Identification Of Genes Research Articles

Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and heart failure.

Non-alcoholic fatty liver disease (NAFLD) and heart failure (HF) are related conditions with an increasing incidence. However, the mechanism underlying their association remains unclear. This study aimed to explore the shared pathogenic mechanisms and common biomarkers of NAFLD and HF through bioinformatics analyses and experimental validation. NAFLD and HF-related transcriptome data were extracted from the Gene Expression Omnibus (GEO) database (GSE126848 and GSE26887). Differential analysis was performed to identify common differentially expressed genes (co-DEGs) between NAFLD and HF. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were conducted to explore the functions and regulatory pathways of co-DEGs. Protein-protein interaction (PPI) network and support vector machine-recursive feature elimination (SVM-RFE) methods were used to screen common key DEGs. The diagnostic value of common key DEGs was assessed by receiver operating characteristic (ROC) curve and validated with external datasets (GSE89632 and GSE57345). Finally, the expression of biomarkers was validated in mouse models. A total of 161 co-DEGs were screened out in NAFLD and HF patients. GO, KEGG, and GSEA analyses indicated that these co-DEGs were mainly enriched in immune-related pathways. PPI network revealed 14 key DEGs, and SVM-RFE model eventually identified two genes (CD163 and CCR1) as common key DEGs for NAFLD and HF. Expression analysis revealed that the expression levels of CD163 and CCR1 were significantly down-regulated in HF and NAFLD patients. ROC curve analysis showed that CD163 and CCR1 had good diagnostic values for HF and NAFLD. Single-gene GSEA suggested that CD163 and CCR1 were mainly engaged in immune responses and inflammation. Experimental validation indicated unbalanced macrophage polarization in HF and NAFLD mouse models, and the expression of CD163 and CCR1 were significantly down-regulated. This study identified M2 polarization impairment characterized by decreased expression of CD163 and CCR1 as a common pathogenic pathway in NAFLD and HF. The downregulation of CD163 and CCR1 may reflect key pathological changes in the development and progression of NAFLD and HF, suggesting their potential as diagnostic and therapeutic targets.

Exogenous interactome analysis of bovine viral diarrhea virus-host using network based-approach and identification of hub genes and important pathways involved in virus pathogenesis

Bovine viral diarrhea (BVD) is one of the most important diseases in livestock, caused by BVD virus (BVDV). During the pathogenesis of the virus, many interactions occur between host and viral proteins. Studying these interactions can help better understand the pathogenesis of the virus, identify putative functional proteins, and find new treatment and prevention strategies. To this aim, a BVDV-host protein-protein interaction (PPI) network map was constructed using Cytoscape and analyzed with cytoHubba, Kyoto Encyclopedia of Genes and Genomics (KEGG), Gene Ontology (GO), and Protein Analysis Through Evolutionary Relationships (PANTHER). Npro with 125 connections had the greatest number of interactions with host proteins. CD46, EEF-2, and TXN genes were detected as hub genes using different ranking algorithms in cytoHubba. BVDV interactions with its host mainly focus on targeting translation, protein synthesis, and cellular metabolism pathways. Different classes of proteins including translational proteins, nucleic acid metabolism proteins, metabolite interconversion enzymes, and protein-modifying enzymes are affected by BVDV. These findings improve our understanding of the effects of the virus on the cell. Hub genes and key pathways identified in the present study can serve as targets for novel BVDV prevention or treatment strategies.

Identification of MYC genes in four Cucurbitaceae species and their roles in the response to temperature stress

