Identification of novel mutations causing familial primary congenital glaucoma in Indian pedigrees

Purpose: To determine the possible molecular genetic defect underlying primary congenital glaucoma (PCG) in India and to identify the pathogenic mutations causing this childhood blindness. Methods: Twenty-two members of five clinically well-characterized consanguineous families were studied. The primary candidate gene CYP1B1 was amplified from genomic DNA, sequenced, and analyzed in control subjects and patients to identify the disease-causing mutations. Results: Five distinct mutations were identified in the coding region of CYP1B1 in eight patients of five PCG-affected families, of which three mutations are novel. These include a novel homozygous frameshift, compound heterozygous missense, and other known mutations. One family showed pseudodominance, whereas others were autosomal recessive with full penetrance. In contrast to all known CYP1B1 mutations, the newly identified frameshift is of special significance, because all functional motifs are missing. This, therefore, represents a rare example of a natural functional CYP1B1 knockout, resulting in a null allele (both patients are blind). Conclusions: The molecular mechanism leading to the development of PCG is unknown. Because CYP1B1 knockout mice did not show a glaucoma phenotype, the functional knockout identified in this study has important implications in elucidating the pathogenesis of PCG. Further understanding of how this molecular defect leads to PCG could influence the development of specific therapies. This is the first study to describe the molecular basis of PCG from the Indian subcontinent and has profound and multiple clinical implications in diagnosis, genetic counseling, genotype-phenotype correlations and prognosis. Hence, it is a step forward in preventing this devastating childhood blindness

Bioinformatics Advance Access published March 22, 2007

designed to find tandem repeats of large size motifs as large as 2000 bases and hence large numbers of microsatellites go unidentified by these methods. Many of these programs do not generate alignments between imperfect microsatellites and their expected perfect counterparts and therefore require additional postprocessing in order to study the mutational events in microsatellites. In view of these lacunae and to aid our systematic analysis of imperfect microsatellites, we developed a program called IMEx (Imperfect Microsatellite Extractor) with a number of discoveryfriendly features. IMEx is fast, highly sensitive and is also flexible where user can set the limits for imperfection (thus can be used for both perfect and imperfect microsatellites). The output comprises of a list of microsatellites each of which with information such as its total imperfection content, point mutations, sequence alignment with its perfect counterpart, whether the locus lies in the coding or non-coding region along with corresponding known details. The IMEx program is available in two modes: as a stand-alone program and also in the form of a web-server. The stand-alone as well as web-server are available from the web-sit

A Hierarchical Approach to Protein Fold Prediction

Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes

Global versus Local Hubs in Human Protein–Protein Interaction Network

In this study, we have constructed tissue-specific protein–protein interaction networks for 70 human tissues and have identified three types of hubs based on their expression breadths: (a) tissue-specific hubs (TSHs) (proteins that are expressed in ≤ 10 tissues and also form hubs in ≤ 10 tissues), (b) tissue-preferred hubs (TPHs) (proteins expressed in ≥ 60 tissues but are highly connected in ≤ 10 tissues), and (c) housekeeping hubs (HKHs) (proteins that are expressed in ≥ 60 tissues and also form hubs in ≥ 60 tissues). Comparative analyses revealed significant differences between TSHs and HKHs and also revealed that TPHs behave more like HKHs. TSHs are lengthier, more disordered, and also quickly evolving proteins as compared with HKHs. Despite having a similar number of binding surfaces and interacting domains, TSHs are associated with a lower degree of centrality as compared with HKHs, suggesting that TSHs are “unsaturated” with regard to their binding capability and are perhaps evolving with regard to their interactions. TSHs are less abundantly expressed as compared with HKHs and are enriched with PEST motifs, indicating their tight regulation. All of these properties of TSHs and HKHs correlate with their distinct functional roles; TSHs are involved in tissue-specific functional roles, viz., secretors, receptors, and signaling proteins, whereas HKHs are involved in core-cellular functions such as transcription, translation, and so on. Our study, therefore, brings forth a clear and distinct classification of hubs simply based on their expression breadth and further assumes significance in the light of the highly debated dichotomy of date and party hubs, which is based on the coexpression pattern of hubs with their partners

