Gene Nomenclature Committee Research Articles

Identifying Key Genes and Approved Medications Associated with Major Depressive Disorder Using Network Analysis and Systems Biology

Objective: Major depressive disorder (MDD) stands as one of the serious psychiatric conditions that detrimentally affect patients' quality of life and leads to a significant part of disability worldwide. Due to the limited understanding of the basic molecular mechanisms of depression and antidepressant medications, a clear understanding of the onset and development of MDD is unavailable. This study aims to figure out the pivotal genes and pathways implicated in the MDD development and identify medications that can potentially improve MDD treatment based on their relation with the key genes. Method: Symbols of human coding genes were retrieved from the HUGO Gene Nomenclature Committee database. These symbols were then queried for MDD-related associations using a Python script in PubMed. Subsequently, genes with two or more related articles to MDD were selected. A union of our search data and MDD-related genes in the DisGeNET database was found. The gene interaction network was generated and analyzed utilizing the STRING and Cytoscape, respectively. Finally, a drug-gene network was constructed and medications that can affect multiple genes were selected. Results: The union of our search data and DisGeNET data contained 1734 genes. Based on network analysis, TNF, IL1B, IL6, STAT1, and STAT3 were identified as the key genes in the MDD pathogenesis. Eleven drugs that affect more than one gene were detected through a drug-gene network. These medications include Acitretin, Adalimumab, Alteplase, Cisplatin, Digoxin, Etanercept, Infliximab, Insulin, Omeprazole, Pentoxifylline, and Rabeprazole. Conclusion: In summary, our findings identified five genes as key genes in MDD development, as well as medications related to key genes. This study provides a new vision of the pathogenesis and treatment of MDD. However, further experimental and clinical studies are necessary.

Small Nucleolar RNAs in Solid Tumors: A Brief Review of the Literature on These Potential Biomarkers.

The objective of this study was to conduct an integrative review, addressing the key findings, biological functions, and clinical significance of these biomolecules in solid tumors. This document analyzes the main data on the involvement of snoRNAs in solid tumors. For this, Pubmed and Science direct were used, with keywords. Additionally, a search for the host gene was conducted using the snoDB tool, and its chromosomal location was identified using the Hugo Gene Nomenclature Committee (HGNC). According to research conducted in the literature, the majority of snoRNAs were found to be overexpressed and described as regulators of processes such as invasion, cellular proliferation, apoptosis, and migration. They are associated with clinical prognostic factors such as metastasis and worse survival. Therefore, it is essential to expand the investigation of snoRNAs in oncology across different types of tumors. The utilization of these biomolecules may pave the way for innovative clinical applications, such as their use in the early detection of neoplasms in non-invasive samples and as therapeutic targets. Broadening research on snoRNAs across various tumor types is crucial.

IL27 and IL1RN are causally associated with acute pancreatitis: a Mendelian randomization study.

The interleukin (IL) plays a role in the development of acute pancreatitis (AP). However, the specific IL in AP has not been fully revealed. Therefore, the association between prospective IL and AP was studied via Mendelian randomization (MR). The HUGO Gene nomenclature committee (HGNC) database provided 47 interleukin related genes (ILRGs). ILRGs and differentially expressed genes (DEGs) from GSE194331 were overlapped to create differently expressed ILRGs (DE-ILRGs). The integrative epidemiology unit (IEU) open genome-wide association study (GWAS) database provided exposure and outcome datasets. Univariate MR (UVMR) analysis using MR-Egger, IVW, simple mode, and weighted mode was done. UVMR results were verified using sensitivity analysis. Drug prediction, MVMR analysis, and PPI network development were also performed. Six DE-ILRGs were obtained. IL27 and IL1RN were substantially causally linked with AP by UVMR analysis (OR = 0.926, P < 0.001 and OR = 1.031, P = 0.023). Our sensitivity analysis showed the dependability of our results. Direct effect of IL27 was suggested by MVMR analysis. In the cytokine receptor binding pathway, IL27 and IL1RN interacted with IL36G and IL1R2. TAE-684, ARQ-680, and 12 other IL1RN and 14 IL27 medications were predicted. IL1RN was identified as a risk factor for acute pancreatitis (AP), but IL27 was found to be a protective factor for AP.

Bioinformatics Architecture for Integrating Genomics Data into Electronic Health Records.

