Functional Enrichment Analysis Research Articles

Deciphering the Molecular Landscape of Dengue Infection: Insights from gene expression profiling and Protein Interactions

Dengue fever (DF) is a viral infection recognized as one of the most rapidly spreading vector-borne diseases among humans. However, the molecular mechanisms underlying disease aetiology and potential therapeutic targets remain unclear. This study presents a thorough examination of differentially expressed genes (DEGs) throughout the spectrum of Dengue infection stages, offering insights into disease mechanisms and therapeutic targets. Through meticulous analysis of DEGs within each infection stage, key genes pertinent to disease progression are identified, highlighting their roles in host-virus interactions. Noteworthy discoveries include the upregulation of IFI27 and USP18 in mild infection, suggesting involvement in viral replication inhibition and host defence, alongside the downregulation of CMTM2, indicating alterations in immune-related functions. Similarly, in severe infection, upregulated genes like IGHV1-69 are linked to illness severity, while downregulated IL-13RA1 signifies disruptions in cytokine signaling. Overlapping DEGs between infection groups unveil shared biological processes, further elucidated through functional enrichment analysis, showcasing coordinated regulation of cellular processes and pathways. Integration of Dengue virus and human interaction network analysis reveals crucial insights into the mechanisms by which the virus mediates the disease progression and offers potential therapeutic opportunities for disease management.

Identification of mitophagy-related gene signatures for predicting delayed graft function and renal allograft loss post-kidney transplantation.

Ischemia-reperfusion injury (IRI) is an unavoidable consequence post-kidney transplantation, which inevitably leads to kidney damage. Numerous studies have demonstrated that mitophagy is implicated in human cancers. However, the function of mitophagy in kidney transplantation remains poorly understood. This study aims to develop mitophagy-related gene (MRGs) signatures to predict delayed graft function (DGF) and renal allograft loss post-kidney transplantation. Differentially expressed genes (DEGs) were identified and intersected with the MRGs to obtain mitophagy-related DEGs (MRDEGs). Functional enrichment analyses were conducted. Subsequently, random forest and SVM-RFE machine learning were employed to identify hub genes. The DGF diagnostic prediction signature was constructed using LASSO regression analysis. The renal allograft prognostic prediction signature was developed through univariate Cox and LASSO regression analysis. In addition, ROC curves, immunological characterization, correlation analysis, and survival analysis were performed. Nineteen MRDEGs were obtained by intersecting 61 DEGs with 4897 MRGs. Seven hub genes were then identified through machine learning. Subsequently, a five-gene DGF diagnostic prediction signature was established, with ROC curves indicating its high diagnostic value for DGF. Immune infiltration analysis revealed that many immune cells were more abundant in the DGF group compared to the Immediate Graft Function (IGF) group. A two-gene prognostic signature was developed, which accurately predicted renal allografts prognosis. The mitophagy-related gene signatures demonstrated high predictive accuracy for DGF and renal allograft loss. Our study may provide new perspectives on prognosis and treatment strategies post-kidney transplantation.

Discovering Explainable Biomarkers for Breast Cancer Anti-PD1 Response via Network Shapley Value Analysis

Background and objectiveImmunotherapy holds promise in enhancing pathological complete response rates in breast cancer, albeit confined to a select cohort of patients. Consequently, pinpointing factors predictive of treatment responsiveness is of paramount importance. Gene expression and regulation, inherently operating within intricate networks, constitute fundamental molecular machinery for cellular processes and often serve as robust biomarkers. Nevertheless, contemporary feature selection approaches grapple with two key challenges: opacity in modeling and scarcity in accounting for gene-gene interactions MethodsTo address these limitations, we devise a novel feature selection methodology grounded in cooperative game theory, harmoniously integrating with sophisticated machine learning models. This approach identifies interconnected gene regulatory network biomarker modules with priori genetic linkage architecture. Specifically, we leverage Shapley values on network to quantify feature importance, while strategically constraining their integration based on network expansion principles and nodal adjacency, thereby fostering enhanced interpretability in feature selection. We apply our methods to a publicly available single-cell RNA sequencing dataset of breast cancer immunotherapy responses, using the identified feature gene set as biomarkers. Functional enrichment analysis with independent validations further illustrates their effective predictive performance ResultsWe demonstrate the sophistication and excellence of the proposed method in data with network structure. It unveiled a cohesive biomarker module encompassing 27 genes for immunotherapy response. Notably, this module proves adept at precisely predicting anti-PD1 therapeutic outcomes in breast cancer patients with classification accuracy of 0.905 and AUC value of 0.971, underscoring its unique capacity to illuminate gene functionalities ConclusionThe proposed method is effective for identifying network module biomarkers, and the detected anti-PD1 response biomarkers can enrich our understanding of the underlying physiological mechanisms of immunotherapy, which have a promising application for realizing precision medicine.

Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach

Barley (Hordeum vulgare L.) is exposed to various biotic and abiotic stresses, making it crucial to fully understand the gene signatures that respond to stress. This study utilizes machine learning to analyze transcriptomic data from 515 RNA-seq profiles across 18 independent studies, covering eleven abiotic and three biotic stress types. Through meticulous data preprocessing, including quality assessment and batch effect correction, we have identified 4,311 genes for further analysis. Feature selection was performed using five weighting algorithms, resulting in the prioritization of 400 core genes. Machine learning models, specifically Random Forest and C4.5, were optimized and evaluated using a 10-fold cross-validation approach. The C4.5 algorithm demonstrated superior accuracy in predicting stress-responsive signatures. Key genes, such as bHLH119 and E3 ubiquitin protein ligase DRIP2, were identified as potential biomarkers. Functional enrichment analysis, conducted through protein-protein interaction networks and Gene Ontology/KEGG pathway analysis, has revealed significant involvement in lipid biosynthesis, signal transduction, and defense response processes. These findings highlight the crucial roles of the identified biomarkers genes in barley's resilience to stress and provide potential targets for genetic improvement. Future research should focus on validating these biomarkers in different barley cultivars and under field conditions to enhance crop resilience against stressors.

Rapid and high-throughput identification of thermal-responsive genes based on cDNA-library in Upland Cotton

Severe high temperature (HT) climate significantly impacts cotton quality and yield. Consequently, it is essential to mine thermal-responsive genes and explore the underlying mechanisms of HT response in cotton. In this study, we employed a high-throughput cDNA-library method in conjunction with the ALRS system to screen thermotolerant genes in Upland cotton. As a result, a total of 16,120, 13,216 and 172 effective survival genes were filtered after HT stress exposure (42 °C, 220 rpm) for 48 h, 60 h and 72 h, respectively. Functional annotation and enrichment analysis revealed that 170 common genes were involved in regulatory processes associated with HT stress, and the relevant transcriptome data indicated that the majority of these genes responded to temperature fluctuations. Twenty-one genes were randomly selected for verification, and it was found that these genes could enhance yeast resistance to HT stress. Additionally, we selected mutants of homologous Arabidopsis genes for four candidate genes to validate plant thermotolerance during flowering; the thermotolerances of SALK_201915 and SALK_120540.1 were significantly worse. The results demonstrate that numerous candidate genes identified from the cDNA-library contribute to the highly complex molecular network that governs the response and resistance to HT stress in Upland cotton. The high-throughput heat-screening method utilized in this study was optimized for mining thermotolerant genes including improvement in yeast library construction, screening system, gradient reverse pressure, and sequencing library construction. We hope that this new method can be applied in future studies on stress in cotton and other species.

Genetic variation analysis of Guanling cattle based on whole-genome resequencing.

The objective of this study was to unravel the genetic traits of Guanling cattle, pinpoint genes advantageous for muscle growth, and lay a foundation for the preservation of genetic diversity and further analysis of regulation mechanism of important economic traits in local cattle breed. In this study, we sequenced the whole genome of 3 Guanling cattle in Guizhou province using the Illumina HiSeq cBo sequencing platform. And, high- multiplex polymerase chain reaction technology was employed to detect high-quality single nucleotide polymorphism (SNP) sites of other 55 Guanling cattle. Our study identified 166,411 non-synonymous SNPs (nsSNPs) and 42,423 insertions and deletions (indels). Through SNP annotation, gene function enrichment analysis, and comparing with Simmental, Angus, and Limousin cattle, we identified six genes (LEPR, AKAP9, SIX4, SPIDR, PRG4, FASN) which are potentially influential on meat quality traits, playing crucial roles in muscle growth, fat metabolism, and bodily support. We also examined polymorphisms at seven SNP sites in Guanling cattle and found that all seven were in Hardy-Weinberg equilibrium. These findings suggested that these gene sites are stable and widespread in the Guanling cattle population. Our research lays the groundwork for future genetic enhancement and variety identification of Guanling cattle.

Proteomic study of cerebrospinal fluid in adult tethered cord syndrome: Chemical structure and function of apolipoprotein B.

Adult tethered cord syndrome (ATCS) has a hidden onset and delayed clinical symptoms. The purpose of this study is to identify hub proteins in the cerebrospinal fluid of ATCS patients through bioinformatics analysis, and to find significant heterogeneity in these proteins between ATCS patients and non ATCS patients (control group). Firstly, differential genes were screened based on proteomic results. Compared with the control group, 18 differentially expressed proteins were upregulated and 18 differentially expressed proteins were downregulated in the cerebrospinal fluid of ATCS patients. Then, GO, KEGG, and GESA functional enrichment analysis showed that ATCS patients were active in biological processes such as coagulation, inflammatory response, and regulation of humoral immune response, suggesting the possibility of spinal cord injury. In addition, protein network interaction analysis indicates that APOB, APOC3, FGA, and FGG are defined as hub proteins. The correlation between ATCS patients and immune characteristics was analyzed using the CIBERSORT algorithm, which may have generated a unique immune microenvironment. Finally, Western blotting was used to experimentally validate APOB, APOC3, FGA, and FGG. The results showed that APOB, APOC3, FGA, and FGG were upregulated in the cerebrospinal fluid of ATCS patients and had an important impact on the repair and functional maintenance of spinal cord injury. They can be used as key proteins for early and accurate diagnosis and treatment of spinal cord thrombosis syndrome, and suggest that the spinal cord of ATCS patients may be damaged, which can serve as potential therapeutic targets.

Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis.

To explore the diagnostic biomarkers for diagnosing endometriosis. Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis. To explore the diagnostic biomarkers for endometriosis diagnosis. Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking results. A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. In vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya. These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.

Identification and validation of autophagy‑related genes in hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant cardiac disorder characterized by ventricular hypertrophy resulting from the disordered arrangement of myocardial cells, which leads to impaired cardiac function or death. Autophagy (AT) is a biochemical process through which lysosomes degrade and recycle damaged or discarded intracellular components to protect cells against external environmental conditions, such as hypoxia and oxidative stress. AT is closely related to HCM, and thus, serves an important role in myocardial hypertrophy. However, the precise mechanism underlying the regulation of AT in cardiac hypertrophy remains elusive. The present study aimed to examine the role and mechanisms of AT-related genes (ARGs) in HCM through bioinformatics analysis and experimental validation and to identify potential targeted drugs for HCM. In this study, cardiac samples were obtained from healthy individuals and patients with HCM from the GEO database, and screened for differentially expressed ARGs to further investigate their potential interactions and functional pathways. These genes were subjected to functional enrichment analysis to identify potential crosstalk and involved pathways. Based on a protein-protein interaction network, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were identified as potential biomarkers for the diagnosis and treatment of HCM. Furthermore, 10 components with therapeutic potential for HCM were predicted based on the aforementioned hub genes. The results of bioinformatics analysis were validated using H9c2 cells stimulated with angiotensin II, which represented an in vitro model of cardiac hypertrophy. Overall, the present study demonstrated that the expression levels of ARGs were substantially altered in HCM. Therefore, these genes may be used as diagnostic biomarkers and therapeutic targets for HCM.

FAM83H regulated by glis3 promotes triple-negative breast cancer tumorigenesis and activates the NF-κB signaling pathway.

