Protein-protein Interaction Network Analysis Research Articles

Genome-Wide Identification and Characterization of bHLH Gene Family in Hevea brasiliensis

The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and characterized in rubber trees using systematic bioinformatics approaches. In total, 180 bHLH genes were identified in the rubber tree genome, distributed unevenly across 18 chromosomes, and phylogenetic analysis classified these genes into 23 distinct subfamilies. Promoter regions revealed a high density of cis-elements responsive to light and hormones. Enrichment analysis indicated involvement in numerous biological processes, including growth, development, hormone responses, abiotic stress resistance, and secondary metabolite biosynthesis. Protein interaction network analysis identified extensive interactions between HbbHLH genes and other functional genes, forming key clusters related to iron homeostasis, plant growth, and stomatal development. Expression profiling of HbbHLH genes have demonstrated varied responses to endogenous and environmental changes. RT-qPCR of eleven HbbHLH genes in different tissues and under ethylene, jasmonic acid, and cold treatments revealed tissue-specific expression patterns and significant responses to these stimuli, highlighting the roles of these genes in hormone and cold stress responses. These findings establish a framework for exploring the molecular functions of bHLH transcription factors in rubber trees.

Identification of potential mechanisms of Schisandrin B in the treatment of idiopathic pulmonary fibrosis by integrating network pharmacology and experimental validation.

Idiopathic pulmonary fibrosis (IPF) is a worsening fibrotic condition characterized by a short survival rate and limited treatment options. This study evaluates the potential anti-fibrotic properties of Schisandrin B (Sch B) through network pharmacology and experimental validation. A mouse model of bleomycin-induced pulmonary fibrosis was established, and the modeled mice were treated with Sch B at three doses (20 mg/kg/day, 40 mg/kg/day, and 80 mg/kg/day). A fibrotic model was developed in NIH/3T3 cells by treating them with TGF-β (10 ng/mL) and administering Sch B at various concentrations (10, 20, and 40 µM). The results revealed that Sch B treatment delayed the development of bleomycin-induced pulmonary fibrosis and substantially decreased the transcription levels of collagen I and α-SMA in TGF-β-induced fibroblasts. Core targets were screened with protein-protein interaction network analysis, molecular complex detection (MCODE), and CytoHubba plugin. The application of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and molecular docking highlighted the significance of the HIF-1α signaling pathway in the potential mechanism of Sch B in IPF therapy. Western blot, PCR, and immunofluorescence were performed to validate the effects of Sch B on HIF-1α. In vivo and in vitro, Sch B administration reduced HIF-1α expression. These outcomes provide valuable insights into the potential mechanism by which Sch B delays IPF development, with HIF-1α potentially serving as a key target. However, further investigation is warranted to assess the safety and efficacy of Sch B in clinical settings.

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

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

Genetic variation perspective reveals potential drug targets for subtypes of endometrial cancer.

The study aims to identify potential drug targets for endometrial cancer (EC) subtypes through a Mendelian randomization (MR) approach, assessing their clinical relevance. We utilized genetic instruments for 4,907 plasma proteins from the deCODE Genetics study dataset, and data with EC (n = 12,906) from a genome-wide study (GWAS) meta-analysis in European populations for MR analyses. Complementary analyses included protein-protein interactions (PPI) network analysis, therapeutic efficacy evaluation, differential gene expression assessment, and prognosis evaluation. The expression levels of key drug targets were quantitatively measured at both the transcriptional and translational stages utilizing reverse transcription quantitative PCR (RT-qPCR) and immunohistochemistry (IHC). Additionally, we analyzed various clinicopathological features. Five drug targets for EC (CBR3, GSTO1, HHIP, IGF2R, and MMP10), seven for endometrioid subtypes (ACAP2, CBR3, GSTO1, HHIP, IGF2R, MMP10, and TLR2), and seven for non-endometrioid subtypes (CST3, DNAJB14, FSTL5, GMPR2, IFI16, MAPK9, and NEO1) were identified. Among these, IGF2R (OR = 1.165; 95% CI 1.067-1.272; p = 1.046 × 10- 2) and CST3 (OR = 0.523; 95% CI 0.339-0.804; p = 7.010 × 10- 3) were highlighted as key drug targets with causal evidence both at transcriptional and translational levels. This study preliminarily confirms that IGF2R and CST3 may serve as novel targets for the treatment of EC, providing a foundational reference for innovative clinical approaches to this disease.

