Focal segmental glomerulosclerosis (FSGS) is a major cause of nephrotic syndrome and progression to end-stage renal disease, yet its molecular pathogenesis remains still incompletely defined. While transcriptional alterations in podocytes have been extensively characterized, the contribution of post-transcriptional regulatory mechanisms is poorly understood. Here, we combined a zebrafish podocyte-specific injury model with glomerulus-resolved transcriptomic profiling to dissect RNA regulatory alterations during FSGS progression. Integrated analyses of bulk RNA sequencing, small RNA profiling, and alternative splicing revealed pronounced, time-dependent remodeling of the glomerular transcriptome. We demonstrate that podocyte injury is associated with loss of key podocyte-specific proteins, activation of inflammatory pathways, remodeling of the extracellular matrix, and altered microRNA expression, such as miR-21 and miR-193. Moreover, we found that alternative splicing influences key podocyte gene expression, affecting genes critical for slit diaphragm integrity, actin cytoskeleton organization, and glomerular basement membrane stability. Isoform analyses identified FSGS-associated isoform switches in SRSF3 and EPB41L5. Importantly, these changes were also evident in glomeruli from FSGS patients, demonstrating that the zebrafish model recapitulates key molecular features of human disease and highlighting alternative splicing as a central regulatory mechanism in FSGS. Post-transcriptional regulations, such as alternative splicing and microRNA dysregulation, are identified as central and underappreciated processes in injured podocytes in focal segmental glomerulosclerosis (FSGS), with disease-associated isoform switches in SRSF3 and EPB41L5. Post-transcriptional regulations, such as alternative splicing and microRNA dysregulation, are identified as central and underappreciated processes in injured podocytes in focal segmental glomerulosclerosis (FSGS), with disease-associated isoform switches in SRSF3 and EPB41L5.

Abstract Background Gene expression profiling is widely used to investigate disease mechanisms, but classical approaches such as differential expression or pairwise correlation analyses provide limited interpretability. Network-based differential co-expression methods that model conditional dependencies through partial correlations offer richer insights, yet their application in high-dimensional settings requires estimation of precision matrices. Numerous precision matrix estimation methods (PMEMs) have been proposed, but their relative performance under various conditions remains unclear. Results Simulated gene expression datasets with known ground truth correlation structures were used to benchmark a broad set of PMEMs. Performance was strongly affected by data characteristics, including covariance structure, matrix density, covariance values, sample size-to-dimension ratio, and sampling distribution. Among the evaluated methods, GLassoElnetFast consistently showed the highest accuracy in recovering differential edges, although high signal-to-noise ratios and sufficient sample sizes remain essential for reliable inference. Conclusions Evaluation across diverse simulation conditions demonstrated that no single metric or condition was sufficient to assess PMEM performance. Therefore, previous less extensive evaluations risked misleading conclusions. Our simulation and benchmarking framework supports future method development and ensures reproducible evaluation of newly developed approaches.

Body size represents a complex phenotype driven by genetic variation and epigenetic regulation, with the molecular processes underlying this trait remaining a central challenge to disentangle. To elucidate these fundamental mechanisms, we apply a multi-omics approach that combines ROH-based selection mapping with growth plate epigenomics in pigs. Taking advantage of divergent selection that separates pigs into miniature and larger-sized groups, we target genomic regions under this intense selection for height, which harbour functional variants with pronounced effects. We assemble a multi-omics dataset, identifying homozygous alternative SNPs in Aachen Minipigs and Mini-LEWE predicted to affect cis-regulatory elements potentially interacting with differentially expressed genes that drive body size in breed-dependent ways. Our results point to an lncRNA (ENSSSCG00000048200) near SDR16C5 and PLAG1, HPX and NET-related pathways, as central players in the growth plates. In summary, our study offers a multi-layered characterisation of regulatory mechanisms in the growth plates in the pig model.

