Protein-protein Interaction Network Analysis Research Articles

Genome-Wide Characterization and Expression Analysis of CsPALs in Cucumber (Cucumis sativus L.) Reveal Their Potential Roles in Abiotic Stress and Aphid Stress Tolerance

Phenylalanine ammonia lyase (PAL) is a pivotal enzyme in the phenylalanine metabolic pathway in plants and has a crucial role in the plant’s response to environmental stress. Although the PAL family has been widely studied in many plant species, limited is known about its particular role in cucumbers under stress. We investigated the physicochemical properties, gene structure, gene duplication events, conserved motifs, cis-acting elements, protein interaction networks, stress-related transcriptome data, and quantitatively validated key stress-related genes. The main results indicated that 15 PAL genes were grouped into four clades: I, II, and III when arranged in a phylogenetic tree of PAL genes in angiosperms. The analysis of the promoter sequence revealed the presence of multiple cis-acting elements related to hormones and stress responses in the cucumber PAL genes (CsPALs). The analysis of protein interaction networks suggested that CsPAL1 interacts with eight other members of the PAL family through CsELI5 and CsHISNA, and directly interacts with multiple proteins in the 4CL family. Further investigation into the expression patterns of CsPAL genes in different tissues and under various stress treatments (NaCl, Cu2+, Zn2+, PEG6000, aphids) demonstrated significant differential expression of CsPALs across cucumber tissues. In summary, our characterization of the CsPAL family offers valuable insights and provides important clues regarding the molecular mechanisms of CsPALs in managing abiotic and biotic stress interactions in cucumbers.

The Vital Role of the CAMTA Gene Family in Phoebe bournei in Response to Drought, Heat, and Light Stress

The calmodulin-binding transcriptional activator (CAMTA) is a small, conserved gene family in plants that plays a crucial role in regulating growth, development, and responses to various abiotic stress. Given the significance of the CAMTA gene family, various studies have been dedicated to uncovering its functional characteristics. In this study, genome-wide identification and bioinformatics analysis were conducted to explore CAMTAs in Phoebe bournei. A total of 17 CAMTA genes, each containing at least one domain from CG-1, TIG, ANK, or IQ, were identified in the P. bournei genome. The diversity of PbCAMTAs could be varied depending on their subcellular localization. An analysis of protein motifs, domains, and gene structure revealed that members within the same subgroup exhibited similar organization, supporting the results of the phylogenetic analysis. Gene duplications occurred among members of the PbCAMTA gene family. According to the cis-regulatory element prediction and protein–protein interaction network analysis, eight genes were subjected to qRT-PCR under drought, heat, and light stresses. The expression profiles indicated that PbCAMTAs, particularly PbCAMTA2, PbCAMTA12, and PbCAMTA16, were induced by abiotic stress. This study provides profound insights into the functions of CAMTAs in P. bournei.

Key Disease-Related Genes and Immune Cell Infiltration Landscape in Inflammatory Bowel Disease: A Bioinformatics Investigation.

Inflammatory Bowel Diseases (IBD), which encompass ulcerative colitis (UC) and Crohn's disease (CD), are characterized by chronic inflammation and tissue damage of the gastrointestinal tract. This study aimed to uncover novel disease-gene signatures, dysregulated pathways, and the immune cell infiltration landscape of inflamed tissues. Eight publicly available transcriptomic datasets, including inflamed and non-inflamed tissues from CD and UC patients were analyzed. Common differentially expressed genes (DEGs) were identified through meta-analysis, revealing 180 DEGs. DEGs were implicated in leukocyte transendothelial migration, PI3K-Akt, chemokine, NOD-like receptors, TNF signaling pathways, and pathways in cancer. Protein-protein interaction network and cluster analysis identified 14 central IBD players, which were validated using eight external datasets. Disease module construction using the NeDRex platform identified nine out of 14 disease-associated genes (CYBB, RAC2, GNAI2, ITGA4, CYBA, NCF4, CPT1A, NCF2, and PCK1). Immune infiltration profile assessment revealed a significantly higher degree of infiltration of neutrophils, activated dendritic cells, plasma cells, mast cells (resting/activated), B cells (memory/naïve), regulatory T cells, and M0 and M1 macrophages in inflamed IBD tissue. Collectively, this study identified the immune infiltration profile and nine disease-associated genes as potential modulators of IBD pathogenesis, offering insights into disease molecular mechanisms, and highlighting potential disease modulators and immune cell dynamics.

