Comprehensive identification of ubiquitin-like 3 (UBL3)-interacting proteins in the mouse brain.

Discovery of novel post-translational modifications provides new insights into changes in protein function, localization, and stability. They are also key elements in understanding disease mechanisms and developing therapeutic strategies. We have previously reported that ubiquitin-like 3 (UBL3) serves as a novel post-translational modifier that is highly expressed in the cerebral cortex and hippocampus, in addition to various other organs, and that 60% of proteins contained in small extracellular vesicles (sEVs), including exosomes, are influenced by UBL3. In this study, we generated transgenic mice expressing biotinylated UBL3 in the forebrain under control of the alpha-CaMKII promoter (Ubl3Tg/+). Western blot analysis revealed that the expression of UBL3 in the cerebral cortex and hippocampus was 6- to 7-fold higher than that in the cerebellum. Therefore, we performed immunoprecipitation of protein extracts from the cerebral cortex of Ubl3+/+ and Ubl3Tg/+ mice using avidin beads to comprehensively discover UBL3 interacting proteins, identifying 35 new UBL3 interacting proteins. Nine proteins were annotated as extracellular exosomes. Gene Ontology (GO) analysis suggested a new relationship between sEVs and RNA metabolism in neurodegenerative diseases. We confirmed the association of endogenous UBL3 with the RNA-binding proteins FUS and HPRT1-both listed in the Neurodegenerative Diseases Variation Database (NDDVD)-and with LYPLA1, which is involved in Huntington's disease, using immunoprecipitation (IP)-western blotting analysis. These UBL3 interacting proteins will accelerate the continued elucidation of sEV research about proteins regulated by novel post-translational modifications by UBL3 in the brain.

Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.

Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

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.

Exploring therapeutic targets and molecular mechanisms for treating diabetes mellitus-associated heart failure with Qishen Yiqi dropping pills: A network pharmacology and bioinformatics approach.

Diabetes mellitus (DM) and heart failure frequently coexist, presenting significant public health challenges. QiShenYiQi Dropping Pills (QSDP) are widely employed in the treatment of diabetes mellitus concomitant with heart failure (DM-HF). Nevertheless, the precise mechanisms underlying their efficacy have yet to be elucidated. Active ingredients and likely targets of QSDP were retrieved from the TCMSP and UniProt databases. Genes associated with DM-HF were pinpointed through searches in the GeneCards, OMIM, DisGeNET, and TTD databases. Differential genes connected to DM-HF were sourced from the GEO database. Enrichment analyses via gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways, as well as immune infiltration assessments, were conducted using R software. Further analysis involved employing molecular docking strategies to explore the interactions between the identified targets and active substances in QSDP that are pertinent to DM-HF treatment. This investigation effectively discerned 108 active compounds and 257 targets relevant to QSDP. A protein-protein interaction network was constructed, highlighting 6 central targets for DM-HF treatment via QSDP. Gene ontology enrichment analysis predominantly linked these targets with responses to hypoxia, metabolism of reactive oxygen species, and cytokine receptor interactions. Analysis of Kyoto Encyclopedia of Genes and Genomes pathways demonstrated that these targets mainly participate in pathways linked to diabetic complications, such as AGE-RAGE signaling, dyslipidemia, arteriosclerosis, the HIF-1 signaling pathway, and the tumor necrosis factor signaling pathway. Further, immune infiltration analysis implied that QSDP's mechanism in treating DM-HF might involve immune-mediated inflammation and crucial signaling pathways. Additionally, molecular docking studies showed that the active substances in QSDP have strong binding affinities with these identified targets. This research presents a new model for addressing DM-HF through the use of QSDP, providing novel insights into incorporating traditional Chinese medicine (TCM) principles in the clinical treatment of DM-HF. The implications of these findings are substantial for both clinical application and further scientific inquiry.

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.

Comparison of ovarian mRNA expression levels in wild and hatchery-produced greater amberjack Seriola dumerili.

