Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses

Subtyping of acute myeloid leukaemia (AML) is predominantly based on recurrent genetic abnormalities, but recent literature indicates that transcriptomic phenotyping holds immense potential to further refine AML classification. Here we integrated five AML transcriptomic datasets with corresponding genetic information to provide an overview (n = 1224) of the transcriptomic AML landscape. Consensus clustering identified 17 robust patient clusters which improved identification of CEBPA-mutated patients with favourable outcomes, and uncovered transcriptomic subtypes for KMT2A rearrangements (2), NPM1 mutations (5), and AML with myelodysplasia-related changes (AML-MRC) (5). Transcriptomic subtypes of KMT2A, NPM1 and AML-MRC showed distinct mutational profiles, cell type differentiation arrests and immune properties, suggesting differences in underlying disease biology. Moreover, our transcriptomic clusters show differences in ex-vivo drug responses, even when corrected for differentiation arrest and superiorly capture differences in drug response compared to genetic classification. In conclusion, our findings underscore the importance of transcriptomics in AML subtyping and offer a basis for future research and personalised treatment strategies. Our transcriptomic compendium is publicly available and we supply an R package to project clusters to new transcriptomic studies.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Expanding the repertoire reveals recurrent, cryptic and hematopoietic HLA class I minor histocompatibility antigens

Allogeneic stem cell transplantation (alloSCT) is a curative treatment for hematological malignancies. After HLA-matched alloSCT, anti-tumor immunity is caused by donor T cells recognizing polymorphic peptides, designated minor histocompatibility antigens (MiHAs), that are presented by HLA on malignant patient cells. However, T cells often target MiHAs on healthy non-hematopoietic tissues of patients, thereby inducing side effects known as Graft-versus-Host Disease. Here, we aimed to identify the dominant repertoire of HLA-I-restricted MiHAs to enable strategies to predict, monitor or modulate immune responses after alloSCT. To systematically identify novel MiHAs by genome-wide association screening, T-cell clones were isolated from 39 transplanted patients and tested for reactivity against 191 EBV-B cell lines of the 1000 Genomes Project. By discovering 81 new MiHAs, we more than doubled the antigen repertoire to 159 MiHAs and demonstrated that, despite many genetic differences between patients and donors, often the same MiHAs are targeted in multiple patients. Furthermore, we showed that one quarter of the antigens are cryptic, i.e. translated from unconventional open reading frames, for example long non-coding RNAs, showing that these antigen types are relevant targets in natural immune responses. Finally, using single cell RNA-seq data, we analyzed tissue expression of MiHA-encoding genes to explore their potential role in clinical outcome, and characterized 11 new hematopoietic-restricted MiHAs as potential targets for immunotherapy. In conclusion, we expanded the repertoire of HLA-I-restricted MiHAs and identified recurrent, cryptic and hematopoietic-restricted antigens, which are fundamental to predict, follow or manipulate immune responses to improve clinical outcome after alloSCT.

Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Quantitative multiple fragment monitoring with enhanced in-source fragmentation/annotation mass spectrometry

Analytical techniques with high sensitivity and selectivity are essential to the quantitative analysis of clinical samples. Liquid chromatography coupled to tandem mass spectrometry is the gold standard in clinical chemistry. However, tandem mass spectrometers come at high capital expenditure and maintenance costs. We recently showed that it is possible to generate very similar results using a much simpler single mass spectrometry detector by performing enhanced in-source fragmentation/annotation (EISA) combined with correlated ion monitoring. Here we provide a step-by-step protocol for optimizing the analytical conditions for EISA, so anyone properly trained in liquid chromatography-mass spectrometry can follow and apply this technique for any given analyte. We exemplify the approach by using 2-hydroxyglutarate (2-HG) which is a clinically relevant metabolite whose D-enantiomer is considered an 'oncometabolite', characteristic of cancers associated with mutated isocitrate dehydrogenases 1 or 2 (IDH1/2). We include procedures for determining quantitative robustness, and show results of these relating to the analysis of DL-2-hydroxyglutarate in cells, as well as in serum samples from patients with acute myeloid leukemia that contain the IDH1/2 mutation. This EISA-mass spectrometry protocol is a broadly applicable and low-cost approach for the quantification of small molecules that has been developed to work well for both single-quadrupole and time-of-flight mass analyzers.

The Role of Genetics in Clinically Suspect Arthralgia and Rheumatoid Arthritis Development: A Large Cross-Sectional Study

OBJECTIVE

To investigate whether established genetic predictors for rheumatoid arthritis (RA) differentiate healthy controls, patients with clinically suspect arthralgia (CSA), and RA patients.