BackgroundMyelocytomatosis (MYC) transcription factors are crucial mediators of the response of plants to environmental stresses through via binding to DNA regulatory regions. However, few systematic characterizations of MYC genes are available in Cucurbitaceae species.ResultsIn this study, we identified 10, 8, 12, and 10 MYC genes in Cucumis sativus, Cucumis melo, Citrullus lanatus, and Benincasa hispida, respectively. Characterization revealed that all of the MYC proteins contain a highly conserved H4-V5-E6-E8-R9-R11-R12 sequence, which is essential for the binding of DNA regulatory regions. Evolutionary analysis enabled us to categorize 40 predicted MYC proteins from seven species into five distinct groups and revealed that the expansion of the MYC genes occurred before the divergence of monocots and dicots. The upstream promoter regions of the MYC genes contain a variety of developmental, stress, and hormone-responsive regulatory elements. The expression of cucumber MYC genes varies significantly across organs, with particularly high expression of CsaV3_3G001710 observed across all organs. Transcriptomic analysis revealed that certain cucumber MYC genes undergo specific upregulation or downregulation in response to both biotic and abiotic stressors. In particular, under temperature stress, the cucumber genes CsaV3_3G007980 and CsaV3_3G001710 were significantly upregulated. Interestingly, the homologs of these two genes in C. lanatus presented a similar expression pattern to that in C. sativus, whereas in B. hispida, they presented the opposite pattern, i.e., significant downregulation. These findings indicated that these two genes indeed respond to temperature stress but with different expression patterns, highlighting the divergent functions of homologous genes across different species.ConclusionsThis study analyzed the size and composition of the MYC gene family in four Cucurbitaceae species and investigated stress-responsive expression profiles, especially under temperature stress. All the results showed that MYC genes play important roles in development and stress responses, laying a theoretical foundation for further investigations of these response mechanisms.

Activin A inhibits the migration of human lung adenocarcinoma A549 cells induced by EGF

Activin A, a member of the transforming growth factor β (TGF-β) superfamily, is involved in tumorigenesis and tumor progression. However, it remains unclear whether activin A can affect the migration of lung adenocarcinoma (LUAD) cells. In this study, the results of differentially expressed genes (DEGs) identification revealed that lung adenocarcinoma tissues exhibited lower expression of activin βA mRNA, but higher expression of epidermal growth factor (EGF) and MMP9 mRNA compared to nontumor tissues. Moreover, we found that activin A inhibited human LUAD A549 cell proliferation promoted by EGF. Additionally, EGF induced A549 cell migration in microfluidic device, while activin A attenuated EGF actions. Simultaneously, EGF increased the levels of migration-related proteins, but activin A played the opposite role. Furthermore, the study revealed that EGF upregulated the ratio of p-ERK/ERK in A549 cells, which was weakened by activin A, and A549 cell migration regulated by activin A was not related to calcium signaling. In addition, the inhibitory effect of activin A on EGF-induced A549 cell migration was attenuated by the ERK inhibitor FR180204. These findings demonstrate that activin A effectively hinders the migration of A549 cells induced by EGF through ERK1/2 signaling, suggesting that targeting activin A may hold promise in the treatment of EGF-dependent LUAD growth and metastasis.

A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches.

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

PCP-bε is a novel positive regulator of pollen germination in Arabidopsis thaliana

small cysteine-rich peptides play essential roles in different stages of the plant reproductive process. Pollen germination is a prerequisite for double fertilization and is directly related to seed formation and crop yield. However, the small cysteine-rich peptides that are involved in pollen germination remain to be identified. In this study, identification and phylogenetic analysis of PCP-Bε genes in sequenced Brassicaceae show that pollen coat protein B-class protein PCP-Bε gene is widespread in Arabidopsis and its high relatives, but lost in some Brassica species. Expression analyses display that AtPCP-Bε gene is expressed in Arabidopsis pollen. Arabidopsis PCP-Bε knockout mutants are generated by CRISPR/Cas9, Phenotypic analyses show that the absence of AtPCP-Bε obviously impairs in vitro pollen germination, but has no influence on pollen tube growth, which demonstrates that AtPCP-Bε is a novel positive regulator of pollen germination. It is speculated that AtPCP-Bε should interact with the receptor from pollen to perform its function. These findings are useful for further analysis on the molecular mechanism of pollen germination.

Comprehensive Identification and Expression Analysis of the Multidrug and Toxic Compound Extrusion (MATE) Gene Family in Brachypodium distachyon.

