Protein-protein Interaction Network Research Articles

Gene expression profile analysis of severe influenza-based modulation of idiopathic pulmonary fibrosis

BackgroundIt is known severe influenza infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other. Till now, no associated mechanism has been reported.MethodWe collected the genetic pattern of expression of severe influenza (GSE111368) and IPF (GSE70866) from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (C-DEGs) were identified from the two datasets, and using this data, we conducted three forms of analyses, functional annotation, protein–protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis.ResultsIn all, 174 C-DEGs were selected for additional analyses. Based on our functional analysis, these C-DEGs mediated inflammatory response and cell differentiation. Furthermore, using cytoHubba, we identified 15 genes, namely, MELK, HJURP, BIRC5, TPX2, TK1, CDT1, UBE2C, UHRF1, CCNA2, TYMS, CDCA5, CDCA8, RAD54L, CCNB2, and ITGAM, which served as hub genes to possibly contribute to severe influenza patients with IPF disease as comorbidity. The hub gene expressions were further confirmed using two stand-alone datasets (GSE101702 for severe influenza and GSE10667 for IPF).ConclusionHerein, we demonstrated the significance of common pathways and critical genes in severe influenza and IPF etiologies. The identified pathways and genes may be employed as possible therapeutic targets for future therapy against severe influenza patients with IPF.

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

Vascular endothelial growth factor/connective tissue growth factor and proteomic analysis of aqueous humor after intravitreal conbercept for proliferative diabetes retinopathy.

To investigate the role of connective tissue growth factor (CTGF) and vascular endothelial growth factor (VEGF) in the protein profile of the aqueous humor in patients with proliferative diabetic retinopathy (PDR) following intravitreal injection of conbercept. This study included 72 PDR patients and 8 cataract patients as controls. PDR patients were divided into 3 groups according to the intervals of 3, 5, and 7d between intravitreal conbercept (IVC, 0.5 mg/0.05 mL) injection and pars plana vitrectomy (PPV) performed. Aqueous humor samples were collected before and after IVC and PPV for VEGF and CTGF levels detected with enzyme-linked immunosorbent assay (ELISA). The differential proteomics of 10 patients who underwent PPV surgery 5d after IVC and 8 normal controls was studied, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the data, and the protein interaction network of 23 differential proteins was studied. Post-IVC, VEGF levels decreased and CTGF levels increased significantly in aqueous humor, with the CTGF/VEGF ratio rising significantly at all intervals. Liquid chromatography tandem mass spectrometry (LC-MS/MS) identified differentially expressed proteins between pre- and post-IVC samples. GO and KEGG analyses revealed involvement in immune response, stress response, complement and coagulation cascades, ferroptosis, and PPAR signaling pathways. PPI analysis highlighted key proteins like APOA1, C3, and transferrin (TF). ELISA assay confirmed the differential expression of proteins such as HBA1, SERPINA1, COL1A1, and ACTB, with significant changes in the IVC groups. The study demonstrates that IVC effectively reduces VEGF levels while increasing CTGF levels, thereby modifying the CTGF/VEGF ratio, and IVC significantly alters the protein profile in the aqueous humor of patients with PDR. Proteomic analysis reveals that these changes are associated with critical biological pathways and protein interactions involved in immune response, stress response, and cellular metabolism.

MicroRNA expression profiles in plasma exosomes of late pregnant giant pandas.

MicroRNAs can regulate various biological functions including cell proliferation, differentiation, embryo formation, and implantation. The giant panda exhibits embryonic diapause, with embryo development resuming in late pregnancy. However, the changes in microRNAs during late pregnancy remain poorly understand. After mating, plasma samples were collected on day 40 of early pregnancy (EP; n = 3) and 30 days before delivery of late pregnancy (LP; n = 3). Following microRNAs screening, a total of 120 microRNAs were detected in the plasma exosomes of pregnant pandas. Nine differentially expressed microRNAs (DEmicroRNAs) were identified in LP compared to EP, including three that were upregulated and six that were downregulated. Notably, miR-25b and miR-47 were significantly downregulated in LP group. All DEmicroRNAs were predicted to target a total of 2,675 genes. Pathway enrichment analysis of these target genes revealed significant enrichment in the MAPK and Rap1 signaling pathways, which are closely related to cell proliferation, differentiation, and cell-cell and cell-matrix interactions. Analysis of protein-protein interaction networks showed that most of the hub genes (five out of eight), including Fgfr1, Fgf2, Fgf18, Erbb4, and Kras within the MAPK and Rap1 pathways are associated with the cell proliferation and differentiation. Significantly, Erbb4 was regulated by significantly differentially expressed miRNA-47. We suggest that plasma exosomal microRNAs are involved in cell proliferation and differentiation during embryonic development by regulating key hub genes within MAPK and Rap1 pathways. These findings provided new insights into the development of giant panda embryos.