Triple-negative breast cancer (TNBC) is a highly aggressive and invasive form of breast cancer (BC) with a high mortality rate and a lack of effective targeted drugs. Family with sequence similarity 83 member H (FAM83H) is critically implicated in tumorigenesis. However, the potential role of FAM83H in TNBC remains elusive. Here, we discovered that FAM83H exhibited high expression in tumor tissues of patients with TNBC and was associated with TNM stage. Gain- or loss-of-function experiments were conducted to explore the biological role of FAM83H in TNBC. Subsequently, functional enrichment analysis confirmed that FAM83H overexpression promoted TNBC cell proliferation, invasion, migration and epithelial-mesenchymal transition (EMT), accompanied by upregulation of cyclin E, cyclin D, Vimentin, N-cadherin and Slug. As observed, FAM83H knockdown showed anti-cancer effects, such as fostering apoptosis and inhibiting tumorigenicity and metastasis of TNBC cells. Mechanistically, FAM83H activated the NF-κB signaling pathway. Moreover, a dual-luciferase reporter assay demonstrated that GLIS family zinc finger 3 (GLIS3) bound to the promoter of FAM83H and enhanced its transcription. Notably, overexpression of GLIS3 significantly stimulated TNBC cell proliferation and invasion, and all of this was reversed by rescue experiments involving the knockdown of FAM83H. Overall, FAM83H exacerbates tumor progression, and in-depth understanding of FAM83H as a therapeutic target for TNBC will provide clinical translational potential for intervention therapy.

Interactions between glucagon like peptide 1 (GLP-1) and estrogens regulates lipid metabolism

Obesity, characterized by excessive fat accumulation in white adipose tissue (WAT), is linked to numerous health issues, including insulin resistance (IR), and type 2 diabetes mellitus (DM2). The distribution of adipose tissue differs by sex, with men typically exhibiting android adiposity and pre-menopausal women displaying gynecoid adiposity. After menopause, women have an increased risk of developing android-type obesity, IR, and DM2. Glucagon-like peptide 1 (GLP-1) receptor agonists (GLP-1RAs) are important in treating obesity and DM2 by regulating insulin secretion, impacting glucose and lipid metabolism. GLP-1Rs are found in various tissues including the pancreas, brain, and adipose tissue. Studies suggest GLP-1RAs and estrogen replacement therapies have similar effects on tissues like the liver, central nervous system, and WAT, probably by converging pathways involving protein kinasesTo investigate these interactions, female rats underwent ovariectomy (OVR) to promote a state of estrogen deficiency. After 20 days, the rats were euthanized and the tissues were incubated with 10 μM of liraglutide, a GLP-1RA. Results showed significant changes in metabolic parameters: OVR increased lipid catabolism in perirenal WAT and basal lipolysis in subcutaneous WAT, while liraglutide treatment enhanced stimulated lipolysis in subcutaneous WAT. Liver responses included increased stimulated lipolysis with liraglutide. Transcriptome analysis revealed distinct gene expression patterns in WAT of OVR rats and those treated with GLP-1RA, highlighting pathways related to lipid and glucose metabolism. Functional enrichment analysis showed estrogen’s pivotal role in these pathways, influencing genes involved in lipid metabolism regulation.Overall, the study underscores GLP-1RA acting directly on adipose tissues and highlights the complex interactions between GLP-1 and estrogen in regulating metabolism, suggesting potential synergistic therapeutic effects in treating metabolic disorders like obesity and DM2.

Reported race-associated differences in control and schizophrenia post-mortem brain transcriptomes implicate stress-related and neuroimmune pathways

BackgroundThe molecular mechanisms underlying racial disparities in schizophrenia (SCZ) illness courses and outcomes are poorly understood. While these differences are thought to arise partly through stressful social gradients, little is known about how these differences are reflected in the brain, nor how they might underlie disparate psychiatric outcomes.MethodsTo better understand the neuro-molecular correlates of social gradients, SCZ, and their overlap, we analyzed post-mortem dorsolateral prefrontal cortex (DLPFC) RNAseq data from two racially diverse cohorts in the CommonMind Consortium (235 reported Black and 546 White, 322 SCZ cases and 459 controls) using differential expression and gene set variation analyses.ResultsWe observed differences in brain gene expression that were consistent across cohorts and reported race. A combined mega-analysis identified 1,514 genes with differential expression (DE) between reported race groups after accounting for diagnosis and other covariates. Functional enrichment analyses identified upregulation of genes involved in stress and immune response, highlighting the potential role of environmental differences between reported race groups. In a race-by-diagnosis interaction analysis, no individual genes passed statistical significance. However, 109 gene sets showed statistically significant differences, implicating metabolic and immune pathways.ConclusionOur results suggest molecular mechanisms uniquely perturbed across reported race groups and identify several candidate pathways associated with SCZ in a reported race-dependent manner. Our results underscore the importance of diverse cohort ascertainment to better capture population-level differences in SCZ pathogenesis.