The Multidrug and Toxic Compound Extrusion (MATE) proteins serve as pivotal transporters responsible for the extrusion of metabolites, thereby playing a significant role in both plant development and the detoxification of toxins. The MATE gene family within the Brachypodium distachyon, which is an important model organism of the Poaceae family, remains largely unexplored. Here, a comprehensive identification and analysis of MATE genes that complement B. distachyon were conducted. The BdMATE genes were systematically categorized into five distinct groups, predicated on an assessment of their phylogenetic affinities and protein structure. Furthermore, our investigation revealed that dispersed duplication has significantly contributed to the expansion of the BdMATE genes, with tandem and segmental duplications showing important roles, suggesting that the MATE genes in Poaceae species have embarked on divergent evolutionary trajectories. Examination of ω values demonstrated that BdMATE genes underwent purifying selection throughout the evolutionary process. Furthermore, collinearity analysis has confirmed a high conservation of MATE genes between B. distachyon and rice. The cis-regulatory elements analysis within BdMATEs promoters, coupled with expression patterns, suggests that BdMATEs play important roles during plant development and in response to phytohormones. Collectively, the findings presented establish a foundational basis for the subsequent detailed characterization of the MATE gene family members in B. distachyon.

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments

BackgroundIschemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features.MethodsTo enhance our understanding of the mechanistic underpinnings of disulfidptosis in IS, we initially retrieved the expression profile of peripheral blood from human IS patients from the GEO database. We then utilized a suite of machine learning algorithms, including LASSO, random forest, and SVM-RFE, to identify and validate pivotal genes. Furthermore, we developed a predictive nomogram model, integrating multifactorial logistic regression analysis and calibration curves, to evaluate the risk of IS. For the analysis of single-cell sequencing data, we employed a range of analytical tools, such as "Monocle" and "CellChat," to assess the status of immune cell infiltration and to characterize intercellular communication networks. Additionally, we utilized an oxygen–glucose deprivation (OGD) model to investigate the effects of SLC7A11 overexpression on microglial polarization.ResultsThis study successfully identified key genes associated with disulfidptosis and developed a reliable nomogram model using machine learning algorithms to predict the risk of ischemic stroke. Examination of single-cell sequencing data showed a robust correlation between disulfidptosis levels and the infiltration of immune cells. Furthermore, "CellChat" analysis elucidated the intricate characteristics of intercellular communication networks. Notably, the TNF signaling pathway was found to be intimately linked with the disulfidptosis signature in ischemic stroke. In an intriguing finding, the OGD model demonstrated that SLC7A11 expression suppresses M1 polarization while promoting M2 polarization in microglia.ConclusionThe significance of our findings lies in their potential to shed light on the pathogenesis of ischemic stroke, particularly by underscoring the pivotal role of disulfidptosis-related genes (DRGs). These insights could pave the way for novel therapeutic strategies targeting DRGs to mitigate the impact of ischemic stroke.

Identification of novel genes regulating the development of the palate.

The International Mouse Phenotyping Consortium (IMPC) has generated thousands of knockout mouse lines, many of which exhibit embryonic or perinatal lethality. Using micro-computed tomography (micro-CT), the IMPC has created and publicly released 3D image datasets of embryos from these lethal and subviable lines. In this study, we leveraged this dataset to screen homozygous null mutants for anomalies in secondary palate development. We analyzed optical sections from 2,987 embryos at embryonic days E15.5 and E18.5, representing 484 homozygous mutant lines. Our analysis identified 45 novel genes implicated in palatogenesis. Gene set enrichment analysis highlighted biological processes and pathways relevant to palate development and uncovered 18 genes jointly regulating the development of the eye and the palate. These findings present a valuable resource for further research, offer novel insights into the molecular mechanisms underlying palatogenesis, and provide important context for understanding the etiology of rare human congenital disorders involving simultaneous malformations of the palate and other organs, including the eyes, ears, kidneys, and lungs.

