Interspecies data mining to predict novel ING-protein interactions in human
- Paul MK Gordon†1,
- Mohamed A Soliman†1, 2, 3,
- Pinaki Bose1, 2,
- Quang Trinh1,
- Christoph W Sensen1 and
- Karl Riabowol1, 2Email author
© Gordon et al; licensee BioMed Central Ltd. 2008
Received: 09 August 2008
Accepted: 18 September 2008
Published: 18 September 2008
The IN hibitor of G rowth (ING) family of type II tumor suppressors (ING1–ING5) is involved in many cellular processes such as cell aging, apoptosis, DNA repair and tumorigenesis. To expand our understanding of the proteins with which the ING proteins interact, we designed a method that did not depend upon large-scale proteomics-based methods, since they may fail to highlight transient or relatively weak interactions. Here we test a cross-species (yeast, fly, and human) bioinformatics-based approach to identify potential human ING-interacting proteins with higher probability and accuracy than approaches based on screens in a single species.
We confirm the validity of this screen and show that ING1 interacts specifically with three of the three proteins tested; p38MAPK, MEKK4 and RAD50. These novel ING-interacting proteins further link ING proteins to cell stress and DNA damage signaling, providing previously unknown upstream links to DNA damage response pathways in which ING1 participates. The bioinformatics approach we describe can be used to create an interaction prediction list for any human proteins with yeast homolog(s).
None of the validated interactions were predicted by the conventional protein-protein interaction tools we tested. Validation of our approach by traditional laboratory techniques shows that we can extract value from the voluminous weak interaction data already elucidated in yeast and fly databases. We therefore propose that the weak (low signal to noise ratio) data from large-scale interaction datasets are currently underutilized.
Protein-protein interactions play vital roles in regulating protein function and can provide valuable insight into the biological activity of proteins and biochemical pathways in which they function. The importance of protein interactions in biology has fueled intense efforts to identify such interactions and a vast repository of data has been accumulated over the years, particularly in relatively simple model organisms that are easier to manipulate genetically and biochemically. A number of bioinformatics-based approaches attempt to predict interactions using various techniques.
The budding yeast, Saccharomyces cerevisiae, is one of the most comprehensively studied eukaryotic organisms and a substantial amount of biochemical and genetic data has been accumulated. After the publication of the S. cerevisiae genome a decade ago , high throughput genetic and proteomic screens aimed at identifying novel genetic and protein interactions began complementing more traditional biochemical approaches [2, 3]. We suspected that the voluminous data from yeast genes with human counterparts could be exploited more fully to provide better insights into human protein-protein interactions. Yeast and humans represent extreme ends of the eukaryotic evolutionary spectrum. Therefore the genes they share are often involved in fundamentally important cellular processes and represent an interesting set of genes which warrant further investigation. An example of a shared gene of particular interest to us was ING1.
The founding member of the ING family of type II tumor suppressors (ING1) was discovered using the method of subtractive hybridization aimed at identifying factors that were differentially expressed in normal mammary epithelial cells as opposed to breast cancer cell lines . Ectopic over-expression of ING1 was subsequently observed to promote G1 arrest and suppression of its expression led to transformation in vitro and tumor formation in vivo. Other ING genes (i.e., ING2, ING3, ING4, and ING5) have been subsequently identified in various mammalian genomes . A significant number of tumors, either (i) harbour mutations within the ING genes, (ii) have reduced expression of ING proteins, or (iii) have altered ING protein subcellular localization. A large spectrum of cancers show attenuation of ING expression (reviewed in [6, 7]) and mechanistic studies have implicated the ING family in apoptosis, gene expression, senescence, hormone signaling and angiogenesis among others (reviewed in ). The major mechanism through which ING proteins exert their effects is through altering chromatin structure by regulating HAT and HDAC activity [9–11]. This involves binding to differentially methylated histone H3 via the ING PHD domain and also via binding to phosphatidylinositol monophosphates through the polybasic region near the PHD [12–15]. Binding is then believed to target associated HAT or HDAC complexes to chromatin regions, resulting in alteration of local histone acetylation states [10, 12, 14]. Other regions of the INGs have been shown to bind to PCNA, 14-3-3, [16, 17] and cytoplasmic proteins such as liprin . Therefore, we wished to establish a comprehensive list of ING interacting proteins that would aid in the understanding of the complex role of this family of tumor suppressors in regulating diverse cellular functions. ING genes are evolutionarily conserved and members of the ING family have been identified across the animal and plant kingdoms including the yeast S. cerevisiae .