Comparative analyses of RNA-seq and phytohormone data of sweetpotatoes inoculated with Dickeya dadantii causing bacterial stem and root rot of sweetpotato.

Bacterial stem and root rot (BSRR) in sweetpotato caused by Dickeya dadantii is one of the ten major diseases of sweetpotatoes in China. However, the molecular mechanism underlying the resistance of sweetpotato to D. dadantii remains unclear. This study adopted a resistance identification assay that conformed Guangshu87 (GS87) as BSRR-resistant and Xinxiang (XX) as susceptible. Compared to XX, GS87 effectively prevented the invasion and dissemination of D. dadantii in planta. An RNA sequencing (RNA-seq) analysis identified 54,844 expressed unigenes between GS87 and XX at four different stages. Further, it revealed that GS87 was more able to regulate the expressions of more unigenes after the inoculation with D. dadantii, including resistance (R) and transcription factors (TF) genes. Moreover, content measurements of disease resistance-related phytohormones showed that both jasmonic acids (JAs) and salicylic acids (SAs) accumulated in D. dadantii-inoculated sweetpotatoes, and JAs may negatively regulate sweetpotato resistance against D. dadantii and accumulated faster than SAs. Meanwhile, determinations of ROS production rate and relevant enzymatic/non-enzymatic activity highlighted the vital roles of reactive oxygen species (ROS) and superoxide dismutase (SOD) in confering GS87 resistance against D. dadantii. Additionally, several hub genes with high connectivity were highlighted through Protein-Protein interaction (PPI) network analysis. In summary, the findings in this study contribute to the understanding of the different responses of resistant and susceptible sweetpotato cultivars to D. dadantii infection, and it also provide the first insight into the relevant candidate genes and phytohormones involved in the resistance of sweetpotato to D. dadantii.

Mining contextually meaningful subgraphs from a vertex-attributed graph.

Networks have emerged as a natural data structure to represent relations among entities. Proteins interact to carry out cellular functions and protein-Protein interaction network analysis has been employed for understanding the cellular machinery. Advances in genomics technologies enabled the collection of large data that annotate proteins in interaction networks. Integrative analysis of interaction networks with gene expression and annotations enables the discovery of context-specific complexes and improves the identification of functional modules and pathways. Extracting subnetworks whose vertices are connected and have high attribute similarity have applications in diverse domains. We present an enumeration approach for mining sets of connected and cohesive subgraphs, where vertices in the subgraphs have similar attribute profile. Due to the large number of cohesive connected subgraphs and to overcome the overlap among these subgraphs, we propose an algorithm for enumerating a set of representative subgraphs, the set of all closed subgraphs. We propose pruning strategies for efficiently enumerating the search tree without missing any pattern or reporting duplicate subgraphs. On a real protein-protein interaction network with attributes representing the dysregulation profile of genes in multiple cancers, we mine closed cohesive connected subnetworks and show their biological significance. Moreover, we conduct a runtime comparison with existing algorithms to show the efficiency of our proposed algorithm.

Molecular Mechanism of Finerenone in Treating Diabetic Nephropathy Based on Bioinformatics