Predicting “pain genes”: multi-modal data integration using probabilistic classifiers and interaction networks

Abstract Accurate identification of pain-related genes remains challenging due to the complex nature of pain pathophysiology and the subjective nature of pain reporting in humans, or inferring pain states in animals on the basis of behaviour. Here, we use a machine learning approach to identify possible “pain genes”. Labelling was based on a gold-standard list of genes with validated involvement across pain conditions, and was trained on a selection of -omics, protein-protein interaction network features, and biological function readouts for each gene. Multiple classifiers were trained, and the top-performing model was selected to predict a “pain score” per gene. The top ranked genes were validated against pain-related human SNPs to validate against human genetics studies. Functional analysis revealed JAK2/STAT3 signal, ErbB, and Rap1 signalling pathways as promising targets for further exploration, while network topological features contribute significantly to the identification of “pain” genes. As such, a network based on top-ranked genes was constructed to reveal previously uncharacterised pain-related genes including CHRFAM7A and UNC79. These analyses can be further explored using the linked open-source database at https://livedataoxford.shinyapps.io/drg-directory/, which is accompanied by a freely accessible code template and user guide for wider adoption across disciplines. Together, the novel insights into pain pathogenesis can indicate promising directions for future experimental research.

Exploring the mechanism of Danggui Sini Decoction in the treatment of myocardial infarction: A systematic review, network pharmacology, and molecular docking.

Myocardial infarction (MI) is one of the leading causes of death worldwide because of its high morbidity and mortality. Traditional Chinese Medicine compounds play a crucial role in preventing cardiovascular diseases. Danggui Sini Decoction (DSD) is widely used clinically for cardiovascular diseases. However, the mechanism, main components, and main targets of DSD in treating MI are still unclear. In this study, we utilized network pharmacology and molecular docking for exploration. MI-related genes were examined using the Genecards database, and the active ingredients of DSD were screened based on System Pharmacology Database and Analysis Platform of Traditional Chinese Medicine by oral bioavailability ≥ 30% and drug-likeness ≥ 0.18. The protein-protein interaction network diagram was generated using the STRING database. The DAVID web platform was used to carry out gene ontology and Kyoto encyclopedia of gene and genome signaling pathway analysis. DSD's screening study revealed 120 primary active ingredients and 561 putative active target genes. The main therapeutic targets were TP53, EGFR, AKT1, IL6, TNF, STAT3, IL1B, CTNNB1, SRC, MYC, JUN, and INS. Gene ontology and Kyoto encyclopedia of gene and genome analyses revealed that DSD treatment of MI mainly involves the positive regulation of the ERK1 and ERK2 cascades, positive regulation of cell proliferation, inflammatory responses, aging, and the MAPK cascade, along with other biological processes. The molecular docking results indicate that DSD drugs may interact with AKT1, EGFR, TP53, and TNF through formononetin, isorhamnetin, β-Sitosterol, and kaempferol, potentially contributing to the treatment of MI. By utilizing a multi-component, multi-pathway, and multi-target mode of action, DSD may have the potential to prevent MI.

Integrating network pharmacology and experimental verification to explore the pharmacological mechanisms of Guanxin Shutong capsule in treating heart failure.