Global versus Local Hubs in Human Protein–Protein Interaction Network

In this study, we have constructed tissue-specific protein–protein interaction networks for 70 human tissues and have identified three types of hubs based on their expression breadths: (a) tissue-specific hubs (TSHs) (proteins that are expressed in ≤ 10 tissues and also form hubs in ≤ 10 tissues), (b) tissue-preferred hubs (TPHs) (proteins expressed in ≥ 60 tissues but are highly connected in ≤ 10 tissues), and (c) housekeeping hubs (HKHs) (proteins that are expressed in ≥ 60 tissues and also form hubs in ≥ 60 tissues). Comparative analyses revealed significant differences between TSHs and HKHs and also revealed that TPHs behave more like HKHs. TSHs are lengthier, more disordered, and also quickly evolving proteins as compared with HKHs. Despite having a similar number of binding surfaces and interacting domains, TSHs are associated with a lower degree of centrality as compared with HKHs, suggesting that TSHs are “unsaturated” with regard to their binding capability and are perhaps evolving with regard to their interactions. TSHs are less abundantly expressed as compared with HKHs and are enriched with PEST motifs, indicating their tight regulation. All of these properties of TSHs and HKHs correlate with their distinct functional roles; TSHs are involved in tissue-specific functional roles, viz., secretors, receptors, and signaling proteins, whereas HKHs are involved in core-cellular functions such as transcription, translation, and so on. Our study, therefore, brings forth a clear and distinct classification of hubs simply based on their expression breadth and further assumes significance in the light of the highly debated dichotomy of date and party hubs, which is based on the coexpression pattern of hubs with their partners

Global versus Local Hubs in Human Protein–Protein Interaction Network

In this study, we have constructed tissue-specific protein–protein interaction networks for 70 human tissues and have identified three types of hubs based on their expression breadths: (a) tissue-specific hubs (TSHs) (proteins that are expressed in ≤ 10 tissues and also form hubs in ≤ 10 tissues), (b) tissue-preferred hubs (TPHs) (proteins expressed in ≥ 60 tissues but are highly connected in ≤ 10 tissues), and (c) housekeeping hubs (HKHs) (proteins that are expressed in ≥ 60 tissues and also form hubs in ≥ 60 tissues). Comparative analyses revealed significant differences between TSHs and HKHs and also revealed that TPHs behave more like HKHs. TSHs are lengthier, more disordered, and also quickly evolving proteins as compared with HKHs. Despite having a similar number of binding surfaces and interacting domains, TSHs are associated with a lower degree of centrality as compared with HKHs, suggesting that TSHs are “unsaturated” with regard to their binding capability and are perhaps evolving with regard to their interactions. TSHs are less abundantly expressed as compared with HKHs and are enriched with PEST motifs, indicating their tight regulation. All of these properties of TSHs and HKHs correlate with their distinct functional roles; TSHs are involved in tissue-specific functional roles, viz., secretors, receptors, and signaling proteins, whereas HKHs are involved in core-cellular functions such as transcription, translation, and so on. Our study, therefore, brings forth a clear and distinct classification of hubs simply based on their expression breadth and further assumes significance in the light of the highly debated dichotomy of date and party hubs, which is based on the coexpression pattern of hubs with their partners

Microsatellite polymorphism across the <it>M. tuberculosis </it>and <it>M. bovis </it>genomes: Implications on genome evolution and plasticity

