A structural locus for coagulation factor XIIIA (F13A) is located distal to the HLA region on chromosome 6p in man

Linkage between the locus for coagulation factor XIIIA (F13A) and HLA-region genes has been revealed during a linkage study between F13A and approximately 40 other polymorphic marker genes. In males, the maximum lod score between F13A and HLA-region genes (HLA-A, -C, -B, -DR; C4A, -B; Bf; and/or C2) is 7.60 at theta 1 = .18. To GLO, the maximum lod score is 2.37 at theta 1 = .19; to PGM3, .22 at theta 1 = .35. Female data indicate a clear sex difference in recombination frequency between F13A and HLA. The present findings, in combination with earlier knowledge of PGM3/GLO/HLA localization and gene distances, show that F13A is distal to HLA on the short arm of chromosome 6 in man. It is thus likely that by including FXIIIA typing in linkage studies, the whole male 6p is within mapping distance of highly polymorphic, classical marker genes. Earlier findings that the Hageman factor gene (F12) is located in the same chromosomal region may indicate the presence of a coagulation factor gene cluster in this region.     

Alterations of mammalian oocyte meiosis I with divalent cations and calmodulin

Experiments using a Ca2+/Mg2+, serum free media were carried out aimed at clarifying proposed effects of these divalent cations on in vitro meiotic maturation of mouse and cow oocytes. Agents known to perturb intracellular Ca2+ or calmodulin were also studied. Total absence of both cations restricts both oocyte species from completing meiosis I. Media containing Mg2+ and no Ca2+ permitted some maturation in both species. Absence or small amounts of Mg2+ in the media containing control amounts of Ca2+ was much more inhibitory for the cow than the mouse oocyte. Studies of mouse oocyte maturation with Verapamil, Epinephrine and A23187 demonstrated an inhibition of maturation perhaps by the intracellular Ca2+ changes these agents are alleged to induce. A dependency of mouse oocyte maturation on active Ca-Calmodulin complexes was suggested by the calmodulin inhibitor studies.     

Identification of a core set of genes that signifies pathways underlying cardiac hypertrophy

Although the molecular signals underlying cardiac hypertrophy have been the subject of intense investigation, the extent of common and distinct gene regulation between different forms of cardiac hypertrophy remains unclear. We hypothesized that a general and comparative analysis of hypertrophic gene expression, using microarray technology in multiple models of cardiac hypertrophy, including aortic banding, myocardial infarction, an arteriovenous shunt and pharmacologically induced hypertrophy, would uncover networks of conserved hypertrophy-specific genes and identify novel genes involved in hypertrophic signalling. From gene expression analyses (8740 probe sets, n = 46) of rat ventricular RNA, we identified a core set of 139 genes with consistent differential expression in all hypertrophy models as compared to their controls, including 78 genes not previously associated with hypertrophy and 61 genes whose altered expression had previously been reported. We identified a single common gene program underlying hypertrophic remodelling, regardless of how the hypertrophy was induced. These genes constitute the molecular basis for the existence of one main form of cardiac hypertrophy and may be useful for prediction of a common therapeutic approach. Supplementary material for this article can be found at: http://www.interscience.wiley.com/jpages/1531-6912/suppmat.     

Reproductive fitness and quinone content of Caenorhabditis elegans clk-1 mutants fed coenzyme Q isoforms of varying length

Caenorhabditis elegans clk-1 mutants lack coenzyme Q9 and accumulate the biosynthetic intermediate demethoxy-Q9. A dietary source of ubiquinone (Q) is required for larval growth and development of the gonad and germ cells. We considered that uptake of the shorter Q8 isoform present in the Escherichia coli food may contribute to the Clk phenotypes of slowed development and reduced brood size observed when the animals are fed Q-replete E. coli. To test the effect of isoprene tail length, N2 and clk-1 animals were fed E. coli engineered to produce Q7, Q8, Q9, or Q10. Wild-type nematodes showed no change in reproductive fitness regardless of the Qn isoform fed. clk-1(e2519) fed the Q9 diet showed increased egg production; however, this diet did not improve reproductive fitness of the clk-1(qm30) animals. Furthermore, animals with the more severe clk-1(qm30) allele become sterile and their progeny inviable when fed Q7-containing bacteria. The content of Q7 in the mitochondria of clk-1 animals was decreased relative to Q8, suggesting less effective transport of Q7 to the mitochondria, impaired retention, or decreased stability. Additionally, regardless of E. coli diet, clk-1(qm30) animals contain a dysfunctional dense form of mitochondria. The gonads of clk-1(qm30) worms fed Q7-containing food were severely shrunken and disordered. The differential fertility of clk-1 mutant nematodes fed Q isoforms may result from changes in Q localization, altered recognition by Q-binding proteins, and/or potential defects in mitochondrial function resulting from the mutant CLK-1 polypeptide itself.     

J-Express: exploring gene expression data using Java

J-Express is a Java application that allows the user to analyze gene expression (microarray) data in a flexible way giving access to multidimensional scaling, clustering, and visualization methods in an integrated manner. Specifically, J-Express includes implementations of hierarchical clustering, k-means, principal component analysis, and self-organizing maps. At present, it does not include methods for comparing two or more experiments for differentially expressed genes. The application is completely portable and requires only that a Java runtime environment 1.2 is installed on the system. Its efficiency allows interactive clustering of thousands of expression profiles on standard personal computers.     

New feature subset selection procedures for classification of expression profiles

Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier. We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be made using only 15-30 genes. Our results have implications for how to select marker genes and how many gene measurements are needed for diagnostic purposes. When looking for differential expression between experiment classes, it may not be sufficient to look at each gene in a separate universe. Evaluating combinations of genes reveals interesting information that will not be discovered otherwise. Our results show that class prediction can be improved by taking advantage of this extra information.     

Structure motif discovery and mining the PDB

MOTIVATION: Many of the most interesting functional and evolutionary relationships among proteins are so ancient that they cannot be reliably detected through sequence analysis and are apparent only through a comparison of the tertiary structures. The conserved features can often be described as structural motifs consisting of a few single residues or Secondary Structure (SS) elements. Confidence in such motifs is greatly boosted when they are found in more than a pair of proteins. RESULTS: We describe an algorithm for the automatic discovery of recurring patterns in protein structures. The patterns consist of individual residues having a defined order along the protein's backbone that come close together in the structure and whose spatial conformations are similar. The residues in a pattern need not be close in the protein's sequence. The work described in this paper builds on an earlier reported algorithm for motif discovery. This paper describes a significant improvement of the algorithm which makes it very efficient. The improved efficiency allows us to use it for doing unsupervised learning of patterns occurring in small subsets in a large set of structures, a non-redundant subset of the Protein Data Bank (PDB) database of all known protein structures.