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Physiol. Genomics 29: 109-117, 2007. First published December 26, 2006; doi:10.1152/physiolgenomics.00226.2006 Free Article
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Received 13 October 2006; accepted in final form 22 December 2006.
Physiological Genomics 29:109-117 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

Functional nonsynonymous single nucleotide polymorphisms from the TGF-ß protein interaction network

Sevtap Savas1,2,3, Ian W. Taylor4,5, Jeff L. Wrana4,5 and Hilmi Ozcelik1,2,3

1 Fred A. Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Toronto, Ontario, Canada
2 Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada
3 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
4 Centre for System Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
5 Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario, Canada


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Protein complexes mediated by protein-protein interactions are essential for many cellular functions. Transforming growth factor (TGF)-ß signaling involves a cascade of protein-protein interactions and malfunctioning of this pathway has been implicated in human diseases. Using an in silico approach, we analyzed the naturally occurring human genetic variations from the proteins involved in the TGF-ß signaling (10 TGF-ß proteins and 242 other proteins interacting with them) to identify the ones that have potential biological consequences. All proteins were searched in the dbSNP database for the presence of nonsynonymous single nucleotide polymorphisms (nsSNPs). A total of 118 validated nsSNPs from 63 proteins were retrieved and analyzed in terms of 1) evolutionary conservation status, 2) being located in a functional protein domain or motif, and 3) altering putative protein motif or phosphorylation sites. Our results indicated the presence of 31 nsSNPs that occurred at evolutionarily conserved residues, 37 nsSNPs were located in protein domains, motifs, or repeats, and 46 nsSNPs were predicted to either create or abolish putative protein motifs or phosphorylation sites. We undertook this study to analyze the human genetic variations that can affect the protein function and the TGF-ß signaling. The nsSNPs reported in here can be characterized by experimental approaches to elucidate their exact biological roles and whether they are related to human disease.

transforming growth factor-ß pathway; protein-protein interactions; evolutionary conservation analysis; protein domains and motifs; phosphorylation sites


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
TRANSFORMING GROWTH FACTOR BETA (TGF-ß) signaling is directly involved in many homeostatic and developmental processes (22), including wound healing, tumorigenesis, cell proliferation and differentiation, neovascularization, and extracellular matrix formation (33). TGF-ß signaling starts with the binding of the secreted TGF-ß ligands to the transmembrane serine/threonine kinase TGF-ß receptors. This binding initiates a cascade of cellular events including the activation of the SMAD proteins, which transduce the TGF-ß signals to nucleus for transcriptional regulation of specific genes (12, 33, 61). Deregulation or abnormalities of the TGF-ß pathway have been implicated in several conditions including the atherosclerosis (20), Alzheimer's disease (6), autoimmune disease (1), familial juvenile polyposis (34), pancreatic cancer (29), breast cancer (56), and prostate cancer (3).

Single nucleotide polymorphisms (SNPs) are the most common genetic variation in human (57). They occur, on the average, once every 300–400 base pairs (24). SNP located within the coding or regulatory regions of genes can cause qualitative and quantitative changes in gene expression, RNA splicing, protein translation, or gene function. Nonsynonymous SNPs (nsSNPs) are located in the coding regions, substitute the amino acids, and are the least frequent form of the SNPs probably because of the selective constraints on protein sequences (7, 16). Since nsSNPs change the amino acid sequences, they are also likely to change the structure and the function of the proteins. As nearly 30% of the nsSNPs are predicted to affect the protein function (9, 37, 48), they have been the topic of the studies aiming to identify the disease-susceptibility loci (8).

