Xiangyin Kong

Dentinogenesis imperfecta 1 with or without progressive hearing loss is associated with distinct mutations in DSPP

Dentinogenesis imperfecta 1 (DGI1, MIM 125490) is an autosomal dominant dental disease characterized by abnormal dentin production and mineralization. The DGI1 locus was recently refined to a 2-Mb interval on 4q21 (ref. 1). Here we study three Chinese families carrying DGI1. We find that the affected individuals of two families also presented with progressive sensorineural high-frequency hearing loss (gene DFNA39). We identified three disease-specific mutations within the dentin sialophosphoprotein gene (DSPP) in these three families. We detected a G→A transition at the donor-splicing site of intron 3 in one family without DFNA39, a mutation predicted to result in the skipping of exon 3. In two other families affected with both DGI1 and DFNA39, however, we identified two independent nucleotide transversions in exons 2 and 3 of DSPP, respectively, that cause missense mutations of two adjacent amino-acid residues in the predicted transmembrane region of the protein. Moreover, transcripts of DSPP previously reported to be expressed specifically in teeth are also detected in the inner ear of mice. We have thus demonstrated for the first time that distinct mutations in DSPP are responsible for the clinical manifestations of DGI1 with or without DFNA39.

Embryonic stem cells generated by nuclear transfer of human somatic nuclei into rabbit oocytes

ABSTRACTTo solve the problem of immune incompatibility, nuclear transplantation has been envisaged as a means to produce cells or tissues for human autologous transplantation. Here we have derived embryonic stem cells by the transfer of human somatic nuclei into rabbit oocytes. The number of blastocysts that developed from the fused nuclear transfer was comparable among nuclear donors at ages of 5, 42, 52 and 60 years, and nuclear transfer (NT) embryonic stem cells (ntES cells) were subsequently derived from each of the four age groups. These results suggest that human somatic nuclei can form ntES cells independent of the age of the donor. The derived ntES cells are human based on karyotype, isogenicity, in situ hybridization, PCR and immunocytochemistry with probes that distinguish between the various species. The ntES cells maintain the capability of sustained growth in an undifferentiated state, and form embryoid bodies, which, on further induction, give rise to cell types such as neuron and muscle, as well as mixed cell populations that express markers representative of all three germ layers. Thus, ntES cells derived from human somatic cells by NT to rabbit eggs retain phenotypes similar to those of conventional human ES cells, including the ability to undergo multilineage cellular differentiation.

Mutant DNA-binding domain of HSF4 is associated with autosomal dominant lamellar and Marner cataract

Congenital cataracts cause 10–30% of all blindness in children, with one-third of cases estimated to have a genetic cause. Lamellar cataract is the most common type of infantile cataract. We carried out whole-genome linkage analysis of Chinese individuals with lamellar cataract, and found that the disorder is associated with inheritance of a 5.11-cM locus on chromosome 16. This locus coincides with one previously described for Marner cataract. We screened individuals of three Chinese families for mutations in HSF4 (a gene at this locus that encodes heat-shock transcription factor 4) and discovered that in each family, a distinct missense mutation, predicted to affect the DNA-binding domain of the protein, segregates with the disorder. We also discovered an association between a missense mutation and Marner cataract in an extensive Danish family. We suggest that HSF4 is critical to lens development.

Predicting drug-target interaction networks based on functional groups and biological features.

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging.

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of proteins metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure proteins metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.

Triphalangeal thumb–polysyndactyly syndrome and syndactyly type IV are caused by genomic duplications involving the long range, limb-specific SHH enhancer

