Regulation of gene expression in the secondary metabolic synthesis pathway of Lonicera japonica Flos by yeast polysaccharides.

Lonicera japonica Flos is a valuable herb in the Lonicerae family. While transcriptomic studies on L. japonica have focused on different tissues (stems, leaves, flowers) or flowering stages, few have investigated the molecular mechanisms underlying chemical composition synthesis influenced by exogenous factors, such as foliar fertilization. Moreover, most transcriptomic studies on L. Japonica have been conducted on chlorogenic acid and luteoloside, and the molecular synthesis mechanism of the overall chemical composition has not been analyzed. Methods: We conducted a single-factor, four-level foliar fertilization experiment using yeast polysaccharides. Different yeast polysaccharides concentrations were sprayed on L. japonica for six consecutive days with dynamic sampling. High-performance liquid chromatography determined the active ingredients in each group. The two groups exhibiting the most significant differences were selected for transcriptomic analysis to identify key synthetic genes responsible for L. japonica's active ingredients. Key results: Principal component analysis conducted on samples collected on September 8 revealed significant differences in the active ingredient amounts between the 0.1 g/L yeast polysaccharides treatment group and the control group. Transcriptome sequencing analysis identified 218 significantly differentially expressed genes, including 60 upregulated and 158 downregulated genes. Twelve differential genes involved in the chemical components synthesis pathway of L. japonica under yeast polysaccharides treatment were identified: PAL1, PAL2, PAL3, 4CL1, 4CL, CHS1, CHS2, CHS, CHI1, CHI2, F3H, and SOH. Conclusions: This study contributes to the theoretical understanding of essential synthetic genes associated with L. japonica's active ingredients. It offers data support for further gene exploration and sheds light on the molecular mechanisms underlying L. japonica quality formation. These findings hold significant implications for enhancing the content of secondary metabolites of L. japonica. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-024-01482-1.

Combined salt and low nitrate stress conditions lead to morphophysiological changes and tissue-specific transcriptome reprogramming in tomato.

Despite intense research towards the understanding of abiotic stress adaptation in tomato, the physiological adjustments and transcriptome modulation induced by combined salt and low nitrate (low N) conditions remain largely unknown. Here, three traditional tomato genotypes were grown under long-term single and combined stresses throughout a complete growth cycle. Physiological, molecular, and growth measurements showed extensive morphophysiological modifications under combined stress compared to the control, and single stress conditions, resulting in the highest penalty in yield and fruit size. The mRNA sequencing performed on both roots and leaves of genotype TRPO0040 indicated that the transcriptomic signature in leaves under combined stress conditions largely overlapped that of the low N treatment, whereas root transcriptomes were highly sensitive to salt stress. Differentially expressed genes were functionally interpreted using GO and KEGG enrichment analysis, which confirmed the stress and the tissue-specific changes. We also disclosed a set of genes underlying the specific response to combined conditions, including ribosome components and nitrate transporters, in leaves, and several genes involved in transport and response to stress in roots. Altogether, our results provide a comprehensive understanding of above- and below-ground physiological and molecular responses of tomato to salt stress and low N treatment, alone or in combination.

Correlation between Complement C1q A Chain (C1QA) and Macrophages in the Progression of Carotid Atherosclerosis.

Background: There is increasing evidence that macrophages are involved in the development of carotid atherosclerosis (CAS), but the specific mechanism is still unclear. We aimed to explore the key genes that play a regulatory role on macrophages in the progression of CAS. Methods: From 2021 August to 2023 August, GEO datasets GSE100927 and GSE43292 were downloaded and the key gene modules related to CAS were identified by weighted Gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genes (KEGG) pathway analysis was performed on the genes of the key modules to identify common gene enrichment pathways. Differential expression analysis of pathway-related genes was performed by the "limma" package of R software. Case groups were categorized into high and low expression groups based on the expression levels of key genes, and ssGSEA immune infiltration analysis was performed. Results: The turquoise module of GSE100924 (threshold=12) and the brown module of GSE43292 (threshold=7) were obtained through WGCNA analysis. The analysis of KEGG showed that the differentially expressed genes in the turquoise and brown modules were co-enriched in the staphylococcus aureus infection signaling pathway. Differential expression analysis identified 18 common differentially expressed genes, all of which were highly expressed in the case group. C1QA is the gene of interest. According to ssGSEA analysis, the high expression group of C1QA showed a significant increase in the number of macrophages (GSE43292, P=0.0011; GSE100927, P=0.025). Conclusion: This study identified the key gene C1QA involved in regulating macrophage functional activity during the CAS process, providing new ideas for effective control of CAS.

