Comparative transcriptomics analysis on Senecavirus A-infected and non-infected cells.

Senecavirus A (SVA) is an emerging virus that causes the vesicular disease in pigs, clinically indistinguishable from other high consequence vesicular diseases. This virus belongs to the genus Senecavirus in the family Picornaviridae. Its genome is a positive-sense, single-stranded RNA, approximately 7,300 nt in length, with a 3' poly(A) tail but without 5'-end capped structure. SVA can efficiently propagate in different cells, including some non-pig-derived cell lines. A wild-type SVA was previously rescued from its cDNA clone using reverse genetics in our laboratory. In the present study, the BSR-T7/5 cell line was inoculated with the passage-5 SVA. At 12 h post-inoculation, SVA-infected and non-infected cells were independently collected for the analysis on comparative transcriptomics. The results totally showed 628 differentially expressed genes, including 565 upregulated and 63 downregulated ones, suggesting that SVA infection significantly stimulated the transcription initiation in cells. GO and KEGG enrichment analyses demonstrated that SVA exerted multiple effects on immunity-related pathways in cells. Furthermore, the RNA sequencing data were subjected to other in-depth analyses, such as the single-nucleotide polymorphism, transcription factors, and protein-protein interactions. The present study, along with our previous proteomics and metabolomics researches, provides a multi-omics insight into the interaction between SVA and its hosts.

Identification of key differentially expressed immune related genes in patients with persistent atrial fibrillation: an integrated bioinformation analysis.

OBJECTIVE: We aimed to investigate key differentially expressed immune related genes in persistent atrial fibrillation. METHODS: Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) using "GEO query" package. "limma" package and "sva" package were used to conduct normalization and eliminate batch effects, respectively. We screened out differentially expressed genes (DEGs) based on "limma" package with the standard of |log fold change (FC)| ≥ 1.5 and false discovery rate (FDR) < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed by "clusterProfler" package. We further applied LASSO to select key DEGs, and intersected key DEGs with immune related genes from ImmPort database. The ROC curve of each DEIRG was constructed to evaluate its diagnostic efficiency for AF. RESULTS: A total of 103 DEGs we were screened out, of them, 48 genes were down-regulated and 55 genes were up-regulated. Result of functional enrichment analysis show that, most of DEGs were related to immune response, inflammation, and oxidative stress. Ultimately, CYBB, RORB, S100A12, and CHGB were determined as key DEIRGs, each of which displayed a favor efficiency for diagnosing persistent AF. CONCLUSION: CYBB, RORB, S100A12, and CHGB were identified as key DEIRGs in persistent AF, and future studies are needed to further explore the underlying roles of CYBB, RORB, S100A12, and CHGB in persistent AF.

Transcriptome profiles of organ tissues from pigs experimentally infected with African swine fever virus in early phase of infection.

African swine fever, caused by African swine fever virus (ASFV), is a highly contagious and fatal disease that poses a significant threat to the global pig industry. The limited information on ASFV pathogenesis and ASFV-host interactions has recently prompted numerous transcriptomic studies. However, most of these studies have focused on elucidating the transcriptome profiles of ASFV-infected porcine alveolar macrophages in vitro. Here, we analyzed dynamic transcriptional patterns in vivo in nine organ tissues (spleen, submandibular lymph node, mesenteric lymph node, inguinal lymph node, tonsils, lungs, liver, kidneys, and heart) obtained from pigs in the early stages of ASFV infection (1 and 3 d after viremia). We observed rapid spread of ASFV to the spleen after viremia, followed by broad transmission to the liver and lungs and subsequently, the submandibular and inguinal lymph nodes. Profound variations in gene expression patterns were observed across all organs and at all time-points, providing an understanding of the distinct defence strategies employed by each organ against ASFV infection. All ASFV-infected organs exhibited a collaborative response, activating immune-associated genes such as S100A8, thereby triggering a pro-inflammatory cytokine storm and interferon activation. Functional analysis suggested that ASFV exploits the PI3K-Akt signalling pathway to evade the host immune system. Overall, our findings provide leads into the mechanisms underlying pathogenesis and host immune responses in different organs during the early stages of infection, which can guide further explorations, aid the development of efficacious antiviral strategies against ASFV, and identify valuable candidate gene targets for vaccine development.

Single-cell RNA sequencing reveals the gene expression profile and cellular communication in human fetal heart development.