The greater amberjack Seriola dumerili is a promising candidate for aquaculture production. This study compares the ovary transcriptome of greater amberjack sampled in the wild (WILD) with hatchery-produced breeders reared in aquaculture sea cages in the Mediterranean Sea. Among the seven sampled cultured fish, three were classified as reproductively dysfunctional (DysF group), while four showed no signs of reproductive alteration (NormalF group). The DysF fish showed 1,166 differentially expressed genes (DEGs) compared to WILD females, and 755 DEGs compared to the NormalF. According to gene ontology (GO) analysis, DysF females exhibited enrichment of genes belonging to the biological categories classified as Secreted, ECM-receptor interaction, and Focal adhesion. Protein-protein interaction analysis revealed proteins involved in the biological categories of ECM-receptor interaction, Enzyme-linked receptor protein signaling, Wnt signal transduction pathways, and Ovulation cycle. KEGG pathway analysis showed DEGs involved in 111 pathways, including Neuroactive ligand-receptor interaction, Steroid hormone biosynthesis, Cell cycle, Oocyte meiosis, Necroptosis, Ferroptosis, Apoptosis, Autophagy, Progesterone-mediated oocyte maturation, Endocytosis and Phagosome, as well as Hedgehog, Apelin, PPAR, Notch, and GnRH signalling pathways. Additionally, DysF females exhibited factors encoded by upregulated genes associated with hypogonadism and polycystic ovary syndrome in mammals. This study -which is part of a broader research effort examining the transcriptome of the entire reproductive axis in greater amberjack of both sexes-, enhances our comprehension of the mechanisms underlying the appearance of reproductive dysfunctions when fish are reared under aquaculture conditions.

Investigating the Molecular Mechanisms of Resveratrol in Treating Cardiometabolic Multimorbidity: A Network Pharmacology and Bioinformatics Approach with Molecular Docking Validation.

BACKGROUND: Resveratrol is a potent phytochemical known for its potential in treating cardiometabolic multimorbidity. However, its underlying mechanisms remain unclear. Our study systematically investigates the effects of resveratrol on cardiometabolic multimorbidity and elucidates its mechanisms using network pharmacology and molecular docking techniques. METHODS: We screened cardiometabolic multimorbidity-related targets using the OMIM, GeneCards, and DisGeNET databases, and utilized the DSigDB drug characterization database to predict resveratrol's effects on cardiometabolic multimorbidity. Target identification for resveratrol was conducted using the TCMSP, SymMap, DrugBank, Swiss Target Prediction, CTD, and UniProt databases. SwissADME and ADMETlab 2.0 simulations were used to predict drug similarity and toxicity profiles of resveratrol. Protein-protein interaction (PPI) networks were constructed using Cytoscape 3.9.1 software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed via the DAVID online platform, and target-pathway networks were established. Molecular docking validated interactions between core targets and resveratrol, followed by molecular dynamics simulations on the optimal core proteins identified through docking. Differential analysis using the GEO dataset validated resveratrol as a core target in cardiometabolic multimorbidity. RESULTS: A total of 585 cardiometabolic multimorbidity target genes were identified, and the predicted results indicated that the phytochemical resveratrol could be a major therapeutic agent for cardiometabolic multimorbidity. SwissADME simulations showed that resveratrol has potential drug-like activity with minimal toxicity. Additionally, 6703 targets of resveratrol were screened. GO and KEGG analyses revealed that the main biological processes involved included positive regulation of cell proliferation, positive regulation of gene expression, and response to estradiol. Significant pathways related to MAPK and PI3K-Akt signaling pathways were also identified. Molecular docking and molecular dynamics simulations demonstrated strong interactions between resveratrol and core targets such as MAPK and EGFR. CONCLUSIONS: This study predicts potential targets and pathways of resveratrol in treating cardiometabolic multimorbidity, offering a new research direction for understanding its molecular mechanisms. Additionally, it establishes a theoretical foundation for the clinical application of resveratrol.

Quantitative Proteomic Analysis Reveals Key Proteins Involved in Testicular Development of Yaks.

