CyTargetLinker app update: A flexible solution for network extension in Cytoscape

Copy number variant risk loci for schizophrenia converge on the BDNF pathway

OBJECTIVES

Schizophrenia genetics is intricate, with common and rare variants' contributions not fully understood. Certain copy number variations (CNVs) elevate risk, pivotal for understanding mental disorder models. Despite CNVs' genome-wide distribution and variable gene and protein effects, we must explore beyond affected genes to interaction partners and molecular pathways.

METHODS

In this study, we developed machine-readable interactive pathways to enable analysis of functional effects of genes within CNV loci and identify ten common pathways across CNVs with high schizophrenia risk using the WikiPathways database, schizophrenia risk gene collections from GWAS studies, and a gene-disease association database.

RESULTS

For CNVs that are pathogenic for schizophrenia, we found overlapping pathways, including BDNF signalling, cytoskeleton, and inflammation. Common schizophrenia risk genes identified by different studies are found in all CNV pathways, but not enriched.

CONCLUSIONS

Our findings suggest that specific pathways - BDNF signalling - are critical contributors to schizophrenia risk conferred by rare CNVs. Our approach highlights the importance of not only investigating deleted or duplicated genes within pathogenic CNV loci, but also study their direct interaction partners, which may explain pleiotropic effects of CNVs on schizophrenia risk and offer a broader field for interventions.

Interactive neuroinflammation pathways and transcriptomics-based identification of drugs and chemical compounds for schizophrenia

OBJECTIVES

Schizophrenia is a psychiatric disorder affecting 1% of the population. Accumulating evidence indicates that neuroinflammation is involved in the pathology of these disorders by altering neurodevelopmental processes and specifically affecting glutamatergic signalling and astrocytic functioning. The aim of this study was to curate interactive biological pathways involved in schizophrenia for the identification of novel pharmacological targets implementing pathway, gene ontology, and network analysis.

METHODS

Neuroinflammatory pathways were created using PathVisio and published in WikiPathways. A transcriptomics dataset, originally created by Narla et al. was selected for data visualisation and analysis. Transcriptomics data was visualised within pathways and networks, extended with transcription factors, pathways, and drugs. Network hubs were determined based on degrees of connectivity.

RESULTS

Glutamatergic, immune, and astrocytic signalling as well as extracellular matrix reorganisation were altered in schizophrenia while we did not find an effect on the complement system. Pharmacological agents that target the glutamate receptor subunits, inflammatory mediators, and metabolic enzymes were identified.

CONCLUSIONS

New neuroinflammatory pathways incorporating the extracellular matrix, glutamatergic neurons, and astrocytes in the aetiology of schizophrenia were established. Transcriptomics based network analysis provided novel targets, including extra-synaptic glutamate receptors, glutamate transporters and extracellular matrix molecules that can be evaluated for therapeutic strategies.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma

Background: Most cholangiocarcinoma (CCA) patients are diagnosed at an advanced stage of disease, and the postoperational recurrence rates are high in those undergoing resection. The lack of satisfying biomarkers for early diagnoses and effective targeting of driver pathways is the leading reason for therapeutic failures. The goal of this study was to find a biomarker for making improved diagnoses with enhanced prognostic capabilities for CCA. Materials and Methods: Our study used bioinformatic analyses of microarray data from the Gene Expression Omnibus (GEO) database and investigated mitotic arrest deficient 2-like protein 1 (MAD2L1) expression in tumor and adjacent non-neoplastic biliary ducts through immunocytochemistry in 42 surgically removed primary CCAs from a single institute. In vitro and in vivo models were used to explore the function of MAD2L1. Results: In total, 297 high probability differentially expressed genes (DEGs) were obtained from overlapping the DEGs from the three individual data sets. Through enrichment assays and protein-protein interaction networks analyses, seven hub genes were identified. MAD2L1 was picked up as a novel biomarker based on hierarchical cluster analyses and Kaplan-Meier survival analyses. MAD2L1 was expressed in cancer tissues but not in the surrounding normal tissue, with 31 (73.81%) of 42 CCAs MAD2L1 positive by immunohistochemistry (IHC). MAD2L1 expression levels were significantly correlated with tumor size, pathological grade, and clinical stage. A Kaplan-Meier survival analysis demonstrated an inverse correlation with MAD2L1 expression. Real-time polymerase chain reaction and immunoblotting results further confirmed the results of IHC and bioinformatic analyses. In vitro and in vivo models demonstrated decreasing MAD2L1 could significantly suppress tumor growth, whereas increasing MAD2L1 could promote tumor growth. Conclusion: MAD2L1 could be used as a biomarker to predict prognosis and potential therapeutic target in CCA. Clinical Trial Registration Number: [2020]KY157-01.

