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A Protein Prioritization Approach Tailored for the FA/BRCA Pathway

  • Anneke Haitjema ,

    Contributed equally to this work with: Anneke Haitjema, Bernd W. Brandt

    Affiliation Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands

  • Bernd W. Brandt ,

    Contributed equally to this work with: Anneke Haitjema, Bernd W. Brandt

    Affiliations Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands, Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and VU University Amsterdam, Amsterdam, The Netherlands

  • Najim Ameziane,

    Affiliation Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands

  • Patrick May,

    Affiliations Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg, Institute for Systems Biology, Seattle, Washington, United States of America

  • Jaap Heringa,

    Affiliation Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, Amsterdam, The Netherlands

  • Johan P. de Winter,

    Affiliation Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands

  • Hans Joenje,

    Affiliation Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands

  • Josephine C. Dorsman

    jc.dorsman@vumc.nl

    Affiliation Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands

A Protein Prioritization Approach Tailored for the FA/BRCA Pathway

  • Anneke Haitjema, 
  • Bernd W. Brandt, 
  • Najim Ameziane, 
  • Patrick May, 
  • Jaap Heringa, 
  • Johan P. de Winter, 
  • Hans Joenje, 
  • Josephine C. Dorsman
PLOS
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Correction

15 Oct 2013: Haitjema A, Brandt BW, Ameziane N, May P, Heringa J, et al. (2013) Correction: A Protein Prioritization Approach Tailored for the FA/BRCA Pathway. PLOS ONE 8(10): 10.1371/annotation/bd2ee3f6-0f2b-4814-9825-7b90a40fb91f. https://doi.org/10.1371/annotation/bd2ee3f6-0f2b-4814-9825-7b90a40fb91f View correction

Abstract

Fanconi anemia (FA) is a heterogeneous recessive disorder associated with a markedly elevated risk to develop cancer. To date sixteen FA genes have been identified, three of which predispose heterozygous mutation carriers to breast cancer. The FA proteins work together in a genome maintenance pathway, the so-called FA/BRCA pathway which is important during the S phase of the cell cycle. Since not all FA patients can be linked to (one of) the sixteen known complementation groups, new FA genes remain to be identified. In addition the complex FA network remains to be further unravelled. One of the FA genes, FANCI, has been identified via a combination of bioinformatic techniques exploiting FA protein properties and genetic linkage. The aim of this study was to develop a prioritization approach for proteins of the entire human proteome that potentially interact with the FA/BRCA pathway or are novel candidate FA genes. To this end, we combined the original bioinformatics approach based on the properties of the first thirteen FA proteins identified with publicly available tools for protein-protein interactions, literature mining (Nermal) and a protein function prediction tool (FuncNet). Importantly, the three newest FA proteins FANCO/RAD51C, FANCP/SLX4, and XRCC2 displayed scores in the range of the already known FA proteins. Likewise, a prime candidate FA gene based on next generation sequencing and having a very low score was subsequently disproven by functional studies for the FA phenotype. Furthermore, the approach strongly enriches for GO terms such as DNA repair, response to DNA damage stimulus, and cell cycle-regulated genes. Additionally, overlaying the top 150 with a haploinsufficiency probability score, renders the approach more tailored for identifying breast cancer related genes. This approach may be useful for prioritization of putative novel FA or breast cancer genes from next generation sequencing efforts.

Introduction

Fanconi anemia (FA) is a rare recessive genetically heterogeneous chromosomal instability disorder with both autosomal and X-linked inheritance. FA is associated with haematological defects, including bone marrow failure, aplastic anemia, myelodysplastic syndrome (MDS) and childhood acute myeloid leukemia (AML). Besides these problems, patients have a high risk for solid tumours, such as head and neck squamous cell carcinoma, gynaecological squamous cell carcinoma and esophageal carcinoma. In addition, the patients can present with liver tumours, skin tumours, brain tumours, and renal tumours [1][4]. The high cancer risk is generally attributed to impaired repair of DNA damage. Cells from FA patients display chromosomal structural abnormalities. Accordingly, patient-derived cells have turned out to be extremely sensitive to bifunctional alkylating or DNA interstrand cross-linking agents, such as mitomycin C or cisplatin [5], [6]. The latter feature is currently used in standard FA diagnostics.