Background: Diabetic Nephropathy (DN) is the leading cause of the end-stage renal disease (ESRD). Finerenone (with the molecular formula C21H22N4O3) is an oral non-steroidal mineralocorticoid antagonist (ns-MRA) that is both highly potent and has strong selectivity for the MR. At present, it has been used to treat DN. However, the molecular mechanism of finerenone in the treatment of diabetic nephropathy remains unclear. Objective: In this study, we employed bioinformatics approaches to investigate the molecular mechanism of finerenone as a novel therapeutic agent for the treatment of DN. Methods: We examined a number of databases, including GEO, DisGeNET, Genecards, and OMIM, to find putative genes linked to DN. We then employed the PubChem database and PharmMapper service platform to identify targets of finerenone. Further analysis was conducted using the DAVID database for enrichment analysis and the STRING database for protein-protein interaction (PPI) networks. Molecular docking (MD) was performed using AutoDockTools software, and results were visualized using PyMOL software. Results: In total, we identified 82 drug-disease targets, primarily associated with lipid and atherosclerosis, diabetic cardiomyopathy, MAPK signaling pathway, and PI3K-Akt signaling pathway. Our PPI network analysis and docking studies demonstrated good binding ability of finerenone to specific targets such as AKT1, MMP-9, IGF1, EGFR, CASP3, PPARG, ESR1, MMP-2, and KDR. Conclusion: Finerenone has the potential to reduce the progression of DN through various pathways, including lipid and atherosclerosis, diabetic cardiomyopathy, MAPK signaling pathway, and PI3KAkt signaling pathway. Moreover, it could exert anti-inflammatory and antifibrotic effects on specific targets, such as AKT1, MMP-9, IGF1, EGFR, CASP3, PPARG, ESR1, MMP-2, and KDR.

Proteomic profiling of oral squamous cell carcinoma tissues reveals altered immune-related proteins: implications for personalized therapy

ABSTRACT Objectives Oral squamous cell carcinoma poses a substantial global health challenge marked by rising mortality rate. Recently, immunotherapy has shown promising results in cancer management by enhancing immune response. Thus, identifying additional immune-related markers is critical for advancing immunotherapy treatments. Methods Data-independent acquisition (DIA) mass spectrometry approach was used to explore differentially expressed immune-related proteins in oral cancer tissues compared to adjacent non-cancerous tissues. Functional significance was identified through Gene Ontology, pathway, and network analysis. Gene expression of identified proteins was validated using transcriptomic data. Results DIA analysis identified 29,459 precursors corresponding to 3429 proteins. Among these, 1060 proteins were differentially expressed, with 166 being immune-related. Differentially regulated proteins were involved in innate immune response, mitochondrial ATP synthesis, and neutrophil degranulation. Pathway analysis of immune-related proteins showed perturbation in anti-tumor immunity-related pathways such as interferon signaling, TCR signaling, PD-1 signaling, and antigen processing and presentation. Significance of these pathways was further reinforced by the strong interactions identified in the protein–protein interaction network analysis. Additionally, gene expression analysis showed similar mRNA expression patterns for key proteins involved in altered pathways, including ISG15, IFIT1/3, HLA-A/C and OAS2/3. Conclusions Further validation of these proteins could establish them as potential targets for personalized therapy.

Proteomic identification of potential biomarkers for heat tolerance in Caracu beef cattle using high and low thermotolerant groups.

Heat stress has deleterious effects on physiological and performance traits in livestock. Within this context, using tropically adapted cattle breeds in pure herds or terminal crossbreeding schemes to explore heterosis is attractive for increasing animal production in warmer climate regions. This study aimed to identify biological processes, pathways, and potential biomarkers related to thermotolerance in Caracu, a tropically adapted beef cattle breed, by proteomic analysis of blood plasma. To achieve this goal, 61 bulls had their thermotolerance evaluated through a heat tolerance index. A subset of 14 extreme animals, including the seven most thermotolerant (HIGH group) and the seven least thermotolerant (LOW group), had their blood plasma samples used for proteomic analysis by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The differentially regulated proteins detected between HIGH and LOW groups were used to perform functional enrichment analysis and a protein-protein interaction network analysis. A total of 217 proteins were detected only in the HIGH thermotolerant group and 51 only in the LOW thermotolerant group. In addition, 81 and 87 proteins had significantly higher and lower abundancies in the HIGH group, respectively. Regarding proteins with the highest absolute log-fold change values, we highlighted those encoded by DUSP5, IGFALS, ROCK2, RTN4, IRAG1, and NNT genes based on their functions. The functional enrichment analysis detected several biological processes, molecular functions, and pathways related to cellular responses to stress, immune system, complement system, and hemostasis in both HIGH and LOW groups, in addition to terms and pathways related to lipids and calcium only in the HIGH group. Protein-protein interaction (PPI) network revealed as important nodes many proteins with roles in response to stress, hemostasis, immune system, inflammation, and homeostasis. Additionally, proteins with high absolute log-fold change values and proteins detected as essential nodes by PPI analysis highlighted herein are potential biomarkers for thermotolerance, such as ADRA1A, APOA1, APOB, APOC3, C4BPA, CAT, CFB, CFH, CLU, CXADR, DNAJB1, DNAJC13, DUSP5, FGA, FGB, FGG, HBA, HBB, HP, HSPD1, IGFALS, IRAG1, KNG1, NNT, OSGIN1, PROC, PROS1, ROCK2, RTN4, RYR1, TGFB2, VLDLR, VTN, and VWF. Identifying potential biomarkers, molecular mechanisms and pathways that act in response to heat stress in tropically adapted beef cattle contributes to developing strategies to improve performance and welfare traits in livestock under tropical climates.

