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.

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.

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.

Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune-mediated necrotizing myopathy.

BACKGROUND: Immune-mediated necrotizing myopathy (IMNM) is an autoimmune myopathy characterized by severe proximal weakness and muscle fiber necrosis, yet its pathogenesis remains unclear. So far, there are few bioinformatics studies on underlying pathogenic genes and infiltrating immune cell profiles of IMNM. Therefore, we aimed to characterize differentially expressed genes (DEGs) and infiltrating cells in IMNM muscle biopsy specimens, which may be useful for elucidating the pathogenesis of IMNM. METHODS: Three datasets (GSE39454, GSE48280 and GSE128470) of gene expression profiling related to IMNM were obtained from the Gene Expression Omnibus database. Data were normalized, and DEG analysis was performed using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using clusterProfiler. The CIBERSORT algorithm was performed to identify infiltrating cells. Machine learning algorithm and gene set enrichment analysis (GSEA) were performed to find distinctive gene signatures and the underlying signaling pathways of IMNM. RESULTS: DEG analysis identified upregulated and downregulated in IMNM muscle compared to the gene expression levels of other groups. GO and KEGG analysis showed that the pathogenesis of IMNM was notable for the under-representation of pathways that were important in dermatomyositis and inclusion body myositis. Three immune cells (M2 macrophages, resting dendritic cells and resting natural killer cells) with differential infiltration and five key genes (NDUFAF7, POLR2J, CD99, ARF5 and SKAP2) in patients with IMNM were identified through the CIBERSORT and machine learning algorithm. The GSEA results revealed that the key genes were remarkably enriched in diverse immunological and muscle metabolism-related pathways. CONCLUSIONS: We comprehensively explored immunological landscape of IMNM, which is indicative for the research of IMNM pathogenesis.

scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data.

Differential expression (DE) gene detection in single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) data is a key step to understand the biological question investigated. Filtering genes is suggested to improve the performance of DE methods, but the influence of filtering genes has not been demonstrated. Furthermore, the optimal methods for different scRNA-seq datasets are divergent, and different datasets should benefit from data-specific DE gene detection strategies. However, existing tools did not take gene filtering into consideration. There is a lack of metrics for evaluating the optimal method on experimental datasets. Based on two new metrics, we propose single-cell Consensus Optimization of Differentially Expressed gene detection, an R package to automatically optimize DE gene detection for each experimental scRNA-seq dataset.

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.

CFHR1 involvement in bile duct carcinoma: Insights from a data mining study.

The aim of this study is to investigate the role of CFHR1 in bile duct carcinoma (BDC) and its mechanism of action, and we hope that our analysis and research will contribute to a better understanding of cholangiocarcinoma (BDC) disease genesis, progression and the development of new therapeutic strategies. The prognostic receiver operating characteristic curve of CFHR1 was generated using survival ROC. The ROC curve for CFHR1 showed that there is a correlation between CFHR1 expression and clinicopathological parameters and has an impact on poor prognosis. STRING was used to predict the protein-protein interaction network of the identified genes, and the Microenvironment Cell Populations counter algorithm was used to analyze immune cell infiltration within the BDC. The combined analysis showed that CFHR1 was found to be upregulated in BDC tissues, along with a total of 20 related differentially expressed genes (DEGs) (8 downregulated and 12 upregulated genes). Also, the results showed that the expression of CFHR1 is correlated with immune cell infiltration in tumor and immune cell markers in BDC (P < 0.05). In addition, we have verified experimentally the biological function of CFHR1. These findings suggest that CFHR1 may be a prognostic marker and a potential therapeutic target for BDC. Information regarding the detailed roles of CFHR1 in BDC could be valuable for improving the diagnosis and treatment of this rare cancer.

The immune response of fairy shrimp Branchinella kugenumaensis against Edwardsiella anguillarum infections by de novo transcriptome analysis.