Male reproductive health is largely determined already in the early development of the testis. Although much work has been carried out to study the mechanisms of testicular development and spermatogenesis, there was previously no information on the differences in the protein composition of yak testicles during early development. In this study, the protein profiles in the testicles of 6- (M6), 18- (M18), and 30-month-old (M30) yaks were comparatively analyzed using TMT proteomics. A total of 5521 proteins were identified, with 13, 1295, and 1397 differentially expressed proteins (DEPs) in 30- vs. 18-, 18- vs. 6-, and 30- vs. 6-month-old testes, respectively. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that DEPs were mainly involved in signaling pathways related to testicular development and spermatogenesis, including the MAPK, PI3K-Akt, Wnt, mTOR, TGF-ß, and AMPK signaling pathways. Furthermore, we also identified eight potential proteins (TEX101, PDCL2, SYCP2, SYCP3, COL1A1, COL1A2, ADAM10, and ATF1) that may be related to the testicular development and spermatogenesis of yaks. This study may provide new insights into the molecular mechanisms of the testicular development and spermatogenesis of yaks.

Identification of ENTPD1 as a novel biomarker linking allergic rhinitis and systemic lupus erythematosus.

Several studies reveal that allergic rhinitis (AR) is a significant risk factor of systemic lupus erythematosus (SLE). However, studies investigating the common pathogenesis linking AR and SLE are lacking. Our study aims to search for the shared biomarkers and mechanisms that may provide new therapeutic targets for preventing AR from developing SLE. GSE50223 for AR and GSE103760 for SLE were downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the functions of shared DEGs. Hub genes were screened by cytoHubba (a plugin of Cytoscape) and validated in another two datasets. Gene set enrichment analysis (GSEA) and single-sample Gene set enrichment analysis (ssGSEA) algorithm were applied to understand the functions of hub gene. ENTPD1 was validated as a hub gene between AR and SLE. GSEA results revealed that ENTPD1 was associated with KRAS_SIGNALING_UP pathway in AR and related to HYPOXIA, TGF_BETA_SIGNALING and TNFA_SIGNALING_VIA_NFKB pathways in SLE. The expression of ENTPD1 was positively correlated with activated CD8 T cell in both diseases. Thus, ENTPD1 may be a novel therapeutic target for preventing AR from developing SLE.

Identification and analysis of oxidative stress-related genes in hypoxic-ischemic brain damage using bioinformatics and experimental verification.

BACKGROUND: Oxidative stress (OS) plays a major role in the progress of hypoxic-ischemic brain damage (HIBD). This study aimed to investigate OS-related genes and their underlying molecular mechanisms in neonatal HIBD. METHODS: Microarray data sets were acquired from the Gene Expression Omnibus (GEO) database to screen the differentially expressed genes (DEGs) between control samples and HIBD samples. OS-related genes were drawn from GeneCards and OS-DEGs in HIBD were obtained by intersecting with the DEGs. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were conducted to determine the underlying mechanisms and functions of OS-DEGs in HIBD. Moreover, the hub genes were screened using the protein-protein interaction network and identified in the GSE144456 data set. CIBERSORT was then performed to evaluate the expression of immunocytes in each sample and perform a correlation analysis of the optimal OS-DEGs and immunocytes. Finally, quantitative reverse transcription polymerase chain reaction (RT-qPCR) and immunohistochemistry were performed to validate the expression levels of the optimal OS-DEGs. RESULTS: In total, 93 OS-DEGs were identified. GO, KEGG, and GSEA enrichment analyses indicated that these genes were predominantly enriched in OS and inflammation. Four OS-related biomarker genes (Jun, Fos, Tlr2, and Atf3) were identified and verified. CIBERSORT analysis revealed the dysregulation of six types of immune cells in the HIBD group. Moreover, 47 drugs that might target four OS-related biomarker genes were screened. Eventually, RT-qPCR and immunohistochemistry results for rat samples further validated the expression levels of Fos, Tlr2, and Atf3. CONCLUSIONS: Fos, Tlr2 and Atf3 are potential OS-related biomarkers of HIBD progression. The mechanisms of OS are associated with those of neonatal HIBD.

m6A modification of lncRNA in middle ear cholesteatoma. / 中耳胆脂瘤中lncRNA的m6A修饰.