Genome-wide identification and biochemical characterization of glycoside hydrolase gene family members in Tilletia Horrida.

Rice kernel smut, caused by Tilletia horrida, is becoming an increasingly serious disease in hybrid rice planting, leading to production losses and quality decline of male-sterile rice varieties. Successful infection requires an efficient energy source that the pathogen obtains from rice plants, such as carbohydrates. Glycoside hydrolases (GHs), one of the largest sub-families in the cell wall-degrading enzyme family, play a key role in the infection progress of pathogens. To investigate their roles in facilitating infection, in this study, we identified and characterized genes encoding GH family proteins of T. horrida and further explored the functions and structures of these genes. Through genome-wide sequencing and bioinformatics analyses, 52 GH genes were identified from T. horrida, named ThGhd_1 to ThGhd_52. The subcellular location, conserved motifs, and structures of ThGhds were identified by bioinformatics analyses. Phylogenetic analysis revealed that ThGhds with similar domains clustered together, although some proteins clustered in different branches, which might reflect functional diversity. Protein-protein interaction network analysis revealed that ThGhds interact with partner proteins involved in reactive oxygen species signaling, protein kinase activity, and plant hormone signal transduction pathways. RNA-sequencing analysis showed that the expression of ThGhd genes responded differently at different infection time points, with dynamic changes detected during the T. horrida infection process, indicating that these genes are involved in interactions with rice and have potential roles in pathogenic mechanisms. The results of this study provide valuable resources for the structure elucidation of GH family proteins of T. horrida and can help to further elucidate their roles in pathogenesis.

Identification of immune-associated genes for the diagnosis of ulcerative colitis-associated carcinogenesis via integrated bioinformatics analysis

BackgroundUC patients suffer more from colorectal cancer (CRC) than the general population, which increases with disease duration. Early colonoscopy is difficult because ulcerative colitis-associated colorectal cancer (UCAC) lesions are flat and multifocal. Our study aimed to identify promising UCAC biomarkers that are complementary endoscopy strategies in the early stages.MethodsThe datasets may be accessed from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The co-expressed modules of UC and CRC were determined via weighted co-expression network analysis (WGCNA). The biological mechanisms of the shared genes were exported for analysis using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. To identify protein interactions and hub genes, a protein-protein interaction network and CytoHubba analysis were conducted. To evaluate gene expression, external datasets and experimental validation of human colon tissues were utilized. The diagnostic value of core genes was examined through receiver operating characteristic (ROC) curves. Immune infiltration analysis was employed to investigate the associations between immune cell populations and hub genes.ResultsThree crucial modules were identified from the WGCNA of UC and CRC tissues, and 33 coexpressed genes that were predominantly enriched in the NF-κB pathway were identified. Two biomarkers (CXCL1 and BCL6) were identified via Cytoscape and validated in external datasets and human colon tissues. CRC patients expressed CXCL1 at the highest level, whereas UC and CRC patients showed higher levels than the controls. The UC cohort expressed BCL6 at the highest level, whereas the UC and CRC cohorts expressed it more highly than the controls. The hub genes exhibited significant diagnostic potential (ROC curve > 0.7). The immune infiltration results revealed a correlation among the hub genes and macrophages, neutrophils and B cells.ConclusionsThe findings of our research suggest that BCL6 and CXCL1 could serve as effective biomarkers for UCAC surveillance. Additionally, they demonstrated a robust correlation with immune cell populations within the CRC tumour microenvironment (TME). Our findings provide a valuable insight about diagnosis and therapy of UCAC.