Background: The Sonic hedgehog (SHH) protein produced in the zone of polarising activity (ZPA) is a major determinant of the identity and numbers of digits in early limb development. Preaxial polydactyly types II (PPD2) and III (PPD3) have been mapped to a critical region at 7q36, and subsequently shown to be caused by point mutations in the ZPA regulatory sequence (ZRS), a long range cis-regulator for the SHH gene. Triphalangeal thumb–polysyndactyly syndrome (TPTPS) and syndactyly type IV (SD4) were also mapped to the 7q36 region but pathogenic mutations in ZRS have not yet been affirmed. Methods and results: We performed linkage and haplotype analysis in six Han Chinese families with TPTPS and/or SD4, and refined the disease locus to an interval of 646 kb containing ZRS. In all families, the affected individuals heterozygous at rs10254391 (a single nucleotide polymorphism within ZRS) revealed a remarkable allele imbalance on sequence chromatogram. Using real-time quantitative polymerase chain reaction (qPCR), we identified duplication of ZRS and found that this duplication segregated with the limb phenotypes in all families but was not detected in unaffected family members or in unrelated control individuals. The duplication was also confirmed by interphase fluorescence in situ hybridisation (FISH) in an affected individual. We designed 17 additional qPCR assays and defined the minimum duplications in all six families, ranging from 131kb to 398kb. Conclusion: Both TPTPS and SD4 are due to duplications involving ZRS, the limb specific SHH enhancer. Point mutations in the ZRS and duplications encompassing the ZRS cause distinctive limb phenotypes.

Genome-wide interaction-based association analysis identified multiple new susceptibility loci for common diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohns disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10−7). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future.

Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties.

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAPs functional association. This research will facilitate the post genome-wide association studies.

Loss-of-Function Mutations in the PRPS1 Gene Cause a Type of Nonsyndromic X-linked Sensorineural Deafness, DFN2

We report a large Chinese family with X-linked postlingual nonsyndromic hearing impairment in which the critical linkage interval spans a genetic distance of 5.41 cM and a physical distance of 15.1 Mb that overlaps the DFN2 locus. Mutation screening of the PRPS1 gene in this family and in the three previously reported DFN2 families identified four different missense mutations in PRPS1. These mutations result in a loss of phosphoribosyl pyrophosphate (PRPP) synthetase 1 activity, as was shown in silico by structural analysis and was shown in vitro by enzymatic activity assays in erythrocytes and fibroblasts from patients. By in situ hybridization, we demonstrate expression of Prps1 in murine vestibular and cochlea hair cells, with continuous expression in hair cells and postnatal expression in the spiral ganglion. Being the second identified gene associated with X-linked nonsyndromic deafness, PRPS1 will be a good candidate gene for genetic testing for X-linked nonsyndromic hearing loss.

Removal of Hsf4 leads to cataract development in mice through down-regulation of γS-crystallin and Bfsp expression

BackgroundHeat-shock transcription factor 4 (HSF4) mutations are associated with autosomal dominant lamellar cataract and Marner cataract. Disruptions of the Hsf4 gene cause lens defects in mice, indicating a requirement for HSF4 in fiber cell differentiation during lens development. However, neither the relationship between HSF4 and crystallins nor the detailed mechanism of maintenance of lens transparency by HSF4 is fully understood.ResultsIn an attempt to determine how the underlying biomedical and physiological mechanisms resulting from loss of HSF4 contribute to cataract formation, we generated an Hsf4 knockout mouse model. We showed that the Hsf4 knockout mouse (Hsf4-/-) partially mimics the human cataract caused by HSF4 mutations. Q-PCR analysis revealed down-regulation of several cataract-relevant genes, including γS-crystallin (Crygs) and lens-specific beaded filament proteins 1 and 2 (Bfsp1 and Bfsp2), in the lens of the Hsf4-/- mouse. Transcription activity analysis using the dual-luciferase system suggested that these cataract-relevant genes are the direct downstream targets of HSF4. The effect of HSF4 on γS-crystallin is exemplified by the cataractogenesis seen in the Hsf4-/-,rncat intercross. The 2D electrophoretic analysis of whole-lens lysates revealed a different expression pattern in 8-week-old Hsf4-/- mice compared with their wild-type counterparts, including the loss of some αA-crystallin modifications and reduced expression of γ-crystallin proteins.ConclusionOur results indicate that HSF4 is sufficiently important to lens development and disruption of the Hsf4 gene leads to cataracts via at least three pathways: 1) down-regulation of γ-crystallin, particularly γS-crystallin; 2) decreased lens beaded filament expression; and 3) loss of post-translational modification of αA-crystallin.