To explore the immune defense mechanisms of the ancient crustacean fairy shrimp (B.kugenumaensis) and uncover antibacterial-related gene resources, the present study analyzed the pathological changes in B. kugenumaensis infected with E. anguillarum. Differential gene expression changes between the infected and uninfected groups were investigated through comparative transcriptome sequencing to elucidate the molecular responses to the infection. Under transmission electron microscopy, the intestinal mucosal structure of B. kugenumaensis was damaged, the microvilli disappeared, the number of mitochondria and endoplasmic reticulum increased, mitochondria vacuolated and arranged disordered. The transcriptome data indicated that a total of 250,520,580 clean reads were assembled into 66,502 unigenes, with an average length of 789 bp and an N50 length of 1326 bp. Following bacterial infection, approximately 2678 differentially expressed genes (DEGs) were identified, with 1732 genes upregulated and 946 genes downregulated. The detected DEGs related to immune responses, particularly involving apoptosis, lysosome, autophagy, phagosome, and MAPK signaling pathways. Moreover, 9 immunity-related genes with different expressions were confirmed by using real-time quantitative PCR (RT-qPCR). This study first reports the pathogenicity of E. anguillarum on B. kugenumaensis and speculates that immune effectors such as lysozyme and lectin, as well as apoptosis, lysosome, and the MAPK signaling pathway, play crucial roles in the innate immunity of fairy shrimp. These findings deepen our understanding of fairy shrimp immune regulatory mechanisms and provide a theoretical foundation for disease prevention and control.

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.

Different development and fecundity between Spodoptera frugiperda USA and China populations, influenced by ecdysone-related genes.

The fall armyworm (FAW), Spodoptera frugiperda, is one of the most harmful plant pests in the world and is globally distributed from the American continent to the Asian region. The FAW USA population (Sf-USA) and China population (Sf-CHN), which belong to corn strain, showed different developmental periods and fecundity rates in lab conditions. Sf-USA had faster development and higher fecundity compared with Sf-CHN. To examine these differences, transcriptomic data from two FAW populations were analyzed and compared. Twelve gigabytes of transcripts were read from each sample and 21,258 differentially expressed genes (DEGs) were detected. DEGs with log2 fold change ≥ 2 were identified and compared in two populations. In comparison to the Sf-CHN, we discovered that 3471 and 3851 individual DEGs upregulated and downregulated, respectively. Comparing transcriptome profiles for differential gene expression revealed several DEGs, including 39 of ecdysone (E)-, 25 of juvenile hormone-, and 15 of insulin-related genes. We selected six of E-related genes, such as Neverland, Shade, Ecdysone receptor, Ecdysone-inducible protein 74 (E74), E75, and E78 from DEGs. Gene expressions were suppressed by RNA interference to confirm the physiological functions of the selected genes from Sf-USA. The Sf-USA showed developmental retardation and a decrease in fecundity rate by suppression of E-related genes. These findings show that biological characteristics between Sf-USA and Sf-CHN are influenced by E-related genes.

Insights on reproduction-related genes in the striped fruit fly, Zeugodacus scutellata (Hendel) (Diptera: Tephritidae).

The striped fruit fly, Zeugodacus scutellata is a significant pest in East and Southeast Asia by damaging Cucurbitaceae blossoms and fruits. To control this pest, a novel strategy to suppress the gene(s) associated with sexually dimorphic phenotypes has been devised and implemented in a laboratory scale. However, comprehensive transcriptomic analysis related to this sex differentiation of Z. scutellata was necessary to determine effective target genes for the genetic control. We performed de novo assembly of the transcript obtained by paired-end sequencing using an Illumina HiSeq platform and let to 217,967 unigenes (i.e., unique genes) with a minimum length of 200 bp. The female produced 31, 604, 442 reads with 97.93% of Q20, 94.76% of Q30, and the male produced 130, 592, 828 reads with 97.93% of Q20 and 94.76 of Q30%. The differentially expressed genes were used to predict genetic factors associated with sex differentiation, which included Rho1, extra-macrochaetae (emc), hopscotch (hop), doublesex (dsx), sex-lethal (sxl), transformer-2 (tra-2), testis-specific serine/threonine-protein kinase (tssk1), tektin1 (tkt1) and 2 (tkt2), odorant binding proteins (OBPs), fruitless (fru), vitellogenin receptor, and hormone receptors in Z. scutellata. In addition, this transcriptome analysis provides the additional gene associated with sex determination and mating behaviors, which would be applied to develop a novel sterile insect technique against Z. scutellata.

Isolation, identification and transcriptome analysis of triadimefon-degrading strain Enterobacter hormaechei TY18.

Triadimefon, a type of triazole systemic fungicide, has been extensively used to control various fungal diseases. However, triadimefon could lead to severe environmental pollution, and even threatens human health. To eliminate triadimefon residues, a triadimefon-degrading bacterial strain TY18 was isolated from a long-term polluted site and was identified as Enterobacter hormaechei. Strain TY18 could grow well in a carbon salt medium with triadimefon as the sole nitrogen source, and could efficiently degrade triadimefon. Under triadimefon stress, a total of 430 differentially expressed genes (DEGs), including 197 up-regulated and 233 down-regulated DEGs, were identified in strain TY18 using transcriptome sequencing (RNA-Seq). Functional classification and enrichment analysis revealed that these DEGs were mainly related to amino acid transport and metabolism, carbohydrate transport and metabolism, small molecule and pyrimidine metabolism. Interestingly, the DEGs encoding monooxygenase and hydrolase activity acting on carbon-nitrogen were highly up-regulated, might be mainly responsible for the metabolism in triadimefon. Our findings in this work suggest that strain E. hormaechei TY18 could efficiently degrade triadimefon for the first time. They provide a great potential to manage triadimefon biodegradation in the environment successfully.

CD6 and CCR7 as Genetic Biomarkers in Evaluating Intracranial Aneurysm Rupture Risk.

BACKGROUND: This study used bioinformatics combined with statistical methods to identify plasma biomarkers that can predict intracranial aneurysm (IA) rupture and provide a strong theoretical basis for the search for new IA rupture prevention methods. METHODS: We downloaded gene expression profiles in the GSE36791 and GSE122897 datasets from the Gene Expression Omnibus (GEO) database. Data were normalized using the "sva" R package and differentially expressed genes (DEGs) were identified using the "limma" R package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used for DEG function analysis. Univariate logistic regression analysis, least absolute shrinkage and selection operator (LASSO) regression modeling, and the support vector machine recursive feature elimination (SVM-RFE) algorithm were used to identify key biomarker genes. Data from GSE122897 and GSE13353 were extracted to verify our findings. RESULTS: Eight co-DEG mRNAs were identified in the GSE36791 and GSE122897 datasets. Genes associated with inflammatory responses were clustered in the co-DEG mRNAs in IAs. CD6 and C-C chemokine receptor 7 (CCR7) were identified as key genes associated with IA. CD6 and CCR7 were upregulated in patients with IA and their expression levels were positively correlated. There were significant differences in the infiltration of immune cells between IAs and normal vascular wall tissues (p < 0.05). A predictive nomogram was designed using this two-gene signature. Binary transformation of CD6 and CCR7 was performed according to the cut-off value to construct the receiver-operating characteristic (ROC) curve and showed a strong predictive ability of the CD6-CCR7 gene signature (p < 0.01; area under the curve (AUC): 0.90; 95% confidence interval (CI): 0.88-0.92). Furthermore, validation of this two-gene signature using the GSE122897 and GSE13353 datasets proved it to be valuable for clinical application. CONCLUSIONS: The identified two-gene signature (CD6-CCR7) for evaluating the risk of IA rupture demonstrated good clinical application value.

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.

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.

Severe Type 2 Inflammation Leads to High Platelet-Activating-Factor-Associated Pathology in Chronic Rhinosinusitis with Nasal Polyps-A Hierarchical Cluster Analysis Using Bulk RNA Barcoding and Sequencing.

Platelet-activating factor (PAF) is a phospholipid-derived inflammatory mediator that triggers various inflammatory conditions, including eosinophil activation and recruitment. This study aimed to evaluate the expressions of PAF-metabolism-associated genes, namely genes coding the enzymes involved in PAF synthesis (LPCAT1, LPCAT2, LPCAT3, and LPCAT4), PAF degradation (PAFAH1B2, PAFAH1B3, and PAFAH2), and the gene for the PAF receptor (PTAFR) in subtypes of CRSwNP classified by clinical- or hierarchal-analysis-based classifications. Transcriptomic analysis using bulk RNA barcoding and sequencing (BRB-seq) was performed with CRSwNP, including eosinophilic CRS (ECRS) (n = 9), nonECRS (n = 8), ECRS with aspirin-exacerbated respiratory disease (Asp) (n = 3), and controls with a normal uncinate process mucosa (n = 6). PTAFR was only upregulated in ECRS and nonECRS. In the hierarchical cluster analysis with clusters 1 and 2 reflecting patients with low-to-moderate and high levels of type 2 inflammation, respectively, cluster 1 exhibited a significant downregulation of LPCAT2 and an upregulation of PTAFR expression, while cluster 2 showed an upregulation of LPCAT1, PAFAH1B2, and PTAFR and downregulation of PAFAH2 expression. Understanding this strong PAF-associated pathophysiology in the severe type 2 inflammation group could provide valuable insights into the treatment and management of CRSwNP.

Effects of functional defects in the NMD pathway on rice phenotype and transcriptome.