METHODS

Using analyses of variance, chi-square tests, and mean risk difference analyses, we investigated the association of an RA polygenic risk score (PRS) and HLA shared epitope (HLA-SE) with all participant groups, both unstratified and stratified for anti-citrullinated protein antibody (ACPA) status. We used 3 separate data sets sampled from the same Dutch population (1,015 healthy controls, 479 CSA patients, and 1,146 early classified RA patients). CSA patients were assessed for conversion to inflammatory arthritis over a period of 2 years, after which they were classified as either CSA converters (n = 84) or CSA nonconverters (n = 395).

RESULTS

The PRS was increased in RA patients (mean ± SD PRS 1.31 ± 0.96) compared to the complete CSA group (1.07 ± 0.94) and compared to CSA converters (1.12 ± 0.94). In ACPA- strata, PRS distributions differed strongly when comparing the complete CSA group (mean ± SD PRS 1.05 ± 0.94) and CSA converters (0.97 ± 0.87) to RA patients (1.20 ± 0.94), while in the ACPA+ strata, the complete CSA group (1.25 ± 0.99) differed clearly from healthy controls (1.05 ± 0.94) and RA patients (1.41 ± 0.96). HLA-SE was more prevalent in the RA group (prevalence 0.64) than the complete CSA group (0.45), with small differences between RA patients and CSA converters (0.64 versus 0.60) and larger differences between CSA converters and CSA nonconverters (0.60 versus 0.42). HLA-SE prevalence differed more strongly within the ACPA+ strata as follows: healthy controls (prevalence 0.43), CSA nonconverters (0.48), complete CSA group (0.59), CSA converters (0.66), and RA patients (0.79).

CONCLUSION

We observed that genetic predisposition increased across pre-RA participant groups. The RA PRS differed in early classified RA and inflammatory pre-disease stages, regardless of ACPA stratification. HLA-SE prevalence differed between arthritis patients, particularly ACPA+ patients, and healthy controls. Genetics seem to fulfill different etiologic roles.

GlycA, a Biomarker of Low-Grade Inflammation, Is Increased in Male Night Shift Workers

Sustained night shift work is associated with various adverse health risks, including an increased risk of cardiovascular disease, type II diabetes, and susceptibility to infectious respiratory diseases. The extent of these adverse health effects, however, seems to greatly vary between night shift workers, yet the underlying reasons and the mechanisms underlying these interindividual differences remain poorly understood. Metabolomics assays in the blood have recently gained much attention as a minimally invasive biomarker platform capturing information predictive of metabolic and cardiovascular diseases. In this cross-sectional study, we explored and compared the metabolic profiles of 1010 night shift workers and 1010 age- and sex-matched day workers (non-shift workers) from the Lifelines Cohort Study. The metabolic profiles were determined using the ¹H-NMR Nightingale platform for the quantification of 250 parameters of metabolism, including routine lipids, extensive lipoprotein subclasses, fatty acid composition, and various low-molecular metabolites, including amino acids, ketone bodies, and gluconeogenesis-related metabolites. Night shift workers had an increased BMI (26.6 vs. 25.9 kg/m²) compared with day workers (non-shift workers) in both sexes, were slightly more likely to be ever smokers (only in males) (54% vs. 46%), worked on average 5.9 ± 3.7 night shifts per month, and had been working in night shifts for 18.3 ± 10.5 years on average. We observed changes in several metabolic markers in male night shift workers compared with non-shift workers, but no changes were observed in women. In men, we observed higher levels of glycoprotein acetyls (GlycA), triglycerides, and fatty acids compared with non-shift workers. The changes were seen in the ratio of triglycerides and cholesterol(esters) to total lipids in different sizes of VLDL particles. Glycoprotein acetyls (GlycAs) are of particular interest as markers since they are known as biomarkers for low-grade chronic inflammation. When the analyses were adjusted for BMI, no significant associations were observed. Further studies are needed to better understand the relationship between night shift work and metabolic profiles, particularly with respect to the role of sex and BMI in this relationship.

Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multilineage development in the human thymus

T cell development in the mouse thymus has been studied extensively, but less is known regarding T cell development in the human thymus. We used a combination of single-cell techniques and functional assays to perform deep immune profiling of human T cell development, focusing on the initial stages of prelineage commitment. We identified three thymus-seeding progenitor populations that also have counterparts in the bone marrow. In addition, we found that the human thymus physiologically supports the development of monocytes, dendritic cells, and NK cells, as well as limited development of B cells. These results are an important step toward monitoring and guiding regenerative therapies in patients after hematopoietic stem cell transplantation.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

OBJECTIVE

To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.

MATERIAL AND METHODS

We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features.

RESULTS

We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles.

DISCUSSION

Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data.