The heart is the central organ of the circulatory system, and its proper development is vital to maintain human life. As fetal heart development is complex and poorly understood, we use single-cell RNA sequencing to profile the gene expression landscapes of human fetal hearts from the four-time points: 8, 10, 11, 17 gestational weeks (GW8, GW10, GW11, GW17), and identified 11 major types of cells: erythroid cells, fibroblasts, heart endothelial cells, ventricular cardiomyocytes, atrial cardiomyocytes, macrophage, DCs, smooth muscle, pericytes, neural cells, schwann cells. In addition, we identified a series of differentially expressed genes and signaling pathways in each cell type between different gestational weeks. Notably, we found that ANNEXIN, MIF, PTN, GRN signalling pathways were simple and fewer intercellular connections in GW8, however, they were significantly more complex and had more intercellular communication in GW10, GW11, and GW17. Notably, the interaction strength of OSM signalling pathways was gradually decreased during this period of time (from GW8 to GW17). Together, in this study, we presented a comprehensive and clear description of the differentiation processes of all the main cell types in the human fetal hearts, which may provide information and reference data for heart regeneration and heart disease treatment.

Ectoine enhances recombinant antibody production in Chinese hamster ovary cells by promoting cell cycle arrest.

Chinese hamster ovary (CHO) cells represent the most preferential host cell system for therapeutic monoclonal antibody (mAb) production. Enhancing mAb production in CHO cells can be achieved by adding chemical compounds that regulate the cell cycle and cell survival pathways. This study investigated the impact of ectoine supplementation on mAb production in CHO cells. The results showed that adding ectoine at a concentration of 100 mM on the 3rd day of cultivation improved mAb production by improving cell viability and extending the culture duration. RNA sequencing analysis revealed differentially expressed genes associated with cell cycle regulation, cell proliferation, and cellular homeostasis, in particular promotion of cell cycle arrest, which was then confirmed by flow cytometry analysis. Ectoine-treated CHO cells exhibited an increase in the number of cells in the G0/G1 phase. In addition, the cell diameter was also increased. These findings support the hypothesis that ectoine enhances mAb production in CHO cells through mechanisms involving cell cycle arrest and cellular homeostasis. Overall, this study highlights the potential of ectoine as a promising supplementation strategy to enhance mAb production not only in CHO cells but also in other cell lines.

Peripheral blood cells RNA-seq identifies differentially expressed gene network linked to lymphocyte subsets alterations and active lupus nephritis associated with declines in renal function.

Background: The aim of this study was to investigate whether quantitative changes in lymphocyte subsets and gene expression in peripheral blood (PB) cells are related to the clinical manifestations and pathogenesis of lupus nephritis (LN). Methods: We enrolled 95 pediatric-onset SLE patients with renal involvement who presented with 450 clinical episodes suspicious for LN flare. Percentages of lymphocyte subsets at each episode were determined. We stratified 55 of 95 patients as high or low subset group according to the median percentage of each lymphocyte subset and the association with changes in the eGFR (ΔeGFR) were analyzed. Peripheral blood bulk RNA-seq to identify differentially expressed genes (DEGs) in 9 active LN vs. 9 inactive LN patients and the DEG-derived network was constructed by Ingenuity Pathway Analysis (IPA). Results: The mean ΔeGFR of low NK-low memory CD4+ T-high naive CD4+ T group (31.01 mL/min/1.73 m2) was significantly greater than that of high NK-high memory CD4+ T-low naive CD4+ T group (11.83 mL/min/1.73 m2; P = 0.0175). Kaplan-Meier analysis showed that the median time for ΔeGFR decline to mean ΔeGFR is approximately 10 years for high NK-high memory CD4+ T-low naive CD4+ T group and approximately 5 years for low NK-low memory CD4+ T-high naive CD4+ T group (log-rank test P = 0.0294). Conclusions: Our study highlighted important connections between DEG-derived network, lymphocyte subset composition, and disease status of LN and GN. A novel scoring system based on lymphocyte subset proportions effectively stratified patients into groups with differential risks for declining renal function.

Particulate matter-induced gene expression patterns in human-derived cells based on 11 public gene expression datasets.