Changes in alternative splicing between groups or conditions contribute to protein-protein interaction rewiring, a consequence often neglected in data analysis. The web server and database DIGGER overcomes this limitation by augmenting a protein-protein interaction network with domain-domain interactions and splicing information. Here, we present DIGGER 2.0, which now features both experimental and newly added predicted domain-domain interactions. In addition to the human interactome, DIGGER 2.0 adds support for mouse as an important model organism. Additionally, we integrated the splicing analysis tool NEASE, which allows users to perform online splicing- and interactome-informed enrichment analysis on RNA-seq data. In two application cases (multiple sclerosis and mice models of cardiac diseases), we show the utility of DIGGER 2.0 for deeper exploration and functional interpretation of changes in alternative splicing in human and mouse disorders. DIGGER 2.0 is available at https://exbio.wzw.tum.de/digger/.

This study examines different configurations of deep convolutional neural networks (CNNs) and the effect of using domain-specific transfer learning for distinguishing Alzheimer's Disease and Mild Cognitive Impairment from normal controls. The data used to train our models was provided by ADNI and included 1118 3D FDG-PET scans in total. We train a binary and a multiclass classifier, as well as chains of binary classifiers, for consecutive multiclass classification. Two chains were trained with different orders: chain A classified cognitively normal (CN) vs. non-CN, followed by Alzheimer's disease (AD) vs. mild cognitive impairment (MCI). Classifier chain B classified AD vs. non-AD first, followed by MCI vs. CN. All classifiers were trained with and without the use of domain-specific transfer learning, using weights from Med3D. All models achieve comparable performance to the state-of-the-art. Classifier chain A even achieved superior performance with an accuracy of 96 %, F1 score of 95 % and AUROC of 99 %. Using domain-specific transfer learning resulted in worse performance among the majority of the models, producing decreases in accuracy of up to 55 %. These results show the potential of binary classifier chains and open some questions about the use of domain-specific transfer learning.

Endoplasmic reticulum unfolded protein responses contribute to cancer development, with activating transcription factor 6 (ATF6) involved in microbiota-dependent tumorigenesis. Here we show the clinical relevance of ATF6 in individuals with early-onset and late colorectal cancer, and link ATF6 signalling to changes in lipid metabolism and intestinal microbiota. Transcriptional analysis in intestinal epithelial cells of ATF6 transgenic mice (nATF6IEC) identifies bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype labelling confirm ATF6-related enrichment of long-chain fatty acids in colonic tissue of humans, mice and organoids. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirm the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumour-relevant microbial taxa, including Desulfovibrio fairfieldensis, is mechanistically linked to long-chain fatty acid exposure using bioorthogonal non-canonical amino acid tagging, and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signalling to select for tumour-promoting microbiota by altering lipid metabolism.

Background Focal Segmental Glomerulosclerosis (FSGS) is a severe kidney disorder with complex and not yet fully understood pathogenesis. Alternative splicing (AS) – the generation of distinct protein isoforms from the same gene – might play a critical role by the regulation of gene functions and disease development. Methods To investigate the role of AS in FSGS, we used a zebrafish model, which mimics key human FSGS features, including foot process effacement, matrix accumulation, podocyte detachment and parietal epithelial cell activation. We performed total RNA sequencing of isolated zebrafish glomeruli and whole larvae, followed by integrative bioinformatic analysis to identify AS events and regulatory miRNAs. Results Our data revealed a downregulation of essential podocyte genes ( nphs1, nphs2, podxl, wt1 ) and an inhibition of pathways associated with nephron development and cytoskeletal organization. We also observed increased expression of the transcription factor stat3 and disease-associated miRNAs such as miR-21 and miR-193. AS analysis identified approximately ∼7,000 splicing events, primarily exon skipping (∼80%), affecting genes such as nphs1 , magi2 , and ptpro . A total of 136 and 612 alternatively spliced genes were found at 5 and 6 days post-fertilization (dpf), respectively. Isoform switch analysis uncovered 70 genes affected by AS in FSGS, including epb41l5 (linked to podocyte adhesion), fgfr1a (fibroblast growth signaling), and members of the SRSF splicing factor family (e.g., srsf3a ). Conclusions These findings emphasize the importance of transcriptional and post-transcriptional regulation, including AS, in FSGS pathogenesis. Furthermore, they support the zebrafish model as a valuable system for identifying novel mechanisms and potential therapeutic targets for kidney diseases. ### Competing Interest Statement The authors have declared no competing interest.

