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Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

Which genes, gene sets or pathways are regulated by certain miRNAs? Which miRNAs regulate a particular target gene or target pathway in a certain physiological context? Answering such common research questions can be time consuming and labor intensive. Especially for researchers without computational experience, the integration of different data sources, selection of the right parameters and concise visualization can be demanding. A comprehensive analysis should be central to present adequate answers to complex biological questions. With miRTargetLink 2.0, we develop an all-in-one solution for human, mouse and rat miRNA networks. Users input in the unidirectional search mode either a single gene, gene set or gene pathway, alternatively a single miRNA, a set of miRNAs or an miRNA pathway. Moreover, genes and miRNAs can jointly be provided to the tool in the bidirectional search mode. For the selected entities, interaction graphs are generated from different data sources and dynamically presented. Connected application programming interfaces (APIs) to the tailored enrichment tools miEAA and GeneTrail facilitate downstream analysis of pathways and context-annotated categories of network nodes. MiRTargetLink 2.0 is freely accessible at https://www.ccb.uni-saarland.de/mirtargetlink2., Graphical Abstract Graphical abstractMiRTargetLink 2.0 offers interactive, web-based functionality to dissect networks of miRNAs and their target genes and pathways in three commonly investigated species.

Feed-forward loops (FFLs) are thought to be one of the most common and important classes of transcriptional network motifs involved in various diseases. Enhancers are cis-regulatory elements that positively regulate protein-coding genes or microRNAs (miRNAs) by recruiting DNA-binding transcription factors (TFs). However, a comprehensive resource to identify, store, and analyze the FFLs of typical enhancer and super-enhancer FFLs is not currently available. Here, we present EnhFFL, an online database to provide a data resource for users to browse and search typical enhancer and super-enhancer FFLs. The current database covers 46 280/7000 TF-enhancer-miRNA FFLs, 9997/236 enhancer-miRNA-gene FFLs, 3 561 164/3 193 182 TF-enhancer-gene FFLs, and 1259/235 TF-enhancer feed-back loops (FBLs) across 91 tissues/cell lines of human and mouse, respectively. Users can browse loops by selecting species, types of tissue/cell line, and types of FFLs. EnhFFL supports searching elements including name/ID, genomic location, and the conservation of miRNA target genes. We also developed tools for users to screen customized FFLs using the threshold of q value as well as the confidence score of miRNA target genes. Disease and functional enrichment analysis showed that master miRNAs that are widely engaged in FFLs including TF-enhancer-miRNAs and enhancer-miRNA-genes are significantly involved in tumorigenesis. Database URL:http://lcbb.swjtu.edu.cn/EnhFFL/.

Aims The early repolarization syndrome (ERS) can cause ventricular fibrillation (VF) and sudden death in young, otherwise healthy individuals. There are limited data suggesting that ERS might be heritable. The aim of this study was to characterize the clinical phenotype and to identify a causal variant in an affected family using an exome-sequencing approach. Methods and results Early repolarization syndrome was diagnosed according to the recently proposed Shanghai ERS Score. After sequencing of known ERS candidate genes, whole-exome sequencing (WES) was performed. The index patient (23 years, female) showed a dynamic inferolateral early repolarization (ER) pattern and electrical storm with intractable VF. Isoproterenol enabled successful termination of electrical storm with no recurrence on hydroquinidine therapy during 33 months of follow-up. The index patient’s brother (25 years) had a persistent inferior ER pattern with malignant features and a history of syncope. Both parents were asymptomatic and showed no ER pattern. While there was no pathogenic variant in candidate genes, WES detected a novel missense variant affecting a highly conserved residue (p. H2245R) in the ANK3 gene encoding Ankyrin-G in the two siblings and the father. Conclusion We identified two siblings with a malignant ERS phenotype sharing a novel ANK3 variant. A potentially pathogenic role of the novel ANK3 variant is suggested by the direct interaction of Ankyrin-G with the cardiac sodium channel, however, more patients with ANK3 variants and ERS would be required to establish ANK3 as novel ERS susceptibility gene. Our study provides additional evidence that ERS might be a heritable condition.

MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome and that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value. Second, the novel webtool miRTaH facilitates guided designs of reporter assay constructs at scale. Third, automated and standardized reporter assays are performed. We evaluated HiTmIR using miR-34a-5p, for which TNF- and TGFBsignaling, and Parkinson’s Disease (PD)-related categories were identified and repeated the pipeline for miR-7-5p. HiTmIR validated 58.9% of the target genes for miR-34a-5p and 46.7% for miR-7-5p. We confirmed the targeting by measuring the endogenous protein levels of targets in a neuronal cell model. The standardized positive and negative targets are collected in the new miRATBase database, representing a resource for training, or benchmarking new target predictors. Applied to 88 target predictors with different confidence scores, TargetScan 7.2 and miRanda outperformed other tools. Our experiments demonstrate the efficiency of HiTmIR and provide evidence for an orchestrated miRNA-gene targeting., Michael J. Fox foundation 14446, Internal funds of Saarland University, Instituto de Salud Carlos III European Commission IFI16/00041 MV19/00058

Enhancer RNA (eRNA) is a type of long non-coding RNA transcribed from DNA enhancer regions. Despite critical roles of eRNA in gene regulation, the expression landscape of eRNAs in normal human tissue remains unexplored. Using numerous samples from the Genotype-Tissue Expression project, we characterized 45 411 detectable eRNAs and identified tens of thousands of associations between eRNAs and traits, including gender, race, and age. We constructed a co-expression network to identify millions of putative eRNA regulators and target genes across different tissues. We further constructed a user-friendly data portal, Human enhancer RNA Atlas (HeRA, https://hanlab.uth.edu/HeRA/). In HeRA, users can search, browse, and download the eRNA expression profile, trait-related eRNAs, and eRNA co-expression network by searching the eRNA ID, gene symbol, and genomic region in one or multiple tissues. HeRA is the first data portal to characterize eRNAs from 9577 samples across 54 human tissues and facilitates functional and mechanistic investigations of eRNAs.

The p53 transcription factor confers its potent tumor suppressor functions primarily through the regulation of a large network of target genes. The recent explosion of next generation sequencing protocols has enabled the study of the p53 gene regulatory network (GRN) and underlying mechanisms at an unprecedented depth and scale, helping us to understand precisely how p53 controls gene regulation. Here, we discuss our current understanding of where and how p53 binds to DNA and chromatin, its pioneer-like role, and how this affects gene regulation. We provide an overview of the p53 GRN and the direct and indirect mechanisms through which p53 affects gene regulation. In particular, we focus on delineating the ubiquitous and cell type-specific network of regulatory elements that p53 engages; reviewing our understanding of how, where, and when p53 binds to DNA and the mechanisms through which these events regulate transcription. Finally, we discuss the evolution of the p53 GRN and how recent work has revealed remarkable differences between vertebrates, which are of particular importance to cancer researchers using mouse models.

Rare coding variants have been proven to be one of the significant factors contributing to spermatogenic failure in patients with non-obstructive azoospermia (NOA) and severe oligospermia (SO). To delineate the molecular characteristics of idiopathic NOA and SO, we performed whole-exome sequencing of 314 unrelated patients of Chinese Han origin and verified our findings by comparing to 400 fertile controls. We detected six pathogenic/likely pathogenic variants and four variants of unknown significance, in genes known to cause NOA/SO, and 9 of which had not been earlier reported. Additionally, we identified 20 novel NOA candidate genes affecting 25 patients. Among them, five (BRDT, CHD5, MCM9, MLH3 and ZFX) were considered as strong candidates based on the evidence obtained from murine functional studies and human single-cell (sc)RNA-sequencing data. These genetic findings provide insight into the aetiology of human NOA/SO and pave the way for further functional analysis and molecular diagnosis of male infertility.

The data set contains all regulatory elements (REMs) and the additional information used to create the EpiRegio webserver (https://epiregio.de). The data set consists of 10 tables (CSV-files): GenomeAnnotation: contains information about genomeVersion, annotationVersion and databaseName (GenomeAnnotation_1.csv.gz) GeneAnnotation: Information of the genes (chr, start, end, geneID, geneSymbol, alternativeGeneID, isTF, strand and annotationVersion) (GeneAnnotation_1.csv.gz) GeneExpression of Blueprint and Roadmap: Per consortium one table containing information about geneID, sampleID, expressionLog2TPM and species (GeneExpression_Blueprint_1.csv.gz and GeneExpressionRoadmap_1.csv.gz) CellTypeInfo: Information of the used cell and tissue types (cellTypeID, cellTypeName and cellOntologyTerm) (CellTypeInfo.csv.gz) sampleInfo of Roadmap and Blueprint: Per consortium one table containing information about sampleID, originalSampleID, cellTypeID, origin and dataType (sampleInfo_Blueprint_1.csv.gz and sampleInfo_Roadmap_1.csv.gz) REMAnnotation: contains all predicted REMs using STITCHIT (chr, start, end, geneID, REMID, regressionCoefficient, pValue, normModelScore, meanDNase1Signal, sdDNase1Signal, consortium and version) (REMAnnotationModelScore_1.csv.gz) REMActivity: This table contains per REM the DNase-signal and the standardised DNase-signal per cell or tissue type (REMID, sampleID, dnase1Log2, standDnase1Log2 and version) (REMActivity_1.csv.gz) clusterREMs: contains all CREMs (REMID, CREMID, chr, start, end, REMsPerCREM and version) (clusterREMs_1.csv.gz) With these tables the underlying database of EpiRegio can easily be reconstructed. The source code for the current version of the EpiRegio webserver version is available at 10.5281/zenodo.3751189. EpiRegio uses the STITCHIT algorithm, which is currently under revision. The preprint is available at http://dx.doi.org/10.1101/585125., This work has been supported by the DZHK (German Centre for Cardiovascular Research, 81Z0200101) and the Cardio-Pulmonary Institute (CPI) [EXC 2026], and the DFG SFB/TRR 267 Noncoding RNAs in the cardiovascular system.