Lipoteichoic Acid from Heyndrickxia coagulans HOM5301 Modulates the Immune Response of RAW 264.7 Macrophages.

Heyndrickxia coagulans (formerly Bacillus coagulans) has been increasingly utilized as an immunomodulatory probiotics. Oral administration of H. coagulans HOM5301 significantly boosted both innate and adaptive immunity in mice, particularly by increasing the phagocytic capacity of monocytes/macrophages. Lipoteichoic acid (LTA), a major microbe-associated molecular pattern (MAMP) in Gram-positive bacteria, exhibits differential immunomodulatory effects due to its structural heterogeneity. We extracted, purified, and characterized LTA from H. coagulans HOM5301. The results showed that HOM5301 LTA consists of a glycerophosphate backbone. Its molecular weight is in the range of 10-16 kDa. HOM5301 LTA induced greater productions of nitric oxide, TNFα, and IL-6 in RAW 264.7 macrophages compared to Staphylococcus aureus LTA. Comparative transcriptome and proteome analyses identified the differentially expressed genes and proteins triggered by HOM5301 LTA. KEGG analyses revealed that HOM5301 LTA transcriptionally and translationally activated macrophages through two immune-related pathways: cytokine-cytokine receptor interaction and phagosome formation. Protein-protein interaction network analysis indicated that the pro-inflammatory response elicited by HOM5301 LTA was TLR2-dependent, possibly requiring the coreceptor CD14, and is mediated via the MAPK and NF-kappaB pathways. Our results demonstrate that LTA is an important MAMP of H. coagulans HOM5301 that boosts immune responses, suggesting that HOM5301 LTA may be a promising immunoadjuvant.

Identification of HDAC10 as a candidate oncogene in clear cell renal carcinoma that facilitates tumor proliferation and metastasis

BackgroundClear cell renal cell carcinoma (ccRCC) remains one of the most lethal urological malignancies even though a great number of improvements in diagnosis and management have achieved over the past few decades. Accumulated evidence revealed that histone deacetylases (HDACs) play vital role in cell proliferation, differentiation and apoptosis. Nevertheless, the biological functions of histone deacetylation modification related genes in ccRCC remains poorly understood.MethodBulk transcriptomic data and clinical information of ccRCC patients were obtained from the TCGA database and collected from the Chinese PLA General Hospital. A total of 36 histone deacetylation genes were selected and studied in our research. Univariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, random forest (RF) analysis, and protein-protein interaction (PPI) network analysis were applied to identify key genes affecting the prognosis of ccRCC. The ‘oncoPredict’ algorithm was utilized for drug-sensitive analysis. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to explore the potential biological function. The ssGSEA algorithm was used for tumor immune microenvironment analysis. The expression levels of HDAC10 were validated by RT-PCR and immunohistochemistry (IHC). 5-ethynyl-2′-deoxyuridine (EdU assay), CCK-8 assay, cell transwell migration and invasion assay and colony formation assay were performed to detect the proliferation and invasion ability of ccRCC cells. A nomogram incorporating HDAC10 and clinicopathological characteristics was established to predict the prognosis of ccRCC patients.ResultTwo machine learning algorithms and PPI analysis identified four histone deacetylation genes that have a significant association with the prognosis of ccRCC, with HDAC10 being the key gene among them. HDAC10 is highly expressed in ccRCC and its high expression is associated with poor prognosis for ccRCC patients. Pathway enrichment and the experiments of EdU staining, CCK-8 assay, cell transwell migration and invasion assay and colony formation assay demonstrated that HDAC10 mediated the proliferation and metastasis of ccRCC cells and involved in reshaping the tumor microenvironment (TME) of ccRCC. A clinically reliable prognostic predictive model was established by incorporating HDAC10 and other clinicopathological characteristics ( https://nomogramhdac10.shinyapps.io/HDAC10_Nomogram/ ).ConclusionOur study found the increased expression of HDAC10 was closely associated with poor prognosis of ccRCC patients. HDAC10 showed a pro-tumorigenic effect on ccRCC and promote the proliferation and metastasis of ccRCC, which may provide new light on targeted therapy for ccRCC.

Transcriptomic landscape of quiescent and proliferating human corneal stromal fibroblasts

This study analyzed the transcriptional changes in primary human corneal stromal fibroblasts (hCSFs) grown under quiescent (serum-free) and proliferating (serum-supplemented) culture conditions to identify genes, pathways, and protein‒protein interaction networks influencing corneal repair and regeneration. Primary hCSFs were isolated from donor human corneas and maintained in serum-free or serum-laden conditions. RNA was extracted from confluent cultures using Qiagen kit and subjected to RNA sequencing (RNAseq) analysis. Differential gene expression (DGE) and pathway enrichment analyses were conducted using DESeq2 and Gene Set Enrichment Analysis (GSEA), respectively. Protein‒protein interaction (PPI) networks were created exploiting the STRING database and analyzed with Cytoscape and the cytoHubba plugin. RNA-seq revealed 5,181 genes that were significantly differentially expressed/changed among the 18,812 annotated genes (p value ˂0.05). A cutoff value of a log2-fold change of ±1.5 or greater was used to identify 674 significantly upregulated and 771 downregulated genes between quiescent and proliferating hCSFs. Pathway enrichment analysis revealed significant changes in genes linked to cell cycle regulation, inflammatory, and oxidative stress response pathways, such as E2F Targets, G2M Checkpoint, and MYC Targets, TNFA signaling via NF-kB, and oxidative phosphorylation. Protein-protein interaction network analysis highlighted critical hub genes. The FGF22, CD34, ASPN, DPT, LUM, FGF10, PDGFRB, ECM2, DCN, VEGFD, OMD, OGN, ANGPT1, CDH5, and PRELP were upregulated, whereas genes linked to cell cycle regulation and mitotic progression, such as BUB1, TTK, KIF23, KIF11, BUB1B, DLGAP5, NUSAP1, CCNA2, CCNB1, BIRC5, CDK1, KIF20A, AURKB, KIF2C, and CDCA8, were downregulated. The RNA sequences and gene count files have been submitted to the Gene Expression Omnibus (accession # GSE260476). Our study provides a comprehensive information on the transcriptional and molecular changes in hCSFs under quiescent and proliferative conditions and highlights key pathways and hub genes.

A protein risk score for all-cause and respiratory-specific mortality in non-Hispanic white and African American individuals who smoke

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31–2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein–protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

Construction of a NETosis-related gene signature for predicting the prognostic status of sepsis patients

BackgroundSepsis is a common traumatic complication of response disorder of the body to infection. Some studies have found that NETosis may be associated with the progression of sepsis. MethodsData of the sepsis samples were acquired from Gene Expression Omnibus (GEO) database. Gene set enrichment score was calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks analysis, and stepwise multivariable regression analysis were performed to identify NETosis-associated genes for sepsis prognosis. To assess the infiltration of immune cells, the ESTIMATE and CIBERPSORT algorithms were used. Functional enrichment analysis was conducted in the clusterProfiler package. ResultsDifferent programmed death pathways were abnormally activated in sepsis patients as compared to normal samples. We screened five important NETosis associated genes, namely, CEACAM8, PGLYRP1, MAPK14, S100A12, and LCN2. These genes were significantly positively correlated with entotic cell death and ferroptosis and negatively correlated with autophagy. A clinical prognostic model based on riskscore was established using the five genes. The ROC curves of the model at 7 days, 14 days and 21 days all had high AUC values, indicating a strong stability of the model. Patients with high riskscore had lower survival rate than those with low riskscore. After the development of a nomogram, calibration curve and decision curve evaluation also showed a strong prediction performance and reliability of the model. As for clinicopathological features, older patients and female patients had a relatively high riskscore. The riskscore was significantly positively correlated with cell cycle-related pathways and significantly negatively correlated with inflammatory pathways. ConclusionWe screened five NETosis-associated genes that affected sepsis prognosis, and then established a riskscore model that can accurately evaluate the prognosis and survival for sepsis patients. Our research may be helpful for the diagnosis and clinical treatment of sepsis.