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus)

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood.

Identification of Two R2R3-MYB Genes Involved in Flavan-3-Ols Biosynthesis as Modulated by Salicylic Acid Through RNA-Seq in Grape Berries (Vitis spp.)

Flavan-3-ols are plant secondary metabolites that play important roles in stress resistance. Our previous studies revealed that salicylic acid (SA) activates R2R3-MYB transcription factors, promoting flavan-3-ol biosynthesis. This study identified two R2R3-MYB genes that exhibited positive responses to both exogenous SA and were probably involved in flavan-3-ol biosynthesis through RNA-sequencing, functional enrichment analysis, and qRT-PCR. The results indicated that the contents of total flavan-3-ols and their monomers, (+)-catechin and (−)-epicatechin, in grape berries after exogenous SA application were substantially increased compared to those in the control. A total of 683 differentially expressed genes in response to exogenous SA treatment were identified using RNA-seq. KEGG analysis revealed enrichment of the ‘flavonoid biosynthesis’ and ‘plant hormone signal transduction’ pathways. A specific module highly associated with flavan-3-ol biosynthesis was identified by constructing a co-expression network. Two candidate genes (VvMYB108B and VvMYB145) likely participating in flavan-3-ol biosynthesis were selected using qRT-PCR. Therefore, these two potential genes that respond to SA and putatively participate in flavan-3-ol biosynthesis were identified for the first time. These results lay a solid basis for a more profound understanding of the molecular regulation of flavan-3-ol biosynthesis in grapes.

Association of B cells and the risk of Esophageal cancer: a bidirectional two-sample mendelian randomization study.

Currently, research on the role of B cells in esophageal cancer (EC) is limited, and existing studies on their impact are controversial. Therefore, this study was conducted to elucidate the complex causal relationship between B cells and EC, expand the understanding of esophageal cancer immunology. Bidirectional two-sample Mendelian randomization (MR) was performed to assess the causal relationships between 190 B cell phenotypes and EC. To complement the MR analysis, Bayesian Weighted Mendelian Randomization (BWMR) was employed, and sensitivity analyses were conducted to evaluate the robustness of the findings. Positive results were further validated in independent cohorts of esophageal cancer studies. In addition, RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) were utilized for validation, incorporating B cell-related gene expression analysis and functional enrichment analysis to support the MR findings. In the primary analysis, significant causal relationships were observed between 5 B cell types and the risk of EC; the onset of EC was causally linked to 3 B cell phenotypes. Validation in other cohorts revealed that 4 outcomes aligned with the primary analysis, included were CD19 on IgD + CD38-, CD20 on IgD- CD27-, CD20 on IgD- CD38br, and CD38 on PB/PC. Further validation using RNA-seq data showed that CD38 mRNA was significantly overexpressed in EC tissues, whereas CD19 and MS4A1 mRNA levels did not differ significantly between tumor and normal tissues. Functional enrichment analysis revealed that CD19, MS4A1, and CD38 are involved in multiple tumor-related immune pathways, suggesting their pivotal role in regulating the tumor immune microenvironment. Our study suggests a potential connection between B cell phenotypes and EC through bidirectional two-sample MR combined with BWMR analysis, providing a preliminary basis for future research.

Novel dysregulated long non-coding RNAs in the acute kidney injury-to-chronic kidney diseases transition unraveled by transcriptomic analysis.

