Global transcriptomic approaches are a common tool used to obtain a better understanding of gene function and regulation. The composition of the transcriptome is the result of a dynamic balance between chromatin state, the activation of gene expression by transcription factors (TFs) and the speed of transcript degradation. The combination of good genomic information and robust gene models paves the way for systematic and consistent gene and gene family naming. This combined with other genomics tools, such as Electronic Fluorescent Pictograph (eFP) browsers to help visualise where a gene is expressed, allows faster identification of gene function in different species. To date, eFP browsers have been successfully developed in plants such as Arabidopsis, tomato, strawberry, and pineapple.
TFs are one of the largest groups of genes in a genome; in Arabidopsis there are over 1500 TFs described, belonging to a number of different classes representing 5% of all genes. In other species TFs represent 3–5% of coding genes, with function often conserved across species. TFs have been grouped into 57 different classes with some classes having multiple types of DNA binding domains. Each class of TF is represented by a gene family. These gene families vary in size from species to species depending on events such as individual gene and genome duplications, leading to expansions of certain or most families. In higher plants the MYB, bHLH and Zinc finger classes of TF contain many hundreds of members. There are numerous examples demonstrating the strong evolutionary maintenance of TF primary protein structure across species, with the homologous genes having a similar gene function. This allows researchers to predict function by homology.
The MADS-box containing TFs form arguably one of the best understood classes of TF. Members of the MADS-box gene family, including the well-known floral organ structure ABCE TFs, determine many aspects of plant development[8, 9]. Even though the fruiting bodies of Angiosperms are homoplasious, with fleshy fruit evolving numerous times within many plant families the function of these genes appear conserved. Angiosperm flower structure and fruiting bodies are remarkably conserved, with whorls of sepals, petals, stamens and carpels. The MADS protein sequence is also conserved with many examples within plants demonstrating similar control mechanisms across many species[7, 11].
Kiwifruit are part of the Actinidiaceae which is a basal family within Ericales, and contains the genus Actinidia comprising of a number of economically important fruit species such as Actinidia chinensis var. deliciosa (green kiwifruit), A. chinensis var. chinensis (gold and red kiwifruit) and A. arguta (hardy kiwifruit or kiwiberries). The green ‘Hayward’ kiwifruit is hexaploid, while a commercially released yellow fleshed variety A. chinensis var. chinensis, ‘Hort16A’, and the red fleshed A. chinensis var. chinensis ‘Hongyang’ are large fruiting diploid genotypes making them ideal for understanding molecular processes in Actinidiaceae. More recently a new Pseudomonas syringae pv. actinidiae (Psa) tolerant tetraploid gold variety, ‘Zesy002’, has replaced ‘Hort16A’ in the markets. The two diploid cultivars have been used to understand the molecular control of many aspects of development including flowering, fruit ripening, colour and flavour development[13–16]. Genomics tools such as CRISPR gene editing have been successfully used to edit the floral repressors in ‘Hort16A’ to create a small fruiting plant that can be used to rapidly test gene function in fruit, further building on their utility.
The first draft kiwifruit genome was of A. chinensis ‘Hongyang’, published in 2013, paving the way for genomics in Actinidiaceae. More recently a second A. chinensis genome of a more inbred related genotype, Red5, further improved the construction and importantly manual annotation of gene models. The manual annotation of the kiwifruit genome improved the quality of the published computer predicted gene models, and provided a quality resource for future gene mining. Here we build on these data by identifying TF genes, analysing their expression over a number of tissues and providing an eFP Browser tool to analyse gene expression.