Identification of deregulated lncRNAs in Alzheimer's disease: an integrated gene co-expression network analysis of hippocampus and fusiform gyrus RNA-seq datasets.

Introduction: The deregulation of lncRNAs expression has been associated with neuronal damage in Alzheimer's disease (AD), but how or whether they can influence its onset is still unknown. We investigated 2 RNA-seq datasets consisting, respectively, of the hippocampal and fusiform gyrus transcriptomic profile of AD patients, matched with non-demented controls. Methods: We performed a differential expression analysis, a gene correlation network analysis (WGCNA) and a pathway enrichment analysis of two RNA-seq datasets. Results: We found deregulated lncRNAs in common between hippocampus and fusiform gyrus and deregulated gene groups associated to functional pathways related to neurotransmission and memory consolidation. lncRNAs, co-expressed with known AD-related coding genes, were identified from the prioritized modules of both brain regions. Discussion: We found common deregulated lncRNAs in the AD hippocampus and fusiform gyrus, that could be considered common signatures of AD pathogenesis, providing an important source of information for understanding the molecular changes of AD.

Transcriptome Profiling of Gene Expression in Atlantic Salmon (Salmo salar) at Early Stage of Development.

For Atlantic salmon development, the most critical phase is the early development stage from egg to fry through alevin. However, the studies investigating the early development of Atlantic salmon based on RNA-seq are scarce and focus only on one stage of development. Therefore, using the RNA-seq technology, the assessment of different gene expressions of various early development stages (egg, alevin, and fry) was performed on a global scale. Over 22 GB of clean data was generated from 9 libraries with three replicates for each stage with over 90% mapping efficiency. A total of 5534 genes were differentially expressed, among which 19, 606, and 826 genes were specifically expressed in each stage, respectively. The transcriptome analysis showed that the number of differentially expressed genes (DEGs) increased as the Atlantic salmon progressed in development from egg to fry stage. In addition, gene ontology enrichment demonstrated that egg and alevin stages are characterized by upregulation of genes involved in spinal cord development, neuron projection morphogenesis, axonogenesis, and cytoplasmic translation. At the fry stage, upregulated genes were enriched in the muscle development process (muscle cell development, striated muscle cell differentiation, and muscle tissue development), immune system (defense response and canonical NF-kappaB signal transduction), as well as epidermis development. These results suggest that the early development of Atlantic salmon is characterized by a dynamic shift in gene expression and DEGs between different stages, which provided a solid foundation for the investigation of Atlantic salmon development.

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

Integrated Bioinformatics-Based Identification and Validation of Neuroinflammation-Related Hub Genes in Primary Open-Angle Glaucoma.

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Identification of hub genes and key pathways in arsenic-treated rice (Oryza sativa L.) based on 9 topological analysis methods of CytoHubba.

BACKGROUND: Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear. METHODS: In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in. RESULTS: Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress. CONCLUSIONS: In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.

Identification of key genes and long non­coding RNA expression profiles in osteoporosis with rheumatoid arthritis based on bioinformatics analysis.

BACKGROUND: Although rheumatoid arthritis (RA) is a chronic systemic tissue disease often accompanied by osteoporosis (OP), the molecular mechanisms underlying this association remain unclear. This study aimed to elucidate the pathogenesis of RA and OP by identifying differentially expressed mRNAs (DEmRNAs) and long non-coding RNAs (lncRNAs) using a bioinformatics approach. METHODS: Expression profiles of individuals diagnosed with OP and RA were retrieved from the Gene Expression Omnibus database. Differential expression analysis was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway enrichment analyses were performed to gain insights into the functional categories and molecular/biochemical pathways associated with DEmRNAs. We identified the intersection of common DEmRNAs and lncRNAs and constructed a protein-protein interaction (PPI) network. Correlation analysis between the common DEmRNAs and lncRNAs facilitated the construction of a coding-non-coding network. Lastly, serum peripheral blood mononuclear cells (PBMCs) from patients with RA and OP, as well as healthy controls, were obtained for TRAP staining and qRT-PCR to validate the findings obtained from the online dataset assessments. RESULTS: A total of 28 DEmRNAs and 2 DElncRNAs were identified in individuals with both RA and OP. Chromosomal distribution analysis of the consensus DEmRNAs revealed that chromosome 1 had the highest number of differential expression genes. GO and KEGG analyses indicated that these DEmRNAs were primarily associated with " platelets (PLTs) degranulation", "platelet alpha granules", "platelet activation", "tight junctions" and "leukocyte transendothelial migration", with many genes functionally related to PLTs. In the PPI network, MT-ATP6 and PTGS1 emerged as potential hub genes, with MT-ATP6 originating from mitochondrial DNA. Co-expression analysis identified two key lncRNA-mRNA pairs: RP11 - 815J21.2 with MT - ATP6 and RP11 - 815J21.2 with PTGS1. Experimental validation confirmed significant differential expression of RP11-815J21.2, MT-ATP6 and PTGS1 between the healthy controls and the RA + OP groups. Notably, knockdown of RP11-815J21.2 attenuated TNF + IL-6-induced osteoclastogenesis. CONCLUSIONS: This study successfully identified shared dysregulated genes and potential therapeutic targets in individuals with RA and OP, highlighting their molecular similarities. These findings provide new insights into the pathogenesis of RA and OP and suggest potential avenues for further research and targeted therapies.