The Guanxin Shutong capsule (GXST), a traditional Chinese medicine, is commonly used for treating cardiovascular disease, it has shown efficacy in improving symptoms and enhancing the quality of life for patients with heart failure (HF). However, the specific mechanism of action of GXST in HF remains unclear. In this study, we employed a comprehensive approach combining network pharmacology, molecular dynamics (MD) simulations, and in vitro validations to investigate the potential targets and molecular mechanisms of GXST against HF. We collected active ingredients and target genes of GXST, as well as related genes of HF, from multiple public databases. Using bioinformatics analysis, we constructed networks of ingredients-disease-targets and performed functional annotations of the core targets. MD simulations were conducted to verify the binding between the core protein-ligand complexes. In vitro evaluations, including cell proliferation, apoptosis, and protein expression in human umbilical vein endothelial cells (HUVECs) and H9C2 cells were treated with GXST, were performed for pharmacodynamics evaluation. Network analysis revealed 320 intersection genes and 74 active ingredients in the Herbs-ingredients-target genes-disease network. We identified key active ingredients and target genes that overlapped. The KEGG pathways of the intersection genes were primarily enriched in the PI3K-Akt signaling pathway and apoptosis. The protein-protein interaction network highlighted proteins such as AKT1, VEGFR2, and eNOS. MD simulations confirmed stable docking and lower binding energy between 4 identified ingredients (kaempferol, quercetin, (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl) chroman-4-one, and ellagic acid) and their respective core proteins (VEGFR2, eNOS, and AKT). In vitro experiments demonstrated the protective effects of GXST against H2O2-induced apoptosis in both HUVECs and H9C2 cells. Notably, consistent with the in silico predictions, GXST effectively activates the VEGFR2/AKT/eNOS signaling pathways in HUVECs. This study provides insights into the underlying mechanism of GXST's therapeutic effects in heart failure. The involvement of the VEGFR2/AKT/eNOS signaling pathways suggests their importance in further elucidating and applying GXST in the clinical treatment of heart failure.

Exploring the Key Pathogenesis and Potential Intervention Targets of Sulforaphane in Acute Kidney Injury in Sepsis Based on Bioinformatics.

The objective was to use bioinformatics to analyze the potential key genes involved in the mechanism of sulforaphane's protective effects in sepsis-associated acute kidney injury (SA-AKI) and to identify potential intervention targets. Gene Expression Omnibus (GEO) gene chip datasets containing gene expression profiles from kidney tissues of SA-AKI patients and normal controls were selected. Upregulated differentially expressed genes (DEGs) were identified using GEO2R. Protein-protein interaction (PPI), GO, and KEGG enrichment analyses were performed. Allyl isothiocyanate's target genes were analyzed in the ChEMBL database, and intersections with the above DEGs were presented in Venn diagrams. Rat tissues were examined for FKBP1A expression using qRT-PCR. A total of 17 DEGs related to SA-AKI were obtained (|log fold change| > 0 and P < .05). KEGG pathway analysis indicated that the primary pathways linked to the elevated DEGs were glycogen breakdown, leukocyte trans-endothelial migration, and T-cell receptor, TNF, and NF-κB signaling. The module and PPI network analysis of common DEGs revealed one cluster and four candidate genes, including OASL, TRRAP, FKBP1A, and BANF. ChEMBL database analysis identified 339 target genes for allyl isothiocyanate, and the intersection with upregulated DEGs related to SA-AKI injury yielded two co-expressed genes, FKBP1A and TRRAP. According to the findings of the qRT-PCR assay, the kidney tissues of the model cohort showed significantly higher expression levels of FKBP1A mRNA than the control cohort (P = .0142). Allyl isothiocyanate may alleviate SA-AKI injury by targeting FKBP1/NF-κB.

Computational approaches for identifications of altered ion channels in keratoconus.