Role of SIRPG gene in type 1 diabetes and lichen planus.

Type 1 diabetes (T1D) is a form of diabetes caused by pancreatic β-cell destruction and absolute insulin deficiency. Lichen planus (LP) is an idiopathic inflammatory skin disease of unclear etiology. The role of SIRPG gene dysregulation in T1D and LP remains unclear. Mendelian randomization (MR) using matched samples was employed to study causal relationship between T1D and increased risk of LP. T1D-related single nucleotide polymorphism identification was conducted. Datasets GSE156035 for T1D and GSE52130 for LP were obtained from gene expression omnibus. Differentially expressed genes were identified, analyses included weighted gene co-expression network analysis, functional enrichment, gene set enrichment analysis, and protein-protein interaction network construction and analysis. Heatmaps of gene expression levels were generated. Comparative toxicogenomics database was used to identify diseases most relevant to core genes. Inverse variance weighted, MR-Egger, weighted median methods estimated genetic predisposition between T1D and LP, showing consistent positive correlations using both weighted median and inverse variance weighted methods. Horizontal pleiotropy analysis with MR-Egger intercept indicated no evidence of significant directional pleiotropy (P = .70645) for LP. There was no evidence of directional pleiotropy effects between T1D and LP. One hundred eighteen differentially expressed genes were identified. In biological processes, they were mainly enriched in apoptosis, inflammatory response, insulin receptor signaling pathway, glucose metabolism. In cellular components, enrichment was observed in mediator complex and replication fork. In molecular function, they were concentrated in leukotriene receptor activity and helicase activity. Kyoto Encyclopedia of Genes and Genomes analysis revealed enrichment in metabolic pathways, PI3K-Akt signaling pathway, cell cycle, p53 signaling pathway, AGE-RAGE signaling pathway in diabetic complications. Weighted gene co-expression network analysis with a soft threshold power of 4. SIRPG showing high expression in both T1D and LP samples. There is a positive causal relationship between T1D and LP. Comparative toxicogenomics database analysis revealed associations of core genes with metabolic syndrome, lipid metabolism disorders, cardiovascular diseases, immune system diseases, peripheral neuropathic pain, and inflammation. SIRPG is highly expressed in both T1D and LP, providing a new insight into the pathogenesis of T1D and LP.

Molecular mechanisms of ferroptosis in renal ischemia-reperfusion injury Investigated via bioinformatics analysis and animal experiments.

Kidney transplantation is a pivotal treatment for end-stage renal disease. However, renal ischemia-reperfusion injury (IRI) during surgery significantly impacts graft function. Despite unclear molecular mechanisms, no specific therapies or preventative measures are available. Gene expression profiles from renal biopsies before and after IRI were downloaded from public databases. Differentially expressed genes were identified using the Wilcoxon rank-sum test and weighted gene co-expression network analysis. Ferroptosis-associated genes were screened using the FerrDb database. The genes with the highest connectivity were identified via the protein-protein interaction (PPI) network and upstream regulatory miRNAs were found through the gene-miRNA network. A mouse renal IRI model was constructed for transcriptome sequencing and quantitative real-time polymerase chain reaction (qRT-PCR) validation to elucidate the relationship between key ferroptosis genes and regulatory miRNAs in renal IRI. Differential analysis identified 15 ferroptosis-associated genes (TNFAIP3, IL6, KLF2, EGR1, JUN, ZFP36, GDF15, CDKN1A, HSPB1, BRD2, PDK4, DUSP1, SLC2A3, DDIT3, and CXCL2) involved in renal IRI regulation. In animal experiments, ferroptosis-related genes were also upregulated in the model group. Enrichment analysis and hematoxylin-eosin pathological staining suggested these genes are primarily involved in renal inflammatory responses. PPI network analysis revealed IL6 as the gene with the highest connectivity, and the gene-miRNA network indicated IL6 might be regulated by miR-let-7a. Animal experiments revealed decreased miR-let-7a and increased IL6 levels in the model group, identifying potential therapeutic targets. MiR-let-7a regulates ferroptosis in renal IRI by targeting IL6, highlighting IL6 as a crucial gene in the ferroptosis process of renal IRI.

