An inhibitor of apoptosis (SfIAP) interacts with SQUAMOSA promoter binding protein (SBP) transcription factors that exhibit pro-cell death characteristics

Abstract

Highlights: SBP transcription factors SlySBP8b and SlySBP12a from tomato interact with an insect inhibitor of apoptosis protein (SfIAP). Both exhibit pro-cell death characteristics while SlySBP12a activity may be regulated through ER membrane tethering. AbstractDespite the functional conservation of programmed cell death (PCD) across broad evolutionary distances, an understanding of the molecular machinery underpinning this fundamental program in plants remains largely elusive. This is despite its critical importance to development, homeostasis, and proper responses to stress. Progress in plant PCD has been hindered by the fact that many core regulators of animal PCD are absent in plant genomes. Remarkably, numerous studies have shown that the ectopic expression of animal anti-PCD genes in plants can suppress cell death imposed by many stresses. In this study, we capitalize on the ectopic expression of an insect inhibitor of apoptosis (SfIAP) to identify novel cell death regulators in plants. A yeast two-hybrid assay was conducted using SfIAP as bait to screen a tomato cDNA library. This screen identified several transcription factors of the SQUAMOSA promoter binding protein (SBP) family as potential SfIAP binding partners. We confirmed this interaction in vivo for our top two interactors, SlySBP8b and SlySBP12a, using coimmunoprecipitation. Interestingly, overexpression of SlySBP8b and SlySBP12a induced spontaneous cell death in Nicotiana benthamiana leaves. Overexpression of these two transcription factors also induced the accumulation of reactive oxygen species and enhanced the growth of the necrotrophic pathogen Alternaria alternata. Fluorescence microscopy confirmed the nuclear localization of both SlySBP8b and SlySBP12a, while SlySBP12a was also localized to the ER membrane. These results support a pro-death role for SlySBP8b and SlySBP12a and provide potential targets that can be utilized to improve stress tolerance in crop plants.3

Modeling of gene and protein networks in the context of plant immunity

“…Multiple experimental variations in Y2H-NGIS have been presented as alternatives to study interactomes from a wide range of model and non-model organisms (Hastie and Pruitt, 2007;Lewis et al, 2012;Schlecht et al, 2017;Erffelinck et al, 2018;Kessens et al, 2018; F., J., S., Yang et al, 2018;Zong et al, 2020). Depending on the availability of prior information, an open reading frame (ORF) or cDNA libraries can be used to perform the screening .…”

“…Hence, there is a need for robust and consistent statistical models that can be implemented with datasets coming from different Y2H-NGIS settings and that can make use of all the available sequence information to recognize true protein-protein interactions. 1) Most current studies focus on experimental optimization rather than analytical development, and do not offer software to statistically analyze Y2H-NGIS datasets (Lewis et al, 2012;Zong et al, 2020;Erffelinck et al, 2018;Kessens et al, 2018;Schlecht et al, 2017;Hastie and Pruitt, 2007;. Currently available pipelines map and quantify total reads while ignoring prey-fusion reads (J., S., , or map fusion reads without quantifying their biological significance to identify interactions .…”

“…ORF libraries do not have this issue because they are assumed to be all in frame with the prey plasmid; however, ORF prey libraries are only available for few model organisms . 2) There is no consensus regarding what negative control(s) are more appropriate to signify the background interactivity of the preys and to help to distinguish true interactions (Hastie and Pruitt, 2007;Lewis et al, 2012;Schlecht et al, 2017;Erffelinck et al, 2018;Kessens et al, 2018;J., S., Yang et al, 2018;Zong et al, 2020). 3) Despite its importance, most existing studies do not assess data normalization, or implement inappropriate normalization methods for Y2H-NGIS data Erffelinck et al, 2018;Zong et al, 2020;Lewis et al, 2012;Kessens et al, 2018;Schlecht et al, 2017;Hastie and Pruitt, 2007).…”

“…2) There is no consensus regarding what negative control(s) are more appropriate to signify the background interactivity of the preys and to help to distinguish true interactions (Hastie and Pruitt, 2007;Lewis et al, 2012;Schlecht et al, 2017;Erffelinck et al, 2018;Kessens et al, 2018;J., S., Yang et al, 2018;Zong et al, 2020). 3) Despite its importance, most existing studies do not assess data normalization, or implement inappropriate normalization methods for Y2H-NGIS data Erffelinck et al, 2018;Zong et al, 2020;Lewis et al, 2012;Kessens et al, 2018;Schlecht et al, 2017;Hastie and Pruitt, 2007). High-throughput sequencing datasets, where read counts quantify signal strength, e.g., RNA-Seq, require normalization, as there are external factors, aside from experimental treatments, that influence read counts (Evans et al, 2018).…”

“…Most if not all prey will therefore be DE in the selected vs. non-selected condition. Finally, 4) with no consensus on the appropriate data analysis, nor even how to report the results, whether ratios of counts, log fold-change from DE analysis, or a custom score function, it is nearly impossible to compare Y2H-NGIS studies (Hastie and Pruitt, 2007;Lewis et al, 2012;Schlecht et al, 2017;Erffelinck et al, 2018;Kessens et al, 2018;J., S., Yang et al, 2018;Zong et al, 2020). Consequently, a unified software to rank the candidate interactors from Y2H-NGIS data should address a diverse range of experimental settings.…”