Pathway And Protein-protein Interaction Research Articles

Kinesin binding as a shared pathway underlying the genetic basis of male factor infertility and insomnia

To study whether male infertility and insomnia share genetic risk variants, and to identify any molecular, cellular and biological interactions between these traits. The in silico study was performed. Two lists of genetics variants were manually curated through a literature review, 1 of those associated with male infertility and the other with insomnia. Genes were assigned to these variants to compose male infertility (454 genes) and insomnia (921 genes)-associated gene lists. Not applicable. Not applicable. Enrichment of biological pathways and protein-protein interaction (PPI) analysis. Twenty-eight genes were common to both lists, representing a greater overlap than would be expected by chance. In the 28 genes contained in the intersection list, there was a significant enrichment of pathways related to kinesin binding. A PPI analysis using the intersection list as input retrieved 25 nodes and indicated that 2 of them were kinesin-related proteins (PLEKHM2 and KCL1). The shared male infertility and insomnia genes, and the biological pathways highlighted in this study, suggest that further functional investigations into the interplay between fertility and sleep are warranted.

Transcriptome analysis for the screening of hub genes and potential drugs in hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP), an immune-mediated subtype of interstitial lung diseases (ILDs), is mediated by repetitive inhalation of certain antigens. Transcriptomic signatures, which provide insight into the genetic alterations associated with a disease, somehow remain obscure for HP. In this study, unique transcriptome fingerprints associated with the pathogenesis of HP were investigated using systems biology approach. Furthermore, the metabolic signatures identified in our earlier study, were correlated with the differentially expressed genes (DEGs) to explore the integrated pathways. Two datasets (GSE150910 and GSE19314) procured from the Gene Expression Omnibus repository were used to identify the DEGs of HP patients. The enriched biological processes, altered pathways and protein-protein interaction (PPI) network associated with the DEGs were determined. Based on topological algorithms, 12 hub genes were screened; among which chemokine (C-X-C motif) ligand 13 (CXCL13), CXCL9 and ribosomal protein large 36A (RPL36A) were identified as the top-ranked hub genes. The expression levels of these three candidate genes were experimentally validated in an HP patient cohort using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The findings indicated a trend similar to that of the bioinformatics observations. We integrated the metabolic signatures identified earlier with these hub genes and found glycolysis and phosphatidylinositol-3-kinase (PI3K/AKT) pathways to be impaired in these patients. Finally, the drug-gene interaction database DGIdb 4.0 indicated rituximab to be a promising drug for the management of HP. The present study is the first of its kind to combine bioinformatics and experimental data to identify and validate the genetic signatures and downstream pathways associated with HP. Our findings open up a new window to probe into the gene-metabolite crosstalk involved in the pathogenesis of HP and highlight promising therapeutic strategies.

Evaluation of the proteomic landscape of HPV E7‑induced alterations in human keratinocytes reveal therapeutically relevant pathways for cervical cancer.

The lack of specific and accurate therapeutic targets poses a challenge in the treatment of cervical cancer (CC). Global proteomics has the potential to characterize the underlying and intricate molecular mechanisms that drive the identification of therapeutic candidates for CC in an unbiased manner. The present study assessed human papillomavirus (HPV)‑induced proteomic alterations to identify key cancer hallmark pathways and protein‑protein interaction (PPI) networks, which offered the opportunity to evaluate the possibility of using these for targeted therapy in CC. Comparative proteomic profiling of HPV‑transfected (HPV16/18 E7), HPV‑transformed (CaSki and HeLa) and normal human keratinocyte (HaCaT) cells was performed using the liquid chromatography‑tandem mass spectrometry (LC‑MS/MS) technique. Both label‑free quantification and differential expression analysis were performed to assess differentially regulated proteins in HPV‑transformed and ‑transfected cells. The present study demonstrated that protein expression was upregulated in HPV‑transfected cells compared with in HPV‑transformed cells. This was probably due to the ectopic expression of E7 protein in the former cell type, in contrast to its constitutive expression in the latter cell type. Subsequent pathway visualization and network construction demonstrated that the upregulated proteins in HPV16/18 E7‑transfected cells were predominantly associated with a diverse array of cancer hallmarks, including the mTORC1 signaling pathway, MYC targets V1, hypoxia and glycolysis. Among the various proteins present in the cancer hallmark enrichment pathways, phosphoglycerate kinase 1 (PGK1) was present across all pathways. Therefore, PGK1 may be considered as a potential biomarker. PPI analysis demonstrated a direct interaction between p130 and polyubiquitin B, which may lead to the degradation of p130 via the ubiquitin‑proteasome proteolytic pathway. In summary, elucidation of the key signaling pathways in HPV16/18‑transfected and ‑transformed cells may aid in the design of novel therapeutic strategies for clinical application such as targeted therapy and immunotherapy against cervical cancer.