BACKGROUND: Exposure to particulate matter (PM) and house dust mite (HDM) can change the expression patterns of inflammation-, oxidative stress-, and cell death-related genes. We investigated the changes in gene expression patterns owing to PM exposure. OBJECTIVE: This study examined the changes in gene expression patterns following PM exposure. METHODS: We searched for differentially expressed genes (DEGs) following PM exposure using five cell line-based RNA-seq or microarray datasets and six human-derived datasets. The enrichment terms of the DEGs were assessed. RESULTS: DEG analysis yielded two gene sets. Thus, enrichment analysis was performed for each gene set, and the enrichment terms related to respiratory diseases were presented. The intersection of six human-derived datasets and two gene sets was obtained, and the expression patterns following PM exposure were observed. CONCLUSIONS: Two gene sets were obtained for cells treated with PM and their expression patterns were presented following verification in human-derived cells. Our findings suggest that exposure to PM2.5 and HDM may reveal changes in genes that are associated with diseases, such as allergies, highlighting the importance of mitigating PM2.5 and HDM exposure for disease prevention.

Identification of key genes related to heart failure by analysis of expression profiles.

Introduction: To elucidate the candidate biomarkers involved in the pathogenesis process of heart failure (HF) via analysis of differentially expressed genes (DEGs) of the dataset from the Gene Expression Omnibus (GEO). Material and methods: The GSE76701 gene expression profiles regarding the HF and control subjects were respectively analysed. Briefly, DEGs were firstly identified and subjected to Cytoscape plug-in ClueGO + CluePedia and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A protein-protein interaction (PPI) network was then built to analyse the interaction between DEGs, followed by the construction of an interaction network by combining with hub genes with the targeted miRNA genes of DEGs to identify the key molecules of HF. In addition, potential drugs targeting key DEGs were sought using the drug-gene interaction database (DGIdb), and a drug-mRNA-miRNA interaction network was also constructed. Results: A total of 489 DEGs were verified between HF and control, which mainly enriched in type I interferon and leukocyte migration according to molecular function. Significantly increased levels of GAPDH, GALM1, MMP9, CCL5, and GNAL2 were found in the HF setting and were identified as the hub genes based on the PPI network. Furthermore, according to the drug-mRNA-miRNA network, FCGR2B, CCND1, and NF-κb, as well as corresponding miRNA-605-5p, miRNA-147a, and miRNA-671-5p were identified as the drug targets of HF. Conclusions: The hub genes GAPDH, GALM1, MMP9, CCL5, and GNAL2 were significantly increased in HF. miRNA-605-5p, miRNA-147a, and miRNA-671-5p were predicted as the drug target-interacted gene-miRNA of HF.

Transcriptomic analysis of the defense response in "Cabernet Sauvignon" grape leaf induced by Apolygus lucorum feeding.

To investigate the molecular mechanism of the defense response of "Cabernet Sauvignon" grapes to feeding by Apolygus lucorum, high-throughput sequencing technology was used to analyze the transcriptome of grape leaves under three different treatments: feeding by A. lucorum, puncture injury, and an untreated control. The research findings indicated that the differentially expressed genes were primarily enriched in three aspects: cellular composition, molecular function, and biological process. These genes were found to be involved in 42 metabolic pathways, particularly in plant hormone signaling metabolism, plant-pathogen interaction, MAPK signaling pathway, and other metabolic pathways associated with plant-induced insect resistance. Feeding by A. lucorum stimulated and upregulated a significant number of genes related to jasmonic acid and calcium ion pathways, suggesting their crucial role in the defense molecular mechanism of "Cabernet Sauvignon" grapes. The consistency between the gene expression and transcriptome sequencing results further supports these findings. This study provides a reference for the further exploration of the defense response in "Cabernet Sauvignon" grapes by elucidating the expression of relevant genes during feeding by A. lucorum.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior.

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Differentially expressed genes associated with high metabolic tumor volume served as diagnostic markers and potential therapeutic targets for pancreatic cancer.

