In mouse brain cDNA libraries generated from small RNA molecules we have identified a total of 201 different expressed RNA sequences potentially encoding novel small non‐messenger RNA species (snmRNAs). Based on sequence and structural motifs, 113 of these RNAs can be assigned to the C/D box or H/ACA box subclass of small nucleolar RNAs (snoRNAs), known as guide RNAs for rRNA. While 30 RNAs represent mouse homologues of previously identified human C/D or H/ACA snoRNAs, 83 correspond to entirely novel snoRNAs. Among these, for the first time, we identified four C/D box snoRNAs and four H/ACA box snoRNAs predicted to direct modifications within U2, U4 or U6 small nuclear RNAs (snRNAs). Furthermore, 25 snoRNAs from either class lacked antisense elements for rRNAs or snRNAs. Therefore, additional snoRNA targets have to be considered. Surprisingly, six C/D box snoRNAs and one H/ACA box snoRNA were expressed exclusively in brain. Of the 88 RNAs not belonging to either snoRNA subclass, at least 26 are probably derived from truncated heterogeneous nuclear RNAs (hnRNAs) or mRNAs. Short interspersed repetitive elements (SINEs) are located on five RNA sequences and may represent rare examples of transcribed SINEs. The remaining RNA species could not as yet be assigned either to any snmRNA class or to a part of a larger hnRNA/mRNA. It is likely that at least some of the latter will represent novel, unclassified snmRNAs.

Eight genes have been identified that function in the regulation, biosynthesis, and transport of rhizobactin 1021, a hydroxamate siderophore produced under iron stress by Sinorhizobium meliloti. The genes were sequenced, and transposon insertion mutants were constructed for phenotypic analysis. Six of the genes, named rhbABCDEF, function in the biosynthesis of the siderophore and were shown to constitute an operon that is repressed under iron-replete conditions. Another gene in the cluster, named rhtA, encodes the outer membrane receptor protein for rhizobactin 1021. It was shown to be regulated by iron and to encode a product having 61% similarity to IutA, the outer membrane receptor for aerobactin. Transcription of both the rhbABCDEF operon and the rhtA gene was found to be positively regulated by the product of the eighth gene in the cluster, named rhrA, which has characteristics of an AraC-type transcriptional activator. The six genes in the rhbABCDEF operon have interesting gene junctions with short base overlaps existing between the genes. Similarities between the protein products of the biosynthesis genes and other proteins suggest that rhizobactin 1021 is synthesized by the formation of a novel siderophore precursor, 1,3-diaminopropane, which is then modified and attached to citrate in steps resembling those of the aerobactin biosynthetic pathway. The cluster of genes is located on the pSyma megaplasmid of S. meliloti 2011. Reverse transcription-PCR with RNA isolated from mature alfalfa nodules yielded no products for rhbF or rhtA at a time when the nifH gene was strongly expressed, indicating that siderophore biosynthesis and transport genes are not strongly expressed when nitrogenase is being formed in root nodules. Mutants having transposon insertions in the biosynthesis or transport genes induced effective nitrogen-fixing nodules on alfalfa plants.

Summary.  Background: Aspirin (acetylsalicylic acid) irreversibly inhibits platelet cyclooxygenase (COX)‐1, the enzyme that converts arachidonic acid (AA) to the potent platelet agonist thromboxane (TX) A2. Despite clear benefit from aspirin in patients with cardiovascular disease (CAD), evidence of heterogeneity in the way individuals respond has given rise to the concept of ‘aspirin resistance.’Aims: To evaluate the hypothesis that incomplete suppression of platelet COX as a consequence of variation in the COX‐1 gene may affect aspirin response and thus contribute to aspirin resistance. Patients and methods: Aspirin response, determined by serum TXB2 levels and AA‐induced platelet aggregation, was prospectively studied in patients (n = 144) with stable CAD taking aspirin (75–300 mg). Patients were genotyped for five single nucleotide polymorphisms in COX‐1 [A‐842G, C22T (R8W), G128A (Q41Q), C644A (G213G) and C714A (L237M)]. Haplotype frequencies and effect of haplotype on two platelet phenotypes were estimated by maximum likelihood. The four most common haplotypes were considered separately and less common haplotypes pooled. Results: COX‐1 haplotype was significantly associated with aspirin response determined by AA‐induced platelet aggregation (P = 0.004; 4 d.f.). Serum TXB2 generation was also related to genotype (P = 0.02; 4 d.f.). Conclusion: Genetic variability in COX‐1 appears to modulate both AA‐induced platelet aggregation and thromboxane generation. Heterogeneity in the way patients respond to aspirin may in part reflect variation in COX‐1 genotype.

Clustering is one of the main mathematical challenges in large-scale gene expression analysis. We describe a clustering procedure based on a sequential k-means algorithm with additional refinements that is able to handle high-throughput data in the order of hundreds of thousands of data items measured on hundreds of variables. The practical motivation for our algorithm is oligonucleotide fingerprinting-a method for simultaneous determination of expression level for every active gene of a specific tissue-although the algorithm can be applied as well to other large-scale projects like EST clustering and qualitative clustering of DNA-chip data. As a pairwise similarity measure between two p-dimensional data points, x and y, we introduce mutual information that can be interpreted as the amount of information about x in y, and vice versa. We show that for our purposes this measure is superior to commonly used metric distances, for example, Euclidean distance. We also introduce a modified version of mutual information as a novel method for validating clustering results when the true clustering is known. The performance of our algorithm with respect to experimental noise is shown by extensive simulation studies. The algorithm is tested on a subset of 2029 cDNA clones coming from 15 different genes from a cDNA library derived from human dendritic cells. Furthermore, the clustering of these 2029 cDNA clones is demonstrated when the entire set of 76,032 cDNA clones is processed.