Complex Network Of Protein-protein Interactions Research Articles

Network Medicine: A Potential Approach for Virtual Drug Screening.

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Proteomic characterization of Pseudogymnoascus spp. isolates from polar and temperate regions

ABSTRACT Proteomics has been used extensively in the field of mycology, mainly in trying to understand the complex network of protein-protein interactions that has been implicated in the molecular functions of fungi. It is also a useful tool to compare metabolic differences within a genus. Species of Pseudogymnoascus, a genus under the phyla Ascomycota, have been shown to play an important role in the soil environment. They have been found in both polar and temperate regions and are a known producer of many extracellular hydrolases that contribute to soil decomposition. Despite the apparent importance of Pseudogymnoascus spp. in the soil ecosystem, investigations into their molecular functions are still very limited. In the present study, proteomic characterization of six Pseudogymnoascus spp. isolated from three biogeographic regions (the Arctic, Antarctic, and temperate regions) was carried out using tandem mass spectrometry. Prior to proteomic analysis, the optimization for protein extraction was carried out. Trichloroacetic acid‑acetone‑phenol was found to be the best extraction method to be used for proteomic profiling of Pseudogymnoascus spp. The proteomic analysis identified 2003 proteins that were successfully mapped to the UniProtKB database. The identified proteins were clustered according to their biological processes and molecular functions. The shared proteins found in all Pseudogymnoascus spp. (1201 proteins) showed a significantly close relationship in their basic cellular functions, despite differences in morphological structures. Analysis of Pseudogymnoascus spp. proteome also identified proteins that were unique to each region. However, a high number of these proteins belonged to protein families of similar molecular functions, namely, transferases and hydrolases. Our proteomic data can be used as a reference for Pseudogymnoascus spp. across different global regions and a foundation for future soil ecosystem function research.

The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis.

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets.

Evolution of techniques and tools for replication fork proteome and protein interaction studies.

Understanding the complex network of protein-protein interactions (PPI) that govern cellular functions is essential for unraveling the molecular basis of biological processes and diseases. Mass spectrometry (MS) has emerged as a powerful tool for studying protein dynamics, enabling comprehensive analysis of protein function, structure, post-translational modifications, interactions, and localization. This article provides an overview of MS techniques and their applications in proteomics studies, with a focus on the replication fork proteome. The replication fork is a multi-protein assembly involved in DNA replication, and its proper functioning is crucial for maintaining genomic integrity. By combining quantitative MS labeling techniques with various data acquisition methods, researchers have made significant strides in elucidating the complex processes and molecular mechanisms at the replication fork. Overall, MS has revolutionized our understanding of protein dynamics, offering valuable insights into cellular processes and potential targets for therapeutic interventions.

A data-driven multilayer approach for the identification of potential therapeutic targets in non-alcoholic steatohepatitis

The traditional computational methods of investigating a disease system are limited by the inability to look at the in-depth association between different molecular layers. For instance, the analysis of the protein–protein interaction (PPI) networks does not capture the perturbation of metabolic reactions. The metabolic networks (MN) analysis identifies critical reactions but cramps to connect them with other crucial pathways. Machine learning (ML) can identify different biomolecules’ predictive capabilities but fails to capture the underline mechanism. However, a joint exploration of these three methods would increase the likelihood of identifying more plausible disease targets. With this motivation, we have developed a systematic approach that first identifies the crucial proteins in the PPI network using a guilt-by-association approach, investigates their classification capability through ML algorithms, and then captures their effect on MN. We believe the proposed methodology will help to predict potential therapeutic targets that can control the complex PPI network and metabolic landscape. Application of the proposed methodology in non-alcoholic steatohepatitis (NASH) identified a set of proteins in close proximity to the disease-associated proteins that can classify the disease, control the disease PPI network, and transform the disease metabolic flux state towards healthy.

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer.

Background: Despite the significant survival benefits of anti-PD-1/PD-L1 immunotherapy, non-small cell lung cancer (NSCLC) remains one of the most common tumors and major causes of cancer-related deaths worldwide. Thus, there is an urgent need to identify new therapeutic targets for this refractory disease. Methods: In this study, microarray datasets GSE27262, GSE75037, GSE102287, and GSE21933 were integrated by Venn diagram. We performed functional clustering and pathway enrichment analyses using R. Through the STRING database and Cytoscape, we conducted protein-protein interaction (PPI) network analysis and identified the key genes, which were verified by the GEPIA2 and UALCAN portal. Validation of actin-binding protein anillin (ANLN) was performed by quantitative real-time polymerase chain reaction and Western blotting. Additionally, Kaplan-Meier methods were used to compute the survival analyses. Results: In total, 126 differentially expressed genes were identified, which were enriched in mitotic nuclear division, mitotic cell cycle G2/M transition, vasculogenesis, spindle, and peroxisome proliferator-activated receptor signaling pathway. 12 central node genes were identified in the PPI network complex. The survival analysis revealed that high transcriptional levels were associated with inferior survival in NSCLC patients. The clinical implication of ANLN was further explored; its protein expression showed a gradually increasing trend from grade I to III. Conclusion: These Key genes may be involved in the carcinogenesis and progression of NSCLC, which may serve as useful targets for NSCLC diagnosis and treatment.

Genome-wide identification of the pectin methylesterase inhibitor genes in Brassica napus and expression analysis of selected members.

Pectin methylesterase inhibitors (PMEIs) modulate the status of pectin methylesterification by inhibiting the activity of pectin methylesterase (PME). Recent advances indicate PMEIs play an important role in regulating plant cell wall properties and defense responses. In this study, a genome-wide analysis of PMEI gene family in Brassica napus (B. napus) was conducted and the expression patterns of PMEI genes in response to Sclerotinia sclerotiorum (S. sclerotiorum) was investigated. A total of 190 PMEI proteins were identified from the genome of B. napus. Chromosomal location, gene structure and properties of the PMEI family were analyzed, and these features were compared with Arabidopsis thaliana (A. thaliana). A total of 123 syntenic ortholog pairs were detected from BnPMEI family by synteny analysis. Results showed the expansion of BnPMEI genes was likely predominately from whole-genome duplication (WGD) or segmental duplications. Multiple cis-elements related to plant growth and development, environmental stress responses, hormone responses were detected in the promoters of BnPMEI genes, implying they were regulated by both internal and external factors. Furthermore, expression analysis of transcriptome data combined with quantitative RT-PCR (qRT-PCR) validation identified several candidates that were strongly responsive to S. sclerotiorum infection. These BnPMEI genes are candidates for manipulation to breed novel and improved genotypes that are more resistant to sclerotinia stem rot (SSR). Extensive interactions were detected among 30 BnPMEI proteins, forming complex protein-protein interaction networks. Besides, 48 BnPMEIs showed interactions with other proteins including a range of cell wall structure-related enzymes. This study provides new insights into the evolution and function of PMEIs in B. napus and lays a foundation for breeding novel genotypes for crop improvement.

Using PPI-GA for identifying protein complexes in protein-protein interaction networks

The major focus of an interdisciplinary field of study termed proteomics is a comprehensive analysis of proteins to evaluate their genetic variety, examine their distinctions, and respond to stresses. Proteomics' major goal is to use protein chemistry, bioinformatics, and biology to detect and measure proteins as well as analyze their post-translational modifications and interactions. Any disruption in the interacting network of proteins can lead to biological abnormalities and diseases like Alzheimer's and cancer. The majority of existing computational approaches for detecting protein complexes are based on certain topological properties of protein-protein networks (PPI). In this paper, it initially provides a novel encoding technique for representing the clarification and solution, and next we recommend the concept of PPI-GA, an innovative clustering based algorithm which is based on genetic approach algorithm which employs a innovative multi based objective excellence function to find out the protein complexes. A different two gold based standard and the real-world based datasets are been used to evaluate proposed algorithm. The obtained result shows that the suggested method can discover key protein complexes and that it offers more accurate results than existing protein complex identification techniques.

An Herbal Product Alleviates Bleomycin-Induced Pulmonary Fibrosis in Mice via Regulating NF-κB/TNF-α Signaling in Macrophages.