CONCLUSION

We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

Longitudinal Dynamics of Human B-Cell Response at the Single-Cell Level in Response to Tdap Vaccination

To mount an adequate immune response against pathogens, stepwise mutation and selection processes are crucial functions of the adaptive immune system. To better characterize a successful vaccination response, we performed longitudinal (days 0, 5, 7, 10, and 14 after Boostrix vaccination) analysis of the single-cell transcriptome as well as the B-cell receptor (BCR) repertoire (scBCR-rep) in plasma cells of an immunized donor and compared it with baseline B-cell characteristics as well as flow cytometry findings. Based on the flow cytometry knowledge and literature findings, we discriminated individual B-cell subsets in the transcriptomics data and traced over-time maturation of plasmablasts/plasma cells (PB/PCs) and identified the pathways associated with the plasma cell maturation. We observed that the repertoire in PB/PCs differed from the baseline B-cell repertoire e.g., regarding expansion of unique clones in post-vaccination visits, high usage of IGHG1 in expanded clones, increased class-switching events post-vaccination represented by clonotypes spanning multiple IGHC classes and positive selection of CDR3 sequences over time. Importantly, the Variable gene family-based clustering of BCRs represented a similar measure as the gene-based clustering, but certainly improved the clustering of BCRs, as BCRs from duplicated Variable gene families could be clustered together. Finally, we developed a query tool to dissect the immune response to the components of the Boostrix vaccine. Using this tool, we could identify the BCRs related to anti-tetanus and anti-pertussis toxoid BCRs. Collectively, we developed a bioinformatic workflow which allows description of the key features of an ongoing (longitudinal) immune response, such as activation of PB/PCs, Ig class switching, somatic hypermutation, and clonal expansion, all of which are hallmarks of antigen exposure, followed by mutation & selection processes.

Population matched (pm) germline allelic variants of immunoglobulin (IG) loci: Relevance in infectious diseases and vaccination studies in human populations

Immunoglobulin (IG) loci harbor inter-individual allelic variants in many different germline IG variable, diversity and joining genes of the IG heavy (IGH), kappa (IGK) and lambda (IGL) loci, which together form the genetic basis of the highly diverse antigen-specific B-cell receptors. These allelic variants can be shared between or be specific to human populations. The current immunogenetics resources gather the germline alleles, however, lack the population specificity of the alleles which poses limitations for disease-association studies related to immune responses in different human populations. Therefore, we systematically identified germline alleles from 26 different human populations around the world, profiled by "1000 Genomes" data. We identified 409 IGHV, 179 IGKV, and 199 IGLV germline alleles supported by at least seven haplotypes. The diversity of germline alleles is the highest in Africans. Remarkably, the variants in the identified novel alleles show strikingly conserved patterns, the same as found in other IG databases, suggesting over-time evolutionary selection processes. We could relate the genetic variants to population-specific immune responses, e.g. IGHV1-69 for flu in Africans. The population matched IG (pmIG) resource will enhance our understanding of the SHM-related B-cell receptor selection processes in (infectious) diseases and vaccination within and between different human populations.

Association of common genetic variants with brain microbleeds: A genome-wide association study

OBJECTIVE

To identify common genetic variants associated with the presence of brain microbleeds (BMBs).

METHODS

We performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.

RESULTS

BMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21-1.45]; p = 2.5 × 10-10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19-1.50]; p = 1.0 × 10-6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86-1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.

CONCLUSIONS

Genetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.

Cerebral small vessel disease genomics and its implications across the lifespan

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Background

Financial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries.

Objective

The aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records.

Methods

Two electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation.

Results

For the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97).

Conclusions

We demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Gene expression identifies patients who develop inflammatory arthritis in a clinically suspect arthralgia cohort

BACKGROUND

Established rheumatoid arthritis (RA) patients display differentially expressed genes coding for cytokine/chemokine-mediated immunity compared to healthy controls. It is unclear, however, if in the pre-arthritis phase of clinically suspect arthralgia (CSA) expression of immune genes differ between patients who do or do not develop clinically evident inflammatory arthritis (IA).

METHODS

Two hundred thirty-six consecutive patients presenting with arthralgia clinically suspected for progression to RA were followed until IA development or else for median 24 months (IQR 12-26). Baseline whole blood RNA expression was determined for a previously identified set of 133 genes associated with the innate and adaptive immune system by dual-color reverse-transcription multiplex ligation-dependent probe amplification (dcRT-MLPA) profiling. Cox proportional hazard models were used.

RESULTS

Twenty percent of CSA patients developed IA. After correction for multiple testing, expression levels of six genes (IFNG, PHEX, IGF-1, IL-7R, CD19, CCR7) at the time of presentation were associated with progression to IA. PHEX and IGF-1 were highly correlated with each other (ρ = 0.97). In multivariable analysis correcting for the different genes, expressions of IL-7R and IGF-1 were independently associated with IA development (p = 0.025, p = 0.046, respectively). Moreover, IL-7R and IGF-1 remained significantly associated even after correction for known predictors (ACPA, CRP, imaging-detected subclinical joint inflammation; p = 0.039, p = 0.005, respectively). These genes are also associated with RA development.

