Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal co rd injury.They can greatly affect nerve regeneration and functional recovery.However,there is still limited understanding of the peripheral immune inflammato ry response in spinal cord inju ry.In this study.we obtained microRNA expression profiles from the peripheral blood of patients with spinal co rd injury using high-throughput sequencing.We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus(GEO)database(GSE151371).We identified 54 differentially expressed microRNAs and 1656 diffe rentially expressed genes using bioinformatics approaches.Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways,such as neutrophil extracellular trap formation pathway,T cell receptor signaling pathway,and nuclear factor-κB signal pathway,we re abnormally activated or inhibited in spinal cord inju ry patient samples.We applied an integrated strategy that combines weighted gene co-expression network analysis,LASSO logistic regression,and SVM-RFE algorithm and identified three biomarke rs associated with spinal cord injury:ANO10,BST1,and ZFP36L2.We verified the expression levels and diagnostic perfo rmance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve.Quantitative polymerase chain reaction results showed that ANO20 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients.We also constructed a small RNA-mRNA interaction network using Cytoscape.Additionally,we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal co rd injury patients using the CIBERSORT tool.The proportions of naive B cells,plasma cells,monocytes,and neutrophils were increased while the proportions of memory B cells,CD8^(+)T cells,resting natural killer cells,resting dendritic cells,and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects,and ANO10,BST1 and ZFP26L2we re closely related to the proportion of certain immune cell types.The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal co rd inju ry and suggest that ANO10,BST2,and ZFP36L2 are potential biomarkers for spinal cord injury.The study was registe red in the Chinese Clinical Trial Registry(registration No.ChiCTR2200066985,December 12,2022).

Predictive value of co-expression patterns of immune checkpoint molecules for clinical outcomes of hematological malignancies

Co-expression of immune checkpoint(IC)molecules can exacerbate T cell exhaustion in patients with hematological malignancies(HMs)and contribute to the immune escape of tumor cells,which is related to poor clinical outcome.It is worth establishing and optimizing an ideal prediction model based on the co-expression patterns of IC molecules to evaluate the immune status of HM patients and predict their clinical outcome.In this perspective,we summarize the co-expression patterns of IC molecules and their importance as biomarkers that predict the prognosis of patients with different HMs,providing new insights for designing dual IC blockades(ICBs).

Combinatorial co-expression of xanthine dehydrogenase and chaperone XdhC from Acinetobacter baumannii and Rhodobacter capsulatus and their applications in decreasing purine content in food

This study investigated the combinatorial expression of xanthine dehydrogenase(XDH)and chaperone XdhC from Acinetobacter baumannii and Rhodobacter capsulatus and their applications in decreasing purine content in the beer,beef and yeast.Naturally occurring xdhABC gene clusters of A.baumannii CICC 10254 and R.capsulatus CGMCC 1.3366 as well as two refactored clusters constructed by exchanging their xdhC genes were overexpressed in Escherichia coli and purified to near homogeneity.RcXDH chaperoned by AbXdhC showed nearly the same catalytic performance as that by RcXdhC,except for the decreased substrate affinity.While the AbXDH co-expressed with RcXdhC displayed enhanced acidic adaptation but weakened catalytic activity.All the XDHs degraded purines in beer,beef and yeast extract effectively,indicating potential applications in low-purine foods to prevent hyperuricemia and gout.The study also presents a method for exploiting the better chaperone XdhC and novel XDHs by functional complement activity using existing XdhCs such as RcXdhC.

Weighted gene co-expression network analysis reveals similarities and differences of molecular features between dilated and ischemic cardiomyopathies

Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.

Identification of Differentially Expressed Genes in Grape Skin at Veraison and Maturity and Construction of Co-expression Network

The ripening process of grape is an important stage during grape growth and development. During this process, color of grape skin is the most obvious change. The molecular mechanism for the ripening of grape(a non-climacteric fruit, which ripens without ethylene and respiration bursts) is still unclear. Although numerous studies have been done on the changes in the contents of metabolites during grape ripening, the differentially expressed genes at veraison and maturity stages have not been systematically analyzed. In this study, 1 524 genes that are significantly differentially expressed in grape(Pinot Noir) skin at veraison and maturity stages were identified, and a co-expression network of these genes was built. Some of the eight co-expression modules we identified may be closely related to the synthesis or metabolism of anthocyanins, sugar acids, and other flavor substances. The transcription factor families WRKY, b ZIP, HSF and WOX may play an important role in the regulation of anthocyanin synthesis or metabolism. The results provide a foundation for further study of the molecular mechanism of grape ripening.