The adequate management of patients' genomic information is essential for any health institution pursuing the Precision Medicine model. Here we approach a bioinformatic architecture that allows the Institution to store its whole genetic test data in a scalable database, and also the integration of that genetic data with the Electronic Health Record through a Clinical Decision Support System. The system complements patient care by suggesting referral to genetic counseling for patients who are potentially at risk of hereditary breast/ovarian cancer, and allowing for proper follow-up of patients with pathogenic variants in BRCA1 or BRCA2 genes. The implemented solution uses the FHIR standard and genetic nomenclatures from the Human Genome Variation Society and the HUGO Gene Nomenclature Committee. The architecture is flexible enough to allow any other health institution to integrate -to their information ecosystem- the whole solution or some of the modules according to its degree of digitization progress.

IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies.

Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases. To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources. In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.

DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms.

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee)and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperabilityand reusability) data representation in DGIdb.

Differential expression of zinc finger CCHC-type superfamily proteins in thyroid carcinoma and their associations with tumor immunity

BackgroundThe zinc-finger CCHC-type (ZCCHC) superfamily proteins are characterized by the shared sequence CX2-CX4-HX4-C and thought to own high affinity to single-stranded nucleic acids, particularly RNAs. In humans, a total of 24 ZCCHC proteins have been annotated in the HUGO Gene Nomenclature Committee (HGNC, https://www.genenames.org/) database with most of these members involved in multiple steps of RNA metabolism. Many studies have indicated that the ZCCHC genes play a regulatory role in the development and progression of solid tumors. To date, the expression pattern and prognostic value of ZCCHC factors in thyroid carcinomas have not been reported.MethodsBioinformatics analyses on the functions of ZCCHC factors in thyroid carcinoma (THCA) patients were performed based on various databases, i.e., TCGA, GEPIA, Kaplan-Meier Plotter, and TIMER.ResultsCompared with normal tissues, the expression of ZCCHC12 mRNA was significantly increased in THCA tissues. And it was associated with the overall survival of THCA patients, based on the Kaplan-Meier Plotter database. Furthermore, the expression levels of all ZCCHCs were correlated with tumor stages, implying its high relevance to THCA, specifically its immunity.ConclusionThe ZCCHC genes, represented by ZCCHC12, are differentially expressed in THCA staging. These genes are associated with immune infiltration of THCA and identified as the potential therapeutic targets for immunotherapy in THCA patients, which are possible novel biomarkers for the treatment of THCA.

Revised fission yeast gene and allele nomenclature guidelines for machine readability.

Standardized nomenclature for genes, gene products, and isoforms is crucial to prevent ambiguity and enable clear communication of scientific data, facilitating efficient biocuration and data sharing. Standardized genotype nomenclature, which describes alleles present in a specific strain that differ from those in the wild-type reference strain, is equally essential to maximize research impact and ensure that results linking genotypes to phenotypes are Findable, Accessible, Interoperable, and Reusable (FAIR). In this publication, we extend the fission yeast clade gene nomenclature guidelines to support the curation efforts at PomBase (www.pombase.org), the Schizosaccharomyces pombe Model Organism Database. This update introduces nomenclature guidelines for noncoding RNA genes, following those set forth by the Human Genome Organisation Gene Nomenclature Committee. Additionally, we provide a significant update to the allele and genotype nomenclature guidelines originally published in 1987, to standardize the diverse range of genetic modifications enabled by the fission yeast genetic toolbox. These updated guidelines reflect a community consensus between numerous fission yeast researchers. Adoption of these rules will improve consistency in gene and genotype nomenclature, and facilitate machine-readability and automated entity recognition of fission yeast genes and alleles in publications or datasets. In conclusion, our updated guidelines provide a valuable resource for the fission yeast research community, promoting consistency, clarity, and FAIRness in genetic data sharing and interpretation.

The VGNC: expanding standardized vertebrate gene nomenclature

The Vertebrate Gene Nomenclature Committee (VGNC) was established in 2016 as a sister project to the HUGO Gene Nomenclature Committee, to approve gene nomenclature in vertebrate species without an existing dedicated nomenclature committee. The VGNC aims to harmonize gene nomenclature across selected vertebrate species in line with human gene nomenclature, with orthologs assigned the same nomenclature where possible. This article presents an overview of the VGNC project and discussion of key findings resulting from this work to date. VGNC-approved nomenclature is accessible at https://vertebrate.genenames.org and is additionally displayed by the NCBI, Ensembl, and UniProt databases.