Until now, fifteen complementation groups, FA-A, -B, -C, -D1, -D2, -E, -F, -G, -I, -J, -L, -M, -N, -O, and -P have been described, each corresponding to one distinct gene causing FA [7][28]. Recently, the sixteenth novel FA group was found to have mutations in XRCC2 [29]. The FA proteins function together in the FA/BRCA pathway, in which the monoubiquitination of FANCD2 and FANCI is the central event. This ubiquitination reaction is catalyzed by the so-called FA core complex, which consists of eight FA proteins (FANC-A, -B, -C, -E, -F, -G, -L, and -M). The activation of FANCD2 and FANCI recruits the other factors of the pathway, FANCD1/BRCA2, FANCJ/BRIP1, FANCN/PALB2, FANCO/RAD51C, FANCP/SLX4, and XRCC2 to repair DNA damage (Figure 1). Beside these sixteen FA proteins, there are several other proteins associating with the FA core complex, but no mutations in the corresponding genes have thus far been found. These proteins are the Fanconi Anemia Associated Proteins (FAAPs): FAAP100, FAAP24, FAAP20, FAAP16/MHF1, and FAAP10/MHF2 [30][33]. A FANCD2/FANCI associated nuclease, FAN1, was also recently identified [34][37], as well as a deubiquitinating enzyme complex consisting of USP1 and UAF1 [38], [39]. Intriguingly, most FA proteins were orphan proteins at the time of their discovery, showing no homology to other proteins and harbouring few known protein domains, while displaying a moderate evolutionary conservation. Since not all FA patients could be linked to (one of) the sixteen known complementation groups, new FA genes remain to be identified. In addition, the complex FA network remains to be further unravelled. In a previous study, bioinformatics, based on the properties of known FA proteins, was successfully combined with genetic linkage in identifying a novel FA gene, FANCI from a candidate list [18].

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Figure 1. The Fanconi anemia pathway.

For further explanation see main text (newest FA member XRCC2 not shown).

https://doi.org/10.1371/journal.pone.0062017.g001

The aim of this study is to develop an approach for the identification of proteins of the entire human proteome that potentially interact with the FA/BRCA pathway or are candidate novel FA genes. To this end, we combined a bioinformatic approach based on properties of the first thirteen identified FA proteins with publicly available tools allowing protein ranking.

Materials and Methods

The FA proteins play a pivotal role in genome maintenance, especially during the S phase of the cell cycle (Figure 1). The proteins of the so-called FA core complex monoubiquitinate FANCD2/FANCI resulting in activation of the downstream operating FA proteins. To generate a prioritization of candidate FA/FA-interacting proteins, an integrated approach was developed that involved screening of the entire human proteome (EnsEMBL) using multiple publicly available bioinformatic tools and databases. One of the sixteen known FA genes, FANCI, was discovered via selection of genes based on (general) FA protein properties from a limited number of ∼300 candidate genes [18]. This bioinformatics strategy was incorporated in the selection scheme, using the information of the first thirteen identified FA proteins available at the time. This information was combined with data from publicly available bioinformatic tools and databases.

The entire proteome was scanned for peptides with intrinsic properties that are shared by the first thirteen identified FA proteins (FANC-A, -B, -C, -D1, -D2, -E, -F, -G, -I, -J, -L, -M, and -N). The pipeline analysis started with the calculation of nuclear localization scores (WoLF PSORT; [40]) and iso-electric points (EMBOSS IEP; [41]). Orthology/paralogy relationships to mouse were obtained via EnsemblCompara [42], and Gene Ontology (GO [43]; from EnsEMBL), protein interaction data (EnVision 2), biosemantics (Nermal; [44]) and protein function analysis (FuncNet; including the literature mining tool iHop; [45]) were used. The combined information was used for ranking.

Data sources and Tools

All 77454 peptides of the human protein-coding genes were downloaded from EnsEMBL (version 56) using Biomart, together with their corresponding EnsEMBL protein, transcript, and gene IDs (ENSP, ENST, and ENSG). The peptides were used to score the 22413 associated genes.

