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Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects ( r BMI   = 0.11, P BMI   = 2.0 × 10 −51 and r TG   = 0.13, P TG   = 1.1 × 10 −68 ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones. Identification of gene expression changes between healthy and diseased individuals can reveal mechanistic insights and biomarkers. Here, the authors propose a bi-directional transcriptome-wide Mendelian Randomization approach to assess causal effects between gene expression and complex traits.

The intestinal microbiome is implicated as an important modulating factor in multiple inflammatory 1 , 2 , neurologic 3 and neoplastic diseases 4 . Recent genome-wide association studies yielded inconsistent, underpowered and rarely replicated results such that the role of human host genetics as a contributing factor to microbiome assembly and structure remains uncertain 5 – 11 . Nevertheless, twin studies clearly suggest host genetics as a driver of microbiome composition 11 . In a genome-wide association analysis of 8,956 German individuals, we identified 38 genetic loci to be associated with single bacteria and overall microbiome composition. Further analyses confirm the identified associations of ABO histo-blood groups and FUT2 secretor status with Bacteroides and Faecalibacterium spp. Mendelian randomization analysis suggests causative and protective effects of gut microbes, with clade-specific effects on inflammatory bowel disease. This holistic investigative approach of the host, its genetics and its associated microbial communities as a ‘metaorganism’ broaden our understanding of disease etiology, and emphasize the potential for implementing microbiota in disease treatment and management. Genome-wide association analysis of 8,956 German individuals identifies 38 genetic loci associated with single bacteria and overall microbiome composition.

The collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, such as by the General Data Protection Regulation (GDPR). These regulations mandate strict data security and data protection and, thus, create major challenges for collecting and using large data sets. Technologies such as federated learning (FL), especially paired with differential privacy (DP) and secure multiparty computation (SMPC), aim to solve these challenges. This scoping review aimed to summarize the current discussion on the legal questions and concerns related to FL systems in medical research. We were particularly interested in whether and to what extent FL applications and training processes are compliant with the GDPR data protection law and whether the use of the aforementioned privacy-enhancing technologies (DP and SMPC) affects this legal compliance. We placed special emphasis on the consequences for medical research and development. We performed a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We reviewed articles on Beck-Online, SSRN, ScienceDirect, arXiv, and Google Scholar published in German or English between 2016 and 2022. We examined 4 questions: whether local and global models are \"personal data\" as per the GDPR; what the \"roles\" as defined by the GDPR of various parties in FL are; who controls the data at various stages of the training process; and how, if at all, the use of privacy-enhancing technologies affects these findings. We identified and summarized the findings of 56 relevant publications on FL. Local and likely also global models constitute personal data according to the GDPR. FL strengthens data protection but is still vulnerable to a number of attacks and the possibility of data leakage. These concerns can be successfully addressed through the privacy-enhancing technologies SMPC and DP. Combining FL with SMPC and DP is necessary to fulfill the legal data protection requirements (GDPR) in medical research dealing with personal data. Even though some technical and legal challenges remain, for example, the possibility of successful attacks on the system, combining FL with SMPC and DP creates enough security to satisfy the legal requirements of the GDPR. This combination thereby provides an attractive technical solution for health institutions willing to collaborate without exposing their data to risk. From a legal perspective, the combination provides enough built-in security measures to satisfy data protection requirements, and from a technical perspective, the combination provides secure systems with comparable performance with centralized machine learning applications.

Genome-wide association studies (GWAS) for atrial fibrillation (AF) have uncovered numerous disease-associated variants. Their underlying molecular mechanisms, especially consequences for mRNA and protein expression remain largely elusive. Thus, refined multi-omics approaches are needed for deciphering the underlying molecular networks. Here, we integrate genomics, transcriptomics, and proteomics of human atrial tissue in a cross-sectional study to identify widespread effects of genetic variants on both transcript ( cis -eQTL) and protein ( cis -pQTL) abundance. We further establish a novel targeted trans -QTL approach based on polygenic risk scores to determine candidates for AF core genes. Using this approach, we identify two trans -eQTLs and five trans -pQTLs for AF GWAS hits, and elucidate the role of the transcription factor NKX2-5 as a link between the GWAS SNP rs9481842 and AF. Altogether, we present an integrative multi-omics method to uncover trans -acting networks in small datasets and provide a rich resource of atrial tissue-specific regulatory variants for transcript and protein levels for cardiovascular disease gene prioritization. Numerous disease-associated variants have been described in GWAS for atrial fibrillation. Here the authors integrate omics data to investigate the consequences of genetic variants for transcript and protein levels in the atrium of the human heart. With this multi-omics approach, authors reveal the regulatory network underlying atrial fibrillation and provide a resource for cardiac gene prioritization.

The kidneys integrate information from continuous systemic processes related to the absorption, distribution, metabolism and excretion (ADME) of metabolites. To identify underlying molecular mechanisms, we performed genome-wide association studies of the urinary concentrations of 1,172 metabolites among 1,627 patients with reduced kidney function. The 240 unique metabolite–locus associations (metabolite quantitative trait loci, mQTLs) that were identified and replicated highlight novel candidate substrates for transport proteins. The identified genes are enriched in ADME-relevant tissues and cell types, and they reveal novel candidates for biotransformation and detoxification reactions. Fine mapping of mQTLs and integration with single-cell gene expression permitted the prioritization of causal genes, functional variants and target cell types. The combination of mQTLs with genetic and health information from 450,000 UK Biobank participants illuminated metabolic mediators, and hence, novel urinary biomarkers of disease risk. This comprehensive resource of genetic targets and their substrates is informative for ADME processes in humans and is relevant to basic science, clinical medicine and pharmaceutical research. Genome-wide association analysis of 1,172 urinary metabolites identifies 240 metabolite–locus associations that when combined with UK Biobank data suggest novel metabolic mediators of disease and markers of disease risk.

