Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations

PURPOSE: Predicting the phenotypic effects of mutations has become an important application in clinical genetic diagnostics. Computational tools evaluate the behavior of the variant over evolutionary time and assume that variations seen during the course of evolution are probably benign in humans. However, current tools do not take into account orthologous/paralogous relationships. Paralogs have dramatically different roles in Mendelian diseases. For example, whereas inactivating mutations in the NPC1 gene cause the neurodegenerative disorder Niemann-Pick C, inactivating mutations in its paralog NPC1L1 are not disease-causing and, moreover, are implicated in protection from coronary heart disease. METHODS: We identified major events in NPC1 evolution and revealed and compared orthologs and paralogs of the human NPC1 gene through phylogenetic and protein sequence analyses. We predicted whether an amino acid substitution affects protein function by reducing the organism’s fitness. RESULTS: Removing the paralogs and distant homologs improved the overall performance of categorizing disease-causing and benign amino acid substitutions. CONCLUSION: The results show that a thorough evolutionary analysis followed by identification of orthologs improves the accuracy in predicting disease-causing missense mutations. We anticipate that this approach will be used as a reference in the interpretation of variants in other genetic diseases as well. Genet Med 18 10, 1029–1036

Three-Dimensional Phylogeny Explorer: Distinguishing paralogs, lateral transfer, and violation of "molecular clock" assumption with 3D visualization

<p>Abstract</p> <p>Background</p> <p>Construction and interpretation of phylogenetic trees has been a major research topic for understanding the evolution of genes. Increases in sequence data and complexity are creating a need for more powerful and insightful tree visualization tools.</p> <p>Results</p> <p>We have developed 3D Phylogeny Explorer (3DPE), a novel phylogeny tree viewer that maps trees onto three spatial axes (species on the X-axis; paralogs on Z; evolutionary distance on Y), enabling one to distinguish at a glance evolutionary features such as speciation; gene duplication and paralog evolution; lateral gene transfer; and violation of the "molecular clock" assumption. Users can input any tree on the online 3DPE, then rotate, scroll, rescale, and explore it interactively as "live" 3D views. All objects in 3DPE are clickable to display subtrees, connectivity path highlighting, sequence alignments, and gene summary views, and etc. To illustrate the value of this visualization approach for microbial genomes, we also generated 3D phylogeny analyses for all clusters from the public COG database. We constructed tree views using well-established methods and graph algorithms. We used Scientific Python to generate VRML2 3D views viewable in any web browser.</p> <p>Conclusion</p> <p>3DPE provides a novel phylogenetic tree projection method into 3D space and its web-based implementation with live 3D features for reconstruction of phylogenetic trees of COG database.</p

Validation of an NSP-based (negative selection pattern) gene family identification strategy

<p>Abstract</p> <p>Background</p> <p>Gene family identification from ESTs can be a valuable resource for analysis of genome evolution but presents unique challenges in organisms for which the entire genome is not yet sequenced. We have developed a novel gene family identification method based on negative selection patterns (NSP) between family members to screen EST-generated contigs. This strategy was tested on five known gene families in Arabidopsis to see if individual paralogs could be identified with accuracy from EST data alone when compared to the actual gene sequences in this fully sequenced genome.</p> <p>Results</p> <p>The NSP method uniquely identified family members in all the gene families tested. Two members of the FtsH gene family, three members each of the PAL, RF1, and ribosomal L6 gene families, and four members of the CAD gene family were correctly identified. Additionally all ESTs from the representative contigs when checked against MapViewer data successfully identify the gene locus predicted.</p> <p>Conclusion</p> <p>We demonstrate the effectiveness of the NSP strategy in identifying specific gene family members in Arabidopsis using only EST data and we describe how this strategy can be used to identify many gene families in agronomically important crop species where they are as yet undiscovered.</p

Nephele: genotyping via complete composition vectors and MapReduce

<p>Abstract</p> <p>Background</p> <p>Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.</p> <p>Results</p> <p>Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.</p> <p>Conclusions</p> <p>We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.</p

Cloning of a gene (SR-A1), encoding for a new member of the human Ser/Arg-rich family of pre-mRNA splicing factors: overexpression in aggressive ovarian cancer