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, inherited skeletal myopathy linked to hypomethylation of the D4Z4 macrosatellite at chromosome 4q35. This epigenetic de-repression permits expression of the transcription factor DUX4, which may drive pathology by direct activation of target genes or through inhibition of the homologous transcription factor PAX7. We demonstrated that PAX7 target gene repression is a superior biomarker of FSHD status compared with DUX4 target gene expression. However, despite importance for clinical trials, there remains no transcriptomic biomarker for FSHD progression. A recent study by Wong et al. [Longitudinal measures of RNA expression and disease activity in FSHD muscle biopsies. Hum. Mol. Genet., 29, 1030–1043] performed MRI, muscle biopsy transcriptomics and histopathology on a cohort of FSHD patients with 1-year follow-up. No significant changes in any biomarkers were reported over this time period. However, the authors did not consider PAX7 target gene repression as a marker of FSHD progression. Here we demonstrate that PAX7 target gene repression increases in these paired FSHD samples from year 1 to year 2 and is thus a marker of FSHD progression over 1 year. Moreover, we show that three validated DUX4 target gene expression biomarkers are not associated with FSHD progression over 1 year. We further confirm that PAX7 target gene repression associates with clinical correlates of FSHD disease activity, measured by MRI and histopathology. Thus, PAX7 target gene repression is a uniquely sensitive biomarker of FSHD progression and pathology, valid over a 1 year time frame, implicating its use in clinical trials.

Human longevity is a complex trait influenced by both genetic and environmental factors, whose interaction is mediated by epigenetic mechanisms like DNA methylation. Here, we generated genome-wide whole-blood methylome data from 267 individuals, of which 71 were long-lived (90–104 years), by applying reduced representation bisulfite sequencing. We followed a stringent two-stage analysis procedure using discovery and replication samples to detect differentially methylated sites (DMSs) between young and long-lived study participants. Additionally, we performed a DNA methylation quantitative trait loci analysis to identify DMSs that underlie the longevity phenotype. We combined the DMSs results with gene expression data as an indicator of functional relevance. This approach yielded 21 new candidate genes, the majority of which are involved in neurophysiological processes or cancer. Notably, two candidates (PVRL2, ERCC1) are located on chromosome 19q, in close proximity to the well-known longevity- and Alzheimer’s disease-associated loci APOE and TOMM40. We propose this region as a longevity hub, operating on both a genetic (APOE, TOMM40) and an epigenetic (PVRL2, ERCC1) level. We hypothesize that the heritable methylation and associated gene expression changes reported here are overall advantageous for the LLI and may prevent/postpone age-related diseases and facilitate survival into very old age.

Multi-drug resistant pathogens have become a major threat to human health and new antibiotics are urgently needed. Most antibiotics are derived from secondary metabolites produced by bacteria. In order to avoid suicide, these bacteria usually encode resistance genes, in some cases within the biosynthetic gene cluster (BGC) of the respective antibiotic compound. Modern genome mining tools enable researchers to computationally detect and predict BGCs that encode the biosynthesis of secondary metabolites. The major challenge now is the prioritization of the most promising BGCs encoding antibiotics with novel modes of action. A recently developed target-directed genome mining approach allows researchers to predict the mode of action of the encoded compound of an uncharacterized BGC based on the presence of resistant target genes. In 2017, we introduced the ‘Antibiotic Resistant Target Seeker’ (ARTS). ARTS allows for specific and efficient genome mining for antibiotics with interesting and novel targets by rapidly linking housekeeping and known resistance genes to BGC proximity, duplication and horizontal gene transfer (HGT) events. Here, we present ARTS 2.0 available at http://arts.ziemertlab.com. ARTS 2.0 now includes options for automated target directed genome mining in all bacterial taxa as well as metagenomic data. Furthermore, it enables comparison of similar BGCs from different genomes and their putative resistance genes.