Krogan et al. (2006) have described the use of tandem affinity purification (TAP) tagging followed by two different mass spectrometry methods, namely MALDI-TOF and LC-MS/MS, with the aim of assigning interacting partners to each of the yeast proteins . An impressive 72% coverage of the predicted yeast proteome was made possible due to the increased sensitivity of tandem affinity purification followed by mass spectrometry. We have used this study as the initiation point to determine the range of proteins that can interact with the ING family of proteins. Here we attempt to elucidate human ING protein interactions based the recently published yeast interactome data , hoping to identify real interactions in the long tail of low probability noisy interactions detected.
Pairwise alignment of YNGs (yeast ING-like proteins) and human INGs
The first member of the ING family (ING1) was discovered in humans  and subsequently four more ING genes have been identified (ING2-5). Homologs of ING proteins also exist throughout the animal and plant kingdoms . Three yeast proteins, YNG1, YNG2 and YNG3 (Pho23), have been shown to bear considerable homology to the human ING1 protein in their C-terminal region and could functionally substitute for each other . Here we generated Needleman-Wunsch pairwise alignments between individual yeast and human ING proteins. . Sequences of ING1-5 (including all known ING1 isoforms) and YNG1-3 were obtained from the NCBI Genbank database http://www.ncbi.nlm.nih.gov/. Additionally, CLUSTAL-W , T-COFFEE  and Geneious (Biomatters Ltd., NZ) were used to generate multiple sequence alignments, from which we derived the additional pairwise alignments shown in the Additional file 1. Although the alignment scores were very close to each other, given the consistency of the results obtained from the various sequence alignment tools used, the following observations can be made: (i) YNG1 shows the highest degree of sequence homology to ING1, (ii) YNG2 shows considerable homology to ING4 and ING5, and (iii) Pho23 and ING3 are similar to each other. These results agree with previous reports of phylogenetic relationships among ING proteins  and also with a recent report which attempts to classify ING proteins with respect to their association with either HAT or HDAC complexes .
ING-interacting protein prediction
Identification of domains
The use of domain-specific models
Since protein-protein interactions are primarily based on specific domains, we tried to generate domain-specific models of amino acid substitutions for the various annotated domains of the ING family proteins. This allowed us to statistically examine the validation of the domain models across species. Results obtained from this method of analysis should be more sensitive and verifiable than the generalized substitution rates used by the current ortholog detection methods based on pairwise alignments. This improved sensitivity may be due to the accounting for the specific evolution of individual protein domains and/or the greater flexibility of HMMs over simple pairwise alignments. The domain structure models generated using this approach were then used to identify possible ING-like proteins in model species for which interaction data is readily available. We investigated D. melanogaster, C. elegans, and S. cerevisiae interactome data as these species have the most extensively consolidated lists of protein-protein interactions.
Identifying human orthologs
Biochemical validation of potential human ING protein interactions
In order to select candidates for biochemical validation of human ING interactors, we compared our data with experimentally validated ING interactions in human, as listed in the STRING database . Nine of the ten experimentally validated human ING interactors with yeast homologs in the 381 gene list had extremely weak interactions (p < 0.017) . It therefore seems reasonable to biochemically validate any of the 5 potential human homologs, even though they had similarly low probability scores according to available yeast data. The fact that none of the 5 candidate human homologs were found in the validated list of ING interaction from the STRING database is not surprising, since the human interactome dataset is at present not nearly as saturated as that of yeast.