Acute kidney injury (AKI)-to-chronic kidney disease (CKD) transition involves a complex pathomechanism, including inflammation, apoptosis, and fibrosis where long non-coding RNAs (lncRNAs) play a crucial role in their regulation. However, to date, only a few lncRNAs have been discovered to be involved in the AKI-to-CKD transition. Therefore, this study aims to investigate the dysregulated lncRNAs in the AKI-to-CKD transition invitro and invivo. To mimic AKI-to-CKD transition both invivo and invitro, bilateral ischemia-reperfusion (IR) kidney injury was performed in Wistar rats (male), and normal rat kidney epithelial cell (NRK52E) cells were treated with exogenous transforming growth factor-β1 (TGF-β1). Further processing and analysis of samples collected from these studies (e.g., biochemical, histopathology, immunofluorescence, and RNA isolation) were also performed, and transcriptomic analysis was performed to identify the dysregulated lncRNAs. Rats subjected to IR showed a significant increase in kidney injury markers (creatinine, blood urea nitrogen (BUN), kidney injury molecule-1(KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) along with altered cell morphology). Apoptosis, inflammation, and fibrosis markers were markedly increased during the AKI-to-CKD transition. Furthermore, transcriptomic analysis revealed 62 and 84 unregulated and 95 and 92 downregulated lncRNAs invivo and invitro, respectively. Additionally, functional enrichment analysis revealed their involvement in various pathways, including the tumor necrosis factor (TNF), wingless-related integration site (Wnt), and hypoxia-inducible factor-1 (HIF-1) signaling pathways. These identified dysregulated lncRNAs significantly contribute to AKI-to-CKD transition, and their knockin/out can aid in developing targeted therapeutic interventions against AKI-to-CKD transition.

Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells

Abstract Objective Dysregulation of chondrogenic differentiation is associated with osteoarthritis (OA). The myokine irisin is beneficial in OA treatment; yet, the underlying mechanism is not fully understood. Long noncoding RNAs (lncRNAs) act as important regulators of chondrocyte differentiation. This study was conducted to address the role of lncRNAs in mediating irisin-induced chondrocyte differentiation. Methods We investigated the irisin-regulated lncRNA profile change in human mesenchymal stem cells (MSCs) using published whole transcriptome sequencing data. We predicted their potential targets and competitive endogenous RNA (ceRNA) prediction and analyzed their molecular functions using functional enrichment analysis. Results More differentially expressed lncRNAs (DElncRNAs) were observed in irisin-treated samples. The top irisin-induced lncRNAs were associated with OA or chondrogenic differentiation, including XIST, PAX8-AS1, CASC15, LINC01618, and DLX6-AS1. The DEGs co-expressed with DElncRNAs were enriched in skeletal system development, extracellular matrix (ECM) organization, cell adhesion, and inflammation associated pathways. Several lncRNAs likely acted as ceRNAs to regulate downstream mRNAs including ROR2 and SORBS1 in in OA or chondrogenic differentiation. Conclusions We demonstrate the global regulation of lncRNAs by irisin during chondrogenic differentiation of human MSCs. Further study is required to characterize the key irisin-regulated lncRNAs in chondrogenic differentiation

Prognostic model development using novel genetic signature associated with adenosine metabolism and immune status for patients with hepatocellular carcinoma.

The high mortality rate of hepatocellular carcinoma (HCC) is partly due to advanced diagnosis, emphasizing the need for effective predictive tools in HCC treatment. The aim of this study is to propose a novel prognostic model for HCC based on adenosine metabolizing genes and explore the potential relationship between them. Regression analysis was performed to identify differentially expressed genes associated with adenosine metabolism in HCC patients using RNA sequencing data obtained from a public database. Adenosine metabolism-related risk score (AMrisk) was derived using the least absolute shrinkage and selection operator (LASSO) Cox regression and verified using another database. Changes in adenosine metabolism in HCC were analyzed using functional enrichment analysis and multiple immune scores. The gene expression levels in patient samples were validated using quantitative reverse transcription polymerase chain reaction. Thirty adenosine metabolism-related differentially expressed genes were identified in HCC, and six genes (ADA, P2RY4, P2RY6, RPIA, SLC6A3, and VEGFA) were used to calculate the AMrisk score; the higher the risk scores, the lower the overall survival. Moreover, immune infiltration activation and immune checkpoints were considerably higher in the high-risk group. Additional in vitro experiments validated the enhanced expression of these six genes in HCC. The established predictive model demonstrated that adenosine metabolism-related genes was significantly associated with prognosis in HCC patients.

