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.2018 May;17(5):961-973.
doi: 10.1074/mcp.RA117.000474. Epub 2018 Feb 1.

Proteome Data Improves Protein Function Prediction in the Interactome ofHelicobacter pylori

Affiliations

Proteome Data Improves Protein Function Prediction in the Interactome ofHelicobacter pylori

Stefan Wuchty et al. Mol Cell Proteomics.2018 May.

Abstract

Helicobacter pylori is a common pathogen that is estimated to infect half of the human population, causing several diseases such as duodenal ulcer. Despite one of the first pathogens to be sequenced, its proteome remains poorly characterized as about one-third of its proteins have no functional annotation. Here, we integrate and analyze known protein interactions with proteomic and genomic data from different sources. We find that proteins with similar abundances tend to interact. Such an observation is accompanied by a trend of interactions to appear between proteins of similar functions, although some show marked cross-talk to others. Protein function prediction with protein interactions is significantly improved when interactions from other bacteria are included in our network, allowing us to obtain putative functions of more than 300 poorly or previously uncharacterized proteins. Proteins that are critical for the topological controllability of the underlying network are significantly enriched with genes that are up-regulated in the spiral compared with the coccoid form ofH. pylori Determining their evolutionary conservation, we present evidence that 80 protein complexes are identical in composition with their counterparts inEscherichia coli, while 85 are partially conserved and 120 complexes are completely absent. Furthermore, we determine network clusters that coincide with related functions, gene essentiality, genetic context, cellular localization, and gene expression in different cellular states.

© 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

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Figures

Fig. 1.
Fig. 1.
The interactome and proteome ofH. pylori. (A) We map all high-quality interactions between proteins in the core protein interaction network and their corresponding abundances inH. pylori. Furthermore, we label all proteins with their protein functions and essentiality. (B) We bin protein abundances in three groups (high: top 20%, low: bottom 20%, intermediate: remainder) and determine the enrichments of functions in each bin. We find that highly abundant proteins preferably are enriched with metabolic functions.
Fig. 2.
Fig. 2.
Essentiality and abundances. (A) Grouping proteins into bins of abundances, we observe that essential proteins are more abundant than their nonessential counterparts (p < 10−2, Student'st test). (B) Using the combined interaction network inH. pylori, proteins appear to predominately interact with proteins of similar abundance. In turn, interactions that involve proteins in low-abundance bins tend to interact with proteins in intermediate-abundance bins.
Fig. 3.
Fig. 3.
Functional crosstalk in the interactome ofH. pylori. Determining the prevalence of interactions between functional groups, we observe that the majority of interactions appear between proteins of the same functional class.
Fig. 4.
Fig. 4.
Functional prediction of unknown proteins inH. pylori using a bacterial meta-interactome. (A) To assess the quality of our classification procedure, we randomly sample 20% of all functionally annotated proteins inH. pylori and utilize the remainder to predict their functions. To measure prediction quality we calculate the area under the receiver operating characteristic curve, suggesting that the addition of the bacterial meta-interactome allows for better functional prediction (p < 10−50, Student'st test). (B) We consider all randomized samples and calculate the mean s-indices of each gene of unknown function (circles) in both the original network ofH. pylori and the augmented network. In the scatter-plot the homogeneity of the functional prediction of the majority of genes (78.6%) benefit from including the bacterial meta-interactome. (C) Combining the network of protein interactions ofH. pylori and the bacterial meta-interactome, we predict the functions of 337 proteins with unknown or poorly characterized functions (FDR < 0.05).
Fig. 5.
Fig. 5.
Controlling theH. pylori protein interaction network. (A) In a toy network we illustrate the concept of critical, intermediate, and redundant nodes. (B) In the table, we present statistics of the protein interaction network ofH. pylori and of its corresponding critical, intermediate, and redundant proteins. Notably, critical proteins are highly connected, while degrees of intermediate and redundant nodes revolve around the mean degree of all proteins (dashed line). (C) We define the top 20% of proteins with the highest node betweeness as a set of bottleneck proteins. Randomly sampling sets of critical, intermediate, and redundant proteins 10,000 times, we find that critical nodes are strongly enriched with bottlenecks. While intermediate nodes are moderately enriched, we also find a significant depletion of redundant nodes in the underlying set of bottleneck proteins. (D) In the Volcano plot of the fold change of proteins that compares their abundance levels in the coccoid and spiral form, we label all proteins with their critical, intermediate, and redundant role in the underlying network of protein interactions ofH. pylori. We define proteins with a fold change of >0.5 and <−0.5 (p < 0.05) as regulated proteins (shaded areas), suggesting that critical, regulated proteins predominantly appear as being present in the spiral form. (E) As a corollary, we randomly sample sets of regulated proteins 10,000 times. We observe that critical proteins are significantly enriched with regulated genes. (F) We determine the enrichment of functions in the set of critical proteins by randomly sampling their functions. We observe that critical proteins predominately appear in transcriptional and posttranslational modification functions.
Fig. 6.
Fig. 6.
Examples of PPI enrichments in and between genomic operons. Interacting proteins are symbolized by connected gene symbols (heteromers) and colored gene symbols (homomers). In theright panel, protein structures of Trp and Moa orthologs highlight detected (solid arrows) and undetected (dashed arrows) intermolecular interactions that are known from the enzymes′ tertiary structures.
Fig. 7.
Fig. 7.
Predicted protein complexes inH. pylori. (A) Proteomes ofE. coli,M. pneumoniae, andH. pylori overlap substantially using orthologous proteins. Proteins not belonging to COGs were excluded. (B) We show selected protein complexes indicating different degrees of complex conservation. Dashed circles indicate proteins inE. coli complexes that are absent inH. pylori. Stoichiometry of protein complexes is indicated if they diverge from one subunit. (C) We count the number of complexes with different degrees of conservation inH. pylori.
Fig. 8.
Fig. 8.
Selected interaction clusters with various enriched functional terms, genomic context, co-expression, co-localization, and phenotypes. We depict interaction clusters that we derive from the combinedH. pylori core protein interaction map with significant enrichments (p < 0.05). All results can be found inSupplemental Table S5. Node labels represent protein names (if available) or the locus number of the corresponding protein. Co-expressed genes based on operons are given in the legends by the transcription unit number as used in (53) and gene expression data (Supplemental Table S5). For each cluster, we provide a separate legend highlighting the enriched properties. In particular, clusters 16 and 17 are enriched for differentially, co-expressed proteins when growth is shifted to low pH values conditions. Clusters 13 and 65 are enriched for essential genes, while cluster 23 is enriched with ribosomal proteins. Clusters 26 and 36 include flagellar/chemotaxis proteins, and cluster 6 is enriched for intra- and interconnected operons.
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