For all protein-coding genes, the associated gene name and database, gene description, Online Mendelian Inheritance in Man (OMIM) morbid and gene accession, and GO cellular component terms were downloaded. The human data were linked to data on mouse. Human to mouse and mouse to human amino acid percentage identity, orthology type and representative (human) protein IDs were retrieved from ComparaGene. The corresponding FASTA sequences of the human proteins were used to calculate the peptide length, iso-electric point (pI; [41]), and Nuclear Localization Signal (NLS; [40]). The GO cellular component annotations for the proteins were analyzed for hierarchic relationships to nucleus (GO:0005634) with the SOAP interface to the Ontology Lookup Service of the European Bioinformatics Institute [46], [47].

Thirteen FA proteins have been used to find interacting proteins using the Molecular Interactions workflow of EnVision 2, Nermal (biosemantics), and FuncNet. The UniProt IDs of the FA proteins have been used as input to the EnVision 2 Molecular Interaction workflow to retrieve interaction data. All thirteen FA proteins were queried against: ChEMBL, DIP, IntAct, iRefIndex, MatrixDB, MINT, MPIDB, Reactome, and Reactome-Functional-Interactions (STRING and BioGrid were not available at the time). Databases that returned output were: DIP, IntAct, iRefIndex, MINT, and Reactome. Next, all UniProt/RefSeq IDs were taken and mapped to EnsEMBL peptide IDs with the Protein Identifier Cross-Reference service (PICR; [48]).

Nermal is a text-mining tool that predicts protein-protein interaction based on the similarity of the context in which proteins appear in the literature [44]. The entire matrix with the full set of all human protein-pair match scores was downloaded from the Nermal website. Since we used thirteen FA proteins, each query protein can have interactions with more than one FA protein. In case multiple interactions are present with the FA cluster, the maximum score of the query protein with any of these thirteen FA proteins was taken. Proteins interacting with one of the thirteen FA proteins according to Nermal were listed. Further, FuncNet, a web-based protein function prediction tool [45] was used to give prediction scores to the whole proteome. The thirteen FA proteins were used as reference set while the entire human proteome was divided over several query sets. The FuncNet and Nermal scores were integrated and a new Fisher score was calculated that includes all FuncNet P-values as well as a P-value for Nermal.

Results and Discussion

The FA pathway consists of sixteen FA proteins and many associating proteins. However, still unclassified FA patients exist, which have no gene defect in any of the described complementation groups or in the associating genes/proteins. This suggests that there are more genes/proteins involved in this pathway. Since the unclassified patient groups are very small or even consist of a single person, discovery of the gene by linkage analysis is hard. Modern techniques, such as whole genome sequencing of DNA from unclassified FA families, result in many candidate genes. Therefore, to further elaborate which proteins participate in the FA pathway, we used the pathway itself to screen the whole proteome for similar proteins.

Properties of FA Proteins

During the identification of FANCI the protein properties of existing FA proteins were used to narrow down the candidate list generated by genetic linkage [18]. Since we could use protein properties to distinguish more likely candidates we decided for a genome wide approach. We first compared the intrinsic protein properties of thirteen established FA proteins in order to determine several ‘common’ FA ranges.

The following protein properties have been compared between the first thirteen identified FA proteins and the latest identified FA proteins, FANCO/RAD51C, FANCP/SLX4, and XRCC2: i) human-mouse conservation (%ID aa); ii) Nuclear Localization Signal (NLS) score; iii) iso-electric points (pI); iv) subcellular localization, and v) mRNA expression patterns in normal tissues (Table 1; first 4 criteria, all sixteen described FA proteins).

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Table 1. Overview of the main intrinsic protein properties of sixteen FA proteins.

https://doi.org/10.1371/journal.pone.0062017.t001

As described previously [18], a significant subset of FA genes encoded orphan proteins at the time of their discovery. The mouse orthologs of the FA proteins display between ∼50 to ∼80% amino acid identity with the corresponding human protein. FANCF has the lowest conservation with an amino acid identity of 47% with its mouse ortholog, FANCI the highest with 80%, while the average for 16 FA proteins is 65% (Table 1). With WoLF PSORT, a subcellular localization prediction has been generated based on the amino acid sequences. FA proteins with a positive NLS score are: FANC-A, -B, -E, -M, -I, -D1, -N, -J, and -P, with an average score of 1.42 (a score of NLS >1 means a predicted specificity of 71% of being nuclear). The FA core complex is assembled from subcomplexes [49], and intriguingly always one member of the subcomplex has a positive NLS score. The score of the most recently identified member of the FA protein family, FANCP/SLX4, turned out to be high (4.91) in comparison to other FA proteins. This may suggest that the prime function of FANCP/SLX4 is in the nucleus, while other FA proteins, such as FANCA and FANCG, may have cytoplasmic functions too.

