Biology subjects

Franke, L.

Publications and source records attributed to Franke, L..

34 records · Page 2Linked to original sources

bioRxiv2021

XopQ induced stromule formation in Nicotiana benthamiana is causally linked to ETI signaling and depends on ADR1 and NRG1

In Nicotiana benthamiana, expression of the Xanthomonas effector XopQ triggers ROQ1-dependent ETI responses and in parallel accumulation of plastids around the nucleus and the formation of stromules. Both processes were proposed to contribute to ETI-related hypersensitive cell death and thereby to plant immunity. Whether these reactions are directly connected to ETI signaling events has not been tested. Here we utilized transient expression experiments to determine whether XopQ-mediated plastid reactions are a result of XopQ perception by ROQ1 or a consequence of XopQ virulence activity. We find that N. benthamiana mutants lacking ROQ1, both RNLs (NRG1 and ADR1) or EDS1, fail to elicit XopQ-dependent host cell death and stromule formation. Mutants lacking only NRG1 lost XopQ-dependent cell death but retained some stromule induction that was abolished in the RNL double mutant. This analysis aligns XopQ-induced stromules with the ETI signaling cascade but not to host programmed cell death. Furthermore, data reveal that XopQ-triggered plastid clustering is not strictly linked to stromule formation during ETI. Our data suggest that stromule formation, in contrast to chloroplast peri-nuclear dynamics, is an integral part of the N. benthamiana ETI response and that both RNL sub-types play a role in this ETI response. One sentence summaryGenetic analysis aligns effector triggered immunity (ETI) induced stromule formation with the ETI signaling cascade but not programmed cell death and questions stromule guided peri-nuclear plastid clustering.

plant biology↗

bioRxiv2021

Single-cell RNA-sequencing reveals widespread personalized, context-specific gene expression regulation in immune cells.

Gene expression and its regulation can be context-dependent. To dissect this, using samples from 120 individuals, we single-cell RNA-sequenced 1.3M peripheral blood mononuclear cells exposed to three different pathogens at two time points or left unexposed. This revealed thousands of cell type-specific expression changes (eQTLs) and pathogen-induced expression changes (response QTLs) that are influenced by genetic variation. In monocytes, the strongest responder to pathogen stimulations, genetics also affected co-expression of 71.4% of these eQTL genes. For example, the pathogen recognition receptor CLEC12A showed many such co-expression interactions, but only in monocytes after 3h pathogen stimulation. Further analysis linked this to interferon-regulating transcription factors, a finding that we recapitulated in an independent cohort of patients with systemic lupus erythematosus, a condition characterized by increased interferon activity. Altogether, this study highlights the importance of context for gaining a better understanding of the mechanisms of gene regulation in health and disease.

genetics↗

bioRxiv2021

Idefix: identifying accidental sample mix-ups in biobanks using polygenic scores

Structured abstractIdentifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex, which can reveal only half of the mix-ups. Here, we describe Idefix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores. In the Lifelines population-based biobank we calculated polygenic scores (PGSs) for 25 traits for 32,786 participants. Idefix then compares the actual phenotypes to PGSs and uses the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idefix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Idefix therefore is not yet able to identify every sample mix-up. However, this will likely improve soon, with highly powered GWAS summary statistics that will likely become available for more commonly measured traits. Nevertheless, Idefix can already be used to identify a high-quality set of participants for whom it is very unlikely that they reflect sample mix-ups, and therefore could be offered a pharmacogenetic passport. For instance, when selecting the 10% of participants for whom predicted phenotypes adhere best to the actually measured phenotypes, we estimate that the proportion of sample mix-ups is reduced 250-fold. Availability and implementationIdefix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix Contactl.h.franke@umcg.nl

bioinformatics↗

bioRxiv2021

Brain expression quantitative trait locus and network analysis reveals downstream effects and putative drivers for brain-related diseases