Background and aim: Pro-inflammatory macrophages aggravated progress of pulmonary fibrosis (PF) both in patients and animal models. Fuzheng Huayu (FZHY) formula, a Chinese herbal product, is effective in treating pulmonary fibrosis in our previous study. But its action mechanism against PF relating to macrophage activation was unclear. This study was designed to evaluate the anti-fibrotic and anti-inflammatory roles of FZHY in pulmonary fibrosis and to elucidate the potential mechanisms. Methods: Network pharmacology was employed to identify the interrelationships among compounds of FZHY, potential targets and putative pathways on anti-pulmonary fibrosis. According to the data of bioinformatics analysis, the key pharmacological target for FZHY against PF was screened. The network pharmacological prediction was validated by a series of experimental assays, including CCK8, western blot and immunofluorescence staining. Then molecular mechanism of FZHY on relating to the predictive target were studied in bleomycin induced pulmonary fibrosis in mice with methylprednisolone as a positive control, and in lipopolysaccharide (LPS) stimulated cultured macrophages in culture, respectively. Results: The network pharmacology analysis reveal that a total of 12 FZHY–PF crossover proteins were filtered into a protein-protein interaction network complex and designated as the potential targets of FZHY against pulmonary fibrosis, while TNF-α signal pathway ranked at the top. FZHY and methylprednisolone could attenuate the lung fibrosis and decrease pulmonary TNF-α expression in bleomycin induced fibrotic mice, without difference between two treatments. While TNF-α was mainly originated from macrophages identified by double fluorescent staining of TNF-α and F4/80. LPS stimulated cultured macrophage polarization and activation demonstrated by the enhance contents of TNF-α and iNOS but decreased level of Arg-1. FZHY could alleviate the LPS stimulated macrophage polarization and activation demonstrated by decreasing TNF-α and iNOS and increasing Arg-1. In particular, FZHY could significantly reduce the production of p65 and the nuclear translocation of phosphorylated p65. Conclusion: Fuzheng Huayu formula has a good effect against pulmonary fibrosis induced by bleomycin in mice, whose action mechanism was associated with down-regulation of NF-κB/TNF-α signaling pathway in pro-inflammatory macrophages. These findings provided an important strategy for developing new agents against lung fibrosis and accelerated FZHY product application on patients with lung fibrosis.

Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network.

Background Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. Methods HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. Result A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. Conclusion The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding

The identification of protein complexes in protein-protein interaction networks is the most fundamental and essential problem for revealing the underlying mechanism of biological processes. However, most existing protein complexes identification methods only consider a network's topology structures, and in doing so, these methods miss the advantage of using nodes' feature information. In protein-protein interaction, both topological structure and node features are essential ingredients for protein complexes. The spectral clustering method utilizes the eigenvalues of the affinity matrix of the data to map to a low-dimensional space. It has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of dimensionality reduction. In this paper, a new version of spectral clustering, named text-associated DeepWalk-Spectral Clustering (TADW-SC), is proposed for attributed networks in which the identified protein complexes have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the effectiveness of the affinity matrix, our proposed method will use the text-associated DeepWalk (TADW) to calculate the embedding vectors of proteins. In the following, the affinity matrix will be computed by utilizing the cosine similarity between the two low dimensional vectors, which will be considerable to improve the accuracy of the affinity matrix. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in both real protein network datasets and synthetic networks.

An Effective Link-Based Clustering Algorithm for Detecting Overlapping Protein Complexes in Protein-Protein Interaction Networks

Protein complexes are one most important kind of functional modules for biological processes in cells. In this regard, their detection is vital for understanding the principle of cell organization and function. A variety of clustering algorithms have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of them are based on a certain clustering criterion. Given the fact that proteins should interact with each other rather than act independently, we reason that clustering upon interactions can better characterize protein complexes than upon proteins, thus improving the detection accuracy. To this end, a link-based clustering algorithm has been proposed in this paper to effectively detect overlapping protein complexes. It first measures the similarity between pairwise interactions from the perspectives of network topology and Gene Ontology. The problem of protein complex detection is then formulated as an optimization problem of link-based clustering, which is resolved by the proposed algorithm. This proposed algorithm explores the intrinsic correlation between protein complexes and interactions for detecting functionally significant protein complexes. Experimental results on five independent PPI datasets collected from the species of yeast and human demonstrate that compared with state-of-the-art algorithms, the proposed algorithm has significantly improved the detection accuracy for protein complexes.

In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology.