Checkpoint Kinase 1 (CHK1) Functions as Both a Diagnostic Marker and a Regulator of Epithelial-to-Mesenchymal Transition (EMT) in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is more difficult to treat and has a higher mortality rate than other subtypes. Although hormone receptor-targeted therapy is an effective treatment to increase survival rate in breast cancer patients, it is not suitable for TNBC patients. To address the issues, differentially expressed genes (DEGs) in TNBC patients from the Gene Expression Omnibus (GEO) database were analyzed. A total of 170 genes were obtained from three Genomic Spatial Events (GSEs) using the intersection of each GSE dataset and 61 DEGs were identified after validation with the gene enrichment analysis. We combined this with the degree scores from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network, of which 7 genes were correlated with survival rate. Finally, a proteomics database revealed that only the CHK1 protein level was differently expressed in basal-like compared with other subtypes. We demonstrated that CHK1 expression was higher in TNBC cell lines compared with non-TNBC cell lines, and CHK1 promotes epithelial to mesenchymal transition (EMT) as well as migration and invasion ability. Our study provides new insight into the TNBC subnetwork that may be useful in the prognosis and treatment of TNBC patients.

Interaction of Neurovascular Signals in the Degraded Condylar Cartilage.

Introduction: Degradation of the condylar cartilage during temporomandibular joint osteoarthritis (TMJ-OA) results in the infiltration of nerves, blood vessels and inflammatory cells from the subchondral bone into the cartilage. The interaction among innervation, angiogenesis and inflammation in the condylar cartilage of TMJ-OA remains largely unknown. Method: In the present study, microarray-based transcriptome analysis was used to detect, and quantitative real-time polymerase chain reaction was used to validate transcriptome changes in the condylar cartilage from a well-established rat TMJ-OA model. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway and protein-protein interaction (PPI) analyses were conducted. Result: There were 1817 differentially expressed genes (DEGs, fold change ≥2, p < 0.05) between TMJ-OA and control cartilages, with 553 up-regulated and 1,264 down-regulated genes. Among those genes, representative DEGs with known/suspected roles in innervation, angiogenesis and inflammation were further validated by enriched GO terms and KEGG pathways. The DEGs related to innervation were predominately enriched in the GO terms of neurogenesis, generation of neurons, and KEGG pathways of cholinergic synapse and neurotrophin signaling. Genes related to angiogenesis were enriched in GO terms of vasculature and blood vessel development, and KEGG pathways of hypoxia-inducible factor 1 (HIF-1) pathway and calcium signaling pathway. For inflammation, the DEGs were enriched in the GO terms of immune system process and immune response, and KEGG pathways of Toll-like receptor and transforming growth factor β (TGFβ) signaling. Analysis with PPI indicated that the aforementioned DEGs were highly-interacted. Several hub genes such as v-akt murine thymoma viral oncogene homolog 1 (Akt1), glycogen synthase kinase 3β (Gsk3b), fibroblast growth factor 2 (Fgf2) and nerve growth factor receptor (Ngfr) were validated. Conclusion: The present study demonstrated, for the first time, that intimate interactions exist among innervation, angiogenesis and inflammation in the condylar cartilage of TMJ-OA.

RNA sequence analysis identified bone morphogenetic protein-2 (BMP2) as a biomarker underlying form deprivation myopia