Since the initial release of miRPathDB, tremendous progress has been made in the field of microRNA (miRNA) research. New miRNA reference databases have emerged, a vast amount of new miRNA candidates has been discovered and the number of experimentally validated target genes has increased considerably. Hence, the demand for a major upgrade of miRPathDB, including extended analysis functionality and intuitive visualizations of query results has emerged. Here, we present the novel release 2.0 of the miRNA Pathway Dictionary Database (miRPathDB) that is freely accessible at https://mpd.bioinf.uni-sb.de/. miRPathDB 2.0 comes with a ten-fold increase of pre-processed data. In total, the updated database provides putative associations between 27 452 (candidate) miRNAs, 28 352 targets and 16 833 pathways for Homo sapiens, as well as interactions of 1978 miRNAs, 24 898 targets and 6511 functional categories for Mus musculus. Additionally, we analyzed publications citing miRPathDB to identify common use-cases and further extensions. Based on this evaluation, we added new functionality for interactive visualizations and down-stream analyses of bulk queries. In summary, the updated version of miRPathDB, with its new custom-tailored features, is one of the most comprehensive and advanced resources for miRNAs and their target pathways.

Enhancers are distal cis-regulatory elements that activate the transcription of their target genes. They regulate a wide range of important biological functions and processes, including embryogenesis, development, and homeostasis. As more and more large-scale technologies were developed for enhancer identification, a comprehensive database is highly desirable for enhancer annotation based on various genome-wide profiling datasets across different species. Here, we present an updated database EnhancerAtlas 2.0 (http://www.enhanceratlas.org/indexv2.php), covering 586 tissue/cell types that include a large number of normal tissues, cancer cell lines, and cells at different development stages across nine species. Overall, the database contains 13 494 603 enhancers, which were obtained from 16 055 datasets using 12 high-throughput experiment methods (e.g. H3K4me1/H3K27ac, DNase-seq/ATAC-seq, P300, POLR2A, CAGE, ChIA-PET, GRO-seq, STARR-seq and MPRA). The updated version is a huge expansion of the first version, which only contains the enhancers in human cells. In addition, we predicted enhancer–target gene relationships in human, mouse and fly. Finally, the users can search enhancers and enhancer–target gene relationships through five user-friendly, interactive modules. We believe the new annotation of enhancers in EnhancerAtlas 2.0 will facilitate users to perform useful functional analysis of enhancers in various genomes.

Aims Genetic contribution to coronary artery disease (CAD) remains largely unillustrated. Although transcriptomic profiles have identified dozens of genes that are differentially expressed in normal and atherosclerotic vessels, whether those genes are genetically associated with CAD remains to be determined. Here, we combined genetic association studies, transcriptome profiles and in vitro and in vivo functional experiments to identify novel susceptibility genes for CAD. Methods and results Through an integrative analysis of transcriptome profiles with genome-wide association studies for CAD, we obtained 18 candidate genes and selected one representative single nucleotide polymorphism (SNP) for each gene for multi-centred validations. We identified an intragenic SNP, rs1056515 in RGS5 gene (odds ratio = 1.17, 95% confidence interval =1.10–1.24, P = 3.72 × 10−8) associated with CAD at genome-wide significance. Rare genetic variants in linkage disequilibrium with rs1056515 were identified in CAD patients leading to a decreased expression of RGS5. The decreased expression was also observed in atherosclerotic vessels and endothelial cells treated by various cardiovascular risk factors. Through siRNA knockdown and adenoviral overexpression, we further showed that RGS5 regulated endothelial inflammation, vascular remodelling, as well as canonical NF-κB signalling activation. Moreover, CXCL12, a specific downstream target of the non-canonical NF-κB pathway, was strongly affected by RGS5. However, the p100 processing, a well-documented marker for non-canonical NF-κB pathway activation, was not altered, suggesting an existence of a novel mechanism by which RGS5 regulates CXCL12. Conclusions We identified RGS5 as a novel susceptibility gene for CAD and showed that the decreased expression of RGS5 impaired endothelial cell function and functionally contributed to atherosclerosis through a variety of molecular mechanisms. How RGS5 regulates the expression of CXCL12 needs further studies.

The epigenetic machinery in conjunction with the transcriptional machinery is responsible for maintaining genome-wide chromatin states and dynamically regulating gene expression. Mendelian disorders of the epigenetic machinery (MDEMs) are genetic disorders resulting from mutations in components of the epigenetic apparatus. Though individually rare, MDEMs have emerged as a collectively common etiology for intellectual disability (ID) and growth disruption. Studies in model organisms and humans have demonstrated dosage sensitivity of this gene group with haploinsufficiency as a predominant disease mechanism. The epigenetic machinery consists of three enzymatic components (writers, erasers and chromatin remodelers) as well as one non-enzymatic group (readers). A tally of the entire census of such factors revealed that although multiple enzymatic activities never coexist within a single component, individual enzymatic activities often coexist with a reader domain. This group of disorders disrupts both the chromatin and transcription states of target genes downstream of the given component but also DNA methylation on a global scale. Elucidation of these global epigenetic changes may inform our understanding of disease pathogenesis and have diagnostic utility. Moreover, many therapies targeting epigenetic marks already exist, and some have proven successful in treating cancer. This, along with the recent observation that neurological dysfunction in these disorders may in fact be treatable in postnatal life, suggests that the scientific community should prioritize this group as a potentially treatable cause of ID. Here we summarize the recent expansion and major characteristics of MDEMs, as well as the unique therapeutic prospects for this group of disorders.

Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer. The mortality of liver cancer worldwide is ranked as fourth between other cancer causes in males and females. In Egypt, it is a major problem due to the high prevalence of Hepatitis C Virus infection. Objective To characterize the expression of new serum non-coding RNA microRNA (hsa-miR519d) and the associated target gene (SQSTM1) to evaluate their usefulness as diagnostic molecular biomarkers for HCC. Patients and Methods we assessed the expression of the microRNA (hsa-miR-519d-3p) and the mRNA of (SQSTM1) gene in serum samples from 50 participants: (34) HCC patients, (11) chronic liver infection patients and (5) normal volunteers, using Quantitative Real Time Polymerase Chain Reaction (qPCR). Results The results of both microRNA (miR-519d-3p) and mRNA of (SQSTM1) gene showed a significant upregulation of their serum level in the HCC group in comparison to chronic liver infection group. In addition, the results of the serum microRNA (miR-519d) and the messenger RNA of (SQSTM1) gene using receiver operating characteristic (ROC) curve showed higher sensitivity and specificity than that of AFP, as it was (91.2%-81.8%), (97.1%-100%) and (76.5%72.7%) respectively. Conclusion The serum microRNA (hsa-mir-519d-3p) and the serum mRNA of its targeted gene (SQSTM1) are both significantly upregulated in the serum of Hepatocellular carcinoma (HCC) patients. And that the (hsa-mir-519d-3p) stimulates the gene (SQSTM1) at the transcriptional level. Finally, we could conclude that the serum microRNA (hsa-mir-519d-3p) and the serum mRNA of (SQSTM1) gene can be used as diagnostic biomarkers for HCC with good sensitivity and specificity even for early stages of HCC in comparison with AFP.

Background and Aims Whole exome sequencing (WES) is becoming part of routine clinical and diagnostic practice and has been extensively applied in research studies as well as for diagnostic utility to detect various novel genes and variants. Filtering of variants and scoring variants in terms of pathogenicity still represents a major challenge and may explain why ∼50% of patients remain without diagnosis after initial assessment. Method In this study, we performed WES to determine the genetic cause of a hepato-renal ciliopathy syndrome in a genetically unsolved consanguineous family from Oman with 2 affected children. For variants filtering and annotation Qiagen Clinical Insight tool was used. Database searches for identical alleles in patients with similar phenotypes were performed using Genomics England, UK Biobank and a Saudi Arabian cohort. RNA studies were used to confirm a splicing defect. This research was made possible through access to the data and findings generated by the 100,000 Genomes Project and from UK Biobank, a major biomedical database. Results Initial bioinformatic analysis of WES data from 2 affected sibs excluded obvious pathogenic variants in known genes associated with primary ciliopathy syndromes with liver and kidney phenotypes. However, by manual curation of variants in candidate genes, a rare homozygous synonymous allele in NPHP3 was identified (c.2805C>T; p.(Gly935Gly)), mid-exon 20 and within a region of shared homozygosity on chromosome 3. Correct segregation was confirmed via Sanger sequencing in the parents and the 2 affected sibs. The variant was rare in gnomAD (2/251374 alleles) and was found heterozygously in just one individual within the UK Biobank cohort of 200,000 exomes. Using various in silico tools, the allele was shown to activate a cryptic splice donor site in the middle of exon 20. RT-PCR with sequencing of parental whole blood RNA confirmed alternative splicing leading to frameshift p.Gly935GlyfsTer47. The identical homozygous allele was identified in 2 additional unsolved families within the Genomics England 100,000 Genomes Project and in 1 Saudi Arabian family with similar hepato-renal phenotypes. Conclusion This study shows that automated filtering of WES data may exclude synonymous variants which are pathogenic, especially if they are mid-exon. Here we identified a recurrent synonymous NPHP3 variant leading to a splice defect as the cause of a hepato-renal ciliopathy phenotype in four families. In unsolved cases, rare synonymous alleles in candidate genes need to be reassessed for impact on RNA splicing and possible pathogenicity.