Network pharmacology-based mechanism analysis of dauricine on the alleviating Aβ-induced neurotoxicity in Caenorhabditis elegans

BackgroundDauricine (DAU), a benzyl tetrahydroisoquinoline alkaloid isolated from the root of Menispermum dauricum DC, exhibits promising anti-Alzheimer’s disease (AD) effects, but its underlying mechanisms remain inadequately investigated. This paper aims to identify potential targets and molecular mechanisms of DAU in AD treatment.MethodsNetwork pharmacology and molecular docking simulation method were used to screen and focus core targets. Various transgenic Caenorhabditis elegans models were chosen to validate the anti-AD efficacy and mechanism of DAU.ResultsThere are 66 potential DAU-AD target intersections identified from 100 DAU and 3036 AD-related targets. Subsequent protein-protein interaction (PPI) network analysis identified 16 core targets of DAU for anti-AD. PIK3CA, AKT1 and mTOR were predicted to be the central targets with the best connectivity through the analysis of “compound-target-biological process-pathway network”. Molecular docking revealed strong binding affinities between DAU and PIK3CA, AKT1, and mTOR. In vivo experiments demonstrated that DAU effectively reduced paralysis in AD nematodes caused by Aβ aggregation toxicity, downregulated expression of PIK3CA, AKT1, and mTOR homologues (age-1, akt-1, let-363), and upregulated expression of autophagy genes and the marker protein LGG-1. Simultaneously, DAU increased lysosomal content and enhanced degradation of the autophagy-related substrate protein P62. Thioflavin T（Th-T）staining experiment revealed that DAU decreased Aβ accumulation in AD nematodes. Further experiments also confirmed DAU’s protein scavenging activity in polyglutamine (polyQ) aggregation nematodes.ConclusionCollectively, the mechanism of DAU against AD may be related to the activation of the autophagy-lysosomal protein clearance pathway, which contributes to the decrease of Aβ aggregation and the restoration of protein homeostasis.

Essential cancer protein identification using graph-based random walk with restart

Protein-protein interaction (PPI) network analysis holds significant promise for cancer diagnosis and drug target identification. This paper introduces a novel random walk-based method called essential cancer protein identification using graph-based random walk with restart (EPI-GBRWR) to address this gap. This proposed method incorporates local and global topological features of proteins, enhancing the accuracy of essential protein identification in PPI networks. Starting with meticulous preprocessing of cancer gene datasets from NCBI, including breast, lung, colorectal, and ovarian cancers, and identifying a core set of common genes. The proposed method constructs PPI networks to capture complex protein interactions from these common cancer genes. Topological analysis, including a centrality measures matrix, is generated to perform the analysis to identify essential nodes. The study revealed that 40 essential proteins among breast, colorectal, lung and ovarian cancer showcase the potency of integrative methodologies in unravelling cancer complexity, signalling a transformative era in cancer research and treatment. The strength of the findings from the study has direct clinical relevance in cancer diseases. It contributes to the field of precision medicine to guide personalized treatment strategies.

Neuregulin1 nuclear signaling influences adult neurogenesis and regulates a schizophrenia susceptibility gene network within the mouse dentate gyrus.

Neuregulin1 (Nrg1) signaling is critical for aspects of neuronal development and function from fate specification to synaptic plasticity. Type III Nrg1 is a synaptic protein which engages in bi-directional signaling with its receptor ErbB4. Forward signaling engages ErbB4 phosphorylation, whereas back signaling engages two known mechanisms: 1. local axonal PI3K-AKT signaling, and 2. cleavage by gamma secretase resulting in cytosolic release of the intracellular domain (ICD), which can traffic to the nucleus (Bao, Wolpowitz et al. 2003, Hancock, Canetta et al. 2008). To dissect the contribution of these alternate signaling strategies to neuronal development we generated a transgenic mouse with a missense mutation (V321L) in the Nrg1 transmembrane domain that disrupts nuclear back signaling with minimal effects on forward signaling or local back-signaling and was previously found to be associated with psychosis (Walss-Bass, Liu et al. 2006). We combined RNA sequencing, retroviral fate mapping of neural stem cells, behavioral analyses, and various network analyses of transcriptomic data to investigate the effect of disrupting Nrg1 nuclear back-signaling in the dentate gyrus (DG) of male and female mice.The V321L mutation impairs nuclear translocation of the Nrg1 ICD and alters gene expression in the DG. V321L mice show reduced stem cell proliferation, altered cell cycle dynamics, fate specification defects, and dendritic dysmorphogenesis. Orthologs of known schizophrenia (SCZ)-susceptibility genes were dysregulated in the V321L DG. These genes coordinated a larger network with other dysregulated genes. WGCNA and protein-interaction network analyses revealed striking similarity between DG transcriptomes of V321L mouse and humans with schizophrenia.Significance statement Synaptic contact is predicted to be a regulator of the generation of nuclear signaling by Nrg1. Here we show that a schizophrenia-associated mutation in Nrg1 disrupts its ability to communicate extracellular signals to the neuronal genome which results in altered expression of a gene network enriched for orthologs of schizophrenia-susceptibility genes. The striking overlap in functional and molecular alterations between a single rare homozygous missense mutation (V321L) and schizophrenia patient data (complex polygenic and environmental burden) underscores potential convergence of rare and common variants on the same cellular and molecular phenotypes. Furthermore, our data indicate that the evolutionarily conserved gene networks that form the basis for this risk are necessary for coordinating neurodevelopmental events in the DG.