To restrict the list of 5 candidates further, we considered the biological relevance of the potential interactions to the known functions of INGs. Accordingly, the choice was amongst PAK1b, MAP3K4 (MEKK4) and p38MAPKa in descending order of homology among the 3 species (e-values 10-98, 10-45, and 10-26, respectively). We also selected another gene (RAD50) that does not fulfill all of the requirements of our method, but which is predicted by STRING. RAD50 has a weak yeast interaction score but an extremely strong homolog (10-156) in humans. We wanted to test if in such cases the yeast data could be used alone in successfully predicting human ING interactors.
Comparison to existing datasets and methods
Comparison of datasets used by Hart et al. for ING protein interaction predictions.
Total number of Interact-ions
Interactions matching our predictions
von Mering ^
von Mering ^
Based on our success in identifying valid ING interacting proteins from the unfiltered dataset in Krogan et al., we strongly agree with Hart et al. that the research community would be much better served by the release of raw interaction datasets in general for comparison and consolidation. Additionally, some original datasets, such as those from Ito el al., were only available via the Internet Archive http://www.archive.org/web/web.php as the original web links referenced in the papers no longer exist.
Comparison of ING results for existing protein interaction prediction tools.
It must be noted that our core predictions (hRPC155, PAK1B, MAP4K3, p38MAPK and GSPT1) do not overlap with other ortholog-based techniques , which would be the most natural comparison to make. Interestingly, our two marginal predictions, PDI1 and CDC37, agree with some methods in Table 2. The fact that none of the core predictions overlaps, but marginal ones do highlights the fact that different techniques were used to define orthologs. All of the methods in Table 2 used either InParanoid  or Homologene  to define interspecies gene mappings. The former maps only YNG1 and Pho23 to the human counterparts we have identified, while the latter maps all three yeast ING equivalents to ING3.
The uniqueness of our core predictions suggests that the technique we have developed provides added value over a straightforward multi-species prediction tools. Given an unfiltered dataset, it is possible that some of the techniques used in Table 2 that employ existing biochemical data would also predict some or all of our five candidate interactions. However, we are unaware of any follow-up studies by the authors of those tools using a raw dataset. It is not unreasonable to assume that the level of false positive predictions from these tools would increase substantially without some changes to their algorithms, which were built for "clean" input datasets. In contrast, we err on the side of false negatives by using strict 1) multi-species criteria and 2) gene-family specific domain models to cast a highly restrictive "lens" on Krogan et al's massive unfiltered dataset. This explains why our methodology is complementary to the existing techniques. We do not suggest that our technique will find all true positives, because interactions are not always shared between multiple species, and not all interactions have been elucidated. Rather, our technique provides guidance for researchers working on proteins whose interactions are not successfully predicted using existing techniques (such as the ING family presented here).
In this study we have shown that a high degree of conservation of the ING proteins exists between human and yeast based on their interactions with analogous proteins across these species. This is consistent with previous reports showing conservation of ING protein sequence, particularly in specific domains , and conservation of function in regulating chromatin structure through associations with HAT and HDAC complexes (reviewed in [8, 47]). In addition to the specific interactions that we have confirmed experimentally, our work highlights the fact that many additional potential and novel interactions may occur between analogous proteins in these two organisms. Over 1,000 proteins were reported to interact with YNGs  and of these proteins, we found that 381 had identified homologs in human cells. Based on our MAGPIE analysis and initial examination of three of the proposed interactions, many of the set of 381 proteins are likely to also interact with human INGs. The in silico approach we have designed allowed us to predict new protein-protein interactions for the human INGs with a high degree of success and confirmed many previously elucidated interactions such as those with p21, Karyopherin, HAT/HDAC proteins and histone H3.
Our findings suggest that ING family proteins are involved in a more diverse array of biological processes than are presently suspected from the current literature and some of the interactions suggest possible additional mechanisms that might underlie their tumor suppressor capabilities. The three new interactions we have elucidated and biochemically confirmed here, RAD50, p38MAPK and MEKK4, further link ING1 to DNA damage/stress response pathways [8, 48]. ING interaction with RAD50, an important component of the MRE11-RAD50-NBS1 complex, gives credence to previous reports linking ING proteins to DNA damage signaling and repair pathways via PCNA and GADD45 [16, 49, 50].
