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).

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.

Transcriptome and weighted gene co-expression network analysis of jujube(Ziziphus jujuba Mill.)fruit reveal putative genes involved in proanthocyanin biosynthesis and regulation

Proanthocyanidin(PA)is an important bioactive compound with multiple physiological benefits in jujube(Ziziphus jujube Mill.).However,the molecular mechanisms underlying PA biosynthesis in jujube fruit have not been investigated.Here,the profiling of PA,(+)-catechin and(–)-epicatechin and transcriptome sequencing of three jujube cultivars from Xinjiang Uyghur Autonomous Region of China at five developmental stages were analyzed.The levels of total PAs and catechin exhibited a decreased trend over jujube ripening,and epicatechin content of two jujube cultivars increased first and then declined.Transcriptome analysis revealed that the differentially expressed genes(DEGs)were mainly enriched in ribosome,glycolysis/gluconeogenesis,fructose and mannose metabolism.17 DEGs encoding PAL,CHS,CHI,CHS,F3'H,LAR,ANR,C4Hs,4CLs,FLSs,DFRs and UFGTs involved in PA biosynthesis were relatively abundant.The highly transcribed LAR gene may greatly contribute to epicatechin accumulation.A weighted gene co-expression network analysis(WGCNA)was performed,and a network module including 1620 genes highly correlated with content of Pas and catechin was established.We identified 58 genes including 9 structural genes and 49 regulatory genes related to PA biosynthesis and regulation in the WGCNA module.Sixteen genes encoding 9 families of transcriptional factors(i.e.,MYB,bHLH,ERF,bZIP,NAC,SBP,MIKC,HB,WRKY)were considered as hub genes.The results of qRT-PCR analysis validating 10 genes were well consistent with the transcriptome data.These findings provide valuable knowledge to facilitate its genetic studies and molecular breeding.

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.

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.

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.

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.

Identification of potential immune-related prognostic biomarkers of lung cancer using gene co-expression network analysis

Objective The objective of this study was to identify new carcinogenetic hub genes and develop the integration of differentially expressed genes to predict the prognosis of lung cancer.Methods GSE139032 microarray data packages were downloaded from the Gene Expression Omnibus for planning,testing,and review of data.We identified KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV from a key module for validation.Results We found that the five genes were related to a poor prognosis,and the expression levels of these genes were associated with tumor stage.Furthermore,Kaplan-Meier plotter showed that the five hub genes had better prognostic values.The mean levels of methylation in lung adenocarcinoma(LUAD)were significantly lower than those in healthy lung tissues for the hub genes.However,gene set enrichment analysis(GSEA)for single hub genes showed that all of them were immune-related.Conclusion Our findings demonstrated that KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV are all candidate diagnostic and prognostic biomarkers for LUAD.They may have clinical implications in LUAD patients not only for the improvement of risk stratification but also for therapeutic decisions and prognosis prediction.

Discovery of leaf region and time point related modules and genes in maize(Zea mays L.)leaves by Weighted Gene Co-expression Network analysis(WGCNA)of gene expression profiles of carbon metabolism

Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.

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;

Weighted gene co-expression network analysis identiffes potential regulators in response to Salmonella Enteritidis challenge in the reproductive tract of laying ducks

Salmonella Enteritidis(SE)is a zoonotic and vertically transmitted pathogen,often colonized in the reproductive tract of adult poultry,which can result in direct contamination of eggs and threaten human health.Previous studies have revealed that some pattern recognition receptors and resistance genes were involved in regulating immune responses to SE invasion in birds.However,the role of these immune response genes was not independent,and the interactions among the genes remained to be further investigated.In this study,SE burden and colonization were determined in reproductive tissue after the ducks were SE-infected,and RNA-sequencing was performed to construct co-expression networks by weighted gene co-expression network analysis(WGCNA).The result showed that SE could be isolated from 22% of infected-birds in any segment of the reproductive tract and the SE was readily colonized in the stroma,small follicle,isthmus,and vagina of the reproductive tracts in morbid ducks.The top central,highly connected genes were subsequently identified three specific modules in the above four tissues at the defined cut-offs(P<0.01),including 60 new candidate regulators and 125 transcription factors.Moreover,those 185 differentially expressed genes(DEGs)in these modules were co-expressed.Moreover,the hub genes(TRAF3,CXCR4 and IL13RA1)were identified to act with many other genes through immune response pathways including NF-kappaB,Toll-like receptor,steroid biosynthesis,and p53signaling pathways.These data provide references that will understand the immune regulatory relationships during SE infection,but also assist in the breeding of SE-resistant lines through potential biomarkers.

Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer

Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide.Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients.In the study,we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes.Subsequently,the prognostic immunerelated gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis.Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance.And we developed a nomogram by combing the risk score with multiple clinical characteristics.CIBERSORT and TIMER algorithms confirmed that there are significant differences in tumorinfiltrating immune cells in different risk groups.In addition,gene set enrichment analysis shows 6 pathways that differ between high-and low-risk group.The immune-related gene signature effectively predicts the survival and immune infiltration of breast cancer patients and is expected to provide more effective immunotherapy targets for the prognosis prediction of breast cancer.

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.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.

scLink:Inferring Sparse Gene Co-expression Networks fromSingle-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease.With the rapid development of single-cell RNA sequencing technologies,it is now possible to investigate gene interactions in a cell type-specific manner.Here we propose the scLink method,which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data.We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis.The scLink R package is available at https://github.com/Vivianstats/scLink.

AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.

Enhanced production of shikimic acid using a multi-gene co-expression system in Escherichia coli

Shikimic acid(SA) is the key synthetic material for the chemical synthesis of Oseltamivir, which is prescribed as the front-line treatment for serious cases of influenza. Multi-gene expression vector can be used for expressing the plurality of the genes in one plasmid, so it is widely applied to increase the yield of metabolites. In the present study, on the basis of a shikimate kinase genetic defect strain Escherichia coli BL21(?aro L/aro K, DE3), the key enzyme genes aro G, aro B, tkt A and aro E of SA pathway were co-expressed and compared systematically by constructing a series of multi-gene expression vectors. The results showed that different gene co-expression combinations(two, three or four genes) or gene orders had different effects on the production of SA. SA production of the recombinant BL21-GBAE reached to 886.38 mg·L^(-1), which was 17-fold(P < 0.05) of the parent strain BL21(?aro L/aro K, DE3).

Identification of key genes regulating the synthesis of quercetin derivatives in Rosa roxburghii through integrated transcriptomics and metabolomics

Rosa roxburghii fruit is rich in flavonoids, but little is known about their biosynthetic pathways. In this study, we employed transcriptomics and metabolomics to study changes related to the flavonoids at five different stages of R. roxburghii fruit development. Flavonoids and the genes related to their biosynthesis were found to undergo significant changes in abundance across different developmental stages, and numerous quercetin derivatives were identified. We found three gene expression modules that were significantly associated with the abundances of the different flavonoids in R. roxburghii and identified three structural UDP-glycosyltransferase genes directly involved in the synthesis of quercetin derivatives within these modules. In addition, we found that RrBEH4, RrLBD1 and RrPIF8could significantly increase the expression of downstream quercetin derivative biosynthesis genes. Taken together,these results provide new insights into the metabolism of flavonoids and the accumulation of quercetin derivatives in R. roxburghii.