PurposeForm deprivation myopia (FDM) is an urgent public issue characterized by pathological changes, but the underlying mechanism remained unclear. The aim was to investigate bone morphogenetic proteins (BMPs) utilizing the pathogenesis of FDM. Material and methodsGene expression omnibus (GEO) database was used to analyze one mRNA profile (GSE89325) of FDM. Sixteen retina samples (8 FDM and 8 controls) were randomly divided into seven groups for differential gene expression analysis in R. software. The gene pathway and protein-protein interaction (PPI) analysis were performed by the DAVID and STRING databases. Cytoscape was used to draw the PPI network. The gene ontology (GO) enrichment and Kyoto encyclopedia of genes and Genomes (KEGG) analysis were determined to achieve gene annotation and visualization. ResultsA total of 18420 differentially expressed genes (DEGs) were identified associated with FDM. The only non-significant gene (BEND6) was separately analyzed between two groups. Thirteen hub genes were discovered, ACVR1, ACVR2A, ACVR2B, RGMB, BMPR2, BMPR1A, BMP2, BMPR1B, CHRD, PTH, PTH1R, PTHLH, and WNT9A. The expression alteration in FDM were mainly enriched in cytokine-cytokine, and neuroactive ligand receptor interaction pathways. BMP2 was the key gene in myopia progression. ConclusionsOf clinical perspective, our findings reveal that expression of BMP2 as an underlying mechanism of FDM, providing an insight for therapeutic interventions.

Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics Analysis.

Background The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM.Methods Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb).Results We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM.Conclusion These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.

Plasma Proteome Profiling of Patients With In-stent Restenosis by Tandem Mass Tag-Based Quantitative Proteomics Approach.

BackgroundDespite the widespread application of new drug-eluting stents, a considerable portion of patients experience in-stent restenosis (ISR). To date, the pathophysiologic mechanisms of ISR remain poorly understood.MethodsIn this study, we collected plasma samples from ISR patients (n = 29) and non-ISR patients (n = 36) after drug-eluting stent implantation, as well as from healthy controls (HCs) (n = 32). Our goal was to investigate differences in plasma protein profiles using tandem mass tag (TMT) labeling coupled with liquid chromatography and tandem mass spectrometry. The proteomic data were validated by enzyme-linked immunosorbent assay (ELISA). Bioinformatic analyses were conducted to analyze potential pathways and protein-protein interaction (PPI) involved in ISR.ResultsA total of 1,696 proteins were identified, of which 278 differed in protein abundance between non-ISR and HCs, 497 between ISR and HCs, and 387 between ISR and non-ISR, respectively. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and PPI, further demonstrated that differentially abundant proteins between ISR and non-ISR are involved in several crucial biological processes and signaling pathways, such as focal adhesion, platelet activation, Rap1 signaling, regulation of actin cytoskeleton, and cholesterol metabolism. Among the identified differentially abundant proteins in ISR, 170 were increased in abundance relative to both non-ISR patients and HCs. Some of these proteins were identified to have critical functions for atherosclerosis development and might be involved in ISR pathology. Among these proteins, 3 proteins with increased abundance including fetuin-B, apolipoprotein C-III (APOC3), and cholesteryl ester transfer protein (CETP) were confirmed by ELISA.ConclusionsThis is the first study provided a comprehensive proteomic profile to understand ISR pathology, which may help identify early diagnostic biomarkers and therapeutic targets.

The Attenuating Effect of Low-Intensity Pulsed Ultrasound on Hypoxia-Induced Rat Chondrocyte Damage in TMJ Osteoarthritis Based on TMT Labeling Quantitative Proteomic Analysis.

Temporomandibular joint osteoarthritis (TMJOA) is a degenerative disease with a complex and multifactorial etiology. An increased intrajoint pressure or weakened penetration can exacerbate the hypoxic state of the condylar cartilage microenvironment. Our group previously simulated the hypoxic environment of TMJOA in vitro. Low-intensity pulsed ultrasound (LIPUS) stimulation attenuates chondrocyte matrix degradation via a hypoxia-inducible factor (HIF) pathway-associated mechanism, but the mode of action of LIPUS is currently poorly understood. Moreover, most recent studies investigated the pathological mechanisms of osteoarthritis, but no biomarkers have been established for assessing the therapeutic effect of LIPUS on TMJOA with high specificity, which results in a lack of guidance regarding clinical application. Here, tandem mass tag (TMT)-based quantitative proteomic technology was used to comprehensively screen the molecular targets and pathways affected by the action of LIPUS on chondrocytes under hypoxic conditions. A bioinformatic analysis identified 902 and 131 differentially expressed proteins (DEPs) in the <1% oxygen treatment group compared with the control group and in the <1% oxygen + LIPUS stimulation group compared with the <1% oxygen treatment group, respectively. The DEPs were analyzed by gene ontology (GO), KEGG pathway and protein-protein interaction (PPI) network analyses. By acting on extracellular matrix (ECM)-associated proteins, LIPUS increases energy production and activates the FAK signaling pathway to regulate cell biological behaviors. DEPs of interest were selected to verify the reliability of the proteomic results. In addition, this experiment demonstrated that LIPUS could upregulate chondrogenic factors (such as Sox9, Collagen Ⅱ and Aggrecan) and increase the mucin sulfate content. Moreover, LIPUS reduced the hydrolytic degradation of the ECM by decreasing the MMP3/TIMP1 ratio and vascularization by downregulating VEGF. Interestingly, LIPUS improved the migration ability of chondrocytes. In summary, LIPUS can regulate complex biological processes in chondrocytes under hypoxic conditions and alter the expression of many functional proteins, which results in reductions in hypoxia-induced chondrocyte damage. ECM proteins such as thrombospondin4, thrombospondin1, IL1RL1, and tissue inhibitors of metalloproteinase 1 play a central role and can be used as specific biomarkers determining the efficacy of LIPUS and viable clinical therapeutic targets of TMJOA.

