STRING: a database of predicted functional associations between proteins

Fusion Genes Landscape of Lung Cancer Patients From Inner Mongolia, China

Lung cancer is the leading cause of cancer-related deaths globally. Gene fusion, a key driver of tumorigenesis, has led to the identification of numerous driver gene fusions for lung cancer diagnosis and treatment. However, previous studies focused on Western populations, leaving the possibility of unrecognized lung cancer-associated gene fusions specific to Inner Mongolia due to its unique genetic background and dietary habits. To address this, we conducted DNA sequencing analysis on tumor and adjacent nontumor tissues from 1200 individuals with lung cancer in Inner Mongolia. Our analysis established a comprehensive fusion gene landscape specific to lung cancer in Inner Mongolia, shedding light on potential region-specific molecular mechanisms underlying the disease. Compared to Western cohorts, we observed a higher occurrence of ALK and RET fusions in Inner Mongolian patients. Additionally, we discovered eight novel fusion genes in three patients: SLC34A2-EPHB1, CCT6P3-GSTP1, BARHL2-APC, HRAS-MELK, FAM134B-ERBB2, ABCB1-GIPC1, GPR98-ALK, and FAM134B-SALL1. These previously unreported fusion genes suggest potential regional specificity. Furthermore, we characterized the fusion genes' structures based on breakpoints and described their impact on major functional gene domains. Importantly, the identified novel fusion genes exhibited significant clinical and pathological relevance. Notably, patients with SLC34A2-EPHB1, CCT6P3-GSTP1, and BARHL2-APC fusions showed sensitivity to the combination of chemotherapy and immunotherapy. Patients with HRAS-MELK, FAM134B-ERBB2, and ABCB1-GIPC1 fusions showed sensitivity to chemotherapy. In summary, our study provides novel insights into the frequency, distribution, and characteristics of specific fusion genes, offering valuable guidance for the development of effective clinical treatments, particularly in Inner Mongolia.

De Novo Multi-Omics Pathway Analysis Designed for Prior Data Independent Inference of Cell Signaling Pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here, we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering reported molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data, and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets of relatively low sample sizes. Transcription factor, kinase, subcellular location, and function prediction algorithms were devised for transcriptome, phosphoproteome, and interactome modules and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome, and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Changes in the Proteome of the Circle of Willis during Aging Reveal Signatures of Vascular Disease

Approximately 70% of all strokes occur in patients over 65 years old, and stroke increases the risk of developing dementia. The circle of Willis (CoW), the ring of arteries at the base of the brain, links the intracerebral arteries to one another to maintain adequate cerebral perfusion. The CoW proteome is affected in cerebrovascular and neurodegenerative diseases, but changes related to aging have not been described. Here, we report on a quantitative proteomics analysis comparing the CoW from five young (2-3-month-old) and five aged male (18-20-month-old) mice using gene ontology (GO) enrichment, ingenuity pathway analysis (IPA), and iPathwayGuide tools. This revealed 242 proteins that were significantly dysregulated with aging, among which 189 were upregulated and 53 downregulated. GO enrichment-based analysis identified blood coagulation as the top biological function that changed with age and integrin binding and extracellular matrix constituents as the top molecular functions. Consistent with these findings, iPathwayGuide-based impact analysis revealed associations between aging and the complement and coagulation, platelet activation, ECM-receptor interaction, and metabolic process pathways. Furthermore, IPA analysis revealed the enrichment of 97 canonical pathways that contribute to inflammatory responses, as well as 59 inflammation-associated upstream regulators including 39 transcription factors and 20 cytokines. Thus, aging-associated changes in the CoW proteome in male mice demonstrate increases in metabolic, thrombotic, and inflammatory processes.

Exploring the evolutionary trajectory and functional landscape of cannabinoid receptors: A comprehensive bioinformatic analysis

The bioinformatic analysis of cannabinoid receptors (CBRs) CB1 and CB2 reveals a detailed picture of their structure, evolution, and physiological significance within the endocannabinoid system (ECS). The study highlights the evolutionary conservation of these receptors evidenced by sequence alignments across diverse species including humans, amphibians, and fish. Both CBRs share a structural hallmark of seven transmembrane (TM) helices, characteristic of class A G-protein-coupled receptors (GPCRs), which are critical for their signalling functions. The study reports a similarity of 44.58 % between both CBR sequences, which suggests that while their evolutionary paths and physiological roles may differ, there is considerable conservation in their structures. Pathway databases like KEGG, Reactome, and WikiPathways were employed to determine the involvement of the receptors in various signalling pathways. The pathway analyses integrated within this study offer a detailed view of the CBRs interactions within a complex network of cannabinoid-related signalling pathways. High-resolution crystal structures (PDB ID: 5U09 for CB1 and 5ZTY for CB2) provided accurate structural information, showing the binding pocket volume and surface area of the receptors, essential for ligand interaction. The comparison between these receptors' natural sequences and their engineered pseudo-CBRs (p-CBRs) showed a high degree of sequence identity, confirming the validity of using p-CBRs in receptor-ligand interaction studies. This comprehensive analysis enhances the understanding of the structural and functional dynamics of cannabinoid receptors, highlighting their physiological roles and their potential as therapeutic targets within the ECS.

Endometrial decidualization status modulates endometrial microvascular complexity and trophoblast outgrowth in gelatin methacryloyl hydrogels

The endometrium undergoes rapid cycles of vascular growth, remodeling, and breakdown during the menstrual cycle and pregnancy. Decidualization is an endometrial differentiation process driven by steroidal sex hormones that is critical for blastocyst-uterine interfacing and blastocyst implantation. Certain pregnancy disorders may be linked to decidualization processes. However, much remains unknown regarding the role of decidualization and reciprocal trophoblast-endometrial interactions on endometrial angiogenesis and trophoblast invasion. Here, we report an engineered endometrial microvascular network embedded in gelatin hydrogels that displays morphological and functional patterns of decidualization. Vessel complexity and biomolecule secretion are sensitive to decidualization and affect trophoblast motility, but that signaling between endometrial and trophoblast cells was not bi-directional. Although endometrial microvascular network decidualization status influences trophoblast cells, trophoblast cells did not induce structural changes in the endometrial microvascular networks. These findings add to a growing literature that the endometrium has biological agency at the uterine-trophoblast interface during implantation. Finally, we form a stratified endometrial tri-culture model, combining engineered microvascular networks with epithelial cells. These endometrial microvascular networks provide a well-characterized platform to investigate dynamic changes in angiogenesis in response to pathological and physiological endometrial states.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph

Motivation

The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking.

Results

Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

The upregulation of TGM2 is associated with poor prognosis and the shaping of the inflammatory tumor microenvironment in lung squamous cell carcinoma

Tissue transglutaminase (TGM2) is a member of the glutamine transferase superfamily, located within cells and their membranes. When secreted, it catalyzes the cross-linking of extracellular matrix proteins and promotes the formation of extracellular matrix scaffolds. To determine the function of TGM2 in the tumorigenesis of lung squamous cell carcinoma (LUSC), we conducted a comprehensive bioinformatics analysis of TGM2. Our findings indicate that high expression of TGM2 in LUSC was associated with a poorer prognosis. Additionally, we found that high expression of TGM2 is closely related to tumor-promoting inflammation and may increase sensitivity to immunotherapy. We further confirmed the cancer-promoting effect of TGM2 in LUSC through in vitro overexpression and knockdown experiments and showed that TGM2 primarily affects cancer cell proliferation, apoptosis, and invasion. In summary, TGM2 promoted the progression of LUSC, and targeting TGM2 is expected to become a new therapeutic approach for LUSC treatment.

Construction and Bioinformatics Analysis of ceRNA Regulatory Networks in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive form of pulmonary fibrosis of unknown etiology. Despite ongoing research, there is currently no cure for this disease. Recent studies have highlighted the significance of competitive endogenous RNA (ceRNA) regulatory networks in IPF development. Therefore, this study investigated the ceRNA network associated with IPF pathogenesis. We obtained gene expression datasets (GSE32538, GSE32537, GSE47460, and GSE24206) from the Gene Expression Omnibus (GEO) database and analyzed them using bioinformatics tools to identify differentially expressed messenger RNAs (DEmRNAs), microRNAs (DEmiRNAs), and long non-coding RNAs (DElncRNA). For DEmRNAs, we conducted an enrichment analysis, constructed protein-protein interaction networks, and identified hub genes. Additionally, we predicted the target genes of differentially expressed mRNAs and their interacting long non-coding RNAs using various databases. Subsequently, we screened RNA molecules with ceRNA regulatory relations in the lncACTdb database based on the screening results. Furthermore, we performed disease and functional enrichment analyses and pathway prediction for miRNAs in the ceRNA network. We also validated the expression levels of candidate DEmRNAs through quantitative real-time reverse transcriptase polymerase chain reaction and analyzed the correlation between the expression of these candidate DEmRNAs and the percent predicted pre-bronchodilator forced vital capacity [%predicted FVC (pre-bd)]. We found that three ceRNA regulatory axes, specifically KCNQ1OT1/XIST/NEAT1-miR-20a-5p-ITGB8, XIST-miR-146b-5p/miR-31-5p- MMP16, and NEAT1-miR-31-5p-MMP16, have the potential to significantly affect IPF progression. Further examination of the underlying regulatory mechanisms within this network enhances our understanding of IPF pathogenesis and may aid in the identification of diagnostic biomarkers and therapeutic targets.

Integrated Bioinformatics Analysis Revealed Immune Checkpoint Genes Relevant to Type 2 Diabetes

Objective

Chronic low-grade inflammation of the pancreatic islets is the characteristic of type 2 diabetes (T2D), and some of the immune checkpoints may play important roles in the pancreatic islet inflammation. Thus, we aim to explore the immune checkpoint genes (ICGs) associated with T2D, thereby revealing the role of ICGs in the pathogenesis of T2D based on bioinformatic analyses.

Methods

Differentially expressed genes (DEGs) and immune checkpoint genes (ICGs) of islets between T2D and control group were screened from datasets of the Gene Expression Omnibus (GEO). A risk model was built based on the coefficients of ICGs calculated by ridge regression. Functional enrichment analysis and immune cell infiltration estimation were conducted. Correlations between ICGs and hub genes, T2D-related disease genes, insulin secretion genes, and beta cell function-related genes were analyzed. Finally, we conducted RT-PCR to verify the expression of these ICGs.

Results

In total, pancreatic islets from 19 cases of T2D and 84 healthy subjects were included. We identified 458 DEGs. Six significantly upregulated ICGs (CD44, CD47, HAVCR2, SIRPA, TNFSF9, and VTCN1) in T2D were screened out. These ICGs were significantly correlated with several hub genes and T2D-related genes; furthermore, they were correlated with insulin secretion and β cell function-related genes. The analysis of immune infiltration showed that the concentrations of eosinophils, T cells CD4 naive, and T cells regulatory (Tregs) were significantly higher, but CD4 memory resting T cells and monocytes were lower in islets of T2D patients. The infiltrated immune cells in T2D pancreatic islet were associated with these six ICGs. Finally, the expression levels of four ICGs were confirmed by RT-PCR, and three ICGs were validated in another independent dataset.

Conclusion

In conclusion, the identified ICGs may play an important role in T2D. Identification of these differential genes may provide new clues for the diagnosis and treatment of T2D.

Profiling of the Proteins Interacting with Amyloid Beta Peptides in Clinical Samples by PACTS-TPP

The accumulation of amyloid beta (Aβ1-42) results in neurotoxicity and is strongly related to neurodegenerative disorders, especially Alzheimer's disease (AD), but the underlying molecular mechanism is still poorly understood. Therefore, there is an urgent need for researchers to discover the proteins that interact with Aβ1-42 to determine the molecular basis. Previously, we developed peptide-ligand-induced changes in the abundance of proTeinS (PACTS)-assisted thermal proteome profiling (TPP) to identify proteins that interact with peptide ligands. In the present study, we applied this technique to analyze clinical samples to identify Aβ1-42-interacting proteins. We detected 115 proteins that interact with Aβ1-42 in human frontal lobe tissue. Pathway enrichment analysis revealed that the differentially expressed proteins were involved mainly in neurodegenerative diseases. Further orthogonal validation revealed that Aβ1-42 interacted with the AD-associated protein mitogen-activated protein kinase 3 (MAPK3), and knockdown of the Aβ1-42 amyloid precursor protein (APP) inhibited the MAPK signaling pathway, suggesting potential functional roles for Aβ1-42 in interacting with MAPK3. Overall, this study demonstrated the application of the PACTS-TPP in clinical samples and provided a valuable data source for research on neurodegenerative diseases.

Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage

Unexplained recurrent miscarriage (URM) is a common pregnancy complication with few effective therapies. Moreover, little is known regarding the role of pyroptosis in the regulation of the URM immune microenvironment. To address this issue, gene expression profiles of publicly available placental datasets GSE22490 and GSE76862 were downloaded from the Gene Expression Omnibus database. Pyroptosis-related differentially expressed genes were identified and a total of 16 differentially expressed genes associated with pyroptosis were detected, among which 1 was upregulated and 15 were downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the functionally enriched modules and pathways of these genes are closely related to immune and inflammatory responses. Four hub genes were identified: BTK, TLR8, NLRC4, and TNFSF13B. BTK, TLR8, and TNFSF13B were highly connected with immune cells, according to the correlation analysis of four hub genes and 20 different types of immune cells (p < 0.05). The four hub genes were used as research objects to construct the interaction networks. Chorionic villus tissue was used for quantitative real-time polymerase chain reaction and western blot to confirm the expression levels of hub genes, and the results showed that the expression of the four hub genes was significantly decreased in the chorionic villus tissue in the URM group. Collectively, the present study indicates that perhaps pyroptosis is essential to the diversity and complexity of the URM immune microenvironment, and provides a theoretical basis and research ideas for subsequent target gene verification and mechanism research.

Mitochondria-Related Gene MAOB is a Key Biomarker of Osteoarthritis and Inhibition of Its Expression Reduces LPS-induced Chondrocyte Damage

The mitochondria are an important organelle in cells responsible for producing energy, and its abnormal function is closely related to the occurrence and development of osteoarthritis. Finding key genes associated with mitochondrial dysfunction in osteoarthritis can provide new ideas for the study of its pathogenesis. Firstly, 371 differential expressed genes (DEGs) were obtained through bioinformatics analysis of the GSE12021 and GSE55235 datasets in the GEO database, and 24 mitochondria-related DEGs (Mito-DEGs) were obtained by crossing differential genes with mitochondrial related genes. Next, KEGG and GO analysis of Mito-DEGs showed that upregulated Mito-DEGs were mainly enriched in small molecule catabolic process and tryptophan metabolism, while downregulated Mito-DEGs were mainly enriched in acetyl-CoA metabolic process and fatty acid biosynthesis. Furthermore, the key genes ME2 and MAOB were obtained through protein-protein interaction network analysis and lasso cox analysis of the 24 Mito-DEGs. In addition, the comparison results of immune cell scores showed differences between T cells CD4 memory resting, T cells regulatory (Tregs), Mast cells resting, and Mast cells activated in the OA group and the control group. More importantly, the potential regulatory mechanisms of key genes were studied through GSEA analysis and their correlation with immune infiltrating cells, immune checkpoints, m6A, and ferroptosis. Finally, in LPS-induced C28/I2 cells, silencing MAOB reduced inflammation injury and inhibited mitochondrial damage. Our research findings suggest that MAOB may hold potential as a target for the diagnosis and treatment of osteoarthritis.

Investigating the potential of mono-chalcone compounds in targeting breast cancer receptors through network pharmacology, molecular docking, molecular dynamics simulation, antiproliferative effects, and gene expressions

The study aims to investigate various aspects of synthesized mono-chalcone compounds 5 and 8 concerning breast cancer, including network pharmacology, molecular docking, molecular dynamics (MD) simulations, antiproliferative effects, and gene expressions. Initially, the compounds underwent a network pharmacology analysis targeting breast cancer-related targets, with MalaCards, SwissTargetPrediction, and PharmMapper identifying 70 breast cancer target receptors. Subsequently, protein-protein interaction (PPI) network analysis revealed two distinct target gene clusters. Survival analysis identified seven significant target genes following Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and Gene Ontology (GO) evaluation. Molecular docking and MD simulations were conducted on these seven target genes (AKT2, BRAF, ESR1, FGFR1, IGF1, IGF1R, and KIT), revealing that compound 8 exhibited the highest binding affinities, as well as better stability and compactness when interacting with the targeted proteins. Next, the compounds underwent cell viability assay and gene expression analysis to validate the in silico findings. Both compounds demonstrated the ability to suppress breast cancer proliferation, with compound 8 showing increased selectivity in targeting breast cancer cells while causing minimal harm to normal breast cells. The suppression of breast cancer cell proliferation was attributed to decreased expression levels of AKT2, BRAF, FGFR1, IGF1, IGF1R, KIT, and ESR1. Hence, the results provide insights into the molecular interaction responsible for the anti-breast cancer capabilities of mono-chalcone compounds.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03991-y.