The isoelectric point (pI) of proteins gives information about the function of proteins. Almost all FA proteins have an acidic pI, except FANCB (7.65) and FANCF (9.20) with the average isoelectric point constituting 6.5. Interestingly, the pI of interacting proteins is usually different, but together result in an optimum pH for the desired location [50], [51]. For example, the isoelectric points of the subcomplex: FANCC-FANCE-FANCF, respectively 6.09, 4.89, 9.20, result in an average pI of 6.73.

Proteins that participate in the same pathway usually display similar tissue-specific expression patterns. With GeneNote [52], it is possible to survey mRNA expression levels for a selected number of tissues. In general, the expression pattern of GeneNote profiles for FA genes ranged between normalized intensity values of ∼10–100 and was similar between the different assayed tissues, although the profile for FANCG was at the higher end of intensity. The same holds true for FANCF and FANCP/SLX4, which, also for other features, belonged to the extremes (compared to the other FA proteins, FANCF has the highest pI, and FANCP/SLX4 the highest NLS, see also Table 1). FANCF is also an exception in that it is the only one-exon FA gene.

The overall picture emerging from these characterization studies is that the vast majority of FA proteins are not highly conserved, already previous described for FANC-A, -B, -C, -D1, -D2, -E, -F, and -G [53]; NLSs - if present - are of moderate strength, proteins are acidic and the corresponding mRNAs are ubiquitously, but not highly, expressed.

Retrieval of Information and Scoring of the Complete Proteome

The same features as queried for the FA proteins were also interrogated for the complete human proteome. Corresponding orthologous mouse-human identities were retrieved. The NLS and pI scores were calculated using the peptide sequences. In addition, other features were retrieved including whether the genes could be linked to specific annotations, such as to Gene Ontology, but also to MIM gene or MIM morbid. Furthermore, all proteins were queried against the 1) Molecular Interactions workflow of EnVision 2) the literature mining tool Nermal and 3) protein function prediction tool FuncNet. It should be noted that in principle for each protein/peptide, features, such as pI and NLS scores, can be determined, while for other queries especially when using the tools related to protein function only a subset of all proteins queried may return results.

The ranking scheme consists of two parts, one using the protein and another using retrieved literature data as starting point. The first part can be further subdivided in calculable features (NLS and pI), and other features, such as connection to Gene Ontology terms (Table 2).

First, the FA protein properties described above were used to aid the scoring of the whole proteome for FA-like proteins. The settings for ranking were based on properties from the first thirteen identified FA proteins, while the most recently identified FA proteins, FANCO/RAD51C [26] and XRCC2 [29] were used for validation, and FANCP/SLX4 [27], [28] was used for fine-tuning. The percentage of identity between human and mouse protein was set between 40 and 85%. Points were given when a protein has an identity within this range. Extra weight was given if the human and mouse orthologs were more similar (i.e. when the difference in percentage identity of amino acids between human-mouse and vice versa was less than or equal to 15%). For the NLS scores, one point was given to a protein if an NLS score ≤2 was detected, which is in line with the thirteen FA proteins. However, the later identified, FANCP/SLX4, which harbours a high NLS score, would not obtain a score. To cover for this extreme, we developed a scoring in which the presence of NLS scores in the higher ranges from 2 to 5 resulted in a linear decrease in assigned points, ranging from 1 to 0. In total 94% of the peptides analysed displayed scores between −0.47 and 2.00, and therefore received points. The pI range chosen, 4<pI <9, results in 75% of the analysed proteins receiving a point. Since the relevance of a nuclear localization for the DNA repair function, the presence of the GO accession “nucleus” or a descendant of nucleus was used to give one point. In case no GO accession was available zero points were given, while one point was subtracted when the accession was not nucleus. Although the first two features evidently lack large discriminative power, these can, nevertheless, contribute to the overall scoring scheme and were therefore retained as parameter.