Background and Aims In recent years, it has been found that targeted mRNA detection of sediment cells in urine can be used as novel biomarkers for the diagnosis of diabetic nephropathy (DN). However, its value in predicting the progression of DN is not clear. The purpose of this study is to seek for urinary mRNA markers that evaluate the prognosis of DN through systems biological screening, clinical verification and prospective studies. Method GEO database and “Nephroseq” platform were searched, and the transcriptome data of DN glomeruli and tubules and their clinical information were obtained. Weighted gene co-expression network analysis (WGCNA) combined with gene ontology (GO) annotation and KEGG pathway enrichment analysis were used to screen the hub genes negatively related to eGFR, and the hub genes were used as candidate markers for mRNA detection in urine sediment in DN patients. A total of 91 patients with DN diagnosed by renal biopsy were included, and 60 patients with type 2 diabetes and 61 healthy people were selected as the control groups. The mRNA expression of candidate molecules was detected by Taqman probe quantitative PCR, and the correlation between mRNA expression and eGFR and urinary protein levels were analyzed. Patients with DN were followed up for a median time of 21 months, and the primary end point was defied as end stage renal disease or eGFR decreasing by more than 50%. Multivariate Cox regression was used to evaluate the value of mRNA in predicting DN progression. Results GSE30528 and GSE30529 datasets were selected for analysis, including mRNA expression data of 9 cases of DN and 13 cases of normal glomeruli; and 10 cases of DN and 12 cases of normal tubules respectively. The clinical data of the patients in this study, including gender, race, age and eGFR, were searched on the Nephroseq platform. The gene modules negatively related to eGFR were screened by WGCNA. GO and KEGG analysis showed that the main function of the gene modules in both datasets were related to the activation of inflammatory cells and chemokines pathway. Through the screening of hub genes and the comparison of expression levels, CCL5, CXCL1, CXCL6 and CXCL12 were finally obtained as candidate genes. Quantitative PCR showed that the levels of CCL5 and CXCL1 was significantly increased in DN group, CCL5 was negatively correlated with eGFR and positively correlated with urinary protein level, while CXCL1 was negatively correlated with eGFR, but had no significant correlation with urinary protein level. Multivariate Cox regression showed that eGFR, urinary protein level, degree of renal fibrosis and urinary CCL5 were independent risk factors of primary end point. Conclusion The activation of chemokine signal pathway in renal tissue is involved in the progression of DN. Urinary CCL5 mRNA can independently predict the prognosis of DN and may be served as a novel biomarker for the progression of DN.

Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.

Key Points Question What genes and genomic processes underlie risk of sporadic Parkinson disease? Findings This genetic association study integrated Parkinson disease genome-wide association study data and brain-derived gene regulation data using various complementary bioinformatic tools and identified 11 candidate genes with evidence of disease-associated regulatory changes. Coexpression and protein level analyses of these genes demonstrated a significant functional association with known mendelian Parkinson disease genes. Meaning This study suggests that gene regulation data may be used to identify candidate genes and pathways involved in sporadic Parkinson disease., Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies., This genetic association study investigates what genes and genomic processes underlie the risk of sporadic Parkinson disease.

Gephebase is a manually-curated database compiling our accumulated knowledge of the genes and mutations that underlie natural, domesticated and experimental phenotypic variation in all Eukaryotes — mostly animals, plants and yeasts. Gephebase aims to compile studies where the genotype-phenotype association (based on linkage mapping, association mapping or a candidate gene approach) is relatively well supported or understood. Human disease and aberrant mutant phenotypes in laboratory model organisms are not included in Gephebase and can be found in other databases (eg. OMIM, OMIA, Monarch Initiative). Gephebase contains more than 1700 entries. Each entry corresponds to an allelic difference at a given gene and its associated phenotypic change(s) between two species or between two individuals of the same species, and is enriched with molecular details, taxonomic information, and bibliographic information. Users can easily browse entries for their topic of interest and perform searches at various levels, whether phenotypic, genetic, taxonomic or bibliographic (eg. transposable elements, cis-regulatory mutations, snakes, carotenoid content, an author name). Data can be searched using keywords and boolean operators and is exportable in spreadsheet format. This database allows to perform meta-analysis to extract general information and global trends about evolution, genetics, and the field of evolutionary genetics itself. Gephebase should also help breeders, conservationists and others to identify the most promising target genes for traits of interest, with potential applications such as crop improvement, parasite and pest control, bioconservation, and genetic diagnostic. It is freely available at www.gephebase.org.