Common molecular and pathophysiological underpinnings of delirium and Alzheimer’s disease: molecular signatures and therapeutic indications

BackgroundDelirium and Alzheimer's disease (AD) are common causes of cognitive dysfunction among older adults. These neurodegenerative diseases share a common and complex relationship, and can occur individually or concurrently, increasing the chance of permanent mental dysfunction. However, the common molecular pathophysiology, key proteomic biomarkers, and functional pathways are largely unknown, whereby delirium is superimposed on AD and dementia.MethodsWe employed an integrated bioinformatics and system biology analysis approach to decipher such common key proteomic signatures, pathophysiological links between delirium and AD by analyzing the gene expression data of AD-affected human brain samples and comparing them with delirium-associated proteins. The present study identified the common drug target hub-proteins examining the protein–protein interaction (PPI) and gene regulatory network analysis. The functional enrichment and pathway analysis was conducted to reveal the common pathophysiological relationship. Finally, the molecular docking and dynamic simulation was used to computationally identify and validate the potential drug target and repurposable drugs for delirium and AD.ResultsWe detected 99 shared differentially expressed genes (sDEGs) associated with AD and delirium. The sDEGs-set enrichment analysis detected the transmission across chemical synapses, neurodegeneration pathways, neuroinflammation and glutamatergic signaling pathway, oxidative stress, and BDNF signaling pathway as the most significant signaling pathways shared by delirium and AD. The disease-sDEGs interaction analysis highlighted the other disease risk factors with delirium and AD development and progression. Among the sDEGs of delirium and AD, the top 10 hub-proteins including ALB, APP, BDNF, CREB1, DLG4, GAD1, GAD2, GFAP, GRIN2B and GRIN2A were found by the PPI network analysis. Based on the maximum molecular docking binding affinities and molecular dynamic simulation (100 ns) results, the ALB and GAD2 were found as prominent drug target proteins when tacrine and donepezil were identified as potential drug candidates for delirium and AD.ConclusionThe study outlined the common key biomolecules and biological pathways shared by delirium and AD. The computationally reported potential drug molecules need a deeper investigation including clinical trials to validate their effectiveness. The outcomes from this study will help to understand the typical pathophysiological relationship between delirium and AD and flag future therapeutic development research for delirium.

Deciphering molecular atlas of Alzheimer’s disease: A comprehensive bioinformatic analysis of gene expression and protein interaction networks

Deciphering molecular atlas of Alzheimer’s disease: A comprehensive bioinformatic analysis of gene expression and protein interaction networks

Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel.

The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.

Bioinformatics analysis and validation of RNA methylation-related genes in osteogenic and adipogenic differentiation of rat bone marrow mesenchymal stem cells