Lysophospholipid (S1P) receptors in GtoPdb v.2023.1

Sphingosine 1-phosphate (S1P) receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Lysophospholipid receptors [96]) are activated by the endogenous lipid sphingosine 1-phosphate (S1P). Originally cloned as orphan members of the endothelial differentiation gene (edg) family [16, 123], the receptors are currently designated as S1P1R through S1P5R [73, 16, 123]. Their gene nomenclature has been codified as human S1PR1, S1PR2, etc. (HUGO Gene Nomenclature Committee, HGNC) and S1pr1, S1pr2, etc. for mice (Mouse Genome Informatics Database, MGI) to reflect species and receptor function. All S1P receptors (S1PRs) have been knocked-out in mice constitutively and in some cases, conditionally. S1PRs, particularly S1P1, are expressed throughout all mammalian organ systems. Ligand delivery occurs via two known carriers (or "chaperones"): albumin and HDL-bound apolipoprotein M (ApoM), the latter of which elicits biased agonist signaling by S1P1 in multiple cell types [18, 53]. The five S1PRs, two chaperones, and active cellular metabolism have complicated analyses of receptor ligand binding in native systems. Signaling pathways and physiological roles have been characterized through radioligand binding in heterologous expression systems, targeted deletion of the different S1PRs, and most recently, mouse models that report in vivo S1P1R activation [101, 103]. The structures of S1P1 [180, 69, 108, 184], S1P2 [32], S1P3[116, 187], and S1P5 [110, 185] are solved, and confirmed aspects of ligand binding, specificity, and receptor activation, determined previously through biochemical and genetic studies [69, 17]. fingolimod (FTY720), the first FDA-approved drug to target any of the lysophospholipid receptors, binds as a phosphorylated metabolite to four of the five S1PRs, and was the first oral therapy for multiple sclerosis (MS) [35]. Second-generation S1PR modulators siponimod, ozanimod, and ponesimod that target S1P1 and S1P5 are also FDA approved for the treatment of various MS forms [16, 123]. In 2021, ozanimod became the first S1PR modulator to be FDA approved for the treatment of ulcerative colitis [145]. The mechanisms of action of fingolimod and other S1PR-modulating drugs now in development include binding S1PRs in multiple organ systems, e.g., immune and nervous systems, although the precise nature of their receptor interactions requires clarification [141, 37, 63, 64].

Lysophospholipid (LPA) receptors in GtoPdb v.2023.1

Lysophosphatidic acid (LPA) receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Lysophospholipid Receptors [62, 23, 91, 144]) are activated by the endogenous phospholipid LPA. The first receptor, LPA1, was identified as ventricular zone gene-1 (vzg-1) [46], This discovery represented the beginning of the de-orphanisation of members of the endothelial differentiation gene (edg) family, as other LPA and sphingosine 1-phosphate (S1P) receptors were found. Five additional LPA receptors (LPA2,3,4,5,6) have since been identified [91] and their gene nomenclature codified for human LPAR1, LPAR2, etc. (HUGO Gene Nomenclature Committee, HGNC) and Lpar1, Lpar2, etc. for mice (Mouse Genome Informatics Database, MGI) to reflect species and receptor function of their corresponding proteins. The crystal structure of LPA1 [17, 80, 2] and LPA6 [128] are solved and indicate that LPA accesses the extracellular binding pocket, consistent with its proposed delivery via autotaxin [17]. These studies have also implicated cross-talk with endocannabinoids via phosphorylated intermediates that can also activate these receptors. The binding affinities to LPA1 of unlabeled, natural LPA and anandamide phosphate (AEAp) were measured using backscattering interferometry (pKd = 9) [92, 115]. Utilization of this method indicated affinities that were 77-fold lower than when measured using radioactivity-based protocols [143]. Targeted deletion of LPA receptors has clarified signalling pathways and identified physiological and pathophysiological roles. Multiple groups have independently published validation of all six LPA receptors described in these tables, and further validation was achieved using a distinct read-out via a novel TGFα "shedding* assay [54]. LPA has been proposed to be a ligand for GPR35 [103], supported by a study revealing that LPA modulates macrophage function through GPR35 [60]. However chemokine (C-X-C motif) ligand 17 (CXCL17) is reported to be a ligand for GPR35/CXCR8 [85]. Moreover, LPA has also been described as an agonist for the transient receptor potential (Trp) ion channels TRPV1 [96] and TRPA1 [65]. All of these proposed non-GPCR receptor identities require confirmation and are not currently recognized as bona fide LPA receptors.

