A Protein Complex Network of Drosophila melanogaster

Guruharsha, K. G.; Rual, Jean François; Zhai, Bo; Mintseris, Julian; Vaidya, Pujita; Vaidya, Namita; Beekman, Chapman; Wong, Christina S.F.; Rhee, David Y.; Cenaj, Odise; McKillip, Emily; Shah, Saumini; Stapleton, Mark; Wan, Kenneth H.; Yu, Charles; Parsa, Bayan; Carlson, Joseph W.; Chen, Xiao; Kapadia, Bhaveen H.; VijayRaghavan, K.; Gygi, Steven P.; Celniker, S; Obar, Robert A.; Artavanis‐Tsakonas, Spyros

doi:10.1016/j.cell.2011.08.047

Cited by 623 publications

(653 citation statements)

References 86 publications

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“…To determine whether the genetic modifiers are interconnected through physical interactions, we placed them in the context of the recently generated Drosophila Protein Interaction Map (DPiM) (13). We first retrieved the set of proteins that copurify with Drosophila Smn in DPiM and asked whether any of the modifiers belong to this Smn subnetwork.…”

Section: Resultsmentioning

confidence: 99%

Genetic circuitry of Survival motor neuron , the gene underlying spinal muscular atrophy

Sen

Dimlich

Guruharsha

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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The clinical severity of the neurodegenerative disorder spinal muscular atrophy (SMA) is dependent on the levels of functional Survival Motor Neuron (SMN) protein. Consequently, current strategies for developing treatments for SMA generally focus on augmenting SMN levels. To identify additional potential therapeutic avenues and achieve a greater understanding of SMN, we applied in vivo, in vitro, and in silico approaches to identify genetic and biochemical interactors of the Drosophila SMN homolog. We identified more than 300 candidate genes that alter an Smn-dependent phenotype in vivo. Integrating the results from our genetic screens, large-scale protein interaction studies, and bioinformatic analysis, we define a unique interactome for SMN that provides a knowledge base for a better understanding of SMA.pinal muscular atrophy (SMA), the leading genetic cause of infant mortality, results from the partial loss of Survival Motor Neuron (SMN) gene activity (1). Numerous studies indicate that SMN functions as a central component of a complex that is responsible for the assembly of spliceosomal small nuclear ribonucleoproteins (snRNPs) (reviewed in ref.2). SMN is also reported to play additional roles, including mRNA trafficking in the axon (3). In humans, SMN is encoded by two nearly identical genes, SMN1 and SMN2, which are located on chromosome 5 (4). SMN2 differs from SMN1 in that only 10% of SMN2 transcripts produce functional SMN due to a single-nucleotide polymorphism that results in inefficient splicing of exon 7 and translation of a truncated, unstable SMN protein (1,5,6). The clinical severity of SMA correlates with the SMN2 copy number, which varies between individuals (7). As the small amount of functional SMN2 protein produced by each copy of the gene is capable of partially compensating for the loss of the SMN1 gene function, higher copy numbers of SMN2 typically result in milder forms of SMA. Therefore, genetic modifiers capable of increasing the abundance and/or specific activity of SMN hold promise as therapeutic targets.The Drosophila genome harbors a single, highly conserved ortholog of SMN1/2, the Smn gene. SMN is essential for cell viability in vertebrates and Drosophila (8, 9). In Drosophila, zygotic loss of Smn function results in recessive larval lethality (not embryonic as might be expected) due to the rescue of early development by maternal contribution of Smn. The larval phenotype includes neuromuscular junction (NMJ) abnormalities that are reminiscent of those associated with the human disease, rendering this invertebrate organism an excellent system to model SMN biology (9-11). We previously described a genetic screen for modifiers of the lethal phenotype resulting from a complete lossof-function Smn allele (12). This screen, although it probed half of the Drosophila genome, identified only a relatively small number of genes that affected the NMJ phenotype associated with Smn loss of function (12). In particular, it did not identify genes involved in snRNP biogenesis, the molecular func...

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Section: Resultsmentioning

confidence: 99%

Genetic circuitry of Survival motor neuron , the gene underlying spinal muscular atrophy

Sen

Dimlich

Guruharsha

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…Twenty-three genes were overexpressed in S2R+ cells by transfection of available constructs with C-terminal HA tags (13) and the proteins encoded by these genes were examined (Fig. 5, Fig.…”

Section: Significancementioning

confidence: 99%

Proteomic mapping in live Drosophila tissues using an engineered ascorbate peroxidase

Chen

Udeshi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Characterization of the proteome of organelles and subcellular domains is essential for understanding cellular organization and identifying protein complexes as well as networks of protein interactions. We established a proteomic mapping platform in live Drosophila tissues using an engineered ascorbate peroxidase (APEX). Upon activation, the APEX enzyme catalyzes the biotinylation of neighboring endogenous proteins that can then be isolated and identified by mass spectrometry. We demonstrate that APEX labeling functions effectively in multiple fly tissues for different subcellular compartments and maps the mitochondrial matrix proteome of Drosophila muscle to demonstrate the power of APEX for characterizing subcellular proteomes in live cells. Further, we generate “MitoMax,” a database that provides an inventory of Drosophila mitochondrial proteins with subcompartmental annotation. Altogether, APEX labeling in live Drosophila tissues provides an opportunity to characterize the organelle proteome of specific cell types in different physiological conditions.

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“…Apart from widely studied model organisms such as yeast [1][2][3][4][5][6][7], fruit fly [8,9] and worm [10], it is now possible to predict complexes from more sophisticated organisms including human [15]. This has provided new opportunities to study complexes under different contexts and across species, thus tracing the functional and evolutionary conservation of complexes.…”

Section: Resultsmentioning

confidence: 99%

“…High-throughput experimental systems including yeast two-hybrid (Y2H), tandem affinity purification followed by mass spectrometry (TAP-MS) and protein complementation assay (PCA) have mapped a considerable fraction of interactions from model organisms including S. cerevisiae [1][2][3][4][5][6][7], Drosophila melanogaster [8,9] and Caenorhabditis elegans [10], thereby fuelling computational methods to systematically analyse these large-scale interaction data. Beginning from classical methods by Spirin & Mirny [11] and Bader & Hogue [12] that work primarily by clustering the network of protein interactions (PPI network), computational methods have come a long way, and current methods integrate diverse information with PPI networks to predict complexes.…”

Section: Introductionmentioning

confidence: 99%

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

et al. 2015

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Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub-complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

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A Protein Complex Network of Drosophila melanogaster

Cited by 623 publications

References 86 publications

Genetic circuitry of Survival motor neuron , the gene underlying spinal muscular atrophy

Genetic circuitry of Survival motor neuron , the gene underlying spinal muscular atrophy

Proteomic mapping in live Drosophila tissues using an engineered ascorbate peroxidase

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

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