Second, we expanded the scoring of the proteome with publicly available interaction data. If a protein in the proteome has an interaction with at least one of the thirteen FA proteins according to the EnVision 2 Molecular Interaction results, one point was given. Further, a point was given when an OMIM entry was present; this was primarily to give known genes, including those not yet related to DNA repair pathways, a higher weight. Often, the genes were already known for a long time (e.g. RAD51C) but not yet related to FA. In addition, known genes in ranked lists can be easier ruled out/or selected for often laborious further analysis as more information is present on these genes. We also obtained scores for the tools Nermal (biosemantics; literature mining) and FuncNet (which includes the literature mining tool iHop). Nermal turned out to have a good coverage of the proteome and for a large number of proteins Nermal was the only or one of the few tools providing a prediction. However, for other proteins FuncNet was more informative. For ranking purposes, we therefore included both the Nermal and the FuncNet scores.

Validation of the Ranking Approach

The ranking of all proteins based on FA-protein properties, resulted in a top 150 list (Table 3). The FA proteins score in general high, as can be expected since the scoring is based on their properties (Table 4). Furthermore, only 1673 unique associated gene names (including the FA proteins and proteins without a Nermal and/or FuncNet score) score 5 points or higher (Table 5) which is 7.5% of all unique associated gene names. The FANCF protein scored relatively low, compared to the other FA proteins. This could be attributed to the fact that in the automated scoring the mouse ortholog FANCF was missing, resulting in zero points for the human-mouse comparison feature. After manual correction of the percentage amino acid identity, FANCF ends up at the same level as FANCJ/BRIP1 and FANCD1/BRCA2 with 6.34 points. We hypothesized that in this particular ranking, a cut-off score ≥5 will select for FA like candidates (Table 4 and 5).

Since only the first thirteen FA proteins were used for pipeline generation, we could also determine how the three newest members of the FA protein family, FANCO/RAD51C, FANCP/SLX4, and XRCC2 were performing (Table 4). The overall score for FANCO/RAD51C and XRCC2 turned out to be in the range of known FA proteins, while FANCP/SLX4 scored lower than known FA proteins. FANCP/SLX4 has a relatively high NLS score compared to the other FA proteins, even though we adjusted the NLS score for extremes. In addition, at the time of data retrieval no OMIM entry was present for SLX4.

In the case of FA, there are strong candidates for FA genes uncovered by biochemical studies. These include the so-called FAAP proteins [30][33], which associate to the core complex, a nuclease FAN1 [34][37], and a deubiquitinating enzyme complex consisting of USP1 and UAF1 [38], [39]. These proteins, upon disruption, display the FA hallmarks including MMC sensitivity. Therefore, we were interested in how these proteins were ending up in our ranking (Table 6). Three of these, FAAP100, USP1, and FAAP24, would also have been suggested with our automated ranking scheme, with a respectively score of 6.15 (rank: 299), 5.44 (rank: 890), and 5.16 (rank: 1011). The protein UAF1 is a highly conserved in mouse (98% aa identity), and therefore had a score of 4.05 (no points for the conservation interval 40–85%). UAF1 functions as an activator of USP1 (by itself USP1 has almost no deubiquitinating activity), and the protein complex together functions as a deubiquitinating enzyme. Interestingly, it has been reported that UAF1 is an abundant protein in human cells, and that UAF1 may have additional functions [39], besides regulating the FA pathway. For the three other FAAPs (FAAP16∶4.05, FAAP20∶2.00, and FAAP10∶1.11) and FAN1 (4.53), manual correction of the orthology feature was required, as was the case FANCF. In addition, FAAP10 was not properly scored, due to the fact that the threshold for scoring of peptides was set at larger than 100 amino acids, and FAAP10 is 81 amino acids in length. Overall, the chosen settings leads to logical candidates in the top. However, awareness is important, since strong candidates may not show up by, for example, wrong mapping of orthologs.

