Drew K et al. (2017), Integration of over 9,000 mass spectrometry exp...

XB-ART-53806

Mol Syst Biol 2017 Jun 08;136:932.

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Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes.

Drew K , Lee C , Huizar RL , Tu F , Borgeson B , McWhite CD , Ma Y , Wallingford JB , Marcotte EM .

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Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k-cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease-associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

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Species referenced: Xenopus
Genes referenced: ankrd55 ccdc138 cplane1 eif3a fh ift52 ift80 KIAA1328 lztfl1 ofd1 pigy wdr19 wdr90
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	Figure 1. Reâanalysis of published APâMS experiments improves overlap among protein interaction networks Graphical schematic of spoke model applied to APâMS datasets. In the spoke model, all interactions must include a bait protein.Venn diagram of overlap between published largeâscale protein interaction networks BioPlex (APâMS), Hein etÂ al (APâMS), and Wan etÂ al (CFâMS). Protein interactions in BioPlex and Hein etÂ al were generated from a spoke model.Graphical schematic of matrix model applied to APâMS datasets. In the matrix model, interactions are allowed between prey proteins.Venn diagram of overlap between protein interaction networks where a weighted matrix model was applied to BioPlex and Hein etÂ al. Sizes of weighted matrix model protein interaction networks were kept constant with published networks (for this analysis only while the full networks were used for integration). Note an increase in the overall number of overlapping interactions when compared to (B).DiagramÂ of protein complex discovery workflow. Three protein interaction networks, BioPlex, Hein etÂ al, and Wan etÂ al, were combined into an integrated protein complex network and clustered to identify protein complexes. Parameters for the SVM and clustering algorithms were optimized on a training set of literatureâcurated complexes and validated on a test set of complexes.
	Figure EV1. Hypothetical example of coâcomplex interactions being scored by weighted matrix model Graphical depiction of six APâMS experiments highlighting the purification of two mutually exclusive complexes AâBâCâD and AâE. Experiments 1, 2, and 3 coâpurify interactions with bait proteins A, D, and E, respectively. Experiments 4, 5, and 6 show nonâspecific interactions for the complexes or subâcomplexes. For the purposes of this example, proteins A, B, C, D, and E are only observed in these six experiments out of a set of 50 experiments (arbitrarily defined).Presenceâabsence matrix for the six APâMS experiments and five proteins described in (A). This matrix represents what the experimenter observes after mass spectrometry analysis.Calculation of weighted matrix model score for protein pairs in highlighted complexes. The True Interaction column represents whether the pair of proteins is coâcomplex or not. The Spoke Model column represents the predictions made by the traditional spoke model. Note the spokes model's falseânegative prediction of interaction B&C. The Number of coâpurifications column represents the number of experiments for which the pair of proteins is coâpurified. The Weighted Matrix Model column represents the â1*log(Pâval) of the hypergeometric test given the experimental overlap value, each protein's total number of observed experiments, and the total number of experiments (nonâdepicted) arbitrarily defined as 50. The panel also shows likely clusters of coâcomplex interactions using three levels of confidence, high, medium, and low. Note, the highâ and mediumâconfidence networks do not show the falseâpositive interactions D&E, C&E, or B&E but do capture the trueâpositive preyâprey interaction B&C.
	Figure 2. Integration of the three largeâscale protein complex datasets substantially improves both precision and recall of known human protein interactions Precisionârecall curves calculated on a leaveâout set of protein interactions from literatureâcurated complexes for different combinations of predictive protein interaction features. The integration of all three datasets outperforms all other networks. Also, note a substantial improvement in performance when the weighted matrix model features are used (no MatrixModel, blue vs. integrated, orange).Performance of parameter optimization for MCL and Newman twoâstage clustering procedures. Each data point represents a set of parameters and is evaluated based on the resulting clusters similarity to both training and test sets of complexes using the FâGrand measure (see Materials and