Biomedical subjects
Publications and source records attributed to M A Huynen.
Eukaryotic cells contain functionally distinct, membrane enclosed compartments called organelles. Here we like to address two questions concerning this architectural lay out. How did this membrane complexity arise during evolution and how is this collection of organelles maintained in multiplying cells to ensure that new cells retain a complete set of them. We will try to address these questions with peroxisomes as a focal point of interest.
BACKGROUND: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to similar disease phenotypes. OBJECTIVE: To investigate whether protein-protein interactions can predict genes for genetically heterogeneous diseases. METHODS: 72,940 protein-protein interactions between 10,894 human proteins were used to search 432 loci for candidate disease genes representing 383 genetically heterogeneous hereditary diseases. For each disease, the protein interaction partners of its known causative genes were compared with the disease associated loci lacking identified causative genes. Interaction partners located within such loci were considered candidate disease gene predictions. Prediction accuracy was tested using a benchmark set of known disease genes. RESULTS: Almost 300 candidate disease gene predictions were made. Some of these have since been confirmed. On average, 10% or more are expected to be genuine disease genes, representing a 10-fold enrichment compared with positional information only. Examples of interesting candidates are AKAP6 for arrythmogenic right ventricular dysplasia 3 and SYN3 for familial partial epilepsy with variable foci. CONCLUSIONS: Exploiting protein-protein interactions can greatly increase the likelihood of finding positional candidate disease genes. When applied on a large scale they can lead to novel candidate gene predictions.
BACKGROUND: Walker-Warburg syndrome (WWS) is an autosomal recessive condition characterised by congenital muscular dystrophy, structural brain defects, and eye malformations. Typical brain abnormalities are hydrocephalus, lissencephaly, agenesis of the corpus callosum, fusion of the hemispheres, cerebellar hypoplasia, and neuronal overmigration, which causes a cobblestone cortex. Ocular abnormalities include cataract, microphthalmia, buphthalmos, and Peters anomaly. WWS patients show defective O-glycosylation of alpha-dystroglycan (alpha-DG), which plays a key role in bridging the cytoskeleton of muscle and CNS cells with extracellular matrix proteins, important for muscle integrity and neuronal migration. In 20% of the WWS patients, hypoglycosylation results from mutations in either the protein O-mannosyltransferase 1 (POMT1), fukutin, or fukutin related protein (FKRP) genes. The other genes for this highly heterogeneous disorder remain to be identified. OBJECTIVE: To look for mutations in POMT2 as a cause of WWS, as both POMT1 and POMT2 are required to achieve protein O-mannosyltransferase activity. METHODS: A candidate gene approach combined with homozygosity mapping. RESULTS: Homozygosity was found for the POMT2 locus at 14q24.3 in four of 11 consanguineous WWS families. Homozygous POMT2 mutations were present in two of these families as well as in one patient from another cohort of six WWS families. Immunohistochemistry in muscle showed severely reduced levels of glycosylated alpha-DG, which is consistent with the postulated role for POMT2 in the O-mannosylation pathway. CONCLUSIONS: A fourth causative gene for WWS was uncovered. These genes account for approximately one third of the WWS cases. Several more genes are anticipated, which are likely to play a role in glycosylation of alpha-DG.
The growing number of completely sequenced genomes adds new dimensions to the use of sequence analysis to predict protein function. Compared with the classical knowledge transfer from one protein to a similar sequence (homology-based function prediction), knowledge about the corresponding genes in other genomes (orthology-based function prediction) provides more specific information about the protein's function, while the analysis of the sequence in its genomic context (context-based function prediction) provides information about its functional context. Whereas homology-based methods predict the molecular function of a protein, genomic context methods predict the biological process in which it plays a role. These complementary approaches can be combined to elucidate complete functional networks and biochemical pathways from the genome sequence of an organism. Here we review recent advances in the field of genomic-context based methods of protein function prediction. Techniques are highlighted with examples, including an analysis that combines information from genomic-context with homology to predict a role of the RNase L inhibitor in the maturation of ribosomal RNA.