OBJECTIVES: Middle ear cholesteatoma is a non-tumorous condition that typically leads to hearing loss, bone destruction, and other severe complications. Despite surgery being the primary treatment, the recurrence rate remains high. Therefore, exploring the molecular mechanisms underlying cholesteatoma is crucial for discovering new therapeutic approaches. This study aims to explore the involvement of N6-methyladenosine (m6A) methylation in long non-coding RNAs (lncRNAs) in the biological functions and related pathways of middle ear cholesteatoma. METHODS: The m6A modification patterns of lncRNA in middle ear cholesteatoma tissues (n=5) and normal post-auricular skin tissues (n=5) were analyzed using an lncRNA m6A transcriptome microarray. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to identify potential biological functions and signaling pathways involved in the pathogenesis of middle ear cholesteatoma. Methylated RNA immunoprecipitation (MeRIP)-PCR was used to validate the m6A modifications in cholesteatoma and normal skin tissues. RESULTS: Compared with normal skin tissues, 1 525 lncRNAs were differentially methylated in middle ear cholesteatoma tissues, with 1 048 showing hypermethylation and 477 showing hypomethylation [fold change (FC)≥3 or <1/3, P<0.05]. GO enrichment analysis indicated that hypermethylated lncRNAs were involved in protein phosphatase inhibitor activity, neuron-neuron synapse, and regulation of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor activity. Hypomethylated lncRNAs were associated with mRNA methyltransferase activity, secretory granule membrane, and mRNA methylation. KEGG analysis revealed that hypermethylated lncRNAs were mainly associated with 5 pathways: the Hedgehog signaling pathway, viral protein interaction with cytokines and cytokine receptors, mitogen-activated protein kinase (MAPK) signaling pathway, cytokine-cytokine receptor interaction, and adrenergic signaling in cardiomyocytes. Hypomethylated lncRNAs were mainly involved in 4 pathways: Renal cell carcinoma, tumor necrosis factor signaling pathway, transcriptional misregulation in cancer, and cytokine-cytokine receptor interaction. Additionally, MeRIP-PCR confirmed the changes in m6A methylation levels in NR_033339, NR_122111, NR_130744, and NR_026800, consistent with microarray analysis. Real-time PCR also confirmed the significant upregulation of MAPK1 and NF-κB, key genes in the MAPK signaling pathway. CONCLUSIONS: This study reveals the m6A modification patterns of lncRNAs in middle ear cholesteatoma, suggests a direction for further research into the role of lncRNA m6A modification in the etiology of cholesteatoma. The findings provide potential therapeutic targets for the treatment of middle ear cholesteatoma.

Co-regulatory mechanisms and potential markers of oxidative stress-related genes in vitiligo and alopecia areata.

BACKGROUND: The specific role of oxidative stress (OS) in vitiligo and alopecia areata (AA) remains unclear. The aim of this study was to analyze and identify the key markers of OS in vitiligo and AA by bioinformatics. METHODS: We obtained vitiligo and AA datasets from gene expression omnibus (GEO) database. The difference-expressed genes of vitiligo and AA were identified by differential analysis, and the functions of difference-expressed genes were identified by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) enrichment analysis. Subsequently, Veen package was used to obtain the intersection genes of OS-related genes with vitiligo and AA. Finally, we used CIBERSORT to assess the infiltration of immune cells in vitiligo and AA. RESULTS: Through enrichment analysis, we found that vitiligo and AA were mainly enriched in cell cycle and cell adhesion molecular channels. We identified KLB and EIF3C as key genes in OS regulation of vitiligo and AA, and found that KLB and EIF3C participate in disease progression by regulating T cells and neutrophils. CONCLUSIONS: According to our findings, KLB and EIF3C play a crucial role in the progression and development of vitiligo and AA, which have been identified as biomarkers and target for early diagnosis of patients.

Phenotypic and transcriptomics characterization uncovers genes underlying tuber yield traits and gene expression marker development in potato under aeroponics.