Quantitative proteomics reveals tissue-specific toxic mechanisms for acute hydrogen sulfide-induced injury of diverse organs in pig

Hydrogen sulfide (H2S) is a highly toxic gas in many environmental and occupational places. It can induce multiple organ injuries particularly in lung, trachea and liver, but the relevant mechanisms remain poorly understood. In this study, we used a TMT-based discovery proteomics to identify key proteins and correlated molecular pathways involved in the pathogenesis of acute H2S-induced toxicity in porcine lung, trachea and liver tissues. Pigs were subjected to acute inhalation exposure of up to 250 ppm of H2S for 5 h for the first time. Changes in hematology and biochemical indexes, serum inflammatory cytokines and histopathology demonstrated that acute H2S exposure induced organs inflammatory injury and dysfunction in the porcine lung, trachea and liver. The proteomic data showed 51, 99 and 84 proteins that were significantly altered in lung, trachea and liver, respectively. Gene ontology (GO) annotation, KEGG pathway and protein-protein interaction (PPI) network analysis revealed that acute H2S exposure affected the three organs via different mechanisms that were relatively similar between lung and trachea. Further analysis showed that acute H2S exposure caused inflammatory damages in the porcine lung and trachea through activating complement and coagulation cascades, and regulating the hyaluronan metabolic process. Whereas antigen presentation was found in the lung but oxidative stress and cell apoptosis was observed exclusively in the trachea. In the liver, an induced dysfunction was associated with protein processing in the endoplasmic reticulum and lipid metabolism. Further validation of some H2S responsive proteins using western blotting indicated that our proteomics data were highly reliable. Collectively, these findings provide insight into toxic molecular mechanisms that could potentially be targeted for therapeutic intervention for acute H2S intoxication.

Overexpression and Activation of αvβ3 Integrin Differentially Affects TGFβ2 Signaling in Human Trabecular Meshwork Cells.

Studies from our laboratory have suggested that activation of αvβ3 integrin-mediated signaling could contribute to the fibrotic-like changes observed in primary open angle glaucoma (POAG) and glucocorticoid-induced glaucoma. To determine how αvβ3 integrin signaling could be involved in this process, RNA-Seq analysis was used to analyze the transcriptomes of immortalized trabecular meshwork (TM) cell lines overexpressing either a control vector or a wild type (WT) or a constitutively active (CA) αvβ3 integrin. Compared to control cells, hierarchical clustering, PANTHER pathway and protein-protein interaction (PPI) analysis of cells overexpressing WT-αvβ3 integrin or CA-αvβ3 integrin resulted in a significant differential expression of genes encoding for transcription factors, adhesion and cytoskeleton proteins, extracellular matrix (ECM) proteins, cytokines and GTPases. Cells overexpressing a CA-αvβ3 integrin also demonstrated an enrichment for genes encoding proteins found in TGFβ2, Wnt and cadherin signaling pathways all of which have been implicated in POAG pathogenesis. These changes were not observed in cells overexpressing WT-αvβ3 integrin. Our results suggest that activation of αvβ3 integrin signaling in TM cells could have significant impacts on TM function and POAG pathogenesis.

LINC02535 promotes cell growth in poorly differentiated gastric cancer.