Key PointsMechanical stretch induced over 3000 alternative splicing events in podocytes, affecting gene expression and protein abundance.Seventeen genes showed consistent splicing events across multiple analysis tools, with key isoform changes.Shroom3 and Myl6 underwent isoform switches under mechanical stretch, altering the C-terminal sequence and interaction properties of Myl6.BackgroundAlterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension.MethodsMurine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing, and several bioinformatic alternative splicing tools.ResultsUsing transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance because of mechanical stretch. RNA-Seq identified over 3000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by quantitative RT-PCR and in situ hybridization. In addition, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence.ConclusionsA comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

Background/Aims Hepatitis C virus (HCV) infection remains a global health challenge, leading to chronic liver disease, cirrhosis, and hepatocellular carcinoma (HCC). Despite the high efficacy of direct-acting antiviral therapy in achieving sustained virologic response (SVR), concerns persist regarding long-term immune alterations and residual risks, particularly in cirrhotic patients. Methods This study investigates 75 soluble immune mediator (SIM) profiles in 102 chronic HCV patients, stratified by cirrhosis status, at therapy initiation, end of treatment, and long-term follow-up (median 96 weeks). Findings were compared with 51 matched healthy controls and validated in an independent cohort of 47 cirrhotic patients, 17 of whom developed HCC. Results We observed significant SIM alterations at baseline, with cirrhotic patients displaying a more profoundly dysregulated inflammatory milieu. Despite an overall decline in inflammatory markers following SVR, persistent alterations were evident, particularly in cirrhotic patients. Notably, those with liver stiffness exceeding 14 kPa exhibited sustained inflammatory dysregulation, correlating with liver elastography values. Key SIM such as interleukin (IL)-6, IL-8, urokinase plasminogen activator, and hepatocellular growth factor remained elevated and were associated with HCC development. Network analysis highlighted their roles in liver fibrosis, regeneration, and carcinogenesis. Conclusions These findings underscore the importance of early antiviral intervention to prevent cirrhosis-related sequelae. Future studies should explore the mechanistic pathways linking chronic inflammation, fibrosis, and oncogenesis to identify predictive biomarkers and novel therapeutic targets. Addressing persistent immune alterations post-HCV clearance may improve long-term outcomes, particularly in patients with advanced liver disease.

Transcription factors play important roles in maintaining normal biological function, and their dys-regulation can lead to the development of diseases. Identifying candidate transcription factors involved in disease pathogenesis is thus an important task for deriving mechanistic insights from gene expression data. We developed Transcriptional Regulator Identification using Prize-collecting Steiner trees (TRIPS), a workflow for identifying candidate transcriptional regulators from case-control expression data. In the first step, TRIPS combines the results of differential expression analysis with a disease module identification step to retrieve perturbed subnetworks comprising an expanded gene list. TRIPS then solves a prize-collecting Steiner tree problem on a gene regulatory network, thereby identifying candidate transcriptional modules and transcription factors. We compare TRIPS to relevant methods using publicly available disease datasets and show that the proposed workflow can recover known disease-associated transcription factors with high precision. Network perturbation analyses demonstrate the reliability of TRIPS results. We further evaluate TRIPS on Alzheimer’s disease, diabetic kidney disease, and prostate cancer single-cell omics datasets. Overall, TRIPS is a useful approach for prioritizing transcriptional mechanisms for further downstream analyses.### Competing Interest StatementThe authors have declared no competing interest.