The overall philosophy of the prediction procedure outlined in Figure 1 is to examine the large number of interactions detected in yeast for a given protein, regardless of their probability scores, and then to reduce the list to a few candidates. This reduction is accomplished by successively keeping only the overlap of: 1) domain occurrence, and 2) interaction pairs, in at least three species. This approach stands in contrast to current automated prediction methods based on just one or two species, which often use only relatively high-scoring interaction data to prevent too many false positive predictions. The success of our approach, with the test case of ING1, suggests that the large quantity of low-scoring interaction data available in yeast is currently underutilized.
Our approach is semi-automatable (see Methods), but the researcher must: 1) assist in creating the multiple sequence alignments of domains, and 2) select biologically tenable interactions from the final shortlist generated. The procedure focuses on one gene (or set of closely related genes) at a time, and is not specific to ING proteins, but rather can be applied to any human gene with equivalents in yeast. We expect researchers who concentrate on a specific gene can beneficially use this focused approach to interaction prediction when generalized, large-scale prediction services do not yield good results.
We have developed a method using comparisons in different organisms in which homologs exist, to predict with a high degree of certainty what particular protein interactions found in unfiltered data may occur in vivo and contribute to the activities of, in this case, the ING proteins. This cross-species (yeast, fly, and human) bioinformatics-based approach was used to identify potential human ING1 interacting proteins with higher probability and accuracy than approaches based on screens in a single species. We confirm the validity of this screen and show that ING1 interacts specifically with three proteins tested: p38MAPK, MEKK4, and RAD50. These novel ING-interacting proteins further link ING proteins to cell stress and the DNA damage signaling, providing previously unknown upstream links to DNA damage response pathways in which ING1 participates.
The process of creating a list of protein interaction predictions consists of 8 broad steps (please refer to Figure 1 for the precise data flow between steps). We assume that the researcher already has a human gene, or closely related set of human genes of interest (GOI) in mind for analysis, which in our case was ING. The first step was to determine if anything resembling ING exists in yeast. This was done using  against the full set of S. cerevisiae genes downloaded from the yeast genome database (SGD) . Given good pairwise matches, we determined that it was worthwhile to proceed with step 2: the construction of domain models to provide a quantification of their conservation among species. In step 2, a multiple sequence alignment (MSA) was performed using CLUSTALW , and adjusted by hand as required. In step 3, the HMMER software http://hmmer.janelia.org/ was used to build and calibrate Hidden Markov Models (HMMs) from multiple, distinct conserved regions, i.e. potential domains, of the MSA. In step 4, these HMMs were used to search for proteins in other species with the same domains, using a DeCypher hardware-based HMM search (ActiveMotif Inc., Carlsbad, CA), although HMMER software could also be used. The database searched against was Uniprot , which provides a non-redundant set of know eukaryotic genes. Each HMM's search results was reviewed by hand, and portions of database sequences deemed matching (primarily those with e-value < 10-5, and few large gaps) were incorporated into the HMM. This searching and extra sequence incorporation was done iteratively (since new sequences in the HMM affect e-value results) until no new matches were found in Uniprot. The end result of step 4 was that for each domain we had an HMM representing the domain's very particular evolution across eukaryotic species. Step 5 was to compare the HMMs against the complete protein sets from model organisms with large amounts of interaction data, namely, worm , fly  and yeast (SGD). This was once again done using the DeCypher HMM search, and identified model organism genes with the same domains as the ING proteins. Given HMM matches in yeast and at least one more model organism (fly, in our case), we proceeded in step 6: to extract its interacting proteins. The source of the interaction data was either 1) yeast data base  or 2) the other model organism database (FlyBase) . Step 7 was to reduce the list of ING-interacting proteins in the model organisms to just those satisfying two conditions: 1) strong pairwise ING-iteracting proteins homology between human, yeast and fly, and 2) having interaction data in both yeast and fly. These filters reduced the list dramatically. Finally, in step 8, we viewed the human homologs of each ING-interacting proteins in the STRING database  and assessed the biological relevance of the potential interactions in humans.
