Hubba: hub objects analyzer--a framework of interactome hubs identification for network biology

Helicobacter pylori infection facilitates cell migration and potentially impact clinical outcomes in gastric cancer

Gastric cancer is a significant health concern worldwide. Helicobacter pylori (HP) infection is associated with gastric cancer risk, but differences between HP-infected and HP-free gastric cancer have not been studied sufficiently. The objective of this study was to investigate the effects of HP infection on the viability and migration of gastric cancer cells and identify potential underlying genetic mechanisms as well as their clinical relevance. Cell counting kit-8, lactate dehydrogenase, wound healing, and transwell assay were applied in the infection model of multiple clones of HP and multiple gastric cancer cell lines. Genes related to HP infection were identified using bioinformatics analysis and subsequently validated using real-time quantitative PCR. The association of these genes with immunity and drug sensitivity of gastric cancer was analyzed. Results showed that HP has no significant impact on viability but increases the migration of gastric cancer cells. We identified 1405 HP-upregulated genes, with their enriched terms relating to cell migration, drug, and immunity. Among these genes, the 82 genes associated with survival showed a significant impact on gastric cancer in consensus clustering and LASSO prognostic model. The top 10 hub HP-associated genes were further identified, and 7 of them were validated in HP-infected cells using real-time quantitative PCR, including ERBB4, DNER, BRINP2, KCTD16, MAPK4, THPO, and VSTM2L. The overexpression experiment showed that KCTD16 medicated the effect of HP on gastric cancer migration. Our findings suggest that HP infection may enhance the migratory potential of gastric cancer cells and these genes might be associated with immunity and drug sensitivity of gastric cancer. In human subjects with gastric cancer, HP presence in tumors may affect migration, immunity, and drug sensitivity.

Identification of the novel exhausted T cell CD8 + markers in breast cancer

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Exploring putative drug properties associated with TNF-alpha inhibition and identification of potential targets in cardiovascular disease using machine learning-assisted QSAR modeling and virtual reverse pharmacology approach

Cardiovascular disease is a chronic inflammatory disease with high mortality rates. TNF-alpha is pro-inflammatory and associated with the disease, but current medications have adverse effects. Therefore, efficient inhibitors are urgently needed as alternatives. This study represents a structural-activity relationship investigation of TNF-alpha, curated from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of different bioactivity groups. The extracted molecules were subjected to PubChem and SubStructure fingerprints, and a QSAR-based Random Forest (QSAR-RF) model was generated using the WEKA tool. The QSAR random Forest model was built based on the SubStructure fingerprint with a correlation coefficient of 0.992 and 0.716 as the respective tenfold cross-validation scores. The variance important plot (VIP) method was used to extract the important features for TNF-alpha inhibition. The Substructure-based QSAR-RF (SS-QSAR-RF) model was validated using molecules from PubChem and ZINC databases. The generated model also predicts the pIC50 value of the molecules selected from the docking study followed by molecular dynamic simulation with the time step of 100 ns. Through virtual reverse pharmacology, we determined the main drug targets from the top four hit compounds obtained via molecular docking study. Our analysis included an integrated bioinformatics approach to pinpoint crucial targets like EGRF, HSP900A1, STAT3, PSEN1, AKT1, and MDM2. Further, GO and KEGG pathways analysis identified relevant cardiovascular disease-related pathways for the hub gene involved. However, this study provides valuable insights, it is important to note that it lacks experimental application. Future research may benefit from conducting in-vitro and in-vivo studies.

A protein network refinement method based on module discovery and biological information

BACKGROUND

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.

METHODS

Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.

RESULTS

To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension

Persistent liver injury triggers a fibrogenic program that causes pathologic remodeling of the hepatic microenvironment (i.e., liver fibrosis) and portal hypertension. The dynamics of gene regulation during liver disease progression and early regression remain understudied. Here, we generated hepatic transcriptome profiles in two well-established liver disease models at peak fibrosis and during spontaneous regression after the removal of the inducing agents. We linked the dynamics of key disease readouts, such as portal pressure, collagen area, and transaminase levels, to differentially expressed genes, enabling the identification of transcriptomic signatures of progressive vs. regressive liver fibrosis and portal hypertension. These candidate biomarkers (e.g., Tcf4, Mmp7, Trem2, Spp1, Scube1, Islr) were validated in RNA sequencing datasets of patients with cirrhosis and portal hypertension, and those cured from hepatitis C infection. Finally, deconvolution identified major cell types and suggested an association of macrophage and portal hepatocyte signatures with portal hypertension and fibrosis area.

EPI-SF: essential protein identification in protein interaction networks using sequence features

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Mechanism of herb pair containing Astragali Radix and Spatholobi Caulis in the treatment of myelosuppression based on network pharmacology and experimental investigation

ETHNOPHARMACOLOGICAL RELEVANCE

The Astragali Radix and Spatholobi Caulis herb pair (ARSC) is one of the most commonly used herbal combinations for bone marrow suppression. According to traditional Chinese medicine, Astragali Radix strengthens the spleen and replenishes qi, while Spatholobi Caulis is a hematinic agent that promotes blood circulation and enrichment. The compatibility of the two helps the body to tonify the spleen and kidneys and compensate for visceral deficiencies. However, the multi-target mechanism of ARSC in bone marrow suppression has remained largely unknown.

AIM OF THE STUDY

The aim of this study is to explore the key targets and signaling pathways of the traditional Chinese herbal pair ARSC for the treatment of bone marrow suppression.

MATERIALS AND METHODS

The active components of ARSC and targets for myelosuppression were screened using network databases. Cytoscape 3.8.0 was used to construct compound-target, compound-disease-target and protein-protein interaction (PPI) networks. Go-function and pathway enrichment analyses were performed to explore the potential mechanism. In vivo animal experiments were conducted to verify the molecular mechanisms.

RESULTS

The 36 active compounds were identified from the ARSC, and a total of 108 genes involved in myelosuppression were screened. VEGFA, IL6, TNF, JUN, STAT3, PTGS2, CASP3 and MMP9 genes were identified as potential drug targets in the PPI network analyzed by CytoHubba. Enrichment analysis indicated that ARSC may treat myelosuppression through various biological processes, such as apoptosis, TNF-α signaling pathway via NF-κB, PI3K/AKT/mTOR signaling pathway, IL6/JAK/STAT3 signaling pathway, P53 signaling pathway and G2/M checkpoint signaling pathway. The results of the experiment showed that the aqueous extract of ARSC significantly alleviated myelosuppression, reduced the apoptosis rate of bone marrow cells, upregulated the mRNA expression levels of TNF-α, IL-6 and VEGF, and promoted NF-κB phosphorylation in myelosuppressed mice.

CONCLUSIONS

This study identified the active components and relevant mechanisms of ARSC in the treatment of myelosuppression. Our findings predicted that ARSC could treat bone marrow suppression through multiple components, multiple targets and multiple pathways. Pharmacological experiments showed that ARSC alleviated fluorouracil-induced myelosuppression by reducing the apoptosis rate of bone marrow cells and regulating the TNF-α/NF-κB signaling pathway.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization

BACKGROUND

In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

RESULTS

We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an interaction streaming source to feed into the evolutionary community discovery algorithm and then identify overlapping communities during the evolution of the dynamic network. SVM recursive feature elimination (RFE) is applied to extract the most informative communities, which are combined with subcellular localization data for classification predictions. We assess the performance of ECDEP by comparing it against ten centrality methods, four shallow machine learning methods with RFE, and two deep learning methods that incorporate multiple biological data sources on Saccharomyces. Cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Mus musculus, and Caenorhabditis elegans. ECDEP achieves an AP value of 0.86 on the H. sapiens dataset and the contribution ratio of community features in classification reaches 0.54 on the S. cerevisiae (Krogan) dataset.

CONCLUSIONS

Our proposed method adeptly integrates network dynamics and yields outstanding results across various datasets. Furthermore, the incorporation of evolutionary community discovery algorithms amplifies the capacity of gene expression data in classification.

Qinzhizhudan formula dampens inflammation in microglia polarization of vascular dementia rats by blocking MyD88/NF-κB signaling pathway: Through integrating network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Qinzhizhudan Formula (QZZD) is composed of Scutellaria baicalensis Georgi (Huang Qin) extract, Gardenia jasminoides (Zhizi) extract and Suis Fellis Pulvis (Zhudanfen) (ratio of 4:5:6). This formula is optimized from Qingkailing (QKL) injection. Regarding brain injury, QZZD is protective. However, the mechanism by which QZZD treats vascular dementia (VD) has not been elucidated.

AIM OF THE STUDY

To ascertain QZZD's effect on the treatment of VD and further investigate the molecular mechanisms.

MATERIALS AND METHODS

In this study, we screened the possible components and targets of QZZD against VD and microglia polarization using network pharmacology (NP), then an animal model of bilateral common carotid artery ligation method (2VO) was induced. Afterward, The Morris water maze was employed to evaluate cognitive ability, and pathological alterations in the CA1 area of the hippocampus were detected using HE and Nissl staining. To confirm the affect of QZZD on VD and its molecular mechanism, the contents of inflammatory factors IL-1β, TNF-α, IL-4, and IL-10 were performed to detect by ELISA, the phenotype polarization of microglia cells was detected by immunofluorescence staining, and the expressions of MyD88, p-IκBα and p-NF-κB p65 in brain tissue were detected by western blot.

RESULTS

A total of 112 active compounds and 363 common targets of QZZD, microglia polarization, and VD were identified, according to the NP analysis. 38 hub targets were screened out from the PPI network. GO analysis and KEGG pathway analysis showed that QZZD may regulate microglia polarization through anti-inflammatory mechanism such as Toll-like receptor signaling pathway and NF-κB signaling pathway. The further results showed that QZZD can alleviate the memory impairment induced by 2VO. QZZD profoundly rescued brain hippocampus neuronal damage and increased the number of neurons. These advantageous outcomes were linked to the control of microglia polarization. QZZD decreased M1 phenotypic marker expression while increasing M2 phenotypic marker expression. QZZD may controll the polarization of the M1 microglia by blocking the core part of Toll-like receptor signaling pathway, that is the MyD88/NF-κB signaling pathway, which reduced the neurotoxic effects of the microglia.

CONCLUSION

Here, we explored the anti-VD microglial polarization characteristic of QZZD for the first time and clarified its mechanisms. These findings will provide valuable clues for the discovery of anti-VD agents.

Transcriptional survey of abiotic stress response in maize (Zea mays) in the level of gene co-expression network and differential gene correlation analysis

Abstract. Maize may be exposed to several abiotic stresses in the field. Therefore, identifying the tolerance mechanisms of natural field stress is mandatory. Gene expression data of maize upon abiotic stress were collected, and 560 differentially expressed genes (DEGs) were identified through meta-analysis. The most significant gene ontology terms in up-regulated genes were 'response to abiotic stress' and 'chitinase activity'. 'Phosphorelay signal transduction system' was the most significant enriched biological process in down-regulated DEGs. The co-expression analysis unveiled seven modules of DEGs, with a notable positive correlation between the modules and abiotic stress. Furthermore, the statistical significance was strikingly high for the turquoise, green and yellow modules. The turquoise group played a central role in orchestrating crucial adaptations in metabolic and stress response pathways in maize when exposed to abiotic stress. Within three up-regulated modules, Zm.7361.1.A1_at, Zm.10386.1.A1_a_at and Zm.10151.1.A1_at emerged as hub genes. These genes might introduce novel candidates implicated in stress tolerance mechanisms, warranting further comprehensive investigation and research. In parallel, the R package glmnet was applied to fit a logistic LASSO regression model on the DEGs profile to select candidate genes associated with abiotic responses in maize. The identified hub genes and LASSO regression genes were validated on an independent microarray dataset. Additionally, Differential Gene Correlation Analysis (DGCA) was performed on LASSO and hub genes to investigate the gene-gene regulatory relationship. The P value of DGCA of 16 pairwise gene comparisons was lower than 0.01, indicating a gene-gene significant change in correlation between control and abiotic stress. Integrated weighted gene correlation network analysis and logistic LASSO analysis revealed Zm.11185.1.S1_at, Zm.2331.1.S1_x_at and Zm.17003.1.S1_at. Notably, these 3 genes were identified in the 16 gene-pair comparisons. This finding highlights the notable significance of these genes in the abiotic stress response. Additional research into maize stress tolerance may focus on these three genes.

An in-silico approach to understand the potential role of Wnt inhibitory factor-1 (WIF-1) in the inhibition of the Wnt signalling pathway

WIF1 (Wnt inhibitory factor 1) is a potent tumour suppressor gene which is epigenetically silenced in numerous malignancies. The associations of WIF1 protein with the Wnt pathway molecules have not been fully explored, despite their involvement in the downregulation of several malignancies. In the present study, a computational approach encompassing the expression, gene ontology analysis and pathway analysis is employed to obtain an insight into the role of the WIF1 protein. Moreover, the interaction of the WIF1 domain with the Wnt pathway molecules was carried out to ascertain the tumour-suppressive role of the domain, along with the determination of their plausible interactions. Initially, the protein-protein interaction network analysis endowed us with the Wnt ligands (such as Wnt1, Wnt3a, Wnt4, Wnt5a, Wnt8a and Wnt9a), along with the Frizzled receptors (Fzd1 and Fzd2) and the low-density lipoprotein complex (Lrp5/6) as the foremost interactors of the protein. Further, the expression analysis of the aforementioned genes and proteins was determined using The Cancer Genome Atlas to comprehend the significance of the signalling molecules in the major cancer subtypes. Moreover, the associations of the aforementioned macromolecular entities with the WIF1 domain were explored using the molecular docking studies, whereas the dynamics and stability of the assemblage were investigated using 100 ns molecular dynamics simulations. Therefore, providing us insights into the plausible roles of WIF1 in inhibiting the Wnt pathways in various malignancies.Communicated by Ramaswamy H. Sarma.

Comprehensive Analysis of PANoptosis-Related Gene Signature of Ulcerative Colitis

Accumulating evidence shows that the abnormal increase in the mortality of intestinal epithelial cells (IECs) caused by apoptosis, pyroptosis, and necroptosis is closely related to the function of mucous membrane immunity and barrier function in patients with ulcerative colitis (UC). As a procedural death path that integrates the above-mentioned many deaths, the role of PANoptosis in UC has not been clarified. This study aims to explore the characterization of PANoptosis patterns and determine the potential biomarkers and therapeutic targets. We constructed a PANoptosis gene set and revealed significant activation of PANoptosis in UC patients based on multiple transcriptome profiles of intestinal mucosal biopsies from the GEO database. Comprehensive bioinformatics analysis revealed five key genes (ZBP1, AIM2, CASP1/8, IRF1) of PANoptosome with good diagnostic value and were highly correlated with an increase in pro-inflammatory immune cells and factors. In addition, we established a reliable ceRNA regulatory network of PANoptosis and predicted three potential small-molecule drugs sharing calcium channel blockers that were identified, among which flunarizine exhibited the highest correlation with a high binding affinity to the targets. Finally, we used the DSS-induced colitis model to validate our findings. This study identifies key genes of PANoptosis associated with UC development and hypothesizes that IRF1 as a TF promotes PANoptosome multicomponent expression, activates PANoptosis, and then induces IECs excessive death.

A Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Pharmacological Mechanism of Astragalus membranaceus for Glioma

Glioma treatment in traditional Chinese medicine has a lengthy history. Astragalus membranaceus, a traditional Chinese herb that is frequently utilized in therapeutic practice, is a component of many Traditional Chinese Medicine formulas that have been documented to have anti-glioma properties. Uncertainty persists regarding the molecular mechanism behind the therapeutic effects. Based on results from network pharmacology and molecular docking, we thoroughly identified the molecular pathways of Astragalus membranaceus' anti-glioma activities in this study. According to the findings of the enrichment analysis, 14 active compounds and 343 targets were eliminated from the screening process. These targets were mainly found in the pathways in cancer, neuroactive ligand-receptor interaction, protein phosphorylation, inflammatory response, positive regulation of phosphorylation, and inflammatory mediator regulation of Transient Receptor Potential (TRP) channels. The results of molecular docking showed that the active substances isoflavanone and 1,7-Dihydroxy-3,9-dimethoxy pterocarpene have strong binding affinities for the respective targets ESR2 and PTGS2. In accordance with the findings of our investigation, Astragalus membranaceus active compounds exhibit a multicomponent and multitarget synergistic therapeutic impact on glioma by actively targeting several targets in various pathways. Additionally, we propose that 1,7-Dihydroxy-3,9-dimethoxy pterocarpene and isoflavanone may be the main active ingredients in the therapy of glioma.

MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification

An early diagnosis and precise prognosis are critical for the treatment of glioma. The mitogen‑activated protein kinase (MAPK) signaling pathway potentially affects glioma, but the exploration of the clinical values of the pathway remains lacking. We accessed data from TCGA, GTEx, CGGA, etc. Up-regulated MAPK signaling pathway genes in glioma were identified and used to cluster the glioma subtypes using consensus clustering. The subtype differences in survival, cancer stemness, and the immune microenvironment were analyzed. A prognostic model was trained with the identified genes using the LASSO method and was validated with three external cohorts. The correlations between the risk model and cancer-associated signatures in cancer were analyzed. Key hub genes of the gene set were identified by hub gene analysis and survival analysis. 47% of the MAPK signaling pathway genes were overexpressed in glioma. Subtypes based on these genes were distinguished in survival, cancer stemness, and the immune microenvironment. A risk model was calculated with high confidence in the prediction of overall survival and was correlated with multiple cancer-associated signatures. 12 hub genes were identified and 8 of them were associated with survival. The MAPK signaling pathway was overexpressed in glioma with prognostic value.

Activating ryanodine receptor improves isoflurane-induced cognitive dysfunction

BACKGROUND

Postoperative cognitive dysfunction (POCD) is characterized by impaired learning and memory. 6 h duration isoflurane anesthesia is an important factor to induce POCD, and the dysfunction of ryanodine receptor (RyR) in the hippocampus may be involved in this process. We investigated the expression of RyR3 in the hippocampus of mice after 6-h duration isoflurane anesthesia, as well as the improvement of RyR receptor agonist caffeine on POCD mice, while attempting to identify the underlying molecular mechanism.

MATERIALS

We constructed a POCD model using 8-week-old male C57BL/6J mice that were exposed to 6-h duration isoflurane. Prior to the three-day cognitive behavioral experiment, RyR agonist caffeine were injected. Fear conditioning and location memory tests were used in behavioral studies. We also exposed the mouse neuroblastoma cell line Neuro-2a (N2A) to 6-h duration isoflurane exposure to simulate the conditions of in vivo cognitive dysfunction. We administered ryanodine receptor agonist (caffeine) and inhibitor (ryanodine) to N2a cells. Following that, we performed a series of bioinformatics analysis to discover proteins that are involved in the development of cognitive dysfunction. Rt-PCR and Western blot were used to assess mRNA level and protein expression.

RESULTS

6-h duration isoflurane anesthesia induced cognitive dysfunction and increased RyR3 mRNA levels in hippocampus. The mRNA levels of RyR3 in cultured N2a cells after anesthesia were comparable to those in vivo, and the RyR agonist caffeine corrected the expression of some cognitive-related phenotypic proteins that were disturbed after anesthesia. Intraperitoneal injection of RyR agonist caffeine can improve cognitive function after isoflurane anesthesia in mice, and bioinformatics analyses suggest that CaMKⅣ may be involved in the molecular mechanism.

CONCLUSION

Ryanodine receptor agonist caffeine may improve cognitive dysfunction in mice after isoflurane anesthesia.

A Construction Method of Dynamic Protein Interaction Networks by Using Relevant Features of Gene Expression Data

Essential proteins play an important role in various life activities and are considered to be a vital part of the organism. Gene expression data are an important dataset to construct dynamic protein-protein interaction networks (DPIN). The existing methods for the construction of DPINs generally utilize all features (or the features in a cycle) of the gene expression data. However, the features observed from successive time points tend to be highly correlated, and thus there are some redundant and irrelevant features in the gene expression data, which will influence the quality of the constructed network and the predictive performance of essential proteins. To address this problem, we propose a construction method of DPINs by using selected relevant features rather than continuous and periodic features. We adopt an improved unsupervised feature selection method based on Laplacian algorithm to remove irrelevant and redundant features from gene expression data, then integrate the chosen relevant features into the static protein-protein interaction network (SPIN) to construct a more concise and effective DPIN (FS-DPIN). To evaluate the effectiveness of the FS-DPIN, we apply 15 network-based centrality methods on the FS-DPIN and compare the results with those on the SPIN and the existing DPINs. Then the predictive performance of the 15 centrality methods is validated in terms of sensitivity, specificity, positive predictive value, negative predictive value, F-measure, accuracy, Jackknife and AUPRC. The experimental results show that the FS-DPIN is superior to the existing DPINs in the identification accuracy of essential proteins.

Predicting congenital renal tract malformation genes using machine learning

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Identification of key genes and therapeutic drugs for cocaine addiction using integrated bioinformatics analysis

Introduction

Cocaine is a highly addictive drug that is abused due to its excitatory effect on the central nervous system. It is critical to reveal the mechanisms of cocaine addiction and identify key genes that play an important role in addiction.

Methods

In this study, we proposed a centrality algorithm integration strategy to identify key genes in a protein-protein interaction (PPI) network constructed by deferential genes from cocaine addiction-related datasets. In order to investigate potential therapeutic drugs for cocaine addiction, a network of targeted relationships between nervous system drugs and key genes was established.

Results

Four key genes (JUN, FOS, EGR1, and IL6) were identified and well validated using CTD database correlation analysis, text mining, independent dataset analysis, and enrichment analysis methods, and they might serve as biomarkers of cocaine addiction. A total of seventeen drugs have been identified from the network of targeted relationships between nervous system drugs and key genes, of which five (disulfiram, cannabidiol, dextroamphetamine, diazepam, and melatonin) have been shown in the literature to play a role in the treatment of cocaine addiction.

Discussion

This study identified key genes and potential therapeutic drugs for cocaine addiction, which provided new ideas for the research of the mechanism of cocaine addiction.

Discovering new pathways toward integration between health and sustainable development goals with natural language processing and network science

BACKGROUND

Research on health and sustainable development is growing at a pace such that conventional literature review methods appear increasingly unable to synthesize all relevant evidence. This paper employs a novel combination of natural language processing (NLP) and network science techniques to address this problem and to answer two questions: (1) how is health thematically interconnected with the Sustainable Development Goals (SDGs) in global science? (2) What specific themes have emerged in research at the intersection between SDG 3 ("Good health and well-being") and other sustainability goals?

METHODS

After a descriptive analysis of the integration between SDGs in twenty years of global science (2001-2020) as indexed by dimensions.ai, we analyze abstracts of articles that are simultaneously relevant to SDG 3 and at least one other SDG (N = 27,928). We use the top2vec algorithm to discover topics in this corpus and measure semantic closeness between these topics. We then use network science methods to describe the network of substantive relationships between the topics and identify 'zipper themes', actionable domains of research and policy to co-advance health and other sustainability goals simultaneously.

RESULTS

We observe a clear increase in scientific research integrating SDG 3 and other SDGs since 2001, both in absolute and relative terms, especially on topics relevant to interconnections between health and SDGs 2 ("Zero hunger"), 4 ("Quality education"), and 11 ("Sustainable cities and communities"). We distill a network of 197 topics from literature on health and sustainable development, with 19 distinct network communities - areas of growing integration with potential to further bridge health and sustainability science and policy. Literature focused explicitly on the SDGs is highly central in this network, while topical overlaps between SDG 3 and the environmental SDGs (12-15) are under-developed.

CONCLUSION

Our analysis demonstrates the feasibility and promise of NLP and network science for synthesizing large amounts of health-related scientific literature and for suggesting novel research and policy domains to co-advance multiple SDGs. Many of the 'zipper themes' identified by our method resonate with the One Health perspective that human, animal, and plant health are closely interdependent. This and similar perspectives will help meet the challenge of 'rewiring' sustainability research to co-advance goals in health and sustainability.

Identification of key biomarkers associated with immune cells infiltration for myocardial injury in dermatomyositis by integrated bioinformatics analysis

BACKGROUND

Dermatomyositis (DM) is an acquired autoimmune disease that can cause damage to various organs, including the heart muscle. However, the mechanisms underlying myocardial injury in DM are not yet fully understood.

METHODS

In this study, we utilized publicly available datasets from the Gene Expression Omnibus (GEO) database to identify hub-genes that are enriched in the immune system process in DM and myocarditis. Weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) analysis, protein-protein interaction (PPI), and gene ontology (GO) analysis were employed to identify these hub-genes. We then used the CIBERSORT method to analyze immune cell infiltration in skeletal muscle specimens of DM and myocardium specimens of myocarditis respectively. Correlation analysis was performed to investigate the relationship between key genes and infiltrating immune cells. Finally, we predicted regulatory miRNAs of hub-genes through miRNet and validated their expression in online datasets and clinical samples.

RESULTS

Using integrated bioinformatics analysis, we identified 10 and 5 hub-genes that were enriched in the immune system process in the database of DM and myocarditis respectively. The subsequent intersections between hub-genes were IFIT3, OAS3, ISG15, and RSAD2. We found M2 macrophages increased in DM and myocarditis compared to the healthy control, associating with the expression of IFIT3, OAS3, ISG15, and RSAD2 in DM and myocarditis positively. Gene function enrichment analysis (GSEA) showed that IFIT3, OAS3, ISG15, and RSAD2 were mainly enriched in type I interferon (IFN) signaling pathway, cellular response to type I interferon, and response to type I interferon. Finally, we verified that the expression of miR-146a-5p was significantly higher in the DM with myocardial injury than those without myocardial injury (p = 0.0009).

CONCLUSION

Our findings suggest that IFIT3, OAS3, ISG15, and RSAD2 may play crucial roles in the underlying mechanism of myocardial injury in DM. Serum miR-146a-5p could be a potential biomarker for myocardial injury in DM.

Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning

MOTIVATION

Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions.

RESULTS

In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines.

AVAILABILITY AND IMPLEMENTATION

The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In silico analysis revealed the potential circRNA-miRNA-mRNA regulative network of non-small cell lung cancer (NSCLC)

BACKGROUND

The primary source of death in the world is non-small cell lung cancer (NSCLC). However, NSCLCs pathophysiology is still not completely understood. The current work sought to study the differential expression of mRNAs involved in NSCLC and their interactions with miRNAs and circRNAs.

METHODS

We utilized three microarray datasets (GSE21933, GSE27262, and GSE33532) from the GEO NCBI database to identify the differentially expressed genes (DEGs) in NSCLC. We employed DAVID Functional annotation tool to investigate the underlying GO biological process, molecular functions, and KEGG pathways involved in NSCLC. We performed the Protein-protein interaction (PPI) network, MCODE, and CytoHubba analysis from Cytoscape software to identify the significant DEGs in NSCLC. We utilized miRnet to anticipate and build interaction between miRNAs and mRNAs in NSCLC and ENCORI to predict the miRNA-circRNA relationships and build the ceRNA regulatory network. Finally, we executed the gene expression and Kaplan-Meier survival analysis to validate the significant DEGs in the ceRNA network utilizing TCGA NSCLC and GEPIA data.

RESULTS

We revealed a total of 156 overlapped DEGs (47 upregulated and 109 downregulated genes) in NSCLC. The PPI network, MCODE, and CytoHubba analysis revealed 12 hub genes (cdkn3, rrm2, ccnb1, aurka, nuf2, tyms, kif11, hmmr, ccnb2, nek2, anln, and birc5) that are associated with NSCLC. We identified that these 12 genes encode 12 mRNAs that are strongly linked with 8 miRNAs, and further, we revealed that 1 circRNA was associated with this 5 miRNA. We constructed the ceRNAs network that contained 1circRNA-5miRNAs-7mRNAs. The expression of these seven significant genes in LUAD & LUSC (NSCLC) was considerably higher in the TCGA database than in normal tissues. Kaplan-Meier survival plot reveals that increased expression of these hub genes was related to a poor survival rate in LUAD.

CONCLUSION

Overall, we developed a circRNA-miRNA-mRNA regulation network to study the probable mechanism of NSCLC.

In-silico evidence of ADAM metalloproteinase pathology in cancer signaling networks

Lack of effective targeted therapies often contributes to poor clinical outcomes of aggressive malignancies associated with drug resistance, angiogenesis and metastasis. Literature mining portrays the major role of ADAM17 in cancer and inflammatory diseases. However, it is quite challenging to design a candidate drug for targeting ADAM17 due to its structural similarity with the catalytic domain of the matrix metalloproteases (MMPs). The present study reports the protein-protein interaction analysis of ADAM17, along with the molecular docking and MD simulation studies for the screened compounds. Our analysis confirms the association of ADAM17 with numerous oncogenes that facilitates cancer progression and inflammation, especially the members of the Notch, receptor tyrosine kinase (RTK) and TNFα pathways. The outcome provides evidence that the prevalent protease ADAM17 could attribute to cancer signaling regulation though the shedding of various inflammatory and oncogenic molecules. We have also exploited the analogues of the existing inhibitors, with an aim at discovering a potent molecule, which could be repurposed as a drug against ADAM17 inflicted cancer progression. Upon stringent screening, we delineated our choice into two specific compounds (I6 and I9; analogues of IK862, a type of y-lactam hydroxamates), possessing the lowest binding energy (-9.1 Kcal/mol), stable MD-simulation studies and superior pharmacodynamic properties. The current information illustrates the avenue to persuade further research on targeting ADAM17 with small molecular compounds (I6 and I9) in cancer therapeutics.Communicated by Ramaswamy H. Sarma.

Identification of candidate aberrant differentially methylated/expressed genes in asthma

BACKGROUND

Asthma is an important non-communicable disease worldwide. DNA methylation is associated with the occurrence and development of asthma. We are aimed at assuring differential expressed genes (DEGs) modified by aberrantly methylated genes (DMGs) and pathways related to asthma by integrating bioinformatics analysis.

METHODS

One mRNA dataset (GSE64913) and one gene methylation dataset (GSE137716) were selected from the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed using GeneCodies 4.0 database. All gene expression matrices were analyzed by Gene set enrichment analysis (GSEA) software. STRING was applied to construct a protein-protein interaction (PPI) network to find the hub genes. Then, electronic validation was performed to verify the hub genes, followed by the evaluation of diagnostic value. Eventually, quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to detect the expression of hub genes.

RESULTS

In total, 14 hypomethylated/high-expression genes and 10 hypermethylated/low-expression genes were obtained in asthma. Among them, 10 hub genes were identified in the PPI network. Functional analysis demonstrated that the differentially methylated/expressed genes were primarily associated with the lung development, cytosol and protein binding. Notably, HLA-DOA was enriched in asthma. FKBP5, WNT5A, TM4SF1, PDK4, EPAS1 and GMPR had potential diagnostic value for asthma.

CONCLUSION

The project explored the pathogenesis of asthma, which may provide a research basis for the prediction and the drug development of asthma.

Evolutionarily conserved gene expression patterns for affective disorders revealed using cross-species brain transcriptomic analyses in humans, rats and zebrafish

Widespread, debilitating and often treatment-resistant, depression and other stress-related neuropsychiatric disorders represent an urgent unmet biomedical and societal problem. Although animal models of these disorders are commonly used to study stress pathogenesis, they are often difficult to translate across species into valuable and meaningful clinically relevant data. To address this problem, here we utilized several cross-species/cross-taxon approaches to identify potential evolutionarily conserved differentially expressed genes and their sets. We also assessed enrichment of these genes for transcription factors DNA-binding sites down- and up- stream from their genetic sequences. For this, we compared our own RNA-seq brain transcriptomic data obtained from chronically stressed rats and zebrafish with publicly available human transcriptomic data for patients with major depression and their respective healthy control groups. Utilizing these data from the three species, we next analyzed their differential gene expression, gene set enrichment and protein-protein interaction networks, combined with validated tools for data pooling. This approach allowed us to identify several key brain proteins (GRIA1, DLG1, CDH1, THRB, PLCG2, NGEF, IKZF1 and FEZF2) as promising, evolutionarily conserved and shared affective 'hub' protein targets, as well as to propose a novel gene set that may be used to further study affective pathogenesis. Overall, these approaches may advance cross-species brain transcriptomic analyses, and call for further cross-species studies into putative shared molecular mechanisms of affective pathogenesis.

ER stress-related mRNA-lncRNA co-expression gene signature predicts the prognosis and immune implications of esophageal cancer

BACKGROUND

Esophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported.

METHODS

Firstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups.

RESULTS

The WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC.

CONCLUSION

In this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.

The Effect of Coumestrol on Hub Genes in Lung Squamous Cell Carcinoma Based on Bioinformatic Strategy

Purpose There is limited treatment for lung squamous cell carcinoma (LUSC), so there is an urgent need to find new antitumor drugs. Materials and Methods We downloaded datasets from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas databases. We used GEO2R and the “limma” package to screen differentially expressed genes. We used the Cytoscape software to screen out hub genes. We screened herbs that act on hub genes on the Chinese medicine website. We then studied the effect of coumestrol (CM) on the hub genes in the H226 cell line. Results Seven hub genes were screened, namely CCNB2, CENPF, KIF11, MELK, nucleolar and spindle-associated protein 1 (NUSAP1), PBK, and RRM2. We observed that CM had a tumor-inhibiting effect on H226 cells by inhibiting the expression of CCNB2, KIF11, and NUSAP1. Conclusion CM, screened by bioinformatics and network pharmacology, can inhibit H226 cells by downregulating CCNB2, KIF11, and NUSAP1.

A novel age-related gene expression signature associates with proliferation and disease progression in breast cancer

BACKGROUND AND OBJECTIVE

Breast cancer (BC) diagnosed at ages <40 years presents with more aggressive tumour phenotypes and poorer clinical outcome compared to older BC patients. Here, we explored transcriptional BC alterations to gain a better understanding of age-related tumour biology, also subtype-stratified.

METHODS

We studied publicly available global BC mRNA expression (n = 3999) and proteomics data (n = 113), exploring differentially expressed genes, enriched gene sets, and gene networks in the young compared to older patients.

RESULTS

We identified transcriptional patterns reflecting increased proliferation and oncogenic signalling in BC of the young, also in subtype-stratified analyses. Six up-regulated hub genes built a novel age-related score, significantly associated with aggressive clinicopathologic features. A high 6 Gene Proliferation Score (6GPS) demonstrated independent prognostic value when adjusted for traditional clinicopathologic variables and the molecular subtypes. The 6GPS significantly associated also with disease-specific survival within the luminal, lymph node-negative and Oncotype Dx intermediate subset.

CONCLUSIONS

We here demonstrate evidence of higher tumour cell proliferation in young BC patients, also when adjusting for molecular subtypes, and identified a novel age-based six-gene signature pointing to aggressive tumour features, tumour proliferation, and reduced survival-also in patient subsets with expected good prognosis.

A deep learning framework for identifying essential proteins based on multiple biological information

Background

Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein–protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information.

Results

We put forward a deep learning framework to predict essential proteins by integrating features obtained from the PPI network, subcellular localization, and gene expression profiles. In our model, the node2vec method is applied to learn continuous feature representations for proteins in the PPI network, which capture the diversity of connectivity patterns in the network. The concept of depthwise separable convolution is employed on gene expression profiles to extract properties and observe the trends of gene expression over time under different experimental conditions. Subcellular localization information is mapped into a long one-dimensional vector to capture its characteristics. Additionally, we use a sampling method to mitigate the impact of imbalanced learning when training the model. With experiments carried out on the data of Saccharomyces cerevisiae, results show that our model outperforms traditional centrality methods and machine learning methods. Likewise, the comparative experiments have manifested that our process of various biological information is preferable.

Conclusions

Our proposed deep learning framework effectively identifies essential proteins by integrating multiple biological data, proving a broader selection of subcellular localization information significantly improves the results of prediction and depthwise separable convolution implemented on gene expression profiles enhances the performance.

Identification of immunophenotypes in esophageal squamous cell carcinoma based on immune gene sets

PURPOSE

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor with high heterogeneity. Research on molecular mechanisms involved in the process of tumor origination and progression is extremely limited to investigating mechanisms of molecular typing for ESCC.

METHODS

After comprehensively analyzing the gene expression profiles in The Cancer Genome Atlas and Gene Expression Omnibus databases, we identified four immunotypes of ESCC (referred to as C1-C4) based on the gene sets of 28 immune cell subpopulations. The discrepancies in prognostic value, clinical features, drug sensitivity, and tumor components between the immunotypes were individually analyzed.

RESULTS

The ranking of immune infiltration is C1 > C4 > C3 > C2. These subtypes are characterized by high and low expression of immune checkpoint proteins, enrichment and insufficiency of immune-related pathways, and differential distribution of immune cell subgroups. Poorer survival was observed in the C1 subtype, which we hypothesized could be caused by an immunosuppressive cell population. Fortunately, C1's susceptibility to anti-PD-1 therapy offers hope for patients with poor prognosis in advanced stages. On the other hand, C4 is sensitive to docetaxel, which may offer novel treatment strategies for ESCC in the future. It is worth noting that immunophenotyping is tightly bound to the abundance of stromal components and stem cells, which could explain the tumor immune escape to some extent. Ultimately, determination of hub genes based on the C1 subtypes provides a reference for the discovery of immunotarget drugs against ESCC.

CONCLUSION

The identification of immunophenotypes in our study provides new therapeutic strategies for patients with ESCC.

Identification of Immune-Related Gene Signature and Prediction of CeRNA Network in Active Ulcerative Colitis

Background

Ulcerative colitis (UC) is an inflammatory disease of the intestinal mucosa, and its incidence is steadily increasing worldwide. Intestinal immune dysfunction has been identified as a central event in UC pathogenesis. However, the underlying mechanisms that regulate dysfunctional immune cells and inflammatory phenotype remain to be fully elucidated.

Methods

Transcriptome profiling of intestinal mucosa biopsies were downloaded from the GEO database. Robust Rank Aggregation (RRA) analysis was performed to identify statistically changed genes and differentially expressed genes (DEGs). Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore potential biological mechanisms. CIBERSORT was used to evaluate the proportion of 22 immune cells in biopsies. Weighted co-expression network analysis (WGCNA) was used to determine key module-related clinical traits. Protein-Protein Interaction (PPI) network and Cytoscape were performed to explore protein interaction network and screen hub genes. We used a validation cohort and colitis mouse model to validate hub genes. Several online websites were used to predict competing endogenous RNA (ceRNA) network.

Results

RRA integrated analysis revealed 1838 statistically changed genes from four training cohorts (adj. p-value < 0.05). GSEA showed that statistically changed genes were enriched in the innate immune system. CIBERSORT analysis uncovered an increase in activated dendritic cells (DCs) and M1 macrophages. The red module of WGCNA was considered the most critical module related to active UC. Based on the results of the PPI network and Cytoscape analyses, we identified six critical genes and transcription factor NF-κB. RT-PCR revealed that andrographolide (AGP) significantly inhibited the expression of hub genes. Finally, we identified XIST and three miRNAs (miR-9-5p, miR-129-5p, and miR-340-5p) as therapeutic targets.

Conclusions

Our integrated analysis identified four hub genes (CXCL1, IL1B, MMP1, and MMP10) regulated by NF-κB. We further revealed that AGP decreased the expression of hub genes by inhibiting NF-κB activation. Lastly, we predicted the involvement of ceRNA network in the regulation of NF-κB expression. Collectively, our results provide valuable information in understanding the molecular mechanisms of active UC. Furthermore, we predict the use of AGP and small RNA combination for the treatment of UC.

TREM2 and CD163 Ameliorate Microglia-Mediated Inflammatory Environment in the Aging Brain

Aging decreases cognitive functions, especially learning and memory. Neuroinflammation is mediated by microglia and occurs in age-related neurodegenerative diseases. The expression profiles in a dataset of cognitively normal controls (GSE11882) were obtained from the Gene Expression Omnibus (GEO) database. Microarray data were used to explore the expression of age-related genes in the human hippocampus. A total of 120 differentially expressed genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed. A total of 18 key genes were identified by the plugin cytoHubba in Cytoscape software. Two genes with a positive impact on cognition during aging were teased out: triggering receptor expressed on myeloid cells 2 (TREM2) and a scavenger receptor (CD163). Finally, the results of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting (WB) verified that the mRNA expression of these two genes was significantly upregulated in aged mice. Moreover, the levels of the inflammatory factors IL-1β and IL-6 were significantly increased. TREM2 and CD163 may be upregulated to alleviate the inflammatory environment resulting from microglial activation in the aging brain, thereby delaying cognitive decline.

Identification of the Potential Key Genes and Pathways Involved in Lens Changes of High Myopia

Aim

Methods

Results

Conclusion

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

Node centrality measures are among the most commonly used analytical techniques for networks. They have long helped analysts to identify “important” nodes that hold power in a social context, where damages could have dire consequences for transportation applications, or who should be a focus for prevention in epidemiology. Given the ubiquity of network data, new measures have been proposed, occasionally motivated by emerging applications or by the ability to interpolate existing measures. Before analysts use these measures and interpret results, the fundamental question is: are these measures likely to complete within the time window allotted to the analysis? In this paper, we comprehensively examine how the time necessary to run 18 new measures (introduced from 2005 to 2020) scales as a function of the number of nodes in the network. Our focus is on giving analysts a simple and practical estimate for sparse networks. As the time consumption depends on the properties in the network, we nuance our analysis by considering whether the network is scale-free, small-world, or random. Our results identify that several metrics run in the order of O(nlogn) and could scale to large networks, whereas others can require O(n²) or O(n³) and may become prime targets in future works for approximation algorithms or distributed implementations.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

Construction and analysis of the protein-protein interaction network for the detoxification enzymes of the silkworm, Bombyx mori

Detoxification enzymes are necessary for insects to metabolize toxic substances and maintain physiological activities. Cytochromes P450 (CYPs), glutathione S-transferases (GSTs), and carboxylesterase (CarEs) are the main detoxification enzymes in insects. In addition, UDP-glucosyltransferase and ATP-binding cassette transporter also participate in the process of material metabolism. This study collected proteins related to detoxification in the silkworm, Bombyx mori (Lepidoptera: Bombycidae). And we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on these proteins to understand their biological function. We constructed the protein-protein interaction network for the silkworm's detoxification enzymes and analyzed the network's topological properties. We found that BGIBMGA014046-TA, BGIBMGA003221-TA, BGIBMGA011092-TA, BGIBMGA000074-TA, and LOC732976 are the essential proteins in the network. These proteins are primarily involved in the process of ribosome biogenesis and may be related to protein synthesis. We integrated GO, KEGG, and network analysis and found that ribosome-associated protein and GSTs played a vital role in the detoxification process.

Identification of prognostic biomarkers associated with tumor microenvironment in ceRNA network for esophageal squamous cell carcinoma: a bioinformatics study based on TCGA database

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer in the world with high incidence rate and poor prognosis. Infiltrated immune and stromal cells are vital components of tumor microenvironment (TME) and have a significant impact on the progression of ESCC. The competitive endogenous RNA (ceRNA) hypothesis has been proved important in the molecular biological mechanisms of tumor development. However, there are few studies on the relationship between ceRNA and ESCC TME.

METHODS

The proportion of tumor-infiltrating immune cells and the amount of stromal and immune cells in ESCC cases were calculated from The Cancer Genome Atlas database using the CIBERSORT and ESTIMATE calculation methods. After stratified identification of differentially expressed genes, WGCNA and miRNA prediction system were applied to construct ceRNA network. Finally, PPI network and survival analysis were selected to discriminate prognostic signature. And the results were verified in two independent groups from Gene Expression Omnibus and Lanzhou, China.

RESULTS

We found that high Stromal and ESTIMATE scores were significantly associated with poor overall survival. Three TME-related key prognostic genes were screened, namely, LCP2, CD86, SLA. And the expression of them was significantly correlated with infiltrated immunocytes. It is also found that ESTIMATE Score and the expression of CD86 were both related to TNM system of ESCC.

CONCLUSIONS

We identified three novel TME-related prognostic markers and their lncRNA-miRNA-mRNA pathway in ESCC patients, which may provide new strategies for the targeted therapy.

Identification of hub genes associated with cognition in the hippocampus of Alzheimer's Disease

Alzheimer’s Disease (AD) is a neurodegenerative disease featured by cognitive impairment. This bioinformatic analysis was used to identify hub genes related to cognitive dysfunction in AD. The gene expression profile GSE48350 in the hippocampus of AD patients aged >70 years was obtained from the Gene Expression Omnibus (GEO) database. A total of 96 differentially expressed genes (DEGs) were identified, and subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses; a protein–protein interaction (PPI) network was constructed. The DEGs were enriched in synapse-related changes. A protein cluster was teased out of PPI. Furthermore, the cognition ranked the first among all the terms of biological process (BP). Next, 4 of 10 hub genes enriched in cognition were identified. The function of these genes was validated using APP/PS1 mice. Cognitive performance was validated by Morris Water Maze (MWM), and gene expression by RT-qPCR, Cholecystokinin (CCK), Tachykinin precursor 1 (TAC1), Calbindin 1 (CALB1) were downregulated in the hippocampus. These genes can provide new directions in the research of the molecular mechanism of AD.

Gene Network Analysis of Alzheimer's Disease Based on Network and Statistical Methods

Gene network associated with Alzheimer’s disease (AD) is constructed from multiple data sources by considering gene co-expression and other factors. The AD gene network is divided into modules by Cluster one, Markov Clustering (MCL), Community Clustering (Glay) and Molecular Complex Detection (MCODE). Then these division methods are evaluated by network structure entropy, and optimal division method, MCODE. Through functional enrichment analysis, the functional module is identified. Furthermore, we use network topology properties to predict essential genes. In addition, the logical regression algorithm under Bayesian framework is used to predict essential genes of AD. Based on network pharmacology, four kinds of AD’s herb-active compounds-active compound targets network and AD common core network are visualized, then the better herbs and herb compounds of AD are selected through enrichment analysis.

Advances in Understanding Leishmania Pathobiology: What Does RNA-Seq Tell Us?

Leishmaniasis is a vector-borne disease caused by a protozoa parasite from over 20 Leishmania species. The clinical manifestations and the outcome of the disease vary greatly. Global RNA sequencing (RNA-Seq) analyses emerged as a powerful technique to profile the changes in the transcriptome that occur in the Leishmania parasites and their infected host cells as the parasites progresses through their life cycle. Following the bite of a sandfly vector, Leishmania are transmitted to a mammalian host where neutrophils and macrophages are key cells mediating the interactions with the parasites and result in either the elimination the infection or contributing to its proliferation. This review focuses on RNA-Seq based transcriptomics analyses and summarizes the main findings derived from this technology. In doing so, we will highlight caveats in our understanding of the parasite's pathobiology and suggest novel directions for research, including integrating more recent data highlighting the role of the bacterial members of the sandfly gut microbiota and the mammalian host skin microbiota in their potential role in influencing the quantitative and qualitative aspects of leishmaniasis pathology.

Down-regulation of BCL2L13 renders poor prognosis in clear cell and papillary renal cell carcinoma

Background

BCL2L13 belongs to the BCL2 super family, with its protein product exhibits capacity of apoptosis-mediating in diversified cell lines. Previous studies have shown that BCL2L13 has functional consequence in several tumor types, including ALL and GBM, however, its function in kidney cancer remains as yet unclearly.

Methods

Multiple web-based portals were employed to analyze the effect of BCL2L13 in kidney cancer using the data from TCGA database. Functional enrichment analysis and hubs of BCL2L13 co-expressed genes in clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were carried out on Cytoscape. Evaluation of BCL2L13 protein level was accomplished through immunohistochemistry on paraffin embedded renal cancer tissue sections. Western blotting and flow cytometry were implemented to further analyze the pro-apoptotic function of BCL2L13 in ccRCC cell line 786-0.

Results

BCL2L13 expression is significantly decreased in ccRCC and pRCC patients, however, mutations and copy number alterations are rarely observed. The poor prognosis of ccRCC that derived from down-regulated BCL2L13 is independent of patients’ gender or tumor grade. Furthermore, BCL2L13 only weakly correlates with the genes that mutated in kidney cancer or the genes that associated with inherited kidney cancer predisposing syndrome, while actively correlates with SLC25A4. As a downstream effector of BCL2L13 in its pro-apoptotic pathway, SLC25A4 is found as one of the hub genes that involved in the physiological function of BCL2L13 in kidney cancer tissues.

Conclusions

Down-regulation of BCL2L13 renders poor prognosis in ccRCC and pRCC. This disadvantageous factor is independent of any well-known kidney cancer related genes, so BCL2L13 can be used as an effective indicator for prognostic evaluation of renal cell carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02039-y.

Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

Integrated analysis and identification of nine-gene signature associated to oral squamous cell carcinoma pathogenesis

Oral squamous cell carcinoma (OSCC) is one of the leading cancers with poor disease survival rate. Herein, we explored molecular basis, in silico identification and in vitro verification of genes associated with OSCC. Five gene expression series including, GSE30784, GSE13601, GSE9844, GSE23558 and GSE37991 were screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by cluster Profiler. Further, protein-protein interaction network was analysed and hub genes were verified. A total of 6476 (up-regulated: 2848; down-regulated: 3628) DEGs were identified among OSCC patients and healthy controls. Gene Ontology analysis indicated DEGs enrichment in cellular motility, invasion and adhesion processes. KEGG analysis revealed enrichment of PI3K-Akt signalling, focal adhesion and regulation of actin cytoskeleton pathways. Subsequently, nine DEGs including APP, EHMT1, ACACB, PCNA, PLAU, FST, HMGA2, LAMC2 and SPP1 were correlated with TCGA expression data along with significant association towards patient's survival, recognized as hub genes. This dysregulated mRNA signature of genes was validated in two OSCC cell lines with an anti-cancer agent, fisetin. Fisetin inhibited the expression of APP, EHMT1, PCNA, PLAU, FST, HMGA2, LAMC2, SPP1 and upregulated the expression of ACACB gene which were associated with growth inhibition of both the OSCC cell lines. The regulatory effect of fisetin supported crucial role of nine hub genes identified in OSCC. This study signified that hub genes and pathways might influence the aggressiveness of OSCC. Thus, the proposed hub genes could be potential diagnostic biomarker and drug targets for OSCC.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02737-4.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

CNS genomic profiling in the mouse chronic social stress model implicates a novel category of candidate genes integrating affective pathogenesis

Despite high prevalence, medical impact and societal burden, anxiety, depression and other affective disorders remain poorly understood and treated. Clinical complexity and polygenic nature complicate their analyses, often revealing genetic overlap and cross-disorder heritability. However, the interplay or overlaps between disordered phenotypes can also be based on shared molecular pathways and 'crosstalk' mechanisms, which themselves may be genetically determined. We have earlier predicted (Kalueff et al., 2014) a new class of 'interlinking' brain genes that do not affect the disordered phenotypes per se, but can instead specifically determine their interrelatedness. To test this hypothesis experimentally, here we applied a well-established rodent chronic social defeat stress model, known to progress in C57BL/6J mice from the Anxiety-like stage on Day 10 to Depression-like stage on Day 20. The present study analyzed mouse whole-genome expression in the prefrontal cortex and hippocampus during the Day 10, the Transitional (Day 15) and Day 20 stages in this model. Our main question here was whether a putative the Transitional stage (Day 15) would reveal distinct characteristic genomic responses from Days 10 and 20 of the model, thus reflecting unique molecular events underlining the transformation or switch from anxiety to depression pathogenesis. Overall, while in the Day 10 (Anxiety) group both brain regions showed major genomic alterations in various neurotransmitter signaling pathways, the Day 15 (Transitional) group revealed uniquely downregulated astrocyte-related genes, and the Day 20 (Depression) group demonstrated multiple downregulated genes of cell adhesion, inflammation and ion transport pathways. Together, these results reveal a complex temporal dynamics of mouse affective phenotypes as they develop. Our genomic profiling findings provide first experimental support to the idea that novel brain genes (activated here only during the Transitional stage) may uniquely integrate anxiety and depression pathogenesis and, hence, determine the progression from one pathological state to another. This concept can potentially be extended to other brain conditions as well. This preclinical study also further implicates cilial and astrocytal mechanisms in the pathogenesis of affective disorders.

A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?

In order to understand and represent the importance of nodes within networks better, most of the studies that investigate graphs compute the nodes' centrality within their network(s) of interest. In the literature, the most frequent measures used are degree, closeness and/or betweenness centrality, even if other measures might be valid candidates for representing the importance of nodes within networks. The main contribution of this paper is the development of a methodology that allows one to understand, compare and validate centrality indices when studying a particular network of interest. The proposed methodology integrates the following steps: choosing the centrality measures for the network of interest; developing a theoretical taxonomy of these measures; identifying, by means of Principal Component Analysis (PCA), latent dimensions of centrality within the network of interest; verifying the proposed taxonomy of centrality measures; and identifying the centrality measures that best represent the network of interest. Also, we applied the proposed methodology to an existing graph of interest, in our case a real friendship student network. We chose eighteen centrality measures that were developed in SNA and are available and computed in a specific library (CINNA), defined them thoroughly, and proposed a theoretical taxonomy of these eighteen measures. PCA showed the emergence of six latent dimensions of centrality within the student network and saturation of most of the centrality indices on the same categories as those proposed by the theoretical taxonomy. Additionally, the results suggest that indices other than the ones most frequently applied might be more relevant for research on friendship student networks. Finally, the integrated methodology that we propose can be applied to other centrality indices and/or other network types than student graphs.

Integrative analyses of molecular pathways and key candidate biomarkers associated with colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths and mining the molecular factors underlying CRC pathogenesis is imperative for alleviating the disease burden.

OBJECTIVE

To highlight key molecular pathways, prioritize hub genes and their regulators related to CRC.

METHODS

Data sets of TCGA-COAD and GTEx were used to identify differentially expressed genes (DEGs) and their functional enrichments in pathways and biological processes were analyzed using bioinformatics tools. Protein-protein interaction network was constructed and hub genes were identified using Cytoscape. Ingenuity Pathway Analysis was used to analyze the relations of the hub genes with diseases and canonical pathways. Key regulators targeting the hub genes such as TFs, miRNAs and their interactions were identified using in silico tools.

RESULTS

AURKA, CDK1, MYC, CDH1, CCNB1, CDC20, UBE2C, PLK1, KIF11, and CCNA2 were prioritized as hub genes based on their topological properties. Enrichment analyses emphasized the roles of DEGs and hub genes in the cell cycle process. Interactions of the hub genes with TFs and miRNAs suggested TP53, EZH2 and KLF4 as being promising candidate biomarkers for CRC.

CONCLUSIONS

Our results provide in silico evidence for candidate biomolecules that may have strong biomarker potential for CRC-related translational strategies.