To further validate the method, we analyzed several proteins that are involved in DNA repair processes that the FA/BRCA pathway is coordinating, which include nucleolytic incision, translesion DNA synthesis (TLS), and homologous recombination [54] (Further see Table 7). Of the 26 analyzed proteins, 20 score above 5 points, of which 12 are also in the top 150: BRCA1, XRCC3, NBN/NBS1, RAD52, MUS81, XRCC2, RAD51L3/RAD51D, RAD51, ERCC1, RBBP8/CtIP, RAD54B, and REV1 (Table 3 and 7). REV1, is involved in translesion DNA synthesis and scores higher (6.26) than REV3 (4.22) and REV7 (3.14) that together form a complex called Pol ζ, mainly because the last two are highly conserved (more than 85%). REV1 functions as a scaffold protein to recruit and coordinate TLS polymerase, such as Pol ζ [55], [56]. Pol ζ has besides translesion DNA synthesis another essential role in cell proliferation [57]. The difference in score suggest that a high score (≥5) can distinguish between proteins that have a direct interaction with the FA/BRCA pathway (USP1 and REV1) and proteins that are coordinated via this link (UAF1 and Pol ζ).

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Table 7. Overview proteins involved in processes coordinated by the FA/BRCA pathway.

https://doi.org/10.1371/journal.pone.0062017.t007

In addition, we investigated a prime candidate for a novel FA gene based on whole-genome next generation sequencing data. The particular gene, PRR12, harboured two possible pathogenic mutations, which followed proper segregation as expected for a recessive disorder (unpublished data). However, based on our ranking system, this gene turned out to be an unlikely candidate, having only 1 point. Further functional testing, confirmed that PRR12 did not display the hallmark FA features. Upon PRR12 disruption by siRNA, the cells were still resistant to MMC. Furthermore, downregulation of PRR12 did not result in a reduction of RAD51 focus formation (Figure 2; Material and Methods S1), which is characteristic for the cells of the patient in which the PRR12 variants were found (unpublished data). This confirmed that a low score in our ranking corresponded to a non-FA protein (in this case).

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Figure 2. Validation of FA candidate PRR12.

MMC growth inhibition assays and RAD51 foci in HeLa cells after siRNA knockdown with specific siRNAs for BRCA2, FANCD2, and PRR12 or with negative control siRNAs. A) MMC growth inhibition assays after siRNA knockdown of BRCA2 and FANCD2 show sensitivity to MMC. siRNA knockdown of PRR12 does not result in MMC sensitivity. Data represent average values from one representative experiment performed in triplicate. B) Relative gene expression was calculated via the 2−ΔΔCT method, normalized against the non targeting oligo and TBP. The normalized expression (average of two different primer sets per gene) of the non targeting oligo was set at 100%, and knockdown efficiency for BRCA2, FANCD2, and PRR12 siRNA has been indicated. C) siRNA knockdown of BRCA2 results in diminished RAD51 foci, whereas siRNA knockdown of FANCD2 and PRR12 show RAD51 foci after knockdown. Green: αRAD51; Red: αTOPRO3.