Network pharmacology and multitarget analysis of Nigella sativa in the management of diabetes and obesity: a computational study

Obesity and diabetes are commonly associated with one another and represent a significant global health issue, with a recent surge in disease incidence. Nigella sativa, also known as black cumin, is believed to possess several health benefits, including anti-diabetic, anticancer, antioxidant, antimicrobial, and anti-obesity properties. In this study, we aimed to identify the active compounds derived from N. sativa, which can potentially inhibit key protein targets and signaling pathways associated with diabesity treatment. We employed an exhaustive in silico search, which led to the identification of 22 potential compounds. Out of these, only five hits were found to be non-toxic, including Arabic and ascorbic acids, dihydrocodeine, catechin, and kaempferol. Our analysis revealed that these hits were associated with genes such as AKT1, IL6, SRC, and EGFR. Finally, we conducted molecular docking and molecular dynamics simulations, which identified kaempferol as the best binder for AKT1 in comparison to the reference molecule. Overall, our in silico integrated pipeline provides a useful approach to identify non-toxic phytocompounds as promising drug candidates to treat diabetes and obesity.Communicated by Ramaswamy H. Sarma.

Quantitative Proteomic Analysis Reveals Functional Alterations of the Peripheral Immune System in Colorectal Cancer

Colorectal cancer (CRC) is characterized by high morbidity, high mortality, and limited response to immunotherapies. The peripheral immune system is an important component of tumor immunity, and enhancements of peripheral immunity help to suppress tumor progression. However, the functional alterations of the peripheral immune system in CRC are unclear. Here, we used mass spectrometry-based quantitative proteomics to establish a protein expression atlas for the peripheral immune system in CRC, including plasma and five types of immune cells (CD4+ T cells, CD8+ T cells, monocytes, natural killer cells, and B cells). Synthesizing the results of the multidimensional analysis, we observed an enhanced inflammatory phenotype in CRC, including elevated expression of plasma inflammatory proteins, activation of the inflammatory pathway in monocytes, and increased inflammation-related ligand-receptor interactions. Notably, we observed tumor effects on peripheral T cells, including altered cell subpopulation ratios and suppression of cell function. Suppression of CD4+ T cell function is mainly mediated by high expression levels of protein tyrosine phosphatases. Among them, the expression of protein tyrosine phosphatase receptor type J (PTPRJ) gradually increased with CRC progression; knockdown of PTPRJ in vitro could promote T cell activation, thereby enhancing peripheral immunity. We also found that the combination of leucine-rich α-2 glycoprotein 1 (LRG1) and apolipoprotein A4 (APOA4) had the best predictive ability for colorectal cancer and has the potential to be a biomarker. Overall, this study provides a comprehensive understanding of the peripheral immune system in CRC. It also offers insights regarding the potential clinical utilities of these peripheral immune characteristics as diagnostic indicators and therapeutic targets.

Computational analysis and expression profiling of two-component system (TCS) gene family members in mango (Mangifera indica) indicated their roles in stress response

The two-component system (TCS) gene family is among the most important signal transduction families in plants and is involved in the regulation of various abiotic stresses, cell growth and division. To understand the role of TCS genes in mango (Mangifera indica ), a comprehensive analysis of TCS gene family was carried out in mango leading to identification of 65 MiTCS genes. Phylogenetic analysis divided MiTCSs into three groups (histidine kinases, histidine-containing phosphotransfer proteins, and response regulators) and 11 subgroups. One tandem duplication and 23 pairs of segmental duplicates were found within the MiTCSs . Promoter analysis revealed that MiTCSs contain a large number of cis -elements associated with environmental stresses, hormone response, light signalling, and plant development. Gene ontology analysis showed their involvement in various biological processes and molecular functions, particularly signal transduction. Protein-protein interaction analysis showed that MiTCS proteins interacted with each other. The expression pattern in various tissues and under many stresses (drought, cold, and disease) showed that expression levels varied among various genes in different conditions. MiTCSs 3D structure predictions showed structural conservation among members of the same groups. This information can be further used to develop improved cultivars and will serve as a foundation for gaining more functional insights into the TCS gene family.

Yigansan ameliorates maternal immune activation-induced autism-like behaviours by regulating the IL-17A/TRAF6/MMP9 pathway: Network analysis and experimental validation

BACKGROUND

Maternal immune activation (MIA) is a significant factor inducing to autism spectrum disorder (ASD) in offspring. The fundamental principle underlying MIA is that inflammation during pregnancy impedes fetal brain development and triggers behavioural alterations in offspring. The intricate pathogenesis of ASD renders drug treatment effects unsatisfactory. Traditional Chinese medicine has strong potential due to its multiple therapeutic targets. Yigansan, composed of seven herbs, is one of the few that has been proven to be effective in treating neuro-psychiatric disorders among numerous traditional Chinese medicine compounds, but its therapeutic effect on ASD remains unknown.

HYPOTHESIS

Yigansan improves MIA-induced ASD-like behaviours in offspring by regulating the IL-17 signalling pathway.

METHODS

Pregnant C57BL/6J mice were intraperitoneally injected with poly(I:C) to construct MIA models and offspring ASD models. Network analysis identified that the IL-17A/TRAF6/MMP9 pathway is a crucial pathway, and molecular docking confirmed the binding affinity between the monomer of Yigansan and target proteins. qRT-PCR and Western blot were used to detect the expression levels of inflammatory factors and pathway proteins, immunofluorescence was used to detect the distribution of IL-17A, and behavioural tests were used to evaluate the ASD-like behaviours of offspring.

RESULTS

We demonstrated that Yigansan can effectively alleviate MIA-induced neuroinflammation of adult offspring by regulating the IL-17A/TRAF6/MMP9 pathway, and the expression of IL-17A was reduced in the prefrontal cortex. Importantly, ASD-like behaviours have been significantly improved. Moreover, we identified that quercetin is the effective monomer for Yigansan to exert therapeutic effects.

CONCLUSION

Overall, this study was firstly to corroborate the positive therapeutic effect of Yigansan in the treatment of ASD. We elucidated the relevant molecular mechanism and regulatory pathway involved, determined the optimal therapeutic dose and effective monomer, providing new solutions for the challenges of drug therapy for ASD.

A bioinformatics approach of specificity protein transcription factors in head and neck squamous cell carcinoma

Background and purpose

The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential.

Experimental approach

Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes' diagnostic and prognostic potential.

Findings/Results

SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential.

Conclusion and implications

Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.

Integrative analysis discovers Imidurea as dual multitargeted inhibitor of CD69, CD40, SHP2, lysozyme, GATA3, cCBL, and S-cysteinase from SARS-CoV-2 and M. tuberculosis

Two of the deadliest infectious diseases, COVID-19 and tuberculosis (TB), have combined to establish a worldwide pandemic, wreaking havoc on economies and claiming countless lives. The optimised, multitargeted medications may diminish resistance and counter them together. Based on computational expression studies, 183 genes were co-expressed in COVID-19 and TB blood samples. We used the multisampling screening algorithms on the top ten co-expressed genes (CD40, SHP2, Lysozyme, GATA3, cCBL, SIVmac239 Nef, CD69, S-adenosylhomocysteinase, Chemokine Receptor-7, and Membrane Protein). Imidurea is a multitargeted inhibitor for COVID-19 and TB, as confirmed by extensive screening and post-filtering utilising MM\GBSA algorithms. Imidurea has shown docking and MM\GBSA scores of -8.21 to -4.75 Kcal/mol and -64.16 to -29.38 Kcal/mol, respectively. The DFT, pharmacokinetics, and interaction patterns suggest that Imidurea may be a drug candidate, and all ten complexes were tested for stability and bond strength using 100 ns for all MD atoms. The modelling findings showed the complex's repurposing potential, with a cumulative deviation and fluctuation of <2 Å and significant intermolecular interaction, which validated the possibilities. Finally, an inhibition test was performed to confirm our in-silico findings on SARS-CoV-2 Delta variant infection, which was suppressed by adding imidurea to Vero E6 cells after infection.

Transcriptomic responses of cumulus granulosa cells to SARS-CoV-2 infection during controlled ovarian stimulation

Cumulus granulosa cells (CGCs) play a crucial role in follicular development, but so far, no research has explored the impact of SARS-CoV-2 infection on ovarian function from the perspective of CGCs. In the present study, we compared the cycle outcomes between infected and uninfected female patients undergoing controlled ovarian stimulation, performed bulk RNA-sequencing of collected CGCs, and used bioinformatic methods to explore transcriptomic changes. The results showed that women with SARS-CoV-2 infection during stimulation had significantly lower number of oocytes retrieved and follicle-oocyte index, while subsequent fertilization and embryo development were similar. CGCs were not directly infected by SARS-CoV-2, but exhibited dramatic differences in gene expression (156 up-regulated and 65 down-regulated). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses demonstrated a high enrichment in antiviral, immune and inflammatory responses with necroptosis. In addition, the pathways related to telomere organization and double strand break repair were significantly affected by infection in gene set enrichment analysis. Further weighted gene co-expression network analysis identified a key module associated with ovarian response traits, which was mainly enriched as a decrease of leukocyte chemotaxis and migration in CGCs. For the first time, our study describes how SARS-CoV-2 infection indirectly affects CGCs at the transcriptional level, which may impair oocyte-CGC crosstalk and consequently lead to poor ovarian response during fertility treatment.

