Exploring large-scale gene coexpression networks in peach (Prunus persica L.): a new tool for predicting gene function

Abstract

Peach is a model for Prunus genetics and genomics, however, identifying and validating genes associated to peach breeding traits is a complex task. A gene coexpression network (GCN) capable of capturing stable gene-gene relationships would help researchers overcome the intrinsic limitations of peach genetics and genomics approaches and outline future research opportunities. In this study, we created four GCNs from 604 Illumina RNA-Seq libraries. We evaluated the performance of every GCN in predicting functional annotations using an algorithm based on the ‘guilty-by-association’ principle. The GCN with the best performance was COO300, encompassing 21 956 genes. To validate its performance predicting gene function, we performed two case studies. In case study 1, we used two genes involved in fruit flesh softening: the endopolygalacturonases PpPG21 and PpPG22. Genes coexpressing with both genes were extracted and referred to as melting flesh (MF) network. Finally, we performed an enrichment analysis of MF network and compared the results with the current knowledge regarding peach fruit softening. The MF network mostly included genes involved in cell wall expansion and remodeling, and with expressions triggered by ripening-related phytohormones, such as ethylene, auxin, and methyl jasmonate. In case study 2, we explored potential targets of the anthocyanin regulator PpMYB10.1 by comparing its gene-centered coexpression network with that of its grapevine orthologues, identifying a common regulatory network. These results validated COO300 as a powerful tool for peach and Prunus research. This network, renamed as PeachGCN v1.0, and the scripts required to perform a function prediction analysis are available at https://github.com/felipecobos/PeachGCN.

Acknowledgements

We acknowledge financial support through the Severo Ochoa Programme for Centers of Excellence in R&D (SEV-2015-0533 and CEX2019-000902-S). Also, this work was funded by the Spanish Ministry of Science and Innovation through the Estate Agency of Research: Project PID2020-118612RR-I00 (Better Almonds) and PID2019-110599RR-I00. Authors F.P. C., I. E., I. B. are grateful to CERCA Program from Generalitat of Catalonia for its support. F. P. C. wishes to acknowledge the receipt of a FPI doctoral fellowship from the Spanish Ministry of Science and Innovation. This work was also supported by grants PID2021-128865NB-I00 and RYC-2017-23645 awarded to J.T.M. and the PRE2019-088044 fellowship awarded to L.O. from the Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain), Agencia Estatal de Investigación (AEI, Spain), and Fondo Europeo de Desarrollo Regional (FEDER, European Union).

Author Contributions

F.P.C. participated in the conceptualization, analysis, and writing the original draft. B.G.G. participated in the analysis and writing the original draft, reviewing, and editing. L.O.R. participated in the analysis, reviewing, and editing. I.B. participated in the supervision, reviewing, editing, project management, and seeking funding. P.A. participated in the supervision, reviewing, editing, and project management. J.T.M. participated in the conceptualization, supervision, reviewing, and editing. I.E. participated in the conceptualization, supervision, reviewing, editing, project management, and seeking funding.

Data Availability

The PeachGCN v1.0, and the scripts necessary to run a function prediction analysis using it, are available at https://github.com/felipecobos/PeachGCN.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

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