https://doi.org/10.1371/journal.pone.0062017.g002

Furthermore, we evaluated the performance of the ranking scheme for identification of proteins enriched for functional classifications relevant to FA. A clear enrichment was found when screening the top 150 of the list for GO annotations “response to DNA damage stimulus” (GO:0006974; observed: 71; expected: 5; P-value: 5.50E−64) and “DNA repair” (GO:0006281; observed: 61; expected: 4; P-value: 1.28E−60; Figure 3). To demonstrate the effect of our combined scoring using multiple criteria, we also compared our top 150 list (“Ranked”) to the top 150 lists of the literature-mining tool “Nermal” and the protein function prediction tool “FuncNet”. When only FuncNet was used, the number of genes with the GO annotation for “response to DNA damage stimulus” (observed: 53; expected: 5; P-value: 4.27E−42) and “DNA repair” (observed: 47; expected: 3; P-value: 1.54E−42) were clearly lower compared to our ranking. However, the top 150 of Nermal “response to DNA damage stimulus” (observed: 65; expected: 5; P-value: 7.10E−56) and “DNA repair” (observed: 62; expected: 3; P-value: 1.24E−62) results in similar number of genes for the “DNA repair” annotation (Ranked: 61 genes and Nermal: 62 genes). To study the difference between our ranking (including Nermal and FuncNet) and Nermal alone, we also analyzed the remaining genes in the top 150 that do not have the “DNA repair” annotation for functional categories (Table 8 and 9). We chose for the “DNA repair” term so that both lists are comparable in size (for Ranked: 150 genes –61 genes with GO term “DNA repair” and 89 genes that do not have the GO term “DNA repair” and for Nermal: 150 genes –62 genes with GO term “DNA repair” and 88 genes that do not have the GO term “DNA repair”). In the case of our approach, the remaining genes were significantly enriched for cell cycle-regulated genes (10 most significant categories; all cell cycle; Table 8). The emphasis on cell cycle parallels the important role for FA proteins in the cell cycle. From the top 10 GO enrichments obtained for Nermal, only 4 GO terms were cell-cycle related (Table 9). The relation between the different cell-cycle related proteins uncovered by our approach (including Nermal and FuncNet) or with Nermal alone is shown in Figure 4.

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Figure 3. GO enrichment top 150 of Ranked, Nermal, and FuncNet.

Number of genes observed with the GO terms “response to DNA damage stimulus” (GO:0006974) and “DNA repair” (GO:0006281) in the top 150 of our combined ranking approach “Ranked”, the literature mining tool “Nermal”, and the protein function prediction tool “FuncNet”. Number of genes observed, number of genes expected (P-value) for GO term “response to DNA damage stimulus” (GO:0006974): Ranked 71, 5 (5.50E−64); Nermal 65, 5 (7.10E−56); FuncNet 53, 5 (4.27E−42). GO term “DNA repair” (GO:0006281): Ranked 61, 4 (1.28E−60); Nermal 62, 3 (1.24E−62); FuncNet 47, 3 (1.54E−42).

https://doi.org/10.1371/journal.pone.0062017.g003

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Figure 4. Overview genes involved in “Cell cycle process”, Ranked vs Nermal.

Analysis of the overlap between our combined approach and Nermal for the GO biological process term “Cell cycle process” (GO:0022402). Top 150 of either our combined ranking or Nermal alone were analyzed for GO term “DNA repair” (GO:0006281) and these genes were discarded. The remaining lists (Ranking combination scheme: 89 genes and Nermal: 88 genes) were further compared. In total, 7 genes were found in common (BACH1, NFE2L3, DDX11, CHEK2, MAPT, BUB1B, UBASH3A). A combined list of the remaining genes (Ranking: 81 genes and Nermal: 82 genes; total 163 genes) was analyzed with the Genomatix Pathway System (GePS). The biological process term “Cell cycle process” was the most enriched (P-value: 4.83E−25). The relation between the different cell cycle proteins is depicted (red: candidates combined ranking scheme, blue: candidates Nermal).

https://doi.org/10.1371/journal.pone.0062017.g004

Proteins that participate in the same pathway usually display similar expression. As mentioned above, FA genes are expressed in a variety of tissues, but not at very high levels (cf. GeneNote profiles). The expression of a subset of mRNAs for FA genes is downregulated upon serum withdrawal and, although less strongly, upon reaching confluence, as for example tested in the human T98G cell model [58]. Genes such as DNA2, BUB1, DCLRE1B, DDX11 and GMNN from the top 150 list are similarly regulated for these cell cycle features and may display an as yet not recognized interaction with the FA network.

