Generation of fruit postharvest gene datasets and a novel motif analysis tool for functional studies: uncovering links between peach fruit heat treatment and cold storage responses

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Main Conclusion

A survey of developed fruit gene-specific datasets and the implementation of a novel cis-element analysis tool indicate specific transcription factors as novel regulatory actors under HT response and CI protection.


Heat treatment (HT) prior to cold storage (CS) has been successfully applied to ameliorate fruit chilling injury (CI) disorders. Molecular studies have identified several HT-driven benefits and putative CI-protective molecules and mechanisms. However, bioinformatic tools and analyses able to integrate fruit-specific information are necessary to begin functional studies and breeding projects. In this work, a HT-responsive gene dataset (HTds) and four fruit expression datasets (FEds), containing gene-specific information from several species and postharvest conditions, were developed and characterized. FEds provided information about HT-responsive genes, not only validating their sensitivity to HT in different systems but also revealing most of them as CS-responsive. A special focus was given to peach heat treatment-sensitive transcriptional regulation by the development of a novel Perl motif analysis software (cisAnalyzer) and a curated plant cis-elements dataset (PASPds). cisAnalyzer is able to assess sequence motifs presence, localization, enrichment and discovery on biological sequences. Its implementation for the enrichment analysis of PASPds motifs on the promoters of HTds genes rendered particular cis-elements that indicate certain transcription factor (TF) families as responsible of fruit HT-sensitive transcription regulation. Phylogenetic and postharvest expression data of these TFs showed a functional diversity of TF families, with members able to fulfil roles under HT, CS and/or both treatments. All integrated datasets and cisAnalyzer tool were deposited in FruitGeneDB (, a new available database with a great potential for fruit gene functional studies, including the markers of HT and CS responses whose study will contribute to unravel HT-driven CI-protection and select tolerant cultivars.

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Chilling injury


Cold storage


Fruit expression datasets


Heat treatment


Heat treatment-responsive gene dataset


Transcription factor


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LDD, MFD, MVL, and CAB are members of the Researcher Career of CONICET. LLM, CM and MG are fellows of the same institution. Financial support was provided by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) and Agencia Nacional de Promoción de Actividades Científicas y Técnicas (ANPCyT, Argentina). The authors thank Dr. Luis Esteban for his suggestions during early stages of cisAnalyzer design.

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Correspondence to Mauro Gismondi or Claudia A. Bustamante.

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Supplementary Fig. S1 Overview of the main biological processes that contain HT-induced or repressed determinants. A. thaliana orthologues of HT-induced (white) or repressed (black) genes were used. MapMan pathway annotations (A) as well as biological process, molecular function and cellular component GO terms entities (B) are depicted in y-axis while the number of genes is shown as scale bars in x-axis. Genes binned to each pathway or term are available in FeaturesIII (Supplementary Table S8). AgriGO web server allowed performing a Singular Enrichment Analysis (SEA) against A. thaliana genome frequencies, and enriched GO terms are indicated with asterisks (FDR ≤ 0.05). TCA, TriCarboxylic Acid; CHO, Carbohydrates (TIF 37090 kb)

Supplementary Fig. S2 Description of the current knowledge about HTds HT-responsive genes and orthologues. Showed information was extracted from FEds datasets. ds, dataset; HT, Heat Treatment (TIF 4047 kb)

Supplementary Fig. S3 Overview of main steps for cisAnalyzer implementation. Supplementary file contains the especial directories structure that allows cisAnalyzer functioning. execution in a unix/linux terminal allows the user to browse and choose among the different program capabilities (Supplementary Table S12) and to input targets obtained from Phytozome BioMart tool and custom motif files (when needed). Next, the Perl program controls’ sequences and motifs quality, reporting any inconvenience. Once ending, execution comprises each motif search on each target of the set, processing subroutines and outputs generation at /MyOuputFiles. Results are reported in classical motifs and target tables but a following exhaustive sub-processing of these data renders tabular and graphical non-classical outputs (TIF 14436 kb)

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Gismondi, M., Daurelio, L.D., Maiorano, C. et al. Generation of fruit postharvest gene datasets and a novel motif analysis tool for functional studies: uncovering links between peach fruit heat treatment and cold storage responses. Planta 251, 53 (2020) doi:10.1007/s00425-020-03340-2

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  • Chilling injury
  • cisAnalyzer
  • Database
  • Dataset
  • Heat treatment
  • Prunus persica
  • Transcriptional regulation