Comparative transcriptome analysis provides insights into the molecular mechanism of the anti-nematode role of Arachis hypogaea(Fabales:Fabaceae)against Meloidogyne incognita(Tylenchida:Heteroderidae)

Background:Plant root-knot nematode(RKN)disease is a serious threat to agricultural production across the world.Meloidogyne incognita is the most prominent pathogen to the vegetables and cash crops cultivated.Arachis hypogaea can effectively inhibit M.incognita,but the underlying defense mechanism is still unclear.Methods:In our study,the chemotaxis and infestation of the second-stage juveniles(J2s)of M.incognita to A.hypogaea root tips were observed by the Pluronic F-127 system and stained with sodium hypochlorite acid fuchsin,respectively.The transcriptome data of A.hypogaea roots with non-infected or infected by J2s were analyzed.Results:The J2s could approach and infect inside of A.hypogaea root tips,and the chemotactic migration rate and infestation rate were 20.72%and 22.50%,respectively.Differential gene expression and pathway enrichment analyses revealed ubiquinone and other terpenoid-quinone biosynthesis pathway,plant hormone signal transduction pathway,and phenylpropanoid biosynthesis pathway in A.hypogaea roots responded to the infestation of M.incognita.Furthermore,the AhHPT gene,encoding homogentisate phytyltransferase,was considered to be an ideal candidate gene due to its higher expression based on the transcriptome data and quantitative real-time PCR analysis.Conclusion:Therefore,the key gene AhHPT might be involved in the A.hypogaea against M.incognita.These findings lay a foundation for revealing the molecular mechanism of A.hypogaea resistance to M.incognita and also provide a prerequisite for further gene function verification,aiming at RKN-resistant molecular breeding.

Identification of candidate genes controlling fiber quality traits in upland cotton through integration of meta-QTL,significant SNP and transcriptomic data

Background:Meta-analysis of quantitative trait locus(QTL)is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies.The combination of meta-QTL intervals,significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants.Results:In our study,884 QTLs associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1,as a result,74 meta-QTLs were identified,including 19 meta-QTLs for fiber length;18 meta-QTLs for fiber strength;11 meta-QTLs for fiber uniformity;11 meta-QTLs for fiber elongation;and 15 meta-QTLs for micronaire.Combined with 8589 significant single nucleotide polymorphisms associated with fiber quality traits collected from 15 studies,297 candidate genes were identified in the meta-QTL intervals,20 of which showed high expression levels specifically in the developing fibers.According to the function annotations,some of the 20 key candidate genes are associated with the fiber development.Conclusions:This study provides not only stable QTLs used for marker-assisted selection,but also candidate genes to uncover the molecular mechanisms for cotton fiber development.

Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)data.Compared with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity network.We mathematically find the optimal expression patterns that can maximize the proposed scoring function.In comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation analysis.Also,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

TSUNAMI:Translational Bioinformatics Tool Suite for Network Analysis and Mining

Gene co-expression network(GCN)mining identifies gene modules with highly correlated expression profiles across samples/conditions.It enables researchers to discover latent gene/molecule interactions,identify novel gene functions,and extract molecular features from certain disease/condition groups,thus helping to identify disease bio-markers.However,there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis,as well as modules that may share common members.To address this need,we developed an online GCN mining tool package:TSUNAMI(Tools SUite for Network Analysis and MIning).TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data(microarray,RNA-seq,or any other numerical omics data),and then performs downstream gene set enrichment analysis for the identified modules.It has several features and advantages:1)a user-friendly interface and real-time co-expression network mining through a web server;2)direct access and search of NCBI Gene Expression Omnibus(GEO)and The Cancer Genome Atlas(TCGA)databases,as well as user-input gene ex-pression matrices for GCN module mining;3)multiple co-expression analysis tools to choose from,all of which are highly flexible in regards to parameter selection options;4)identified GCN modules are summarized to eigengenes,which are convenient for users to check their correlation with other clinical traits;5)integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools;and 6)visualization of gene loci by Circos plot in any step of the process.The web service is freely accessible through URL:https://biolearns.medicine.iu.edu/.Source code is available at https://github.com/huangzhii/TSUNAMI/.