A comparison of myocardial magnetic resonance extracellular volume mapping at 3 T against histology of tissue collagen in severe aortic valve stenosis and obstructive hypertrophic cardiomyopathy.

OBJECTIVE: Quantitative extracellular volume fraction (ECV) mapping with MRI is commonly used to investigate in vivo diffuse myocardial fibrosis. This study aimed to validate ECV measurements against ex vivo histology of myocardial tissue samples from patients with aortic valve stenosis or hypertrophic cardiomyopathy. MATERIALS AND METHODS: Sixteen patients underwent MRI examination at 3 T to acquire native T1 maps and post-contrast T1 maps after gadobutrol administration, from which hematocrit-corrected ECV maps were estimated. Intra-operatively obtained myocardial tissue samples from the same patients were stained with picrosirius red for quantitative histology of myocardial interstitial fibrosis. Correlations between in vivo ECV and ex vivo myocardial collagen content were evaluated with regression analyses. RESULTS: Septal ECV was 30.3% ± 4.6% and correlated strongly (n = 16, r = 0.70; p = 0.003) with myocardial collagen content. Myocardial native T1 values (1206 ± 36 ms) did not correlate with septal ECV (r = 0.41; p = 0.111) or with myocardial collagen content (r = 0.32; p = 0.227). DISCUSSION: We compared myocardial ECV mapping at 3 T against ex vivo histology of myocardial collagen content, adding evidence to the notion that ECV mapping is a surrogate marker for in vivo diffuse myocardial fibrosis.

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response.

Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection.

shRNAs Targeting a Common KCNQ1 Variant Could Alleviate Long-QT1 Disease Severity by Inhibiting a Mutant Allele.

Long-QT syndrome type 1 (LQT1) is caused by mutations in KCNQ1. Patients heterozygous for such a mutation co-assemble both mutant and wild-type KCNQ1-encoded subunits into tetrameric Kv7.1 potassium channels. Here, we investigated whether allele-specific inhibition of mutant KCNQ1 by targeting a common variant can shift the balance towards increased incorporation of the wild-type allele to alleviate the disease in human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs). We identified the single nucleotide polymorphisms (SNP) rs1057128 (G/A) in KCNQ1, with a heterozygosity of 27% in the European population. Next, we determined allele-specificity of short-hairpin RNAs (shRNAs) targeting either allele of this SNP in hiPSC-CMs that carry an LQT1 mutation. Our shRNAs downregulated 60% of the A allele and 40% of the G allele without affecting the non-targeted allele. Suppression of the mutant KCNQ1 allele by 60% decreased the occurrence of arrhythmic events in hiPSC-CMs measured by a voltage-sensitive reporter, while suppression of the wild-type allele increased the occurrence of arrhythmic events. Furthermore, computer simulations based on another LQT1 mutation revealed that 60% suppression of the mutant KCNQ1 allele shortens the prolonged action potential in an adult cardiomyocyte model. We conclude that allele-specific inhibition of a mutant KCNQ1 allele by targeting a common variant may alleviate the disease. This novel approach avoids the need to design shRNAs to target every single mutation and opens up the exciting possibility of treating multiple LQT1-causing mutations with only two shRNAs.

Quantification of Myocardial Creatine and Triglyceride Content in the Human Heart: Precision and Accuracy of in vivo Proton Magnetic Resonance Spectroscopy.

BACKGROUND: Proton magnetic resonance spectroscopy (1 H-MRS) of the human heart is deemed to be a quantitative method to investigate myocardial metabolite content, but thorough validations of in vivo measurements against invasive techniques are lacking. PURPOSE: To determine measurement precision and accuracy for quantifications of myocardial total creatine and triglyceride content with localized 1 H-MRS. STUDY TYPE: Test-retest repeatability and measurement validation study. SUBJECTS: Sixteen volunteers and 22 patients scheduled for open-heart aortic valve replacement or septal myectomy. FIELD STRENGTH/SEQUENCE: Prospectively ECG-triggered respiratory-gated free-breathing single-voxel point-resolved spectroscopy (PRESS) sequence at 3 T. ASSESSMENT: Myocardial total creatine and triglyceride content were quantified relative to the total water content by fitting the 1 H-MR spectra. Precision was assessed with measurement repeatability. Accuracy was assessed by validating in vivo 1 H-MRS measurements against biochemical assays in myocardial tissue from the same subjects. STATISTICAL TESTS: Intrasession and intersession repeatability was assessed using Bland-Altman analyses. Agreement between 1 H-MRS measurements and biochemical assay was tested with regression analyses. RESULTS: The intersession repeatability coefficient for myocardial total creatine content was 41.8% with a mean value of 0.083% ± 0.020% of the total water signal, and 36.7% for myocardial triglyceride content with a mean value of 0.35% ± 0.13% of the total water signal. Ex vivo myocardial total creatine concentrations in tissue samples correlated with the in vivo myocardial total creatine content measured with 1 H-MRS: n = 22, r = 0.44; P < 0.05. Likewise, ex vivo myocardial triglyceride concentrations correlated with the in vivo myocardial triglyceride content: n = 20, r = 0.50; P < 0.05. DATA CONCLUSION: We validated the use of localized 1 H-MRS of the human heart at 3 T for quantitative assessments of in vivo myocardial tissue metabolite content by estimating the measurement precision and accuracy. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).