BACKGROUND: The lack of distinct biomarkers for pancreatic cancer is a major cause of early-stage detection difficulty. The pancreatic cancer patient group with high metabolic tumor volume (MTV), one of the values measured from positron emission tomography-a confirmatory method and standard care for pancreatic cancer, showed a poorer prognosis than those with low MTV. Therefore, MTV-associated differentially expressed genes (DEGs) may be candidates for distinctive markers for pancreatic cancer. This study aimed to evaluate the possibility of MTV-related DEGs as markers or therapeutic targets for pancreatic cancer. METHODS: Tumor tissues and their normal counterparts were obtained from patients undergoing preoperative 18F-FDG PET/CT. The tissues were classified into MTV-low and MTV-high groups (7 for each) based on the MTV2.5 value of 4.5 (MTV-low: MTV2.5 < 4.5, MTV-high: MTV2.5 ≥ 4.5). Gene expression fold change was first calculated in cancer tissue compared to its normal counter and then compared between low and high MTV groups to obtain significant DEGs. To assess the suitability of the DEGs for clinical application, the correlation of the DEGs with tumor grades and clinical outcomes was analyzed in TCGA-PAAD, a large dataset without MTV information. RESULTS: Total RNA-sequencing (MTV RNA-Seq) revealed that 44 genes were upregulated and 56 were downregulated in the high MTV group. We selected the 29 genes matching MTV RNA-seq patterns in the TCGA-PAAD dataset, a large clinical dataset without MTV information, as MTV-associated genes (MAGs). In the analysis with the TCGA dataset, MAGs were significantly associated with patient survival, treatment outcomes, TCGA-PAAD-suggested markers, and CEACAM family proteins. Some MAGs showed an inverse correlation with miRNAs and were confirmed to be differentially expressed between normal and cancerous pancreatic tissues. Overexpression of KIF11 and RCC1 and underexpression of ADCY1 and SDK1 were detected in ~ 60% of grade 2 pancreatic cancer patients and associated with ~ 60% mortality in stages I and II. CONCLUSIONS: MAGs may serve as diagnostic markers and miRNA therapeutic targets for pancreatic cancer. Among the MAGs, KIF11, RCC1, ADCY, and SDK1 may be early diagnostic markers.

Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance.

BACKGROUND: The transcriptome and metabolome dissection of the skeletal muscle of high- and low- growing individuals from a crossbred population of the indigenous Chongming white goat and the Boer goat were performed to discover the potential functional differentially expressed genes (DEGs) and differential expression metabolites (DEMs). RESULTS: A total of 2812 DEGs were detected in 6 groups at three time stages (3,6,12 Month) in skeletal muscle using the RNA-seq method. A DEGs set containing seven muscle function related genes (TNNT1, TNNC1, TNNI1, MYBPC2, MYL2, MHY7, and CSRP3) was discovered, and their expression tended to increase as goat muscle development progressed. Seven DEGs (TNNT1, FABP3, TPM3, DES, PPP1R27, RCAN1, LMOD2) in the skeletal muscle of goats in the fast-growing and slow-growing groups was verified their expression difference by reverse transcription-quantitative polymerase chain reaction. Further, through the Liquid chromatography-mass spectrometry (LC-MS) approach, a total of 183 DEMs in various groups of the muscle samples and these DEMs such as Queuine and Keto-PGF1α, which demonstrated different abundance between the goat fast-growing group and slow-growing group. Through weighted correlation network analysis (WGCNA), the study correlated the DEGs with the DEMs and identified 4 DEGs modules associated with 18 metabolites. CONCLUSION: This study benefits to dissection candidate genes and regulatory networks related to goat meat production performance, and the joint analysis of transcriptomic and metabolomic data provided insights into the study of goat muscle development.

Identification of HIST1H2BH as the hub gene associated with multiple myeloma using integrated bioinformatics analysis.

OBJECTIVES: Identifying the specific biomarkers and molecular signatures of MM might provide novel evidence for MM prognosis and targeted therapy. METHODS: Bioinformatic analyses were performed through GEO and TCGA datasets. The differential expression of HIST1H2BH in MM sample was validated by the qRT-PCR. And the CCK-8 assay was performed to detect the proliferation activity of HIST1H2BH on MM cell lines. RESULTS: A total of 793 DEGs were identified between bone marrow plasma cells from newly diagnosed myeloma and normal donors in GSE6477. Among them, four vital genes (HIST1H2AC, HIST1H2BH, CCND1 and TCF7L2) modeling were constructed. The increased HIST1H2BH expression was correlated with worse survival of MM based on TCGA datasets. The transcriptional expression of HIST1H2BH was significantly up-regulated in primary MM patients. And knockdown HIST1H2BH decreased the proliferation of MM cell lines. CONCLUSIONS: We have identified up-regulated HIST1H2BH in MM patients associated with poor prognosis using integrated bioinformatical methods.

Identification of differentially expressed genes and metabolism signaling pathway in the spleen of broilers supplemented with probiotic Bacillus spp.