Nonsense-mediated mRNA decay (NMD) is an important RNA quality control pathway. It aids in degrading harmful erroneous mRNA, thereby preserving a stable and healthy internal environment. In this study, we employed CRISPR/Cas9 and amiRNA technology to generate knock out or knock down mutants of realted genes in the rice NMD pathway. Through transcriptome sequencing and observing phenotype changes, the study explored the impact of NMD pathway defects on rice gene expression and alternative splicing. The results suggest that even partial defects will induce phenotypic changes such as plant height and pollen vitality to different degrees, showing necessity of NMD factors. Gene expression analysis reveals that most differentially expressed genes are upregulated in the mutants, with ko-upf1-like and kd-upf1 defects having a more significant impact than kd-upf2 and kd-upf3. Specifically, NMD pathway defects result in increased expression levels of rice defense response-related genes and decreased expression levels of secondary metabolism-related genes, with a wider range of affected genes observed in 60-day-old senescence mutants. Transcript analysis indicates that different NMD related genes defects alter hundreds of alternative splicing events, mostly enriched in genes involving alternative splicing regulatory pathways. Approximately half of these events are shared among different mutants, and a substantial number of affected transcripts show NMD target features. NMD could affect both the transcript abundance and their splicing subtypes to regulate the defense response and early-senescence associated pathways, which plays a vital role in rice growth and reproduction.

[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.

Transcriptome analysis of critical genes related to flowering in Mikania micrantha at different altitudes provides insights for a potential control.

BACKGROUND: Mikania micrantha is a vine with strong invasion ability, and its strong sexual reproduction ability is not only the main factor of harm, but also a serious obstacle to control. M. micrantha spreads mainly through seed production. Therefore, inhibiting the flowering and seed production of M. micrantha is an effective strategy to prevent from continuing to spread. RESULT: The flowering number of M. micrantha is different at different altitudes. A total of 67.01 Gb of clean data were obtained from nine cDNA libraries, and more than 83.47% of the clean reads were mapped to the reference genome. In total, 5878 and 7686 significantly differentially expressed genes (DEGs) were found in E2 vs. E9 and E13 vs. E9, respectively. Based on the background annotation and gene expression, some candidate genes related to the flowering pathway were initially screened, and their expression levels in the three different altitudes in flower bud differentiation showed the same trend. That is, at an altitude of 1300 m, the flower integration gene and flower meristem gene were downregulated (such as SOC1 and AP1), and the flowering inhibition gene was upregulated (such as FRI and SVP). Additionally, the results showed that there were many DEGs involved in the hormone signal transduction pathway in the flower bud differentiation of M. micrantha at different altitudes. CONCLUSIONS: Our results provide abundant sequence resources for clarifying the underlying mechanisms of flower bud differentiation and mining the key factors inhibiting the flowering and seed production of M. micrantha to provide technical support for the discovery of an efficient control method.

Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected individuals that have hypertension or cardiovascular comorbidities have an elevated risk of serious coronavirus disease 2019 (COVID-19) disease and high rates of mortality but how COVID-$19$ and cardiovascular diseases interact are unclear. We therefore sought to identify novel mechanisms of interaction by identifying genes with altered expression in SARS-CoV-$2$ infection that are relevant to the pathogenesis of cardiovascular disease and hypertension. Some recent research shows the SARS-CoV-$2$ uses the angiotensin converting enzyme-$2$ (ACE-$2$) as a receptor to infect human susceptible cells. The ACE2 gene is expressed in many human tissues, including intestine, testis, kidneys, heart and lungs. ACE2 usually converts Angiotensin I in the renin-angiotensin-aldosterone system to Angiotensin II, which affects blood pressure levels. ACE inhibitors prescribed for cardiovascular disease and hypertension may increase the levels of ACE-$2$, although there are claims that such medications actually reduce lung injury caused by COVID-$19$. We employed bioinformatics and systematic approaches to identify such genetic links, using messenger RNA data peripheral blood cells from COVID-$19$ patients and compared them with blood samples from patients with either chronic heart failure disease or hypertensive diseases. We have also considered the immune response genes with elevated expression in COVID-$19$ to those active in cardiovascular diseases and hypertension. Differentially expressed genes (DEGs) common to COVID-$19$ and chronic heart failure, and common to COVID-$19$ and hypertension, were identified; the involvement of these common genes in the signalling pathways and ontologies studied. COVID-$19$ does not share a large number of differentially expressed genes with the conditions under consideration. However, those that were identified included genes playing roles in T cell functions, toll-like receptor pathways, cytokines, chemokines, cell stress, type 2 diabetes and gastric cancer. We also identified protein-protein interactions, gene regulatory networks and suggested drug and chemical compound interactions using the differentially expressed genes. The result of this study may help in identifying significant targets of treatment that can combat the ongoing pandemic due to SARS-CoV-$2$ infection.