BackgroundThe regulatory mechanisms of RNA methylation during the processes of osteogenic and adipogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) have yet to be fully understood. The objective of our study was to analyze and validate the contribution of RNA methylation regulators to the mechanisms underlying the osteogenic and adipogenic differentiation of rat BMSCs. MethodsWe downloaded the GSE186026 from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened using the DESeq2 package in R software (version 3.6.3). A total of 50 RNA methylation genes obtained from literature review and summary were intersected with the previous DEGs to obtain RNA methylation genes, which have different expressions (RM-DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to reveal the functional enrichment. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate RM-DEGs. Protein-protein interaction network (PPI) analysis and visual analysis were performed using STRING and Cytoscape. RM-DEGs regulatory network was constructed to analyze the top 10 hub genes. The relationship between RM-DEGs, some enriched GO and pathways was also been analyzed. The miRNAs and RM-DEGs regulatory networks were established by using miRWalk and TargetScan. ResultsAs part of our research, we detected varying levels of expression for m6A regulators Mettl3 and Rbm15, as well as m7G regulators Mettl1 and Wdr4, in relation to osteogenic differentiation, along with m6A regulator Fmr1 in adipogenic differentiation. The protein-protein interaction (PPI) networks were constructed for 49 differentially expressed genes (DEGs) related to RNA methylation during the process of osteogenic differentiation, and 13 DEGs for adipogenic differentiation. Moreover, top10 hub genes were calculated. In osteogenic differentiation, Mettl3 regulated the Wnt pathway and Hippo pathway by regulating Smad3, Rbm15 regulated the Notch pathway by Notch1, Mettl1 regulated the PI3K-Akt pathway by Gnb4. In adipogenic differentiation, Fmr1 regulated the PI3K-Akt pathway by Egfr. M6A methylation sites of Smad3, Notch1 and Gnb4 were predicted, and the results showed that all three genes were possibly methylated by m6A, and more than 9 sites per gene were possibly methylated. Finally, we constructed the regulatory networks of Mettl3, Rbm15, Mettl1, and Wdr4 and 109 miRNAs in osteogenic differentiation, Fmr1 and 118 miRNAs in adipogenic differentiation. ConclusionsMettl3(m6A), Rbm15(m6A), Wdr4 and Mettl1(m7G) were differentially expressed in osteogenic differentiation, while Fmr1(m6A) was differentially expressed in adipogenic differentiation. These findings offered potential candidates for further research on the involvement of RNA methylation in the osteogenic and adipogenic differentiation of BMSCs.

CRP and IHF act as host regulators in Royal Jelly's antibacterial activity

Royal Jelly (RJ) is a natural substance produced by honeybees, serving not only as nutrition for bee brood and queens but also as a functional food due to its health-promoting properties. Despite its well-known broad-spectrum antibacterial activity, the precise molecular mechanism underlying its antibacterial action has remained elusive. In this study, we investigated the impact of RJ on the bacteria model MG1655 at its half-maximal inhibitory concentration, employing LC–MS/MS to analyze proteomic changes. The differentially expressed proteins were found to primarily contribute to the suppression of gene expression processes, specifically transcription and translation, disrupting nutrition and energy metabolism, and inducing oxidative stress. Notably, RJ treatment led to a marked inhibition of superoxide dismutase and catalase activities, resulting in heightened oxidative damage and lipid peroxidation. Furthermore, through a protein–protein interaction network analysis using the STRING database, we identified CRP and IHF as crucial host regulators responsive to RJ. These regulators were found to play a pivotal role in suppressing essential hub genes associated with energy production and antioxidant capabilities. Our findings significantly contribute to the understanding of RJ's antibacterial mechanism, highlighting its potential as a natural alternative to conventional antibiotics. The identification of CRP and IHF as central players highlights the intricate regulatory networks involved in RJ's action, offering new targets for developing innovative antimicrobial strategies.

Single-cell RNA sequencing reveals the effects of high-fat diet on oocyte and early embryo development in female mice

BackgroundObesity is a global health issue with detrimental effects on various human organs, including the reproductive system. Observational human data and several lines of animal experimental data suggest that maternal obesity impairs ovarian function and early embryo development, but the precise pathogenesis remains unclear.MethodsWe established a high-fat diet (HFD)-induced obese female mouse model to assess systemic metabolism, ovarian morphology, and oocyte function in mice. For the first time, this study employed single-cell RNA sequencing to explore the altered transcriptomic landscape of preimplantation embryos at different stages in HFD-induced obese mice. Differential gene expression analysis, enrichment analysis and protein-protein interactions network analysis were performed.ResultsHFD-induced obese female mice exhibited impaired glucolipid metabolism and insulin resistance. The ovaries of HFD mice had a reduced total follicle number, an increased proportion of atretic follicles, and irregular granulosa cell arrangement. Furthermore, the maturation rate of embryonic development by in vitro fertilization of oocytes was significantly decreased in HFD mice. Additionally, the transcriptional landscapes of preimplantation embryos at different stages in mice induced by different diets were significantly distinguished. The maternal-to-zygotic transition was also affected by the failure to remove maternal RNAs and to turn off zygotic genome expression.ConclusionsHFD-induced obesity impaired ovarian morphology and oocyte function in female mice and further led to alterations in the transcriptional landscape of preimplantation embryos at different stages of HFD mice.