Objective To characterize the molecular and clinical phenotypic basis of developmental and epileptic encephalopathies caused by rare biallelic variants in CACNA2D2. Methods Two affected individuals from a family with clinical features of early onset epileptic encephalopathy were recruited for exome sequencing at the Centers for Mendelian Genomics to identify their molecular diagnosis. GeneMatcher facilitated identification of a second family with a shared candidate disease gene identified through clinical gene panel‐based testing. Results Rare biallelic CACNA2D2 variants have been previously reported in three families with developmental and epileptic encephalopathy, and one family with congenital ataxia. We identified three individuals in two unrelated families with novel homozygous rare variants in CACNA2D2 with clinical features of developmental and epileptic encephalopathy and cerebellar atrophy. Family 1 includes two affected siblings with a likely damaging homozygous rare missense variant c.1778G>C; p.(Arg593Pro) in CACNA2D2. Family 2 includes a proband with a homozygous rare nonsense variant c.485_486del; p.(Tyr162Ter) in CACNA2D2. We compared clinical and molecular findings from all nine individuals reported to date and note that cerebellar atrophy is shared among all. Interpretation Our study supports the candidacy of CACNA2D2 as a disease gene associated with a phenotypic spectrum of neurological disease that include features of developmental and epileptic encephalopathy, ataxia, and cerebellar atrophy. Age at presentation may affect apparent penetrance of neurogenetic trait manifestations and of a particular clinical neurological endophenotype, for example, seizures or ataxia.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. miR-484 is previously reported to be a crucial modulator during the process from precancerous lesion to cancer. Tumour suppressor candidate 5 (TUSC5) is a potential tumour suppressor, but its expression and function in HCC are obscure. In this study, we aimed to explore the roles of miR-484 and TUSC5 in HCC, and clarify the relationship between them. We demonstrated that miR-484 was significantly up-regulated in HCC, while TUSC5 was down-regulated. TUSC5 was validated as the target gene of miR-484 and both of them were associated with the prognosis of HCC patients. miR-484 mimics markedly promoted the malignant phenotypes while TUSC5 plasmid had the opposite effect. In conclusion, miR-484/TUSC5 is potential diagnostic biomarkers and therapy targets for HCC.

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder characterized by progressive muscular atrophy and respiratory failure. The G4C2 repeat expansion in the C9orf72 gene is the most prevalent genetic risk for ALS. Mutation carriers (C9ALS) display variability in phenotypes such as age-at-onset and duration, suggesting the existence of additional genetic factors. Here we introduce a three-step gene discovery strategy to identify genetic factors modifying the risk of both C9ALS and sporadic ALS (sALS) using limited samples. We first identified 135 candidate genetic modifiers of C9ALS using whole-genome sequencing (WGS) of extreme C9ALS cases diagnosed ~30 years apart. We then performed an unbiased genetic screen using a Drosophila model of the G4C2 repeat expansion with the genes identified from WGS analysis. This genetic screen identified the novel genetic interaction between G4C2 repeat-associated toxicity and 18 genetic factors, suggesting their potential association with C9ALS risk. We went on to test if 14 out of the 18 genes, those which were not known to be risk factors for ALS previously, are also associated with ALS risk in sALS cases. Gene-based-statistical analyses of targeted resequencing and WGS were performed. These analyses together reveal that rare variants in MYH15 represent a likely genetic risk factor for ALS. Furthermore, we show that MYH15 could modulate the toxicity of dipeptides produced from expanded G4C2 repeat. Our study presented here demonstrates the power of combining WGS with fly genetics to facilitate the discovery of fundamental genetic components of complex traits with a limited number of samples.

Facioscapulohumeral muscular dystrophy (FSHD) is a prevalent, incurable skeletal myopathy. The condition is linked to hypomethylation of the D4Z4 macrosatellite repeat at chromosome 4q35, leading to epigenetic derepression of the transcription factor DUX4; coupled with a permissive 4qA haplotype supplying a poly(A) signal. DUX4 may drive FSHD pathology via both induction of target genes and inhibition of the function of the myogenic master regulator PAX7. Biomarkers for FSHD have focused on DUX4 target gene expression. We have, however, reported that PAX7 target gene repression is a hallmark of FSHD skeletal muscle. Here we demonstrate that PAX7 target gene repression is an equivalent biomarker to DUX4 target gene expression when considering RNA-Sequencing data from magnetic resonance imaging-guided muscle biopsies. Moreover, PAX7 target gene repression correlates with active disease, independent to DUX4 target gene expression. PAX7 target genes are also repressed in RNA-Sequencing data from single cells, representing a significantly better biomarker of FSHD cells than DUX4 target gene expression. Importantly, PAX7 target gene repression is a significant biomarker in the majority of FSHD cells that are DUX4 target gene negative, and on which the DUX4 biomarker is indiscriminate. To facilitate the evaluation of validated biomarkers we provide a simple tool that outputs biomarker values from a normalized expression data matrix. In summary, PAX7 target gene repression in FSHD correlates with disease severity, independently of DUX4 target gene expression. At the single-cell level, PAX7 target gene repression can efficiently discriminate FSHD cells, even when no DUX4 target genes are detectable.