<p>Abstract</p> <p>Background</p> <p>Microsatellites are the tandem repeats of nucleotide motifs of size 1–6 bp observed in all known genomes. These repeats show length polymorphism characterized by either insertion or deletion (indels) of the repeat units, which in and around the coding regions affect transcription and translation of genes.</p> <p>Results</p> <p>Systematic comparison of all the equivalent microsatellites in the coding regions of the three mycobacterial genomes, viz. <it>Mycobacterium tuberculosis </it>H37Rv, <it>Mycobacterium tuberculosis </it>CDC1551 and <it>Mycobacterium bovis</it>, revealed for the first time the presence of several polymorphic microsatellites. The coding regions affected by frame-shifts owing to microsatellite indels have undergone changes indicative of gene fission/fusion, premature termination and length variation. Interestingly, the genes affected by frame-shift mutations code for membrane proteins, transporters, PPE, PE_PGRS, cell-wall synthesis proteins and hypothetical proteins.</p> <p>Conclusion</p> <p>This study has revealed the role of microsatellite indel mutations in imparting novel functions and a certain degree of plasticity to the mycobacterial genomes. There seems to be some correlation between microsatellite polymorphism and the variations in virulence, host-pathogen interactions mediated by surface antigen variations, and adaptation of the pathogens. Several of the polymorphic microsatellites reported in this study can be tested for their polymorphic nature by screening clinical isolates and various mycobacterial strains, for establishing correlations between microsatellite polymorphism and the phenotypic variations among these pathogens.</p

Simple sequence repeats in mycobacterial genomes

Simple sequence repeats (SSRs) or microsatellites are the repetitive nucleotide sequences of motifs of length 1–6 bp. They are scattered throughout the genomes of all the known organisms ranging from viruses to eukaryotes. Microsatellites undergo mutations in the form of insertions and deletions (INDELS) of their repeat units with some bias towards insertions that lead to microsatellite tract expansion. Although prokaryotic genomes derive some plasticity due to microsatellite mutations they have in-built mechanisms to arrest undue expansions of microsatellites and one such mechanism is constituted by post-replicative DNA repair enzymes MutL, MutH and MutS. The mycobacterial genomes lack these enzymes and as a null hypothesis one could expect these genomes to harbour many long tracts. It is therefore interesting to analyse the mycobacterial genomes for distribution and abundance of microsatellites tracts and to look for potentially polymorphic microsatellites. Available mycobacterial genomes, Mycobacterium avium, M. leprae, M. bovis and the two strains of M. tuberculosis (CDC1551 and H37Rv) were analysed for frequencies and abundance of SSRs. Our analysis revealed that the SSRs are distributed throughout the mycobacterial genomes at an average of 220–230 SSR tracts per kb. All the mycobacterial genomes contain few regions that are conspicuously denser or poorer in microsatellites compared to their expected genome averages. The genomes distinctly show scarcity of long microsatellites despite the absence of a post-replicative DNA repair system. Such severe scarcity of long microsatellites could arise as a result of strong selection pressures operating against long and unstable sequences although influence of GC-content and role of point mutations in arresting microsatellite expansions can not be ruled out. Nonetheless, the long tracts occasionally found in coding as well as non-coding regions may account for limited genome plasticity in these genomes

MICdb: database of prokaryotic microsatellites

The MICdb (Microsatellites Database) (http://www.cdfd.org.in/micas) is a comprehensive relational database of non-redundant microsatellites extracted from fully sequenced prokaryotic genomes. The current version (1.0) of the database has been compiled from 83 genomes belonging to different phylogenetic groups. This database has been linked to MICAS, the web-based Microstatellite Analysis Server. MICAS provides a user-friendly front-end to systematically extract data on microsatellite tracts from genomes. The database contains the following information pertaining to the microsatellites: the regions (coding/non-coding, if coding, their GenBank annotations) containing microsatellite tracts; the frequencies of their occurrences, the size and the number of repeating motifs; and the sequences of the tracts. MICAS also provides an interface to Autoprimer, a primer design program to automatically design primers for selected microsatellite loci