Open Access

Predicting candidate genes for human deafness disorders: a bioinformatics approach

BMC Genomics20067:180

DOI: 10.1186/1471-2164-7-180

Received: 20 March 2006

Accepted: 19 July 2006

Published: 19 July 2006

Abstract

Background

There are more than 50 genes for autosomal dominant and autosomal recessive nonsyndromic hereditary deafness that are yet to be cloned. The human genome sequence and expression profiles of transcripts in the inner ear have aided positional cloning approaches. The knowledge of protein interactions offers additional advantages in selecting candidate genes within a mapped region.

Results

We have employed a bioinformatic approach to assemble the genes encoded by genomic regions that harbor various deafness loci. The genes were then in silico analyzed for their candidacy by expression pattern and ability to interact with other proteins. Such analyses have narrowed a list of 2400 genes from suspected regions of the genome to a manageable number of about 140 for further analysis.

Conclusion

We have established a list of strong candidate genes encoded by the regions linked to various nonsyndromic hereditary hearing loss phenotypes by using a novel bioinformatic approach. The candidates presented here provide a starting point for mutational analysis in well-characterized families along with genetic linkage to refine the loci. The advantages and shortcomings of this bioinformatic approach are discussed.

Background

Hearing loss, acquired or genetic, is a major worldwide public health concern. Numerous genes have been linked to hearing disorders [1]. These disorders may be syndromic or nonsyndromic; conductive, sensorineural, or mixed; and prelingual or postlingual [2]. The various genetic forms of hearing loss are distinguished based on otologic, audiologic and physical examination combined with linkage analysis. Some representative deafness genes that have been identified include the Alport syndrome (COL4A3, COL4A4 or COL4A5 genes), branchio-oto-renal syndrome (EYA1 gene), Mohr-Tranebjaerg syndrome (TIMM8A gene), Pendred syndrome (SLC26A4 gene), Jervell and Lange-Nielsen Syndrome (KVLQT1 and KCNE1 genes), Usher syndrome with its several types, Norrie disease (NDP gene), DFNB1 (GJB2 gene), DFN3 (POU3F4 gene), DFNB4 (SLC26A4 gene), DFNA6/14 (WFS1 gene), and several others [3, 4]. The mutational analysis of genes such as GJB2 (encoding the protein connexin 26) and GJB6 (encoding the protein connexin 30) [3, 5, 6] has aided diagnosis and geneticcounselling.

Syndromic hearing loss is associated with a variety of other clinical findings and is relatively less prevalent. In contrast, nonsyndromic hearing loss accounts for more than 70% of deafness cases and involves autosomal as well as X or Y -linked deafness phenotypes [7]. The molecular causes of nearly all nonsyndromic hearing loss are associated with inner ear structural damage, and changes in both the inner and the middle ear [8]. Mutations in genes such as the ACTG1, COCH, COL11A2, DFNA5, EYA4, GJB2, GJB6, KCNQ4, MYO6, MYO7A, TECTA, TMC1, and WFS1, as well as altered expression of genes such as GJB3 and MYO1A have been associated with the autosomal dominant types that are generally progressive and involve changes in inner ear [911]. The autosomal recessive phenotypes are associated with mutations in genes such as the CDH23, CLDN14, ESPN, GJB2, GJB6, MYO15A, MYO6, MYO7A, OTOF, PCDH15, SLC26A4, STRC, TECTA, TMC1, TMIE, TMPRSS3, and USH1C, as well as altered expression of GJB3 [8].

The map locations of a large number of nonsyndromic autosomal recessive deafness phenotypes are known, but the specific genes responsible for all these phenotypes have not been identified [4]. The cloning of genes involved in such phenotypes requires refinement of the suspected genomic interval to as short a region as possible by linkage analysis. However, it is not always possible to map a gene within an interval that is amenable for mutation analysis. The mutation analysis of all genes encoded by a large genomic interval is extremely labor-intensive. We describe here a bioinformatic approach that can reduce the candidate genes to a manageable number for mutation analysis. Initially, all the genes from a particular locus are cross-referenced to the databases of expressed mouse inner ear genes and the expressed human cochlear genes. The alternative procedure included a search for interacting proteins for the gene products mapping to the candidate region. As presented here, this approach has led to a set of specific candidate genes.

