Time-resolved fluorescence imaging reveals differential interactions of N-glycan processing enzymes across the Golgi stack in planta

N-glycan processing is one of the most important cellular protein modifications in plants and as such is essential for plant development and defense mechanisms. The accuracy of Golgi-located processing steps is governed by the strict intra-Golgi localization of sequentially acting glycosidases and glycosyltransferases. Their differential distribution goes hand in hand with the compartmentalization of the Golgi stack into cis-, medial and trans-cisternae, which separate early from late processing steps. The mechanisms that direct differential enzyme concentration are still unknown, but formation of multi-enzyme complexes is considered a feasible Golgi protein localization strategy. In this study we used two-photon (2P)-excitation Förster resonance energy transfer (FRET)-fluorescence lifetime imaging microscopy (FLIM) to determine the interaction of N-glycan processing enzymes with differential intra-Golgi locations. Following the coexpression of fluorescent protein-tagged N-terminal Golgi targeting sequences (cytoplasmic-transmembrane-stem region, designated CTS) of enzyme pairs in leaves of tobacco (Nicotiana tabacum or Nicotiana benthamiana), we observed that all tested cis- and medial-Golgi enzymes, namely MNS1, GnTI, GMII and XylT, form homo- and heterodimers, whereas among the late-acting enzymes GALT1, FUT13 and ST (a non-plant Golgi marker) only GALT1 and GMII were found to form a heterodimer. Furthermore, the efficiency of energy transfer indicating the formation of interactions decreased considerably in a cis-to-trans fashion. The comparative 2P-FRET-FLIM analysis of several full-length cis- and medial-Golgi enzymes and their respective catalytic domain-deleted CTS clones further suggested that the formation of protein-protein interactions can occur through their N-terminal CTS region

The transmembrane domain of N-acetylglucosaminyltransferase I is the key determinant for its Golgi subcompartmentation

Golgi-resident type–II membrane proteins are asymmetrically distributed across the Golgi stack. The intrinsic features of the protein that determine its subcompartment-specific concentration are still largely unknown. Here, we used a series of chimeric proteins to investigate the contribution of the cytoplasmic, transmembrane and stem region of Nicotiana benthamiana N–acetylglucosaminyltransferase I (GnTI) for its cis/medial-Golgi localization and for protein–protein interaction in the Golgi. The individual GnTI protein domains were replaced with those from the well-known trans-Golgi enzyme α2,6–sialyltransferase (ST) and transiently expressed in Nicotiana benthamiana. Using co-localization analysis and N–glycan profiling, we show that the transmembrane domain of GnTI is the major determinant for its cis/medial-Golgi localization. By contrast, the stem region of GnTI contributes predominately to homomeric and heteromeric protein complex formation. Importantly, in transgenic Arabidopsis thaliana, a chimeric GnTI variant with altered sub-Golgi localization was not able to complement the GnTI-dependent glycosylation defect. Our results suggest that sequence-specific features in the transmembrane domain of GnTI account for its steady-state distribution in the cis/medial-Golgi in plants, which is a prerequisite for efficient N–glycan processing in vivo

Unraveling the function of Arabidopsis thaliana OS9 in the endoplasmic reticulum-associated degradation of glycoproteins

In the endoplasmic reticulum, immature polypeptides coincide with terminally misfolded proteins. Consequently, cells need a well-balanced quality control system, which decides about the fate of individual proteins and maintains protein homeostasis. Misfolded and unassembled proteins are sent for destruction via the endoplasmic reticulum-associated degradation (ERAD) machinery to prevent the accumulation of potentially toxic protein aggregates. Here, we report the identification of Arabidopsis thaliana OS9 as a component of the plant ERAD pathway. OS9 is an ER-resident glycoprotein containing a mannose-6-phosphate receptor homology domain, which is also found in yeast and mammalian lectins involved in ERAD. OS9 fused to the C-terminal domain of YOS9 can complement the ERAD defect of the corresponding yeast Δyos9 mutant. An A. thaliana OS9 loss-of-function line suppresses the severe growth phenotype of the bri1-5 and bri1-9 mutant plants, which harbour mutated forms of the brassinosteroid receptor BRI1. Co-immunoprecipitation studies demonstrated that OS9 associates with Arabidopsis SEL1L/HRD3, which is part of the plant ERAD complex and with the ERAD substrates BRI1-5 and BRI1-9, but only the binding to BRI1-5 occurs in a glycan-dependent way. OS9-deficiency results in activation of the unfolded protein response and reduces salt tolerance, highlighting the role of OS9 during ER stress. We propose that OS9 is a component of the plant ERAD machinery and may act specifically in the glycoprotein degradation pathway

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines