Biology subjects

Strauch, K.

Publications and source records attributed to Strauch, K..

14 recordsLinked to original sources

bioRxiv2025

GeDi: Simplifying Gene Set Distances for Enhanced Omics Interpretation in R/Bioconductor

BackgroundFunctional enrichment analysis is a standard component in many omics data analysis workflows, supported by a variety of methods and algorithms. However, despite their utility and wide application, these methods often return the results as an extensive and redundant list of gene sets, impeding interpretation and hypothesis generation. Moreover, network based information can provide additional biological context through functional interaction data, yet this is often overlooked by existing tools. ResultsWe developed GeDi, an R/Bioconductor package designed to streamline and standardize the interpretation of functional enrichment results. GeDi aggregates gene sets into biologically meaningful clusters using a suite of gene set distance metrics and clustering algorithms, aimed to reduce redundancy and improve clarity. GeDi also enables the integration of protein-protein interaction (PPI) data, through the implementation of a weighted distance metric, providing a richer biological context by capturing functional connectivity between pathways and their components. The package offers visualizations, aggregation, and automated reporting, and is available as both a stand-alone R-package and an interactive Shiny application. ConclusionGeDi facilitates clearer, faster interpretation of enrichment results by combining clustering and network context. Application to a public RNA-seq dataset revealed coherent biological themes, supporting both experimental and computational research. GeDi is freely available in the Bioconductor project under the MIT license (https://bioconductor.org/packages/GeDi), and a demo instance is accessible on the Shiny server (http://shiny.imbei.uni-mainz.de:3838/GeDi).

bioinformatics↗

bioRxiv2025

Exploration of oncogenic cooperation between germline variation and somatic mutation in prostate cancer progression

Prostate carcinoma (PCa) is the most common cancer of men, associated with a still unresolved issue of accurate risk-stratification. While recent advances in omics technologies have provided clues as to how molecular changes shape the onset and progression of PCa, it remains largely unclear whether germline variants and somatic mutations cooperate to contribute to PCa progression and outcome. Thus, we explored whether oncogenic cooperation between regulatory germline variants and somatic driver mutations can help explain why some PCa patients develop a more aggressive phenotype, which may have implications for risk-adapted medical treatment. Here, by employing an integrative functional genomics approach, we identified receptor-type protein-tyrosine phosphatase kappa (PTPRK) as a TMPRSS2::ERG (TE)-modulated gene associated with PCa progression whose expression is controlled by cooperation of the TE-fusion with a regulatory single nucleotide polymorphism (SNP). Analysis of available clinically annotated patient cohorts demonstrated that PTPRK is overexpressed in TE-positive PCa tumors and associated with higher Gleason scores and metastatic disease. TE knockdown in PCa cell lines reduced PTPRK expression, while ectopic overexpression of the fusion in TE-negative PCa cell lines and prostatic epithelium cells induced its expression. Functionally, PTPRK silencing inhibited cellular proliferation, cell cycle progression, and clonogenic growth of PCa cells, which was mirrored by dysregulation of corresponding gene and protein signatures in global transcriptomic and phospho-proteomic analyses after PTPRK knockdown. Analysis of TE ChIP-Seq and Hi-C data from PCa cells highlighted a proximal TE-bound DNA element whose TE-dependent enhancer activity was validated in reporter assays and which could be abrogated by a regulatory SNP. Collectively, our results provide evidence of how exploration of oncogenic cooperation may help to identify novel biomarkers and potentially druggable pathways and highlight the role of the regulatory genome in PCa progression.

cancer biology↗

bioRxiv2023

Genomic and phenotypic stability of fusion-driven pediatric Ewing sarcoma cell lines