Connection between MiR-490 and CCND1 and GSK3β genes play an effective role in Wnt signaling pathway in colorectal cancer

The Wnt signaling pathway is identified as one of the main disrupted pathways in Colorectal cancer (CRC). Results from studies focusing on this route will aid greatly in the detection and treatment of CRC. MicroRNAs (MiRs), particularly MiR-490, has emerged as key regulator of gene expression in biological pathways, making it an attractive research target. This is notably true for the Wnt signaling pathway, which is usually disordered in CRC tissues. This study aimed to evaluate the expression level of MiR-490 isomiRs and determine some of its key target genes involved in Wnt signaling pathway in CRC tissues and cell lines, based on experimental and bioinformatics analysis. Elevated expression of GSK3β and CCND1 indicate that the progression of CRC tumor is associated with the inhibitory effect of MiR-490 isomiRs on the Wnt/β-catenin signaling pathway. This finding was supported by the observation of a positive connection between the expression pattern of miR-490-3p and 5p, and CCND1 and GSK3β in CRC. The valuable results of this study provide a means of identifying biomarkers with the potential to either inhibit or activate CRC cellular pathways.

Troponin T1 in tumorigenesis and immune modulation: Insights into multiple cancers and kidney renal clear cell carcinoma

Troponin T1 (TNNT1) plays a crucial role in muscle contraction but its role in cancer, particularly in kidney renal clear cell carcinoma (KIRC), is not well-understood. This study explores the expression, clinical significance and biological functions of TNNT1 in various cancers, with an emphasis on its involvement in KIRC. We analysed TNNT1 expression in cancers using databases like TCGA and GTEx, assessing its prognostic value, mutation patterns, methylation status and functional implications. The study also examined TNNT1's effect on the tumour microenvironment and drug sensitivity in KIRC, complemented by in vitro TNNT1 knockdown experiments in KIRC cells. TNNT1 is overexpressed in several cancers and linked to adverse outcomes, showing frequent upregulation mutations and abnormal methylation. Functionally, TNNT1 connects to muscle and cancer pathways, affects immune infiltration and drug responses, and its overexpression in KIRC is associated with advanced disease and reduced survival. Knocking down TNNT1 curbed KIRC cell growth. TNNT1's aberrant expression plays a significant role in tumorigenesis and immune modulation, highlighting its value as a prognostic biomarker and a potential therapeutic target in KIRC and other cancers. Further studies are essential to understand TNNT1's oncogenic mechanisms in KIRC.

Genome-wide identification of the ICS family genes and its role in resistance to Plasmodiophora brassicae in Brassica napus L

The isochorismate synthase (ICS) proteins are essential regulators of salicylic acid (SA) synthesis, which has been reported to regulate resistance to biotic and abiotic stresses in plants. Clubroot caused by Plasmodiophora brassicae is a common disease that threatens the yield and quality of Oilseed rape (Brassica napus L.). Exogenous application of salicylic acid reduced the incidence of clubroot in oilseed rape. However, the potential importance of the ICS genes family in B. napus and its diploid progenitors has been unclear. Here, we identified 16, 9, and 10 ICS genes in the allotetraploid B. napus, diploid ancestor Brassica rapa and Brassica oleracea, respectively. These ICS genes were classified into three subfamilies (I-III), and member of the same subfamilies showed relatively conserved gene structures, motifs, and protein domains. Furthermore, many hormone-response and stress-related promoter cis-acting elements were observed in the BnaICS genes. Exogenous application of SA delayed the growth of clubroot galls, and the expression of BnaICS genes was significantly different compared to the control groups. Protein-protein interaction analysis identified 58 proteins involved in the regulation of ICS in response to P. brassicae in B. napus. These results provide new clues for understanding the resistance mechanism to P. brassicae.

Network pharmacology-based screening of active constituents of Avicennia marina and their clinical biochemistry related mechanism against breast cancer

Breast cancer is the second major cause of cancer death in women globally. Avicennia marina is a medicinal plant that belongs to the family Acanthaceae and is known as grey or white mangrove. It has antioxidant, antiviral, anticancer, anti-inflammatory, and antibacterial activity in the treatment of various diseases including cancer. The goal of the study is to use a network pharmacology method to identify the potential phenomena of bioactive compounds of A. marina in the treatment of breast cancer and explore clinical biochemistry related aspects. A total of 74 active compounds of A. marina were retrieved from various databases as well as a literature review and collectively 429 targets of these compounds were identified by STITCH and Swiss Target Prediction databases. Breast cancer related 15606 potential targets were retrieved from the GeneCards database. A Venn diagram was drawn to find common key targets. To check the biological functions, the GO enrichment and KEGG pathways analysis of 171 key targets were performed through the DAVID database. To understand the interactions among key targets, Protein-protein interaction (PPI) studies were completed using the STRING database, and the Protein-Protein Interaction (PPI) network, as well as the compound-target-pathway network, was constructed using Cytoscape 3.9.0. Finally, molecular docking analysis of 5 hub genes named tumor protein 53 (TP53), catenin beta 1 (CTNNB1), interleukin 6 (IL6), tumor necrosis factor (TNF), and RAC-alpha serine/threonine protein kinases 1 (AKT1) with the active constituent of A. marina against breast cancer were performed. Additionally, a molecular docking study demonstrates that active drugs have a higher affinity for the target that may be used to decrease breast cancer. The molecular dynamic simulation analysis predicted the very stable behavior of docked complexes with no global structure deviations seen. The MMGBSA further supported strong intermolecular interactions with net energy values as; AKT1_Betulinic_acid (-20.97 kcal/mol), AKT1_Stigmasterol (-44.56 kcal/mol), TNF_Betulinic_acid (-28.68 kcal/mol) and TNF_Stigmastero (-29.47 kcal/mol).Communicated by Ramaswamy H. Sarma.

SLC7A5 correlated with malignancies and immunotherapy response in bladder cancer

BACKGROUND

Metabolic reprogramming contributes to bladder cancer development. This study aimed to understand the role of SLC7A5 in bladder cancer.

METHODS

We systematically analyzed the correlation between SLC7A5 and bladder cancer through various approaches, including bioinformatics, western blotting, cell cycle analysis, cell proliferation assays, and invasion experiments. We also investigated the immunological features within the tumor microenvironment (TME), encompassing cancer immune cycles, immune modulators, immune checkpoints, tumor-infiltrating immune cells (TIIC), T cell inflammation scores, and treatment responses. Additionally, for a comprehensive assessment of the expression patterns and immunological roles of SLC7A5, pan-cancer analysis was performed using cancer genomics datasets.

RESULTS

SLC7A5 was associated with adverse prognosis in bladder cancer patients, activating the Wnt pathway and promoting bladder cancer cell cycle progression, proliferation, migration, and invasion. Based on the evidence that SLC7A5 positively correlated with immunomodulators, TIIC, the cancer immune cycle, immune checkpoint and T cell inflammation scores, we also found that SLC7A5 was associated with the inflammatory tumor immune microenvironment. EGFR-targeted therapy, cancer immunotherapy, and radiation therapy were effective for patients with high SLC7A5 expression in bladder cancer. Low SLC7A5 patients were, however, sensitive to targeted therapies and anti-angiogenic therapy, such as blocking β-catenin network, PPAR-γ and FGFR3 signaling. Anti-SLC7A5 combined with cancer immunotherapy may have greater effectiveness than either therapy alone. Furthermore, we observed specific overexpression of SLC7A5 in TME of various cancers.

CONCLUSION

SLC7A5 can predict therapeutic response to immunotherapy, radiotherapy and chemotherapy in bladder cancer patients. Targeting SLC7A5 in combination with immunotherapy may be a potentially appropriate treatment option.

Anti-papillary thyroid carcinoma effects of dioscorea bulbifera L. through ferroptosis and the PI3K/AKT pathway based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer with a rising global incidence. Despite favorable prognoses, a significant recurrence rate persists. Dioscorea bulbifera L. (DBL), a traditional Chinese medicine, has been historically used for thyroid-related disorders. However, its therapeutic effects and mechanisms of action on PTC remain unclear.

AIM OF THE STUDY

To explore the potential therapeutic effects, principal active components, and molecular mechanisms of DBL in the treatment of PTC through network pharmacology and molecular docking, with experimental validation conducted to corroborate these findings.