Analysis of single-cell RNA sequencing in human oocytes with diminished ovarian reserve uncovers mitochondrial dysregulation and translation deficiency.

Diminished ovarian reserve (DOR) is clinically characterized by a decrease in the number of available ovarian follicles and a decline in the quality of oocytes, accompanied by hormonal changes. Low quality of DOR oocyte leads to impaired embryo development, an increased risk of aneuploid pregnancies and miscarriages. However, the specific pathogenic mechanism remains unclear, posing a significant challenge for assisted reproductive technology. For the first time, our study employed single-cell RNA sequencing to reveal the altered transcriptomic landscape of DOR oocytes at GV stage after ovarian stimulation. Differentially expressed genes analysis (DEGs), functional enrichment analysis, weighted gene co-expression network analysis (WGCNA) and protein-protein interactions network analysis were performed. We found 132 up-regulated genes and 466 down-regulated genes in DOR oocytes, with the down-regulated genes primarily enriched in mitochondrial function and translation. Hub genes, identified through integrated analysis of WGCNA and DEGs, were further validated in DOR and control oocytes using RT-qPCR. By utilizing hub genes and employing transcription factor enrichment tools, it had been predicted that pleomorphic adenoma gene 1 (PLAG1) played a crucial role as a transcriptional regulatory factor in DOR oocytes. Additionally, we conformed the PLAG1-IGF2 axis was dysregulated in DOR oocytes. Transcriptome analysis revealed that DOR oocytes exhibited mitochondrial dysfunction and translational defects, and the PLAG1-IGF2 axis might be a potential contributor for the low quality of DOR oocytes.

Blood mir-331-3p is a potential diagnostic marker for giant panda (Ailuropoda melanoleuca) testicular tumor.

In recent years, several giant pandas have suffered from testicular tumor, which has seriously affected giant panda health. However, the pathogenesis of testicular tumor in giant panda is still unclear. Studies have shown that miRNAs are involved in the occurrence and development of a variety of cancers. However, the effect of miRNAs on giant panda testicular tumor has been little studied. Therefore, this study explored the pathogenesis of giant panda testicular tumor through miRNA and mRNA sequencing, and screened out diagnostic markers of testicular tumor. Combined with phenotypic symptoms and pathological section results, three giant pandas were diagnosed with testicular tumor and divided into tumor group, and three other giant pandas were divided into normal group. A total of 29 differentially expressed miRNAs (DEmiRNAs) were screened by blood miRNA-seq, and 3149 target gene candidates were predicted. Functional enrichment analysis showed that the target genes were mainly involved in intermembrane lipid transfer and ATP-dependent chromatin remodeling. However, only 5 DEmiRNAs were screened by miRNA-seq of blood-derived exosomes and 364 target genes were predicted, which were mainly involved in antigen processing and presentation. In addition, 216 differentially expressed genes (DEGs) were screened by RNA-seq, and functional enrichment analysis showed that tumor-specific DEGs significantly enriched to protein phosphorylation. Spearman correlation analysis of miRNA-mRNA showed that the expressions of miR-331-3p and PKIG were significantly positively correlated (spearman = 0.943, p < 0.01), while the expressions of miR-331-3p and ENSAMEG00000013628 were significantly negatively correlated (spearman= -0.829, p < 0.05). RT-PCR showed that the expression of miR-331-3p was significantly decreased in giant panda with tumor (p < 0.01). blood miRNAs and exosomal miRNAs exhibit distinct regulatory patterns concerning giant panda testicular tumor, potentially reflecting divergent biological processes in the disease's etiology. Meanwhile, miR-331-3p could be used as a potential diagnostic marker for giant panda testicular tumor. Our findings are conducive to the rapid clinical diagnosis of testicular tumor in giant pandas, and are also expected to provide scientific reference for further research on the pathogenesis of testicular tumor.