Much has been learned about the cellular pathology of Friedreich's ataxia, a recessive neurodegenerative disease resulting from insufficient expression of the mitochondrial protein frataxin. However, the biochemical function of frataxin has remained obscure, hampering attempts at therapeutic intervention. To predict functional interactions of frataxin with other proteins we investigated whether its gene specifically co-occurs with any other genes in sequenced genomes. In 56 available genomes we identified two genes with identical phylogenetic distributions to the frataxin/cyaY gene: hscA and hscB/JAC1. These genes have not only emerged in the same evolutionary lineage as the frataxin gene, they have also been lost at least twice with it, and they have been horizontally transferred with it in the evolution of the mitochondria. The proteins encoded by hscA and hscB, the chaperone HSP66 and the co-chaperone HSP20, have been shown to be required for the synthesis of 2Fe-2S clusters on ferredoxin in proteobacteria. JAC1, an ortholog of hscB, and SSQ1, a paralog of hscA, have been shown to be required for iron-sulfur cluster assembly in mitochondria of Saccharomyces cerevisiae. Combining data on the co-occurrence of genes in genomes with experimental and predicted cellular localization data of their proteins supports the hypothesis that frataxin is directly involved in iron-sulfur cluster protein assembly. They indicate that frataxin is specifically involved in the same sub-process as HSP20/Jac1p.
Pyrococcus furiosus uses a variant of the Embden-Meyerhof pathway during growth on sugars. All but one of the genes that encode the glycolytic enzymes of P. furiosus have previously been identified, either by homology searching of its genome or by reversed genetics. We here report the isolation of the missing link of the pyrococcal glycolysis, the phosphoglucose isomerase (PGI), which was purified to homogeneity from P. furiosus and biochemically characterized. The P. furiosus PGI, a dimer of identical 23.5-kDa subunits, catalyzes the reversible isomerization of glucose 6-phosphate to fructose 6-phosphate, with K(m) values of 1.99 and 0.63 mm, respectively. An optimum pH of 7.0 has been determined in both directions, and at its optimum temperature of 90 degrees C the enzyme has a half-life of 2.4 h. The N-terminal sequence was used for the identification of the pgiA gene in the P. furiosus genome. The pgiA transcription start site has been determined, and a monocistronic messenger was detected in P. furiosus during growth on maltose and pyruvate. The pgiA gene was functionally expressed in Escherichia coli BL21(DE3). The deduced amino acid sequence of this first archaeal PGI revealed that it is not related to its bacterial and eukaryal counterparts. In contrast, this archaeal PGI shares similarity with the cupin superfamily that consists of a variety of proteins that are generally involved in sugar metabolism in both prokaryotes and eukaryotes. As for the P. furiosus PGI, distinct phylogenetic origins have previously been reported for other enzymes from the pyrococcal glycolytic pathway. Apparently, convergent evolution by recruitment of several unique enzymes has resulted in the unique Pyrococcus glycolysis.
Comparisons of the gene order in closely related genomes reveal a major role for inversions in the genome shuffling process. In contrast to prokaryotes, where the inversions are predominantly large, half of the inversions between Saccharomyces cerevisiae and Candida albicans appear to be small, often encompassing only a single gene. Overall the genome rearrangement rate appears higher in eukaryotes than in prokaryotes, and the current genome data do not indicate that functional constraints on the co-expression of neighboring genes have a large role in conserving eukaryotic gene order. Nevertheless, qualitatively interesting examples of conservation of gene order in eukaryotes can be observed.
The repeated occurrence of genes in each other's neighbourhood on genomes has been shown to indicate a functional association between the proteins they encode. Here we introduce STRING (search tool for recurring instances of neighbouring genes), a tool to retrieve and display the genes a query gene repeatedly occurs with in clusters on the genome. The tool performs iterative searches and visualises the results in their genomic context. By finding the genomically associated genes for a query, it delineates a set of potentially functionally associated genes. The usefulness of STRING is illustrated with an example that suggests a functional context for an RNA methylase with unknown specificity.
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The presence of genes encoding enzymes involved in the citric-acid cycle has been studied in 19 completely sequenced genomes. In the majority of species, the cycle appears to be incomplete or absent. Several distinct, incomplete cycles reflect adaptations to different environments. Their distribution over the phylogenetic tree hints at precursors in the evolution of the citric-acid cycle.