BackgroundAbnormal long non‐coding RNA (lncRNA) expression plays important roles in gastric cancer. However, the functions of many lncRNAs in poorly differentiated gastric cancer (PDGC) remain unknown.MethodsThree sets of paired tissues from patients with PDGC were used, and transcriptome sequencing was performed, followed by the construction and sequencing of a library and mapping of the reads. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein‐protein interaction (PPI) networks were analysed, and canonical pathway significance was calculated among the differentially expressed genes (DEGs; p < 0.05). Gene expression in 30 paired PDGC specimens and four cell lines was validated through quantitative PCR. Cell proliferation, migration, invasion, apoptosis, and wound healing were analysed.ResultsA total of 499 upregulated DEGs and 627 downregulated DEGs were identified between peritumoral and gastric cancer tissues. The proportions of positive and negative correlations between LINC02535 and the DEGs were 98.40% and 92.66%, respectively, while the Spearman's correlation coefficient was greater than 0.5. The PPI network showed that approximately 73.15% of the top five genes were directly correlated with LINC02535 according to the STRING database. Based on KEGG analysis, the functions of LINC02535 target genes were enriched in signalling pathways related to cancer cell growth. Furthermore, cell function studies showed that LINC02535 upregulation contributed to cell proliferation, migration, invasion, and wound healing and that its inhibition facilitated cell apoptosis.ConclusionLINC02535 expression was upregulated in PDGC and contributed to cell proliferation, migration, invasion and wound healing, whereas its inhibition in PDGC facilitated cell apoptosis.

Contribution of endothelial cell-derived transcriptomes to the colon cancer based on bioinformatics analysis.

By analysis of CTECs-derived gene expression profiling dataset, we identified differentially expressed genes (DEGs) between CTECs and colon normal endothelial cells (CNECs). In addition, we identified the significant pathways and protein-protein interaction (PPI) network that was significantly associated with the DEGs. Furthermore, we identified hub genes whose expression was significantly associated with prognosis and immune cell infiltrations in colon cancer. Finally, we identified the significant correlations between the prognostic hub genes and immune-inhibitory markers in colon cancer. We identified 362 DEGs in CTECs relative to the CNECs, including117 up-regulated genes and 245 down-regulated genes in the CTECs. In addition, we identified significantly up-regulated pathways in CTECs that were mainly involved in cancer and immune regulation. Furthermore, we identified hub genes (such as SPARC, COL1A1, COL1A2 and IGFBP3) that are associated with prognosis and immune cells infiltrations in colon cancer. Interestingly, we found that prognosis-associated hub genes (SPARC, COL1A1, COL1A2 and IGFBP3) are positively correlated with immune-inhibitory markers of various immunosuppressive cells, including TAM, M2 macrophage, Tregs and T cell exhaustion. Finally, our findings revealed that prognosis-associated upregulated hub genes are positively correlated with immune checkpoint markers, including PD-L1 and PD-L2 and the immunosuppressive markers including TGFB1 and TGFBR1. The identification of CTECs-specific transcriptomes may provide crucial insights into the colon tumor microenvironment that mediates the development of colon cancer.

RNA sequencing analysis and T-cell receptor repertoire in triple-negative breast cancer (TNBC).

1075 Background: Triple negative breast cancer (TNBC) is defined by the lack of two hormone receptors (HR) and human epidermal growth factor receptor 2 (HER2), and well known to have poor prognosis. In this study, we conducted a RNA sequencing including T-cell receptor (TCR) repertoire analysis to develop prognostic biomarker in patients with TNBC. In addition, genes and signaling pathways that correlated with selected biomarker were also investigated. Methods: Total of 78 tumor tissues from TNBC patients were participated for RNA-seq (Illumina Hiseq) analysis. Groups of significant genes were selected by differentially expressed genes (DEGs) analysis, whose expression levels differed more than 1.5 times between patients and normal, or early stage and advanced stage TNBC. Transcript expression levels for prognostic biomarker were analyzed based on R v3.4.3. Using CBS ProbePINGS, a genomic big data analytics platform, we evaluated druggable pathways and protein-protein interaction (PPI). The Interaction Frequency Ratio Score (IFRS) was calculated by investigating highly interactive pathways, and the drugs were matched to patients. TCR repertoire analysis was performed by MiXCR. Results: Ten candidate gene signatures were selected based on RNA sequencing data of each sample. Cross-validation through machine learning showed that the accuracy of the first-ranked signature was 92.3%, the second was 92.0%, and the third was 90.3%. The accuracy of 4th to 6th was 88.7%, and the accuracy of 7th to 10th was over 88.0%. Cross-validated gene signature, age, and TNM staging showed significant discriminant power under univariate Cox regression analysis (p &lt; 0.05). In the CBS ProbePINGS, human papillomavirus infection, MAPK pathway, and tumorigenesis pathway were correlated with cell signaling. CDK2, FN1, and JUN genes were highly interactive each other. In addition, the drug matching result according to IFRS value suggested imatinib and regorafenib could be possible candidates. TCR repertoire analysis presented that number of clonecount was lower in recurrent or metastatic TNBC than early stage cancer. Conclusions: This study revealed a specific gene signatures that can accurately determine recurrence and metastasis in patients with TNBC based on RNA sequencing analysis. TCR repertoire analysis and CBS ProbePINGS could be valuable method in treatment selection