Human cancer cell lines are the mainstay of cancer research. Recent reports showed that highly mutated adult carcinoma cell lines (mainly HeLa and MCF-7) present striking diversity across laboratories and that long-term continuous culturing results in genomic/transcriptomic heterogeneity with strong phenotypical implications. This highlighted how despite human cell line models being powerful tools for cancer research, the findings derived from their use may present limitations in terms of reproducibility. However, to what extent these conclusions can be generalized to the majority of cancer cell lines remained unexplored. Here, we hypothesized that oligomutated pediatric sarcoma cell lines driven by a chimeric oncogenic transcription factor (COTF), such as Ewing sarcoma (EwS), were genetically and phenotypically more stable than the previously investigated (adult) carcinoma cell lines. A comprehensive molecular and phenotypic characterization of multiple EwS cell line strains in direct comparison to the HeLa and MCF-7 cell lines, together with a simultaneous analysis during 12 months of continuous cell culture showed that COTF-driven pediatric sarcoma cell line strains are genomically more stable than adult carcinoma strains, display remarkably stable and homogenous transcriptomes, and exhibit uniform and stable drug response. The analysis of multiple EwS cell lines subjected to long-term continuous culture conditions revealed that variable degrees of genomic/transcriptomic/phenotypic may be observed among COTF-driven cell lines, further exemplifying that the potential for reproducibility of in vitro scientific results may be rather understood as a spectrum, even within the same tumor entity.

cell biology↗

bioRxiv2021

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation

Cell lines have been essential for major discoveries in cancer including Ewing sarcoma (EwS). EwS is a highly aggressive pediatric bone or soft-tissue cancer characterized by oncogenic EWSR1-ETS fusion transcription factors converting polymorphic GGAA-microsatellites (mSats) into neo-enhancers. However, further detailed mechanistic evaluation of gene regulation in EwS have been hindered by the limited number of well-characterized cell line models. Here, we present the Ewing Sarcoma Cell Line Atlas (ESCLA) comprising 18 EwS cell lines with inducible EWSR1-ETS knockdown that were profiled by whole-genome-sequencing, DNA methylation arrays, gene expression and splicing arrays, mass spectrometry-based proteomics, and ChIP-seq for EWSR1-ETS and histone marks. Systematic analysis of these multi-dimensional data illuminated hundreds of new potential EWSR1-ETS target genes, the nature of EWSR1-ETS-preferred GGAA-mSats, and potential indirect modes of EWSR1-ETS-mediated gene regulation. Moreover, we identified putative co-regulatory transcription factors and heterogeneously regulated EWSR1-ETS target genes that may have implications for the clinical heterogeneity of EwS. Collectively, our freely available ESCLA constitutes an extremely rich resource for EwS research and highlights the power of leveraging multidimensional and comprehensive datasets to unravel principles of heterogeneous gene regulation by dominant fusion oncogenes.

genomics↗

bioRxiv2021

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BackgroundThe interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats - normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. ResultsWe developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. ConclusionGeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both birds-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

bioinformatics↗

bioRxiv2020

Network reconstruction for trans acting genetic loci using multi-omics data and prior information

BackgroundMolecular multi-omics data provide an in-depth view on biological systems, and their integration is crucial to gain insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans -QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information has been proposed to alleviate network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans -QTL hotspots using human cohort data and data-driven prior information. ResultsWe devised a strategy to integrate QTL with human population scale multi-omics data and comprehensively curated prior information from large-scale biological databases. State-of-the art network inference methods applied to these data and priors were used to recover the regulatory networks underlying trans -QTL hotspots. We benchmarked inference methods and showed, that Bayesian strategies using biologically-informed priors outperform methods without prior data in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. ConclusionWe demonstrate, that existing biological knowledge can be leveraged for the integrative analysis of networks underlying trans associations to deduce novel hypotheses on cell regulatory mechanisms.

bioinformatics↗

bioRxiv2020

Genome wide association analysis in dilated cardiomyopathy reveals two new key players in systolic heart failure on chromosome 3p25.1 and 22q11.23

We present the results of the largest genome wide association study (GWAS) performed so far in dilated cardiomyopathy (DCM), a leading cause of systolic heart failure and cardiovascular death, with 2,719 cases and 4,440 controls in the discovery population. We identified and replicated two new DCM-associated loci, one on chromosome 3p25.1 (lead SNP rs62232870, p = 8.7 x 10-11 and 7.7 x 10-4 in the discovery and replication step, respectively) and the second on chromosome 22q11.23 (lead SNP rs7284877, p = 3.3 x 10-8 and 1.4 x 10-3 in the discovery and replication step, respectively) while confirming two previously identified DCM loci on chromosome 10 and 1, BAG3 and HSPB7. The genetic risk score constructed from the number of lead risk-alleles at these four DCM loci revealed that individuals with 8 risk-alleles were at a 27% increased risk of DCM compared to individuals with 5 risk alleles (median of the referral population). We estimated the genome wide heritability at 31% {+/-} 8%. In silico annotation and functional 4C-sequencing analysis on iPSC-derived cardiomyocytes strongly suggest SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine and beta-alanine transporter whose involvement in myocardial dysfunction and DCM is supported by recent observations in humans and mice. Although less easy to discriminate the better candidate at the 22q11.23 locus, SMARCB1 appears as the strongest one. This study provides both a better understanding of the genetic architecture of DCM and new knowledge on novel biological pathways underlying heart failure, with the potential for a therapeutic perspective.

