Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

Comparative transcriptomics analysis identifies crucial genes and pathways during goose spleen development

As the largest peripheral lymphoid organ in poultry, the spleen plays an essential role in regulating the body's immune capacity. However, compared with chickens and ducks, information about the age- and breed-related changes in the goose spleen remains scarce. In this study, we systematically analyzed and compared the age-dependent changes in the morphological, histological, and transcriptomic characteristics between Landes goose (LG; Anser anser) and Sichuan White goose (SWG; Anser cygnoides). The results showed a gradual increase in the splenic weights for both LG and SWG until week 10, while their splenic organ indexes reached the peak at week 6. Meanwhile, the splenic histological indexes of both goose breeds continuously increased with age, reaching the highest levels at week 30. The red pulp (RP) area was significantly higher in SWG than in LG at week 0, while the splenic corpuscle (AL) diameter was significantly larger in LG than in SWG at week 30. At the transcriptomic level, a total of 1710 and 1266 differentially expressed genes (DEGs) between week 0 and week 30 were identified in spleens of LG and SWG, respectively. Meanwhile, a total of 911 and 808 DEGs in spleens between LG and SWG were identified at weeks 0 and 30, respectively. Both GO and KEGG enrichment analysis showed that the age-related DEGs of LG or SWG were dominantly enriched in the Cell cycle, TGF-beta signaling, and Wnt signaling pathways, while most of the breed-related DEGs were enriched in the Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction, ECM-receptor interaction, and metabolic pathways. Furthermore, through construction of protein-protein interaction networks using significant DEGs, it was inferred that three hub genes including BUB1, BUB1B, and TTK could play crucial roles in regulating age-dependent goose spleen development while GRIA2, GRIA4, and RYR2 could be crucial for the breed-specific goose spleen development. These data provide novel insights into the splenic developmental differences between Chinese and European domestic geese, and the identified crucial pathways and genes are helpful for a better understanding of the mechanisms regulating goose immune functions.

Exploration of the potential mechanism of Duhuo Jisheng Decoction in osteoarthritis treatment by using network pharmacology and molecular dynamics simulation

In this study, the active ingredients of 15 Chinese herbal medicines of Duhuo Jisheng Decoction and their corresponding targets were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The microarray data of Osteoarthritis (OA) were obtained through the GEO database for differential analysis and then a drug target-OA-related gene protein-protein interaction (PPI) network was established. The potential targets of Duhuo Jisheng Decoction in the treatment of OA were acquired by intersecting the OA-associated genes with the target genes of active ingredients. Random walk with restart (RWR) analysis of PPI networks was performed using potential targets as seed, and the top 50 genes of affinity coefficients were used as key action genes of Duhuo Jisheng Decoction in the treatment of OA. A drug-active ingredient-gene interaction network was established. AKT1, a key target of Duhuo Jisheng Decoction in the treatment of OA, was obtained by topological analysis of the gene interaction network. Molecular docking and molecular dynamics verified the binding of AKT1 to its corresponding drug active ingredients. CETSA assay demonstrated that the combination of luteolin and AKT1 increased the stability of AKT1, and the combination efficiency was high. In conclusion, the molecular mechanism of Duhuo Jisheng Decoction in treating OA featured by multiple components, targets, and pathways had been further investigated in this study, which is of significance for discovering as well as developing new drugs for this disease. The findings can also offer personalized diagnosis and treatment strategies for patients with OA in clinical practice.

Identifying diseases associated with Post-COVID syndrome through an integrated network biology approach

A growing body of research shows that COVID-19 is now recognized as a multi-organ disease with a wide range of manifestations that can have long-lasting repercussions, referred to as post-COVID-19 syndrome. It is unknown why the vast majority of COVID-19 patients develop post-COVID-19 syndrome, or why patients with pre-existing disorders are more likely to experience severe COVID-19. This study used an integrated network biology approach to obtain a comprehensive understanding of the relationship between COVID-19 and other disorders. The approach involved building a PPI network with COVID-19 genes and identifying highly interconnected regions. The molecular information contained within these subnetworks, as well as the pathway annotations, were used to reveal the link between COVID-19 and other disorders. Using Fisher's exact test and disease-specific gene information, significant COVID-19-disease associations were discovered. The study discovered diseases that affect multiple organs and organ systems, thus proving the theory of multiple organ damage caused by COVID-19. Cancers, neurological disorders, hepatic diseases, cardiac disorders, pulmonary diseases, and hypertensive diseases are just a few of the conditions linked to COVID-19. Pathway enrichment analysis of shared proteins revealed the shared molecular mechanism of COVID-19 and these diseases. The findings of the study shed new light on the major COVID-19-associated disease conditions and how their molecular mechanisms interact with COVID-19. The novelty of studying disease associations in the context of COVID-19 provides new insights into the management of rapidly evolving long-COVID and post-COVID syndromes, which have significant global implications.Communicated by Ramaswamy H. Sarma.

Identification of common candidate genes and pathways for Spina Bifida and Wilm's Tumor using an integrative bioinformatics analysis

Spina Bifida (SB) and Wilm's Tumor (WT) are conditions, both associated with children. Several studies have shown that WT later develops in SB patients, which led us to elucidate common key genes and linked pathways of both conditions, aimed at their concurrent therapeutic management. For this, integrated bioinformatics analysis was employed. A comprehensive manual curation of genes identified 133 and 139 genes associated with SB and WT, respectively, which were used to construct a single protein-protein interaction (PPI) network. Topological parameters analysis of the network showed its scale-free and hierarchical nature. Centrality-based analysis of the network identified 116 hubs, of which, 6 were called the key genes attributed to being common between SB and WT besides being the hubs. Gene enrichment analysis of the 5 most essential modules, identified important biological processes and pathways possibly linking SB to WT. Additionally, miRNA-key gene-transcription factor (TF) regulatory network elucidated a few important miRNAs and TFs that regulate our key genes. In closing, we put forward TP53, DICER1, NCAM1, PAX3, PTCH1, MTHFR; hsa-mir-107, hsa-mir-137, hsa-mir-122, hsa-let-7d; and YY1, SOX4, MYC, STAT3; key genes, miRNAs and TFs, respectively, as the key regulators. Further, MD simulation studies of wild and Glu429Ala forms of MTHFR proteins showed that there is a slight change in MTHFR protein structure due to Glu429Ala polymorphism. We anticipate that the interplay of these three entities will be an interesting area of research to explore the regulatory mechanism of SB and WT and may serve as candidate target molecules to diagnose, monitor, and treat SB and WT, parallelly.Communicated by Ramaswamy H. Sarma.

Integrative analysis of immune-related signature profiles in eosinophilic chronic rhinosinusitis with nasal polyposis

Eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) is a subtype of chronic rhinosinusitis (CRS) that is associated with the nasal cavity and sinus polyps, elevated levels of eosinophils, and dysregulated immune responses to environmental triggers. The underlying cause of ECRSwNP is not well understood, and few studies have focused on the unique features of this subtype of CRS. Our study integrated proteomic and transcriptomic data with multi-omic bioinformatics analyses. We collected nasal polyps from three ECRSwNP patients and three control patients and identified 360 differentially expressed (DE) proteins, including 119 upregulated and 241 downregulated proteins. Functional analyses revealed several significant associations with ECRSwNP, including focal adhesion, hypertrophic cardiomyopathy, and extracellular matrix (ECM)-receptor interactions. Additionally, a protein-protein interaction (PPI) network revealed seven hub proteins that may play crucial roles in the development of ECRSwNP. We also compared the proteomic data with publicly available transcriptomic data and identified a total of 1077 DE genes. Pathways enriched by the DE genes involved angiogenesis, positive regulation of cell motility, and immune responses. Furthermore, we investigated immune cell infiltration and identified biomarkers associated with eosinophil and M2 macrophage infiltration using CIBERSORT and Weighted Gene Correlation Network Analysis (WGCNA). Our results provide a more complete picture of the immune-related mechanisms underlying ECRSwNP, which could contribute to the development of more precise treatment strategies for this condition.

Insights into the identification and evolutionary conservation of key genes in the transcriptional circuits of meiosis initiation and commitment in budding yeast

Initiation of meiosis in budding yeast does not commit the cells for meiosis. Thus, two distinct signaling cascades may differentially regulate meiosis initiation and commitment in budding yeast. To distinguish between the role of these signaling cascades, we reconstructed protein-protein interaction networks and gene regulatory networks with upregulated genes in meiosis initiation and commitment. Analyzing the integrated networks, we identified four master regulators (MRs) [Ume6p, Msn2p, Met31p, Ino2p], three transcription factors (TFs), and 279 target genes (TGs) unique for meiosis initiation, and three MRs [Ndt80p, Aro80p, Rds2p], 11 TFs, and 948 TGs unique for meiosis commitment. Functional enrichment analysis of these distinct members from the transcriptional cascades for meiosis initiation and commitment revealed that nutritional cues rewire gene expression for initiating meiosis and chromosomal recombination commits cells to meiosis. As meiotic chromosomal recombination is highly conserved in eukaryotes, we compared the evolutionary rate of unique members in the transcriptional cascade of two meiotic phases of Saccharomyces cerevisiae with members of the phylum Ascomycota, revealing that the transcriptional cascade governing chromosomal recombination during meiosis commitment has experienced greater purifying selection pressure (P value = 0.0013, 0.0382, 0.0448, 0.0369, 0.02967, 0.04937, 0.03046, 0.03357 and < 0.00001 for Ashbya gossypii, Yarrowia lipolytica, Debaryomyces hansenii, Aspergillus fumigatus, Neurospora crassa, Kluyveromyces lactis, Schizosaccharomyces pombe, Schizosaccharomyces cryophilus, and Schizosaccharomyces octosporus, respectively). This study demarcates crucial players driving meiosis initiation and commitment and demonstrates their differential rate of evolution in budding yeast.

Circadian clock crosstalks with autism

BACKGROUND

The mechanism underlying autism spectrum disorder (ASD) remains incompletely understood, but researchers have identified over a thousand genes involved in complex interactions within the brain, nervous, and immune systems, particularly during the mechanism of brain development. Various contributory environmental effects including circadian rhythm have also been studied in ASD. Thus, capturing the global picture of the ASD-clock network in combined form is critical.

METHODS

We reconstructed the protein-protein interaction network of ASD and circadian rhythm to understand the connection between autism and the circadian clock. A graph theoretical study is undertaken to evaluate whether the network attributes are biologically realistic. The gene ontology enrichment analyses provide information about the most important biological processes.

RESULTS

This study takes a fresh look at metabolic mechanisms and the identification of potential key proteins/pathways (ribosome biogenesis, oxidative stress, insulin/IGF pathway, Wnt pathway, and mTOR pathway), as well as the effects of specific conditions (such as maternal stress or disruption of circadian rhythm) on the development of ASD due to environmental factors.

CONCLUSION

Understanding the relationship between circadian rhythm and ASD provides insight into the involvement of these essential pathways in the pathogenesis/etiology of ASD, as well as potential early intervention options and chronotherapeutic strategies for treating or preventing the neurodevelopmental disorder.

Computational Pipeline for Analysis of Biomedical Networks with BioNAR

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

Integrating network pharmacology and experimental validation to decipher the mechanism of the Chinese herbal prescription modified Shen-Yan-Fang-Shuai formula in treating diabetic nephropathy

CONTEXT

Diabetic nephropathy (DN) is the main cause of end-stage renal disease. Modified Shen-Yan-Fang-Shuai formula (M-SYFSF) has excellent clinical efficacy in treating diabetic kidney disease. However, the potential mechanism of M-SYFSF remains unknown.

OBJECTIVE

To investigate the mechanism of M-SYFSF against DN by network pharmacological analysis and biological experiments.

MATERIALS AND METHODS

Utilizing a web-based pharmacology database, the potential mechanisms of M-SYFSF against DN were identified. In vivo experiments, male SD rats were injected with streptozotocin (50 mg/kg) and got uninephrectomy to construct a model of DN. M-SYFSF (11.34 g/kg/d) was gavaged once per day for 12 weeks after model establishment. In vitro experiments, human proximal tubular cells (HK-2) were performed with advanced glycation end-products (AGEs) (100 μg/mL), then intervened with M-SYFSF freeze-dried powder. Pathological staining, WB, IHC, ELISA were conducted to explore the mechanism of M-SYFSF against DN.

RESULTS

Network pharmacological analysis showed that MAPK pathway was the potential pathway. Results showed that compared with the Model group, M-SYFSF significantly reduced 24h urine albumin, UACR, and serum creatinine levels (54.90 ± 26.67 vs. 111.78 ± 4.28, 8.87 ± 1.69 vs. 53.94 ± 16.01, 11.56 ± 1.70 vs. 118.70 ± 49.57, respectively), and improved renal pathological changes. Furthermore, the intervention of M-SYFSF reduced the expression of pro-inflammatory cytokines and inhibited the activation of MAPK pathway in AGEs-treated HK-2 cells.

DISCUSSION AND CONCLUSION

M-SYFSF is likely to reduce inflammation in DN by inhibiting the MAPK pathway. It provides a theoretical basis for the clinical application of M-SYFSF in the treatment of DN.

Identification of potential molecular targets and repurposed drugs for tuberculosis using network-based screening approach, molecular docking, and simulation

The spread of drug-resistant strains of tuberculosis has hampered efforts to control the disease worldwide. The Mycobacterium tuberculosis cell wall envelope is dynamic, with complex features that protect it from the host immunological response. As a result, the bacterial cell wall components represent a potential target for drug discovery. Protein-protein interaction networks (PPIN) are critical for understanding disease conditions and identifying precise therapeutic targets. We used a rational theoretical approach by constructing a PPIN with the proteins involved in cell wall biosynthesis. The PPIN was constructed through the STRING database and embB was identified as a key protein by using four topological measures, betweenness, closeness, degree, and eigenvector, in the CytoNCA tool in Cytoscape. The 'Drug repurposing' approach was employed to find suitable inhibitors against embB. We used the Schrödinger suites for molecular docking, molecular dynamics simulation, and binding free energy calculations to validate the binding of protein with the ligand. FDA-approved drugs from the ZINC database and DrugBank were screened against embB (PDB ID: 7BVF) using high-throughput virtual screening, standard precision, and extra precision docking. The drugs were screened based on the XP docking score of the standard drug ethambutol. Accordingly, from the top five hits, azilsartan and dihydroergotamine were selected based on the binding free energy values and were further subjected to Molecular Dynamics Simulation studies for 100 ns. Our study confirms that Azilsartan and Dihydroergotamine form stable complexes with embB and can be used as potential lead molecules based on further in vitro and in vivo experimental validation.Communicated by Ramaswamy H. Sarma.

Exploring the role of Hmox1 in ferroptosis and immune infiltration during renal ischemia-reperfusion injury

Ischemia-reperfusion injury (IRI) is inevitable in kidney transplantations and, as a complex pathophysiological process, it can be greatly impacted by ferroptosis and immune inflammation. Our study aimed to identify the biomarkers of renal IRI (RIRI) and elucidate their relationship with immune infiltration. In this study, the GSE148420 database was used as a training set to analyze differential genes and overlap them with ferroptosis-related genes to identify hub genes using a protein-protein interaction (PPI) network, the least absolute shrinkage and selection operator (LASSO), and random forest algorithm (RFA). We verified the hub gene and ferroptosis-related phenotypes in a verification set and animal experiments involving unilateral IRI with contralateral nephrectomy in rats. Gene set enrichment analysis (GSEA) of single genes was conducted according to the hub gene to predict related endogenous RNAs (ceRNAs) and drugs to establish a network. Finally, we used the Cibersort to analyze immunological infiltration and conducted Spearman's correlation analysis. We identified 5456 differential genes and obtained 26 ferroptosis-related differentially expressed genes. Through PPI, LASSO, and RFA, Hmox1 was identified as the only hub gene and its expression levels were verified using verification sets. In animal experiments, Hmox1 was verified as a key biomarker. GSEA of single genes revealed the seven most related pathways, and the ceRNAs network included 138 mRNAs and miRNAs. We predicted 11 related drugs and their three-dimensional structural maps. Thus, Hmox1 was identified as a key biomarker and regulator of ferroptosis in RIRI and its regulation of ferroptosis was closely related to immune infiltration.

PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease

Introduction

Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability.

Methods

To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD.

Results

The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation.

Discussion

By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.

Whole-Genome Identification of Regulatory Function of CDPK Gene Families in Cold Stress Response for Prunus mume and Prunus mume var. Tortuosa

Calcium-dependent protein kinases (CDPK) are known to mediate plant growth and development and respond to various environmental changes. Here, we performed whole-genome identification of CDPK families in cultivated and wild mei (Prunus mume). We identified 14 and 17 CDPK genes in P. mume and P. mume var. Tortuosa genomes, respectively. All 270 CPDK proteins were classified into four clade, displaying frequent homologies between these two genomes and those of other Rosaceae species. Exon/intron structure, motif and synteny blocks were conserved between P. mume and P. mume var. Tortuosa. The interaction network revealed all PmCDPK and PmvCDPK proteins is interacted with respiratory burst oxidase homologs (RBOHs) and mitogen-activated protein kinase (MAPK). RNA-seq data analysis of cold experiments show that cis-acting elements in the PmCDPK genes, especially PmCDPK14, are associated with cold hardiness. Our results provide and broad insights into CDPK gene families in mei and their role in modulating cold stress response in plants.

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.

Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.Communicated by Ramaswamy H. Sarma.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

The Expression Pattern and Clinical Significance of Lysyl Oxidase Family in Gliomas

LOX (Lysyl oxidase) family participates in the catalysis of collagen and elastin to maintain ECM homeostasis. Glioma is the most common primary brain tumor and LOX family has not been systemic studied in glioma. In this study, we found LOX family members are upregulated expressed in gliomas samples. A protein-protein interaction network (PPIN) was construct to visualize and understand the differential expression pattern, as well as functional annotation, for LOX family and their interacting proteins, which involved in collagen fibril organization and MAPK signaling pathway. Through subcellular localization distribution, the LOX family members distribute both intracellular and extracellular. All five LOX members are consistently significantly correlate with dendritic cell both in immune infiltrate of GBM and LGG. Survival analysis showed that high expression of LOX family is associated with a poor prognosis of gliomas patients. These analyses provide important clues to identify the potential biological roles for LOX family in gliomas, which might serve as diagnosis markers.

Exploration of anti-stress mechanisms in high temperature exposed juvenile golden cuttlefish (Sepia esculenta) based on transcriptome profiling

Sepia esculenta is a cephalopod widely distributed in the Western Pacific Ocean, and there has been growing research interest due to its high economic and nutritional value. The limited anti-stress capacity of larvae renders challenges for their adaptation to high ambient temperatures. Exposure to high temperatures produces intense stress responses, thereby affecting survival, metabolism, immunity, and other life activities. Notably, the molecular mechanisms by which larval cuttlefish cope with high temperatures are not well understood. As such, in the present study, transcriptome sequencing of S. esculenta larvae was performed and 1,927 differentially expressed genes (DEGs) were identified. DEGs were subjected to functional enrichment analyses using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The top 20 terms of biological processes in GO and 20 high-temperature stress-related pathways in KEGG functional enrichment analysis were identified. A protein-protein interaction network was constructed to investigate the interaction between temperature stress-related genes. A total of 30 key genes with a high degree of participation in KEGG signaling pathways or protein-protein interactions were identified and subsequently validated using quantitative RT-PCR. Through a comprehensive analysis of the protein-protein interaction network and KEGG signaling pathway, the functions of three hub genes (HSP90AA1, PSMD6, and PSMA5), which belong to the heat shock protein family and proteasome, were explored. The present results can facilitate further understanding of the mechanism of high temperature resistance in invertebrates and provide a reference for the S. esculenta industry in the context of global warming.

Natural Antioxidant By-Product Mixture Counteracts the Effects of Aflatoxin B1 and Ochratoxin A Exposure of Piglets after Weaning: A Proteomic Survey on Liver Microsomal Fraction

Mycotoxins are toxic compounds produced by certain strains of fungi that can contaminate raw feed materials. Once ingested, even in small doses, they cause multiple health issues for animals and, downstream, for people consuming meat. It was proposed that inclusion of antioxidant-rich plant-derived feed might diminish the harmful effects of mycotoxins, maintaining the farm animals' health and meat quality for human consumption. This work investigates the large scale proteomic effects on piglets' liver of aflatoxin B1 and ochratoxin A mycotoxins and the potential compensatory effects of grapeseed and sea buckthorn meal administration as dietary byproduct antioxidants against mycotoxins' damage. Forty cross-bred TOPIGS-40 hybrid piglets after weaning were assigned to three (n = 10) experimental groups (A, M, AM) and one control group (C) and fed with experimental diets for 30 days. After 4 weeks, liver samples were collected, and the microsomal fraction was isolated. Unbiased label-free, library-free, data-independent acquisition (DIA) mass spectrometry SWATH methods were able to relatively quantify 1878 proteins from piglets' liver microsomes, confirming previously reported effects on metabolism of xenobiotics by cytochrome P450, TCA cycle, glutathione synthesis and use, and oxidative phosphorylation. Pathways enrichment revealed that fatty acid metabolism, steroid biosynthesis, regulation of actin cytoskeleton, regulation of gene expression by spliceosomes, membrane trafficking, peroxisome, thermogenesis, retinol, pyruvate, and amino acids metabolism pathways are also affected by the mycotoxins. Antioxidants restored expression level of proteins PRDX3, AGL, PYGL, fatty acids biosynthesis, endoplasmic reticulum, peroxisome, amino acid synthesis pathways, and, partially, OXPHOS mitochondrial subunits. However, excess of antioxidants might cause significant changes in CYP2C301, PPP4R4, COL18A1, UBASH3A, and other proteins expression levels. Future analysis of proteomics data corelated to animals growing performance and meat quality studies are necessary.

Targeting FOS attenuates malignant phenotypes of breast cancer: Evidence from in silico and in vitro studies

Data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases can reveal important information behind molecular biomarkers and their associated oncogenesis. Therefore, this study was based on in silico predictions and in vitro experiments to explore regulatory network associated with breast carcinogenesis. The breast cancer (BC)-related data sets were retrieved from GEO database, followed by differential analysis and protein-protein interaction (PPI) analysis. Then, Fos proto-oncogene, AP-1 transcription factor subunit (FOS)-associated gene network was constructed, and the key gene-related genes in BC were screened by LinkedOmics. Finally, FOS expression was determined in BC tissues and cells, and gain-of-function assays were performed to define the role of FOS in BC cells. It was noted that seven differentially expressed genes (EGR1, RASSF9, FOSB, CDC20, KLF4, PTGS2, and FOS) were obtained from BC microarray data sets. FOS was the gene with the most nodes in PPI analysis. Poor FOS mRNA expression was identified in BC patients. Furthermore, FOS was mainly located in the extracellular matrix and was involved in cell processes. FOS was downregulated in BC tissues and cells, and FOS overexpression restrained the malignant phenotypes of BC cells. Collectively, ectopic expression of FOS curtails the development of BC.

A protein microarray-based serum proteomic investigation reveals distinct autoantibody signature in colorectal cancer

PURPOSE

Colorectal cancer (CRC) has been reported as the second leading cause of cancer death worldwide. The 5-year annual survival is around 50%, mainly due to late diagnosis, striking necessity for early detection. This study aims to identify autoantibody in patients' sera for early screening of cancer.

EXPERIMENTAL DESIGN

The study used a high-density human proteome array with approximately 17,000 recombinant proteins. Screening of sera from healthy individuals, CRC from Indian origin, and CRC from middle-east Asia origin were performed. Bio-statistical analysis was performed to identify significant autoantibodies altered. Pathway analysis was performed to explore the underlying mechanism of the disease.

RESULTS

The comprehensive proteomic analysis revealed dysregulation of 15 panels of proteins including CORO7, KCNAB1, WRAP53, NDUFS6, KRT30, and COLGALT2. Further biological pathway analysis for the top dysregulated autoantigenic proteins revealed perturbation in important biological pathways such as ECM degradation and cytoskeletal remodeling etc. CONCLUSIONS AND CLINICAL RELEVANCE: The generation of an autoimmune response against cancer-linked pathways could be linked to the screening of the disease. The process of immune surveillance can be detected at an early stage of cancer. Moreover, AAbs can be easily extracted from blood serum through the least invasive test for disease screening.

Network and pathway-based analysis of candidate genes associated with esophageal adenocarcinoma

Background

Previous studies have made some headway in analyzing esophageal adenocarcinoma (EA) with respect to pathogenic factors, treatment methods, and prognosis. However, far less is known about the molecular mechanisms. Thus, a comprehensive analysis focusing on the biological function and interaction of EA genes would provide valuable information for understanding the pathogenesis of EA, which may provide new insights into gene function as well as potential therapy targets.

Methods

We selected 109 genes related to EA by reviewing 458 publications from the PubMed database. In addition, performing gene enrichment assays, pathway enrichment assays, pathway crosstalk analysis, and extraction of EA-specific subnetwork were used to describe the relevant biochemical processes.

Results

Function analysis revealed that biological processes and biochemical pathways associated with apoptotic and metabolic processes, a variety of cancers, and drug reaction pathways. Further, 12 novel genes (PTHLH, SUMO2, TYMS, APP, PTGIR, SP1, UBC, COL1A1, GSTO1, TRAF6, BMP7, and RAB40B) were identified in the EA-specific network, which might provide helpful information for clinical application.

Conclusions

Overall, by integrating pathways and networks to explore the pathogenetic mechanisms underlying EA, our results could significantly improve our understanding of the molecular mechanisms of EA and form a basis for selection of potential molecular targets for further exploration.

Identification of key candidate genes and pathways in rheumatoid arthritis and osteoarthritis by integrated bioinformatical analysis

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the most common joint disorders. Although they have shown analogous clinical manifestations, the pathogenesis of RA and OA are different. In this study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE153015 to identify gene signatures between RA and OA joints. The relevant data on 8 subjects obtained from large joints of RA patients (RA-LJ), 8 subjects obtained from small joints of RA patients (RA-SJ), and 4 subjects with OA were investigated. Differentially expressed genes (DEGs) were screened. Functional enrichment analysis of DEGs including the Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, which were mainly associated with T cell activation or chemokine activity. Besides, protein-protein interaction (PPI) network analysis was performed, and key modules were identified. Hub genes of RA-LJ and OA groups were screened, they were CD8A, GZMB, CCL5, CD2, and CXCL9, whereas CD8A, CD2, IL7R, CD27, and GZMB were hub genes of RA-SJ and OA group. The novel DEGs and functional pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms and therapeutic strategies of RA and OA.

Preventing Respiratory Viral Diseases with Antimicrobial Peptide Master Regulators in the Lung Airway Habitat

The vast surface area of the respiratory system acts as an initial site of contact for microbes and foreign particles. The whole respiratory epithelium is covered with a thin layer of the airway and alveolar secretions. Respiratory secretions contain host defense peptides (HDPs), such as defensins and cathelicidins, which are the best-studied antimicrobial components expressed in the respiratory tract. HDPs have an important role in the human body's initial line of defense against pathogenic microbes. Epithelial and immunological cells produce HDPs in the surface fluids of the lungs, which act as endogenous antibiotics in the respiratory tract. The production and action of these antimicrobial peptides (AMPs) are critical in the host's defense against respiratory infections. In this study, we have described all the HDPs secreted in the respiratory tract as well as how their expression is regulated during respiratory disorders. We focused on the transcriptional expression and regulation mechanisms of respiratory tract HDPs. Understanding how HDPs are controlled throughout infections might provide an alternative to relying on the host's innate immunity to combat respiratory viral infections.

A disease-related essential protein prediction model based on the transfer neural network

Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.

Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

Identification of Hypoxia-related Genes in Acute Myocardial Infarction using Bioinformatics Analysis

BACKGROUND

Acute myocardial infarction (AMI) remains one of the most fatal diseases worldwide. Persistent ischemia and hypoxia are implicated as significant mechanisms in the development of AMI. However, no hypoxia-related gene targets of AMI have been identified to date. This study aimed to identify potential genes and drugs for AMI using bioinformatics analysis.

MATERIALS AND METHODS

Two datasets both related to AMI (GSE76387 and GSE161427) were downloaded from the Gene Expression Omnibus to identify differentially expressed genes (DEGs) between AMI and sham mice. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed to identify hub genes using Cytoscape. Candidate genes were identified by the intersection of hub genes and hypoxia-related genes. Western blotting was used to validate the candidate genes in the AMI mouse model. Furthermore, the Drug-Gene Interaction Database was used to predict potential therapeutic drugs targeting all hub genes.

RESULTS

Fifty-three upregulated and 16 downregulated genes closely related to AMI were identified. The DEGs were primarily enriched in protein, heparin, and integrin binding. KEGG analysis suggested that focal adhesion, PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction are crucial pathways for AMI. The PPI network analysis identified 14 hub genes, two of which were hypoxia-related. Several agents were found to have therapeutic potential for AMI.

CONCLUSION

This study suggests that connective tissue growth factors and the collagen family members may be candidate targets in treating AMI. Agents targeting these candidates may be potential treatments.

Identifying Genes Associated with Alzheimer's Disease Using Gene-Based Polygenic Risk Score

BACKGROUND

Except APOE, Alzheimer's disease (AD) associated genes identified in recent large-scale genome-wide association studies (GWAS) had small effects and explained a small portion of heritability. Many AD-associated genes have even smaller effects thereby sub-threshold p-values in large-scale GWAS and remain to be identified. For some AD-associated genes, drug targeting them may have limited efficacies due to their small effect sizes.

OBJECTIVE

The purpose of this study is to identify AD-associated genes with sub-threshold p-values and prioritize drugs targeting AD-associated genes that have large efficacies.

METHODS

We developed a gene-based polygenic risk score (PRS) to identify AD genes. It was calculated using SNPs located within genes and having the same directions of effects in different study cohorts to exclude cohort-specific findings and false positives. Gene co-expression modules and protein-protein interaction networks were used to identify AD-associated genes that interact with multiple other genes, as drugs targeting them have large efficacies via co-regulation or interactions.

RESULTS

Gene-based PRS identified 389 genes with 164 of them not previously reported as AD-associated. These 389 genes explained 56.12% -97.46% SNP heritability; and they were enriched in brain tissues and 164 biological processes, most of which are related to AD and other neurodegenerative diseases. We prioritized 688 drugs targeting 64 genes that were in the same co-expression modules and/or PPI networks.

CONCLUSIONS

Gene-based PRS is a cost-effective way to identify AD-associated genes without substantially increasing the sample size. Co-expression modules and PPI networks can be used to identify drugs having large efficacies.

Screening of Key Part in IFN Pathway for Herpes Zoster: Evidence from Bioinformatics Analysis

BACKGROUND

Herpes zoster is one of the most common diseases in middle and old ages, and the incidence rate is constantly increasing. Long-term, severe neuropathological pain continues to afflict the patients, causing trouble and even the inability to live a normal life. Since the occurrence and development of herpes zoster are related to many mechanisms, there is no uniform conclusion and specific treatment method, and only a limited number of people are currently vaccinated against HZ.

OBJECTIVE

This study aimed at exploring the potential mechanism or biomarkers for Herpes zoster.

METHODS

In this study, a data set GSE165112 containing 12 samples was downloaded, out of which, 6 samples were treated with interferon, and 6 samples were not treated. Differentially expressed genes (DEG) analysis, KEGG, GO enrichment analysis, and GSEA were carried out.

RESULTS

A total of 264 DEGs were identified, including 32 uP-regulated DEGs and 232 downregulated DEGs. DEGs are mainly enriched in immune response, inflammatory response, chemotaxis, etc. Four key pathways were found to be related to HZ, including IL2-STAT5 signaling, inflammatory response, TNF-a signaling via NF-κB, and IFN-α. Moreover, ten hub genes were also identified.

CONCLUSION

This study shows that exploring DEGs and pathways through bioinformatics analysis is of great significance for understanding the molecular mechanism of HZ, especially the defect of the IFN pathway. It may be helpful in improving the treatment for HZ.

Whole-transcriptome analyses of sheep embryonic testicular cells infected with the bluetongue virus

Introduction

bluetongue virus (BTV) infection triggers dramatic and complex changes in the host's transcriptional profile to favor its own survival and reproduction. However, there is no whole-transcriptome study of susceptible animal cells with BTV infection, which impedes the in-depth and systematical understanding of the comprehensive characterization of BTV-host interactome, as well as BTV infection and pathogenic mechanisms.

Methods

to systematically understand these changes, we performed whole-transcriptome sequencing in BTV serotype 1 (BTV-1)-infected and mock-infected sheep embryonic testicular cells, and subsequently conducted bioinformatics differential analyses.

Results

there were 1504 differentially expressed mRNAs, 78 differentially expressed microRNAs, 872 differentially expressed long non-coding RNAs, and 59 differentially expressed circular RNAs identified in total. Annotation from the Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of competing endogenous RNA networks revealed differentially expressed RNAs primarily related to virus-sensing and signaling transduction pathways, antiviral and immune responses, inflammation, and development and metabolism related pathways. Furthermore, a protein-protein interaction network analysis found that BTV may contribute to abnormal spermatogenesis by reducing steroid biosynthesis. Finally, real-time quantitative PCR and western blotting results showed that the expression trends of differentially expressed RNAs were consistent with the whole-transcriptome sequencing data.

Discussion

this study provides more insights of comprehensive characterization of BTV-host interactome, and BTV infection and pathogenic mechanisms.

Systematic identification of the interventional mechanism of Qingfei Xiaoyan Wan (QFXYW) in treatment of the cytokine storm in acute lung injury using transcriptomics-based system pharmacological analyses

CONTEXT

Acute lung injury (ALI) is a complex, severe inflammation disease with high mortality, and there is no specific and effective treatment for ALI. Qingfei Xiaoyan Wan (QFXYW) has been widely used to treat lung-related diseases for centuries.

OBJECTIVE

This study evaluates the potential effects and elucidates the therapeutic mechanism of QFXYW against LPS induced ALI in mice.

MATERIALS AND METHODS

BALB/c Mice in each group were first orally administered medicines (0.9% saline solution for the control group, 0.5 mg/kg Dexamethasone, or 1.3, 2.6, 5.2 g/kg QFXYW), after 4 h, the groups were injected LPS (1.0 mg/kg) to induce ALI, then the same medicines were administered repeatedly. The transcriptomics-based system pharmacological analyses were applied to screen the hub genes, RT-PCR, ELISA, and protein array assay was applied to verify the predicted hub genes and key pathways.

RESULTS

QFXYW significantly decreased the number of leukocytes from (6.34 ± 0.51) × 10⁵/mL to (4.01 ± 0.11) × 10⁵/mL, accompanied by the neutrophil from (1.41 ± 0.19) × 10⁵/mL to (0.77 ± 0.10) × 10⁵/mL in bronchoalveolar lavage fluid (BALF). Based on Degree of node connection (Degree) and BottleNeck (BN), important parameters of network topology, the protein-protein interaction (PPI) network screened hub genes, including IL-6, TNF-α, CCL2, TLR2, CXCL1, and MMP-9. The results of RT-PCR, ELISA, and protein chip assay revealed that QFXYW could effectively inhibit ALI via multiple key targets and the cytokine-cytokine signalling pathway.

CONCLUSIONS

This study showed that QFXYW decreased the number of leukocytes and neutrophils by attenuating inflammatory response, which provides an important basis for the use of QFXYW in the treatment of ALI.

Identification and validation of potential hub genes in rheumatoid arthritis by bioinformatics analysis

OBJECTIVE

Rheumatoid arthritis (RA) is considered to be a chronic immune disease pathologically characterized by synovial inflammation and bone destruction. At present, the potential pathogenesis of RA is still unclear. Hub genes are recognized to play a pivotal role in the occurrence and progression of RA.

METHODS

Firstly, we attempted to screen hub genes that are associated with RA, to clarify the underlying pathological mechanisms of RA, and to offer potential treatment methods for RA. We acquired these datasets (GSE12021, GSE55235, and GSE55457) of RA patients and healthy samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized via R software. Then, Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to deeply explore the underlying biological functions and pathways closely associated with RA. In addition, a protein-protein interaction (PPI) network was built to further evaluate and screen for hub genes. Finally, on the basis of the results of PPI analysis, we confirmed the mRNA expression levels of five hub genes in the synovial tissue of rats modeled with RA.

RESULTS

In the human microarray datasets, LCK, JAK2, SOCS3, STAT1, and EGFR were identified as hub genes associated with RA by bioinformatics analysis. Furthermore, we verified the differential expression levels of hub genes in rat synovial tissues via qRT-PCR (P < 0.05).

CONCLUSIONS

Our findings suggest that the hub genes LCK, JAK2, SOCS3, STAT1, and EGFR might have vital roles in the progression of RA and may offer novel therapeutic treatments for RA.

Identification of potential gene markers in gestational diabetes mellitus

This study aims to investigate underlying mechanisms of gestational diabetes mellitus (GDM). In this work, the GSE70493 dataset from GDM and control samples was acquired from Gene Expression Omnibus (GEO) database. Afterward, differentially expressed genes (DEGs) were screened between GDM and control samples. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis of these DEGs were carried out. Furthermore, significant sub‐modules were identified, and the functional analysis was also performed. Finally, we undertook a quantitative real‐time polymerase chain reaction (qRT‐PCR) with the purpose of confirming several key genes in GDM development. There were totally 528 up‐regulated and 684 down‐regulated DEGs between GDM and healthy samples. The functional analyses suggested that the above genes were dramatically enriched in type 1 diabetes mellitus (T1DM) process and immune‐related pathways. Moreover, PPI analysis revealed that several members of human leukocyte antigen (HLA) superfamily, including down‐regulated HLA‐DQA1, HLA‐DRB1, HLA‐DPA1, and HLA‐DQB1 served as hub genes. In addition, six significant sub‐clusters were extracted and functional analysis suggested that these four genes in sub‐module 1 were also associated with immune and T1DM‐related pathways. Finally, they were also confirmed by qRT‐PCR array. Besides, the four members of HLA superfamily might be implicated with molecular mechanisms of GDM, contributing to a deeper understanding of GDM development.

SGII: Systematic Identification of Essential lncRNAs in Mouse and Human Genome With lncRNA-Protein-Protein Heterogeneous Interaction Network

Long noncoding RNAs (lncRNAs) play important roles in a variety of biological processes. Knocking out or knocking down some lncRNA genes can lead to death or infertility. These lncRNAs are called essential lncRNAs. Identifying the essential lncRNA is of importance for complex disease diagnosis and treatments. However, experimental methods for identifying essential lncRNAs are always costly and time consuming. Therefore, computational methods can be considered as an alternative approach. We propose a method to identify essential lncRNAs by combining network centrality measures and lncRNA sequence information. By constructing a lncRNA-protein-protein interaction network, we measure the essentiality of lncRNAs from their role in the network and their sequence together. We name our method as the systematic gene importance index (SGII). As far as we can tell, this is the first attempt to identify essential lncRNAs by combining sequence and network information together. The results of our method indicated that essential lncRNAs have similar roles in the LPPI network as the essential coding genes in the PPI network. Another encouraging observation is that the network information can significantly boost the predictive performance of sequence-based method. All source code and dataset of SGII have been deposited in a GitHub repository (https://github.com/ninglolo/SGII).

An insight into major signaling pathways and protein-protein interaction networks involved in the pathogenesis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is associated with the increase of glucose in the blood rather than being absorbed by the cells. A better understanding of the signaling pathways is necessary to understand the pathophysiology of GDM. This study provides details about a series of signaling pathways and protein-protein interactions involved in the pathogenesis of GDM and their evaluations in GDM development. Protein-protein interactions were found between proteins of several signaling pathways that suggest interlink between these signaling pathways. Protein-protein interactions were generated with high confidence interaction scores based on textmining, co-occurrence, coexpression, neighborhood, gene fusion, experiments and databases. The dysregulation of signaling pathways may also contribute to the increased risk of complications associated with GDM in the mother and child. Further, studies on signaling pathways involved in the pathogenesis of GDM would help in the development of an effective intervention to prevent GDM along with the identification of key targets for effective therapies in the future. This article is protected by copyright. All rights reserved.

Genome-Wide Identification of Cotton (Gossypium spp.) Glycerol-3-Phosphate Dehydrogenase (GPDH) Family Members and the Role of GhGPDH5 in Response to Drought Stress

Glycerol-3-phosphate dehydrogenase (GPDH) is a key enzyme in plant glycerol synthesis and metabolism, and plays an important role in plant resistance to abiotic stress. Here, we identified 6, 7, 14 and 14 GPDH genes derived from Gossypium arboreum, Gossypium raimondii, Gossypium barbadense and Gossypium hirsutum, respectively. Phylogenetic analysis assigned these genes into three classes, and most of the genes within the family were expanded by whole-genome duplication (WGD) and segmental duplications. Moreover, determination of the nonsynonymous substitution rate/synonymous substitution rate (Ka/Ks) ratio showed that the GPDH had an evolutionary preference for purifying selection. Transcriptome data revealed that GPDH genes were more active in the early stages of fiber development. Additionally, numerous stress-related cis-elements were identified in the potential promoter region. Then, a protein-protein-interaction (PPI) network of GPDH5 in G. hirsutum was constructed. In addition, we predicted 30 underlying miRNAs in G. hirsutum. Functional validation results indicated that silencing GhGPDH5 diminished drought tolerance in the upland cotton TM-1 line. In summary, this study provides a fundamental understanding of the GPDH gene family in cotton, GhGPDH5 exerts a positive effect during drought stress and is potentially involved in stomatal closure movements.

Identification of hub genes and pathways in bladder cancer using bioinformatics analysis

Bladder cancer (BC) is the most common malignant tumor of urinary tract system. The aim of this study was to investigate the genetic signatures of bladder cancer (BC) and identify its potential molecular mechanisms. The gene expression profiles of GSE3167 (50 samples, including 41BC and 9 non-cancerous urothelial cells) was downloaded from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were performed to identify enriched pathways, and a protein-protein interaction (PPI) network was used to identify hub genes and for module analysis. Moreover, we conducted expression and survival analyses to screen and validate hub genes. In total, 1528 DEGs were identified in bladder cancer (BC), including 1212 up-regulated genes and 316 down-regulated genes. Up-regulated differentially expressed genes (DEGs) were significantly enriched in negative regulation of macromolecule metabolic process, macromolecule catabolic process, proteolysis and regulation of cell death, while the down-regulated differentially expressed genes (DEGs) were mainly involved in cell surface receptor linked signal transduction, ion transport, cell-cell signaling and defense response. The top 10 hub genes with the highest degrees were selected from the PPI network. These genes included HSP90AA1, MYH11, MYL9, CNN1, ACTC1, RAN, ENO1, HNRNPC, ACTG2 and YWHAZ. From sub-networks, we found these genes were involved in the proteasome, pathways in cancer and cell cycle. Hence, the identified DEGs and hub genes may be beneficial to elucidate the mechanisms underlying BC.

Potential biomarkers and signaling pathways associated with the pathogenesis of primary salivary gland carcinoma: a bioinformatics study

Salivary gland carcinoma (SGC) is rare cancer, constituting 6% of neoplasms in the head and neck area. The most responsible genes and pathways involved in the pathology of this disorder have not been fully understood. We aimed to identify differentially expressed genes (DEGs), the most critical hub genes, transcription factors, signaling pathways, and biological processes (BPs) associated with the pathogenesis of primary SGC. The mRNA dataset GSE153283 in the Gene Expression Omnibus database was re-analyzed for determining DEGs in cancer tissue of patients with primary SGC compared to the adjacent normal tissue (adjusted p-value < 0.001; |Log2 fold change| > 1). A protein interaction map (PIM) was built, and the main modules within the network were identified and focused on the different pathways and BP analyses. The hub genes of PIM were discovered, and their associated gene regulatory network was built to determine the master regulators involved in the pathogenesis of primary SGC. A total of 137 genes were found to be differentially expressed in primary SGC. The most significant pathways and BPs that were deregulated in the primary disease condition were associated with the cell cycle and fibroblast proliferation procedures. TP53, EGF, FN1, NOTCH1, EZH2, COL1A1, SPP1, CDKN2A, WNT5A, PDGFRB, CCNB1, and H2AFX were demonstrated to be the most critical genes linked with the primary SGC. SPIB, FOXM1, and POLR2A significantly regulate all the hub genes. This study illustrated several hub genes and their master regulators that might be appropriate targets for the therapeutic aims of primary SGC.

Identification and Validation of Autophagy-Related Genes in Diabetic Retinopathy

BACKGROUND

Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes, which is associated with damage of blood-retinal barrier and ischemia of retinal vasculature. It devastates visual acuity due to leakage of retinal vessels and aberrant pathological angiogenesis in diabetic patients. The etiology of DR is complex, accumulated studies have shown that autophagy plays an important role in the pathogenesis of DR, but its specific mechanism needs to be further studied.

METHODS

This study chose the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE146615 to carry on the research. Autophagy-related genes that were potentially differentially expressed in DR were screened by R software. Then, the differentially expressed autophagy-related genes were analyzed by correlation analysis, tissue-specific gene expression, gene-ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis. Finally, retinal pigment epithelial cell line (ARPE-19) incubated with high glucose (HG) was used to mimic the DR model, and the mRNA level of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR) in vitro.

RESULTS

A total of 23 differentially expressed autophagy-related genes (9 up-regulated genes and 14 down-regulated genes) were identified by differential expression analysis. The analysis of tissue-specific gene expression showed that these differentially expressed autophagy-related genes were enriched in the retina. GO and KEGG enrichment analysis showed that differentially expressed autophagy-related genes were significantly enriched in autophagy-related pathways such as regulation of autophagy and macroautophagy. Then 10 hub genes were identified by PPI network analysis and construction of key modules. Finally, qRT-PCR confirmed that the expression of MAPK3 in the DR model was consistent with the results of bioinformatics analysis of mRNA chip.

CONCLUSION

Through bioinformatics analysis, we identified 23 potential DR autophagy-related genes, among which the down-regulated expression of MAPK3 may affect the occurrence and development of DR by regulating autophagy. It provides a novel insight into the pathogenesis of DR.

A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma

Lymphoma is a serious type of cancer, especially for adolescents and elder adults, although this malignancy is quite rare compared with other types of cancer. The cause of this malignancy remains ambiguous. Genetic factor is deemed to be highly associated with the initiation and progression of lymphoma, and several genes have been related to this disease. Determining the pathogeny of lymphoma by identifying the related genes is important. In this study, we presented a random walk-based method to infer the novel lymphoma-associated genes. From the reported 1,458 lymphoma-associated genes and protein–protein interaction network, raw candidate genes were mined by using the random walk with restart algorithm. The determined raw genes were further filtered by using three screening tests (i.e., permutation, linkage, and enrichment tests). These tests could control false-positive genes and screen out essential candidate genes with strong linkages to validate the lymphoma-associated genes. A total of 108 inferred genes were obtained. Analytical results indicated that some inferred genes, such as RAC3, TEC, IRAK2/3/4, PRKCE, SMAD3, BLK, TXK, PRKCQ, were associated with the initiation and progression of lymphoma.

Identification of Hub Genes Correlated With Poor Prognosis for Patients With Uterine Corpus Endometrial Carcinoma by Integrated Bioinformatics Analysis and Experimental Validation

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the most common malignancies of the female genital tract and there remains a major public health problem. Although significant progress has been made in explaining the progression of UCEC, it is still warranted that molecular mechanisms underlying the tumorigenesis of UCEC are to be elucidated. The aim of the current study was to investigate key modules and hub genes related to UCEC pathogenesis, and to explore potential biomarkers and therapeutic targets for UCEC. The RNA-seq dataset and corresponding clinical information for UCEC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened between 23 paired UCEC tissues and adjacent non-cancerous tissues. Subsequently, the co-expression network of DEGs was determined via weighted gene co-expression network analysis (WGCNA). The Blue and Brown modules were identified to be significantly positively associated with neoplasm histologic grade. The highly connected genes of the two modules were then investigated as potential key factors related to tumor differentiation. Additionally, a protein-protein interaction (PPI) network for all genes in the two modules was constructed to obtain key modules and nodes. 10 genes were identified by both WGCNA and PPI analyses, and it was shown by Kaplan-Meier curve analysis that 6 out of the 10 genes were significantly negatively related to the 5-year overall survival (OS) in patients (AURKA, BUB1, CDCA8, DLGAP5, KIF2C, TPX2). Besides, according to the DEGs from the two modules, lncRNA-miRNA-mRNA and lncRNA-TF-mRNA networks were constructed to explore the molecular mechanism of UCEC-related lncRNAs. 3 lncRNAs were identified as being significantly negatively related to the 5-year OS (AC015849.16, DUXAP8 and DGCR5), with higher expression in UCEC tissues compared to non-tumor tissues. Finally, quantitative Real-time PCR was applied to validate the expression patterns of hub genes. Cell proliferation and colony formation assays, as well as cell cycle distribution and apoptosis analysis, were performed to test the effects of representative hub genes. Altogether, this study not only promotes our understanding of the molecular mechanisms for the pathogenesis of UCEC but also identifies several promising biomarkers in UCEC development, providing potential therapeutic targets for UCEC.

Transcriptional Regulation Based on Network of Autophagy Identifies Key Genes and Potential Mechanisms in Human Osteoarthritis

OBJECTIVE

Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory.

DESIGN

We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword "autophagy" were identified using the scanGEO online analysis tool. DEGs representing the same expression trend were screened using the MATCH function. Clinical synovial specimens were collected for identification, pathological diagnosis, hematoxylin and eosin staining, and real-time polymerase chain reaction analysis. Differential expression of mRNAs in the synovial membrane tissues and chondrocyte monolayer samples from OA patients was used to identify potential OA biomarkers. Protein-protein interactions were established by the STRING website and visualized with Cytoscape. Functional and pathway enrichment analyses were performed using the Metascape database.

RESULTS

GABARAPL1, GABARAPL2, and ATG13 were obtained as co-expressed autogenes in the 3 data sets. They were all downregulated among OA synovial tissues compared with non-OA synovial tissues (P < 0.01). A protein-protein interaction network was constructed based on these 3 genes and included 63 genes. A functional analysis revealed that these genes were associated with autophagy-related functions. The top hub genes in the protein-protein interaction network were presented. Furthermore, 3 key modules were extracted to be core control modules.

CONCLUSIONS

These results offer an important molecular understanding of the key transcriptional regulatory genes and modules based on the network of potential autophagy mechanisms in human OA.

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.

Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection

For studying the pathogenesis of complex diseases, it is important to identify the disease modules in the system level. Since the protein-protein interaction (PPI) networks contain a number of incomplete and incorrect interactome, most existing methods often lead to many disease proteins isolating from disease modules. In this paper, we propose an effective disease module identification method IDMCSS, where the used human PPI networks are obtained by adding some potential missing interactions from existing PPI networks, as well as removing some potential incorrect interactions. In IDMCSS, a network adjustment strategy is developed to add or remove links around disease proteins based on both topological and semantic information. Next, neighboring proteins of disease proteins are prioritized according to a suggested similarity between each of them and disease proteins, and the protein with the largest similarity with disease proteins is added into a candidate disease protein set one by one. The stopping criterion is set to the boundary of the disease proteins. Finally, the connected subnetwork having the largest number of disease proteins is selected as a disease module. Experimental results on asthma demonstrate the effectiveness of the method in comparison to existing algorithms for disease module identification. It is also shown that the proposed IDMCSS can obtain the disease modules having crucial biological processes of asthma and 12 targets for drug intervention can be predicted.

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

Transcriptomic analyses and experimental verification reveal potential biomarkers and biological pathways of urinary tract infection

Urinary tract infection (UTI) is a common infectious disease. Urinary tract pathogenic Escherichia coli (UPEC) is the main cause of UTIs. At present, antibiotics are mainly used for the treatment of UTIs. However, with the increase of drug resistance, the course of the disease is prolonged. Therefore, identifying the receptors and signal pathways of host cells and tissues will further our understanding of the pathogenesis of UTIs and help in the development of new drug treatments. We used two public microarray datasets (GSE43790, GSE124917) in the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between UTI and normal cell samples. A functional analysis based on Gene Ontology (GO) data, a pathway enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) data and a protein-protein interaction analysis identified the main potential biomarkers and verified them in animal tissues. A total of 147 up-regulated genes and 40 down-regulated genes were identified. GO enrichment analysis showed that these functional changes relate to the terms response to lipopolysaccharide, regulation of cytokine production, and regulation of the inflammatory response. KEGG analysis indicated that urinary tract infections likely involve the TNF-αsignaling pathways. The 20 hub genes were selected from the protein-protein interaction network, and the highly significant hub genes were verified by animal experiments. Our findings provide potential targets for exploring new treatments for urinary tract infections. After a comprehensive analysis of the GEO database, these results may facilitate development of new diagnosis and treatment strategies for urinary tract infections.

Molecular signatures from multi-omics of autism spectrum disorders and schizophrenia

The genetic and phenotypic heterogeneity of autism spectrum disorder (ASD) impedes the unification of multiple biological hypotheses in an attempt to explain the complex features of ASD, such as impaired social communication, social interaction deficits, and restricted and repetitive patterns of behavior. However, recent psychiatric genetic studies have identified numerous risk genes and chromosome loci (copy number variation: CNV) which enable us to analyze at the single gene level and utilize system-level approaches. In this review, we focus on ASD as a major neurodevelopmental disorder and review recent findings mainly from the bioinformatics of omics studies. Additionally, by comparing these data with other major psychiatric disorders, including schizophrenia (SCZ), we identify unique characteristics of both diseases from multiple enrichment, pathway, and protein-protein interaction networks (PPIs) analyses using susceptible genes found in recent large-scale genetic studies. These unified, systematic approaches highlight unique characteristics of both disorders from multiple aspects and demonstrate how convergent pathways can contribute to an understanding of the complex etiology of such neurodevelopmental and neuropsychiatric disorders.