Genome-wide RNAi screening identifies human proteins with a regulatory function in the early secretory pathway
School of Biology and Environmental Science & Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland. Nature Cell Biology
(Impact Factor: 19.68).
06/2012; 14(7):764-74. DOI: 10.1038/ncb2510
The secretory pathway in mammalian cells has evolved to facilitate the transfer of cargo molecules to internal and cell surface membranes. Use of automated microscopy-based genome-wide RNA interference screens in cultured human cells allowed us to identify 554 proteins influencing secretion. Cloning, fluorescent-tagging and subcellular localization analysis of 179 of these proteins revealed that more than two-thirds localize to either the cytoplasm or membranes of the secretory and endocytic pathways. The depletion of 143 of them resulted in perturbations in the organization of the COPII and/or COPI vesicular coat complexes of the early secretory pathway, or the morphology of the Golgi complex. Network analyses revealed a so far unappreciated link between early secretory pathway function, small GTP-binding protein regulation, actin cytoskeleton organization and EGF-receptor-mediated signalling. This work provides an important resource for an integrative understanding of global cellular organization and regulation of the secretory pathway in mammalian cells.
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Available from: Brian Storrie
- "The importance of Rab1 and Rab11 were additionally implicated in secretion of soluble cargo proteins, but no other Rab proteins were identified as central to these processes (Bard et al., 2006). Similarly, a genome wide RNAi screen with human HeLa cells revealed a single Rab, Rab18, as being significantly important within the early secretory pathway (Simpson et al., 2012). Rab-centric screens based on reductions in GTPase activity achieved through the expression of mutant GAPs, Rab-specific guanine nucleotide activating proteins (Haas et al., 2005, 2007), or overexpression of wild-type or engineered Rab mutations (Dejgaard et al., 2008) have implicated 3 of ~70 mammalian Rab proteins required for Golgi ribbon organization: Rab1, Rab18, and Rab43 (mis-identified as Rab41 in Haas et al., 2005). "
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ABSTRACT: Rab proteins, small GTPases, are key regulators of mammalian Golgi apparatus organization. Based on the effect of Rab activation state, Rab proteins fall into two functional classes. In Class1, inactivation induces Golgi ribbon fragmentation and/or redistribution of Golgi enzymes to the Endoplasmic Reticulum, while overexpression of wild type or activation has little, if any, effect on Golgi ribbon organization. In Class 2, the reverse is true. We give emphasis to Rab6, the most abundant Golgi-associated Rab protein. Rab6 depletion in HeLa cells causes an increase in Golgi cisternal number, longer, more continuous cisternae, and a pronounced accumulation of vesicles; the effect of Rab6 on Golgi ribbon organization is probably through regulation of vesicle transport. In effector studies, motor proteins and their regulators are found to be key Rab6 effectors. A related Rab, Rab41, affects Golgi ribbon organization in a contrasting manner. The balance between minus- and plus-end directed motor recruitment may well be the major Rab-dependent factor in Golgi ribbon organization.
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International review of cell and molecular biology 02/2015; 315:1-22. DOI:10.1016/bs.ircmb.2014.12.002 · 3.42 Impact Factor
Available from: Keith Ireton
- "A possible explanation for the apparent discrepancy between the bacterial spreading and host secretion data is that expression of EGFP-InlC or depletion of Sec31A might cause minor impairments in secretion that are difficult to detect in SEAP assays. Indeed, recent reports indicate that RNAi-mediated depletion of Sec31A, Sec13 or other COPII components cause little or no measurable inhibition in secretion of cargo thought to be trafficked through conventional-sized COPII vesicles (Townley et al., 2008; Simpson et al., 2012; Cutrona et al., 2013). This is in contrast to the strong impairment of trafficking of SEAP or other conventional cargo by mutant alleles of Sar1 (Kuge et al., 1994; Kagan et al., 2004) or BFA (de Silva et al., 1990; Irurzun et al., 1993; Kim et al., 2007; Clements et al., 2011). "
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ABSTRACT: Listeria monocytogenes is a food-borne pathogen that uses actin-dependent motility to spread between human cells. Cell-to-cell spread involves the formation by motile bacteria of plasma membrane-derived structures termed 'protrusions'. In cultured enterocytes, the secreted Listeria protein InlC promotes protrusion formation by binding and inhibiting the human scaffolding protein Tuba. Here we demonstrate that protrusions are controlled by human COPII components that direct trafficking from the endoplasmic reticulum. Co-precipitation experiments indicated that the COPII proteins Sec31A and Sec13 interact directly with a Src Homology 3 domain in Tuba. This interaction was antagonized by InlC. Depletion of Sec31A or Sec13 restored normal protrusion formation to a Listeria mutant lacking inlC, without affecting spread of wild-type bacteria. Genetic impairment of the COPII component Sar1 or treatment of cells with brefeldin A affected protrusions similarly to Sec31A or Sec13 depletion. These findings indicated that InlC relieves a host-mediated restriction of Listeria spread otherwise imposed by COPII. Inhibition of Sec31A, Sec13, or Sar1 or brefeldin A treatment also perturbed the structure of cell-cell junctions. Collectively, these findings demonstrate an important role for COPII in controlling Listeria spread. We propose that COPII may act by delivering host proteins that generate tension at cell junctions.
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Cellular Microbiology 12/2014; 17(6). DOI:10.1111/cmi.12409 · 4.92 Impact Factor
Available from: Douglas Lowrie
- "In such a view, it would be of greater biological relevance if " omics " data were trained in the context of proteinprotein interactions   . As a precedent, candidate genes identified from a RNAi functional screen for host genes important for regulating M. tb survival in macrophages have been analyzed in the context of protein-protein interaction data. "
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ABSTRACT: Network analysis of transcriptional signature typically relies on direct interaction between two highly expressed genes. However, this approach misses indirect and biological relevant interactions through a third factor (hub). Here we determine whether a hub-based network analysis can select an improved signature subset that correlates with a biological change in a stronger manner than the original signature. We have previously reported an interferon-related transcriptional signature (THP1r2Mtb-induced) from Mycobacterium tuberculosis (M. tb)-infected THP-1 human macrophage. We selected hub-connected THP1r2Mtb-induced genes into the refined network signature TMtb-iNet and grouped the excluded genes into the excluded signature TMtb-iEx. TMtb-iNet retained the enrichment of binding sites of interferon-related transcription factors and contained relatively more interferon-related interacting genes when compared to THP1r2Mtb-induced signature. TMtb-iNet correlated as strongly as THP1r2Mtb-induced signature on a public transcriptional dataset of patients with pulmonary tuberculosis (PTB). TMtb-iNet correlated more strongly in CD4(+) and CD8(+) T cells from PTB patients than THP1r2Mtb-induced signature and TMtb-iEx. When TMtb-iNet was applied to data during clinical therapy of tuberculosis, it resulted in the most pronounced response and the weakest correlation. Correlation on dataset from patients with AIDS or malaria was stronger for TMtb-iNet, indicating an involvement of TMtb-iNet in these chronic human infections. Collectively, the significance of this work is twofold: (1) we disseminate a hub-based approach in generating a biologically meaningful and clinically useful signature; (2) using this approach we introduce a new network-based signature and demonstrate its promising applications in understanding host responses to infections.
BioMed Research International 08/2014; 2014:713071. DOI:10.1155/2014/713071 · 2.71 Impact Factor
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