Paeoniflorin-mediated downregulation of VEGFA: unveiling the therapeutic mechanism of buyang huanwu decoction in diabetic retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness globally. Buyang Huanwu decoction (BHD) is a traditional Chinese medicine for treating DR, but its therapeutic mechanisms are not fully understood. This study aimed to elucidate and validate the underlying mechanisms of BHD in DR treatment through network pharmacology and in vitro experiments. We identified active compounds in BHD and their associated targets using the TCMSP and SwissTargetPrediction. DR-related targets were sourced from GeneCards, NCBI, and OMIM databases. The protein-protein interaction (PPI) network and enrichment analyses were employed to predict common targets and pathways. Subsequent molecular docking and in vitro experiments, including cell viability assays, RT-qPCR, flow cytometry, and Western blot, were conducted to validate the anti-DR mechanism of BHD. Network pharmacology identified paeoniflorin as a key active compound in BHD for treating DR, with VEGFA emerging as a central target. Molecular docking suggested a strong binding affinity between paeoniflorin and VEGFA. In vitro experiments confirmed that paeoniflorin attenuated high glucose-induced increases in cell viability, migration, apoptosis, and inflammatory cytokine expression in retinal pigment epithelial cells. The therapeutic effect of paeoniflorin was primarily mediated through the downregulation of VEGFA expression. Our study demonstrates that paeoniflorin, a key active compound in BHD, effectively mitigates DR by downregulating VEGFA expression and reducing high glucose-induced cellular alterations, thereby highlighting its potential as a therapeutic agent for DR.

Transcriptomics analysis of differential gene expression and immune and inflammatory response mechanisms in patients with typical and non-criteria obstetric antiphospholipid syndrome (OAPS and NC-OAPS)

In this study, we investigated the molecular differences between patients with typical obstetric antiphospholipid syndrome (OAPS) and patients with non-criteria obstetric antiphospholipid syndrome (NC-OAPS) patients through transcriptome sequencing of peripheral blood samples from ten OAPS patients and ten NC-OAPS patients. Differentially expressed genes (DEGs) were identified, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, protein-protein interaction (PPI) analysis, and competitive endogenous RNA (ceRNA) network construction to identify hub genes. Verification was performed via Quantitative Real-time PCR (qPCR) in OAPS (n=9) and NC-OAPS (n=12) samples. We identified 240 DEGs in two groups. GO and KEGG analyses reviewed upregulated in pathways related to the inflammatory response; immune response; antigen processing and presentation; Th1, Th2, and Th17 cell differentiation; and NK cell-mediated cytotoxicity in OAPS patients. PPI and ceRNA network analyses identified key genes, with significant upregulation of CXCR2, JAK2, and MPO found in the OAPS group, which correlated with severe inflammation, JAK-STAT pathway activation, and increased NET activity in neutrophils. Other genes such as CD4, IL2RB, and NKG7, are involved in T-cell and NK cell regulation. Our results indicate enhanced inflammatory and immune responses in OAPS patients, suggesting more severe immune activity than in NC-OAPS patients, providing a basis for precise diagnostic and therapeutic strategies.

Trem2/Tyrobp Signaling Protects Against Aortic Dissection and Rupture by Inhibiting Macrophage Activation in Mice.