Keratoconus is an etiologically complex, degenerative corneal disease that eventually leads to loss of corneal integrity. Cells in corneal epithelium and endothelium express various types of ion channels that play important roles in ocular pathology. This emphasizes the need of understanding alterations of ion channels in keratoconus. Differential gene expression analysis was performed to identify deregulated ion channels in keratoconus patients using transcriptomic data. Thereafter correlation analysis of ion channel expression was performed to obtain the changed correlation between ion channels' expression in keratoconus patients versus control samples. Moreover, Protein-protein interaction networks and a pathway map was constructed to identify cellular processes altered due to the deregulation of ion channels. Furthermore, drugs interacting with deregulated ion channels were identified. Total 75 ion channels were found to be deregulated in keratoconus, of which 12 were upregulated and 63 were downregulated. Correlations between ion channel expressions found to be different in control and keratoconus samples. Thereafter, protein-protein interactions network was generated to identify hub ion channels in network. Furthermore, the pathway map was constructed to depict calcium signalling, MAPK signalling, synthesis and secretion of cortisol, and cAMP signalling. The 19 FDA- approved drugs that interact with the 6 deregulated ion channels were identified. Down-regulation of voltage-gated calcium channels can be attributed to reduced cell proliferation and differentiation. Additionally, deregulated ion channels in 3',5'- cyclic adenosine monophosphate signalling may be responsible for elevated cortisol level in progressive keratoconus patients.

Berberine Ameliorates High-fat-induced Insulin Resistance in HepG2 Cells by Modulating PPARs Signaling Pathway.

Berberine (BBR), also known as berberine hydrochloride, was isolated from the rhizomes of the Coptis chinensis. Studies have reported that BBR plays an important role in glycolipid metabolism, including insulin (IR). The targets, and molecular mechanisms of BBR against hyperlipid-induced IR is worthy to be further studied. The related targets of BBR were identified via Pharmmapper database and relevant targets of diabetes were obtained through GeneCards and Online Mendelian Inheritance in Man (OMIM) database. The common targets were employed with the STRING database and visualized with the protein-protein interactions (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the biological progress and pathways. In vitro, human hepatocellular carcinomas (HepG2) cell was used as experimental cell line, and an insulin resistant HepG2 cell model (IR-HepG2) was constructed using free fatty acid induction. After intervention with BBR, glucose consumption and uptake in HepG2 cells were observed. Molecular docking was used to test the interaction between BBR and key targets, and real-time fluorescence quantitative PCR was used to detect the regulatory effect of BBR on related targets. 262 overlapped targets were extracted from BBR and diabetes. In the KEGG enrichment analysis, the peroxisome proliferator activated receptor (PPAR) signaling pathway was included. In vitro experiments, BBR can significantly increase sugar consumption and uptake in IR HepG2 cells, while PPAR inhibitors can weaken the effect of BBR on IR-HepG2. The PPAR signaling pathway is one of the important pathways for BBR to improve high-fat-induced insulin resistance in HepG2 cells.

Decoding the Genetic Basis of Mast Cell Hypersensitivity and Infection Risk in Hypermobile Ehlers-Danlos Syndrome.

Hypermobile Ehlers-Danlos syndrome (hEDS) is a connective tissue disorder marked by joint hypermobility, skin hyperextensibility, and tissue fragility. Recent studies have linked hEDS with mast cell activation syndrome (MCAS), suggesting a genetic interplay affecting immune regulation and infection susceptibility. This study aims to decode the genetic basis of mast cell hypersensitivity and increased infection risk in hEDS by identifying specific genetic variants associated with these conditions. We conducted whole-genome sequencing (WGS) on 18 hEDS participants and 7 first-degree relatives as controls, focusing on identifying genetic variants associated with mast cell dysregulation. Participants underwent clinical assessments to document hEDS symptoms and mast cell hypersensitivity, with particular attention to past infections and antihistamine response. Our analysis identified specific genetic variants in MT-CYB, HTT, MUC3A, HLA-B and HLA-DRB1, which are implicated in hEDS and MCAS. Protein-protein interaction (PPI) network analysis revealed significant interactions among identified variants, highlighting their involvement in pathways related to antigen processing, mucosal protection, and collagen synthesis. Notably, 61.1% of the hEDS cohort reported recurrent infections compared to 28.5% in controls, and 72.2% had documented mast cell hypersensitivity versus 14.2% in controls. These findings provide a plausible explanation for the complex interplay between connective tissue abnormalities and immune dysregulation in hEDS. The identified genetic variants offer insights into potential therapeutic targets for modulating mast cell activity and improving patient outcomes. Future research should validate these findings in larger cohorts and explore the functional implications of these variants to develop effective treatment strategies for hEDS and related mast cell disorders.