Biological systems have evolved to utilize proteins to accomplish nearly all functional roles needed to sustain life. A majority of biological functions occur within the crowded environment inside cells and subcellular compartments where proteins exist in a densely packed complex network of protein-protein interactions. The structural biology field has experienced a renaissance with recent advances in crystallography, NMR, and CryoEM that now produce stunning models of large and complex structures previously unimaginable. Nevertheless, measurements of such structural detail within cellular environments remain elusive. This review will highlight how advances in mass spectrometry, chemical labeling, and informatics capabilities are merging to provide structural insights on proteins, complexes, and networks that exist inside cells. Because of the molecular detection specificity provided by mass spectrometry and proteomics, these approaches provide systems-level information that not only benefits from conventional structural analysis, but also is highly complementary. Although far from comprehensive in their current form, these approaches are currently providing systems structural biology information that can uniquely reveal how conformations and interactions involving many proteins change inside cells with perturbations such as disease, drug treatment, or phenotypic differences. With continued advancements and more widespread adaptation, systems structural biology based on in-cell labeling and mass spectrometry will provide an even greater wealth of structural knowledge.

LncRNA-Associated Competitive Endogenous RNA Regulatory Network in an Aβ25-35-Induced AD Mouse Model Treated with Tripterygium Glycoside.

BackgroundTripterygium glycoside (TG) has been suggested to have protective effects on the diseases of the central nervous system including Alzheimer’s disease (AD). The mechanisms involving lncRNA-associated competing endogenous RNAs (ceRNAs) were shown to play important roles in the development of AD. However, the ceRNA mechanism of TG in treating AD is still unknown. Thus, we aimed to explore the ceRNA mechanism in the treatment of AD with TG.MethodsA total of 32 C57BL/6J mice were administered 3 µL of Aβ25-35 (dual side, 1 mg/mL) by a single stereotactic injection in the brain to conduct AD mouse model. AD mouse models were randomly selected and divided into the AD+normal saline (NS) group (n=16) and the AD+TG group (n=16). The expression data of lncRNAs, mRNAs, and miRNAs in the hippocampus of mice from AD+NS group (n=3) and the AD+TG group (n=3) were obtained by microarray analysis. The MuTaME method was used to predict the ceRNA regulatory network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. A protein–protein interaction (PPI) network was constructed by using STRING software.ResultsTG can significantly improve spatial memory and inhibit the production of p-tau in an Aβ25-35-induced AD mouse model. A total of 661 differentially expressed lncRNAs, 503 mRNAs, and 13 miRNAs were identified. A ceRNA network involving the top 200 mRNA-miRNA-lncRNA pairs with 16 lncRNAs, 11 miRNAs, and 52 mRNAs was visualized. And a PPI network complex filtered 26 gene nodes in DEGs was predicted.ConclusionWe have identified 503 DEGs, 661 DElncRNAs, and 13 DEmiRNAs during treatment with TG in Aβ25-35-induced AD mouse model. A ceRNA network based on the DElncRNAs, DEmRNAs, and DEmiRNAs was conducted, which provided new insight into the lncRNA-mediated ceRNA regulatory mechanisms underlying the effects of TG in the treatment of AD.

PC2P: parameter-free network-based prediction of protein complexes.

MotivationPrediction of protein complexes from protein–protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction.ResultsTo address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks.Availability and implementation https://github.com/SaraOmranian/PC2P.Supplementary information Supplementary data are available at Bioinformatics online.

Bioinformatics analysis of differentially expressed genes in tumor and paracancerous tissues of patients with lung adenocarcinoma.

BackgroundLung adenocarcinoma is the main pathological type of non-small cell lung cancer (NSCLC). In this study, we analyzed the gene expression profile of lung adenocarcinoma tumor and paracancerous tissues by bioinformatics to assess the genes and signal pathways related to lung adenocarcinoma.MethodsThe expression data of GSE7670, GSE27262, and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database. The three microarray data sets were integrated to obtain common differential expression genes of lung adenocarcinoma tumor and adjacent tissues. The STRING database was used to construct the protein-protein interaction (PPI) network of lung adenocarcinoma and mine the gene modules and core genes in the network, and the online tools, GEPIA and Kaplan-Meier plotter were used to further verify and analyze the core genes.ResultsThere were 109 pairs of lung adenocarcinoma tissues and matched paracancerous normal lung tissues in the three data sets. Eighty-three differentially expressed genes were identified, including 16 up-regulated and 67 down-regulated genes, and 60 differentially expressed genes were successfully incorporated into the PPI network complex. Eleven core genes were identified in the PPI network complex, including three up-regulated (COMP, SPP1, COL1A1) and eight down-regulated genes (CDH5, CAV1, CLDN5, LYVE1, IL6, VWF, TEK, PECAM1). These core genes were verified by the GEPIA tumor database. Survival analysis showed that expression of the core genes was significantly related to the prognosis of lung adenocarcinoma. KEGG pathway analysis of core genes showed six genes (COMP, SPP1, COL1A1, IL6, VWF, TEK) were significantly enriched in the PI3K-Akt signaling-pathway (P=1.62E-06).ConclusionsBy analyzing the differential expression genes of lung adenocarcinoma and paracancerous normal tissues with bioinformatics, 11 genes with significant differential expression and significant influence on prognosis were identified. The findings may provide new concepts for developing diagnosis and treatment targets and prognosis markers for lung adenocarcinoma.

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis.

PurposeStomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment.Materials and MethodsWe acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.ResultsA total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues.ConclusionThe multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Protein Dynamics in F-like Bacterial Conjugation

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

Proteomics analysis reveals the effect of Aeromonas hydrophila sirtuin CobB on biological functions

Our previous studies have profiled lysine acetylation and succinylation modifications in Aeromonas hydrophila protein and have found that CobB may be involved in lysine deacylation; however, its effects on bacterial biological function are still unknown. In this study, a data-independent acquisition (DIA)-based proteomics method was used to compare the protein abundance between cob-deleted mutants and wild-type strains. Of the total 2385 identified proteins, 385 were found to have increased abundance, while only 46 showed decreased abundance. Data analysis revealed that many proteins in six metabolic pathways, ribosome, the bacterial secretion system, protein export, RNA degradation, beta-Lactam resistance and oxidative phosphorylation, were affected by the deletion of cobB. Some proteins, such as outer membrane proteins, the two-component regulatory systems and transcriptional factor, were also regulated by cobB. The following phenotype assays confirmed that the ΔcobB mutant produced more biofilm, migrated farther in soft agar, and was more sensitive to oxidative stress than its WT parent. Taken together, the results presented herein provide insights into the behaviors of sirtuin protein CobB in bacteria and demonstrate its important biological functions in A. hydrophila. Biological significanceThe sirtuin protein CobB play crucial roles on lysine deacylation, such as desuccinylation and deacetylation in many bacterial species, while the intrinsic behavior of CobB on bacteria remains elusive. The current DIA-based quantitative proteomics analysis showed that the deletion of A. hydrophila cobB significantly affect the intracellular biological processes. Further phenotype assays validated proteomics results. Overall, our data further confirmed the important roles of CobB on the complex protein-protein interaction network regulation in A. hydrophila.

Identification and validation of HELLS (Helicase, Lymphoid-Specific) and ICAM1 (Intercellular adhesion molecule 1) as potential diagnostic biomarkers of lung cancer.

Although lung cancer is one of the greatest threats to human health, its signaling pathway and related genes are still unknown. This study integrates data from three groups of people to study potential key candidate genes and pathways related to lung cancer. Expression profiles (GSE18842, GSE19188 and GSE27262), including 162 tumor tissue and 135 adjacent normal lung tissue samples, were integrated and analyzed. Differentially expressed genes (DEGs) and candidate genes were identified, their expression pathways were analyzed, and the diethylene glycol-related protein–protein interaction (PPI) network was analyzed. We identified 232 shared DEGs (40 upregulated and 192 down-regulated) from the three GSE datasets. The DEGs were clustered according to function and signaling pathway for significant enrichment analysis. In total, 129 nodes/DEGs were identified from the DEG PPI network complex. An improved prognosis was associated with increased Helicase, Lymphoid-Specific (HELLS) and decreased Intercellular adhesion molecule 1 (ICAM1) mRNA expression in lung cancer patients. In conclusion, we used integrated bioinformatics analysis to identify candidate genes and pathways in lung cancer to show that HELLS and ICAM1 might be the key genes related to tumorigenesis or tumor progression in lung cancer. Additional studies are needed to further explore the involved functional mechanisms.