Results and discussion

The locations of 23 autosomal dominant and 27 autosomal recessive nonsyndromic deafness phenotypes mapped to several chromosomes downloaded from hereditary hearing loss homepage are shown in Tables 1 and 2[4]. Additional loci for nonsyndromic conditions are mapped to chromosomes 1, 8, X and Y [4]. The hereditary hearing loss homepage is updated on a regular basis. The marker boundaries of these locations encompass between 1.4 and 18.6 million basepairs (Mbp) for various loci. To generate a set of candidate genes for the listed loci, a strategy schematically represented in Figure 1 was followed. The determination of coding sequences and/or genes in a genomic region was made by Unigene [12]. However, the genes encoded in a large genomic interval are too many to be characterized by mutational analysis in a gene-by-gene approach. Therefore, we used the human cochlea and mouse inner ear expression databases [13, 14] to eliminate from the candidate list certain genes that were not expressed in these organs. Such in silico expression analysis relies on the assumption that the expression databases are comprehensive. However, the characterization of all transcripts expressed in the ear is far from complete. We, therefore, introduced another step in our candidate gene strategy by taking advantage of the human protein reference database (HPRD) and generated a list of interacting genes for every gene mapping to candidate deafness loci [15]. The rationale for protein interaction is as follows. If a gene encoded in the candidate region interacts with a gene that is either involved in inner ear development/function, or a protein shows interaction with more than one candidate genes mapping to different loci, then such a gene is likely to be involved in the phenotype in question. The interaction pattern of the gene products from Usher syndrome is a good example to illustrate this point. The known gene products for several Usher syndrome loci are known to form interactions in vivo [16]. The mutation of each one of these genes affects protein interactions and influences Usher type 1 phenotype [17]. The five forms of Usher syndrome have defects in myosin VIIA, harmonin, cadherin 23, protocadherin 15, and a putative scaffolding protein sans. Harmonin binds sans, and it also binds myosin VIIA and protocadherin 15 [34]. The role of cadherins in mediating cell-cell interaction is well-characterized. Furthermore, interactions of harmonin (USH1C) with USH2A, USH2C and USH2B are mediated by PDZ domains [35, 36]. In retrospect, if the interacting protein strategy had been used to select candidate genes for Usher syndrome subtypes, it is likely that several genes could have been eliminated from consideration. Therefore, it is reasonable to assume that physical interactions will exist between proteins that are involved in inner ear developmental pathway or inner ear signal transduction pathways, and mutations in any one protein of the pathway is likely to give the same altered phenotype. If proteins of interacting networks can be identified or predicted, then such genes are natural candidates for a given phenotype. The above hypothesis is the underlying rationale for incorporating interacting proteins as a criterion for selecting candidate genes presented in this paper. Briefly, the strategy is as follows. First, assemble the genes encoded in all candidate intervals, list the proteins that interact with genes in the candidate region, and then search for candidates on different loci that interact with a common protein. Such a criterion will fulfil the rationale of putative involvement of proteins at two different loci involved in a common biological process, and by association the respective genes mapping to two different loci will be considered as candidates.
Table 1

Autosomal dominant nonsyndromic loci.

Locus Name

Location

DFNA7

1q21–q23

DFNA16

2q24

DFNA18

3q22

DFNA21

6p21

DFNA23

14q21–q22

DFNA24

4q

DFNA25

12q21–24

DFNA27

4q12

DFNA30

15q25–26

DFNA31

6p21.3

DFNA32

11p15

DFNA34

1q44

DFNA37

1p21

DFNA41

12q24–qter

DFNA42

5q31.1–32

DFNA43

2p12

DFNA44

3q28–29

DFNA47

9p21–22

DFNA49

1q21–q23

DFNA50

7q32

DFNA53

14q11–q12

DFNA54

5q31

Table 2

Autosomal recessive nonsyndromic loci.

Locus Name

Location

DFNB5

14q12

DFNB13

7q34–36

DFNB14

7q31

DFNB15

3q21–q25, 19p13

DFNB17

7q31

DFNB19

18p11

DFNB20

11q25–qter

DFNB24

11q23

DFNB25

4p15.3–q12

DFNB26

4q31

DFNB27

2q23–q31

DFNB28

22q13

DFNB32

1p13.3–22.1

DFNB33

9q34.3

DFNB35

14q24.1–24.3

DFNB38

6q26–q27

DFNB39

7q11.22–q21.12

DFNB40

22q

DFNB42

3q13.31–q22.3

DFNB44

7p14.1–q11.22

DFNB46

18p11.32–p11.31

DFNB48

15q23–q25.1

DFNB49

5q12.3–q14.1.

DFNB55

4q12–q13.2

DFNB58

2q14.1–q21.2

DFNB60

5q22–q31

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-7-180/MediaObjects/12864_2006_Article_563_Fig1_HTML.jpg
Figure 1

Schematic flow for information processing to predict candidate genes. The rectangles contain tasks that were processed in the sequence as indicated by arrows.

The application of candidate gene isolation is demonstrated for the autosomal dominant condition DFNA27. The gene is mapped to the genomic interval 4q12 spanning 15 Mbp [4]. This region codes for 36 known and 30 hypothetical proteins (Table 3) [18]. The comparison of these genes to expression databases reduced the list to 10 genes from the human cochlear database and three found in the mouse inner ear (Table 4) [13, 14, 19]. The possibility remained for the elimination of a stronger candidate just on the basis that it did not score a hit in expression databases. To avoid such an error, we have assembled lists of interacting proteins by using the human protein reference database (HPRD) [15] for every gene identified by GeneRetriever® from the candidate region. If an interactor of a gene in the candidate interval is expressed in inner ear then the gene is considered a candidate. Alternatively, the interacting genes from a specific locus list were compared against lists from other loci to identify if a hit was scored against proteins among two or more lists. The original genes corresponding to such interactor(s) were considered as candidates for the respective deafness loci. The strong candidates, based on the above analyses, for various deafness loci are presented in Table 5.
Table 3

GeneRetriever list of known genes found within the DFNA27 locus.

Gene ID

Gene Entrez

Type

GeneDescription

Expressed in Cochlear Library

Interactor Cochlear Protein

KDR

3791

Known

kinase insert domain receptor (a type III receptor tyrosine kinase)

Yes

VEGF A, Grb2, CDH5

FLJ13352

79644

Known

hypothetical protein FLJ13352

No

 

TPARL

55858

Known

TPA regulated locus

Yes

 

CLOCK

9575

Known

clock homolog (mouse)

No

 

PDCL2

132954

Known

phosducin-like 2

No

 

NMU

10874

Known

neuromedin U

Yes

 

SEC3L1

55763

Known

SEC3-like 1 (S. cerevisiae)

No

 

KIAA0635

9662

Known

KIAA0635

Yes

 

KIAA1211

57482

Known

KIAA1211 protein

No

 

MRPL22P1

359738

Known

mitochondrial ribosomal protein L22 pseudogene 1

No

 

NRPS998

132949

Known

2-aminoadipic 6-semialdehyde dehydrogenase

No

 

PPAT

5471

Known

phosphoribosyl pyrophosphate amidotransferase

Yes

 

PAICS

10606

Known

phosphoribosylaminoimidazole carboxylase, succinocarboxamide synthetase

No

 

SRP72

6731

Known

signal recognition particle 72kDa

No*

Caspase 3

ARL9

132946

Known

ADP-ribosylation factor-like 9

No

 

GLDCP

2732

Known

glycine dehydrogenase (decarboxylase) pseudogene

No

 

HOP

84525

Known

homeodomain-only protein

Yes

HDAC2

SPINK2

6691

Known

serine protease inhibitor, Kazal type 2 (acrosin-trypsin inhibitor)

No

 

REST

5978

Known

RE1-silencing transcription factor

Yes

 

C4orf14

84273

Known

chromosome 4 open reading frame 14

No

 

POLR2B

5431

Known

polymerase (RNA) II (DNA directed) polypeptide B, 140kDa

Yes

 

IGFBP7

3490

Known

insulin-like growth factor binding protein 7

No*

VEGF A, IGF1

SRIL

6644

Known

sorcin-like

No

 

LPHN3

23284

Known

latrophilin 3

No

 

EPHA5

2044

Known

EphA5

No

 