Aortic dimensions and distensibility are key risk factors for aortic aneurysms and dissections, as well as for other cardiovascular and cerebrovascular diseases. We present genome-wide associations of ascending and descending aortic distensibility and area derived from cardiac magnetic resonance imaging (MRI) data of up to 32,590 Caucasian individuals in UK Biobank. We identify 102 loci (including 27 novel associations) tagging genes related to cardiovascular development, extracellular matrix production, smooth muscle cell contraction and heritable aortic diseases. Functional analyses highlight four signalling pathways associated with aortic distensibility (TGF-β, IGF, VEGF and PDGF). We identify distinct sex-specific associations with aortic traits. We develop co-expression networks associated with aortic traits and apply phenome-wide Mendelian randomization (MR-PheWAS), generating evidence for a causal role for aortic distensibility in development of aortic aneurysms. Multivariable MR suggests a causal relationship between aortic distensibility and cerebral white matter hyperintensities, mechanistically linking aortic traits and brain small vessel disease. Aortic distensibility is a risk factor for multiple cardiovascular events, but the genetic etiology is not well understood. Here, the authors identify genetic variants linked to aortic distensibility, highlighting mechanistic pathways and causal relationships between distensibility and both aortic aneurysms and brain small vessel disease.

Staphylococcus aureus is a major human pathogen that causes a range of infections from acute invasive to chronic and difficult-to-treat. Infection strategies associated with persisting S. aureus infections are bacterial host cell invasion and the bacterial ability to dynamically change phenotypes from the aggressive wild-type to small colony variants (SCVs), which are adapted for intracellular long-term persistence. The underlying mechanisms of the bacterial switching and adaptation mechanisms appear to be very dynamic, but are largely unknown. Here, we analyzed the role and the crosstalk of the global S. aureus regulators agr, sarA and SigB by generating single, double and triple mutants, and testing them with proteome analysis and in different in vitro and in vivo infection models. We were able to demonstrate that SigB is the crucial factor for adaptation in chronic infections. During acute infection, the bacteria require the simultaneous action of the agr and sarA loci to defend against invading immune cells by causing inflammation and cytotoxicity and to escape from phagosomes in their host cells that enable them to settle an infection at high bacterial density. To persist intracellularly the bacteria subsequently need to silence agr and sarA. Indeed agr and sarA deletion mutants expressed a much lower number of virulence factors and could persist at high numbers intracellularly. SigB plays a crucial function to promote bacterial intracellular persistence. In fact, ΔsigB-mutants did not generate SCVs and were completely cleared by the host cells within a few days. In this study we identified SigB as an essential factor that enables the bacteria to switch from the highly aggressive phenotype that settles an acute infection to a silent SCV-phenotype that allows for long-term intracellular persistence. Consequently, the SigB-operon represents a possible target to develop preventive and therapeutic strategies against chronic and therapy-refractory infections.

The tegument, as the surface layer of adult male and female Schistosoma spp. represents the protective barrier of the worms to the hostile environment of the host bloodstream. Here we present the first comparative analysis of sex-specific tegument proteins of paired or virgin Schistosoma mansoni . We applied a new and highly sensitive workflow, allowing detection of even low abundance proteins. Therefore, a streptavidin–biotin affinity purification technique in combination with single pot solid-phase enhanced sample preparation was established for subsequent LC–MS/MS analysis. We were able to identify 1519 tegument proteins for male and female virgin and paired worms and categorized them by sex. Bioinformatic analysis revealed an involvement of female-specific tegument proteins in signaling pathways of cellular processes and antioxidant mechanisms. Male-specific proteins were found to be enriched in processes linked to phosphorylation and signal transduction. This suggests a task sharing between the sexes that might be necessary for survival in the host. Our datasets provide a basis for further studies to understand and ultimately decipher the strategies of the two worm sexes to evade the immune system.

The global obesity epidemic constitutes a major cause of morbidity and mortality challenging public health care systems worldwide. Thus, a better understanding of its pathophysiology and the development of novel therapeutic options are urgently needed. Recently, alterations of the intestinal microbiome in the obese have been discussed as a promoting factor in the pathophysiology of obesity and as a contributing factor to related diseases such as type 2 diabetes and metabolic syndrome. The present pilot study investigated the effect of a structured weight loss program on fecal microbiota in obese type 2 diabetics. Twelve study subjects received a low-calorie formula diet for six weeks, followed by a nine week food reintroduction and stabilization period. Fecal microbiota were determined by 16S rRNA gene sequencing of stool samples at baseline, after six weeks and at the end of the study after fifteen weeks. All study subjects lost weight continuously throughout the program. Changes in fecal microbiota were most pronounced after six weeks of low-calorie formula diet, but reverted partially until the end of the study. However, the gut microbiota phylogenetic diversity increased persistently. The abundance of Collinsella, which has previously been associated with atherosclerosis, decreased significantly during the weight loss program. This study underlines the impact of dietary changes on the intestinal microbiome and further demonstrates the beneficial effects of weight loss on gut microbiota. Trial registration: ClinicalTrials.gov NCT02970838.

Genomic data holds huge potential for medical progress but requires strict safety measures due to its sensitive nature to comply with data protection laws. This conflict is especially pronounced in genome-wide association studies (GWAS) which rely on vast amounts of genomic data to improve medical diagnoses. To ensure both their benefits and sufficient data security, we propose a federated approach in combination with privacy-enhancing technologies utilising the findings from a systematic review on federated learning and legal regulations in general and applying these to GWAS.