Methods). Final parameter sets were selected based only on FâGrand measure for the training set.Precisionârecall curves evaluating protein interactions on leaveâout set before (integrated) and after (hu.MAP) clustering procedure. Note an improvement in performance after clustering suggests the clustering procedure successfully removed falseâpositive interactions.Distribution of protein interactions in the final protein interaction network based on input evidence. Note the weighted matrix model interactions produce many highâconfident interactions. Also, the âMultipleâ category shows predominately highâconfident interactions, which validates the integration of multiple datasets mitigating false positives.Protein interactions from our complex map substantially overlap with other protein interaction datasets across a variety of experimental types.
	Figure EV2. Performance evaluation of networks with alternative feature sets and hu.MAP compared to leaveâout set of coâcomplex interactions and complexes Precisionârecall curves calculated using leaveâout set of coâcomplex interactions to evaluate networks trained on BioPlex only and BioPlexÂ +Â weighted matrix model features. Note improvement of performance when weighted matrix model features are included.Precisionârecall curves calculated using leaveâout set of coâcomplex interactions to evaluate networks trained on Hein only and HeinÂ +Â weighted matrix model features. Note improvement of performance when weighted matrix model features are included.Precisionârecall curves calculated using leaveâout set of coâcomplex interactions to evaluate networks trained on all features (integrated, orange), all features except HumanNet features SCâLC, SCâCC, CEâLC, and CEâCC (dashed blue), and all features except HumanNet (green). Note negligible performance loss when HumanNet features are excluded.Comparison of hu.MAP and published complex maps to leaveâout set of complexes using precisionârecall product measure (Song & Singh, 2009).Comparison of hu.MAP and published complex maps to leaveâout set of complexes using Fâweighted kâclique score.Distribution of number of subunits for complexes in hu.MAP.
	Figure 3. Weighted matrix model edges identify large synaptic bouton complex Presence/absence matrix of BioPlex APâMS experiments as rows and pulled down proteins as columns for four complexes identified in our complex map. The Exosome, eIF3 Complex, and 19S Proteasome all have multiple baitâbait interactions whereas the novel synaptic bouton complex does not have baitâbait interactions but does have substantial density in the nonâbait region of the matrix. This density is identified by the weighted matrix model and highlights the model's ability to discover protein complexes.RNA expression profiles of proteins in the synaptic bouton complex across different tissues sampled by the Human Protein Atlas. This shows the complex is highly specific for cerebral cortex tissue. No less than six replicates were used for each tissue type. Boxes indicate median (inner band), first quartile (bottom) and third (top) quartile. Whiskers indicate 1.5 interquartile range. Dots indicate outliers.Correlation coefficient distributions of Allen Brain Map tissue expression profiles between synaptic bouton complex proteins and random set of proteins. This shows coherence of expression among proteins in the complex suggesting a functional module.Significantly enriched Gene Ontology annotations for proteins in the synaptic bouton complex shows enrichment for neuron development and synaptic transmission.
	Figure 4. hu.MAP consists of predominately core human complexes and covers a large fraction of disease genes Complex map coverage of Human Protein Atlas RNA tissue specificity classifications showing majority of complexes are ubiquitously expressed and likely core cellular machinery.Fraction of complexes with significantly enriched annotation terms (g:Profiler hypergeometric test with FDR (BenjaminiâHochberg) correction on each complex and further corrected at an FDR of 5% given a set of shuffled complexes; see Materials and Methods) from various ontologies.Protein coverage of highâlevel Disease Ontology terms and ciliaârelated annotations for complex map as well as three published maps (Wan etÂ al, BioPlex, Boldt etÂ al) and three published interaction network (Hein etÂ al, Gupta etÂ al and Boldt etÂ al).
	Figure EV3. Distribution of most significant annotation for each complex in hu.MAP and shuffled complexesWe selected the largest âlog(Pâvalue) for each complex in hu.MAP and shuffled complexes as calculated by g:Profiler. hu.MAP has a substantially higher number of complexes with highly enriched annotations than permuted complexes. This distribution was used to calculate the false discovery rate of 5% in FigÂ 4B.