Weighted Gene Co-expression Network Analysis of Gene Modules for the Prognosis of Esophageal Cancer

Esophageal cancer is a common malignant tumor, whose pathogenesis and prognosis factors are not fully understood. This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer. The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas(TCGA), and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma. The result showed that 12 modules were associated with one or more survival data such as recurrence status, recurrence time, vital status or vital time. Furthermore, survival analysis showed that 5 out of the 12 modules were related to progression-free survival(PFS) or overall survival(OS). As the most important module, the midnight blue module with 82 genes was related to PFS, apart from the patient age, tumor grade, primary treatment success, and duration of smoking and tumor histological type. Gene ontology enrichment analysis revealed that 'glycoprotein binding' was the top enriched function of midnight blue module genes. Additionally, the blue module was the exclusive gene clusters related to OS. Platelet activating factor receptor(PTAFR) and feline Gardner-Rasheed(FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database. In conclusion, our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.

Predicting Arabidopsis thaliana Gene Function by Transitiving Co-expression in Shortest-path

The present paper predicted the function of unknow genes by analyzing the co-expression data of Arabidopsis thaliana from biological pathway based on the shortest-path algorithm. This paper proposed that transitive co-expression among genes can be used as an important attribute to link genes of the same biological pathway. The genes from the same biological pathway with similar functions are strongly correlated in expression. Moreover,the function of unknown genes can be predicted by the known genes where they are strongly correlated in expression lying on the same shortest-path from the biological pathway. Analyzing the Arabidopsis thaliana from the biological pathway,this study showed that this method can reliably reveal function of the unknown Arabidopsis thaliana genes and the approach of predicting gene function by transitiving co-expression in shortest-path is feasible and effective.

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

AIM: To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis(WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study.METHODS: Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus(GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes.The function of the genes were annotated by gene ontology(GO).RESULTS: In this study, we identified four co-expression modules significantly correlated with clinictraits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location(sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter(LTD). Additionally, we identified the hug gene(top connectivity with other genes) in each module. The hub gene RPS15 A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma.CONCLUSION: From WGCNA analysis and hub gene calculation, we identified RPS15 A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

Identification of key genes involved in axon regeneration and Wallerian degeneration by weighted gene co-expression network analysis

Peripheral nerve injury repair requires a certain degree of cooperation between axon regeneration and Wallerian degeneration.Therefore,investigating how axon regeneration and degeneration work together to repair peripheral nerve injury may uncover the molecular mechanisms and signal cascades underlying peripheral nerve repair and provide potential strategies for improving the low axon regeneration capacity of the central nervous system.In this study,we applied weighted gene co-expression network analysis to identify differentially expressed genes in proximal and distal sciatic nerve segments from rats with sciatic nerve injury.We identified 31 and 15 co-expression modules from the proximal and distal sciatic nerve segments,respectively.Functional enrichment analysis revealed that the differentially expressed genes in proximal modules promoted regeneration,while the differentially expressed genes in distal modules promoted neurodegeneration.Next,we constructed hub gene networks for selected modules and identified a key hub gene,Kif22,which was up-regulated in both nerve segments.In vitro experiments confirmed that Kif22 knockdown inhibited proliferation and migration of Schwann cells by modulating the activity of the extracellular signal-regulated kinase signaling pathway.Collectively,our findings provide a comparative framework of gene modules that are co-expressed in injured proximal and distal sciatic nerve segments,and identify Kif22 as a potential therapeutic target for promoting peripheral nerve injury repair via Schwann cell proliferation and migration.All animal experiments were approved by the Institutional Animal Ethics Committee of Nantong University,China(approval No.S20210322-008)on March 22,2021.

CO-EXPRESSION OF MACROPHAGE COLONY-STIMULATING FACTOR WITH ITS RECEPTOR IN HUMAN HEPATOMA CELLS AND ITS POTENTIAL ROLES

Objective: To investigate the potential role of macrophage colony-stimulating factor (M-CSF) and macrophage colony-stimulating factor receptor (M-CSF-R) on the growth of human hepatoma cells. Methods: Specimens of different origin, including tissues of human hepatocellular carcinoma (HCC), human fetal liver (FL) and normal liver (NL), the hepatoma cell lines, as well as the peripheral blood mononuclear cells (PBMC) from patients with HCC or liver metastatic tumor (LMT), were used to detect the expression levels of M-CSF and M-CSF-R by ABC immunohistochemistry staining and reverse transcription polymerase chain reaction methods the expression levels of M-CSF and M-CSF-R. Influence of monoclonal antibody against M-CSF (B5) or M-CSF-R (RE2) on proliferation ability of hepatoma cell linesin vitro was also studied. Results: The results showed that hepatoma tissues produced elevated levels of both M-CSF and M-CSF-R compared with those of fetal liver (P<0.001). The M-CSF/M-CSF-R expression levels of PBMC from hepatoma patients were higher than those of LMT patients (P<0.01,P<0.05) and the normal people (P<0.001). The hepatoma cell lines showed strong positive for M-CSF and M-CSF-R production. Both B5 and RE2 displayed a dose-dependent inhibitory effect on the growth and proliferation of hepatoma cells. Conclusion: The study indicates a co-expression model for M-CSF-R in hepatoma cells, suggesting an involvement of M-CSF/M-CSF-R in growth signaling of those malignant cells. The M-CSF/M-CSF-R seems to function through an autonomy mechanism in human hepatoma.

Identification of Potential Therapeutic Targets of Alzheimer's Disease By Weighted Gene Co-Expression Network Analysis

Objective Alzheimer's disease(AD)is the most common cause of dementia.The pathophysiology of the disease mostly remains unearthed,thereby challenging drug development for AD.This study aims to screen high throughput gene expression data using weighted co-expression network analysis(WGCNA)to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus(GEO)database.Normalization,quality control,filtration,and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules.Furthermore,the correlation coefiidents between the modules and clinical traits were computed to identify the key modules.Gene ontology and pathway enrichment analyses were performed on the key module genes.The STRING database was used to construct the protein-protein interaction(PPI)networks,which were further analyzed by Cytoscape app(MCODE).Finally,validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules,among which 6 modules were identified as the key module relating to AD occurrence.These key modules are primarily involved in chemical synaptic transmission(G0:0007268),the tricarboxylic acid(TCA)cycle and respiratory electron transport(R-HSA-1428517).WDR47,OXCT1,C3orfl4,ATP6V1A,SLC25A14,NAPB were found as the hub genes and their expression were validated by external datasets.Conclusions Through modules co-expression network analyses and PPI network analyses,we identified the hub genes of AD,including WDR47,0XCT1,C3orfl4i ATP6V1A,SLC25A14 and NAPB.Among them,three hub genes(ATP6V1A,SLC25A14,OXCT1)might contribute to AD pathogenesis through pathway of TCA cycle.

Identification of Potential Zinc Deficiency Responsive Genes and Regulatory Pathways in Rice by Weighted Gene Co-expression Network Analysis

Zinc(Zn)malnutrition is a major public health issue.Genetic biofortification of Zn in rice grain can alleviate global Zn malnutrition.Therefore,elucidating the genetic mechanisms regulating Zn deprivation response in rice is essential to identify elite genes useful for breeding high grain Zn rice varieties.Here,a meta-analysis of previous RNA-Seq studies involving Zn deficient conditions was conducted using the weighted gene co-expression network analysis(WGCNA)and other in silico prediction tools to identify modules(denoting cluster of genes with related expression pattern)of co-expressed genes,modular genes which are conserved differentially expressed genes(DEGs)across independent RNA-Seq studies,and the molecular pathways of the conserved modular DEGs.WGCNA identified 16 modules of co-expressed genes.Twenty-eight and five modular DEGs were conserved in leaf and crown,and root tissues across two independent RNA-Seq studies.Functional enrichment analysis showed that 24 of the 28 conserved modular DEGs from leaf and crown tissues significantly up-regulated 2 Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways and 15 Gene Ontology(GO)terms,including the substrate-specific transmembrane transporter and the small molecule metabolic process.Further,the well-studied transcription factors(OsWOX11 and OsbHLH120),protein kinase(OsCDPK20 and OsMPK17),and miRNAs(OSA-MIR397A and OSA-MIR397B)were predicted to target some of the identified conserved modular DEGs.Out of the 24 conserved and up-regulated modular DEGs,19 were yet to be experimentally validated as Zn deficiency responsive genes.Findings from this study provide a comprehensive insight on the molecular mechanisms of Zn deficiency response and may facilitate gene and pathway prioritization for improving Zn use efficiency and Zn biofortification in rice.

Co-expression network analysis predicts a key role of microRNAs in the adaptation of the porcine skeletal muscle to nutrient supply

Background:The role of non-coding RNAs in the porcine muscle metabolism is poorly understood,with few studies investigating their expression patterns in response to nutrient supply.Therefore,we aimed to investigate the changes in microRNAs(miRNAs),long intergenic non-coding RNAs(lincRNAs)and mRNAs muscle expression before and after food intake.Results:We measured the miRNA,lincRNA and mRNA expression levels in the gluteus medius muscle of 12 gilts in a fasting condition(AL-T0)and 24 gilts fed ad libitum during either 5 h.(AL-T1,N=12)or 7 h.(AL-T2,N=12)prior to slaughter.The small RNA fraction was extracted from muscle samples retrieved from the 36 gilts and sequenced,whereas lincRNA and mRNA expression data were already available.In terms of mean and variance,the expression profiles of miRNAs and lincRNAs in the porcine muscle were quite different than those of mRNAs.Food intake induced the differential expression of 149(AL-T0/AL-T1)and 435(AL-T0/AL-T2)mRNAs,6(AL-T0/AL-T1)and 28(AL-T0/AL-T2)miRNAs and none lincRNAs,while the number of differentially dispersed genes was much lower.Among the set of differentially expressed miRNAs,we identified ssc-miR-148a-3p,ssc-miR-22-3p and ssc-miR-1,which play key roles in the regulation of glucose and lipid metabolism.Besides,co-expression network analyses revealed several miRNAs that putatively interact with mRNAs playing key metabolic roles and that also showed differential expression before and after feeding.One case example was represented by seven miRNAs(ssc-miR-148a-3p,ssc-miR-151-3p,ssc-miR-30a-3p,ssc-miR-30e-3p,ssc-miR-421-5p,ssc-miR-493-5p and ssc-miR-503)which putatively interact with the PDK4 mRNA,one of the master regulators of glucose utilization and fatty acid oxidation.Conclusions:As a whole,our results evidence that microRNAs are likely to play an important role in the porcine skeletal muscle metabolic adaptation to nutrient availability.

Co-expression Network Analysis Identifies Fourteen Hub Genes Associated with Prognosis in Clear Cell Renal Cell Carcinoma

Summary:Renal cancer is a common genitourinary malignance,of which clear cell renal cell carcinoma(ccRCC)has high aggressiveness and leads to most cancer-related deaths.Identification of sensitive and reliable biomarkers for predicting tumorigenesis and progression has great significance in guiding the diagnosis and treatment of ccRCC.Here,we identified 2397 common difTerentially expressed genes(DEGs)using paired normal and tumor ccRCC tissues from GSE53757 and The Cancer Genome Atlas(TCGA).Then,we performed weighted gene co-expression network analysis and protein-protein interaction network analysis,17 candidate hub genes were identified.These candidate hub genes were further validated in GSE36895 and Oncomine database and 14 real hub genes were identified.All the hub genes were up-regulated and significantly positively correlated with pathological stage and histologic grade of ccRCC.Survival analysis showed that the higher expression level of each hub gene tended to predict a worse clinical outcome.ROC analysis showed that all the hub genes can accurately distinguish between tumor and normal samples,and between early stage and advanced stage ccRCC.Moreover,all the hub genes were positively associated with distant metastasis,lymph node infiltration,tumor recurrence and the expression of MKi67,suggesting these genes might promote tumor proliferation,invasion and metastasis.Furthermore,the functional annotation demonstrated that most genes were enriched in cell-cycle related biological function.In summary,our study identified 14 potential biomarkers for predicting tumorigenesis and progression,which might contribute to early diagnosis,prognosis prediction and therapeutic intervention.

Identification of potential key molecules and signaling pathways for psoriasis based on weighted gene co-expression network analysis

BACKGROUND Psoriasis is a chronic inflammatory skin disease,the pathogenesis of which is more complicated and often requires long-term treatment.In particular,moderate to severe psoriasis usually requires systemic treatment.Psoriasis is also associated with many diseases,such as cardiometabolic diseases,malignant tumors,infections,and mood disorders.Psoriasis can appear at any age,and lead to a substantial burden for individuals and society.At present,psoriasis is still a treatable,but incurable,disease.Previous studies have found that micro RNAs(mi RNAs)play an important regulatory role in the progression of various diseases.Currently,mi RNAs studies in psoriasis and dermatology are relatively new.Therefore,the identification of key mi RNAs in psoriasis is helpful to elucidate the molecular mechanism of psoriasis.AIM To identify key molecular markers and signaling pathways to provide potential basis for the treatment and management of psoriasis.METHODS The mi RNA and m RNA data were obtained from the Gene Expression Omnibus database.Then,differentially expressed m RNAs(DEm RNAs)and differentially expressed mi RNAs(DEmi RNAs)were screened out by limma R package.Subsequently,DEm RNAs were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomics functional enrichment.The“WGCNA”R package was used to analyze the co-expression network of all mi RNAs.In addition,we constructed mi RNA-m RNA regulatory networks based on identified hub mi RNAs.Finally,in vitro validation was performed.All experimental procedures were approved by the ethics committee of Chinese PLA General Hospital(S2021-012-01).RESULTS A total of 639 DEm RNAs and 84 DEmi RNAs were identified.DEm RNAs screening criteria were adjusted P(adj.P)value<0.01 and|log Fold Change|(|log FC|)>1.DEmi RNAs screening criteria were adj.P value<0.01 and|logFC|>1.5.KEGG functional analysis demonstrated that DEm RNAs were significantly enriched in immune-related biological functions,for example,tolllike receptor signaling pathway,cytokine-cytokine receptor interaction,and chemokine signaling pathway.In weighted gene co-expression network analysis,turquoise module was the hub module.Moreover,10 hub mi RNAs were identified.Among these 10 hub mi RNAs,only 8 hub mi RNAs predicted the corresponding target m RNAs.97 negatively regulated mi RNA-m RNA pairs were involved in the mi RNA-m RNA regulatory network,for example,hsa-mi R-21-5 pclaudin 8(CLDN8),hsa-mi R-30 a-3 p-interleukin-1 B(IL-1 B),and hsa-mi R-181 a-5 p/hsa-mi R-30 c-2-3 p-C-X-C motif chemokine ligand 9(CXCL9).Real-time polymerase chain reaction results showed that IL-1 B and CXCL9 were up-regulated and CLDN8 was down-regulated in psoriasis with statistically significant differences.CONCLUSION The identification of potential key molecular markers and signaling pathways provides potential research directions for further understanding the molecular mechanisms of psoriasis.This may also provide new research ideas for the prevention and treatment of psoriasis in the future.

Co-expression of Clostridium perfringens α and ε Toxins and Their Immunogenicity

The genc CPA and ETX encoding α and e toxins were amplified from the standard strain C60-2 of type D Clostridium perfingens by PCR, and then they were inserted into pet-Duct-1 vecto, respectively for the construction of co-expression plasmid pETDoet-1-CPA-ETX. The co-expression plasmid was transformed into BL21 （DE3） competent cells, at and e toxins proteins were induced by IPTG before analysis by SDS-PAGE. As a result, both of α and e toxins were detected at 43 and 34 KU by western-blot. Mice immunized with the co-expressed α and e toxins proteins produced high titers of neutralizing antibodies in the serum, which protected the mice against the attack of type D C. perfringens culture filtrate. In addition, mice immunized with the produced co-expressed α and e toxins proteins showed significantly higher surviving rate than with ε toxins protein alone when infected with culture filtrate of type D C. perfringens . These results indicated that co-expression of α and e toxins proteins could be used as a new method to prepare vaccines against the pathogens of multiple serotypes.

3-80 Identify the Signature Genes for Diagnose of Uveal Melanoma by Weight Gene Co-expression Network Analysis

Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful tool which is applied to investigate the relationship between gene expression levels and patient clinic traits[1;2]. In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment;module purple positively correlates with tumor location (sclera) and negatively correlates with patient age;

Co-expression network analysis of virulence genes exoS and exoU of pseudomonas aeruginosa in lower respiratory tract based on histological data expression profiles

Objective:To use the gene chip of pseudomonas aeruginosa as a research sample and to explore it at an omics level,aiming at elucidating the co-expression network characteristics of the virulence genes exoS and exoU of pseudomonas aeruginosa in the lower respiratory tract from the perspective of molecular biology and identifying its key regulatory genes.Methods:From March 2016 to May 2018,312 patients infected with pseudomonas aeruginosa in the lower respiratory tract who were admitted to Department of Respiratory Medicine of Baogang Hospital and given follow-up treatments in the hospital were selected as subjects by use of cluster sampling.Alveolar lavage fluid and sputum collected from those patients were used as biological specimens.The genes of pseudomonas aeruginosa were detected with the help of oligonucleotide probes to make a pre-processing of chip data.A total of 8 common antibiotics(ceftazidime,gentamicin,piperacillin,amikacin,ciprofloxacin,levofloxacin,doripenem and ticarcillin)against Gram-negative bacteria were selected to determine the drug resistance of biological specimens.MCODE algorithm was used to construct a co-expression network model of the drug-resistance genes focused on exoS/exoU.Results:The expression level of exoS/exoU in the drug-resistance group was significantly higher than that in the non-resistance group(p<0.05).The top 5 differentially expressed genes in the alveolar lavage fluid specimens from the drug-resistance group were RAC1,ITGB1,ITGB5,CRK and IGF1R in the order from high to low.In the sputum specimens,the top 5 differentially expressed genes were RAC1,CRK,IGF1R,ITGB1 and ITGB5.In the alveolar lavage fluid specimens,only RAC1 had a positive correlation with the expression of exoS and exoU(p<0.05).In the sputum specimens,RAC1,ITGB1,ITGB5,CRK and IGF1R were positively correlated with the expression of exoS and exoU(p<0.05).The genes included in the co-expression network contained exoS,exoU,RAC1,ITGB1,ITGB5,CRK,CAMK2D,RHOA,FLNA,IGF1R,TGFBR2 and FOS.Among them,RAC1 had a highest score in the aspect of regulatory ability(72.00)and the largest number of regulatory genes(6);followed by ITGB1,ITGB5 and CRK genes.Conclusions:The high expression of exoS and exoU in the sputum specimens suggests that pseudomonas aeruginosa has a higher probability to get resistant to antibiotics;RAC1,ITGB1,ITGB5 and CRK genes may be the key genes that can regulate the expression of exoS and exoU.

WheatCENet:A Database for Comparative Co-expression Network Analysis of Allohexaploid Wheat and Its Progenitors

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks.Here,using large-scale transcriptome data,we constructed co-expression networks for diploid,tetraploid,and hexaploid wheat species,and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors,named WheatCENet.WheatCENet is a platform for searching and comparing specific functional coexpression networks,as well as identifying the related functions of the genes clustered therein.Functional annotations like pathways,gene families,protein-protein interactions,microRNAs(miRNAs),and several lines of epigenome data are integrated into this platform,and Gene Ontology(GO)annotation,gene set enrichment analysis(GSEA),motif identification,and other useful tools are also included.Using WheatCENet,we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION I(WAPOI)has more co-expressed genes related to spike development in hexaploid wheat than its progenitors.We also found a novel motif of CCWWWWWWGG(CArG)specifically in the promoter region of WAPO-Al,suggesting that neofunctionalization of the WAPO-AI gene affects spikelet development in hexaploid wheat.WheatCENet is useful for investigating co-expression networks and conducting other analyses,and thus facilitates comparative and functional genomic studies in wheat.

Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

A large number of putative risk genes for autism spectrum disorder(ASD)have been reported.The functions of most of these susceptibility genes in developing brains remain unknown,and causal relationships between their variation and autism traits have not been established.The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes(hcASDs).The results showed that three gene features–gene size,mRNA abundance,and guanine-cytosine content–affect the genome-wide co-expression profiles of hcASDs.To circumvent the interference of these features in gene co-expression analysis,we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes.This method is referred to as"matched-gene co-expression analysis"(MGCA).With MGCA,we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction.The results of analysis of two recently-reported ASD candidate genes,CDH11 and CDH9,suggested the involvement of CDH11,but not CDH9,in ASD.Consistent with this prediction,behavioral studies showed that Cdh11-null mice,but not Cdh9-null mice,have multiple autism-like behavioral alterations.This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.