment strategy. Studies were identified from Cochrane Library, SCOPUS, PubMed and Web of Science up to February 2018. The domain used was MR. The determinant was LV-GLS; other methods of deformation imaging were excluded. The examined outcome was LV dysfunction after surgery. A total of 144 articles were retrieved, of which 11 publications met the inclusion criteria, including a total of 2415 patients. Ten studies showed a significant correlation between preoperative LV-GLS and LV dysfunction postoperatively; one study reported a negative correlation. These studies suggest that LV-GLS is a predictor of LV dysfunction after surgery in asymptomatic patients with chronic MR. Hence, incorporation of LV-GLS for clinical decision-making in these patients might be of additional value. Further research is needed to confirm the role of LV-GLS in postoperative patients, and additionally in asymptomatic MR patients during a ‘watchful waiting’ strategy.

RNA-Seq in 296 phased trios provides a high-resolution map of genomic imprinting

Background

Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression.

Results

We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting.

Conclusions

Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0674-0) contains supplementary material, which is available to authorized users.

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Feasibility of a quantitative polymerase chain reaction assay for diagnosing pneumococcal pneumonia using oropharyngeal swabs

Streptococcus pneumoniae is the most important pathogen causing community-acquired pneumonia (CAP). The current diagnostic microbial standard detects S. pneumoniae in less than 30% of CAP cases. A quantitative polymerase chain reaction (PCR) targeting autolysin (lytA) is able to increase the rate of detection. The aim of this study is validation of this quantitative PCR in vitro using different available strains and in vivo using clinical samples (oropharyngeal swabs). The PCR autolysin (lytA) was validated by testing the intra- and inter-run variability. Also, the in vitro specificity and sensitivity, including the lower limit of detection was determined. In addition, a pilot-study was performed using samples from patients (n = 28) with pneumococcal pneumonia and patients (n = 28) with a pneumonia without detection of S. pneumoniae with the current diagnostic microbial standard, but with detection of either a viral and or another bacterial pathogen to validate this test further. The intra- and inter-run variability were relatively low (SD's ranging from 0.08 to 0.96 cycle thresholds). The lower limit of detection turned out to be 1-10 DNA copies/reaction. In-vitro sensitivity and specificity of the tested specimens (8 strains carrying lytA and 6 strains negative for lytA) were both 100%. In patients with pneumococcal and non-pneumococcal pneumonia a cut-off value of 6.000 copies/mL would lead to a sensitivity of 57.1% and a specificity of 85.7%. We were able to develop a quantitative PCR targeting lytA with good in-vitro test characteristics.

Multivariate genome-wide analyses of the well-being spectrum

We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N_obs = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the well-being spectrum.

Publisher Correction: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.

Longitudinal echocardiographic and clinical follow-up of patients undergoing mitral valve surgery without concomitant tricuspid valve repair

Background

In patients with mild to moderate functional tricuspid regurgitation (TR) and absence of right ventricular dysfunction or tricuspid annulus (TA) dilatation, there is currently no indication for concomitant tricuspid valve (TV) repair during elective mitral valve (MV) surgery. However, long-term results are conflicting. Here, we sought to determine the clinical outcome of this cohort, the rate of TR progression after MV surgery and the role of MV aetiology.

Methods

Patients for elective MV surgery without concomitant TV repair were retrospectively analysed with longitudinal echocardiographic and clinical follow-up, focusing on TR progression and MV aetiology. Linear regression analysis was performed for change in TR at follow-up, using pre-determined variables and confounders.

Results

In total 204 patients without TV repair were analysed. Development of more than moderate TR after a median of 3.1 [1.6–4.6] years was rarely seen: only in 2 out of 161 patients (1.2%) with known TR grade at follow-up. Overall, median preoperative and late postoperative TR grade were equal (p = 0.116). Subanalysis showed no significant difference in MV aetiology subgroups. Preoperative TR grade and male gender were inversely correlated to change in TR. Mortality was not influenced by the 1‑year postoperative TR severity.

Conclusion

Our data showed that in a study population of patients with mild to moderate TR undergoing MV surgery without concomitant TV repair, significant late TR was rarely seen. Based on our study, it is safe to waive concomitant TV repair in this specific patient cohort.

Electronic supplementary material

The online version of this article (10.1007/s12471-018-1159-4) contains supplementary material, which is available to authorized users.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

A characterization of postzygotic mutations identified in monozygotic twins

Postzygotic mutations are DNA changes acquired from the zygote stage onwards throughout the lifespan. These changes lead to differences in DNA sequence among cells of an individual, potentially contributing to the etiology of complex disorders. Here we compared whole genome DNA sequence data of two monozygotic twin pairs, 40 and 100 years old, to detect somatic mosaicism. DNA samples were sequenced twice on two Illumina platforms (13X and 40X read depth) for increased specificity. Using differences in allelic ratios resulted in sets of 1,720 and 1,739 putative postzygotic mutations in the 40-year-old twin pair and 100-year-old twin pair, respectively, for subsequent enrichment analysis. This set of putative mutations was strongly (p < 4.37e-91) enriched in both twin pairs for regulatory elements. The corresponding genes were significantly enriched for genes that are alternatively spliced, and for genes involved in GTPase activity. This research shows that somatic mosaicism can be detected in monozygotic twin pairs by using allelic ratios calculated from DNA sequence data and that the mutations which are found by this approach are not randomly distributed throughout the genome.