Identification and characterization of aldehyde dehydrogenase (ALDH) gene superfamily in garlic and expression profiling in response to drought, salinity, and ABA

In response to biotic and abiotic stressors, aldehydes are detoxified and converted to carboxylic acids by aldehyde dehydrogenases (ALDHs), which are enzymes that use NAD+/NADP+ as cofactors. Garlic (Allium sativum L.) has not yet undergone a systematic examination of the ALDH superfamily, despite the genome sequence having been made public. In this investigation, we identified, characterized, and profiled the expression of the garlic ALDH gene family over the entire genome. The ALDH Gene Nomenclature Committee (AGNC) classification was used to classify and name the 34 ALDH genes that were discovered. Except for chromosome 8, all AsALDH genes were dispersed across the chromosomes. AsALDH genes have various localizations, according to predictions about subcellular localization. The AsALDH proteins are more varied and closely related to rice than to Arabidopsis, according to a study of conserved motifs and phylogenetic relationships. The presence of stress modulation pathways is indicated by the abundance of stress-related cis-elements in the AsALDH genes' promoter regions. Analysis of the RNA-seq data showed that AsALDHs expressed differently in various tissues and at various developmental stages. Nine AsALDHs were chosen for study using RT-qPCR, and the results revealed that the majority of the genes were upregulated in response to ABA and downregulated in response to salinity and drought. The results of this study improved our knowledge of the traits, evolutionary background, and biological functions of AsALDHs genes in growth and development.

Identification of recurrent 3q13.31 chromosomal rearrangement indicates LSAMP as a tumor suppressor gene in neuroblastoma.

Neuroblastoma (NB) is a childhood malignancy of the sympathetic nervous system. NB is mainly driven by copy number alterations, such as MYCN amplification, large deletions of chromosome arm 11q and gain of chromosome arm 17q, which are all markers of high‑risk disease. Genes targeted by recurrent, smaller, focal alterations include CDKN2A/B, TERT, PTPRD and ATRX. Our previous study on relapsed NB detected recurrent structural alterations centered at limbic system‑associated membrane protein (LSAMP; HUGO Gene Nomenclature Committee: 6705; chromosomal location 3q13.31), which is a gene frequently reported to be deleted or downregulated in other types of cancer. Notably, in cancer, LSAMP has been shown to have tumor‑suppressing functions. The present study performed an expanded investigation using whole genome sequencing of tumors from 35 patients, mainly with high‑risk NB. Focal duplications or deletions targeting LSAMP were detected in six cases (17%), whereas single nucleotide polymorphism‑microarray analysis of 16 NB cell lines detected segmental alterations at 3q13.31 in seven out of the 16 NB cell lines (44%). Furthermore, low expression of LSAMP in NB tumors was significantly associated with poor overall and event‑free survival. Invitro, knockdown of LSAMP in NB cell lines increased cell proliferation, whereas overexpression decreased proliferation and viability. These findings supported a tumor suppressor role for LSAMP in NB. However, the higher incidence of LSAMP aberrations in cell lines and in relapsed NB tumors suggested that these alterations were a late event predominantly in advanced NB with a poor prognosis, indicating a role of LSAMP in tumor progression rather than in tumor initiation. In conclusion, the present study demonstrated recurrent genomic aberrations of chromosomal region 3q13.31 that targeted the LSAMP gene, which encodes a membrane protein involved in cell adhesion, central nervous system development and neurite outgrowth. The frequent aberrations affecting LSAMP, together with functional evidence, suggested an anti‑proliferative role of LSAMP in NB.

Five glycosylation-related gene signatures predict the prognostic risks of lung adenocarcinoma.

This study aimed to identify glycosylation-related genes associated with lung adenocarcinoma (LUAD) prognosis through comprehensive bioinformatic analysis. Glycosylation-related genes were identified from the Human Gene Nomenclature Committee, and LUAD prognostic genes were screened from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)-GSE68465 datasets. Glycosylation risk score (GLRS) was calculated to predict LUAD prognostic risk. Samples were grouped into GLRS-high and GLRS-low and compared. The Tumor Immune Dysfunction and Exclusion (TIDE) score was computed to assess the antitumor immune escape possibility after immunotherapy. From 213 glycosylation-related genes, five gene signatures served as prognostic LUAD predictors using univariate and stepwise Cox regression analyses. GLRS-based models were constructed using TCGA and GSE68465 samples; their sensitivity and specificity in predicting LUAD prognosis were confirmed. GLRS was an independent LUAD prognostic factor and contributed to the nomogram to predict patient survival. High GLRS was associated with advanced tumor stage and higher mutation frequencies, estimate scores, and TIDE scores. GLRS-high and GLRS-low patients differed in immune cell infiltration and epithelial-mesenchymal transition (EMT)-related gene expression. Thus, we propose five glycosylation-related gene signatures to predict overall survival and prognostic risks of LUAD. Their regulatory roles may be related to immune invasion, immunotherapy response, mutation, and EMT.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance.

Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients. Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry. We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p&lt;0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments. The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Genenames.org: the HGNC resources in 2023.

The HUGO Gene Nomenclature Committee (HGNC) assigns unique symbols and names to human genes. The HGNC database (www.genenames.org) currently contains over 43 000 approved gene symbols, over 19 200 of which are assigned to protein-coding genes, 14 000 to pseudogenes and nearly 9000 to non-coding RNA genes. The public website, www.genenames.org, displays all approved nomenclature within Symbol Reports that contain data curated by HGNC nomenclature advisors and links to related genomic, clinical, and proteomic information. Here, we describe updates to our resource, including improvements to our search facility and new download features.

Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach.

Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein–protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)–DEG, DEG–microRNA (miRNA) and DEG–disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF–DEG and DEG–miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129–2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.

Gene Updater: a web tool that autocorrects and updates for Excel misidentified gene names

Opening and processing gene expression data files in Excel runs into the inadvertent risk of converting gene names to dates. As pathway analysis tools rely on gene symbols to query against pathway databases, the genes that are converted to dates will not be recognized, potentially causing voids in pathway analysis. Molecular pathways related to cell division, exocytosis, cilium assembly, protein ubiquitination and nitric oxide biosynthesis were found to be most affected by Excel auto-conversion. A plausible solution is hence to update these genes and dates to the newly approved gene names as recommended by the HUGO Gene Nomenclature Committee (HGNC), which are resilient to Excel auto-conversion. Herein, we developed a web tool with Streamlit that can convert old gene names and dates back into the new gene names recommended by HGNC. The web app is named Gene Updater, which is open source and can be either hosted locally or at https://share.streamlit.io/kuanrongchan/date-to-gene-converter/main/date_gene_tool.py. Additionally, as Mar-01 and Mar-02 can each be potentially mapped to 2 different gene names, users can assign the date terms to the appropriate gene names within the Gene Updater web tool. This user-friendly web tool ensures that the accuracy and integrity of gene expression data is preserved by minimizing errors in labelling gene names due to Excel auto-conversions.

A standardised nomenclature for long non-coding RNAs.

The HUGO Gene Nomenclature Committee (HGNC) is the sole group with the authority to approve symbols for human genes, including long non-coding RNA (lncRNA) genes. Use of approved symbols ensures that publications and biomedical databases are easily searchable and reduces the risks of confusion that can be caused by using the same symbol to refer to different genes or using many different symbols for the same gene. Here, we describe how the HGNC names lncRNA genes and review the nomenclature of the seven lncRNA genes most mentioned in the scientific literature.

Aldehyde dehydrogenase superfamily in sorghum: genome-wide identification, evolution, and transcript profiling during development stages and stress conditions

BackgroundAldehyde dehydrogenases (ALDHs) are a family of NAD(P)+ dependent enzymes that detoxify aldehydes by promoting their oxidation to respective carboxylic acids. The role of ALDH enzymes in various plant species has been extensively studied, revealing their critical role in salinity, drought, heat, and heavy metal stress tolerance. Despite their physiological significance, ALDH genes in Sorghum bicolor have yet to be studied thoroughly.ResultsIn this study, a total of 19 ALDH genes have been identified that have been grouped into ten families based on the criteria of the ALDH gene nomenclature committee. Segmental duplication assisted more in the enhancement of SbALDH gene family members than tandem duplication. All the identified SbALDH members made a cluster with monocot rice and maize in the phylogenetic tree rather than dicot species, suggesting the pre-eudicot-monocot separation of the ALDH superfamily members. The gene structure and protein domain were found to be mostly conserved in separate phylogenetic classes, indicating that each family played an important role in evolution. Expression analysis revealed that several SbALDHs were expressed in various tissues, developmental stages, and in response to abiotic stresses, indicating that they can play roles in plant growth, development, or stress adaptation. Interestingly, the majority of the SbALDH genes were found to be highly responsive to drought stress, and the SbALDH18B1 transcript showed maximum enhancement in all the stress conditions. The presence of cis-acting elements (mainly ABRE and MBS) in the promoter region of these genes might have a significant role in drought tolerance.ConclusionsOur findings add to the current understanding, evolutionary history, and contribution of SbALDHs in stress tolerance, and smooth the path of further functional validation of these genes.