Species phylogenies derived from comparisons of single genes are rarely consistent with each other, due to horizontal gene transfer, unrecognized paralogy and highly variable rates of evolution. The advent of completely sequenced genomes allows the construction of a phylogeny that is less sensitive to such inconsistencies and more representative of whole-genomes than are single-gene trees. Here, we present a distance-based phylogeny constructed on the basis of gene content, rather than on sequence identity, of 13 completely sequenced genomes of unicellular species. The similarity between two species is defined as the number of genes that they have in common divided by their total number of genes. In this type of phylogenetic analysis, evolutionary distance can be interpreted in terms of evolutionary events such as the acquisition and loss of genes, whereas the underlying properties (the gene content) can be interpreted in terms of function. As such, it takes a position intermediate to phylogenies based on single genes and phylogenies based on phenotypic characteristics. Although our comprehensive genome phylogeny is independent of phylogenies based on the level of sequence identity of individual genes, it correlates with the standard reference of prokarytic phylogeny based on sequence similarity of 16s rRNA. Thus, shared gene content between genomes is quantitatively determined by phylogeny, rather than by phenotype, and horizontal gene transfer has only a limited role in determining the gene content of genomes.
We propose a new method for detecting conserved RNA secondary structures in a family of related RNA sequences. Our method is based on a combination of thermodynamic structure prediction and phylogenetic comparison. In contrast to purely phylogenetic methods, our algorithm can be used for small data sets of approximately 10 sequences, efficiently exploiting the information contained in the sequence variability. The procedure constructs a prediction only for those parts of sequences that are consistent with a single conserved structure. Our implementation produces reasonable consensus structures without user interference. As an example we have analysed the complete HIV-1 and hepatitis C virus (HCV) genomes as well as the small segment of hantavirus. Our method confirms the known structures in HIV-1 and predicts previously unknown conserved RNA secondary structures in HCV.
The determination of complete genome sequences provides us with an opportunity to describe and analyze evolution at the comprehensive level of genomes. Here we compare nine genomes with respect to their protein coding genes at two levels: (i) we compare genomes as "bags of genes" and measure the fraction of orthologs shared between genomes and (ii) we quantify correlations between genes with respect to their relative positions in genomes. Distances between the genomes are related to their divergence times, measured as the number of amino acid substitutions per site in a set of 34 orthologous genes that are shared among all the genomes compared. We establish a hierarchy of rates at which genomes have changed during evolution. Protein sequence identity is the most conserved, followed by the complement of genes within the genome. Next is the degree of conservation of the order of genes, whereas gene regulation appears to evolve at the highest rate. Finally, we show that some genomes are more highly organized than others: they show a higher degree of the clustering of genes that have orthologs in other genomes.
We compare the frequency distribution of gene family sizes in the complete genomes of six bacteria (Escherichia coli, Haemophilus influenzae, Helicobacter pylori, Mycoplasma genitalium, Mycoplasma pneumoniae, and Synechocystis sp. PCC6803), two Archaea (Methanococcus jannaschii and Methanobacterium thermoautotrophicum), one eukaryote (Saccharomyces cerevisiae), the vaccinia virus, and the bacteriophage T4. The sizes of the gene families versus their frequencies show power-law distributions that tend to become flatter (have a larger exponent) as the number of genes in the genome increases. Power-law distributions generally occur as the limit distribution of a multiplicative stochastic process with a boundary constraint. We discuss various models that can account for a multiplicative process determining the sizes of gene families in the genome. In particular, we argue that, in order to explain the observed distributions, gene families have to behave in a coherent fashion within the genome; i.e., the probabilities of duplications of genes within a gene family are not independent of each other. Likewise, the probabilities of deletions of genes within a gene family are not independent of each other.
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RNA secondary structure folding algorithms predict the existence of connected networks of RNA sequences with identical structure. On such networks, evolving populations split into subpopulations, which diffuse independently in sequence space. This demands a distinction between two mutation thresholds: one at which genotypic information is lost and one at which phenotypic information is lost. In between, diffusion enables the search of vast areas in genotype space while still preserving the dominant phenotype. By this dynamic the success of phenotypic adaptation becomes much less sensitive to the initial conditions in genotype space.