MAIN CONCLUSION: Transcriptome analysis in potato varieties revealed genes associated with tuber yield-related traits and developed gene expression markers. This study aimed to identify genes involved in high tuber yield and its component traits in test potato varieties (Kufri Frysona, Kufri Khyati, and Kufri Mohan) compared to control (Kufri Sutlej). The aeroponic evaluation showed significant differences in yield-related traits in the varieties. Total RNA sequencing was performed using tuber and leaf tissues on the Illumina platform. The high-quality reads (QV > 25) mapping with the reference potato genomes revealed statistically significant (P < 0.05) differentially expressed genes (DEGs) into two categories: up-regulated (> 2 Log2 fold change) and down-regulated (< -2 Log2 fold change). DEGs were characterized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Collectively, we identified genes participating in sugar metabolism, stress response, transcription factors, phytohormones, kinase proteins, and other genes greatly affecting tuber yield and its related traits. A few selected genes were UDP-glucose glucosyltransferase, glutathion S-transferase, GDSL esterase/lipase, transcription factors (MYB, WRKY, bHLH63, and BURP), phytohormones (auxin-induced protein X10A, and GA20 oxidase), kinase proteins (Kunitz-type tuber invertase inhibitor, BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1) and laccase. Based on the selected 17 peptide sequences representing 13 genes, a phylogeny tree and motifs were analyzed. Real time-quantitative polymerase chain reaction (RT-qPCR) analysis was used to validate the RNA-seq results. RT-qPCR based gene expression markers were developed for the genes such as 101 kDa heat shock protein, catechol oxidase B chloroplastic, cysteine protease inhibitor 1, Kunitz-type tuber invertase inhibitor, and laccase to identify high yielding potato genotypes. Thus, our study paved the path for potential genes associated with tuber yield traits in potato under aeroponics.

Exploration of PVT1 as a biomarker in prostate cancer.

Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.

A comparative cross-platform analysis of cuproptosis-related genes in human nonobstructive azoospermia: An observational study.

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of "homologous recombination" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis.

Comparative transcriptomics of Venus flytrap (Dionaea muscipula) across stages of prey capture and digestion.

The Venus flytrap, Dionaea muscipula, is perhaps the world's best-known botanical carnivore. The act of prey capture and digestion along with its rapidly closing, charismatic traps make this species a compelling model for studying the evolution and fundamental biology of carnivorous plants. There is a growing body of research on the genome, transcriptome, and digestome of Dionaea muscipula, but surprisingly limited information on changes in trap transcript abundance over time since feeding. Here we present the results of a comparative transcriptomics project exploring the transcriptomic changes across seven timepoints in a 72-hour time series of prey digestion and three timepoints directly comparing triggered traps with and without prey items. We document a dynamic response to prey capture including changes in abundance of transcripts with Gene Ontology (GO) annotations related to digestion and nutrient uptake. Comparisons of traps with and without prey documented 174 significantly differentially expressed genes at 1 hour after triggering and 151 genes with significantly different abundances at 24 hours. Approximately 50% of annotated protein-coding genes in Venus flytrap genome exhibit change (10041 of 21135) in transcript abundance following prey capture. Whereas peak abundance for most of these genes was observed within 3 hours, an expression cluster of 3009 genes exhibited continuously increasing abundance over the 72-hour sampling period, and transcript for these genes with GO annotation terms including both catabolism and nutrient transport may continue to accumulate beyond 72 hours.

mRNA expression insights: Unraveling the relationship between COPD and lung cancer.

BACKGROUND: Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to. METHOD: Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups. RESULT: After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log2 fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3 × 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × -0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion. CONCLUSION: In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.

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.

Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis.

Cell immortalization, a hallmark of cancer development, is a process that cells can undergo on their path to carcinogenesis. Spontaneously immortalized mouse embryonic fibroblasts (MEFs) have been used for decades; however, changes in the global transcriptome during this process have been poorly described. In our research, we characterized the poly-A RNA transcriptome changes after spontaneous immortalization. To this end, differentially expressed genes (DEGs) were screened using DESeq2 and characterized by gene ontology enrichment analysis and protein-protein interaction (PPI) network analysis to identify the potential hub genes. In our study, we identified changes in the expression of genes involved in proliferation regulation, cell adhesion, immune response and transcriptional regulation in immortalized MEFs. In addition, we performed a comparative analysis with previously reported MEF immortalization data, where we propose a predicted gene regulatory network model in immortalized MEFs based on the altered expression of Mapk11, Cdh1, Chl1, Zic1, Hoxd10 and the novel hub genes Il6 and Itgb2.