CENPC1

1060

Known

centromere protein C 1

No

 

BRDG1

26228

Known

BCR downstream signaling 1

No*

KIT

FLJ10808

55236

Known

hypothetical protein FLJ10808

Yes

 

GNRHR

2798

Known

gonadotropin-releasing hormone receptor

No

 

HAT

9407

Known

airway trypsin-like protease

Yes

PAR-2

FLJ16046

389208

Known

FLJ16046 protein

No

 

YT521

91746

Known

splicing factor YT521-B

No*

KHDRBS3, FYN

DESC1

28983

Known

DESC1 protein

No

 

UGT2B17

7367

Known

UDP glycosyltransferase 2 family, polypeptide B17

No

 

*These genes are not listed in the human cochlear database. However, their interactors are present in the cochlear database.

Table 4

Cochlear-expressed EST found within DFNA27 locus.

Locus

Location

Genes from Cochlea

Genes from Mouse

Genes from Known Disorders

DFNA27

4q12

FLJ10808

EPHA5

None

  

HAT

HAT

 
  

HOP

KDR

 
  

KIAA0635

  
  

KDR

  
  

NMU

  
  

POLR2B

  
  

PPAT

  
  

REST

  
  

TPARL

  
Table 5

List of candidates for various deafness loci.

Loci

Location

Candidates

Loci

Location

Candidates

DFNA7

1q21–q23

ATP1B1

DFNB5

14q12

**

  

F5

DFNB13

7q34–36

SLC37A3

  

MYOC

DFNB17

7q31

WNT2

  

SLC19A2

DFNB19

18p11

LAMA1

  

POU2F1

DFNB20

11q25–qter

KCNJ1

DFNA16

2q24

*

  

TECTA

DFNA18

3q22

**

  

SLC37A2

DFNA21

6p21

*

DFNB27

2q23–q31

ITGA6

DFNA23

14q21–q22

**

  

SP3

DFNA25

12q21–24

HAL

DFNB28

22q13

KCNJ4

  

SLC25A3

  

MT

  

IGF1

  

SOX10

DFNA27

4q12

HAT

DFNB32

1p13.3–22.1

COL11A1

  

KDR/VEGFR2

  

DR1

DFNA30

15q25–26

**

  

F3

DFNA31

6p21.3

TNF

DFNB33

9q34.3

TUBB2

  

POU5F1

  

SLC34A3

DFNA34

1q44

**

DFNB35

14q24.1–24.3

NUMB

DFNA42

5q31.1

FGF1

  

FOS

  

GFRA3

DFNB38

6q26–q27

QK1

  

IK

DFNB39

7q11.22–q21.12

POR

  

PCDH1

DFNB40

22q

CRYBB2

  

DIAPH1

  

SLC25A1

  

POU4F3

  

TBX1

DFNA47

9p21–22

**

   

*The chromosomal regions for DFNA16 and DFNA21 code for 7 and 9 genes, respectively. However, none of these genes were listed in the mouse or human inner ear databases. The candidates as described in the text are based on their functional significance.

**The chromosomal regions for DFNA18, DFNA23, DFNA30, DFNA34, DFNA47 and DFNB5 code for a substantial number of genes. A small fraction of these genes are listed in human and mouse inner ear databases as shown in Table 6. Furthermore, no hits were scored by the protein interaction approach. Therefore the genes scoring hits in the ear databases may be considered as candidates and prioritized based on their function. Some of these priority candidates are described in the text.

In principle, the interactions-based strategy can be targeted to identify candidates for deafness if a database for interacting proteins involved in inner ear development and function is available. For example, oncomodulin and prestin are expressed in outer hair cells [20]. The protein interaction approach could link the possible candidate genes to specific cochlear cells by identifying known interactants. If the interactors happen to map to a region harboring a deafness gene, such interactors are obvious candidates for mutational analysis. However, such an approach will require identification of interacting proteins. The primary limitation of the in silico approach described here is inadequate description of interacting protein networks.