	Figure 5. Select complexes in the map are strongly linked to human ciliopathiesEight complexes are highlighted with ciliopathyâlinked subunits (bold outlines), predicted ciliopathy subunits (dashed outlines), and their association with four representative ciliopathy phenotypes (AâD). We predict links to ciliopathies for uncharacterized proteins (green) that are coâcomplex with known ciliopathy genes. All edges to ciliopathy phenotypes are mapped from OMIM (Amberger etÂ al, 2015) or direct from literature (Krock & Perkins, 2008; Keady etÂ al, 2011; Chang etÂ al, 2015; Toriyama etÂ al, 2016). Cystic kidney phenotype represented by polycystic kidneys from patient with OFD1 variant, adapted from ChettyâJohn etÂ al (2010).Digit malformations represented by polydactyly of BardetâBiedl syndrome patient with LZTFL1 (BBS17) variant, adapted from Schaefer etÂ al (2014).Shortârib phenotype represented by chest narrowing of Jeune asphyxiating thoracic dystrophy individual with IFT80 variant, adapted from Beales etÂ al (2007).Maculopathy represented by retinitis pigmentosa of SeniorâLoken syndrome patient with mutation in WDR19 (Coussa etÂ al, 2013).
	Figure 6. Oroâfacialâdigital syndrome 1 (OFD1) interaction partners are centriole and centriolar satellite proteins, suggesting new components of ciliary basal bodies Network of ciliopathy complex and closely interacting centrosomal complex. Edge weights represent SVM confidence scores where gray are intracomplex edges and purple are interâcomplex edges. Color of nodes follows FigÂ 5 conventions.Matrix of APâMS evidence supporting both complexes. The matrix shows strong support for interactions within each complex. Bait proteins that are members of either complex are labeled on the left.Experimental validation of ciliary proteins using multiâciliated epithelial cells in Xenopus laevis. Localization assays for the three uncharacterized proteins in the OFD1 complex confirm that all three proteins localize to basal bodies at the base of the cilia in a manner similar to known components of the complex. Scale bars: 1Â Î¼m. Each image is representative of nine cells from three different embryos.
	Figure EV4. hu.MAP recapitulates IFTâA and IFTâB complexes Network view of IFTâA and IFTâB complexes. Node colors follow FigÂ 5 conventions.Matrix of APâMS experiments shows IFTâA and IFTâB are well separated and supported by multiple experiments.
	Figure 7. ANKRD55 is a new component of the intraflagellar transport (IFT) particle, is important for ciliogenesis, and has a role in neural tube closure Network view of two IFT subâcomplexes associated with ANKRD55.Matrix of APâMS experiments shows strong support for ANKRD55 association with known IFT proteins.ANKRD55 localizes to cilia as predicted from coâcomplex interactions, as assayed inÂ vivo in multiâciliated Xenopus laevis epithelial cells. Scale bar: 10Â Î¼m. Each image is representative of 18 cells from six different embryos. Kymograph of ANKRD55 localized to cilia inÂ vivo reveals rapid trafficking along the length of the cilia (representative out of 36 multiâciliated cells).Morpholino knockdown of ANKRD55 results in reduced count and length of cilia, in a manner similar to the control IFT52 knockdown, supporting a role in ciliogenesis for ANKRD55. Scale bar: 10Â Î¼m. Each image is representative of 18 cells from six different embryos.Dorsal view of stage 19 X.Â laevis embryos displays that ANKRD55 knockdown causes neural tube closure defects that are rescued by wildâtype ANKRD55 mRNA. The Tukey box plot displays average distance between neural folds in control (nÂ =Â 32), morphant (nÂ =Â 22), and rescue (nÂ =Â 24) embryos. ***PÂ <Â 0.0001, twoâsample KolmogorovâSmirnov test. Boxes indicate median (inner band), first quartile (bottom) and third (top) quartile. Whiskers indicate 1.5 interquartile range. Dots indicate outliers.
	Figure EV5. ANKRD55 localization and movement along axonemes is consistent with being a component of the IFTâB particle Twoâcolor kymograph generated by coâexpression of ANKRD55âGFP (green) and mCherryâCLUAP1 (magenta) reveals that ANKRD55 travels along axonemes in association with other IFT proteins. Scale bar: 10Â Î¼m. Kymograph is representative out of 22 multiâciliated cells.RTâPCR demonstrates the efficiency of ANKRD55 MO to disrupt splicing of ANKRD55 mRNA in Xenopus embryos. GAPDH is used as a control.Morpholino knockdown of JBTS17, known to specifically affect IFTâB localization, results in the accumulation of ANKRD55âGFP in axonemes (green: ANKRD55âGFP, magenta: membrane RFP). Scale bar: 10Â Î¼m. Each image is representative of 18 cells from six different embryos.

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