Co-expression networks are essential tools to infer biological associations between gene products and predict gene annotation. Global networks can be analyzed at the transcriptome-wide scale or after querying them with a set of guide genes to capture the transcriptional landscape of a given pathway in a process named Pathway Level Coexpression (PLC). A critical step in network construction remains the definition of gene co-expression. In the present work, we compared how Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), their respective ranked values (Highest Reciprocal Rank (HRR)), Mutual Information (MI) and Partial Correlations (PC) performed on global networks and PLCs. This evaluation was conducted on the model plantArabidopsis thalianausing microarray and differently pre-processed RNA-seq datasets. We particularly evaluated how dataset × distance measurement combinations performed in 5 PLCs corresponding to 4 well described plant metabolic pathways (phenylpropanoid, carbohydrate, fatty acid and terpene metabolisms) and the cytokinin signaling pathway. Our present work highlights how PCC ranked with HRR is better suited for global network construction and PLC with microarray and RNA-seq data than other distance methods, especially to cluster genes in partitions similar to biological subpathways.

Plants deploy distinct secondary metabolisms to cope with environment pressure and to face bio-aggressors notably through the production of biologically active alkaloids. This metabolism-type is particularly elaborated inCatharanthus roseusthat synthesizes more than a hundred different monoterpene indole alkaloids (MIAs). While the characterization of their biosynthetic pathway now reaches completion, still little is known about the role of MIAs during biotic attacks. As a consequence, we developed a new plant/herbivore interaction system by challengingC. roseusleaves withManduca sextalarvae. Transcriptomic and metabolic analyses demonstrated thatC. roseusrespond to folivory by both local and systemic processes relying on the activation of specific gene sets and biosynthesis of distinct MIAs following jasmonate production. While a huge local accumulation of strictosidine was monitored in attacked leaves that could repel caterpillars through its protein reticulation properties, newly developed leaves displayed an increased biosynthesis of the toxic strictosidine-derived MIAs, vindoline and catharanthine, produced by up-regulation of MIA biosynthetic genes. In this context, leaf consumption resulted in a rapid death of caterpillars that could be linked to the MIA dimerization observed in intestinal tracts. Furthermore, this study also highlights the overall transcriptomic control of the plant defense processes occurring during herbivory.

Large-scale gene co-expression networks are an effective methodology to analyze sets of co-expressed genes and discover new gene functions or associations. Distances between genes are estimated according to their expression profiles and are visualized in networks that may be further partitioned to reveal communities of co-expressed genes. Creating expression profiles is now eased by the large amounts of publicly available expression data (microarrays and RNA-seq). Although many distance calculation methods have been intensively compared and reviewed in the past, it is unclear how to proceed when many samples reflecting a wide range of different conditions are available. Should as many samples as possible be integrated into network construction or be partitioned into smaller sets of more related samples? Previous studies have indicated a saturation in network performances to capture known associations once a certain number of samples is included in distance calculations. Here, we examined the influence of sample size on co-expression network construction using microarray and RNA-seq expression data from three plant species. We tested different down-sampling methods and compared network performances in recovering known gene associations to networks obtained from full datasets. We further examined how aggregating networks may help increase this performance by testing six aggregation methods.

Cytokinin signaling is a key regulatory pathway of many aspects in plant development and environmental stresses. Herein, we initiated the identification and functional characterization of the five CHASE-containing Histidine Kinases (CHK) in the economically important Malus domestica species. These cytokinin receptors named MdCHK2, MdCHK3a/MdCHK3b and MdCHK4a/MdCHK4b by homology with Arabidopsis AHK clearly displayed three distinct profiles. The three groups exhibited architectural variations, especially in the N-terminal part including the cytokinin sensing domain. Using a yeast complementation assay, we showed that MdCHK2 perceives a broad spectrum of cytokinins with a substantial sensitivity whereas both MdCHK4 homologs exhibit a narrow spectrum. Both MdCHK3 homologs perceived some cytokinins but surprisingly they exhibited a basal constitutive activity. Interaction studies revealed that MdCHK2, MdCHK4a and MdCHK4b homodimerized whereas MdCHK3a and MdCHK3b did not. Finally, qPCR analysis and bioinformatics approach pointed out contrasted expression patterns among the three MdCHK groups as well as distinct sets of co-expressed genes. Our study characterized for the first time the five cytokinin receptors in apple tree and provided a framework for their further functional studies.

Cluster headache is a severe primary headache characterized by extremely painful attacks of unilateral headache. Verapamil is commonly used as a prophylactic treatment with good effect. In order to search for new pathways involved in the pathophysiology of cluster headache, we analyzed genetic variants that were previously linked to verapamil response in migraine in a Swedish cluster headache case-control sample. We used TaqMan qPCR for genetic screening and performed a gene expression analysis on associated genes in patient-derived fibroblasts, and further investigated which reference genes were suitable for analysis in fibroblasts from cluster headache patients. We discovered a significant association between anoctamin 3, a gene encoding a calcium-activated ion channel, and cluster headache. The association was not dependent on verapamil treatment since the associated variant, rs1531394, was also overrepresented in patients not using verapamil. No difference was found in the anoctamin 3 gene expression between controls and patients. Also, we determined that TBP, IPO8 and PDHB were suitable reference genes in cluster headache fibroblasts. This finding is the first report of an association between a variant in a gene encoding an ion-channel and cluster headache, and the first significant genetic evidence of calcium involvement in cluster headache pathophysiology.