MATERIALS AND METHODS

The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) was utilized as a systematic tool for collecting and screening the phytochemical components of DBL, and for establishing associations between these components and molecular targets. Based on this, network data was visually processed using Cytoscape software (version 3.8.0). Concurrently, precise molecular docking studies of the principal active components of DBL and their corresponding targets were conducted using Autodock software. Additionally, PTC-related genes were selected through the GeneCards and GEO databases. We further employed the DAVID bioinformatics resources to conduct comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the intersecting genes between DBL and PTC. These analyses aid in predicting the potential therapeutic actions of DBL on PTC and its mechanisms of action. To validate these findings, corresponding in vitro experimental studies were also conducted.

RESULTS

In this investigation, 14 bioactive compounds of DBL and 195 corresponding molecular targets were identified, with 127 common targets shared between DBL and PTC. Molecular docking revealed strong binding affinities between major bioactive compounds and target proteins. GO enrichment analysis unveiled key processes involved in DBL's action. KEGG analysis highlighted DBL's modulation of the PI3K/AKT signaling pathway. Experimental outcomes demonstrated DBL's potential in inhibiting PTC cell proliferation and migration, suppressing PI3K/AKT pathway activation, and promoting ferroptosis.

CONCLUSION

In conclusion, DBL offers a multifaceted therapeutic approach for PTC, targeting multiple molecular entities and influencing diverse biological pathways. Network pharmacology and molecular docking shed light on DBL's potential utility in PTC treatment, substantiated by experimental validation. This study contributes valuable insights into using DBL as a promising therapeutic agent for PTC management.

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.

Computational framework for identifying and evaluating mutagenic and xenoestrogenic potential of food additives

Food additives are chemicals incorporated in food to enhance its flavor, color and prevent spoilage. Some of these are associated with substantial health hazards, including developmental disorders, increase cancer risk, and hormone disruption. Hence, this study aimed to comprehend the in-silico toxicology framework for evaluating mutagenic and xenoestrogenic potential of food additives and their association with breast cancer. A total of 2885 food additives were screened for toxicity based on Threshold of Toxicological Concern (TTC), mutagenicity endpoint prediction, and mutagenic structural alerts/toxicophores identification. Ten food additives were identified as having mutagenic potential based on toxicity screening. Furthermore, Protein-Protein Interaction (PPI) analysis identified ESR1, as a key hub gene in breast cancer. KEGG pathway analysis verified that ESR1 plays a significant role in breast cancer pathogenesis. Additionally, competitive interaction studies of the predicted potential mutagenic food additives with the estrogen receptor-α were evaluated at agonist and antagonist binding sites. Indole, Dichloromethane, Trichloroethylene, Quinoline, 6-methyl quinoline, Ethyl nitrite, and 4-methyl quinoline could act as agonists, and Paraldehyde, Azodicarbonamide, and 2-acetylfuranmay as antagonists. The systematic risk assessment framework reported in this study enables the exploration of mutagenic and xenoestrogenic potential associated with food additives for hazard identification and management.

Integrated Analysis of CD1A Immune Infiltration and Competing Endogenous RNA Networks in COAD

Background

The CD1A gene, a key component of the human immune system and part of the CD1 family, plays a crucial role in presenting lipid antigens to T cells. Abnormal CD1A expression is associated with various immune-related diseases and tumors. However, the biological function of CD1A in COAD is unclear.

Methods

Multiple databases were systematically employed to conduct an analysis of CD1A expression in pan-cancer and COAD, along with its clinical-pathological features. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of CD1A were performed using the 'clusterProfiler' package. The Protein-protein interaction (PPI) analysis of CD1A was used the STRING database. Additionally, TIMER and ssGSEA tools were used to explore the relationship between CD1A expression in COAD and immune cell infiltration. The study also investigated the association between CD1A expression and N6-methyladenosine (m6A) modification genes in the TCGA COAD cohort and constructed a CD1A-centric competing endogenous RNA (ceRNA) regulatory network.

Results

CD1A displays varying expression levels in various tumors, including COAD, and is closely linked to clinical-pathological characteristics. GO analysis suggests that CD1A plays a role in important processes like antigen processing and presentation, leukocyte-mediated immunity, and lymphocyte-mediated immunity. KEGG analysis identifies CD1A's involvement in key pathways such as the Chemokine signaling pathway and Cytokine-cytokine receptor interaction. PPI analysis highlights CD1A's interactions with CD207, CD1C, CD1E, FOXP3, and ITGB2. ssGSEA analysis indicates a significant relationship between CD1A expression and the infiltration of various immune cells in COAD. Significant associations were found between CD1A and m6A modification genes in COAD. Furthermore, a CD1A-centered ceRNA regulatory network has been constructed.

Conclusion

CD1A emerges as a potential biomarker for the diagnosis and treatment of COAD, showing a strong association with tumor immune infiltration, m6A modification, and the ceRNA network.

Comparative genomic profiling of transport inhibitor Response1/Auxin signaling F-box (TIR1/AFB) genes in eight Pyrus genomes revealed the intraspecies diversity and stress responsiveness patterns

In the genomics of plants and the phytoecosystem, Pyrus (pear) is among the most nutritious fruits and contains fiber that has great health benefits to humans. It is mostly cultivated in temperate regions and is one of the most cultivated pome fruits globally. Pears are highly subjected to biotic and abiotic stresses that affect their yield. TIR1/AFB proteins act as auxin co-receptors during the signaling of nuclear auxins and play a primary role in development-related regulatory processes and responses to biotic and abiotic stresses. However, this gene family and its members have not been explored in Pyrus genomes, and understanding these genes will help obtain useful insights into stress tolerance and ultimately help maintain a high yield of pears. This study reports a pangenome-wide investigation of TIR1/AFB genes from eight Pyrus genomes: Cuiguan (Pyrus pyrifolia), Shanxi Duli (P. betulifolia), Zhongai 1 [(P. ussuriensis × communis) × spp.], Nijisseiki (P. pyrifolia), Yunhong No.1 (P. pyrifolia), d'Anjou (P. communis), Bartlett v2.0 (P. communis), and Dangshansuli v.1.1 (P. bretschneideri). These genes were randomly distributed on 17 chromosomes in each genome. Based on phylogenetics, the identified TIR1/AFB genes were divided into six groups. Their gene structure and motif pattern showed the intraspecific structural conservation as well as evolutionary patterns of Pyrus TIR1/AFBs. The expansion of this gene family in Pyrus is mainly caused by segmental duplication; however, a few genes showed tandem duplication. Moreover, positive and negative selection pressure equally directed the gene's duplication process. The GO and PPI analysis showed that Pyrus TIR1/AFB genes are associated with abiotic stress- and development-related signaling pathways. The promoter regions of Pyrus TIR1/AFB genes were enriched in hormone-, light-, development-, and stress-related cis elements. Furthermore, publicly available RNA-seq data analysis showed that DaTIR1/AFBs have varied levels of expression in various tissues and developmental stages, fruit hardening disease conditions, and drought stress conditions. This indicated that DaTIR1/AFB genes might play critical roles in response to biotic and abiotic stresses. The DaTIR1/AFBs have similar protein structures, which show that they are involved in the same function. Hence, this study will broaden our knowledge of the TIR1/AFB gene family in Pyrus, elucidating their contribution to conferring resistance against various environmental stresses, and will also provide valuable insights for future researchers.

Unveiling the molecular complexity of proliferative diabetic retinopathy through scRNA-seq, AlphaFold 2, and machine learning

Background

Proliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR.

Methods

We analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein-protein interaction (PPI) networks and the interplay among key ORGs.

Results

Our scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis.

Conclusion

This study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

Exploring the mechanism of agarwood moxa smoke in treating sleep disorders based on GC-MS and network pharmacology

Background

Agarwood moxibustion is a folk therapy developed by individuals of the Li nationality in China. There is evidence that agarwood moxa smoke (AMS) generated during agarwood moxibustion therapy can treat sleep disorders via traditional Chinese medicines' multiple target and pathway characteristics. However, the specific components and mechanisms involved have yet to be explored.

Objective

GC-MS (Gas Chromatography-Mass Spectrometry) and network pharmacology were used to investigate AMS's molecular basis and mechanism in treating sleep deprivation.

Method

GC-MS was used to determine the chemical composition of AMS; component target information was collected from TCMSP (Traditional Chinese Medicine Systems Pharmacology), PubChem (Public Chemical Database), GeneCards (Human Gene Database), and DisGeNet (Database of Genes and Diseases) were used to identify disease targets, and JVenn (Joint Venn) was used to identify the common targets of AMS and sleep disorders. STRING was used to construct a protein interaction network, Cytoscape 3.9.1 was used to build a multilevel network diagram of the "core components-efficacy targets-action pathways," the targets were imported into Metascape and DAVID for GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and Autodock was used for molecular docking. This research used a network pharmacology methodology to investigate the therapeutic potential of Agarwood Moxa Smoke (AMS) in treating sleep problems. Examining the target genes and chemical constituents of AMS offers insights into the molecular processes and targets of the disease.