Fanconi Anemia a Rare Disease

Fanconi anemia (FA) is a genetically and phenotypically recessive autosomal illness. Fanconi anemia (FA), a rare genetic illness, is currently receiving more attention from hematologists, cancer biologists, and fundamental scientists studying DNA repair and ubiquitin biology. Chromosome instability, progressive bone marrow failure, cancer susceptibility, and several other congenital anomalies are its defining characteristics. All three blood cell lines are included. This is a fatal illness that typically strikes children under the age of five. One of the fastest-growing fields of medical study is FA. The discovery of 15 distinct FA genes and the clarification of the FA molecular pathways have contributed to our knowledge of the pathogenic mechanism and, in many cases, the development of treatment guidelines. Because FA possesses distinct traits in many different biological areas, investigations on FA provided significant material for studies on malignancies. Research has demonstrated a genetic relationship between FA and cancer, showing that both cancer genes and FA genes are present in malignancies. FA is therefore identified as a prototypical illness for the comprehension of aging and cancer. Here we review the incidence of FA, Genetics, Pathophysiology, impact of FA, Identification of FA genes and delineation of FA pathways, Symptoms and indication, Diagnosis, Management, Complication and Prevention, and patient education about FA.

Clinical pharmacology functional disorders of narcolepsy in central nervous system

Excessive daytime sleepiness (EDS), cataplexy, and/or other dissociated REM sleep symptoms such hypnagogic hallucinations and sleep paralysis are characteristics of narcolepsy. Antidepressants are being used to treat cataplexy and EDS in narcolepsy patients. These medications have an amphetamine-like effect on the central nervous system (CNS). Other kinds of medications are also used, including gamma-hydroxybutyrate (GHB), a short-acting sedative for EDS/fragmented nighttime sleep, and modafinil, a non-amphetamine wake-promoting drug for EDS. Based on the identification of narcolepsy genes in animals, the primary pathophysiology of human narcolepsy has recently been clarified. The genes responsible for the pathogenesis of canine narcolepsy (hypocretin/orexin ligand and its receptor) have been discovered using both forward (positional cloning) and reverse (mouse gene knockout) genetics. Novel hypothalamic neuropeptides called hypocretins and orexins have a role in a number of hypothalamic processes, including neuroendocrine and energy balance. Although hypocretin-related gene mutations are uncommon in humans, hypocretin-ligand deficiency is a common cause of narcolepsy-cataplexy. The clinical, pathophysiological, and pharmacological facets of narcolepsy are covered in this review.

Gene identification for ocular congenital cranial motor neuron disorders using human sequencing, zebrafish screening, and protein binding microarrays.

To functionally evaluate novel human sequence-derived candidate genes and variants for unsolved ocular congenital cranial dysinnervation disorders (oCCDDs). Through exome and genome sequencing of a genetically unsolved human oCCDD cohort, we previously identified variants in 80 strong candidate genes. Here, we further prioritized a subset of these (43 human genes, 57 zebrafish genes) using a G0 CRISPR/Cas9-based knockout assay in zebrafish and generated F2 germline mutants for seventeen. We tested the functionality of variants of uncertain significance in known and novel candidate transcription factor-encoding genes through protein binding microarrays. We first demonstrated the feasibility of the G0 screen by targeting known oCCDD genes phox2a and mafba. 70-90% of gene-targeted G0 zebrafish embryos recapitulated germline homozygous null-equivalent phenotypes. Using this approach, we then identified three novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) with putative contributions to human and zebrafish cranial motor development. In addition, protein binding microarrays demonstrated reduced or abolished DNA binding of human variants of uncertain significance in known and novel sequence-derived transcription factors PHOX2A (p.(Trp137Cys)), MAFB (p.(Glu223Lys)), and OLIG2 (p.(Arg156Leu)). This study nominates three strong novel candidate oCCDD genes (SEMA3F, OLIG2, and FRMD4B) and supports the functionality and putative pathogenicity of transcription factor candidate variants PHOX2A p.(Trp137Cys), MAFB p.(Glu223Lys), and OLIG2 p.(Arg156Leu). Our findings support that G0 loss-of-function screening in zebrafish can be coupled with human sequence analysis and protein binding microarrays to aid in prioritizing oCCDD candidate genes/variants.