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indications

Type 2 diabetes (T2D) and Clear-cell renal cell carcinoma (ccRCC) are both complicated diseases which incidence rates gradually increasing. Population based studies show that severity of ccRCC might be associated with T2D. However, so far, no researcher yet investigated about the molecular mechanisms of their association. This study explored T2D and ccRCC causing shared key genes (sKGs) from multiple transcriptomics profiles to investigate their common pathogenetic processes and associated drug molecules. We identified 259 shared differentially expressed genes (sDEGs) that can separate both T2D and ccRCC patients from control samples. Local correlation analysis based on the expressions of sDEGs indicated significant association between T2D and ccRCC. Then ten sDEGs (CDC42, SCARB1, GOT2, CXCL8, FN1, IL1B, JUN, TLR2, TLR4, and VIM) were selected as the sKGs through the protein–protein interaction (PPI) network analysis. These sKGs were found significantly associated with different CpG sites of DNA methylation that might be the cause of ccRCC. The sKGs-set enrichment analysis with Gene Ontology (GO) terms and KEGG pathways revealed some crucial shared molecular functions, biological process, cellular components and KEGG pathways that might be associated with development of both T2D and ccRCC. The regulatory network analysis of sKGs identified six post-transcriptional regulators (hsa-mir-93-5p, hsa-mir-203a-3p, hsa-mir-204-5p, hsa-mir-335-5p, hsa-mir-26b-5p, and hsa-mir-1-3p) and five transcriptional regulators (YY1, FOXL1, FOXC1, NR2F1 and GATA2) of sKGs. Finally, sKGs-guided top-ranked three repurposable drug molecules (Digoxin, Imatinib, and Dovitinib) were recommended as the common treatment for both T2D and ccRCC by molecular docking and ADME/T analysis. Therefore, the results of this study may be useful for diagnosis and therapies of ccRCC patients who are also suffering from T2D.

Comparative Transcriptomes of Canine and Human Prostate Cancers Identify Mediators of Castration Resistance.

Prostate cancer continues to be one of the most lethal cancers in men. While androgen-deprivation therapy is initially effective in treating prostate cancer, most cases of advanced prostate cancer eventually progress to castration-resistant prostate cancer (CRPC), which is incurable. Similarly, the most aggressive form of prostatic carcinoma occurs in dogs that have been castrated. To identify molecular similarities between canine prostate cancer and human CRPC, we performed a comparative analysis of gene expression profiles. Through this transcriptomic analysis, we found that prostatic carcinoma in castrated dogs demonstrate an androgen indifferent phenotype, characterized by low androgen receptor and neuroendocrine associated genes. Notably, we identified two genes, ISG15 and AZGP1 that were consistently up- and downregulated, respectively, in both canine prostatic carcinoma and human CRPC. Additionally, we identified several other genes, including GPX3, S100P, and IFITM1, that exhibited similar expression patterns in both species. Protein-protein interaction network analysis demonstrated that these 5 genes were part of a larger network of interferon-induced genes, suggesting that they may act together in signaling pathways that are disrupted in prostate cancer. Accordingly, our findings suggest that the interferon pathway may play a role in the development and progression of CRPC in both dogs and humans and chart a new therapeutic approach.

Genome-Wide Identification of a Maize Chitinase Gene Family and the Induction of Its Expression by Fusarium verticillioides (Sacc.) Nirenberg (1976) Infection.

Maize chitinases are involved in chitin hydrolysis. Chitinases are distributed across various organisms including animals, plants, and fungi and are grouped into different glycosyl hydrolase families and classes, depending on protein structure. However, many chitinase functions and their interactions with other plant proteins remain unknown. The economic importance of maize (Zea mays L.) makes it relevant for studying the function of plant chitinases and their biological roles. This work aims to identify chitinase genes in the maize genome to study their gene structure, family/class classification, cis-related elements, and gene expression under biotic stress, such as Fusarium verticillioides infection. Thirty-nine chitinase genes were identified and found to be distributed in three glycosyl hydrolase (GH) families (18, 19 and 20). Likewise, the conserved domains and motifs were identified in each GH family member. The identified cis-regulatory elements are involved in plant development, hormone response, defense, and abiotic stress response. Chitinase protein-interaction network analysis predicted that they interact mainly with cell wall proteins. qRT-PCR analysis confirmed in silico data showing that ten different maize chitinase genes are induced in the presence of F. verticillioides, and that they could have several roles in pathogen infection depending on chitinase structure and cell wall localization.