By using the positional cloning gene approach, we were able to identify a novel gene encoding for a serine/arginine-rich protein, which appears to be the human homologue of the rat A1 gene. We named this new gene SR-A1. Members of the SR family of proteins have been shown to interact with the C-terminal domain (CTD) of the large subunit of RNA polymerase II and participate in pre-mRNA splicing. We have localized the SR-A1 gene between the known genes IRF3 and RRAS on chromosome 19q13.3. The novel gene spans 16.7 kb of genomic sequence and it is formed of 11 exons and 10 intervening introns. The SR-A1 protein is composed of 1312 amino acids, with a molecular mass of 139.3 kDa and a theoretical isoelectric point of 9.31. The SR-A1 protein contains an SR-rich domain as well as a CTD-binding domain present only in a subset of SR-proteins. Through interactions with the pre-mRNA and the CTD domain of the Polymerase II, SR proteins have been shown to regulate alternative splicing. The SR-A1 gene is expressed in all tissues tested, with highest levels found in fetal brain and fetal liver. Our data suggest that this gene is overexpressed in a subset of ovarian cancers which are clinically more aggressive. Studies with the steroid hormone receptor-positive breast and prostate carcinoma cell lines ZR-75-1, BT-474 and LNCaP, respectively, suggest that SR-A1 is constitutively expressed. Furthermore, the mRNA of the SR-A1 gene in these cell lines appears to increase by estrogens, androgens and glucocorticoids, and to a lesser extend by progestins. © 2001 Cancer Research Campaign http://www.bjcancer.co

Evaluation of the bacterial diversity of Pressure ulcers using bTEFAP pyrosequencing

<p>Abstract</p> <p>Background</p> <p>Decubitus ulcers, also known as bedsores or pressure ulcers, affect millions of hospitalized patients each year. The microflora of chronic wounds such as ulcers most commonly exist in the biofilm phenotype and have been known to significantly impair normal healing trajectories.</p> <p>Methods</p> <p>Bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP), a universal bacterial identification method, was used to identify bacterial populations in 49 decubitus ulcers. Diversity estimators were utilized and wound community compositions analyzed in relation to metadata such as Age, race, gender, and comorbidities.</p> <p>Results</p> <p>Decubitus ulcers are shown to be polymicrobial in nature with no single bacterium exclusively colonizing the wounds. The microbial community among such ulcers is highly variable. While there are between 3 and 10 primary populations in each wound there can be hundreds of different species present many of which are in trace amounts. There is no clearly significant differences in the microbial ecology of decubitus ulcer in relation to metadata except when considering diabetes. The microbial populations and composition in the decubitus ulcers of diabetics may be significantly different from the communities in non-diabetics.</p> <p>Conclusions</p> <p>Based upon the continued elucidation of chronic wound bioburdens as polymicrobial infections, it is recommended that, in addition to traditional biofilm-based wound care strategies, an antimicrobial/antibiofilm treatment program can be tailored to each patient's respective wound microflora.</p

Regulation of pH During Amelogenesis

During amelogenesis, extracellular matrix proteins interact with growing hydroxyapatite crystals to create one of the most architecturally complex biological tissues. The process of enamel formation is a unique biomineralizing system characterized first by an increase in crystallite length during the secretory phase of amelogenesis, followed by a vast increase in crystallite width and thickness in the later maturation phase when organic complexes are enzymatically removed. Crystal growth is modulated by changes in the pH of the enamel microenvironment that is critical for proper enamel biomineralization. Whereas the genetic bases for most abnormal enamel phenotypes (amelogenesis imperfecta) are generally associated with mutations to enamel matrix specific genes, mutations to genes involved in pH regulation may result in severely affected enamel structure, highlighting the importance of pH regulation for normal enamel development. This review summarizes the intra- and extracellular mechanisms employed by the enamel-forming cells, ameloblasts, to maintain pH homeostasis and, also, discusses the enamel phenotypes associated with disruptions to genes involved in pH regulation

Immunoglobulin Genomics in the Guinea Pig (Cavia porcellus)

In science, the guinea pig is known as one of the gold standards for modeling human disease. It is especially important as a molecular and cellular biology model for studying the human immune system, as its immunological genes are more similar to human genes than are those of mice. The utility of the guinea pig as a model organism can be further enhanced by further characterization of the genes encoding components of the immune system. Here, we report the genomic organization of the guinea pig immunoglobulin (Ig) heavy and light chain genes. The guinea pig IgH locus is located in genomic scaffolds 54 and 75, and spans approximately 6,480 kb. 507 VH segments (94 potentially functional genes and 413 pseudogenes), 41 DH segments, six JH segments, four constant region genes (μ, γ, ε, and α), and one reverse δ remnant fragment were identified within the two scaffolds. Many VH pseudogenes were found within the guinea pig, and likely constituted a potential donor pool for gene conversion during evolution. The Igκ locus mapped to a 4,029 kb region of scaffold 37 and 24 is composed of 349 Vκ (111 potentially functional genes and 238 pseudogenes), three Jκ and one Cκ genes. The Igλ locus spans 1,642 kb in scaffold 4 and consists of 142 Vλ (58 potentially functional genes and 84 pseudogenes) and 11 Jλ -Cλ clusters. Phylogenetic analysis suggested the guinea pig’s large germline VH gene segments appear to form limited gene families. Therefore, this species may generate antibody diversity via a gene conversion-like mechanism associated with its pseudogene reserves