CONCLUSIONS

IL-7R and IGF-1 were differentially expressed between CSA patients who did or did not progress to IA, independent from regularly used predictors. These biomarkers may become helpful in prognostication of CSA patients. Furthermore, because both genes are associated with T cell functioning, T cell dysregulation may mediate progression from arthralgia to arthritis.

Metabolic Age Based on the BBMRI-NL 1H-NMR Metabolomics Repository as Biomarker of Age-related Disease

BACKGROUND

The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.

METHODS

We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts.

RESULTS

Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data.

CONCLUSIONS

In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.

Broad phenotype of cysteine-altering NOTCH3 variants in UK Biobank: CADASIL to nonpenetrance

OBJECTIVE

To determine the small vessel disease spectrum associated with cysteine-altering NOTCH3 variants in community-dwelling individuals by analyzing the clinical and neuroimaging features of UK Biobank participants harboring such variants.

METHODS

The exome and genome sequencing datasets of the UK Biobank (n = 50,000) and cohorts of cognitively healthy elderly (n = 751) were queried for cysteine-altering NOTCH3 variants. Brain MRIs of individuals harboring such variants were scored according to Standards for Reporting Vascular Changes on Neuroimaging criteria, and clinical information was extracted with ICD-10 codes. Clinical and neuroimaging data were compared to age- and sex-matched UK Biobank controls and clinically diagnosed patients from the Dutch cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) registry.

RESULTS

We identified 108 individuals harboring a cysteine-altering NOTCH3 variant (2.2 of 1,000), of whom 75% have a variant that has previously been reported in CADASIL pedigrees. Almost all variants were located in 1 of the NOTCH3 protein epidermal growth factor-like repeat domains 7 to 34. White matter hyperintensity lesion load was higher in individuals with NOTCH3 variants than in controls (p = 0.006) but lower than in patients with CADASIL with the same variants (p < 0.001). Almost half of the 24 individuals with brain MRI had a Fazekas score of 0 or 1 up to age 70 years. There was no increased risk of stroke.

CONCLUSIONS

Although community-dwelling individuals harboring a cysteine-altering NOTCH3 variant have a higher small vessel disease MRI burden than controls, almost half have no MRI abnormalities up to age 70 years. This shows that NOTCH3 cysteine altering variants are associated with an extremely broad phenotypic spectrum, ranging from CADASIL to nonpenetrance.

A Comprehensive Workflow for Applying Single-Cell Clustering and Pseudotime Analysis to Flow Cytometry Data

The introduction of single-cell platforms inspired the development of high-dimensional single-cell analysis tools to comprehensively characterize the underlying cellular heterogeneity. Flow cytometry data are traditionally analyzed by (subjective) gating of subpopulations on two-dimensional plots. However, the increasing number of parameters measured by conventional and spectral flow cytometry reinforces the need to apply many of the recently developed tools for single-cell analysis on flow cytometry data, as well. However, the myriads of analysis options offered by the continuously released novel packages can be overwhelming to the immunologist with limited computational background. In this article, we explain the main concepts of such analyses and provide a detailed workflow to illustrate their implications and additional prerequisites when applied on flow cytometry data. Moreover, we provide readily applicable R code covering transformation, normalization, dimensionality reduction, clustering, and pseudotime analysis that can serve as a template for future analyses. We demonstrate the merit of our workflow by reanalyzing a public human dataset. Compared with standard gating, the results of our workflow provide new insights in cellular subsets, alternative classifications, and hypothetical trajectories. Taken together, we present a well-documented workflow, which utilizes existing high-dimensional single-cell analysis tools to reveal cellular heterogeneity and intercellular relationships in flow cytometry data.

Lifestyle-Intervention-Induced Reduction of Abdominal Fat Is Reflected by a Decreased Circulating Glycerol Level and an Increased HDL Diameter

Scope

Abdominal obesity is one of the main modifiable risk factors of age‐related cardiometabolic disease. Cardiometabolic disease risk and its associated high abdominal fat mass, cholesterol, and glucose concentrations can be reduced by a healthier lifestyle. Hence, the aim is to understand the relation between lifestyle‐induced changes in body composition, and specifically abdominal fat, and accompanying changes in circulating metabolic biomarkers.

Methods and results

Data from the Growing Old Together (GOTO) study was used, which is a single arm lifestyle intervention in which 164 older adults (mean age 63 years, BMI 23–35 kg/m²) changed their lifestyle during 13 weeks by 12.5% caloric restriction plus 12.5% increase in energy expenditure. It is shown here that levels of circulating metabolic biomarkers, even after adjustment for body mass index, specifically associate with abdominal fat mass. The applied lifestyle intervention mainly reduces abdominal fat mass (−2.6%, SD = 3.0) and this reduction, when adjusted for general weight loss, is highly associated with decreased circulating glycerol concentrations and increased HDL diameter.

Conclusion

The lifestyle‐induced reduction of abdominal fat mass is particularly associated, independent of body mass index or general weight loss, with decreased circulating glycerol concentrations and increased HDL diameter.

Comprehensive diagnostics of acute myeloid leukemia by whole transcriptome RNA sequencing

Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics. We developed HAMLET (Human AML Expedited Transcriptomics) as bioinformatics pipeline for simultaneous detection of fusion genes, small variants, tandem duplications, and gene expression with all information assembled in an annotated, user-friendly output file. Whole transcriptome RNA sequencing was performed on 100 AML cases and HAMLET results were validated by reference assays and targeted resequencing. The data showed that HAMLET accurately detected all fusion genes and overexpression of EVI1 irrespective of 3q26 aberrations. In addition, small variants in 13 genes that are often mutated in AML were called with 99.2% sensitivity and 100% specificity, and tandem duplications in FLT3 and KMT2A were detected by a novel algorithm based on soft-clipped reads with 100% sensitivity and 97.1% specificity. In conclusion, HAMLET has the potential to provide accurate comprehensive diagnostic information relevant for AML classification, risk assessment and targeted therapy on a single technology platform.

ImSpectR - R package to quantify immune repertoire diversity in spectratype and repertoire sequencing data

SUMMARY

An effective immune system is characterized by a diverse immune repertoire. There is a strong demand for accurate and quantitative methods to assess the diversity of the immune repertoire for various (pre-)clinical applications, including the diagnosis and prognosis of primary immune deficiencies, or to assess the response to therapy. Current strategies for immune diversity assessment generally comprise the visual inspection of the length distribution of rearranged T- and B-cell receptors. Visual inspections, however, are prone to subjective assessments and thus lead to biases. Here, we introduce ImSpectR, a unified approach to quantify immunodiversity using either spectratype, repertoire sequencing or single cell RNA sequencing data. ImSpectR scores various types of deviations from the expected length distribution and integrates these into one measure, allowing for robust quantitative comparisons of immune diversity across individuals or conditions.

AVAILABILITY

R-package is available for download on GitHub at https://github.com/martijn-cordes/ImSpectR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

Predicting longer-term mortality risk requires collection of clinical data, which is often cumbersome. Therefore, we use a well-standardized metabolomics platform to identify metabolic predictors of long-term mortality in the circulation of 44,168 individuals (age at baseline 18-109), of whom 5512 died during follow-up. We apply a stepwise (forward-backward) procedure based on meta-analysis results and identify 14 circulating biomarkers independently associating with all-cause mortality. Overall, these associations are similar in men and women and across different age strata. We subsequently show that the prediction accuracy of 5- and 10-year mortality based on a model containing the identified biomarkers and sex (C-statistic = 0.837 and 0.830, respectively) is better than that of a model containing conventional risk factors for mortality (C-statistic = 0.772 and 0.790, respectively). The use of the identified metabolic profile as a predictor of mortality or surrogate endpoint in clinical studies needs further investigation.

A genome-wide association study identifies genetic loci associated with specific lobar brain volumes

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications (DAAM1 and THBS3), or close to genes causing Mendelian brain-related diseases (ZIC4 and FGFRL1). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.

The effect of standardized food intake on the association between BMI and 1H-NMR metabolites

Multiple studies have shown that levels of ¹H-NMR metabolites are associated with disease and risk factors of disease such as BMI. While most previous investigations have been performed in fasting samples, meta-analysis often includes both cohorts with fasting and non-fasting blood samples. In the present study comprising 153 participants (mean age 63 years; mean BMI 27 kg/m²) we analyzed the effect of a standardized liquid meal (SLM) on metabolite levels and how the SLM influenced the association between metabolites and BMI. We observed that many metabolites, including glycolysis related metabolites, multiple amino acids, LDL diameter, VLDL and HDL lipid concentration changed within 35 minutes after a standardized liquid meal (SLM), similarly for all individuals. Remarkable, however, is that the correlations of metabolite levels with BMI remained highly similar before and after the SLM. Hence, as exemplified with the disease risk factor BMI, our results suggest that the applicability of ¹H-NMR metabolites as disease biomarkers depends on the standardization of the fasting status rather than on the fasting status itself. Future studies are required to investigate the dependency of metabolite biomarkers for other disease risk factors on the fasting status.