The strong candidate list includes genes such as various cadherins, collagens, some cytoskeletal components and a number of growth factors and inner ear specific transcripts. For example, HAT (Human airway trypsin-like protease) from the DFNA27 locus is known to enhance cell growth and IL-8 production. It has been implicated in induction of PAR-2 (protease activated receptor)-mediated IL-8 release in psoriasis vulgaris [21]. Because HAT is expressed in the ear, and protease activated receptor (PAR-2) has the ability to activate G-proteins followed by an increase in calcium ion concentration, we consider HAT as a candidate. KDR(kinase insert domain receptor), a vascular endothelial growth factor(VEGF) receptor-type 2, from the same locus shows age-dependent expression in the inner ear [22]. Our analyses indicated that only a fraction (200/2400) of genes mapping to various genomic intervals was expressed in the inner ear. We attribute these observations to depth of inner ear libraries. It is likely that the genes being scored in these libraries have multiplicity for certain transcripts and absence of other transcripts. For example, out of 153 genes at the DFNA7 locus, only 18 genes are present in the cochlear library. We cannot reasonably rule out the expression of the remaining 135 genes in the inner ear. Therefore, the approach presented here will be more comprehensive if we do not include ear expression in this scheme. Consequently, in a second attempt to mine the protein-interaction data obtained from the HRPD, we analyzed all genes encoded in the candidate intervals for their interactors. The interaction data were considerably exhaustive and resulted in many more possible candidates with their expression not reported in the ear expression library. A summary of gene numbers at different loci before and after interacting proteins analysis using the ear-expression scheme is presented in Table 6. The mouse syntenic genes are also indicated in these results. The number of unfiltered candidate genes for each locus obtained by interacting proteins analysis is shown in Table 7. To elucidate the relevance of genes not found in the ear-expression library as possible candidates, we performed a literature search cross-referencing the identified gene with any reported hearing-associated condition in humans or other model animals. Some of these genes were linked to ear-development or hearing impairment as a secondary or unrelated symptom of other conditions. For example, Neurod1 gene mapping to DFNB27 locus was not reported in any of the inner ear libraries. However, it appears to participate in the development of the auditory system as NeuroD1 null mice exhibited severe reduction of sensory neurons in the cochlear-vestibular ganglion [23]. E2F3, a transcription factor of the E2F family mapping to the DFNA21 locus, may be indirectly implicated by its ability to regulate cell proliferation possibly during the developmental stages [24]. Other candidate genes from the unfiltered candidate analysis for the various loci are listed in Table 8. Thus the unfiltered strategy adds 51 candidates for 25 loci and expands the candidate list to 92 genes for further mutation analysis.

Our approach indicated the presence of possible candidates within most of the mapped loci. However, prediction of candidate genes was not easy for loci indicated by asterisks in Table 7, because the genes mapping to these loci did not fulfil the criteria we have employed. We further examined these genes on the basis of their reported function. The following description pertains to specific genes that are not indicated in the candidate lists. Within the DFNA16 locus, SCNA3 and SCNA2, both being voltage-gated sodium channels, can be considered candidates based on involvement of related sodium channels in hearing [25]. Similarly, ATP2C1 in DFNA18 locus is a likely candidate because mutations in a related ATPase have been shown in mice that are profoundly deaf and have a balance defect [26]. The EphB1 gene, within the DFNA18 locus, plays a major role during the development of the inner ear in mice [27]. The DFNA23 locus has six1 gene that plays a pivotal role in the control of the mouse otic vesicle patterning [28]. Neugrin, mapping to DFNA30 locus, appeared to be an appropriate candidate as it was shown to be up-regulated throughout neuronal differentiation [29]. A possible candidate for the DFNA47 locus is the transcription factor Nfib, an essential player in the maturation of lungs and brain development [30]. The splicing regulation carried out by Pnn, mapping to the DFNB5 region, is a reasonable candidate [31]. We believe the genes presented in this article may serve as starting candidates toward identifying molecular mechanisms for specific deafness phenotypes.
Table 6

Summary of gene numbers from expression-library filtered analysis.

