A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.

BACKGROUND: It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS: The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS: The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS: The implemented workflow could be used for other multifactorial diseases.

Integrative analysis of an endoplasmic reticulum stress-related signature in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS-related signature of MM and assess its diagnostic value. METHODS: We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS-related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein-protein interaction network, a transcription factor-mRNA network, a miRNA-mRNA network and a drug-mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs. RESULTS: We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein-protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples. CONCLUSION: The nine ERS-related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.

A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches.

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis.

With the development of biotechnology, a large number of phosphorylation sites have been experimentally confirmed and collected, but only a few of them have kinase annotations. Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https://github.com/guoxinyunncu/PKSPS.

Improving protein-protein interaction prediction using protein language model and protein network features.

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interaction (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the multilayer perceptron to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96 %. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

Transcriptional landscape of intestinal environment in DSS-induced ulcerative colitis mouse model.

Ulcerative colitis (UC) is a chronic inflammatory disease that targets the colon and has seen an increasing prevalence worldwide. In our pursuit of new diagnostic and therapeutic approaches for UC, we undertook a sequencing of colons from UC mouse models. We focused on analyzing their differentially expressed genes (DEGs), enriching pathways, and constructing protein-protein interaction (PPI) and Competing Endogenous RNA (ceRNA) networks. Our analysis highlighted novel DEGs such as Tppp3, Saa3, Cemip, Pappa, and Nr1d1. These DEGs predominantly play roles in pathways like cytokine-mediated signaling, extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization. This suggests that the UC pathogenesis is intricately linked to the interactions between immune and non-immune cells with the extracellular matrix (ECM). To corroborate our findings, we also verified certain DEGs through quantitative real-time PCR. Within the PPI network, nodes like Stat3, Il1b, Mmp3, and Lgals3 emerged as significant and were identified to be involved in the crucial cytokine-mediated signaling pathway, which is central to inflammation. Our ceRNA network analysis further brought to light the role of the Smad7 Long non-coding RNA (lncRNA). Key MicroRNA (miRNAs) in the ceRNA network were pinpointed as mmu-miR-17-5p, mmu-miR-93-5p, mmu-miR-20b-5p, mmu-miR-16-5p, and mmu-miR-106a-5p, while central mRNAs included Egln3, Plagl2, Sema7a, Arrdc3, and Stat3. These insights imply that ceRNA networks are influential in UC progression and could provide further clarity on its pathogenesis. In conclusion, this research deepens our understanding of UC pathogenesis and paves the way for potential new diagnostic and therapeutic methods. Nevertheless, to solidify our findings, additional experiments are essential to confirm the roles and molecular interplay of the identified DEGs in UC.

Causal relationship between varicose veins and mean corpuscular hemoglobin concentration based on Mendelian randomization study.

BACKGROUND: Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood. METHODS: Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network. RESULTS: Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results. CONCLUSION: This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future.

Effect of Calebin-A on Critical Genes Related to NAFLD: A Protein-Protein Interaction Network and Molecular Docking Study.

Background: Calebin-A is a minor phytoconstituent of turmeric known for its activity against inflammation, oxidative stress, cancerous, and metabolic disorders like Non-alcoholic fatty liver disease(NAFLD). Based on bioinformatic tools. Subsequently, the details of the interaction of critical proteins with Calebin-A were investigated using the molecular docking technique. Methods: We first probed the intersection of genes/ proteins between NAFLD and Calebin-A through online databases. Besides, we performed an enrichment analysis using the ClueGO plugin to investigate signaling pathways and gene ontology. Next, we evaluate the possible interaction of Calebin-A with significant hub proteins involved in NAFLD through a molecular docking study. Results: We identified 87 intersection genes Calebin-A targets associated with NAFLD. PPI network analysis introduced 10 hub genes (TP53, TNF, STAT3, HSP90AA1, PTGS2, HDAC6, ABCB1, CCT2, NR1I2, and GUSB). In KEGG enrichment, most were associated with Sphingolipid, vascular endothelial growth factor A (VEGFA), C-type lectin receptor, and mitogen-activated protein kinase (MAPK) signaling pathways. The biological processes described in 87 intersection genes are mostly concerned with regulating the apoptotic process, cytokine production, and intracellular signal transduction. Molecular docking results also directed that Calebin-A had a high affinity to bind hub proteins linked to NAFLD. Conclusion: Here, we showed that Calebin-A, through its effect on several critical genes/ proteins and pathways, might repress the progression of NAFLD.

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data.

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

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment.

BACKGROUND: Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored. METHODS: The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated. RESULTS: By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels. CONCLUSIONS: Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

New insights on the regulatory network of drought-responsive key genes in Arabidopsis thaliana.

Drought stress is complex abiotic stress that seriously affects crop productivity and yield. Many genes with various functions are induced in response to drought stress. The present study aimed to identify drought-responsive hub genes and their related regulation network in Arabidopsis thaliana under drought stress. In this study, RNA-sequencing data of well-watered and drought treatment samples of Arabidopsis were analyzed, and differential expression genes were identified. The gene ontology enrichment and protein-protein interaction network analyses were performed for differential expression genes. Then, the most important hub genes, gene ontology enrichment, co-expression network, and prediction of related miRNAs of hub genes were investigated by in silico approaches. A total of 2462 genes were expressed differentially, of which 1926 transcripts were up-regulated under drought stress, and the rest were down-regulated. WRKY33, WRKY40, AT1G19020, STZ, SYP122, CNI1, CML37, BCS1, AT3G02840, and AT5G54490 were identified as hub genes in drought stress. The gene ontology analysis showed that hub genes significantly enriched in response to hypoxia, chitin, wounding, and salicylic acid-mediated signaling pathway. The hub genes were co-expressed with important drought-responsive genes such as WRKY46, WRKY60, CML38, ERF6, ERF104, and ERF1A. They were regulated by many stress-responsive miRNAs, such as ath-miR5021, miR413, miR5998, and miR162, that could be used as candidate miRNAs for regulating key genes under drought stress. It seems that the regulation network was involved in signaling pathways and protein degradation under drought stress, and it consists of several important genes and miRNAs that are potential candidates for plant improvement and breeding programs.

Immune infiltration and diagnostic value of immune-related genes in periodontitis using bioinformatics analysis.

BACKGROUND AND OBJECTIVES: Periodontitis, which is a chronic inflammatory periodontal disease resulting in destroyed periodontal tissue, is the leading cause of tooth loss in adults. Many studies have found that inflammatory immune responses are involved in the risk of periodontal tissue damage. Therefore, we analyzed the association between immunity and periodontitis using bioinformatics methods to further understand this disease. MATERIALS AND METHODS: First, the expression profiles of periodontitis and healthy samples were downloaded from the GEO database, including a training dataset GSE16134 and an external validation dataset GSE10334. Then, differentially expressed genes were identified using the limma package. Subsequently, immune cell infiltration was calculated by using the CIBERSORT algorithm. We further identified genes linking periodontitis and immunity from the ImmPort and DisGeNet databases. In addition, some of them were selected to construct a diagnostic model via a logistic stepwise regression analysis. RESULTS AND CONCLUSIONS: Two hundred sixty differentially expressed genes were identified and found to be involved in responses to bacterial and immune-related processes. Subsequently, immune cell infiltration analysis demonstrates significant differences in the abundance of most immune cells between periodontitis and healthy samples, especially in plasma cells. These results suggested that immunity doses play a non-negligible role in periodontitis. Twenty-one genes linking periodontitis and immunity were further identified. And nine hub genes of them were identified that may be key genes involved in the development of periodontitis. Gene ontology analyses showed that these genes are involved in response to molecules of bacterial origin, cell chemotaxis, and response to chemokines. In addition, three genes of them were selected to construct a diagnostic model. And its good diagnostic performance was demonstrated by the receiver operating characteristic curves, with an area under the curve of 0.9424 for the training dataset and 0.9244 for the external validation dataset.

Potential biomarkers of aortic dissection based on expression network analysis.

BACKGROUND: Aortic dissection (AD) is a rare disease with severe morbidity and high mortality. Presently, the pathogenesis of aortic dissection is still not completely clear, and studying its pathogenesis will have important clinical significance. METHODS: We downloaded 28 samples from the Gene Expression Omnibus (GEO) database (Accession numbers: GSE147026 and GSE190635), including 14 aortic dissection samples and 14 healthy controls (HC) samples. The Limma package was used to screen differentially expressed genes. The StarBasev2.0 tool was used to predict the upstream molecular circRNA of the selected miRNAs, and Cytoscape software was used to process the obtained data. STRING database was used to analyze the interacting protein pairs of differentially expressed genes under medium filtration conditions. The R package "org.hs.eg.db" was used for functional enrichment analysis. RESULTS: Two hundred genes associated with aortic dissection were screened. Functional enrichment analysis was performed based on these 200 genes. At the same time, 2720 paired miRNAs were predicted based on these 200 genes, among which hsa-miR-650, hsa-miR-625-5p, hsa-miR-491-5p and hsa-miR-760 paired mRNAs were the most. Based on these four miRNAs, 7106 pairs of circRNAs were predicted to be paired with them. The genes most related to these four miRNAs were screened from 200 differentially expressed genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1). CONCLUSIONS: The study demonstrates that miRNA-associated circRNA-mRNA networks are altered in AD, implying that miRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.

Identification of Potential Genes and Critical Pathways in Postoperative Recurrence of Crohn's Disease by Machine Learning And WGCNA Network Analysis.

Background: Crohn's disease (CD) is a chronic idiopathic inflammatory bowel disease affecting the entire gastrointestinal tract from the mouth to the anus. These patients often experience a period of symptomatic relapse and remission. A 20 - 30% symptomatic recurrence rate is reported in the first year after surgery, with a 10% increase each subsequent year. Thus, surgery is done only to relieve symptoms and not for the complete cure of the disease. The determinants and the genetic factors of this disease recurrence are also not well-defined. Therefore, enhanced diagnostic efficiency and prognostic outcome are critical for confronting CD recurrence. Methods: We analysed ileal mucosa samples collected from neo-terminal ileum six months after surgery (M6=121 samples) from Crohn's disease dataset (GSE186582). The primary aim of this study is to identify the potential genes and critical pathways in post-operative recurrence of Crohn's disease. We combined the differential gene expression analysis with Recursive feature elimination (RFE), a machine learning approach to get five critical genes for the postoperative recurrence of Crohn's disease. The features (genes) selected by different methods were validated using five binary classifiers for recurrence and remission samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbor (KNN) with 10-fold cross-validation. We also performed weighted gene co-expression network analysis (WGCNA) to select specific modules and feature genes associated with Crohn's disease postoperative recurrence, smoking, and biological sex. Combined with other biological interpretations, including Gene Ontology (GO) analysis, pathway enrichment, and protein-protein interaction (PPI) network analysis, our current study sheds light on the in-depth research of CD diagnosis and prognosis in postoperative recurrence. Results: PLOD2, ZNF165, BOK, CX3CR1, and ARMCX4, are the important genes identified from the machine learning approach. These genes are reported to be involved in the viral protein interaction with cytokine and cytokine receptors, lysine degradation, and apoptosis. They are also linked with various cellular and molecular functions such as Peptidyl-lysine hydroxylation, Central nervous system maturation, G protein-coupled chemoattractant receptor activity, BCL-2 homology (BH) domain binding, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mitochondrial translational termination, mitochondrial translation, mitochondrial respiratory chain complex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mitochondrial depolarization pathway is linked with CD recurrence leading to oxidative stress in promoting inflammation in CD patients. Conclusion: These key genes serve as the novel diagnostic biomarker for the postoperative recurrence of Crohn's disease. Thus, among other treatment options present until now, these biomarkers would provide success in both diagnosis and prognosis, aiming for a long-lasting remission to prevent further complications in CD.

Nitrate-responsive transcriptome analysis of rice RGA1 mutant reveals the role of G-protein alpha subunit in negative regulation of nitrogen-sensitivity and use efficiency.

KEY MESSAGE: Nitrate-responsive transcriptomic, phenotypic and physiological analyses of rice RGA1 mutant revealed many novel RGA1-regulated genes/processes/traits related to nitrogen use efficiency, and provided robust genetic evidence of RGA1-regulation of NUE. Nitrogen (N) use efficiency (NUE) is important for sustainable agriculture. G-protein signalling was implicated in N-response/NUE in rice, but needed firm genetic characterization of the role of alpha subunit (RGA1). The knock-out mutant of RGA1 in japonica rice exhibited lesser nitrate-dose sensitivity than the wild type (WT), in yield and NUE. We, therefore, investigated its genomewide nitrate-response relative to WT. It revealed 3416 differentially expressed genes (DEGs), including 719 associated with development, grain yield and phenotypic traits for NUE. The upregulated DEGs were related to photosynthesis, chlorophyll, tetrapyrrole and porphyrin biosynthesis, while the downregulated DEGs belonged to cellular protein metabolism and transport, small GTPase signalling, cell redox homeostasis, etc. We validated 26 nitrate-responsive DEGs across functional categories by RT-qPCR. Physiological validation of nitrate-response in the mutant and the WT at 1.5 and 15 mM doses revealed higher chlorophyll and stomatal length but decreased stomatal density, conductance and transpiration. The consequent increase in photosynthesis and water use efficiency may have contributed to better yield and NUE in the mutant, whereas the WT was N-dose sensitive. The mutant was not as N-dose-responsive as the WT in shoot/root growth, productive tillers and heading date, but equally responsive as WT in total N and protein content. The RGA1 mutant was less impacted by higher N-dose or salt stress in terms of yield, protein content, photosynthetic performance, relative water content, water use efficiency and catalase activity. PPI network analyses revealed known NUE-related proteins as RGA1 interactors. Therefore, RGA1 negatively regulates N-dose sensitivity and NUE in rice.

Association Between IL10 Polymorphisms and the Susceptibility to Sepsis: A Meta-Analysis.

Many genetic variations have been identified to associate with sepsis in numerous studies, but the function of these variants in influencing sepsis is a complex process. We make use of mate-analysis and other analytic strategies (eQTL analysis and PPI network) to investigate the effect of interleukin-10 (IL10) on sepsis. Single-nucleotide polymorphisms (SNPs) in IL10 were analyzed in 3011 septic cases and 2976 controls from 22 studies. In results, the IL10-rs1800871 showed a significant association with sepsis in Asians (P < 0.05). Moreover, there is a association between rs1800896 and sepsis in pooled populations and Asians (P < 0.05). However, there is no association between rs1800872 and sepsis in different models. The three polymorphisms were also identified for the regulation of IL10 expression in an eQTL analyze, and the increased IL10 expression was related to the development of sepsis. Furthermore, the IL10 was discovered to associate with the expression of DRD1, TANK, MKL1, and STARD3NL genes in another independent cohort, which are functionally enriched for IRF3 (interferon regulatory factor 3)/IRF7 (interferon regulatory factor 7) and hormone pathways. In conclusion, the study confirmed the association between IL10 polymorphisms (rs1800871, rs1800872, rs1800896) and sepsis, and suggested the role of the variants in inflammatory pathologies.

Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach.

Colorectal cancer is the third deadliest and fourth most diagnosed cancer. It is heterogeneously driven by varied mutations and mutagens, and thus, it is challenging for targeted therapy. The rapid advancement of high-throughput technology presents considerable opportunities for discovering new colon cancer biomarkers. In the present study, we have explored and identified the biomarkers based on molecular interactions. We curated cancer datasets that were not micro-dissected and performed gene expression analysis. The protein-protein interactions were curated, and a network was constructed for the up-regulated genes. The hub genes were analyzed using 12 different topological parameters. The correlation analysis selected TOP2A, CDK1, CCNB1, AURKA, and MAD2L1 as hub genes. Further, survival analysis was performed to determine the effectiveness of the hub gene on the patient's survival rate. Our findings explore various transcription factors such as E2F4, FOXM1, E2F6, MAX, and SIN3A, along with kinases CSNK2A1, MAPK14, CDK1, CDK4, and CDK2, as potential molecular signatures and aid researchers in understanding the pathophysiological mechanisms underlying CRC development and thus providing novel therapeutic and diagnostic recourse. Furthermore, investigating miRNAs, we focused on hsa-miR-215-5p, hsa-miR-192-5p, and hsa-miR-193b-3p due to their observed impact on a diverse set of colorectal cancer genes. Thereby, the current approach brings into light CRC- related genes at the RNA and protein levels that can potentially act as novel biomarkers opening doors to diagnostic and treatment purposes.

Integrated analysis of transcriptome-wide m6A methylation in a Cd-induced kidney injury rat model.

BACKGROUND: Accumulating evidence has demonstrated that N6-methyladenosine (m6A) plays important roles in a variety of diseases. However, the specific functions of m6A in CdCl2-induced kidney injury remain unclear. OBJECTIVE: Here, we investigate a transcriptome-wide map of m6A modifications and explore the effects of m6A on Cd-induced kidney injury. MATERIALS AND METHODS: The rat kidney injury model was constructed by subcutaneous injection of CdCl2 (0.5, 1.0, and 2.0 mg/kg). The m6A levels were measured by colorimetry. The level of expression of m6A-related enzymes were detected by reverse transcription quantitative real-time PCR analysis. Transcriptome-wide m6A methylome in CdCl2 (2.0 mg/kg) and the control group were profiled by methylated RNA immunoprecipitation sequencing (MeRIP-seq). Subsequently, the sequencing data were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), while gene set enrichment analysis (GSEA) confirmed the functional enrichment pathways of sequencing genes. In addition, a protein-protein interaction (PPI) network was applied to select hub genes. RESULTS: The levels of m6A and m6A regulators (METTL3, METTL14, WTAP, YTHDF2) were significantly increased in CdCl2 groups. We identified a total of 2615 differentially expressed m6A peaks, 868 differentially expressed genes and 200 genes with significant changes in both m6A modification and gene expression levels. GO, KEGG, and GSEA analyses indicated that these genes were mainly enriched in inflammation and metabolism-related pathways such as in IL-17 signaling and fatty acid metabolism. According the result of the conjoint analysis, we identified the top ten hub genes (Fos, Hsp90aa1, Gata3, Fcer1g, Cftr, Cspg4, Atf3, Cdkn1a, Ptgs2, and Npy) which may be regulated by m6A and involve in CdCl2-induced kidney damage. CONCLUSION: This study established a m6A transcriptional map in a CdCl2-induced kidney injury model and suggested that m6A may affect CdCl2 induced kidney injury via regulated the inflammation and metabolism related gene.

Construction of a diagnostic classifier for cervical intraepithelial neoplasia and cervical cancer based on XGBoost feature selection and random forest model.

BACKGROUND: The pathological phenotype of early-stage cervical cancer (CC) is similar to that of cervical intraepithelial neoplasia (CIN), which provides a challenge for the diagnosis of cervical precancerous lesions. Meanwhile, the existing diagnostic methods have certain subjectivity and limitations, resulting in the possibility of misdiagnosis or missed diagnosis. Hence, some methods are needed to assist diagnosis of CC and CIN. METHODS: Based on the data of CIN and CC in gene expression omnibus (GEO) dataset, the eXtreme Gradient Boosting (XGBoost) algorithm was used to screen the feature genes between CIN and CC for constructing the classifier. Incremental feature selection (IFS) curve was also used for screening. The classifier was validated for reliability using principal component analysis (PCA) dimensionality reduction analysis and heat map analysis of gene expression. Then, differentially expressed genes of CIN and CC were intersected with the classifier genes. Genes in the intersection were used as seeds for protein-protein interaction network construction and restart random walk analysis. And the genes with the top 50 affinity coefficients were selected for gene ontology (GO) and kyoto encyclopedia of genes and genome (KEGG) enrichment analyses to observe the biological functions with differences between CIN and CC. RESULTS: The peripheral blood genes of CIN and CC were analyzed, and seven genes were screened. Using this gene for classifier construction, IFS curve screening revealed that the three-feature gene classifier constructed according to the random forest model had the best effect. The results of PCA dimensionality reduction analysis and gene expression heat map analysis showed that the three-gene classifier could effectively distinguish CIN from CC. CONCLUSION: A three-gene diagnostic classifier can effectively distinguish CIN patients from CC patients and provide a reference for the clinical diagnosis of early CC.