Result

Nine active ingredients comprising anti-inflammatory substances and antioxidants, such as caryophyllene and p-cymene, found seven sleep-regulating signaling pathways and eight targets linked to sleep disorders. GC-MS was used to identify the 94 active ingredients in AMS, and the active ingredients had strong binding with the key targets. Key findings included active components with known medicinal properties, such as p-cymene, eucalyptol, and caryophyllene. An investigation of network pharmacology revealed seven signaling pathways for sleep regulation and eight targets linked to sleep disorders, shedding light on AMS's effectiveness in enhancing sleep quality.

Conclusion

AMS may alleviate sleep disorders by modulating cellular and synaptic signaling, controlling hormone and neurotransmitter pathways, etc. Understanding AMS's material basis and mechanism of action provides a foundation for future research on treating sleep disorders with AMS. According to the study, Agarwood Moxa Smoke (AMS) may improve sleep quality by modifying cellular and synaptic signaling pathways for those who suffer from sleep problems. This might lead to the development of innovative therapies with fewer side effects.

RRP8, associated with immune infiltration, is a prospective therapeutic target in hepatocellular carcinoma

BACKGROUND

Ribosomal RNA Processing 8 (RRP8) is a nucleolar Rossman fold-like methyltransferase that exhibits increased expression in many malignant tumours. However, the role of RRP8 in hepatocellular carcinoma (HCC) is still uncertain. We explored the relationships between RRP8 and prognosis and immune infiltration, as well as the putative pathological function and mechanism of RRP8 in HCC.

METHODS

Analysis of RRP8 expression across cancers was performed by using multiple databases. Associations between RRP8 expression and clinicopathological factors were further examined. Gene enrichment analysis was used to identify various putative biological activities and regulatory networks of RRP8 in HCC. The relationship between RRP8 expression and immune infiltration was confirmed by single-sample gene set enrichment analysis (ssGSEA). Univariate and multivariate Cox regression analyses were conducted to assess the impact of clinical variables on patient outcomes. Furthermore, a nomogram was constructed to estimate survival probability based on multivariate Cox regression analysis. Functional validation of RRP8 in HCC was performed with two different systems: doxycycline-inducible shRNA knockdown and CRISPR-Cas9 knockout.

RESULTS

RRP8 was markedly overexpressed in HCC clinical specimens compared to adjacent normal tissues. Further analysis demonstrated that RRP8 was directly connected to multiple clinical characteristics and strongly associated with various immune markers in HCC. Moreover, elevated RRP8 expression indicated an unfavourable prognosis. Our functional studies revealed that both knockdown and knockout of RRP8 dramatically attenuated liver cancer cells to proliferate and migrate. Knockout of RRP8 decreased the phosphorylation of MEK1/2 and β-catenin-(Y654) signalling pathway components; downregulated downstream signalling effectors, including Cyclin D1 and N-cadherin; and upregulated E-cadherin.

CONCLUSIONS

RRP8 is strongly implicated in immune infiltration and could be a potential therapeutic target in HCC.

Inhibition of ADORA3 promotes microglial phagocytosis and alleviates chronic ischemic white matter injury

BACKGROUND

Adenosine A3 receptor (ADORA3) belongs to the adenosine receptor families and the role of ADORA3 in vascular dementia (VaD) is largely unexplored. The present study sought to determine the therapeutic role of ADORA3 antagonist in a mouse model of VaD.

METHODS

The GSE122063 dataset was selected to screen the differential expression genes and pathways between VaD patients and controls. A mouse model of bilateral carotid artery stenosis (BCAS) was established. The cognitive functions were examined by the novel object recognition test, Y maze test, and fear of conditioning test. The white matter injury (WMI) was examined by 9.4 T MRI, western blot, and immunofluorescence staining. The mechanisms of ADORA3-regulated phagocytosis by microglia were examined using qPCR, western blot, dual immunofluorescence staining, and flow cytometry.

RESULTS

The expression of ADORA3 was elevated in brain tissues of VaD patients and ADORA3 was indicated as a key gene for VaD in the GSE122063. In BCAS mice, the expression of ADORA3 was predominantly elevated in microglia in the corpus callosum. ADORA3 antagonist promotes microglial phagocytosis to myelin debris by facilitating cAMP/PKA/p-CREB pathway and thereby ameliorates WMI and cognitive impairment in BCAS mice. The therapeutic effect of ADORA3 antagonist was partially reversed by the inhibition of the cAMP/PKA pathway.

CONCLUSIONS

ADORA3 antagonist alleviates chronic ischemic WMI by modulating myelin clearance of microglia, which may be a potential therapeutic target for the treatment of VaD.

Bioinformation Analysis of Differential Expression Proteins in Different Processes of COVID-19

COVID-19 is a highly infectious respiratory disease whose progression has been associated with multiple factors. From SARS-CoV-2 infection to death, biomarkers capable of predicting different disease processes are needed to help us further understand the molecular progression of COVID-19 disease. The aim is to find differentially expressed proteins that are associated with the progression of COVID-19 disease or can be potential biomarkers, and to provide a reference for further understanding of the molecular mechanisms of COVID-19 occurrence, progression, and treatment. Data-independent Acquisition (DIA) proteomics to obtain sample protein expression data, using R language screening differentially expressed proteins. Gene Ontology and Kyoto Encyclopedia for Genes and Genomes analysis was performed on differential proteins and protein-protein interaction (PPI) network was constructed to screen key proteins. A total of 47 differentially expressed proteins were obtained from COVID-19 incubation patients and healthy population (L/H), mainly enriched in platelet-related functions, and complement and coagulation cascade reaction pathways, such as platelet degranulation and platelet aggregation. A total of 42 differential proteins were obtained in clinical and latent phase patients (C/L), also mainly enriched in platelet-related functions and in complement and coagulation cascade reactions, platelet activation pathways. A total of 10 differential proteins were screened in recovery and clinical phase patients (R/C), mostly immune-related proteins. The differentially expressed proteins in different stages of COVID-19 are mostly closely associated with coagulation, and key differential proteins, such as FGA, FGB, FGG, ACTB, PFN1, VCL, SERPZNCL, APOC3, LTF, and DEFA1, have the potential to be used as early diagnostic markers.

Colorectal cancer subtyping and immune landscape analysis based on natural killer cell-related genes

BACKGROUND AND STUDY AIMS

The prognosis of colorectal cancer (CRC) is related to natural killer (NK) cells, but the molecular subtype features of CRC based on NK cells are still unknown. This study aimed to identify NK cell-related molecular subtypes of CRC and analyze the survival status and immune landscape of patients with different subtypes.

PATIENTS/MATERIAL AND METHODS

mRNA expression data, single nucleotide variant (SNV) data, and clinical information of CRC patients were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were obtained through differential analysis, and the intersection was taken with NK cell-associated genes to obtain 103 NK cell-associated CRC DEGs (NCDEGs). Based on NCDEGs, CRC samples were divided into three clusters through unsupervised clustering analysis. Survival analysis, immune analysis, Gene Set Enrichment Analysis (GSEA), and tumor mutation burden (TMB) analysis were performed. Finally, NCDEG-related small-molecule drugs were screened using the CMap database.

RESULTS

Survival analysis revealed that cluster2 had a lower survival rate than cluster1 and cluster3 (p < 0.05). Immune infiltration analysis found that the immune infiltration levels and immune checkpoint expression levels of cluster1_3 were substantially higher than those of cluster2, and the tumor purity was the opposite (p < 0.05). GSEA presented that cluster1_3 was significantly enriched in the chemokine signaling pathway, ECM receptor interaction, and antigen processing and presentation pathways (p < 0.05). The TMB of cluster1_3 was significantly higher than that of cluster2 (p < 0.05). Genes with the highest mutation rate in CRC were APC, TP53, TTN, and KRAS. Drug prediction results showed that small-molecule drugs that reverse the upregulation of NCDEGs, deoxycholic acid, dipivefrine, phenformin, and other drugs may improve the prognosis of CRC.

CONCLUSION

NK cell-associated CRC subtypes can be used to evaluate the tumor characteristics of CRC patients and provide an important reference for CRC patients.

Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation Analysis Reveal Insights into the Molecular Mechanism of Cordia myxa in the Treatment of Liver Cancer

Traditional treatments of cancer have faced various challenges, including toxicity, medication resistance, and financial burdens. On the other hand, bioactive phytochemicals employed in complementary alternative medicine have recently gained interest due to their ability to control a wide range of molecular pathways while being less harmful. As a result, we used a network pharmacology approach to study the possible regulatory mechanisms of active constituents of Cordia myxa for the treatment of liver cancer (LC). Active constituents were retrieved from the IMPPAT database and the literature review, and their targets were retrieved from the STITCH and Swiss Target Prediction databases. LC-related targets were retrieved from expression datasets (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790) through gene expression omnibus (GEO). The DAVID Gene Ontology (GO) database was used to annotate target proteins, while the Kyoto Encyclopedia and Genome Database (KEGG) was used to analyze signaling pathway enrichment. STRING and Cytoscape were used to create protein-protein interaction networks (PPI), while the degree scoring algorithm of CytoHubba was used to identify hub genes. The GEPIA2 server was used for survival analysis, and PyRx was used for molecular docking analysis. Survival and network analysis revealed that five genes named heat shot protein 90 AA1 (HSP90AA1), estrogen receptor 1 (ESR1), cytochrome P450 3A4 (CYP3A4), cyclin-dependent kinase 1 (CDK1), and matrix metalloproteinase-9 (MMP9) are linked with the survival of LC patients. Finally, we conclude that four extremely active ingredients, namely cosmosiin, rosmarinic acid, quercetin, and rubinin influence the expression of HSP90AA1, which may serve as a potential therapeutic target for LC. These results were further validated by molecular dynamics simulation analysis, which predicted the complexes with highly stable dynamics. The residues of the targeted protein showed a highly stable nature except for the N-terminal domain without affecting the drug binding. An integrated network pharmacology and docking study demonstrated that C. myxa had a promising preventative effect on LC by working on cancer-related signaling pathways.

Comparative analysis of prognosis and gene expression in prostate cancer patients with site-specific visceral metastases

INTRODUCTION

Prostate cancer patients with visceral metastases often exhibited poor prognoses. Few researches had compared the prognostic impact and gene expression profiles among distinct visceral metastatic sites. Therefore, we conducted a comprehensive study utilizing data from the Surveillance, Epidemiology, and End Results (SEER) database and the Gene Expression Omnibus database.

PATIENTS AND METHODS

We analyzed the prostate cancer-specific mortality (PCSM) risk for 8,170 patients diagnosed with metastatic prostate cancer (mPCa) between 2000 and 2019, utilizing data from the SEER 17 registry database. Patients with metastatic disease in nonregional lymph nodes, bones, brains, livers, and lungs were identified. Competing risks regression was employed to evaluate the effect of visceral metastatic disease sites on PCSM. Differentially expressed genes (DEGs) between visceral metastases were assessed using data from the GSE6752 dataset. A relative protein-protein interaction (PPI) network was constructed based on STRING analysis. Furthermore, we explored the distribution of DEGs in various normal tissues and tumor tissues using the Human Protein Atlas and GEPIA.

RESULTS

Competing risks regression analysis revealed that liver and lung metastases had a substantial impact on PCSM (hazard ratio 2.24, 95% confidence interval 1.70-2.95, P < 0.001; hazard ratio 1.30, 95% confidence interval 1.06-1.59, P = 0.012, respectively). Seven significant DEGs were identified from samples of liver and lung metastases (HERV-FRD, NUDT12, FAM63A, SCGB3A1, CEACAM6, LOC440416, SFTPB) and were associated with respiratory gaseous exchange, pulmonary surfactant metabolism, and fibronectin matrix formation in PPI network analysis. Notably, the expression levels of the three DEGs significantly upregulated in lung metastases were also found to be higher in normal lung tissues compared to normal liver tissues.

CONCLUSION

Patients diagnosed with mPCa and presenting with liver and/or lung metastases exhibit poorer prognoses. SCGB3A1, identified as a tumor suppressor gene, may contribute to the better survival prognosis observed in patients with prostate cancer lung metastases compared to those with liver metastases. The gene expression profiles in these two specific metastatic sites reveal a combination of both heterogeneity and homogeneity.

Regulatory role of AGC genes in heat stress adaptation in maize (Zea mays)

Heat stress represents a significant environmental challenge that restricts maize (Zea mays ) growth and yield on a global scale. Within the plant kingdom, the AGC gene family, encoding a group of protein kinases, has emerged as crucial players in various stress responses. Nevertheless, a comprehensive understanding of AGC genes in Z. mays under heat-stress conditions remains elusive. A genome-wide analysis was done using bioinformatics techniques to identify 39 AGC genes in Z. mays , categorising them into three subfamilies based on their conserved domains. We investigated their phylogenetic relationships, gene structures (including intron-exon configurations), and expression patterns. These genes are likely involved in diverse signalling pathways, fulfilling distinct roles when exposed to heat stress conditions. Notably, most ZmAGC1.5, ZmAGC1.9, ZmNDR3, ZmNDR5 and ZmIRE3 exhibited significant changes in expression levels under heat stress, featuring a high G-box ratio. Furthermore, we pinpointed a subset of AGC genes displaying highly coordinated expression, implying their potential involvement in the heat stress response pathway. Our study offers valuable insights into the contribution of AGC genes to Z. mays 's heat stress response, thus facilitating the development of heat-tolerant Z. mays varieties.

Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer

Background

Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.

Materials and methods

Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.

Results

An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.

Conclusion

The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.

Pan-cancer prognostic model and immune microenvironment analysis of natural killer cell-related genes

Background

Natural killer (NK) cells play a significant role in antitumor immunity and are closely related to tumor prognosis and recurrence. NK cell-based tumor immunotherapy, including immune checkpoint inhibition and CAR-engineered NK cells, is a promising area of research. However, there is a need for better NK cell-related models and associated biomarkers.

Methods

The sequences of NK cell-related genes were obtained from the published NK cell CRISPR/Cas9 library data, and the common genes were selected as NK cell-related genes. The RNA sequencing (RNA-seq) and clinical data of 32 solid tumors from The Cancer Genome Atlas (TCGA) were downloaded from the UCSC Xena database, and the RNA-seq data of normal samples were downloaded from the Genotype-Tissue Expression (GTEx) database. The differentially expressed NK cell-related genes (DENKGs) between the tumor and normal samples were analyzed. The DENKGs related to the prognosis of solid tumors were selected via univariate Cox analysis, and 32 kinds of solid tumor prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis. Survival, receiver operating characteristic (ROC), and independent prognostic analyses were employed to test the effectiveness of the model, along with a nomogram model and prediction curve. Differences in the immune pathways and microenvironment cells were analyzed between the high- and low-risk groups identified by the model.

Results

We constructed a pan-cancer prognostic model with 63 NK cell-related genes and further identified DEPDC1 and ASPM as potentially offering new directions in tumor research by literature screening.

Conclusions

In this study, 63 prognostic solid tumor markers were investigated using NK cell-related genes, and for the first time, a pan-cancer prognostic model was constructed to analyze their role in the immune microenvironment, which may contribute new insights into tumor research.

Integrated bioinformatics analysis and experimental animal models identify a robust biomarker and its correlation with the immune microenvironment in pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) represents a substantial global risk to human health. This study aims to identify diagnostic biomarkers for PAH and assess their association with the immune microenvironment through the utilization of sophisticated bioinformatics techniques.

Methods

Based on two microarray datasets, differentially expressed genes (DEGs) were detected, and hub genes underwent a sequence of machine learning analyses. After pathways associated with PAH were assessed by gene enrichment analysis, the identified genes were validated using external datasets and confirmed in a monocrotaline (MCT)-induced rat model. In addition, three algorithms were employed to estimate the proportions of various immune cell types, and the link between hub genes and immune cells was substantiated.

Results

Using SVM, LASSO, and WGCNA, we identified seven hub genes, including (BPIFA1, HBA2, HBB, LOC441081, PI15, S100A9, and WIF1), of which only BPIFA1 remained stable in the external datasets and was validated in an MCT-induced rat model. Furthermore, the results of the functional enrichment analysis established a link between PAH and both metabolism and the immune system. Correlation assessment showed that BPIFA1 expression in the MCP-counter algorithm was negatively associated with various immune cell types, positively correlated with macrophages in the ssGSEA algorithm, and correlated with M1 and M2 macrophages in the CIBERSORT algorithm.

Conclusion

BPIFA1 serves as a modulator of PAH, with the potential to impact the immune microenvironment and disease progression, possibly through its regulatory influence on both M1 and M2 macrophages.

Effect of Poria cocos Terpenes: Verifying Modes of Action Using Molecular Docking, Drug-Induced Transcriptomes, and Diffusion Network Analyses

We characterized the therapeutic biological modes of action of several terpenes in Poria cocos F.A Wolf (PC) and proposed a broad therapeutic mode of action for PC. Molecular docking and drug-induced transcriptome analysis were performed to confirm the pharmacological mechanism of PC terpene, and a new analysis method, namely diffusion network analysis, was proposed to verify the mechanism of action against Alzheimer's disease. We confirmed that the compound that exists only in PC has a unique mechanism through statistical-based docking analysis. Also, docking and transcriptomic analysis results could reflect results in clinical practice when used complementarily. The detailed pharmacological mechanism of PC was confirmed by constructing and analyzing the Alzheimer's disease diffusion network, and the antioxidant activity based on microglial cells was verified. In this study, we used two bioinformatics approaches to reveal PC's broad mode of action while also using diffusion networks to identify its detailed pharmacological mechanisms of action. The results of this study provide evidence that future pharmacological mechanism analysis should simultaneously consider complementary docking and transcriptomics and suggest diffusion network analysis, a new method to derive pharmacological mechanisms based on natural complex compounds.

Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth

Background

Intrauterine growth restriction (IUGR) and preeclampsia (PE) are intricately linked with specific maternal health conditions, exhibit shared placental abnormalities, and play pivotal roles in precipitating preterm birth (PTB) incidences. However, the molecular mechanism underlying the association between PE and IUGR has not been determined. Therefore, we aimed to analyze the data of females with PE and those with PE + IUGR to identify the key gene(s), their molecular pathways, and potential therapeutic interactions.

Methods

In this study, a comprehensive relationship analysis of both PE and PE + IUGR was conducted using RNA sequence datasets. Using two datasets (GSE148241 and GSE114691), differential gene expression analysis via DESeq2 through R-programming was performed. Gene set enrichment analysis was performed using ClusterProfiler, protein‒protein interaction (PPI) networks were constructed, and cluster analyses were conducted using String and MCODE in Cytoscape. Functional enrichment analyses of the resulting subnetworks were performed using ClueGO software. The hub genes were identified under both conditions using the CytoHubba method. Finally, the most common hub protein was docked against a library of bioactive flavonoids and PTB drugs using the PyRx AutoDock tool, followed by molecular dynamic (MD) simulation analysis. Pharmacokinetic analysis was performed to determine the ADMET properties of the compounds using pkCSM.

Results

We identified eight hub genes highly expressed in the case of PE, namely, PTGS2, ENG, KIT, MME, CGA, GAPDH, GPX3, and P4HA1, and the network of the PE + IUGR gene set demonstrated that nine hub genes were overexpressed, namely, PTGS2, FGF7, FGF10, IL10, SPP1, MPO, THBS1, CYBB, and PF4. PTGS2 was the most common hub gene found under both conditions (PE and PEIUGR). Moreover, the greater (-9.1 kcal/mol) molecular binding of flavoxate to PTGS2 was found to have satisfactory pharmacokinetic properties compared with those of other compounds. The flavoxate-bound PTGS2 protein complex remained stable throughout the simulation; with a ligand fit to protein, i.e., a RMSD ranging from ∼2.0 to 4.0 Å and a RMSF ranging from ∼0.5 to 2.9 Å, was observed throughout the 100 ns analysis.

Conclusion

The findings of this study may be useful for treating PE and IUGR in the management of PTB.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Comparative genomics analysis of pheophorbide a oxygenase (PAO) genes in eight pyrus genomes and their regulatory role in multiple stress responses in Chinese pear (Pyrus bretschneideri)

Pyrus (pear) is among the most nutritious fruits and contains fibers that have great health benefits to humans. It is mostly cultivated in temperate regions globally and is highly subjected to biotic and abiotic stresses which affect its yield. Pheophorbide a oxygenase (PAO) is an essential component of the chlorophyll degradation system and contributes to the senescence of leaves. It is responsible for opening the pheophorbide a porphyrin macrocycle and forming the main fluorescent chlorophyll catabolite However, this gene family and its members have not been explored in Pyrus genomes. Here we report a pangenome-wide investigation has been conducted on eight Pyrus genomes: Cuiguan, Shanxi Duli, Zhongai 1, Nijisseiki, Yunhong No.1, d'Anjou, Bartlett v2.0, and Dangshansuli v.1.1. The phylogenetic history, their gene structure, conservation patterns of motifs, their distribution on chromosomes, and gene duplication are studied in detail which shows the intraspecific structural conservation as well as evolutionary patterns of Pyrus PAOs. Cis-elements, protein-protein interactions (PPI), and the Gene Ontology (GO) enrichment analyses show their potential biological functions. Furthermore, their expression in various tissues, fruit hardening conditions, and drought stress conditions is also studied. Based on phylogenetics, the identified PAOs were divided into four groups. The expansion of this gene family in Pyrus is caused by both tandem and segmental duplication. Moreover, positive and negative selection pressure equally directed the gene's duplication process. The Pyrus PAO genes were enriched in hormones-related, light, development, and stress-related elements. RNA-seq data analysis showed that PAOs have varied levels of expression under diseased and abiotic stress conditions. The 3D structures of PAOs are also predicted to get more insights into functional conservation. Our research can be used further to get a deeper knowledge of the PAO gene family in Pyrus and to guide future research on improving the genetic composition of Pyrus to enhance stress tolerance.

Integrative metabolomic and transcriptomic analyses reveals the accumulation patterns of key metabolites associated with flavonoids and terpenoids of Gynostemma pentaphyllum (Thunb.) Makino

Gynostemma pentaphyllum (Thunb.) Makino (G. pentaphyllum) is a medicinal and edible plant with multiple functions of liver protection, anti-tumor, anti-inflammation, balancing blood sugar and blood lipids. The nutritional value of the G. pentaphyllum plant is mainly due to its rich variety of biologically active substances, such as flavonoids, terpenes and polysaccharides. In this study, we performed a comprehensive analysis combining metabolomics and root, stem and leaf transcriptomic data of G. pentaphyllum. We used transcriptomics and metabolomics data to construct a dynamic regulatory network diagram of G. pentaphyllum flavonoids and terpenoids, and screened the transcription factors involved in flavonoids and terpenoids, including basic helix-loop-helix (bHLH), myb-related, WRKY, AP2/ERF. Transcriptome analysis results showed that among the DEGs related to the synthesis of flavonoids and terpenoids, dihydroflavonol 4-reductase (DFR) and geranylgeranyl diphosphate synthases (GGPPS) were core genes. This study presents a dynamic image of gene expression in different tissues of G. pentaphyllum, elucidating the key genes and metabolites of flavonoids and terpenoids. This study is beneficial to a deeper understanding of the medicinal plants of G. pentaphyllum, and also provides a scientific basis for further regulatory mechanisms of plant natural product synthesis pathways and drug development.

Bioinformatics analysis revealed underlying molecular mechanisms associated with asthma severity and identified GABAergic related pathway as a potential therapy for Th2-high endotype asthma

Background

Asthma, a principally T helper 2 (Th2) cell mediated immunological disease, is categorized into Th2-high and Th2-low endotypes. The influence of these endotypes on clinical characteristics and treatment responsiveness in asthma is yet to be completely understood. This study delves into the underlying molecular mechanisms of Th2 endotypes on asthma.

Methods

Transcriptomics data of airway epithelial and corresponding clinical information were sourced from the GEO. The co-expression modules were established by WGCNA. Cytoscape was applied to construct PPI networks, and hub genes were determined via the Cytohubba plugin. Additionally, a functional enrichment analysis was conducted on the co-expressed genes from the relevant modules. The relative abundances levels of 22 different types of immune cells in asthma patients were evaluated by CIBERSORT algorithm.

Results

There were 471 genes in the pink module significantly correlated with Th2 endotype. Overall, 151 DEGs were identified in the various Th2 endotypes, and 66 were obtained through intersection with the pink module. In the PPI network, the ten most important genes that regulate Th2 endotypes were selected as hub genes. In Th2-high endotype asthma, the hub genes were significantly related to γ-aminobutyric acid (GABA) pathways, indicating that hub genes can mainly regulate Th2-high endotype asthma through GABAergic system.

Conclusions

The severity of asthma is influenced by different Th2 endotypes. GABAergic related hub genes may provide innovative insights for the treatment of Th2-high asthma.

Unraveling the anti-breast cancer activity of Cimicifugae rhizoma using biological network pathways and molecular dynamics simulation

Cimicifugae is a commonly used treatment for breast cancer, but the specific molecular mechanisms underlying its effectiveness remain unclear. In this research, we employ a combination of network pharmacology, molecular docking, and molecular dynamics simulations to uncover the most potent phytochemical within Cimicifugae rhizoma in order to delve into its interaction with the target protein in breast cancer treatment. We identified 18 active compounds and 89 associated targets, primarily associated to various biological processes such as lipid metabolism, the signaling pathway in diabetes, viral infections, and cancer-related pathways. Molecular docking analysis revealed that the two most active compounds, Formononetin and Cimigenol, exhibit strong binding to the target protein AKT1. Through molecular dynamics simulations, we found that the Cimigenol-AKT1 complex exhibits greater structural stability and lower interaction energy compared to the stigmasterol-AKT1 complex. Our study demonstrates that Cimicifugae rhizoma exerts its effects in breast cancer treatment through a multi-component, multi-target synergistic approach. Furthermore, we propose that Cimigenol, targeting AKT-1, represents the most effective compound, offering valuable insights into the molecular mechanisms underpinning its role in breast cancer therapy.