Condition

Map Location

Interval

Number of Genes

Mouse Synteny/Chromo #

Mouse Genes

Human Cochlear Genes

Mouse Inner ear Genes

Interacting Proteins

Shared Interactors

DFNA7

1q21–q23

18.6 Mb

153

1

161

18

6

15

5

DFNA16

2q24

2.6 Mb

7

2

16

0

0

0

0

DFNA18

3q22

12 Mb

116

6, 9

111

18

4

9

1

DFNA21

6p21

3.5 Mb

9

10

48

0

0

0

0

DFNA23

14q21–q22

8 Mb

79

12

60

7

1

4

0

DFNA25

12q21–24

14 Mb

108

10, 5

102

17

2

11

3

DFNA27

4q12

15 Mb

71

5

97

10

3

7

2

DFNA30

15q25–26

7 Mb

74

7

58

6

1

2

0

DFNA31

6p21.3

7.5 Mb

304

13, 17

385

13

1

8

2

DFNA34

1q44

5 Mb

88

1

23

3

0

1

0

DFNA42

5q31

12 Mb

176

13, 18

153

21

5

16

6

DFNA47

9p21–22

9 Mb

65

4

79

6

1

3

0

DFNB5

14q12

5.5 Mb

18

12

37

2

0

2

0

DFNB13

7q34–36

1.4 Mb

14

6

15

1

0

2

1

DFNB17

7q31

6.5 Mb

26

6

43

3

1

2

1

DFNB19

18p11

2.8 Mb

13

17

11

1

1

1

1

DFNB20

11q25–qter

13.4 Mb

152

9

263

14

1

6

3

DFNB27

2q23–q31

11 Mb

79

2

95

14

3

3

2

DFNB28

22q13

6.5 Mb

146

15

154

15

5

7

3

DFNB32

1p13.3–22.1

16 Mb

74

3, 5

89

13

3

3

3

DFNB33

9q34.3

3 Mb

86

2

54

3

4

2

2

DFNB35

14q24.1–24.3

8.2 Mb

116

12

103

8

4

3

2

DFNB38

6q26–q27

3.4 Mb

5

17

3

1

0

1

1

DFNB39

7q11.22–q21.12

18 Mb

114

5

108

10

2

2

0

DFNB40

22q11.21–12.1

9 Mb

285

5, 10, 16

76

10

4

4

3

Total

  

2378

 

2344

214

52

114

41

Table 7

Summary of gene numbers from unfiltered analysis.

Condition

Map Location

Interval

Number of Genes

Interacting Proteins

DFNA7

1q21–q23

18.6 Mb

153

29

DFNA16

2q24

2.6 Mb

7

1

DFNA18

3q22

12 Mb

116

21

DFNA21

6p21

3.5 Mb

9

3

DFNA23

14q21–q22

8 Mb

79

29

DFNA25

12q21–24

14 Mb

108

33

DFNA27

4q12

15 Mb

71

9

DFNA30

15q25–26

7 Mb

74

18

DFNA31

6p21.3

7.5 Mb

304

48

DFNA34

1q44

5 Mb

88

19

DFNA42

5q31

12 Mb

176

48

DFNA47

9p21–22

9 Mb

65

19

DFNB5

14q12

5.5 Mb

18

5

DFNB13

7q34–36

1.4 Mb

14

6

DFNB17

7q31

6.5 Mb

26

9

DFNB19

18p11

2.8 Mb

13

3

DFNB20

11q25–qter

13.4 Mb

152

24

DFNB27

2q23–q31

11 Mb

79

12

DFNB28

22q13

6.5 Mb

146

27

DFNB32

1p13.3–22.1

16 Mb

74

11

DFNB33

9q34.3

3 Mb

86

8

DFNB35

14q24.1–24.3

8.2 Mb

116

19

DFNB38

6q26–q27

3.4 Mb

5

1

DFNB39

7q11.22–q21.12

18 Mb

114

12

DFNB40

22q11.21–12.1

9 Mb

285

25

Total

  

2378

439

Table 8

Candidate genes from unfiltered HPRD analysis*.

