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The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration.

Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. TRANSLATIONAL STATEMENT: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients., (© 2023. The Author(s).)

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

Celiac disease is an auto-immune disease in which an immune response to dietary gluten leads to inflammation and subsequent atrophy of small intestinal villi, causing severe bowel discomfort and malabsorption of nutrients. The major instigating factor for the immune response in celiac disease is the activation of gluten-specific CD4+ T cells expressing T cell receptors that recognize gluten peptides presented in the context of HLA-DQ2 and DQ8. Here we provide an in-depth characterization of 28 gluten-specific T cell clones. We assess their transcriptional and epigenetic response to T cell receptor stimulation and link this to genetic factors associated with celiac disease. Gluten-specific T cells have a distinct transcriptional profile that mostly resembles that of Th1 cells but also express cytokines characteristic of other types of T-helper cells. This transcriptional response appears not to be regulated by changes in chromatin state, but rather by early upregulation of transcription factors and non-coding RNAs that likely orchestrate the subsequent activation of genes that play a role in immune pathways. Finally, integration of chromatin and transcription factor binding profiles suggest that genes activated by T cell receptor stimulation of gluten‑specific T cells may be impacted by genetic variation at several genetic loci associated with celiac disease.

Myocardial infarction (MI) is a leading cause of death worldwide. Reperfusion is considered as an optimal therapy following cardiac ischemia. However, the promotion of a rapid elevation of O 2 levels in ischemic cells produces high amounts of reactive oxygen species (ROS) leading to myocardial tissue injury. This phenomenon is called ischemia reperfusion injury (IRI). We aimed at identifying new and effective compounds to treat MI and minimize IRI. We previously studied heart regeneration following myocardial injury in zebrafish and described each step of the regeneration process, from the day of injury until complete recovery, in terms of transcriptional responses. Here, we mined the data and performed a deep in silico analysis to identify drugs highly likely to induce cardiac regeneration. Fisetin was identified as the top candidate. We validated its effects in an in vitro model of MI/IRI in mammalian cardiac cells. Fisetin enhances viability of rat cardiomyocytes following hypoxia/starvation - reoxygenation. It inhibits apoptosis, decreases ROS generation and caspase activation and protects from DNA damage. Interestingly, fisetin also activates genes involved in cell proliferation. Fisetin is thus a highly promising candidate drug with clinical potential to protect from ischemic damage following MI and to overcome IRI.