Growth-regulating factor 15-mediated gene regulatory network enhances salt tolerance in poplar

Abstract

Soil salinity is an important determinant of crop productivity and triggers salt stress response pathways in plants. The salt stress response is controlled by transcriptional regulatory networks that maintain regulatory homeostasis through combinations of transcription factor (TF)–DNA and TF–TF interactions. We investigated the transcriptome of poplar 84 K (Populus alba × Populus glandulosa) under salt stress using samples collected at 4 or 6 h intervals within 2 days of salt stress treatment. We detected 24,973 differentially expressed genes, including 2,231 TFs that might be responsive to salt stress. To explore these interactions and targets of TFs in perennial woody plants, we combined gene regulatory network, DNA affinity purification sequencing (DAP-seq), yeast two-hybrid-sequencing (Y2H-seq), and multi-gene association approaches. Growth-regulating factor 15 (PagGRF15) and its target, high-affinity K+ transporter 6 (PagHAK6), were identified as an important regulatory module in the salt stress response. Overexpression of PagGRF15 and PagHAK6 in transgenic lines improved salt tolerance by enhancing Na+ transport and modulating H2O2 accumulation in poplar. Yeast two-hybrid (Y2H) assays identified more than 420 PagGRF15-interacting proteins, including ETHYLENE RESPONSE FACTOR (ERF) TFs and a zinc finger protein (C2H2) that are produced in response to a variety of phytohormones and environmental signals and are likely involved in abiotic stress. Therefore, our findings demonstrate that PagGRF15 is a multifunctional TF involved in growth, development and salt stress tolerance, highlighting the capability of a multifaceted approach in identifying regulatory nodes in plants.

Conserved hierarchical gene regulatory networks for drought and cold stress response in Myrica rubra

“…It is of great importance to enhance the comprehensive resistance of plants by means of core functional genes in molecular breeding. (Hu et al, 2017;Xu et al, 2023). In this study, we constructed a hightemporal-resolution dynamic transcriptome landscape of drought and cold stress responses using nine time points.…”

“…Construction of a multi-layered hierarchical gene regulatory network using the BWERF algorithm Using PlantPAN v.2.0, it was determined that cis-regulatory elements were found in the 2 kb promoter region of the candidate genes (Chow et al, 2016;Xu et al, 2022). It was predicted that these motifs are TF target sites based on 80% confidence values.…”

“…It was predicted that these motifs are TF target sites based on 80% confidence values. Then, a backward elimination random forest (BWERF) algorithm is employed to construct ML-hGRNs, which uses the genes and TFs encoding transport, photosynthesis and oxidoreductases (Deng et al, 2017;Xu et al, 2022).…”

“…The statistical significance of treatment differences was assessed with either one way or twoway analysis of variance, with SPSS 17.0 (IBM, Chicago, IL, USA) and Excel 2013 (Microsoft Corp., Redmond, WA) on the basis of prior research (Xu et al, 2022). For the calculation of P values, the Students't test (* P < 0.05, * * P < 0.01) was adopted.…”

“…For the calculation of P values, the Students't test (* P < 0.05, * * P < 0.01) was adopted. After normalization, all samples had a normal distribution with respect to variance homogeneity (Xu et al, 2022).…”