DFNB13

RAB19B

DFNA7

CREG

DFNB17

CAV2

 

DPT

 

CAPZA2

DFNA18

PLXND1

 

KCND2

DFNA21

E2F3

 

ING3

DFNA23

DAAM1

DFNB19

PTPRM

 

MNAT1

DFNB20

GRIK4

DFNA25

HSyn

 

NRGN

 

SELPLG

 

RICS

DFNA27

SRP72

DFNB27

MTX2

 

IGFBP7

 

TTN

 

BRDG1

 

NEUROD1

 

YT521

DFNB28

RANGAP1

DFNA30

CIB1

DFNB32

NTNG1

 

PRC1

DFNB33

TRAF2

DFNA31

ABT1

 

NPDC1

 

UBD

DFNB35

PGF

DFNA34

SMYD3

 

NGB

 

CIAS1

DFNB39

UPK3B

DFNA42

NEUROG1

 

PCLO

 

TTID

 

GRM3

 

NRG2

DFNB40

CLTCL1

 

PCDHAC1

 

TXNRD2

 

PCDHAC2

 

SDF2L1

 

NDFIP1

 

MMP11

DFNA47

CDKN2A

 

CABIN1

  

*The products of these genes shared interacting proteins with genes mapping to other deafness intervals. The above genes may be combined with the candidates predicted by the functional analysis and listed in Table 5.

Conclusion

We have used an in silico strategy to assemble a list of candidate genes that are positionally linked to and could be causing specific nonsyndromic hereditary hearing loss conditions. As presented here, a list of 2378 genes mapping to various genomic intervals have been narrowed down to 92 genes as candidates. These candidates may be analyzed for mutations in various deafness phenotypes in parallel with attempts to further narrow down the suspected region by genetic linkage analysis. It warrants mention that the potential of the approach presented here will be better harnessed as more information becomes available about inner ear transcripts and protein interaction networks.

Methods

Generating list of loci for in silico prediction

The list of most current information and identified loci for the various nonsyndromic hearing loss and syndromic forms was obtained from the Hereditary Hearing Loss Homepage and the survey of latest literature [4]. The list of deafness loci with unknown specific genes for the autosomal dominant, autosomal recessive, and syndromic forms was also compiled from the same web based source.

GeneRetriever for EST identification within each deafness locus

A list of all cloned and identified genes from within each of the listed genomic intervals was obtained using GeneRetriever®, a Perl-based data mining software that has a simple graphical user interfaces [12]. It automatically retrieves from either NCBI or Ensembl databases information that includes all genes and transcripts located in a genomic interval flanked by two genetic markers.

Database analysis

The list of genes and transcripts for each specific locus obtained using GeneRetriever® was compared against two sets of ear gene-expression databases. The first set includes genes expressed in the developing ear [13]. This list is a compilation of the numerous genes that are expressed at different stages during inner ear development in two animal species. The second set was obtained from fetal cochlear cDNA library and EST database (updated as of 2002) of the Morton Hearing Research Group [14]. The data present in this set was adapted from Unigene [12]. The database has 14,805 ESTs, and 12,624 ESTs are sorted by Unigene into 4,519 independent clusters. Unigene did not classify the remaining ESTs due to factors such as possible contaminating sequences, very small inserts, or excessive repetitive elements. For a gene within a particular locus to be considered for candidacy, it has to be present in either of the above two databases. Genes that were not present in either expression databases were initially eliminated from consideration. It warrants mention that functional significance of expressed sequences in human and mouse inner ear has been used to propose deafness candidates [32, 33].

Human reference protein database

In comparing the two sets of databases to the list of genes and transcripts within each hereditary hearing loss locus obtained using GeneRetriever®, we were able to compile a list of possible candidate genes for the various loci. To further narrow-down and refine this list, we obtained a list of all known interacting genes for each of the known and candidate genes using the Human Reference Protein Database (HRPD)[15]. The interacting proteins for all the genes within the mapped loci were obtained regardless of whether the gene is present in the two data sets of inner-ear expressed transcripts. In our first attempt of mining the data, if a gene is not present in the data set but its interacting proteins are expressed or present in the cochlea or identified in the table of gene expression in the developing ear, then this gene is considered a candidate. In our second attempt, we removed the ear-expression filter requirement. Therefore, any interacting and repeating protein was given consideration. Identifying candidate interacting genes that repeat in many loci supported their candidacy, resulting in a more comprehensive candidate list.

Declarations

Acknowledgements

This work was supported in part by Faculty Research Award and Ames Award to RPK.

Authors’ Affiliations

(1)
Department of Biological Sciences, Fordham University Bronx

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© Alsaber et al; licensee BioMed Central Ltd. 2006

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