Probiotics are essential in the body's nutrients, improving the ratio of meat to meat, immune response, and preventing diseases. In this study, RNA-sequencing (RNA-seq) was used to identify the differentially expressed genes (DEGs), enriched related pathways, and Gene Ontology (GO) terms among blank negative control (NC), supplemented with Bacillus spp. (BS) and commercial probiotic (PC) groups after a 42-day fed supplementation. The results showed that 2005, 1356, and 2189 DEGs were significantly altered in BS vs. NC, PC vs NC, and BS vs PC groups, respectively. On the other hand, 9 DEGs were further validated by qRT-PCR, indicating that the qRT-PCR and RNA-Seq results were more consistent. Therefore, the GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs showed that the DEGs were mainly enriched to metabolism signalling pathways (alpha-linolenic acid metabolism, linoleic acid metabolism, tryptophan metabolism, tyrosine metabolism, ether lipid metabolism, and metabolic pathway, etc) and immune response pathways (cytokine-cytokine receptor interaction, MAPK signalling pathway, and intestinal immune network for IgA production, neuroactive ligand-receptor interaction etc). These results will provide a better understanding of the role of probiotics in chicken development and provide basic information on the genetic development of chickens.

Genome-Wide Identification and Expression Analysis of Kiwifruit Leucine-Rich Repeat Receptor-Like Proteins Reveal Their Roles in Biotic and Abiotic Stress Responses.

Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we performed family identification, characterization, transcriptome data analysis, and differential gene expression analysis of kiwifruit LRR-RLPs. We identified totals of 101, 164, and 105 LRR-RLPs in Actinidia chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5', respectively. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Based on RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.

Identification of idiopathic pulmonary fibrosis hub genes and exploration of the mechanisms of action of Jinshui Huanxian formula.

Idiopathic pulmonary fibrosis (IPF) is a common and heterogeneous chronic disease, and the mechanism of Jinshui Huanxian formula (JHF) on IPF remains unclear. For a total of 385 lung normal tissue samples from the Gene Expression Omnibus database, 37,777,639 gene pairs were identified through microarray and RNA-seq platforms. Using the individualized differentially expressed gene (DEG) analysis algorithm RankComp (FDR < 0.01), we identified 344 genes as DEGs in at least 95 % (n = 81) of the IPF samples. Of these genes, IGF1, IFNGR1, GLI2, HMGCR, DNM1, KIF4A, and TNFRSF11A were identified as hub genes. These genes were verified using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in mice with pulmonary fibrosis (PF) and MRC-5 cells, and they were highly effective at classifying IPF samples in the independent dataset GSE134692 (AUC = 0.587-0.788) and mice with PF (AUC = 0.806-1.000). Moreover, JHF ameliorated the pathological changes in mice with PF and significantly reversed the changes in hub gene expression (KIF4A, IFNGR1, and HMGCR). In conclusion, a series of IPF hub genes was identified, and validated in an independent dataset, mice with PF, and MRC-5 cells. Moreover, the abnormal gene expression was normalized by JHF. These findings provide guidance for further exploration of the pathogenesis and treatment of IPF.

[Microglia differential genes and their functions in paraquat-induced Parkinson's disease-like in mice's brains based on single-cell RNA sequencing].

