NRPs (N-rich proteins) were identified as targets of a novel adaptive pathway that integrates endoplasmic reticulum (ER)and osmotic stress signals based on coordinate regulation and synergistic up-regulation by tunicamycin and polyethylene glycol treatments. This integrated pathway diverges from the molecular chaperone-inducing branch of the unfolded protein response (UPR) in several ways. While UPR-specific targets were inversely regulated by ER and osmotic stresses, NRPs required both signals for full activation. Furthermore, BiP (binding protein) overexpression in soybean prevented activation of the UPR by ER stress inducers, but did not affect activation of NRPs. We also found that this integrated pathway transduces a PCD signal generated by ER and osmotic stresses that result in the appearance of markers associated with leaf senescence. Overexpression of NRPs in soybean protoplasts induced caspase-3-like activity and promoted extensive DNA fragmentation. Furthermore, transient expression of NRPs in planta caused leaf yellowing, chlorophyll loss, malondialdehyde production, ethylene evolution, and induction of the senescence marker gene CP1. This phenotype was alleviated by the cytokinin zeatin, a potent senescence inhibitor. Collectively, these results indicate that ER stress induces leaf senescence through activation of plant-specific NRPs via a novel branch of the ER stress response.

Background -: Sucrose content is a highly desirable trait in sugarcane as the worldwide demand for cost-effective biofuels surges. Sugarcane cultivars differ in their capacity to accumulate sucrose and breeding programs routinely perform crosses to identify genotypes able to produce more sucrose. Sucrose content in the mature internodes reach around 20% of the culms dry weight. Genotypes in the populations reflect their genetic program and may display contrasting growth, development, and physiology, all of which affect carbohydrate metabolism. Few studies have profiled gene expression related to sugarcane's sugar content. The identification of signal transduction components and transcription factors that might regulate sugar accumulation is highly desirable if we are to improve this characteristic of sugarcane plants.

Background: Despite the potential of the endoplasmic reticulum (ER) stress response to accommodate adaptive pathways, its integration with other environmental-induced responses is poorly understood in plants. We have previously demonstrated that the ER-stress sensor binding protein (BiP) from soybean exhibits an unusual response to drought. The members of the soybean BiP gene family are differentially regulated by osmotic stress and soybean BiP confers tolerance to drought. While these results may reflect crosstalk between the osmotic and ER-stress signaling pathways, the lack of mutants, transcriptional response profiles to stresses and genome sequence information of this relevant crop has limited our attempts to identify integrated networks between osmotic and ER stress-induced adaptive responses. As a fundamental step towards this goal, we performed global expression profiling on soybean leaves exposed to polyethylene glycol treatment (osmotic stress) or to ER stress inducers.

The molecular chaperone binding protein (BiP) participates in the constitutive function of the endoplasmic reticulum (ER) and protects the cell against stresses. In this study, we investigated the underlying mechanism by which BiP protects plant cells from stress-induced cell death. We found that enhanced expression of BiP in soybean (Glycine max) attenuated ER stress-and osmotic stress-mediated cell death. Ectopic expression of BiP in transgenic lines attenuated the leaf necrotic lesions that are caused by the ER stress inducer tunicamycin and also maintained shoot turgidity upon polyethylene glycol-induced dehydration. BiP-mediated attenuation of stress-induced cell death was confirmed by the decreased percentage of dead cell, the reduced induction of the senescence-associated marker gene GmCystP, and reduced DNA fragmentation in BiPoverexpressing lines. These phenotypes were accompanied by a delay in the induction of the cell death marker genes N-RICH PROTEIN-A (NRP-A), NRP-B, and GmNAC6, which are involved in transducing a cell death signal generated by ER stress and osmotic stress through the NRP-mediated signaling pathway. The prosurvival effect of BiP was associated with modulation of the ER stress-and osmotic stress-induced NRP-mediated cell death signaling, as determined in transgenic tobacco (Nicotiana tabacum) lines with enhanced (sense) and suppressed (antisense) BiP levels. Enhanced expression of BiP prevented NRP-and NAC6-mediated chlorosis and the appearance of senescence-associated markers, whereas silencing of endogenous BiP accelerated the onset of leaf senescence mediated by NRPs and GmNAC6. Collectively, these results implicate BiP as a negative regulator of the stress-induced NRP-mediated cell death response.

Spatial variation is a recurrent issue in field trials and can cause obstacles in terms of genetic selection. Analyses that account for spatial variation within location can lead breeders to predict genetic values accurately across locations in multi-environment trials (METs). The present study aims to fit spatial models for analyzing soybean [Glycine max (L.) Merr.] seed composition traits using a two-stage analysis pipeline and to assess its efficiency relative to a single-stage analysis setting. Seed protein content (SPC), seed oil content (SOC), and seed storage protein content (SSP) data were collected from 283 soybean genotypes tested in four environments (C1, C2, V1, and V2). In Stage 1 of the two-stage analysis, a randomized complete block (RCB) design model as well as four two-dimensional first-order (AR1 ⊗ AR1) spatial models were fit in each dataset to determine the most suitable model for genetic prediction. Predicted genetic values were used as input data for Stage 2. The most used spatial model [5] in Stage 1 of this study had accommodated local and global residuals. The autocorrelation estimates depicted spatial trends, especially in terms of rows, while column autocorrelation coefficients were low for C1 and C2 because of the limited number of blocks and their short length. Broad-sense heritability, mean accuracy, and selection gains were greater for all traits in the two-stage analysis than in the single-stage analysis. The two-stage analysis leveraged the spatial model fitting in the Stage 1 and proved to be advantageous for soybean seed composition breeding.