TMT-based proteomics analysis reveals the protective effects of Xuefu Zhuyu decoction in a rat model of traumatic brain injury

Ethnopharmacological relevanceXuefu Zhuyu decoction (XFZYD) is a traditional Chinese herbal prescription. It is effective in treating traumatic brain injury (TBI). However, the underlying molecular mechanisms remain unclear. Aim of the studyThis study aimed to reveal the possible mechanisms of XFZYD in treating acute TBI through proteomics clues. Materials and methodsControlled Cortical Impact (CCI) rats were given gavage administration of XFZYD (9 g/kg/d) or distilled water (equal volume) for three days. The Modified Neurological Severity Score (mNSS), brain water content, HE staining, Nissl staining and immunohistochemistry were performed to assess the effects of XFZYD for TBI treatment. Additionally, tandem mass tag-based (TMT) quantitative proteomics technology was applied to detect proteins of brain cortex. Bioinformatics analysis including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Protein-protein interaction (PPI) networks were used to analyze differentially expressed proteins (DEPs). Bioinformatics Analysis Tool for Molecular mechanism of TCM (BATMAN-TCM) was conducted to anchor diseases and pathways. Besides, western blotting and immunofluorescence were exerted to verify related proteins. ResultsXFZYD improved neurologic functions, reduced encephaledema and ameliorated cell morphology around the injured area in CCI rats. A total of 6099 proteins were identified with false discovery rate (FDR) < 1%. Overlapping DEPs (105 DEPs) were identified (295 DEPs and 804 DEPs in CCI/Sham or XFZYD/CCI group, respectively). Of these DEPs, 17 were regulated by XFZYD. Bioinformatics analysis showed that the 17 DEPs were predominantly related to platelet activation and PI3K-Akt signaling pathway. Next, PLG and CD34 were verified with molecular biotechnology. ConclusionsXFZYD exerts therapeutic effects through multi-pathways regulation in the treatment of TBI. This work may provide proteomics clues for the continuation of research on TBI treatment with XFZYD.

LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

BackgroundIschemic stroke (IS) was a significant public health concern and long-chain noncoding RNAs (lncRNAs) were gaining particular importance in stroke biology, however, the potential mechanism of lncRNAs in IS was not fully understood. MethodsIn this study, three diagnosed patients with IS and three controls were selected to establish the lncRNA library. Weighted gene co-expression network analysis (WGCNA) was applied to screen key lncRNA modules associated with IS. The key lncRNAs were identified by module membership (MM) and gene significance (GS). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways and protein-protein interaction (PPI) network method was used to identify the key genes. ResultsA total of 3627 lncRNAs were investigated, followed by an analysis of 17 modules, and only one module was highly associated with the IS. The top 10 lncRNAs were identified based on GS and MM. KEGG pathways analysis revealed the top two pathways of the Human T cell Lymphotropic Virus-1 (HTLV-1) infection and the mTOR signaling pathway might influence the progress of IS. Further, genes meeting the top two degree (AKT1 and MAPK14) were selected as the hub genes in the PPI network. ConclusionTo summarize, this study identified the key pathways and genes, which might serve as biomarkers and targets for precise diagnosis and treatment of IS in the future.