Metabolic effects of a 13-weeks lifestyle intervention in older adults: The Growing Old Together Study

For people in their 40s and 50s, lifestyle programs have been shown to improve metabolic health. For older adults, however, it is not clear whether these programs are equally healthy. In the Growing Old Together study, we applied a 13-weeks lifestyle program, with a target of 12.5% caloric restriction and 12.5% increase in energy expenditure through an increase in physical activity, in 164 older adults (mean age=63.2 years; BMI=23-35 kg/m²). Mean weight loss was 4.2% (SE=2.8%) of baseline weight, which is comparable to a previous study in younger adults. Fasting insulin levels, however, showed a much smaller decrease (0.30 mU/L (SE=3.21)) and a more heterogeneous response (range=2.0-29.6 mU/L). Many other parameters of metabolic health, such as blood pressure, and thyroid, glucose and lipid metabolism improved significantly. Many ¹H-NMR metabolites changed in a direction previously associated with a low risk of type 2 diabetes and cardiovascular disease and partially independently of weight loss. In conclusion, 25% reduction in energy balance for 13 weeks induced a metabolic health benefit in older adults, monitored by traditional and novel metabolic markers.

Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health

Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex₄HexNAc₂ to Hex₇HexNAc₆dHex₁Neu5Ac₄, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.

Fibroblast growth factor 21 reflects liver fat accumulation and dysregulation of signalling pathways in the liver of C57BL/6J mice

Fibroblast growth factor 21 (Fgf21) has emerged as a potential plasma marker to diagnose non-alcoholic fatty liver disease (NAFLD). To study the molecular processes underlying the association of plasma Fgf21 with NAFLD, we explored the liver transcriptome data of a mild NAFLD model of aging C57BL/6J mice at 12, 24, and 28 months of age. The plasma Fgf21 level significantly correlated with intrahepatic triglyceride content. At the molecular level, elevated plasma Fgf21 levels were associated with dysregulated metabolic and cancer-related pathways. The up-regulated Fgf21 levels in NAFLD were implied to be a protective response against the NAFLD-induced adverse effects, e.g. lipotoxicity, oxidative stress and endoplasmic reticulum stress. An in vivo PPARα challenge demonstrated the dysregulation of PPARα signalling in the presence of NAFLD, which resulted in a stochastically increasing hepatic expression of Fgf21. Notably, elevated plasma Fgf21 was associated with declining expression of Klb, Fgf21’s crucial co-receptor, which suggests a resistance to Fgf21. Therefore, although liver fat accumulation is a benign stage of NAFLD, the elevated plasma Fgf21 likely indicated vulnerability to metabolic stressors that may contribute towards progression to end-stage NAFLD. In conclusion, plasma levels of Fgf21 reflect liver fat accumulation and dysregulation of metabolic pathways in the liver.

Employing biomarkers of healthy ageing for leveraging genetic studies into human longevity

Genetic studies have thus far identified a limited number of loci associated with human longevity by applying age at death or survival up to advanced ages as phenotype. As an alternative approach, one could first try to identify biomarkers of healthy ageing and the genetic variants associated with these traits and subsequently determine the association of these variants with human longevity. In the present study, we used this approach by testing whether the 35 baseline serum parameters measured in the Leiden Longevity Study (LLS) meet the proposed criteria for a biomarker of healthy ageing. The LLS consists of 421 families with long-lived siblings of European descent, who were recruited together with their offspring and the spouses of the offspring (controls). To test the four criteria for a biomarker of healthy ageing in the LLS, we determined the association of the serum parameters with chronological age, familial longevity, general practitioner-reported general health, and mortality. Out of the 35 serum parameters, we identified glucose, insulin, and triglycerides as biomarkers of healthy ageing, meeting all four criteria in the LLS. We subsequently showed that the genetic variants previously associated with these parameters are significantly enriched in the largest genome-wide association study for human longevity. In conclusion, we showed that biomarkers of healthy ageing can be used to leverage genetic studies into human longevity. We identified several genetic variants influencing the variation in glucose, insulin and triglycerides that contribute to human longevity.

IL7R gene expression network associates with human healthy ageing

Background

The level of expression of the interleukin 7 receptor (IL7R) gene in blood has recently been found to be associated with familial longevity and healthy ageing. IL7R is crucial for T cell development and important for immune competence. To further investigate the IL7R pathway in ageing, we identified the closest interacting genes to construct an IL7R gene network that consisted of IL7R and six interacting genes: IL2RG, IL7, TSLP, CRLF2, JAK1 and JAK3. This network was explored for association with chronological age, familial longevity and immune-related diseases (type 2 diabetes, chronic obstructive pulmonary disease and rheumatoid arthritis) in 87 nonagenarians, 337 of their middle-aged offspring and 321 middle-aged controls from the Leiden Longevity Study (LLS).

