Yeast homologues of higher eukaryotic TFIID subunits

In eukaryotic cells the TATA-binding protein (TBP) associates with other proteins known as TBP-associated factors (TAFs) to form multisubunit transcription factors important for gene expression by all three nuclear RNA polymerases. Computer searching of the complete Saccharomyces cerevisiae genome revealed five previously unidentified yeast genes with significant sequence similarity to known human and Drosophila RNA polymerase II TAFs. Each of these genes is essential for viability. A sixth essential gene (FUN81) has previously been noted to be similar to human TAFII18. Coimmunoprecipitation experiments show that all six proteins are associated with TBP, demonstrating that they are true TAFs. Furthermore, these proteins are present in complexes containing the TAFII130 subunit, indicating that they are components of TFIID. Based on their predicted molecular weights, these genes have been designated TAF67, TAF61(68), TAF40, TAF23(25), TAF19(FUN81), and TAF17. Yeast TAF61 is significantly larger than its higher eukaryotic homologues, and deletion analysis demonstrates that the evolutionarily conserved, histone-like domain is sufficient and necessary to support viability.

Blood-borne tissue factor: Another view of thrombosis

Arterial thrombosis is considered to arise from the interaction of tissue factor (TF) in the vascular wall with platelets and coagulation factors in circulating blood. According to this paradigm, coagulation is initiated after a vessel is damaged and blood is exposed to vessel-wall TF. We have examined thrombus formation on pig arterial media (which contains no stainable TF) and on collagen-coated glass slides (which are devoid of TF) exposed to flowing native human blood. In both systems the thrombi that formed during a 5-min perfusion stained intensely for TF, much of which was not associated with cells. Antibodies against TF caused ≈70% reduction in the amount of thrombus formed on the pig arterial media and also reduced thrombi on the collagen-coated glass slides. TF deposited on the slides was active, as there was abundant fibrin in the thrombi. Factor VIIai, a potent inhibitor of TF, essentially abolished fibrin production and markedly reduced the mass of the thrombi. Immunoelectron microscopy revealed TF-positive membrane vesicles that we frequently observed in large clusters near the surface of platelets. TF, measured by factor Xa formation, was extracted from whole blood and plasma of healthy subjects. By using immunostaining, TF-containing neutrophils and monocytes were identified in peripheral blood; our data raise the possibility that leukocytes are the main source of blood TF. We suggest that blood-borne TF is inherently thrombogenic and may be involved in thrombus propagation at the site of vascular injury.

ALFRED: an allele frequency database for diverse populations and DNA polymorphisms—an update

ALFRED (the ALelle FREquency Database) is designed to store and disseminate frequencies of alleles at human polymorphic sites for multiple populations, primarily for the population genetics and molecular anthropology communities. Currently ALFRED has information on over 180 polymorphic sites for more than 70 populations. Since our initial release of the database we have focussed on increasing the quantity and quality of data, making reciprocal links between ALFRED and other related databases, and providing useful tools to make the data more comprehensible to the end user. ALFRED is accessible from the Kidd Lab home page (http://info.med.yale.edu/genetics/kkidd/) or from ALFRED directly (http://alfred.med.yale.edu/alfred/index.asp).

Digging for dead genes: an analysis of the characteristics of the pseudogene population in the Caenorhabditis elegans genome

Pseudogenes are non-functioning copies of genes in genomic DNA, which may either result from reverse transcription from an mRNA transcript (processed pseudogenes) or from gene duplication and subsequent disablement (non-processed pseudogenes). As pseudogenes are apparently ‘dead’, they usually have a variety of obvious disablements (e.g., insertions, deletions, frameshifts and truncations) relative to their functioning homologs. We have derived an initial estimate of the size, distribution and characteristics of the pseudogene population in the Caenorhabditis elegans genome, performing a survey in ‘molecular archaeology’. Corresponding to the 18 576 annotated proteins in the worm (i.e., in Wormpep18), we have found an estimated total of 2168 pseudogenes, about one for every eight genes. Few of these appear to be processed. Details of our pseudogene assignments are available from http://bioinfo.mbb.yale.edu/genome/worm/pseudogene. The population of pseudogenes differs significantly from that of genes in a number of respects: (i) pseudogenes are distributed unevenly across the genome relative to genes, with a disproportionate number on chromosome IV; (ii) the density of pseudogenes is higher on the arms of the chromosomes; (iii) the amino acid composition of pseudogenes is midway between that of genes and (translations of) random intergenic DNA, with enrichment of Phe, Ile, Leu and Lys, and depletion of Asp, Ala, Glu and Gly relative to the worm proteome; and (iv) the most common protein folds and families differ somewhat between genes and pseudogenes—whereas the most common fold found in the worm proteome is the immunoglobulin fold and the most common ‘pseudofold’ is the C-type lectin. In addition, the size of a gene family bears little overall relationship to the size of its corresponding pseudogene complement, indicating a highly dynamic genome. There are in fact a number of families associated with large populations of pseudogenes. For example, one family of seven-transmembrane receptors (represented by gene B0334.7) has one pseudogene for every four genes, and another uncharacterized family (represented by gene B0403.1) is approximately two-thirds pseudogenic. Furthermore, over a hundred apparent pseudogenic fragments do not have any obvious homologs in the worm.

PartsList: a web-based system for dynamically ranking protein folds based on disparate attributes, including whole-genome expression and interaction information

As the number of protein folds is quite limited, a mode of analysis that will be increasingly common in the future, especially with the advent of structural genomics, is to survey and re-survey the finite parts list of folds from an expanding number of perspectives. We have developed a new resource, called PartsList, that lets one dynamically perform these comparative fold surveys. It is available on the web at http://bioinfo.mbb.yale.edu/partslist and http://www.partslist.org. The system is based on the existing fold classifications and functions as a form of companion annotation for them, providing ‘global views’ of many already completed fold surveys. The central idea in the system is that of comparison through ranking; PartsList will rank the approximately 420 folds based on more than 180 attributes. These include: (i) occurrence in a number of completely sequenced genomes (e.g. it will show the most common folds in the worm versus yeast); (ii) occurrence in the structure databank (e.g. most common folds in the PDB); (iii) both absolute and relative gene expression information (e.g. most changing folds in expression over the cell cycle); (iv) protein–protein interactions, based on experimental data in yeast and comprehensive PDB surveys (e.g. most interacting fold); (v) sensitivity to inserted transposons; (vi) the number of functions associated with the fold (e.g. most multi-functional folds); (vii) amino acid composition (e.g. most Cys-rich folds); (viii) protein motions (e.g. most mobile folds); and (ix) the level of similarity based on a comprehensive set of structural alignments (e.g. most structurally variable folds). The integration of whole-genome expression and protein–protein interaction data with structural information is a particularly novel feature of our system. We provide three ways of visualizing the rankings: a profiler emphasizing the progression of high and low ranks across many pre-selected attributes, a dynamic comparer for custom comparisons and a numerical rankings correlator. These allow one to directly compare very different attributes of a fold (e.g. expression level, genome occurrence and maximum motion) in the uniform numerical format of ranks. This uniform framework, in turn, highlights the way that the frequency of many of the attributes falls off with approximate power-law behavior (i.e. according to V–b, for attribute value V and constant exponent b), with a few folds having large values and most having small values.