Publications by authors named "Lude Franke"

Microbiome influences multiple human systems, but its effects on gene methylation is unknown. We investigated the relations between gene methylation in blood and the abundance of common gut bacteria profiled by 16s rRNA gene sequencing in two population-based Dutch cohorts: LifeLines-Deep (LLD, n = 616, discovery) and the Netherlands Twin Register (NTR, n = 296, replication). In LLD, we also explored microbial pathways using data generated by shotgun metagenomic sequencing (n = 683).

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  • Genetic variants found in GWAS are mostly non-coding and have subtle effects on genes, while Mendelian disease variants are coding and directly influence diseases.
  • The study connects common and rare genetic diseases by analyzing how common variants affect gene co-expression across various tissues, using a tool called Downstreamer on 88 GWAS traits.
  • Key findings show that important downstream genes related to common traits, like height, are often linked to Mendelian disease genes, with many being located outside GWAS loci, indicating complex regulatory interactions.
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Background: H-NMR metabolomics and DNA methylation in blood are widely known biomarkers predicting age-related physiological decline and mortality yet exert mutually independent mortality and frailty signals.

Methods: Leveraging multi-omics data in four Dutch population studies (N = 5238, ∼40% of which male) we investigated whether the mortality signal captured by H-NMR metabolomics could guide the construction of DNA methylation-based mortality predictors.

Findings: We trained DNA methylation-based surrogates for 64 metabolomic analytes and found that analytes marking inflammation, fluid balance, or HDL/VLDL metabolism could be accurately reconstructed using DNA-methylation assays.

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Background: The plasma metabolome reflects the physiological state of various biological processes and can serve as a proxy for disease risk. Plasma metabolite variation, influenced by genetic and epigenetic mechanisms, can also affect the cellular microenvironment and blood cell epigenetics. The interplay between the plasma metabolome and the blood cell epigenome remains elusive.

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  • The study explores the complex genetic background of cancer, focusing on the connection between somatic mutations and germline risk factors from genome-wide association studies (GWAS).
  • Researchers analyzed co-expression patterns of genes identified in GWAS for various cancers, finding that many of these genes are significant in their relationship with known cancer driver genes.
  • The findings suggest that tissue-specific co-expression networks can explain how different sets of genes—those with germline risk factors and those with somatic mutations—can lead to the same type of cancer, thus bridging the gap between these two genetic influences.
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  • Scientists looked at the timing of when girls start their periods (called menarche) and how it can affect their health later in life.
  • They studied about 800,000 women and found over a thousand genetic signals that influence when menstruation starts.
  • Some women have a much higher chance of starting their periods too early or too late based on their genetic makeup, suggesting that genes play a big role in this process!
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Human induced pluripotent stem cell (hiPSC)-derived intestinal organoids are valuable tools for researching developmental biology and personalized therapies, but their closed topology and relative immature state limit applications. Here, we use organ-on-chip technology to develop a hiPSC-derived intestinal barrier with apical and basolateral access in a more physiological in vitro microenvironment. To replicate growth factor gradients along the crypt-villus axis, we locally expose the cells to expansion and differentiation media.

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Encounters with pathogens and other molecules can imprint long-lasting effects on our immune system, influencing future physiological outcomes. Given the wide range of microbes to which humans are exposed, their collective impact on health is not fully understood. To explore relations between exposures and biological aging and inflammation, we profiled an antibody-binding repertoire against 2,815 microbial, viral, and environmental peptides in a population cohort of 1,443 participants.

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Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes.

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  • - Understanding the immune system's role in STEMI could help reduce the risk of recurrent cardiovascular events, but current anti-inflammatory treatments may have serious side effects due to their broad impact.
  • - A study involving 38 STEMI patients and 38 controls revealed significant differences in immune cell types and gene expression, particularly in monocytes, right after STEMI and up to 24 hours later.
  • - Results indicated specific immune pathways affected by STEMI, suggesting that personalized approaches based on genetic variants and disease stages may optimize anti-inflammatory treatments for individual patients.
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  • Recent advances in single-cell RNA-sequencing (scRNA-seq) allow researchers to explore biological phenomena at the individual cell level.
  • Using droplets that mix cells from different individuals, scientists can determine which cell came from whom by identifying genetic differences.
  • A new tool called Demuxafy enhances this process by effectively distinguishing single cells from doubles (which contain multiple cells), improving the accuracy of assigning cells to their respective donors.
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  • - This study conducted a genome-wide association analysis on metabolic traits in over 136,000 participants, revealing over 400 genetic loci that influence human metabolism and complex diseases.
  • - Researchers used advanced techniques like nuclear magnetic resonance spectroscopy to link specific genetic variants with how they affect lipoprotein metabolism and other metabolic processes.
  • - The findings highlight the genetic connections between metabolism and conditions such as hypertension, providing valuable data for further research on metabolic-related diseases.
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Longevity and disease-free survival are influenced by a combination of genetics and lifestyle. Biological age (BioAge), a measure of aging based on composite biomarkers, may outperform chronological age in predicting health and longevity. This study investigated the relationship between genetic risks, lifestyle factors, and delta age (Δage), estimated as the difference between biological and chronological age.

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Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets.

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Pubertal timing varies considerably and has been associated with a range of health outcomes in later life. To elucidate the underlying biological mechanisms, we performed multi-ancestry genetic analyses in ~800,000 women, identifying 1,080 independent signals associated with age at menarche. Collectively these loci explained 11% of the trait variance in an independent sample, with women at the top and bottom 1% of polygenic risk exhibiting a ~11 and ~14-fold higher risk of delayed and precocious pubertal development, respectively.

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The mapping of human-entered data to codified data formats that can be analysed is a common problem across medical research and health care. To identify risk and protective factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility and coronavirus disease 2019 (COVID-19) severity, frequent questionnaires were sent out to participants of the Lifelines Cohort Study starting 30 March 2020. Because specific drugs were suspected COVID-19 risk factors, the questionnaires contained multiple-choice questions about commonly used drugs and open-ended questions to capture all other drugs used.

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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.

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  • Human genetic variation has helped identify key regulators of hemoglobin switching, notably BCL11A, leading to therapeutic advancements, but understanding of the broader regulatory mechanisms remains limited.
  • A large genome-wide association study involving 28,279 individuals from 5 continents identified 178 significant genetic variants affecting fetal hemoglobin regulation.
  • The research pinpointed BACH2 as a new regulator and clarified how certain genetic variations, including rare deletions, interact to influence fetal hemoglobin levels, paving the way for improved treatments for conditions like sickle cell disease and β-thalassemia.
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Most existing TWAS tools require individual-level eQTL reference data and thus are not applicable to summary-level reference eQTL datasets. The development of TWAS methods that can harness summary-level reference data is valuable to enable TWAS in broader settings and enhance power due to increased reference sample size. Thus, we develop a TWAS framework called OTTERS (Omnibus Transcriptome Test using Expression Reference Summary data) that adapts multiple polygenic risk score (PRS) methods to estimate eQTL weights from summary-level eQTL reference data and conducts an omnibus TWAS.

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Both gene expression and protein concentrations are regulated by genetic variants. Exploring the regulation of both eQTLs and pQTLs simultaneously in a context- and cell-type dependent manner may help to unravel mechanistic basis for genetic regulation of pQTLs. Here, we performed meta-analysis of -induced pQTLs from two population-based cohorts and intersected the results with -induced cell-type specific expression association data (eQTL).

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Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples.

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Immune cell function can be altered by lipids in circulation, a process potentially relevant to lipid-associated inflammatory diseases including atherosclerosis and rheumatoid arthritis. To gain further insight in the molecular changes involved, we here perform a transcriptome-wide association analysis of blood triglycerides, HDL cholesterol, and LDL cholesterol in 3229 individuals, followed by a systematic bidirectional Mendelian randomization analysis to assess the direction of effects and control for pleiotropy. Triglycerides are found to induce transcriptional changes in 55 genes and HDL cholesterol in 5 genes.

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