A translational study: Involvement of miR-21-5p in development and maintenance of neuropathic pain via immune-related targets CCL5 and YWHAE

Neuropathic pain occurs in more than half of the patients suffering from peripheral neuropathies. We investigated the role of microRNA (miR)-21 in neuropathic pain using a murine-human translational approach. We applied the spared nerve injury (SNI) model at the sciatic nerve of mice and assessed the potential analgesic effect of perineurial miR-21-5p inhibitor application. Immune-related targets of miR-21-5p were determined by a qRT-PCR based cytokine and chemokine array. Bioinformatical analysis identified potential miR-21-5p targets interacting with CC-chemokine ligand (CCL)5. We validated CCL5 and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAE), an interaction partner of miR-21-5p and CCL5, by qRT-PCR in murine common peroneal and tibial nerves. Validated candidates were then investigated in white blood cell and sural nerve biopsy samples of patients with focal to generalized pain syndromes, i.e. small fiber neuropathy (SFN), polyneuropathy (PNP), and nerve lesion (NL). We showed that perineurial miR-21-5p inhibition reverses SNI-induced mechanical and heat hypersensitivity in mice and found a reduction of the SNI-induced increase of the pro-inflammatory mediators CCL5 (p < 0.01), CCL17 (p < 0.05), and IL-12ß (p < 0.05) in miR-21-5p inhibitor-treated mice. In silico analysis revealed several predicted and validated targets for miR-21-5p with CCL5 interaction. Among these, we found lower YWHAE gene expression in mice after SNI and perineurial injections of a scrambled oligonucleotide compared to naïve mice (p < 0.05), but this was not changed by miR-21-5p inhibition. Furthermore, miR-21-5p inhibition led to a further increase of the SNI-induced increase in TGFß (p < 0.01). Patient biomaterial revealed different systemic expression patterns of miR-21-5p, with higher expression in SFN and lower expression in NL. Further, we showed higher systemic expression of pro-inflammatory mediators in white blood cells of SFN patients compared to healthy controls. We have conducted a translational study comparing results from animal models to human patients with three different neuropathic pain syndromes. We identified CCL5 as a miR-21 dependent common player in the mouse SNI model and the human painful disease SFN.

Whole transcriptome RNA-seq reveals key regulatory factors involved in type 2 diabetes pathology in peripheral fat of Asian Indians

The prevalence of Type 2 Diabetes has reached an epidemic proportion particularly in south Asian countries. We have earlier shown that the anatomical fat distribution, termed ‘thin fat phenotype’ in this population indeed plays a major role for their T2D-predisposition it is indeed the sick fat or adiposopathy, which is the root cause of metabolic syndrome and diabetes and affects both—peripheral, as well as visceral adipose tissue compartments. In present study, we have attempted to unravel the altered regulatory mechanisms at the level of transcription factors, and miRNAs those may likely accounts to T2D pathophysiology in femoral subcutaneous adipose tissue. We prioritized transcription factors and protein kinases as likely upstream regulators of obtained differentially expressed genes in this RNA-seq study. An inferred network of these upstream regulators was then derived and the role of TFs and miRNAs in T2D pathophysiology was explored. In conclusions, this RNS-Seq study finds that peripheral subcutaneous adipose tissue among Asian Indians show pathology characterized by altered lipid, glucose and protein metabolism, adipogenesis defect and inflammation. A network of regulatory transcription factors, protein kinases and microRNAs have been imputed which converge on the process of adipogenesis. As the majority of these genes also showed altered expression in diabetics and some of them are also circulatory, therefore they deserve further investigation for potential clinical diagnostic and therapeutic applications.

A resource to explore the discovery of rare diseases and their causative genes

Here, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166 rare monogenic diseases and linked them to 3163 causative genes, annotated with OMIM and Ensembl identifiers and HGNC symbols. The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com, and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. This dataset relies on publicly available data and publications with a PubMed identifier, but by our effort to make the data interoperable and linked, we can now analyse this data. Our analysis revealed the timeline of rare disease and causative gene discovery and links them to developments in methods.

Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation

Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.

Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

OBJECTIVES

Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a robust, data-driven insight in molecular disease mechanisms.

METHODS

Information about changed gene expression was extracted from five previously published studies, integrated and the resulting differentially expressed genes were analysed using overrepresentation analysis of biological pathways and gene ontology, and network analysis.

RESULTS

We identified a set of genes, which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. We found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organisation via MEF2C and CAPG.

CONCLUSIONS

Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available.

Revealing Clusters of Connected Pathways Through Multisource Data Integration in Huntington's Disease and Spastic Ataxia

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary "connectors" to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.