PSX-3 Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle

Abstract Feed cost represents a major economic determinant within cattle production, amounting to an estimated 75% of the total variable costs. Consequently, comprehensive approaches such as optimizing feed utilization through alternative feed sources, alongside the selection of feed-efficient animals, are of great significance. Here, we investigate the effect of two diets, traditional corn-grain fed and alternative by-product based, on 14 phenotypes related to feed, methane emission and production efficiency and on multi-tissue transcriptomics data from liver, muscle, and rumen wall, derived from Nellore bulls (n = 52), 26 on each diet. To this end, diets were contrasted at the level of phenotype, gene expression, and gene-phenotype network connectivity. As regards the phenotypic level, at a P-value < 0.05, significant differences were found in favor of the alternative diet for average daily weight gain at finishing, dry matter intake at finishing, methane emission, carcass yield and subcutaneous fat thickness at the rib-eye muscle area. In terms of the transcriptional level of the 14,776 genes expressed across the examined tissues, we found 487, 484, and 499 genes differentially expressed due to diet in liver, muscle, and rumen, respectively (P-value < 0.01). To explore differentially connected phenotypes across both diet-based networks, we focused on the phenotypes with the largest change in average number of connections within diets and tissues, namely methane emission and carcass yield, highlighting, in particular, gene expression changes involving SREBF2, and revealing the largest differential connectivity in rumen and muscle, respectively. Similarly, from examination of differentially connected genes across diets, the top-ranked most differentially connected regulators within each tissue were MEOX1, PTTG1, and BASP1 in liver, muscle, and rumen, respectively. Changes in gene co-expression patterns suggest activation or suppression of specific biological processes and pathways in response to dietary interventions, consequently impacting the phenotype. The identification of genes that respond differently to diets and their associated phenotypic effects serves as a crucial steppingstone for further investigations, aiming to build upon our discoveries. Ultimately, such advancements hold the promise of improving animal welfare, productivity, and sustainability in livestock farming.

PSIX-18 Multiomic analysis to identify host and microbiome contributions to digestibility in beef cattle

Abstract This study evaluated beef heifers selected for high (efficient) or low (inefficient) digestible fiber intake (DFI). Initial analysis showed that high DFI animals had reduced methane production versus low-DFI under a high forage diet. Using the same cohort of animals maintained on a further 4 diets of varying forage:concentrate ratios, we employed multi-kingdom amplicon sequencing and metagenome shotgun sequencing of rumen digesta and feces alongside RNA sequencing of rumen epimural samples to evaluate the compositional and functional interplay between different microbial groups, and their relationship with host gene expression in cattle divergent for DFI. Samples were collected from 16 cattle during 2 metabolism trials, comprising 5 diets. Amplicon sequencing analysis was conducted using QIIME2; 16S rRNA and 18S rRNA reads were analyzed using the SILVA database, while LSU (fungal) sequences were analyzed using a custom D1/D2 database. Additional analysis of archaeal 16S rRNA sequences was conducted using the Rumen and Intestinal Methanogens (RIM) database. Metagenome shotgun reads underwent a two-pass classification with Kraken2 using a database of prokaryotic genomes derived from the GTDB taxonomy, with the unclassified output undergoing classification using a custom database containing all NCBI protozoa, fungi, and phage genomes, enriched with selected rumen-specific ciliate and fungal genomes. Downstream analysis of taxonomic data from all microbiome work was conducted in R, and differentially abundant taxa were identified using ANCOM-BC and Aldex2. Functional analysis of metagenome contigs using the CAZY database implemented in dbCAN3 is ongoing. RNA-seq data were analyzed using the ARS-UCD reference genome, with identification of DE genes conducted using DeSeq2. Preliminary results indicate no major effect of DFI ranking on host gene expression, bacterial 16S rRNA, or metagenome compositional profiles. Several bacterial genera were differentially abundant between digestibility groups (P < 0.05), but these were all minor (< 0.01%) members of the microbiome. Fungal and methanogen communities different significantly (P < 0.05) according to DFI group, with efficient (high DFI) containing and more diverse communities under high-grain diet (P < 0.05). The same difference showed a tendency toward significance for the 18S rRNA protozoa data (P < 0.1). These preliminary data indicate that the microbial factors underpinning divergence in efficiency measured by DFI vary according to diet and may be more prominent in the non-bacterial fraction of the microbiome. Ongoing functional analysis of metagenome data as well as integration of multiomic data will provide deeper insight into these relationships and how they contribute to feed digestibility and efficiency in cattle.