Doxorubicin downregulates cell cycle regulatory hub genes in breast cancer cells.

Breast cancer (BC) is the leading commonly diagnosed cancer in the world, with complex mechanisms underlying its development. There is an urgent need to enlighten key genes as potential therapeutic targets crucial to advancing BC treatment. This study sought to investigate the influence of doxorubicin (DOX) on identified key genes consistent across numerous BC datasets obtained through bioinformatic analysis. To date, a meta-analysis of publicly available coding datasets for expression profiling by array from the Gene Expression Omnibus (GEO) has been carried out. Differentially Expressed Genes (DEGs) identified using GEO2R revealed a total of 23 common DEGs, including nine upregulated genes and 14 downregulated genes among the datasets of three platforms (GPL570, GPL6244, and GPL17586), and the commonly upregulated DEGs, showed significant enrichment in the cell cycle in KEGG analysis. The top nine genes, NUSAP1, CENPF, TPX2, PRC1, ANLN, BUB1B, AURKA, CCNB2, and CDK-1, with higher degree values and MCODE scores in the cytoscape program, were regarded as hub genes. The hub genes were activated in disease states commonly across all the subclasses of BC and correlated with the unfavorable overall survival of BC patients, as verified by the GEPIA and UALCAN databases. qRT-PCR confirmed that DOX treatment resulted in reduced expression of these genes in BC cell lines, which reinforces the evidence that DOX remains an effective drug for BC and suggests that developing modified formulations of doxorubicin to reduce toxicity and resistance, could enhance its efficacy as an effective therapeutic option for BC.

Multi-omics analysis of the biological mechanism of the pathogenesis of non-alcoholic fatty liver disease.

Background: Non-alcoholic fatty liver disease (NAFLD) is a type of liver metabolic syndrome. Employing multi-omics analyses encompassing the microbiome, metabolome and transcriptome is crucial for comprehensively elucidating the biological processes underlying NAFLD. Methods: Hepatic tissue, blood and fecal samples were obtained from 9 NAFLD model mice and 8 normal control mice. Total fecal microbiota DNA was extracted, and 16S rRNA was amplified, to analyze alterations in the gut microbiota (GM) induced by NAFLD. Subsequently, diagnostic strains for NAFLD were screened, and their functional aspects were examined. Differential metabolites and differentially expressed genes were also screened, followed by enrichment analysis. Correlations between the differential microbiota and metabolites, as well as between the DEGs and differential metabolites were studied. A collinear network involving key genes-, microbiota-and metabolites was constructed. Results: Ileibacterium and Ruminococcaceae, both belonging to Firmicutes; Olsenella, Duncaniella and Paramuribaculum from Bacteroidota; and Bifidobacterium, Coriobacteriaceae_UCG_002 and Olsenella from Actinobacteriota were identified as characteristic strains associated with NAFLD. Additionally, differentially expressed metabolites were predominantly enriched in tryptophan, linoleic acid and methylhistidine metabolism pathways. The functions of 2,510 differentially expressed genes were found to be associated with disease occurrence. Furthermore, a network comprising 8 key strains, 14 key genes and 83 key metabolites was constructed. Conclusion: Through this study, we conducted a comprehensive analysis of NAFLD alterations, exploring the gut microbiota, genes and metabolites of the results offer insights into the speculated biological mechanisms underlying NAFLD.

CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets. METHODS: Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases. RESULTS: Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients. CONCLUSION: A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics.

BACKGROUND: Effective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals. RESULTS: We present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh's method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl . CONCLUSIONS: The REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

The molecular mechanism responsible for HbSC retinopathy may depend on the action of the angiogenesis-related genes ROBO1 and SLC38A5.