So far, only biallelic/recessive mutations in the genes of the upstream branch (FANC-A, -B, -C, -E, -F, -G, -L, and -M) cause a clinical phenotype. The downstream branch, harbouring FANCD1/BRCA2, FANCN/PALB2, and FANCJ/BRIP1, both biallelic and/or monoallelic mutations result in a disease phenotype, notably breast cancer predisposition [59]. The top candidate in our ranking BRCA1, is another breast cancer predisposition gene. Interestingly, recently biallelic deleterious BRCA1 mutations were discovered in a 28 year-old woman diagnosed with ovarian cancer, who was extremely sensitive to DNA interstrand cross-linking chemotherapy [60]. Haploinsufficiency defines the state of a given gene where a single copy is insufficient to maintain normal function, which is one of the major causes of dominant diseases. We used recent data from Huang et al. (2010 [61]) to estimate the haploinsufficiency probability p(HI) in our top 150 genes. A p(HI) of 1 indicates high probability of haploinsufficiency, while 0 indicates haplosufficiency. We could determine p(HI) values for 143 genes resulting in a mean probability of 0.45 which is significantly different from the mean of 0.29 for the whole human set for which p(HI) could be predicted (Figure 5). To determine how the p(HI) is distributed over the 143 genes in our top 150 we plotted these against each other (Figure 6) and both genes with low and high p(HI) were detected, as expected. When comparing the group of genes with both a high ranking score (>6.75), and with almost opposite p(HI) values (<0.2 versus >0.9), four FA genes ended up in the first group, while established breast cancer predisposition genes ended up in the latter group. However, PALB2 has a low p(HI) value (Table 10). Interestingly, the five genes with a ranked score above 6.75 and a p(HI) value above 0.9 (TP53, BRCA1, RPA1, BUB1, and CHEK2) have Medical Subject Heading (MeSH) terms for small cell carcinoma (C04.557.470.200.380) and glioblastoma (C04.557.580.625.600.380.080.335, C04.557.470.670.380.080.335, C04.557.465.625.600.380.080.335). These data suggest that by overlaying the p(HI) score the top 150 can be divided in two groups, one containing the upstream branch and related genes, and the other group with the downstream branch related genes.

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Figure 5. Distribution of haploinsufficiency probabilities (p(HI)).

Distribution of haploinsufficiency probabilities (p(HI)) for the human genome (from Huang et al. [61]). The mean p(HI) values for all human genes and 143 genes of our top 150 set are given as respectively, light blue and orange circles on the x-axis. p(HI)  = 1 means highly haploinsufficient, p(HI)  = 0 haplosufficient.

https://doi.org/10.1371/journal.pone.0062017.g005

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Figure 6. Distribution of scores of the top 150 vs p(HI).

The scores of the top 150 of our ranked approach were plotted against the p(HI) values of Huang et al. [61]. Shown are 143 genes, since 7 genes were not available in Huang’s data set.

https://doi.org/10.1371/journal.pone.0062017.g006

Application of Prioritization Approach

Ranking tools can be valuable to sort/sift through candidates obtained through whole exome sequencing experiments. In principle, the identification of a causal gene in the case of a recessive disorder, such as Fanconi anemia, is straightforward; the causal gene should harbour two pathogenic mutations (1 mutation in both copies). In practice, the situation may be more cumbersome. Genes harbouring two possible pathogenic mutations may turn out to be not the cause of the disease (see also above), while genes for which only one mutation has been reported may in fact require further study due to the fact that mutations could be missed owing to imperfections in the sequencing procedure, such as lack of coverage or complex mutation types not detectable by the technique. Lists of genes harbouring only one mutation will be inevitably significantly longer than lists of genes harbouring two mutations. Sifting through these lists may be significantly aided by specially tailored ranking tools which should be combined with manual curation.

Conclusion

We show how we used publicly available bioinformatics tools and databases to generate ranked lists tailored for FA-like genes. While the tools by themselves can be discriminative, an integrative approach exploiting multiple intrinsic features of FA proteins combined with functional and text mining resources may result in lists that are highly enriched for proteins of the network of interest. This kind of prioritization can be useful for Next Generation Sequencing projects to prioritize possible disease genes from an extensive list of candidates. Furthermore, since some of the FA proteins have been identified as genes involved in breast cancer we believe that our prioritization strategy can also be applied in studies to screen for breast cancer predisposition genes.

Acknowledgments

We express gratitude to all members of the Oncogenetic section for useful discussions. We are grateful to the children affected with FA and their families who have donated tissues allowing the responsible FA genes to be cloned and the discovery of the FA pathway.

Author Contributions

Conceived and designed the experiments: JCD. Performed the experiments: AH NA. Analyzed the data: AH BWB PM. Wrote the manuscript: AH BWB JCD. Reviewed and edited manuscript: JH JPdW HJ.

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