genomics↗

bioRxiv2019

A genome-wide analysis of DNA methylation identifies a novel association signal for Lp(a) concentrations in the LPA promoter

Lipoprotein(a) [Lp(a)] is a major cardiovascular risk factor, which is largely genetically determined by one major gene locus, the LPA gene. Many aspects of the transcriptional regulation of LPA are poorly understood and the role of epigenetics has not been addressed yet. Therefore, we conducted an epigenome-wide analysis of DNA methylation on Lp(a) levels in two population-based studies (total n=2208). We identified a CpG site in the LPA promoter which was significantly associated with Lp(a) concentrations. Surprisingly, the identified CpG site was found to overlap the SNP rs76735376. We genotyped this SNP de-novo in three studies (total n=7512). The minor allele of rs76735376 (1.1% minor allele frequency) was associated with increased Lp(a) values (p=1.01e-59) and explained 3.5% of the variation of Lp(a). Statistical mediation analysis showed that the effect on Lp(a) is rather originating from the base change itself and is not mediated by DNA methylation levels. This finding is supported by eQTL data from 153 liver tissue samples from the GTEx project, which shows a significant association of the rs76735376 minor allele with increased LPA expression. In summary, our data supports a functional role of rs76735376 in the regulation of LPA expression.

genetics↗

bioRxiv2019

Biomarker-based outcome prediction in prostate adenocarcinoma depends on the TMPRSS2-ERG status

BackgroundProstate adenocarcinoma (PCa) with/without the TMPRSS2-ERG (T2E)-fusion represent distinct molecular subtypes. ObjectiveTo investigate gene-signatures associated with metastasis in T2E-positive and -negative PCa, and to identify and validate subtype-specific prognostic biomarkers. Design, setting and participantsGene expression and clinicopathological data of two discovery PCa cohorts (total n=783) were separately analyzed regarding the T2E-status. Selected subtype-specific biomarkers were validated in two additional cohorts (total n=405). Outcome measurements and statistical analysisFrom both discovery cohorts, we generated two gene lists ranked by their differential intratumoral expression in patients with/without metastases stratified by T2E-status, which were subjected to gene set enrichment and leading-edge analyses. The resulting top 20 gene-signatures of both gene lists associated with metastasis were analyzed for overlaps between T2E-positive and -negative cases. Genes shared by several functional gene-signatures were tested for their association with event-free survival using the Kaplan-Meier method in a validation cohort. Immunohistochemistry was performed in another validation cohort. Results and limitationsMetastatic T2E-positive and -negative PCa are characterized by different gene-signatures. Five genes (ASPN, BGN, COL1A1, RRM2 and TYMS) were identified whose high expression was significantly associated with worse outcome exclusively in T2E-negative PCa. This was validated in an independent cohort for all genes and additionally for RRM2 by immunohistochemistry in a separate validation cohort. No prognostic biomarkers were identified exclusively for T2E-positive tumors. ConclusionsOur study demonstrates that the prognostic value of biomarkers critically depends on the molecular subtype, i.e. the T2E-status, which should be considered when screening for and applying novel prognostic biomarkers for outcome prediction in PCa. Patient summaryOutcome prediction for PCa is complex. The results of this study highlight that the validity of prognostic biomarkers depends on the molecular subtype, specifically the presence/absence of T2E. The reported new subtype-specific biomarkers exemplify that biomarker-based outcome prediction in PCa should consider the T2E-status.