We have focused on the use of thorough methods in our approach to maximize the sensitivity of our results. It would be possible to substitute certain methods, such as BLAST  for Smith-Waterman, or InterPro models searches  for HMM building in these steps, with the caveat of reduced predictive value of the results.
HEK293 (ATCC CRL-1573) cells were maintained in Dulbecco's Modified Eagle's medium (DMEM; Gibco-RBL) supplemented with 10% fetal bovine serum and 100 units/ml of penicillin and 100 mg/ml of streptomycin (Gibco-BRL) in an incubator with 5% CO2 at 37°C. Cells were seeded in 10 cm or 15 cm dishes 24 hrs prior to transfection.
The pCI-ING1b plasmid has been described in , and the pTP11 (Rad50 with C-terminal his tag), MEKK4 and p38MAPK constructs were kind gifts from Drs. Tanya Paull (Rad50), Steve Pellech (p38MAPK), Richard Vaillancourt (MEKK4) and James Woodgett (p38MAPK).
In vitro transfection and UV-irradiation
HEK293 cells were transiently transfected with the plasmids mentioned above at 60–70% confluence using a standard calcium phosphate protocol. Media was removed after 24 hrs, cells were washed with PBS and either exposed to 40 J/m2 of UV radiation or left untreated. Fresh media was added and cells were incubated for 2 hrs before they were harvested.
Co-immunoprecipitation and western blotting
Transfected and untransfected HEK293 cells were harvested and lysed under non-denaturing conditions in ice-cold RIPA buffer containing protease inhibitors (Complete Mini, EDTA-free protease inhibitor cocktail tablets from Roche Diagnostics). Cell lysates were sonicated on ice and centrifuged at 14,000 × g at 4°C for 15 min. The supernatants thus obtained were precleared by incubation with 20 μl 1:1 slurry of protein G-Sepharose (Amersham) for 30 min at 4°C and then incubated with 5 μg of specific antibody and 40 μl of protein G-Sepharose (1:1 slurry) or an equivalent amount of mouse anti-ING1 preconjugated with 40 μl of protein G-Sepharose (1:1 slurry) at 4°C for 3 hrs on a roller system. The immunocomplexes recovered on beads were washed two times for 5 min with 1 ml of RIPA buffer before the addition of Laemmli sample buffer. Proteins were resolved by sodium dodecyl sulfate-PAGE and transferred to nitrocellulose membranes (Hybond; Amersham). Immunoblotting was performed with a cocktail of four mouse anti-ING1 monoclonal antibodies or with rabbit anti-RAD50 polyclonal (Abcam), anti-p38MAPK (Zymed) or anti-MEKK4 (a gift from Dr. Richard Vaillancourt). Immunoreactive bands were visualized using an enhanced chemiluminescence reagent (Amersham Biosciences).
We thank Drs. Tanya Paull for Rad50, Steve Pellech and James Woodgett for p38MAPK and Richard Vaillancourt for MEKK4 DNA expression constructs. Bioinformatics work was supported by grants to CWS from Genome Canada through Genome Alberta's Integrated and Distributed Bioinformatics Platform Project, as well as by The Alberta Science and Research Authority, Western Economic Diversification, The Alberta Network for Proteomics Innovation and the Canada Foundation for Innovation. Verification of bioinformatics predictions was supported by grants from the Canadian Institutes of Health Research and the Alberta Cancer Board to KR. MAS is recipient of Alberta Heritage Foundation for Medical Research (AHFMR) and Alberta Cancer Board Studentships, CWS is the iCORE/Sun Microsystems Industrial Chair for Applied Bioinformatics and KR is an AHFMR Scientist.
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