Advances in single-cell RNA-sequencing techniques reveal the existence of distinct cell subpopulations. Identification of transcription factors (TFs) that define the identity of these subpopulations poses a challenge. Here, we postulate that identity depends on background subpopulations, and is determined by a synergistic core combination of TFs mainly uniquely expressed in each subpopulation, but also TFs more broadly expressed across background subpopulations. Building on this view, we develop a new computational method for determining such synergistic identity cores of subpopulations within a given cell population. Our method utilizes an information-theoretic measure for quantifying transcriptional synergy, and implements a novel algorithm for searching for optimal synergistic cores. It requires only single-cell RNA-seq data as input, and does not rely on any prior knowledge of candidate genes or gene regulatory networks. Hence, it can be directly applied to any cellular systems, including those containing novel subpopulations. The method is capable of recapitulating known experimentally validated identity TFs in eight published single-cell RNA-seq datasets. Furthermore, some of these identity TFs are known to trigger cell conversions between subpopulations. Thus, this methodology can help design strategies for cell conversion within a cell population, guiding experimentalists in the field of stem cell research and regenerative medicine.

The genomic predisposition to oncology-drug-induced cardiovascular toxicity has been postulated for many decades. Only recently has it become possible to experimentally validate this hypothesis via the use of patient-specific human-induced pluripotent stem cells (hiPSCs) and suitably powered genome-wide association studies (GWAS). Identifying the individual single nucleotide polymorphisms (SNPs) responsible for the susceptibility to toxicity from a specific drug is a daunting task as this precludes the use of one of the most powerful tools in genomics: comparing phenotypes to close relatives, as these are highly unlikely to have been treated with the same drug. Great strides have been made through the use of candidate gene association studies (CGAS) and increasingly large GWAS studies, as well as in vivo whole-organism studies to further our mechanistic understanding of this toxicity. The hiPSC model is a powerful technology to build on this work and identify and validate causal variants in mechanistic pathways through directed genomic editing such as CRISPR. The causative variants identified through these studies can then be implemented clinically to identify those likely to experience cardiovascular toxicity and guide treatment options. Additionally, targets identified through hiPSC studies can inform future drug development. Through careful phenotypic characterization, identification of genomic variants that contribute to gene function and expression, and genomic editing to verify mechanistic pathways, hiPSC technology is a critical tool for drug discovery and the realization of precision medicine in cardio-oncology.

MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA–target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (∼1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA–target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10−8). Our result highlighted that miRNA–target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.

Analysis linking directly genomics, neuroimaging phenotypes and clinical measurements is crucial for understanding psychiatric disorders, but remains rare. Here, we describe a multi-scale analysis using genome-wide SNPs, gene expression, grey matter volume (GMV), and the positive and negative syndrome scale scores (PANSS) to explore the etiology of schizophrenia. With 72 drug-naive schizophrenic first episode patients (FEPs) and 73 matched heathy controls, we identified 108 genes, from schizophrenia risk genes, that correlated significantly with GMV, which are highly co-expressed in the brain during development. Among these 108 candidates, 19 distinct genes were found associated with 16 brain regions referred to as hot clusters (HCs), primarily in the frontal cortex, sensory-motor regions and temporal and parietal regions. The patients were subtyped into three groups with distinguishable PANSS scores by the GMV of the identified HCs. Furthermore, we found that HCs with common GMV among patient groups are related to genes that mostly mapped to pathways relevant to neural signaling, which are associated with the risk for schizophrenia. Our results provide an integrated view of how genetic variants may affect brain structures that lead to distinct disease phenotypes. The method of multi-scale analysis that was described in this research, may help to advance the understanding of the etiology of schizophrenia.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

Facioscapulohumeral muscular dystrophy (FSHD) is a common, dominantly inherited disease caused by the epigenetic de-repression of the DUX4 gene, a transcription factor normally repressed in skeletal muscle. As targeted therapies are now possible in FSHD, a better understanding of the relationship between DUX4 activity, muscle pathology and muscle magnetic resonance imaging (MRI) changes is crucial both to understand disease mechanisms and for the design of future clinical trials. Here, we performed MRIs of the lower extremities in 36 individuals with FSHD, followed by needle muscle biopsies in safely accessible muscles. We examined the correlation between MRI characteristics, muscle pathology and expression of DUX4 target genes. Results show that the presence of elevated MRI short tau inversion recovery signal has substantial predictive value in identifying muscles with active disease as determined by histopathology and DUX4 target gene expression. In addition, DUX4 target gene expression was detected only in FSHD-affected muscles and not in control muscles. These results support the use of MRI to identify FSHD muscles most likely to have active disease and higher levels of DUX4 target gene expression and might be useful in early phase therapeutic trials to demonstrate target engagement in therapies aiming to suppress DUX4 expression.