Bioinformatics analysis to screen key genes in papillary thyroid carcinoma.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid carcinoma, and its incidence has been on the increase in recent years. However, the molecular mechanism of PTC is unclear and misdiagnosis remains a major issue. Therefore, the present study aimed to investigate this mechanism, and to identify key prognostic biomarkers. Integrated analysis was used to explore differentially expressed genes (DEGs) between PTC and healthy thyroid tissue. To investigate the functions and pathways associated with DEGs, Gene Ontology, pathway and protein-protein interaction (PPI) network analyses were performed. The predictive accuracy of DEGs was evaluated using the receiver operating characteristic (ROC) curve. Based on the four microarray datasets obtained from the Gene Expression Omnibus database, namely GSE33630, GSE27155, GSE3467 and GSE3678, a total of 153 DEGs were identified, including 66 upregulated and 87 downregulated DEGs in PTC compared with controls. These DEGs were significantly enriched in cancer-related pathways and the phosphoinositide 3-kinase-AKT signaling pathway. PPI network analysis screened out key genes, including acetyl-CoA carboxylase beta, cyclin D1, BCL2, and serpin peptidase inhibitor clade A member 1, which may serve important roles in PTC pathogenesis. ROC analysis revealed that these DEGs had excellent predictive performance, thus verifying their potential for clinical diagnosis. Taken together, the findings of the present study suggest that these genes and related pathways are involved in key events of PTC progression and facilitate the identification of prognostic biomarkers.

Identification of Hub Genes and Pathways in a Rat Model of Renal Ischemia-Reperfusion Injury Using Bioinformatics Analysis of the Gene Expression Omnibus (GEO) Dataset and Integration of Gene Expression Profiles.

BackgroundThis study aimed to identify hub genes and pathways in a rat model of renal ischemia-reperfusion injury (IRI) using bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset and integration of gene expression profiles.Material/MethodsGEO software and the GEO2R calculation method were used to analyze two mRNA profiles, including GSE 39548 and GSE 108195. The co-expression of differentially expressed genes (DEGs) were identified and searched in the DAVID and STRING databases for pathway and protein-protein interaction (PPI) analysis. Cytoscape was used to draw the PPI network. DEGs were also analyzed using the Molecular Complex Detection (MCODE) algorithm. Cytoscape and cytoHubba were used to analyze the hub genes and visualize the molecular interaction networks. Rats (n=20) included the IRI model group (n=10) and a control group (n=10). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to measure and compare the expression of the identified genes in rat renal tissue in the IRI model and the control group.ResultsTen hub genes were identified, STAT3, CD44, ITGAM, CCL2, TIMP1, MYC, THBS1, IGF1, SOCS3, and CD14. Apart from IGF1, qRT-PCR showed that expression of these genes was significantly increased in renal tissue in the rat model of IRI. The HIF-1α signaling pathway was involved in IRI in the rat model, which was supported by MCODE analysis.ConclusionsIn a rat model of renal IRI, bioinformatics analysis of the GEO dataset and integration of gene expression profiles identified involvement of HIF-1α signaling and the STAT3 hub gene.

Potential Applications of DNA, RNA and Protein Biomarkers in Diagnosis, Therapy and Prognosis for Colorectal Cancer: A Study from Databases to AI-Assisted Verification.

In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC.

Crosstalk analysis of dysregulated pathways in preeclampsia.

A crosstalk between multiple biological pathways has been proposed in biological processes. However, the existence and degree of this phenomenon in patients with preeclampsia (PE) have not been strictly investigated. Thus, this study explored an dysregulated pathway set (DPS) for PE based on pathway crosstalk network (PCN) related analysis. In the present study, four steps were performed in the inference of DPS: acquiring data of gene expression, pathway and protein-protein interaction (PPI) construction; building a PCN through integrating the information in these datasets and Pearson's correlation coefficient (PCC). A principal component analysis (PCA) approach was used to compute the activity of every pathway for selecting seed pathway of PCN. DPS was evaluated by measuring of an area under the receiver operating characteristics curve (AUC) and seed pathway from PCN. Consequently, a total of 420 pathways and 6,032 crosstalks were mapped to the PCN, in which RIG-I/MDA5-mediated induction of IFN-α/β pathways was identified as the seed pathway that had the greatest changes in activity scores across PE patients and normal controls. DPS was composed of 15 dysregulated pathways and 46 crosstalks, in which CLEC7A (Dectin-1) signaling possessed the highest degree of 12, which indicated it exerted an important role in the DPS. Our results revealed crosstalk between pathways and the DPS crucial for PE pathogenesis, which aid in excavating potential biomarkers of PE therapy and unveil the underlying pathological mechanism of this disease.