Pathway-based approaches are beneficial because they help the molecular biologists and epidemiologists to investigate multiple genes by focusing on specific physiological processes and relate them to human disease (62). These kinds of approaches also provide a logical basis for examining the gene-gene and SNP-SNP interactions (epistasis) in human health and disease predisposition (11). In this regard, we and other groups have previously analyzed and evaluated the functionalities of the nsSNPs from a variety of cellular pathways (10, 26, 53, 54). In this study, we aimed to systematically investigate the nsSNPs from the TGF-ß signaling protein interaction network. A total of 10 TGF-ß superfamily members have been chosen as the TGF-ß core proteins, and a TGF-ß protein interaction network was constructed. These members of the TGF-ß superfamily were chosen based on their occurrence as bait in a high-throughput screen (2), where literature curated interactions were validated by the LUMIER technique and found to be >70% true-positive using conservative cutoffs. The 10 core proteins included the two bone morphogenetic protein receptors (ALK2/ACVR1 and ALK6/BMPR1B), one transmembrane receptor for TGF-ß ligand (TGFBR1), five SMAD members that transduce the TGF-ß signals in cells (SMAD1, SMAD2, SMAD3, SMAD4, and SMAD7), and two E3 ligases that facilitate SMAD receptor destruction by ubiquitin-mediated protein degradation (SMURF1 and SMURF2) (33). Our results point to the notion that nsSNPs that can cause functional alterations in the TGF-ß interaction network, which can be utilized for further studies to functionally characterize and investigate their relationship to human disease and health.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
TGF-ß pathway interacting proteins.
Literature-curated protein-protein interactions (PPIs) were taken from the MedLine Database at the National Center for Biotechnology Information between February and April 2004. Interactions were detected for well-characterized components of the TGF-ß superfamily of signal transduction pathway (TGFBR1, ALK2/ACVR1, ALK6/BMPR1B, SMAD1, SMAD2, SMAD3, SMAD4, SMAD7, SMURF1, and SMURF2).To search for interactions in the MedLine database each molecule and all known synonyms including alternative spellings from human, mouse, Drosophila melanogaster, and Caenorhabditis elegans were entered in the search field separated by the Boolean operator "OR". The resulting list of abstracts was manually curated by a trained, graduate-level scientist (I. W. Taylor) for interactions of proteins with the component of the TGF-ß pathway. In the case of ambiguous language in the abstracts the scientist examined the primary manuscript for direct evidence in the data figures relating to the abstract. Interacting proteins were then mapped to a unique identifier with the UniGene database (65) (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene). When interactions were detected in model organisms, the human UniGene ID was used to describe the homologous human gene. The protein interactions are depicted in the Supplementary Figure (http://www.ozceliklab.com/savas/TGFbeta_PPIs). (The online version of this article contains supplemental material.)

nsSNP.
The validated nsSNPs for the TGF-ß interaction network proteins were retrieved from the dbSNP build 124 (58) (http://www.ncbi.nlm.nih.gov/SNP/). Only the nsSNPs that are found in at least two chromosomes in a sample panel of at least 20 chromosomes were included into this study (validated nsSNPs). Throughout this paper, the SNPs that were reported with lower or more than 5% of the samples are annotated as rare and common SNPs, respectively. In some cases, SNPs were detected with lower and higher than 5% minor allele frequencies in different submissions: for simplicity, we classified these SNPs as common SNPs together with the SNPs with minor allele frequencies of ≥5%.

Prediction of functional consequences of nsSNPs.
A precomputed PolyPhen resource (courtesy of Dr. Shamil Sunyaev) was utilized to retrieve PolyPhen predictions for the nsSNPs (48) (http://www.bork.embl-heidelberg.de/PolyPhen/). All predictions were based on protein alignments; the predictions based on fewer than five proteins in the alignment were considered unreliable and thus are annotated as "noninformative" in this study.

Protein domain and motif analysis.
The information related to the positions of the protein functional domains and motifs was retrieved from the Swiss-Prot database feature table (5) (http://www.expasy.org/sprot/sprot-search.html) and the Interpro database (36) (http://www.ebi.ac.uk/Interpro). In those cases when the Interpro entry was not available, we have run the InterproScan program (47) (http://www.ebi.ac.uk/InterProScan/) to predict the protein domains and motifs. The Human Protein Reference Database (HPRD, Ref. 43; http://www.hprd.org) was also utilized to see whether the nsSNPs were located in protein domains, motifs, or at experimentally verified posttranslational modification sites of the proteins. NetPhos (4) was utilized to predict putative phosphorylation sites (http://www.cbs.dtu.dk/services/NetPhos/). ScanSite (40) was utilized to predict the putative protein motifs (a total of 63 motifs including phosphorylation sites, modular signaling, and interaction domains) (http://scansite.mit.edu/motifscan_seq.phtml). ScanSite was run at high-stringency conditions.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
A total of 242 proteins interacting with the 10 TGF-ß core proteins (TGFBR1, ACVR1, BMPR1B, SMAD1, SMAD2, SMAD3, SMAD4, SMAD7, SMURF1 and SMURF2) were identified. The number of proteins interacting with each of these proteins was as follows: TGFBR1 (n = 55), ACVR1 (n = 1), BMPR1B (n = 10), SMAD1 (n = 40), SMAD2 (n = 41), SMAD3 (n = 71), SMAD4 (n = 24), SMAD7 (n = 7), SMURF1 (n = 1), and SMURF2 (n = 2) (Supplementary Figure; http://www.ozceliklab.com/savas/TGFbeta_PPIs). All proteins were searched in the dbSNP database for the presence of validated nsSNPs. As a result, a total of 118 validated nsSNPs from 63 proteins were retrieved at the time of data collection (Table 1). When we categorized the nsSNPs based on their minor allele frequencies (mAFs; see METHODS), the majority of the nsSNPs (n = 94, 80.0%) were reported as common variations in the dbSNP database (Table 1).


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  		Table 1. Summary of the data

 
Informative PolyPhen predictions were available for 70/118 (59.0%) nsSNPs. Among these, 31 (44.3%, 31/70) nsSNPs were predicted to affect the protein function (either possibly or probably damaging predictions; Tables 1 and 2). No functional nsSNP was identified in ACVR1, SMAD7, SMURF1, or SMURF2 nor in the proteins interacting with them (n = 11). However, at least one functional nsSNPs were identified in proteins interacting with TGFBR1 (n = 5), BMPR1B (n = 1), SMAD1 (n = 2), SMAD2 (n = 8), SMAD3 (n = 7), SMAD4 (n = 1) (Table 2). Similar to the data from the whole set of nsSNPs (see above), 27/31 (87.1%) of the functional nsSNPs were common nsSNPs with mAFs ≥5%. Information curated in the Swiss-Prot, Interpro, and the HPRD databases indicated that 37/118 (31.0%) nsSNPs were located in a protein domain, motif, or a repeat sequence (Table 2). Among this group of nsSNPs, PolyPhen predicted 16 to affect protein function.


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  		Table 2. nsSNPs that are either predicted to be functional by PolyPhen or located in a functional protein domain, motif, and/or a repeat

 
We also utilized the ScanSite (40) and NetPhos (4) programs to predict impact of the nsSNPs on the putative protein motifs and phosphorylation sites along the TGF-ß protein interaction network proteins. As a result, a total of 46 (39%, 46/118) nsSNPs from 34 genes have been predicted to modify putative protein motifs and phosphorylation sites (Table 3). ScanSite predicted that in the case of seven nsSNPs (ALDH3A1-G309E, BRCA2-N289H, FBXO34-G704V, MYC-N11S, NCOA1-P1272S, REV3L-R1892H, and TCF7-G256R), a short protein motif was either created or abolished. NetPhos predicted that 29 nsSNPs altered (either created or abolished) putative kinase recognition motifs and phosphorylation sites. The BRCA2-S599F, DAB2-T586I, HIF1A-P582S, NEDD9-T577M, REV3L-R1892H, and SNX1-D466N are predicted to alter two motifs and phosphorylation sites (Table 3). PolyPhen predicted 15 of the nsSNPs, which alter protein motif or phosphorylation sites based on NetPhos and ScanSite results, as also affecting the protein function (Table 2).


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  		Table 3. nsSNPs altering short putative protein motifs and phosphorylation sites

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
PPIs that form functional protein complexes are essential for a variety of biological processes such as signal transduction, DNA repair, and cell cycle (19, 32, 39, 42, 64). These interactions are mainly facilitated by specific interaction domains and motifs (42). One protein can have multiple interaction domain/binding motifs and thus multiple interaction partners. Not surprisingly, mutations affecting PPIs have been linked to abnormal cellular functions and human diseases (35, 38).

TGF-ß is a signaling pathway where PPIs are quite abundant. We had previously suggested that the functional nsSNPs from proteins interacting with each other could affect the function of protein complexes by means of affecting either the function of individual proteins or the dynamics of the PPIs (54). Similarly, in this study, our goal was to analyze the possible consequences of genetic variations on functions of the TGF-ß interaction network proteins via an in silico approach. Based on the published information, we have retrieved the proteins interacting with a group of TGF-ß proteins to construct a TGF-ß protein interaction network. This network consisted of a total of 252 proteins, and the highest numbers of protein interactions were with the SMAD3 (n = 71), TGFBR1 (n = 55), SMAD2 (n = 41), SMAD1 (n = 40), and SMAD4 (n = 24) proteins.

Our results demonstrated that a total of 63 nsSNPs (31 damaging the protein function and/or 32 nsSNPs creating/abolishing short protein motifs and phosphorylation sites) could have biological consequences (Tables 2, 3). A literature search revealed that some of the nsSNPs were already implicated in human diseases. AGER-G82S has been previously implicated in the susceptibility to diabetes-associated microvascular dermatoses (21) and rheumatoid arthritis (18), and the AGER-82S allele was found to increase the inflammatory response compared with AGER-G82 allele (18). HIF1A-P582S has an elevated transcription activity (60) and was associated with the maximal oxygen consumption before and after aerobic exercise training in older humans (46) and Type 2 diabetes (67). PPARG-P12A was associated with Type 2 diabetes in Oji-Cree women (17) and reduced risks of renal cell carcinoma (59) and myocardial infarction (50). TP53-72R induces apoptosis better than the proline allele (13) although the proline allele is more efficient in G1 arrest (44). This nsSNP is associated with a variety of cancers, including breast cancer in Jewish women (41) and colorectal cancer (23). NR3C1-N363S (Table 3) was associated with the coronary artery disease (31), obesity (30), and susceptibility to overweight in patients with Type 2 diabetes mellitus (52). Association of AGER, HIF1A, PPARG, and NR3C1 genes with complications related to diabetes is not surprising considering the fact that the biological roles of these genes are consistent with the observed association. For example, AGER, an advanced glycosylation end product-specific receptor, protects tissues from glucose-mediated damage (63); HIF1A induces expression of genes involved in glucose metabolism under hypoxia (28); PPARG modulates a variety of process including insulin sensitivity (27); and NR3C1, the glucocorticoid receptor, plays a role in expression of glucocorticoid responsive genes (51). On the other hand, an nsSNPs in BMP4, BMP4-V152A, was reported to be associated with bone density in postmenopausal women (49), although our analysis suggests that it is functionally benign (Table 2). This nsSNP may represent either a false-negative prediction (see below) or may be in linkage disequilibrium with another genetic factor responsible for the phenotype. Nevertheless, our results suggest that the nsSNPs in Tables 2 and 3 are good candidates for further genetics and molecular analyses to reveal whether they have functional consequences and their effects are linked to human disease.

