A tetracycline derivative, minocycline, reduces inflammation and protects against focal cerebral ischemia with a wide therapeutic window

The only treatment of patients with acute ischemic stroke is thrombolytic therapy, which benefits only a fraction of stroke patients. Both human and experimental studies indicate that ischemic stroke involves secondary inflammation that significantly contributes to the outcome after ischemic insult. Minocycline is a semisynthetic second-generation tetracycline that exerts antiinflammatory effects that are completely separate from its antimicrobial action. Because tetracycline treatment is clinically well tolerated, we investigated whether minocycline protects against focal brain ischemia with a wide therapeutic window. Using a rat model of transient middle cerebral artery occlusion, we show that daily treatment with minocycline reduces cortical infarction volume by 76 ± 22% when the treatment is started 12 h before ischemia and by 63 ± 35% when started even 4 h after the onset of ischemia. The treatment inhibits morphological activation of microglia in the area adjacent to the infarction, inhibits induction of IL-1β-converting enzyme, and reduces cyclooxygenase-2 expression and prostaglandin E2 production. Minocycline had no effect on astrogliosis or spreading depression, a wave of ionic transients thought to contribute to enlargement of cortical infarction. Treatment with minocycline may act directly on brain cells, because cultured primary neurons were also salvaged from glutamate toxicity. Minocycline may represent a prototype of an antiinflammatory compound that provides protection against ischemic stroke and has a clinically relevant therapeutic window.

Yeast microarrays for genome wide parallel genetic and gene expression analysis

We have developed high-density DNA microarrays of yeast ORFs. These microarrays can monitor hybridization to ORFs for applications such as quantitative differential gene expression analysis and screening for sequence polymorphisms. Automated scripts retrieved sequence information from public databases to locate predicted ORFs and select appropriate primers for amplification. The primers were used to amplify yeast ORFs in 96-well plates, and the resulting products were arrayed using an automated micro arraying device. Arrays containing up to 2,479 yeast ORFs were printed on a single slide. The hybridization of fluorescently labeled samples to the array were detected and quantitated with a laser confocal scanning microscope. Applications of the microarrays are shown for genetic and gene expression analysis at the whole genome level.

Cluster analysis and display of genome-wide expression patterns

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

A genome-wide search for susceptibility genes in human systemic lupus erythematosus sib-pair families

Systemic lupus erythematosus (SLE) is an autoimmune multisystem inflammatory disease characterized by the production of pathogenic autoantibodies. Previous genetic studies have suggested associations with HLA Class II alleles, complement gene deficiencies, and Fc receptor polymorphisms; however, it is likely that other genes contribute to SLE susceptibility and pathogenesis. Here, we report the results of a genome-wide microsatellite marker screen in 105 SLE sib-pair families. By using multipoint nonparametric methods, the strongest evidence for linkage was found near the HLA locus (6p11-p21) [D6S257, logarithm of odds (lod) = 3.90, P = 0.000011] and at three additional regions: 16q13 (D16S415, lod = 3.64, P = 0.000022), 14q21–23 (D14S276, lod = 2.81, P = 0.00016), and 20p12 (D20S186, lod = 2.62, P = 0.00025). Another nine regions (1p36, 1p13, 1q42, 2p15, 2q21–33, 3cent-q11, 4q28, 11p15, and 15q26) were identified with lod scores ≥1.00. These data support the hypothesis that multiple genes, including one in the HLA region, influence susceptibility to human SLE.

Linking genome and proteome by mass spectrometry: Large-scale identification of yeast proteins from two dimensional gels

The function of many of the uncharacterized open reading frames discovered by genomic sequencing can be determined at the level of expressed gene products, the proteome. However, identifying the cognate gene from minute amounts of protein has been one of the major problems in molecular biology. Using yeast as an example, we demonstrate here that mass spectrometric protein identification is a general solution to this problem given a completely sequenced genome. As a first screen, our strategy uses automated laser desorption ionization mass spectrometry of the peptide mixtures produced by in-gel tryptic digestion of a protein. Up to 90% of proteins are identified by searching sequence data bases by lists of peptide masses obtained with high accuracy. The remaining proteins are identified by partially sequencing several peptides of the unseparated mixture by nanoelectrospray tandem mass spectrometry followed by data base searching with multiple peptide sequence tags. In blind trials, the method led to unambiguous identification in all cases. In the largest individual protein identification project to date, a total of 150 gel spots—many of them at subpicomole amounts—were successfully analyzed, greatly enlarging a yeast two-dimensional gel data base. More than 32 proteins were novel and matched to previously uncharacterized open reading frames in the yeast genome. This study establishes that mass spectrometry provides the required throughput, the certainty of identification, and the general applicability to serve as the method of choice to connect genome and proteome.

A host-specific function is required for ligation of a wide variety of ribozyme-processed RNAs

Hepatitis δ virus (HDV) replicates its circular RNA genome via a rolling circle mechanism. During this process, cis-acting ribozymes cleave adjacent upstream sequences and thereby resolve replication intermediates to unit-length RNA. The subsequent ligation of these 5′OH and 2′,3′-cyclic phosphate termini to form circular RNA is an essential step in the life cycle of the virus. Here we present evidence for the involvement of a host activity in the ligation of HDV RNA. We used both HDV and hammerhead ribozymes to generate a panel of HDV and non-HDV RNA substrates that bear 5′ hydroxyl and 2′,3′- cyclic phosphate termini. We found that ligation of these substrates occurred in host cells, but not in vitro or in Escherichia coli. The host-specific ligation activity was capable of joining RNA in both bimolecular and intramolecular reactions and functioned in a sequence-independent manner. We conclude that mammalian cells contain a default pathway that efficiently circularizes ribozyme processed RNAs. This pathway could be exploited in the delivery of stable antisense and decoy RNA to the nucleus.

Singular value decomposition for genome-wide expression data processing and modeling

We describe the use of singular value decomposition in transforming genome-wide expression data from genes × arrays space to reduced diagonalized “eigengenes” × “eigenarrays” space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.