The development of aortic dissection (AD) is closely associated with inflammation. The Trem2 (triggering receptor expressed on myeloid cells 2)/Tyrobp (TYRO protein tyrosine kinase-binding protein) signaling pathway critically regulates innate immunity and has emerged as an important target in cardiovascular diseases; however, its role in AD remains unclear. Transcriptome data from human and mouse ADs were used to perform differentially expressed gene-based protein-protein interaction network analyses. Tyrobp knockout (Tyrobp-/-), myeloid cell-specific Tyrobp-/- (Tyrobpfl/fl Lyz2cre), and Trem2 knockout (Trem2-/-) mice were given β-aminopropionitrile monofumarate in drinking water to induce AD. To dissect the role of macrophages in Tyrobp deficiency-mediated AD progression, macrophages were depleted using clodronate liposomes. Bulk and single-cell RNA sequencing, immunofluorescence staining, and quantitative real-time polymerase chain reaction were performed to assess inflammation and the underlying mechanisms of Tyrobp in AD. Network analysis identified Tyrobp as a hub gene of AD, with elevated levels observed in both human and mouse ADs. Global deletion and myeloid cell-specific deficiency of Tyrobp in mice significantly increased AD incidence and exacerbated extracellular matrix degradation and macrophage infiltration within the aortic wall. Macrophage depletion mitigated the adverse effects of Tyrobp deficiency on AD progression. Additionally, Tyrobp deficiency enhanced TLR (Toll-like receptor)-4 signaling and macrophage activation, which were abrogated by TLR4 inhibitors. Furthermore, deletion of the Tyrobp-associated receptor Trem2 significantly aggravated mouse AD development, whereas Trem2 agonist treatment conferred protection against AD. Our findings suggest a novel role for the Trem2/Tyrobp axis in AD development in mice. Enhancement of Trem2/Tyrobp signaling may represent a promising strategy for the prevention and treatment of AD. Future studies to clarify the role of Trem2/Tyrobp in human AD are warranted.

Yigu decoction regulates plasma miRNA in postmenopausal osteoporosis patients: a randomized controlled trial

BackgroundPostmenopausal osteoporosis (PMOP) is a serious condition that affects elderly individuals. Our previous study revealed that Yigu decoction (YGD) effectively improved bone mineral density (BMD) in elderly individuals, but the mechanism underlying this effect remains unclear. In this study, we investigated the relationships among YGD, microRNAs (miRNAs), and bone metabolism by assessing the effects of YGD on the miRNA levels in patient plasma to provide a scientific basis for treating PMOP with YGD.MethodsIn this clinical trial, 60 patients were randomly assigned to the YGD group or the control group (ratio of 1:1) and treated for 3 months. The primary outcome measure was BMD, and the secondary outcome measures included plasma miRNA levels, visual analogue scale (VAS) scores, alkaline phosphatase (ALP) levels, anti-tartrate acid phosphatase (TRACP-5b) levels and traditional Chinese medicine (TCM) syndrome scores. We assessed the regulatory roles of miRNAs in PMOP patients by analysing publicly available data from the Gene Expression Omnibus (GEO) database. Bioinformatics methods were also used to explore the mechanism by which YGD regulates miRNAs that are involved in bone metabolism.ResultsCompared with those before treatment, the BMD, ALP levels, TRACP-5b levels, TCM syndrome scores and VAS scores improved in both groups after 3 months of treatment (P < 0.05). A total of 82 miRNAs differed between the groups. After analysing data from the GEO database, we confirmed that miR-133a-3p is the key molecule that mediates the effects of YGD intervention on PMOP. GO analysis of key genes suggested that gene enrichment was more pronounced in response to hormones, cellular response to growth factor stimulation, and positive regulation of physiological and metabolic processes. KEGG analysis revealed that these genes were enriched mainly in the PI3K-Akt, FOXO, and JAK-STAT pathways and other pathways. The results of the protein‒protein interaction (PPI) network analysis revealed that epidermal growth factor receptor (EGFR), Insulin-like growth factor 1 (IGF-1), Caveolin-1 (Cav-1) and others were core proteins.ConclusionThis study demonstrated that YGD is beneficial in the treatment of PMOP, ameliorating clinical symptoms and bone turnover indices. Moreover, the inhibition of miR-133a-3p expression may be the key mechanisms by which YGD regulates bone metabolism in the treatment of PMOP, although YGD regulates bone metabolism in a multitarget and multipathway manner.

Study on the Mechanism of Raspberry (Rubi fructus)in Treating Type 2 Diabetes Based on UPLC-Q-Exactive Orbitrap MS, Network Pharmacology, and Experimental Validation.