HbSC disease, a less severe form of sickle cell disease, affects the retina more frequently and patients have higher rates of proliferative retinopathy that can progress to vision loss. This study aimed to identify differences in the expression of endothelial cell-derived molecules associated with the pathophysiology of proliferative sickle cell retinopathy (PSCR). RNAseq was used to compare the gene expression profile of circulating endothelial colony-forming cells from patients with SC hemoglobinopathy and proliferative retinopathy (n = 5), versus SC patients without retinopathy (n = 3). Real-time polymerase chain reaction (qRT-PCR) was used to validate the RNAseq results. A total of 134 differentially expressed genes (DEGs) were found. DEGs were mainly associated with vasodilatation, type I interferon signaling, innate immunity and angiogenesis. Among the DEGs identified, we highlight the most up-regulated genes ROBO1 (log2FoldChange = 4.32, FDR = 1.35E-11) and SLC38A5 (log2FoldChange = 3.36 FDR = 1.59E-07). ROBO1, an axon-guided receptor, promotes endothelial cell migration and contributes to the development of retinal angiogenesis and pathological ocular neovascularization. Endothelial SLC38A5, an amino acid (AA) transporter, regulates developmental and pathological retinal angiogenesis by controlling the uptake of AA nutrient, which may serve as metabolic fuel for the proliferation of endothelial cells (ECs) and consequent promotion of angiogenesis. Our data provide an important step towards elucidating the molecular pathophysiology of PSCR that may explain the differences in ocular manifestations between individuals with hemoglobinopathies and afford insights for new alternative strategies to inhibit pathological angiogenesis.

A comparison of the large-scale gene expression patterns in summer and fall migratory Pantala flavescens (Fabricius) in northern China.

Pantala flavescens (Fabricius) is the most well-known seasonal migratory insect. This research focused on the molecular response of P. flavescens migration in summer and fall. A total of 17,810 assembled unigenes were obtained and 624 differentially expressed genes (DEGs) were identified in summer migration compared to fall migration. A number of DEGs, including cpr49Ae, itm2b, chitinase, cpr11B, laccase2, nd5, vtg2 and so on, had previously been reported to be involved in cold- and high-temperature resistance. Functional enrichment analysis showed three pathways 'that antibacterial humoral response, response to bacterial, and lipid transporter activity' were significantly enriched in summer migration while that six pathways 'structural constituent of cuticle, chitin binding, mitochondrion, propanoate metabolism, citrate cycle, hypertrophic cardiomyopathy' were significantly enriched in fall migration. These results will provide a valuable baseline for further understanding of the molecular mechanisms of insect adaptation to different climate migrations.

Differences in T cell-associated serum markers between ischemic cardiomyopathy and dilated cardiomyopathy.

Background: Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) have similar clinical manifestations but differ in pathogenesis. We aimed to identify T cell-associated serum markers that can be used to distinguish between ICM and DCM. Methods: We identified differentially expressed genes (DEGs) with transcriptome sequencing data in GSE116250, and then conducted enrichment analysis of DEGs in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks were used to analyze the relationship between T cells-related genes and identify hub genes. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect T cell-associated proteins in serum, and receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of these serum markers. Results: Using the limma package and Venn plots, we found that the non-failing donors (NFD) and DCM groups shared many of the same DEGs and DEGs-enriched functions compared to the ICM group, which were involved in T cell activation and differentiation, among other functions. Subsequently, the immune cell score showed no difference between NFD and DCM, but they were significantly different from ICM patients in CD8 T cells CD4 T cells memory resting and activated, T cells follicular helper, and M1 macrophage. After analyzing T cell-associated DEGs, it was found that 4 DEGs encoding secreted proteins were highly expressed in the ICM group compared with the NFD and DCM groups, namely chemokine (C-C motif) ligand 21 (CCL21), interleukin (IL)-1ß, lymphocyte-activation gene 3 (LAG3), and vascular cell adhesion molecule-1 (VCAM-1). Importantly, the serum levels of CCL21, IL-1ß, LAG3, and VCAM-1 in ICM patients were all significantly higher than those in DCM patients. The ROC curves showed that the area under the curve (AUC) values of serum CCL21, IL-1ß, LAG3, and VCAM-1 were 0.775, 0.868, 0.934, and 0.903, respectively. Conclusions: We have identified four T cell-associated serum markers, CCL21, IL-1ß, LAG3, and VCAM-1, as potential diagnostic serum markers that differentiate ICM from DCM.

Bioinformatics reveals the potential mechanisms and biomarkers of necroptosis in neuroblastoma.