Results

We observed that expression levels within the IL7R gene network were significantly different between the nonagenarians and middle-aged controls (P = 4.6 × 10⁻⁴), being driven by significantly lower levels of expression in the elderly of IL7, IL2RG and IL7R. After adjustment for multiple testing and white blood cell composition and in comparison with similarly aged controls, middle-aged offspring of nonagenarian siblings exhibit a lower expression level of IL7R only (P = 0.006). Higher IL7R gene expression in the combined group of middle-aged offspring and controls is associated with a higher prevalence of immune-related disease (P = 0.001). On the one hand, our results indicate that lower IL7R expression levels, as exhibited by the members of long-lived families that can be considered as ‘healthy agers’, are beneficial in middle age. This is augmented by the observation that higher IL7R gene expression associates with immune-related disease. On the other hand, IL7R gene expression in blood is lower in older individuals, indicating that low IL7R gene expression might associate with reduced health. Interestingly, this contradictory result is supported by the observation that a higher IL7R gene expression level is associated with better prospective survival, both in the nonagenarians (Hazard ratio (HR) = 0.63, P = 0.037) and the middle-aged individuals (HR = 0.33, P = 1.9 × 10^–4).

Conclusions

Overall, we conclude that the IL7R network reflected by gene expression levels in blood may be involved in the rate of ageing and health status of elderly individuals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12979-015-0048-6) contains supplementary material, which is available to authorized users.

Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis

OBJECTIVES

To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches.

METHODS

Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid).

RESULTS

OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (β=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (β=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2α/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes.

CONCLUSIONS

Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach.

Meta-analysis on blood transcriptomic studies identifies consistently coexpressed protein-protein interaction modules as robust markers of human aging

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein–protein Interaction (PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module (P = 3.5 × 10⁻³⁸) enriched for ‘T-cell activation’ marking age-associated shifts in lymphocyte blood cell counts (R² = 0.603; P = 1.9 × 10⁻¹⁰). Adjusting the analysis in the compendium for the ‘T-cell activation’ module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for ‘Translational elongation’, ‘Cytolysis’ and ‘DNA metabolic process’. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging.

Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age

The genetic contribution to the variation in human lifespan is ∼25%. Despite the large number of identified disease-susceptibility loci, it is not known which loci influence population mortality. We performed a genome-wide association meta-analysis of 7729 long-lived individuals of European descent (≥85 years) and 16 121 younger controls (<65 years) followed by replication in an additional set of 13 060 long-lived individuals and 61 156 controls. In addition, we performed a subset analysis in cases aged ≥90 years. We observed genome-wide significant association with longevity, as reflected by survival to ages beyond 90 years, at a novel locus, rs2149954, on chromosome 5q33.3 (OR = 1.10, P = 1.74 × 10⁻⁸). We also confirmed association of rs4420638 on chromosome 19q13.32 (OR = 0.72, P = 3.40 × 10⁻³⁶), representing the TOMM40/APOE/APOC1 locus. In a prospective meta-analysis (n = 34 103), the minor allele of rs2149954 (T) on chromosome 5q33.3 associates with increased survival (HR = 0.95, P = 0.003). This allele has previously been reported to associate with low blood pressure in middle age. Interestingly, the minor allele (T) associates with decreased cardiovascular mortality risk, independent of blood pressure. We report on the first GWAS-identified longevity locus on chromosome 5q33.3 influencing survival in the general European population. The minor allele of this locus associates with low blood pressure in middle age, although the contribution of this allele to survival may be less dependent on blood pressure. Hence, the pleiotropic mechanisms by which this intragenic variation contributes to lifespan regulation have to be elucidated.

Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array

BACKGROUND

DNA methylation has been recognized as a key mechanism in cell differentiation. Various studies have compared tissues to characterize epigenetically regulated genomic regions, but due to differences in study design and focus there still is no consensus as to the annotation of genomic regions predominantly involved in tissue-specific methylation. We used a new algorithm to identify and annotate tissue-specific differentially methylated regions (tDMRs) from Illumina 450k chip data for four peripheral tissues (blood, saliva, buccal swabs and hair follicles) and six internal tissues (liver, muscle, pancreas, subcutaneous fat, omentum and spleen with matched blood samples).

RESULTS

The majority of tDMRs, in both relative and absolute terms, occurred in CpG-poor regions. Further analysis revealed that these regions were associated with alternative transcription events (alternative first exons, mutually exclusive exons and cassette exons). Only a minority of tDMRs mapped to gene-body CpG islands (13%) or CpG islands shores (25%) suggesting a less prominent role for these regions than indicated previously. Implementation of ENCODE annotations showed enrichment of tDMRs in DNase hypersensitive sites and transcription factor binding sites. Despite the predominance of tissue differences, inter-individual differences in DNA methylation in internal tissues were correlated with those for blood for a subset of CpG sites in a locus- and tissue-specific manner.