The aim of this study is to analyze the chemical composition of raspberry using liquid chromatography-mass spectrometry (LC-MS) technology, predict the potential effects of raspberry in treating type 2 diabetes through network pharmacology, and conduct preliminary validation through invitro experiments. A Waters CORTECS C18 column (3.0 mm × 100 mm, 2.7 μm) was used; mobile phase A consisted of 0.1% formic acid in water and mobile phase B consisted of 0.1% formic acid in acetonitrile. Gradient elution was performed with full-scan mode in both positive and negative ion modes, covering a mass range of m/z 100-1500. The chemical components of raspberry were analyzed and identified based on secondary spectra from databases and relevant literature. The disease targets related to type 2 diabetes were searched, and protein-protein interaction network analysis as well as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted on the intersecting targets of the active components of raspberry and the disease. HepG2 cells were used for experimental validation, with high glucose-induced insulin resistance models established. The CCK-8 method was employed to assess the effects of raspberry on cell proliferation, while Western blotting was used to measure the expression of proteins related to the AGE/RAGE signaling pathway. A total of 47 components were identified, including 10 organic acids, 15 flavonoids, 12 phenols, 2 alkaloids, 4 terpenoids, 1 miscellaneous compound, 1 stilbene, 1 steroid and its derivatives, and 1 diterpenoid. Through database screening, seven active components were identified: kaempferol, epicatechin, ellagic acid, crocetin, stigmasterol, fisetin, and isorhamnetin. KEGG and GO results indicated that the therapeutic effects of raspberry on type 2 diabetes may be related to the advanced glycation end product (AGE)- receptor for advanced glycation end product (RAGE) signaling pathway. Establishment of an insulin resistance model in HepG2 cells demonstrated that, compared to the control group, the raspberry treatment group upregulated p53 protein expression while downregulating the expression of RAGE, Akt1, and Caspase-3 proteins. This study preliminarily elucidates that the therapeutic effects of raspberry in treating type 2 diabetes may be mediated through the inhibition of the AGE-RAGE signaling pathway, providing important references for the study of the pharmacological basis and clinical application of raspberry.

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes—MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5—were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies.

Overlapping Transcriptome Alterations Reveal the Mechanism of Interaction between Selenium and Zinc and Their Common Effect on Essential Nutrient Metabolism in Mung Bean (Vigna radiata L.).

Selenium (Se) and zinc (Zn) deficiencies have become serious global food security and public health problems. Biofortification through foliar fertilizer is a nonspecific, low-tech, and cost-effective strategy. Se and Zn have overlapping physiological roles and interacting relationships in plants. Mung bean is superior for Se enrichment and an excellent Zn carrier. However, the molecular mechanism underlying the interaction between Se and Zn in the mung bean remains unclear. Herein, Se and Zn accumulation, antioxidant activities, physiological determination, and transcriptomic analysis were performed under both Se and Zn treatments. Common essential roles of Se and Zn in mung bean were reflected by the comprehensively altered ten physiological indexes under both Se2 (24 g·ha-1) and Zn1 (1.2 kg·ha-1) treatments. Overlapping transcriptome changes and common DEGs in two compared groups revealed that the upregulated expression of sulfate transporters (SULTRs), phosphate transporters (PHTs), and Zinc-regulated/Iron-regulated-like protein (ZIP) family genes under Se and Zn treatments directly promoted both Se and Zn intakes. Furthermore, the altered Se/Sulfur, nitrogen, and carbohydrate metabolisms are closely interlinked with the uptake and assimilation of Se and Zn via the 20 key genes that we filtered through the protein-protein interaction (PPI) network analysis. Further analysis indicated that l-methionine γ-lyase (E 4.4.1.11) genes may play an important role in the transamination of selenomethionine and its derivatives; glutamine synthetase (GS), nitrate reductase (NR), and starch synthase (SS) genes may regulate the nitrogen assimilation and carbohydrate metabolism, which provide more carriers for Se and Zn; glutathione peroxidase (GPx), glutamate-cysteine ligase catalytic subunit (GCLC), and serine acetyltransferase (SAT) genes may accelerate the GSH-GSSH cycle and promote Se and Zn storages. This study provides new molecular insights into the comprehensive improvement of the nutritional quality of mung beans in Se and Zn biofortification productions.