Gaining insight into the downstream consequences of non-coding variants is an essential step towards the identification of therapeutic targets from genome-wide association study (GWAS) findings. Here we have harmonized and integrated 8,727 RNA-seq samples with accompanying genotype data from multiple brain-regions from 14 datasets. This sample size enabled us to perform both cis- and trans-expression quantitative locus (eQTL) mapping. Upon comparing the brain cortex cis-eQTLs (for 12,307 unique genes at FDR<0.05) with a large blood cis-eQTL analysis (n=31,684 samples), we observed that brain eQTLs are more tissue specific than previously assumed. We inferred the brain cell type for 1,515 cis-eQTLs by using cell type proportion information. We conducted Mendelian Randomization on 31 brain-related traits using cis-eQTLs as instruments and found 159 significant findings that also passed colocalization. Furthermore, two multiple sclerosis (MS) findings had cell type specific signals, a neuron-specific cis-eQTL for CYP24A1 and a macrophage specific cis-eQTL for CLECL1. To further interpret GWAS hits, we performed trans-eQTL analysis. We identified 2,589 trans-eQTLs (at FDR<0.05) for 373 unique SNPs, affecting 1,263 unique genes, and 21 replicated significantly using single-nucleus RNA-seq data from excitatory neurons. We also generated a brain-specific gene-coregulation network that we used to predict which genes have brain-specific functions, and to perform a novel network analysis of Alzheimers disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS) and Parkinsons disease (PD) GWAS data. This resulted in the identification of distinct sets of genes that show significantly enriched co-regulation with genes inside the associated GWAS loci, and which might reflect drivers of these diseases.

genomics↗

bioRxiv2020

Imbalanced expression for predicted high-impact, autosomal-dominant variants in a cohort of 3,818 healthy samples

BackgroundOne of the growing problems in genome diagnostics is the increasing number of variants that get identified through genetic testing but for which it is unknown what the significance for the disease is (Variants of Unknown Significance - VUS)1,2. When these variants are observed in patients, clinicians need to be able to determine their relevance for causing the patients disease. Here we investigated whether allele-specific expression (ASE) can be used to prioritize disease-relevant VUS and therefore assist diagnostics. In order to do so, we conducted ASE analysis in RNA-seq data from 3,818 blood samples (part of the the Dutch BIOS biobank consortium), to ascertain how VUS affect gene expression. We compared the effect of VUS variants to variants that are predicted to have a high impact, and variants that are predicted to be pathogenic but are either recessive or autosomal-dominant with low penetrance. ResultsFor immune and haematological disorders, we observed that 24.7% of known pathogenic variants from ClinVar show allelic imbalance in blood, as compared to 6.6% of known benign variants with matching allele frequencies. However, for other types of disorders, ASE information from blood did not distinguish (likely) pathogenic variants from benign variants. Unexpectedly, we identified 5 genes (ALOX5, COMT, PRPF8, PSTPIP1 and SH3BP2) in which seven population-based samples had a predicted high impact, autosomal-dominant variant. For these genes the imbalanced expression of the major allele compensates for the lower expression of the minor allele. ConclusionsOur analysis in a large population-based gene expression cohort reveals examples of high impact, autosomal-dominant variants that are compensated for by imbalanced expression. Additionally, we observed that ASE analyses in blood are informative for predicting pathogenic variants that are associated with immune and haematological conditions. We have made all our ASE results, including many ASE calls for rare variants (MAF < 1%), available at https://molgenis15.gcc.rug.nl/.

genomics↗

bioRxiv2020

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

BackgroundAging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. ResultsHere, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P [≤] 2.5 x 10-6). Moreover, 511 genes ([~]18% of the 2,808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. ConclusionsWe conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

bioinformatics↗

bioRxiv2020

The role of gene expression on human sexual dimorphism: too early to call

The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. To explore this scenario, we performed a genome-wide analysis of sex-specific whole blood RNA-seq eQTLs from 3,447 individuals. Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-specific cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-specific eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-specific trait-associations. Power analyses using real eQTL- and causal effect sizes showed that millions of samples would be necessary to observe sex-specific trait associations that are fully driven by sex-specific cis-eQTLs. Compensatory effects may further hamper their detection. In line with this observation, we confirmed that the sex-specific trait-associations detected so far are not driven by sex-specific cis-eQTLs.

genetics↗

bioRxiv2020

Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption

Coffee and tea are extensively consumed beverages worldwide. Observational studies have shown contradictory findings for the association between consumption of these beverages and different health outcomes. Epigenetics is suggested as a mechanism mediating the effects of dietary and lifestyle factors on disease onset. We conducted epigenome-wide association studies (EWAS) on coffee and tea consumptions in 15,789 participants of European and African-American ancestries from 15 cohorts. EWAS meta-analysis revealed 11 CpG sites significantly associated with coffee consumption (P-value <1.1x10-7), nine of them annotated to the genes AHRR, F2RL3, FLJ43663, HDAC4, GFI1 and PHGDH, and two CpGs suggestively associated with tea consumption (P-value<5.0x10-6). Among these, cg14476101 was significantly associated with expression of its annotated gene PHGDH and risk of fatty liver disease. Knockdown of PHGDH expression in liver cells showed a correlation with expression levels of lipid-associated genes, suggesting a role of PHGDH in hepatic-lipid metabolism. Collectively, this study indicates that coffee consumption is associated with differential DNA methylation levels at multiple CpGs, and that coffee-associated epigenetic variations may explain the mechanism of action of coffee consumption in conferring disease risk.

