Golgi and Related Vesicle Proteomics: Simplify to Identify

Gannon, Joan; Bergeron, John J.M.; Nilsson, Tommy

doi:10.1101/cshperspect.a005421

Cited by 16 publications

(13 citation statements)

References 63 publications

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“…5C). This directly supports the maturation model for Golgi transport as argued elsewhere Bergeron et al 2010;Nakano and Luini 2010;Gannon et al 2011). An important note here is the combination of enriched Golgi membranes and the generation of vesicles using such membranes in vitro.…”

Section: Protein Classification and Lessons Learned In The Golgi Appasupporting

confidence: 68%

Cell Biology of the Endoplasmic Reticulum and the Golgi Apparatus through Proteomics

Smirle

Jain

et al. 2013

Cold Spring Harbor Perspectives in Biology

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Section: Protein Classification and Lessons Learned In The Golgi Appasupporting

confidence: 68%

Cell Biology of the Endoplasmic Reticulum and the Golgi Apparatus through Proteomics

Smirle

Jain

et al. 2013

Cold Spring Harbor Perspectives in Biology

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“…Despite important advances in the field 27 , current strategies to engineer the secretory 236 pathway have remained predominantly empirical 28,29 . Recent modeling approaches, however, have 237 enabled the analysis of the metabolic capabilities of important eukaryotic cells under different genetic 238 and environmental conditions 17,[30][31][32] . With the development of genome-scale models of protein-producing 239 cells, such as CHO 17 , HEK-293 33 and hybridomas 34,35 , it is now possible to gain a systems-level 240 understanding of the mammalian protein production phenotype 36 .…”

Section: Supplementary Data 3) 171mentioning

confidence: 99%

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

Gutierrez

Feizi

et al. 2018

Preprint

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35Keywords 36 Metabolic network, secretory pathway, biotherapeutic production, systems biotechnology 37Indeed, recent studies have incorporated portions of the secretory pathway in metabolic models of yeast 55 3-5 . Furthermore, Lund and colleagues reconstructed a genetic interaction network of the mouse secretory 56 pathway and the unfolded protein response and analyzed it in the context of CHO cells 6 . However, such a 57 network does not encompass a stoichiometric reconstruction of the biochemical reactions involved in the 58 secretory pathway nor it is coupled to existing metabolic networks of mammalian cells. 59Here we present the first genome-scale stoichiometric reconstructions and computational models of 60 mammalian metabolism coupled to protein secretion. Specifically, we constructed these for human, 61 mouse, and CHO cells, called RECON2.2s, iMM1685s, and iCHO2048s, respectively. We first derive an 62 expression for computing the energetic cost of synthesizing and secreting a product in terms of molecules 63 of ATP equivalents per protein molecule. We use this expression and analyze how the energetic burden 64 of protein secretion has led to an overall suppression of more expensive secreted host cell proteins in 65 mammalian cells. Given its dominant role in biotherapeutic production, we further focus on the 66 biosynthetic capabilities of CHO cells. We then demonstrate that product-specific secretory pathway 67 models can be built to estimate CHO cell growth rates given the specific productivity of the recombinant 68 3 product as a constraint. We identify the features of secreted proteins that have the highest impact on 69 protein cost and productivity rates. Finally, we use our model to identify proteins that compete for cell 70 resources, thereby presenting targets for cell engineering. Through this study we demonstrate that a 71 systems-view of the secretory pathway now enables the analysis of many biomolecular mechanisms 72 controlling the efficacy and cost of protein expression in mammalian cells. We envision our models as 73 valuable tools for the study of normal physiological processes and engineering cell bioprocesses in 74 biotechnology. All models and data used in this study are freely available at 75 https://github.com/LewisLabUCSD/MammalianSecretoryRecon. 76 77 RESULTS 78 A stoichiometric expression of protein secretion energetics 79In any cell, the secretory machinery is concurrently processing thousands of secreted and membrane 80 proteins, which all compete for secretory pathway resources and pose a metabolic burden. To quantify 81 this burden, we estimated the energetic cost of synthesizing and/or secreting 5,641 and 3,538 82 endogenous proteins in the CHO and human secretome and membrane proteome in terms of total 83 number of ATP equivalent molecules consumed (see Methods). These protein costs were compared to 84 the cost of five recombinant proteins commonly produced in CHO cells (Fig. 1a). To refine estimates, we 85 predicted signal peptides 7 , GPI anchor attachment signals 8 , an...

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“…We consider this unlikely, however, based on the collective experimental evidence. Immuno electron microscopy-based observations of anterograde cargo in COPI vesicles is controversial and more recent organelle proteomics readily identified Golgi resident proteins but no exocytic cargo in COPI vesicles (reviewed in [49]). Moreover, functional data in yeast have provided unequivocal evidence for a role of the SNARE in Golgi enzyme trafficking.…”

Section: Discussionmentioning

confidence: 99%

A Model for the Self-Organization of Vesicular Flux and Protein Distributions in the Golgi Apparatus

Ispolatov

Müsch

2013

PLoS Comput Biol

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The generation of two non-identical membrane compartments via exchange of vesicles is considered to require two types of vesicles specified by distinct cytosolic coats that selectively recruit cargo, and two membrane-bound SNARE pairs that specify fusion and differ in their affinities for each type of vesicles. The mammalian Golgi complex is composed of 6–8 non-identical cisternae that undergo gradual maturation and replacement yet features only two SNARE pairs. We present a model that explains how distinct composition of Golgi cisternae can be generated with two and even a single SNARE pair and one vesicle coat. A decay of active SNARE concentration in aging cisternae provides the seed for a cis trans SNARE gradient that generates the predominantly retrograde vesicle flux which further enhances the gradient. This flux in turn yields the observed inhomogeneous steady-state distribution of Golgi enzymes, which compete with each other and with the SNAREs for incorporation into transport vesicles. We show analytically that the steady state SNARE concentration decays exponentially with the cisterna number. Numerical solutions of rate equations reproduce the experimentally observed SNARE gradients, overlapping enzyme peaks in cis, medial and trans and the reported change in vesicle nature across the Golgi: Vesicles originating from younger cisternae mostly contain Golgi enzymes and SNAREs enriched in these cisternae and extensively recycle through the Endoplasmic Reticulum (ER), while the other subpopulation of vesicles contains Golgi proteins prevalent in older cisternae and hardly reaches the ER.

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Golgi and Related Vesicle Proteomics: Simplify to Identify

Cited by 16 publications

References 63 publications

Cell Biology of the Endoplasmic Reticulum and the Golgi Apparatus through Proteomics

Cell Biology of the Endoplasmic Reticulum and the Golgi Apparatus through Proteomics

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

A Model for the Self-Organization of Vesicular Flux and Protein Distributions in the Golgi Apparatus

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