Female Specific Restrictive Cardiomyopathy and Metabolic Dysregulation in transgenic mice expressing a Peptide of the Amino-Terminus of GRK2.

Cardiovascular disease and heart failure are a major health challenge, with sex differences in pathophysiology and treatment responses critically influencing patient outcomes. G protein-coupled receptor (GPCR) kinase 2 (GRK2) is a pivotal regulator of cellular signaling whose elevation is a hallmark of heart failure progression. Its complex network of protein interactions impact a wide range of physiological and pathophysiological processes including cardiac function. In this study, we examined the effects of cardiac-restricted expression of an amino-terminal peptide of GRK2 (βARKnt) in female mice subjected to acute and chronic pressure overload. Our findings reveal that that βARKnt affects hypertrophy development and cardiac function differently in female mice than in males, leading to a transition to heart failure not observed in control females or βARKnt males. Notably, the βARKnt female mice exhibited baseline hypertrophy with distinct left atrial morphology, increased fibrosis, and immune cell infiltration compared to the controls, which progressed under chronic stress, indicating adverse cardiac remodeling. Furthermore, βARKnt female mice, unlike males, exhibit impaired tissue respiration following acute pressure overload and altered glucose sensitivity and insulin tolerance, highlighting significant remodeling of cardiac and systemic metabolism.

Genome-Scale Identification of Wild Soybean Serine/Arginine-Rich Protein Family Genes and Their Responses to Abiotic Stresses.

Serine/arginine-rich (SR) proteins mostly function as splicing factors for pre-mRNA splicing in spliceosomes and play critical roles in plant development and adaptation to environments. However, detailed study about SR proteins in legume plants is still lacking. In this report, we performed a genome-wide investigation of SR protein genes in wild soybean (Glycine soja) and identified a total of 31 GsSR genes from the wild soybean genome. The analyses of chromosome location and synteny show that the GsSRs are unevenly distributed on 15 chromosomes and are mainly under the purifying selection. The GsSR proteins can be phylogenetically classified into six sub-families and are conserved in evolution. Prediction of protein phosphorylation sites indicates that GsSR proteins are highly phosphorylated proteins. The protein-protein interaction network implies that there exist numerous interactions between GsSR proteins. We experimentally confirmed their physical interactions with the representative SR proteins of spliceosome-associated components such as U1-70K or U2AF35 by yeast two-hybrid assays. In addition, we identified various stress-/hormone-responsive cis-acting elements in the promoter regions of these GsSR genes and verified their expression patterns by RT-qPCR analyses. The results show most GsSR genes are highly expressed in root and stem tissues and are responsive to salt and alkali stresses. Splicing analysis showed that the splicing patterns of GsSRs were in a tissue- and stress-dependent manner. Overall, these results will help us to further investigate the biological functions of leguminous plant SR proteins and shed new light on uncovering the regulatory mechanisms of plant SR proteins in growth, development, and stress responses.

Unique and Shared Molecular Mechanisms of Alcoholic and Non-Alcoholic Liver Cirrhosis Identified Through Transcriptomics Data Integration.

Liver cirrhosis is a severe chronic disease that results from various etiological factors and leads to substantial morbidity and mortality. Alcoholic cirrhosis (AC) and non-AC (NAC) arise from prolonged and excessive consumption of alcohol and metabolic syndromes, respectively. Precise molecular mechanisms of AC and NAC are yet to be comprehensively understood for diagnostics and therapeutic advances to materialize. This study reports novel findings to this end by utilizing high-throughput RNA sequencing and microarray data from the Gene Expression Omnibus (GEO). We performed a meta-analysis of transcriptomics data to identify the differentially expressed genes specific to AC and NAC. Functional enrichment and protein-protein interaction network analyses uncovered novel hub genes and transcription factors (TFs) critical to AC and NAC. We found that AC is primarily driven by metabolic dysregulation and oxidative stress, with key TFs such as RELA, NFKB1, and STAT3. NAC was characterized by fibrosis and tissue remodeling associated with metabolic dysfunction, with TFs including USF1, MYCN, and HIF1A. Key hub genes such as ESR1, JUN, FOS, and PKM in AC, and CD8A, MAPK3, CCND1, and CXCR4 in NAC were identified, along with their associated TFs, pointing to potential therapeutic targets. Our results underscore the unique and shared molecular mechanisms that underlie AC and NAC and inform the efforts toward precision medicine and improved patient outcomes in liver cirrhosis.

Brain-Region-Specific Differences in Protein Citrullination/Deimination in a Pre-Motor Parkinson's Disease Rat Model.

The detection of early molecular mechanisms and potential biomarkers in Parkinson's disease (PD) remains a challenge. Recent research has pointed to novel roles for post-translational citrullination/deimination caused by peptidylarginine deiminases (PADs), a family of calcium-activated enzymes, in the early stages of the disease. The current study assessed brain-region-specific citrullinated protein targets and their associated protein-protein interaction networks alongside PAD isozymes in the 6-hydroxydopamine (6-OHDA) induced rat model of pre-motor PD. Six brain regions (cortex, hippocampus, striatum, midbrain, cerebellum and olfactory bulb) were compared between controls/shams and the pre-motor PD model. For all brain regions, there was a significant difference in citrullinated protein IDs between the PD model and the controls. Citrullinated protein hits were most abundant in cortex and hippocampus, followed by cerebellum, midbrain, olfactory bulb and striatum. Citrullinome-associated pathway enrichment analysis showed correspondingly considerable differences between the six brain regions; some were overlapping for controls and PD, some were identified for the PD model only, and some were identified in control brains only. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways identified in PD brains only were associated with neurological, metabolic, immune and hormonal functions and included the following: "Axon guidance"; "Spinocerebellar ataxia"; "Hippo signalling pathway"; "NOD-like receptor signalling pathway"; "Phosphatidylinositol signalling system"; "Rap1 signalling pathway"; "Platelet activation"; "Yersinia infection"; "Fc gamma R-mediated phagocytosis"; "Human cytomegalovirus infection"; "Inositol phosphate metabolism"; "Thyroid hormone signalling pathway"; "Progesterone-mediated oocyte maturation"; "Oocyte meiosis"; and "Choline metabolism in cancer". Some brain-region-specific differences were furthermore observed for the five PAD isozymes (PADs 1, 2, 3, 4 and 6), with most changes in PAD 2, 3 and 4 when comparing control and PD brain regions. Our findings indicate that PAD-mediated protein citrullination plays roles in metabolic, immune, cell signalling and neurodegenerative disease-related pathways across brain regions in early pre-motor stages of PD, highlighting PADs as targets for future therapeutic avenues.

Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data.

Aortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD. Four AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein-protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model. A total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice. By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data.

Oral cancer, representing 2-4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein-Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.

Multi-omics PGT: re-evaluation of euploid blastocysts for implantation potential based on RNA sequencing.