The necessity of incorporating non-genetic risk factors into polygenic risk score models.

The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study.

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities. Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37-73 years, data collected 2006-2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0-93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis. Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855-0.894 for prevalence and 0.819-0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements. Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation. Funding: University Medical Center Groningen.

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.

BACKGROUND: Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS: We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION: Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.

Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure.

The host's gene expression and gene regulatory response to pathogen exposure can be influenced by a combination of the host's genetic background, the type of and exposure time to pathogens. Here we provide a detailed dissection of this using single-cell RNA-sequencing of 1.3M peripheral blood mononuclear cells from 120 individuals, longitudinally exposed to three different pathogens. These analyses indicate that cell-type-specificity is a more prominent factor than pathogen-specificity regarding contexts that affect how genetics influences gene expression (i.e., eQTL) and co-expression (i.e., co-expression QTL). In monocytes, the strongest responder to pathogen stimulations, 71.4% of the genetic variants whose effect on gene expression is influenced by pathogen exposure (i.e., response QTL) also affect the co-expression between genes. This indicates widespread, context-specific changes in gene expression level and its regulation that are driven by genetics. Pathway analysis on the CLEC12A gene that exemplifies cell-type-, exposure-time- and genetic-background-dependent co-expression interactions, shows enrichment of the interferon (IFN) pathway specifically at 3-h post-exposure in monocytes. Similar genetic background-dependent association between IFN activity and CLEC12A co-expression patterns is confirmed in systemic lupus erythematosus by in silico analysis, which implies that CLEC12A might be an IFN-regulated gene. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

Lactobacillus reuteri Colonisation of Extremely Preterm Infants in a Randomised Placebo-Controlled Trial.

Lactobacillus reuteri DSM 17938 supplementation reduces morbidities in very low birth weight infants (<1500 g), while the effect on extremely low birth weight infants (ELBW, <1000 g) is still questioned. In a randomised placebo-controlled trial (ClinicalTrials.gov ID NCT01603368), head growth, but not feeding tolerance or morbidities, improved in L. reuteri-supplemented preterm ELBW infants. Here, we investigate colonisation with the probiotic strain in preterm ELBW infants who received L. reuteri DSM 17938 or a placebo from birth to postmenstrual week (PMW) 36. Quantitative PCR was used on 582 faecal DNA samples collected from 132 ELBW infants at one, two, three, and four weeks, at PMW 36, and at two years of age. Human milk oligosaccharides were measured in 31 milk samples at two weeks postpartum. At least 86% of the ELBW infants in the L. reuteri group were colonised with the probiotic strain during the neonatal period, despite low gestational age, high antibiotic pressure, and independent of infant feeding mode. Higher concentrations of lacto-N-tetraose, sialyl-lacto-N-neotetraose c, and 6'-sialyllactose in mother's milk weakly correlated with lower L. reuteri abundance. Within the L. reuteri group, higher L. reuteri abundance weakly correlated with a shorter time to reach full enteral feeding. Female sex and L. reuteri colonisation improved head growth from birth to four weeks of age. In conclusion, L. reuteri DSM 17938 supplementation leads to successful colonisation in ELBW infants.

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.

An integrative approach for building personalized gene regulatory networks for precision medicine.

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs.

Genome-wide association studies have identified thousands of genetic variants that are associated with disease 1 . Most of these variants have small effect sizes, but their downstream expression effects, so-called expression quantitative trait loci (eQTLs), are often large 2 and celltype-specific3-5. To identify these celltype-specific eQTLs using an unbiased approach, we used single-cell RNA sequencing to generate expression profiles of ~25,000 peripheral blood mononuclear cells from 45 donors. We identified previously reported cis-eQTLs, but also identified new celltype-specific cis-eQTLs. Finally, we generated personalized co-expression networks and identified genetic variants that significantly alter co-expression relationships (which we termed 'co-expression QTLs'). Single-cell eQTL analysis thus allows for the identification of genetic variants that impact regulatory networks.