cancer biology↗

bioRxiv2019

Cis-epistasis at the LPA locus and risk of coronary artery disease

Identification of epistasis affecting complex human traits has been challenging. Focusing on known coronary artery disease (CAD) risk loci, we explore pairwise statistical interactions between 8,068 SNPs from ten CAD genome-wide association studies (n=30,180). We discovered rs1800769 and rs9458001 in the vicinity of the LPA locus to interact in modulating CAD risk (P=1.75x10-13). Specific genotypes (e.g., rs1800769 CT) displayed either significantly decreased or increased risk for CAD in the context of genotypes of the respective other SNP (e.g., rs9458001 GG vs. AA). In the UK Biobank (n=450,112) significant interaction of this SNP pair was replicated for CAD (P=3.09x10-22), and was also found for aortic valve stenosis (P=6.95x10-7) and peripheral arterial disease (P=2.32x10-4). Identical interaction patterns affected circulating lipoprotein(a) (n=5,953; P=8.7x10-32) and hepatic apolipoprotein(a) (apo(a)) expression (n=522, P=2.6x10-11). We further interrogated potential biological implications of the variants and propose a mechanism explaining epistasis that ultimately may translate to substantial cardiovascular risks.

genetics↗

bioRxiv2018

Evaluation of the causal effect of fibrinogen on incident coronary heart disease via Mendelian randomization

BackgroundFibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies.\n\nMethods and FindingsWe evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score incorporated data from 11 European-ancestry prospective cohorts to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI).\n\nIn single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models.\n\nConclusionsA small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.\n\nAuthor SummaryInitial Mendelian Randomization (MR) analyses of the causal effect of fibrinogen on coronary heart disease (CHD) utilized single variants and did not take advantage of modern, multivariant approaches. This manuscript provides an important update to these initial analyses by incorporating larger sample sizes and employing multiple, modern multi-variant MR approaches to account for pleiotropy. We used incident cases to perform a MR study of the causal effect of fibrinogen on incident CHD and the nested outcome of myocardial infarction (MI) using an allele score approach. Then using data from a case-control genome-wide association study for CHD and MI we performed two sample MR analyses with multiple, pleiotropy robust approaches. Overall, the results indicated that associations between fibrinogen and CHD in observational studies are likely upwardly biased from any underlying causal effect. Single variant MR approaches show little evidence of a causal effect of fibrinogen on CHD or MI. Multi-variant MR analyses of fibrinogen on CHD indicate there may be a small positive effect, however this result needs to be interpreted carefully as the 95% confidence intervals were still consistent with a null effect. Multi-variant MR approaches did not suggest evidence of even a small causal effect of fibrinogen on MI.

genetics↗

bioRxiv2018

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

BACKGROUNDUntargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in epidemiological studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.\n\nMETHODSWe investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established genetically metabolic quantitative trait loci.\n\nRESULTSRun day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations (MICE) performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.\n\nCONCLUSIONMissing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.\n\nKey messagesO_LIUntargeted MS-based metabolomics data show missing values due to both batch-specific LOD-based and non-LOD-based effects.\nC_LIO_LIStatistical evaluation of multiple imputation methods was conducted on both simulated and real datasets.\nC_LIO_LIBiological evaluation on real data assessed the ability of imputation methods to preserve statistical inference of biochemical pathways and correctly estimate effects of genetic variants on metabolite levels.\nC_LIO_LIKNN-based imputation on observations with variable pre-selection and K = 10 showed robust performance for all data scenarios across all evaluation schemes.\nC_LI

systems biology↗

bioRxiv2016

Network based conditional genome wide association analysis of human metabolomics

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

genomics↗

bioRxiv2016

Connecting genetic risk to disease endpoints through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence for mapping disease associations and translating them into clinical applications. However, although hundreds of genetic risk variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits across multiple chromosome locations are key in establishing functional links between GWAS-identified risk-variants and disease endpoints. Here, we describe a GWAS performed with a highly multiplexed aptamer-based affinity proteomics platform. We quantified associations between protein level changes and gene variants in a German cohort and replicated this GWAS in an Arab/Asian cohort. We identified many independent, SNP-protein associations, which represent novel, inter-chromosomal links, related to autoimmune disorders, Alzheimer's disease, cardiovascular disease, cancer, and many other disease endpoints. We integrated this information into a genome-proteome network, and created an interactive web-tool for interrogations. Our results provide a basis for new approaches to pharmaceutical and diagnostic applications.

genetics↗