A key parameter in the experimental design of RNA-seq projects is the choice of sequencing depth. Considering a limited budget, one needs to find a tradeoff between the number of samples and the sensitivity of the analysis, particularly concerning lowly expressed genes. While previous studies have proposed a lower bound for the comprehensive analysis of differential gene expression, for the analysis of alternative splicing, it has only been proposed for human adipose tissue. However, alternative splicing differs across tissues and conditions. We analyzed publicly available and newly generated deep-sequenced paired-end RNA-seq samples (between 150 and >500 million reads, read length 50-150 bp) from human buffy coat cells and diverse sets of tissues, including gluteal subcutaneous fat, heart, and hypothalamus. Our results show that the sequencing depth typically used in published cohorts is not sufficient to comprehensively capture the landscape of alternative splicing. This motivates the use of deeper sequencing or long-read technologies in future studies. Toward this goal, we offer guidelines for choosing a suitable sequencing depth. ### Competing Interest Statement The authors have declared no competing interest.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Abstract Background Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). Methods The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study’s main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. Ethics and dissemination The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. Trial registration German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. Availability and implementation BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

Finding new indications for approved drugs is a promising alternative to the often very lengthy and expensive process of de novo drug development. Systems medicine has brought forth several different approaches to tackle this important task. We recently published NeDRex, a network medicine tool for the identification of disease modules and drug repurposing. NeDRex-Web (https://web.nedrex.net) brings existing and new features of the NeDRex platform to a user-friendly and research-oriented web application, enabling online exploration of large heterogeneous molecular networks. Focusing mainly on drug repurposing, NeDRex-Web implements customizable disease module identification and drug prioritization workflows to support users of diverse backgrounds in their research. Users are assisted during every step of their analysis, including the definition of relevant input sets, the selection from various algorithms for module identification or drug prioritization, and the prioritization of the results by their statistical significance. A guided connectivity search provides an easy way to identify links between node sets of interest and can be used to create user-specific induced networks.

Background Chronic hepatitis C virus (HCV) infection can lead to cirrhosis, hepatocellular carcinoma (HCC) and extrahepatic manifestations. A sustained virological response (SVR) is achieved with direct-acting antivirals (DAA) in over 95% of the patients, but sequelae of chronic HCV infections do not improve in all patients, suggesting permanent biological alterations. Therefore, we investigated the influence of chronic HCV infection, viral elimination and cirrhosis on soluble immune mediators (SIM).

Motivation The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.Results To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.Availability and implementation MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.

Background and Aims: Chronic hepatitis C virus (HCV) infection can lead to cirrhosis, development of hepatocellular carcinoma (HCC) and several extrahepatic manifestations. A sustained virological response (SVR) is achieved with direct-acting antivirals (DAA) in over 95% of the patients, but sequelae do not improve in all patients, suggesting permanent biological alterations induced by HCV infection. Therefore, we investigated the influence of chronic HCV infection, viral elimination and cirrhosis on inflammatory immune mediators. Approach and Results: In 102 chronic HCV patients, 46 with and 56 without cirrhosis, 92 soluble immune mediators (SIM) were measured in plasma samples at therapy start, end of treatment and long-term follow-up (median 96 weeks). 39 HBsAg positive persons with HBeAg negative infection served as controls. At baseline, 42 SIM were altered in chronic HCV patients (adj.p <0.05). Notably, patients with cirrhosis displayed a higher frequency and severity of alterations. At long-term follow-up, the SIM profile of the non-cirrhotic patients recovered to the level of the control group, while 41 SIM remained altered in cirrhotic patients. 33 of these SIM correlated with elastography, among them SIM linked to carcinogenesis as e.g. HGF, IL8 and IL6 (KEGG Pathways hsa05202, hsa05200). Conclusions: HCV-related changes in the inflammatory milieu can persist even after HCV elimination, specifically in cirrhotic patients. These changes are closely associated with liver damage and carcinogenesis. Our findings underscore the need for HCV elimination before extensive liver injury occurs and suggest further investigation of the relationship between persistent inflammatory milieu changes and long-term sequelae after HCV elimination.