Background: Neuroblastoma (NB) is a malignant tumor primarily found in children, presenting significant challenges in its development and prognosis. The role of necroptosis in the pathogenesis of NB has been acknowledged as crucial for treatment. This study aimed to investigate the key genes and functional pathways associated with necroptosis, as well as immune infiltration analysis, in NB. Furthermore, we aimed to evaluate the diagnostic significance of these genes for prognostic assessment and explore their potential immunological characteristics. Methods: The NB dataset (GSE19274, GSE73517, and GSE85047) was obtained from the Gene Expression Omnibus (GEO) database, and genes associated with necroptosis were collected from GeneCards and previous literature. First, we conducted differential expression analysis and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We employed gene set enrichment analysis (GSEA) to identify overlapping enriched functional pathways from the NB dataset. In addition, we constructed a protein-protein interaction (PPI) network, predicting relevant microRNAs (miRNAs) and transcription factors (TFs), as well as their corresponding drug predictions. Furthermore, the diagnostic value was assessed using receiver operating characteristic (ROC) curves. Finally, an immune infiltration analysis was performed. Results: We identified six necroptosis-related differentially expressed genes (NRDEGs) closely associated with necroptosis in NB. They were enriched in Tuberculosis, Apoptosis-multiple species, Salmonella infection, legionellosis, and platinum drug resistance. GSEA and PPI network analyses, along with mRNA-drug interaction network, revealed 38 potential drugs corresponding to BIRC2, CAMK2G, CASP3, and IL8. ROC curve analysis showed that in GSE19274, FLOT2 with area under the ROC curve (AUC) of 0.850 and DAPK1 with AUC of 0.789. Conclusions: Our study elucidates the key genes and functional pathways associated with necroptosis in NB, offering valuable insights to enhance our comprehension of the pathogenesis of NB, and improve prognosis assessment.

Identifying key pathogenic mechanisms and potential intervention targets for recurrence after laryngeal cancer treatment through bioinformatics screening.

Background: Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC. Methods: Gene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence. Results: A total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., RRAGA, SLC38A9, WDR24, ATP6V1B1, and LAMTOR3). The construction of the TF-DEG-miRNA network indicated that ATP6V1B1 might be regulated by one TF and interact with 17 miRNAs. The KEGG and GSEA analyses suggested that ATP6V1B1 may influence LC recurrence through the involvement of pro-inflammatory and pro-fibrotic mediators, glutathione metabolism, matrix metalloproteinases, immune regulation, and lymphocyte interactions. The recurrence rate of the 83 LC patients included in the study was 19.3% (16/83). The immunohistochemistry results indicated that ATP6V1B1 was highly expressed in patients with recurrent LC. The univariate and multivariate logistic regression analyses revealed that tumor stage T3 (P=0.04), tumor stage T4 (P=0.01), and a high expression of ATP6V1B1 (P=0.02) were risk factors for recurrence after surgical treatment in LC patients. Conclusions: The key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC. ATP6V1B1 may promote the recurrence of LC by weakening the immune phenotype. Our findings provide a theoretical basis for further research into clinical diagnostics and treatment strategies for LC.

Comparative global profiling of Perilla leaf and stem via transcriptomics and metabolomics.

Perilla (Perilla frutescens L.) is a time-honored herbal plant with widespread applications in both medicine and culinary practices around the world. Profiling the essential organs and tissues with medicinal significance on a global scale offers valuable insights for enhancing the yield of desirable compounds in Perilla and other medicinal plants. In the present study, genome-wide RNA-sequencing (RNA-seq) and assessing the global spectrum of metabolites were carried out in the two major organs/tissues of stem (PfST) and leaf (PfLE) in Perilla. The results showed a total of 18,490 transcripts as the DEGs (differentially expressed genes) and 144 metabolites as the DAMs (differentially accumulated metabolites) through the comparative profiling of PfST vs PfLE, and all the DEGs and DAMs exhibited tissue-specific trends. An association analysis between the transcriptomics and metabolomics revealed 14 significantly enriched pathways for both DEGs and DAMs, among which the pathways of Glycine, serine and threonine metabolism (ko00260), Glyoxylate and dicarboxylate metabolism (ko00630), and Glucagon signaling pathway (ko04922) involved relatively more DEGs and DAMs. The results of qRT-PCR assays of 18 selected DEGs confirmed the distinct tissue-specific characteristics of all identified DEGs between PfST and PfLE. Notably, all eight genes associated with the flavonoid biosynthesis/metabolism pathways exhibited significantly elevated expression levels in PfLE compared to PfST. This observation suggests a heightened accumulation of metabolites related to flavonoids in Perilla leaves. The findings of this study offer a comprehensive overview of the organs and tissues in Perilla that have medicinal significance.