Insights into pathogenicity mechanisms of Phytophthora meadii, the abnormal leaf fall pathogen in rubber tree, through whole genome sequencing and analysis

Hevea brasiliensis (Willd. ex A.Juss.) Müll. Arg., also known as the Para rubber tree is the main commercial source of natural rubber. Phytophthora meadii causing abnormal leaf fall (ALF) disease is the most destructive and recurrent disease of rubber trees in India, leading to significant annual economic losses. To comprehend its pathogenic mechanisms and investigate stratagems for disease control, whole-genome sequencing and analysis of P. meadii were conducted. The P. meadii genome was sequenced using the standard Illumina Novaseq 6000 Platform. The assembled P. meadii genome is approximately 53 Mb in size, consisting of 13,171 contigs with an N50 of 7105 and a BUSCO completeness of 96 %. A total of 14,646 protein-coding genes were predicted, with an average GC content of 49.96 %. P. meadii's genome contains 750 carbohydrate-active enzymes (CAZymes), which is notably higher compared to closely related species. Five genomic regions potentially encoding enzymes for secondary metabolite production and 1877 pathogenic proteins were also identified. Our data confirm the phylogenetic positioning of P. meadii as a distinct species and indicate that it forms a closely related clade with P. citrophthora. Analyzing the P. meadii genome not only aids in understanding its genomic traits but also facilitates the identification of genes related to pathogenesis and will give a better understanding of the Hevea – Phytophthora interaction.

Transcriptome profiles of the skin associated with the color of the black and white coat of Angora goats

The coat color of mammals, determined by the distribution of melanin, particularly eumelanin and pheomelanin, reflects intricate genetic and molecular processes. However, our understanding of the relationship between coat color, gene expression, and polymorphisms in goats remains nascent. This study investigates transcriptomic differences between black and white Angora goats across three distinct hair growth phases. Skin tissue samples from both colored and white Angora goats were analyzed using mRNA expression profiling. Three skin samples were taken from each goat as biological duplicates at every stage of hair follicle growth (September, January, and March). In total, 36 samples were analyzed in this study, including samples from two Angora goat varieties, three developmental stages (three biological replicates), and two technical replicates for RNA sequencing. Significant differences in gene expression were observed between black and white goats at each growth phase, particularly in genes associated with the melanogenesis pathway. Specifically, several pigmentation genes were identified solely in black goats, indicating phase-specific and breed-specific regulation. Noteworthy genes, such as SLC2A1, STAR, and SLC7A5, exhibited differential expression patterns across growth phases in black goats, further highlighting the complexity of melanogenesis regulation. This is the first study to use mRNA expression profiling of skin tissues to analyze coat color differences between black and white coated Angora goats at the anagen, catagen, and telogen stages. The identification of phase-specific and black goat-specific pigmentation genes provides valuable insights into the complex mechanisms governing coat color formation.

Identification of autophagy-related genes as potential biomarkers correlated with immune infiltration in bipolar disorder: a bioinformatics analysis

BackgroundBipolar disorder (BPD) is a kind of manic and depressive phase alternate episodes of serious mental illness, and it is correlated with well-documented cortical brain abnormalities. Emerging evidence supports that autophagy dysfunction in neuronal system contributes to pathophysiological changes in neurological disease. However, the role of autophagy in bipolar disorder has rarely been elucidated. This study aimed to identify the autophagy-related gene as a potential biomarker Correlated to immune infiltration in BPD.MethodsThe microarray dataset GSE23848 and autophagy-related genes (ARGs) were downloaded. Differentially expressed genes (DEGs) between normal and BPD samples were screened using the R software. Machine learning algorithms were performed to screen the significant candidate biomarker from autophagy-related differentially expressed genes (ARDEGs). The correlation between the screened ARDEGs and infiltrating immune cells was explored through correlation analysis.ResultsIn this study, the autophagy pathway was abundantly enriched and activated in BPD, as indicated by Pathway enrichment analysis. We identified 16 ARDEGs in BPD compared to the normal group. A signature of 4 ARDEGs (ERN1, ATG3, CTSB, and EIF2AK3) was screened. ROC analysis showed that the above genes have good diagnostic performance. In addition, immune correlation analysis considered that the above four genes significantly correlated with immune cells in BPD.ConclusionsAutophagy - immune cell axis mediates pathophysiological changes in BPD. Four important ARDEGs are prospective to be potential biomarkers associated with immune infiltration in BPD and helpful for the prediction or diagnosis of BPD.

StarTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It’s worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

Artificial intelligence empowering rare diseases: a bibliometric perspective over the last two decades

ObjectiveTo conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of international collaboration, tracking the emergence of research hotspots, and detecting trends through keyword bursts.MethodsIn this bibliometric study, we identified and retrieved publications on AI applications in RDs spanning 2003 to 2023 from the Web of Science (WoS). We conducted a global research landscape analysis and utilized CiteSpace to perform keyword clustering and burst detection in this field.ResultsA total of 1501 publications were included in this study. The evolution of AI applications in RDs progressed through three stages: the start-up period (2003–2010), the steady development period (2011–2018), and the accelerated growth period (2019–2023), reflecting this field’s increasing importance and impact at the time of the study. These studies originated from 85 countries, with the United States as the leading contributor. “Mutation”, “Diagnosis”, and “Management” were the top three keywords with high frequency. Keyword clustering analysis identified gene identification, effective management, and personalized treatment as three primary research areas of AI applications in RDs. Furthermore, the keyword burst detection indicated a growing interest in the areas of “biomarker”, “predictive model”, and “data mining”, highlighting their potential to shape future research directions.ConclusionsOver two decades, research on the AI applications in RDs has made remarkable progress and shown promising results in the development. Advancing international transboundary cooperation is essential moving forward. Utilizing AI will play a more crucial role across the spectrum of RDs management, encompassing rapid diagnosis, personalized treatment, drug development, data integration and sharing, and continuous monitoring and care.

Differentially expressed extracellular matrix genes functionally separate ameloblastoma from odontogenic keratocyst

BackgroundAmeloblastoma and odontogenic keratocyst (OKC) are odontogenic tumors that develop from remnants of odontogenic epithelium. Both display locally invasive growth characteristics and high predilection for recurrence after surgical removal. Most ameloblastomas harbor BRAFV600E mutation while OKCs are associated with PATCH1 gene mutation but distinctive indicators of ameloblastoma growth characteristics relative to OKC are still unclear. The aim of this study was to assess hub genes that underlie ameloblastoma growth characteristics using bioinformatic analysis, ameloblastoma samples and mouse xenografts of human epithelial-derived ameloblastoma cells.MethodsRNA expression profiles were extracted from GSE186489 gene expression dataset acquired from Gene Expression Ominibus (GEO) database. Galaxy and iDEP online analysis tools were used to identify differentially expressed genes that were further characterized by gene ontology (GO) and pathway analysis using ShineyGO. The protein-protein interaction (PPI) network was constructed for significantly upregulated differentially expressed genes using online database STRING. The PPI network visualization was performed using Cytoscape and hub gene identification with cytoHubba. Top ten nodes were selected using maximum neighborhood component, degree and closeness algorithms and analysis of overlap was performed to confirm the hub genes. Epithelial-derived ameloblastoma cells from conventional ameloblastoma were transplanted into immunocompromised mice to recreate ameloblastoma in vivo based on the mouse xenograft model. The top 3 hub genes FN1, COL I and IGF-1 were validated by immunostaining and quantitative analysis of staining intensities to ameloblastoma, OKC samples and mouse ameloblastoma xenografts tissues.ResultsSeven hub genes were identified among which FN1, COL1A1/COL1A2 and IGF-1 are associated with extracellular matrix organization, collagen binding, cell adhesion and cell surface interaction. These were further validated by positive immunoreactivity within the stroma of ameloblastoma samples but both ameloblastoma xenograft and OKC displayed only FN1 and IGF-1 immunoreactivity while COL 1 was unreactive. The expression levels of both FN1 and IGF-1 were much lower in OKC relative to ameloblastoma.ConclusionThis study further validates a differentially upregulated expression of matrix proteins FN1, COL I and IGF-1 in ameloblastoma relative to OKC. It suggests that differential stromal architecture and growth characteristics of ameloblastoma relative to OKC could be an interplay of differentially upregulated genes in ameloblastoma.