It has been shown that proteins that interact with multiple partners are more likely to be essential (25), and the biological impact of functional nsSNPs from such proteins is likely to be large and diverse. In our data set, SMAD4 has 24 other proteins interacting with it, and it has one nsSNP that occurred at an evolutionarily conserved residue (SMAD4-W101G; Table 2). Therefore, it is feasible to speculate that this nsSNP could affect the functions of a variety of protein complexes involving SMAD4. In this regard among all, SMAD4-W101G nsSNP can be prioritized for further studies.

Protein domains, motifs, and posttranslational sites such as phosphorylation sites are important for the function, structure, and stability of proteins. Therefore, amino acid substitutions in these regions can change the intrinsic properties of proteins. Previously, the impact of the nsSNPs on short binding motifs and phosphorylation sites has been proposed and analyzed for proteins from other cellular pathways (10, 55). In the present study, we found that 37 nsSNPs were located in a protein domain. These nsSNPs, especially the ones that occur at evolutionarily conserved residues (n = 16, Table 2), can alter the structure/features of protein domains and thus the function of the protein. Literature search and the information of the Swiss-Prot feature table or the HPRD did not reveal any of the nsSNPs in Table 3 to be an experimentally verified phosphorylation sites. However, the sensitivity of the NetPhos predictions is 69–96% and with a false positive rate of 0–26% for tyrosine, 0–11% for serine, and 0–14% for threonine (4). In case of the ScanSite, the accuracy of the program was reported as ~70% (66). Therefore, although some of these predictions are likely to be false positives, a considerable portion of the predictions is supposed to be correct. Thus, these nsSNPs deserve further analysis.

Interestingly, 80% of the all nsSNPs analyzed and 44% of the nsSNPs with a reliable PolyPhen prediction that were predicted to affect the protein function were presented as common genetic variations (minor allele frequencies ≥5%). It has been hypothesized that common variations in the contemporary human population contribute to disease susceptibility (8). Thus, it is tempting to say that the common and functional nsSNPs reported in this study can also be utilized for disease association studies to investigate their potential roles in disease proposition.

In conclusion, here we report an analysis of proteins acting with the 10 TGF-ß core proteins through protein-protein interactions, and present nsSNPs that might be important for the protein function based on the evolutionary conservation and protein domain and motif analyses. Those nsSNPs that are found to be functional based on evolutionary conservation analysis and/or altering putative protein binding motifs and phosphorylation sites are good candidates for further functional studies. Given that these nsSNPs are located in candidate proteins, they may also be utilized in disease association studies to test their potential contribution to the altered disease risk. Furthermore, it is possible that in such complexes, some of the nsSNPs could have coevolved together to compensate for the functional impact on each other (10, 11, 14, 15, 54). Thus, further analysis of these nsSNPs can also help with the elucidation of the epistatic relationships among the nsSNPs either on the same protein or individual proteins of protein complexes.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
This work was supported by Canadian Breast Cancer Foundation (H. Ozcelik) and by Genome Canada through Ontario Genomics Institute (J. L. Wrana). S. Savas is supported, in part, by a "CIHR Strategic Training Program Grant - The Samuel Lunenfeld Research Institute Training Program: Applying Genomics to Human Health" fellowship.


   ACKNOWLEDGMENTS
 
Authors thank Dr. Hamdi Jarjanazi for the automatic retrieval of the SNP information from the dbSNP database and Dr. Shamil Sunyaev for the precomputed PolyPhen resource.


   FOOTNOTES
 
Address for reprint requests and other correspondence: H. Ozcelik, Mount Sinai Hospital Samuel Lunenfeld Research Inst., 60 Murray St. L6–303, Box 29, Toronto ON Canada M5T 3L9 (e-mail: ozcelik{at}mshri.on.ca).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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