genomics↗

bioRxiv2020

Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping using trans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

genetics↗

bioRxiv2020

Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a master regulator of IFNγ signalling in celiac disease

BackgroundCeliac disease (CeD) is a complex T cell-mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD. ResultsWe used four different in silico approaches - Mendelian Randomization inverse variance weighting, COLOC, LD overlap and DEPICT - to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signalling and T cell activation pathways. 51 of these genes are targets of known drug compounds and likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene-combinations that were affected by our CeD-prioritized genes in trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAFD1, and were found to be involved in IFN{gamma} signalling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments that validated the role of TRAFD1 as an immune regulator acting in trans. ConclusionsOur strategy has confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and the IFN{gamma} signalling and MHC I antigen processing pathways, both major players of immune activation and CeD pathogenesis.

genetics↗

bioRxiv2019

Relationship between gut microbiota and circulating metabolites in population-based cohorts

Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. As recent development in metabolomics allows classifying the lipoprotein particles into more details, we aimed to examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance (1H-NMR) technology in 2,309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort in whom gut microbiota was profiled using 16S rRNA gene sequencing. The relationship between gut microbiota and metabolites was assessed by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. Our analysis revealed association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, amino acids, and acute phase reaction markers. These observations provide novel insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.

epidemiology↗

bioRxiv2019

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

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, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R [≥] 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and assign them to a cell type with Decon-eQTL using the predicted cell proportions from Decon-cell. Deconvoluted eQTLs show excellent allelic directional concordance with those of eQTL([≥] 96%) and chromatin mark QTL ([≥]87%) studies that used either purified cell subpopulations or single-cell RNA-seq. Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain 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).

genomics↗

bioRxiv2018

Systematic identification of human SNPs affecting regulatory element activity

Most of the millions of single-nucleotide polymorphisms (SNPs) in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies have linked many of these SNPs to human traits or to gene expression levels, but rarely with sufficient resolution to identify the causal SNPs. Functional screens based on reporter assays have previously been of insufficient throughput to test the vast space of SNPs for possible effects on enhancer and promoter activity. Here, we have leveraged the throughput of the SuRE reporter technology to survey a total of 5.9 million SNPs, including 57% of the known common SNPs. We identified more than 30 thousand SNPs that alter the activity of putative regulatory elements, often in a cell-type specific manner. These data indicate that a large proportion of human non-coding SNPs may affect gene regulation. Integration of these SuRE data with genome-wide association studies may help pinpoint SNPs that underlie human traits.

genomics↗

bioRxiv2018

Improving the diagnostic yield of exome-sequencing, by predicting gene-phenotype associations using large-scale gene expression analysis

Clinical interpretation of exome and genome sequencing data remains challenging and time consuming, with many variants with unknown effects found in genes with unknown functions. Automated prioritization of these variants can improve the speed of current diagnostics and identify previously unknown disease genes. Here, we used 31,499 RNA-seq samples to predict the phenotypic consequences of variants in genes. We developed GeneNetwork Assisted Diagnostic Optimization (GADO), a tool that uses these predictions in combination with a patients phenotype, denoted using HPO terms, to prioritize identified variants and ease interpretation. GADO is unique because it does not rely on existing knowledge of a gene and can therefore prioritize variants missed by tools that rely on existing annotations or pathway membership. In a validation trial on patients with a known genetic diagnosis, GADO prioritized the causative gene within the top 3 for 41% of the cases. Applying GADO to a cohort of 38 patients without genetic diagnosis, yielded new candidate genes for seven cases. Our results highlight the added value of GADO (www.genenetwork.nl) for increasing diagnostic yield and for implicating previously unknown disease-causing genes.

genetics↗

bioRxiv2018

A linear mixed model approach to study multivariate gene-environment interactions

Different environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

genetics↗

bioRxiv2017

Genome-wide identification of directed gene networks using large-scale population genomics data

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 x 10-10), among which transcription factors were overrepresented (P = 3.3 x 10-7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

genomics↗