CONCLUSIONS

We conclude that tDMRs preferentially occur in CpG-poor regions and are associated with alternative transcription. Furthermore, our data suggest the utility of creating an atlas cataloguing variably methylated regions in internal tissues that correlate to DNA methylation measured in easy accessible peripheral tissues.

Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study

Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11 (LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1228 unrelated nonagenarian and 1907 geographically matched controls. Using a fixed-effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (P-value = 9.6 × 10(-8) ). By combined modeling of linkage and association, we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with P-value = 0.02 and P-value = 1.0 × 10(-5) , respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.

Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze the combined effect of a SNP set grouped per pathway or gene region. We used this approach to study the joint effect on human longevity of genetic variation in two candidate pathways, the insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway and the telomere maintenance (TM) pathway. For the analyses, we used genotyped GWAS data of 403 unrelated nonagenarians from long-lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. We analyzed 1,021 SNPs in 68 IIS pathway genes and 88 SNPs in 13 TM pathway genes using four self-contained pathway tests (PLINK set-based test, Global test, GRASS and SNP ratio test). Although we observed small differences between the results of the different pathway tests, they showed consistent significant association of the IIS and TM pathway SNP sets with longevity. Analysis of gene SNP sets from these pathways indicates that the association of the IIS pathway is scattered over several genes (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK, SGK2, and YWHAG), while the association of the TM pathway seems to be mainly determined by one gene (POT1). In conclusion, this study shows that genetic variation in genes involved in the IIS and TM pathways is associated with human longevity.

A genome-wide association study identifies a region at chromosome 12 as a potential susceptibility locus for restenosis after percutaneous coronary intervention

Percutaneous coronary intervention (PCI) has become an effective therapy to treat obstructive coronary artery diseases (CAD). However, one of the major drawbacks of PCI is the occurrence of restenosis in 5-25% of all initially treated patients. Restenosis is defined as the re-narrowing of the lumen of the blood vessel, resulting in renewed symptoms and the need for repeated intervention. To identify genetic variants that are associated with restenosis, a genome-wide association study (GWAS) was conducted in 295 patients who developed restenosis (cases) and 571 who did not (controls) from the GENetic Determinants of Restenosis (GENDER) study. Analysis of ~550 000 single nucleotide polymorphisms (SNPs) in GENDER was followed by a replication phase in three independent case-control populations (533 cases and 3067 controls). A potential susceptibility locus for restenosis at chromosome 12, including rs10861032 (P(combined) = 1.11 × 10(-7)) and rs9804922 (P(combined) = 1.45 × 10(-6)), was identified in the GWAS and replication phase. In addition, both SNPs were also associated with coronary events (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023) in a trial based cohort set of elderly patients with (enhanced risk of) CAD (PROSPER) and all-cause mortality in PROSPER (rs10861032, P(additive) = 0.007; rs9804922, P(additive) = 0.013) and GENDER (rs10861032, P(additive) = 0.005; rs9804922, P(additive) = 0.023). Further analysis suggests that this locus could be involved in regulatory functions.

Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

By studying the loci that contribute to human longevity, we aim to identify mechanisms that contribute to healthy aging. To identify such loci, we performed a genome-wide association study (GWAS) comparing 403 unrelated nonagenarians from long-living families included in the Leiden Longevity Study (LLS) and 1670 younger population controls. The strongest candidate SNPs from this GWAS have been analyzed in a meta-analysis of nonagenarian cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort. Only one of the 62 prioritized SNPs from the GWAS analysis (P < 1 × 10⁻⁴) showed genome-wide significance with survival into old age in the meta-analysis of 4149 nonagenarian cases and 7582 younger controls [OR = 0.71 (95% CI 0.65–0.77), P = 3.39 × 10⁻¹⁷]. This SNP, rs2075650, is located in TOMM40 at chromosome 19q13.32 close to the apolipoprotein E (APOE) gene. Although there was only moderate linkage disequilibrium between rs2075650 and the ApoE ε4 defining SNP rs429358, we could not find an APOE-independent effect of rs2075650 on longevity, either in cross-sectional or in longitudinal analyses. As expected, rs429358 associated with metabolic phenotypes in the offspring of the nonagenarian cases from the LLS and their partners. In addition, we observed a novel association between this locus and serum levels of IGF-1 in women (P = 0.005). In conclusion, the major locus determining familial longevity up to high age as detected by GWAS was marked by rs2075650, which tags the deleterious effects of the ApoE ε4 allele. No other major longevity locus was found.