In addition to chromosomal euploidy, can the transcriptome of blastocysts be used as a novel predictor of embryo implantation potential? This retrospective analysis showed that based on differentially expressed genes (DEGs) between euploid blastocysts which resulted and did not result in a clinical pregnancy, machine learning models could help improve implantation rates by blastocyst optimization. Embryo implantation is a multifaceted process, with implantation loss and pregnancy failure related not only to blastocyst euploidy but also to the intricate dialog between blastocyst and endometrium. Although in vitro studies have revealed the characteristics of trophectoderm (TE) differentiation in implanted blastocysts and the function of TE placentation at the implantation site, the precise molecular mechanisms of embryo implantation and their clinical application remain to be fully elucidated. This study involved 102 patients who underwent 111 cycles for preimplantation genetic testing for aneuploidies (PGT-A) between March 2022 and July 2023. The study included 412 blastocysts biopsied at Day 5 [D5] or Day 6 [D6] for patients who underwent PGT-A. The biopsy lysates were split and subjected to DNA and RNA sequencing (DNA- and RNA-seq). One part was used for PGT-A to detect DNA copy number variations, whereas the other part was assessed simultaneously by RNA-seq to determine the transcriptome characteristics. To validate the reliability and accuracy of RNA-seq obtained from this strategy, we initially analyzed the transcriptome of blastocysts with chromosomal aneuploidies. Subsequently, we compared the transcriptomic features of blastocysts with different rates of formation (D5 vs D6) and investigated the network of interactions between key blastulation genes and the receptive endometrium. Then to evaluate the implantation potential of euploid blastocysts, we identified DEGs between euploid blastocysts that resulted in clinical pregnancy (defined as the presence of a gestational sac detected by ultrasound after 5 weeks) and those that did not. These DEGs were then employed to construct a predictive model for optimizing blastocyst selection. The successful detection rate of PGT-A was remarkably high at 99.8%. The RNA data may infer aneuploidy for both trisomy and monosomy. Between the euploid blastocysts that formed on D5 and D6, 187 DEGs were predominantly involved in cell differentiation for embryonic placenta development, the PPAR signaling pathway, and the Notch signaling pathway. These D5/D6 DEGs also exhibited a functional dialog with the receptive phase endometrium-specific genes through protein-protein interaction networks, indicating that the embryo undergoes further differentiation for post-implantation development. Furthermore, a modeling strategy using 280 DEGs between blastocysts leading to successful clinical pregnancies or failing to produce clinical pregnancies was implemented to refine the euploid embryo optimization, achieving areas under the curves of 0.88, 0.71, and 0.84 for the random forest (RF), support vector machine, and linear discriminant analysis models, respectively. Finally, a retrospective analysis of 83 transferred euploid blastocysts using the RF model identified three types of euploid embryos with a decreasing trend in implantation potential. Notably, the implantation rate of the good group was significantly higher than that of the moderate group (88.6% vs 50.0% P = 0.001) and that of the moderate group was higher than that of the poor group (50.0% vs 20.8%, P = 0.035). The sample size was insufficient; thus, a prospective study is needed to verify the clinical effectiveness of the above model. Because we did not analyze blastocysts that led only to biochemical pregnancies but failed clinical pregnancies separately, our classification system still must be modified to screen these embryos. Transcriptomic analysis of blastocysts offers a novel approach for predicting embryo implantation potential, which can be utilized to optimize clinical embryo selection. The ranking system may be effective in reducing the times and costs involved in achieving a clinical pregnancy. This study was funded by the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (No. 2023C03034), the National Natural Science Foundation of China (82101709), and the National Key Research and Development Program for Young Scientists of China (No. 2022YFC2702300). The authors state no competing interests. N/A.

EEF1A2 identified as a hub gene associated with the severity of metabolic dysfunction-associated steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease that ranges from metabolic dysfunction-associated steatotic liver (MASL) to metabolic dysfunction-associated steatohepatitis (MASH), and may eventually progress to cirrhosis and hepatocellular carcinoma (HCC). The underlying mechanism of MASLD remains incompletely understood. This study aimed to identify key gene implicated in MASLD pathogenesis and validate its correlation with disease severity through an integration of bioinformatics and experimental approaches. Liver transcriptome data from MASLD patients were obtained from the Gene Expression Omnibus (GEO) database. A diet-induced MASLD mouse model was developed, and liver RNA-sequencing was performed. Liver specimens and clinical data from patients were collected for further analysis. A total of 120 differentially expressed genes (DEGs) were shared between datasets GSE89632 and GSE213621, with functional enrichment in inflammatory, metabolic, and cell cycle-related pathways. Protein-protein interaction (PPI) network analysis identified three modules associated with MASLD, with the cell cycle-related module being the most notable. EEF1A2 was identified as a novel hub gene and revealed to be elevated with MASLD progression through dataset analysis. EEF1A2 was confirmed to be highly expressed in the livers of both MASLD mouse models and patients. Moreover, the increased expression of EEF1A2 in MASH was positively correlated with higher serum alanine aminotransferase (ALT), alanine aminotransferase (AST), total cholesterol (TC), and body mass index (BMI). In conclusion, EEF1A2 is a novel hub gene significantly associated with MASLD severity and is a promising biomarker and therapeutic target for MASLD.

A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation.

Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA). Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes. Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05). A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.