Objective: To analyze the differential genes and related signaling pathways of microglia subpopulations in Parkinson's disease (PD) -like mouse brains induced by paraquat (PQ) based on single-cell RNA sequencing, and provide clues to elucidate the mechanism of PQ-induced PD-like changes in the brain of animals. Methods: In September 2021, six male 6-week-old C57BL/6 mice were randomly divided into control group and experimental group (three mice in each group) . The mice were injected with saline, 10.0 mg/kg PQ intraperitoneally, once every three days, and 10 consecutive injections were used for modeling. After infection, the brains of mice were taken and 10×Genomics single-cell RNA sequencing was performed. Microglia subpopulations were screened based on gene expression characteristics, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The differential genes of microglia subpopulations between the experimental group and control group were further screened, and functional enrichment analysis was performed using bioinformatics tools. Mouse microglia (BV2 cells) were treated with 0, 60, 90 µmol/L PQ solution, respectively. And real-time fluorescence quantitative PCR experiments were conducted to validate the expressions of differential genes hexokinase 2 (Hk2) , ATPase H+ Transporting V0 Subunit B (Atp6v0b) and Neuregulin 1 (Nrg1) . Results: Cluster 7 and Cluster 20 were identified as microglia subpopulations based on the signature genes inositol polyphosphate-5-phosphatase d, Inpp5d (Inpp5d) and transforming growth factor beta receptor 1 (Tgfbr1) , and they reflected the microglia-activated M2 phenotype. The bioinformatics analysis showed that the characteristic genes of identified microglia subpopulations were enriched in endocytosis. In terms of molecular function, it mainly enriched in transmembrane receptor protein kinase activity and cytokine binding. The up-regulated genes of Cluster 7 were mainly enriched in lysosomal pathway, endocytosis pathway, and down-regulated genes were mainly enriched in neurodegenerative disease and other signaling pathways. The up-regulated genes of Cluster 20 were mainly enriched in signaling pathways related to PD, and down-regulated genes were mainly enriched in cyclic adenosine 3', 5'-monophosphate (cAMP) signaling pathways, neurological development, synaptic function and other signaling pathways. The results of real-time fluorescence quantitative PCR showed that the expressions of Hk2 mRNA and Atp6v0b mRNA increased and the expression of Nrg1 mRNA decreased in the 90 µmol/L PQ-treated BV2 cells compared with the 0 µmol/L, and the differences were statistically significant (P<0.05) . Conclusion: Microglia are activated in the PQ-induced PD-like mouse model and polarized toward the M2 phenotype. And their functions are associated with lysosomal (endocytosis) , synaptic functions and the regulation of PD-related pathways.

Gene expression profiling of peripheral blood in patients with steroid-induced osteonecrosis of the femoral head.

Aim: Steroid-induced osteonecrosis of the femoral head (SONFH) is a severe complication following glucocorticoid therapy. This study aimed to identify the differential mRNA expression and investigate the molecular mechanisms of SONFH. Materials & methods: RNA sequencing was performed in eight SONFH patients, five non-SONFH patients and five healthy individuals. Results: A total of 1555, 3997 and 5276 differentially expressed mRNAs existed between the following combinations: SONFH versus non-SONFH, SONFH versus healthy subjects and non-SONFH versus healthy subjects. Increased ISM1 expression might contribute to a high risk of SONFH through antiangiogenesis. Decreased FOLR3 expression might affect the metabolism of homocysteine, leading to avascular necrosis of the femoral head. KCNJ2, which plays a pivotal role in regulating bone development, was also deregulated. Conclusion: ISM1, FOLR3 and KCNJ2 might be related to the occurrence of SONFH.

[Box: see text].

Identification of Hub Genes and Potential Pathogenesis in Gastric Cancer Based on Integrated Gene Expression Profile Analysis.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies and ranks third in terms of cancer-related mortality. This study aims to identify the hub genes and potential mechanisms in GC using a bioinformatics approach. METHODS: Microarray data GSE54129, GSE79973, GSE55696 were extracted from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) was identified using Benjamini-Hochberg method in the limma package. GO and KEGG pathway enrichment analyses of the DEGs were conducted. Furthermore, protein-protein interaction network was constructed the STRING platform, and the hub genes were discovered using Maximal Clique Centrality method via cytoHubba. The predictive significance of hub genes was evaluated through GSE15459 dataset. RESULTS: A total of 73 genes was identified as DEGs in GC. Volcano plots and heatmaps of DEGs were visualized. Functional enrichment analysis revealed that the genes were mostly enriched in response to xenobiotic stimulus, digestion, cellular hormone metabolic process, extracellular matrix structural constituent, calcium-dependent cysteine-type endopeptidase activity, aromatase activity, apical part of cell, basal part of cell, and apical plasma membrane. Regarding KEGG pathway-enrichment, the genes were mainly involved in Drug metabolism-cytochrome P450, Retinol metabolism, Chemical carcinogenesis-DNA adducts, Gastric acid secretion, and Metabolism of xenobiotics by cytochrome P450. By combining the results of Cytohubba, the top five intersecting genes identified were SPP1, INHBA, MMP7, THBS2 and FAP. Kapplan-Meier analysis results showed that these 5 hub genes were highly related to the overall survival of patients. CONCLUSION: SPP1, INHBA, MMP7, THBS2, and FAP were identified as prospective biomarkers and therapeutic targets for GC that might be utilized for prognostic evaluation and scheme selection.

Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing.

The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein-protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets.