The integration of transcriptomic and transgenic analyses reveals the involvement of the SA response pathway in the defense of chrysanthemum against the necrotrophic fungus Alternaria sp.

Chrysanthemum morifolium cv. 'Huaihuang' has ornamental, edible, medicinal, and tea product uses. However, its field growth, yield, and quality are negatively affected by black spot disease caused by Alternaria sp. (Strain: HQJH10092301; GenBank accession number: KF688111). In this study, we transcriptionally and transgenically characterized a new cultivar, 'Huaiju 2#' (Henan Traditional Chinese Medicine Plant Cultivar identification number: 2016002), which was bred from 'Huaihuang' and shows resistance to Alternaria sp. Numerous 'Huaiju 2#' plants were inoculated with Alternaria sp. for three or five days. Metabolic analysis showed increases in both salicylic acid (SA) and jasmonic acid (JA) in infected plants compared to the control. Protein activity analysis also revealed a significant increase in defense enzyme activities in infected plants. RNA-Seq of plants infected for 3 or 5 days produced a total of 58.6 GB of clean reads. Among these reads, 16,550 and 13,559 differentially expressed genes (DEGs) were identified in Cm_3 dpi (sample from 3 days post-inoculation labeled as Cm_3 dpi) and Cm_5 dpi (sample from 5 days post-inoculation labeled as Cm_5 dpi), respectively, compared with their controls (Cm_0 d: a mixture samples from 0 d (before inoculation) and those treated with sterile distilled water at 3 dpi and 5 dpi). Gene annotation and cluster analysis of the DEGs revealed a variety of defense responses to Alternaria sp. infection, which were characterized by increases in resistance (R) proteins and the reactive oxygen species (ROS), Ca2+, mitogen-activated protein kinase (MAPK), and JA signaling pathways. In particular, SA signaling was highly responsive to Alternaria sp. infection. The qPCR analysis of 12 DEG candidates supported their differential expression characterized by using the RNA-Seq data. One candidate was CmNPR1 (nonexpressor of pathogenesis-related gene 1), an important positive regulator of SA in systemic acquired resistance (SAR). Overexpression of CmNPR1 in 'Huaiju 2#' increased the resistance of transgenic plants to black spot. These findings indicate that the SA response pathway is likely involved in the defense of 'Huaiju 2#' against Alternaria sp. pathogens.

Identifying local co-regulation relationships in gene expression data.

Identifying interesting relationships between pairs of genes, presented over some of experimental conditions in gene expression data set, is useful for discovering novel functional gene interactions. In this paper, we introduce a new method for id entifying L ocal C o-regulation R elationships (IdLCR). These local relationships describe the behaviors of pairwise genes, which are either up- or down-regulated throughout the identified condition subset. IdLCR firstly detects the pairwise gene-gene relationships taking functional forms and the condition subsets by using a regression spline model. Then it measures the relationships using a penalized Pearson correlation and ranks the responding gene pairs by their scores. By this way, those relationships without clearly biological interpretations can be filtered out and the local co-regulation relationships can be obtained. In the simulation data sets, ten different functional relationships are embedded. Applying IdLCR to these data sets, the results show its ability to identify functional relationships and the condition subsets. For micro-array and RNA-seq gene expression data, IdLCR can identify novel biological relationships which are different from those uncovered by IFGR and MINE.

Biclustering of linear patterns in gene expression data.

Identifying a bicluster, or submatrix of a gene expression dataset wherein the genes express similar behavior over the columns, is useful for discovering novel functional gene interactions. In this article, we introduce a new algorithm for finding biClusters with Linear Patterns (CLiP). Instead of solely maximizing Pearson correlation, we introduce a fitness function that also considers the correlation of complementary genes and conditions. This eliminates the need for a priori determination of the bicluster size. We employ both greedy search and the genetic algorithm in optimization, incorporating resampling for more robust discovery. When applied to both real and simulation datasets, our results show that CLiP is superior to existing methods. In analyzing RNA-seq fly and worm time-course data from modENCODE, we uncover a set of similarly expressed genes suggesting maternal dependence. Supplementary Material is available online (at www.liebertonline.com/cmb).