The PRINTS database: a resource for identification of protein families

The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours; this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.     

Phenotypic hypersusceptibility to multiple protease inhibitors and low replicative capacity in patients who are chronically infected with human immunodeficiency virus type 1

Increased susceptibility to the protease inhibitors saquinavir and amprenavir has been observed in human immunodeficiency virus type 1 (HIV-1) with specific mutations in protease (V82T and N88S). Increased susceptibility to ritonavir has also been described in some viruses from antiretroviral agent-naive patients with primary HIV-1 infection in association with combinations of amino acid changes at polymorphic sites in the protease. Many of the viruses displaying increased susceptibility to protease inhibitors also had low replication capacity. In this retrospective study, we analyze the drug susceptibility phenotype and the replication capacity of virus isolates obtained at the peaks of viremia during five consecutive structured treatment interruptions in 12 chronically HIV-1-infected patients. Ten out of 12 patients had at least one sample with protease inhibitor hypersusceptibility (change 相似文献   

The Whole Genome Expression Analysis using Two Microarray Technologies to Identify Gene Networks That Mediate the Myocardial Phenotype of CD36 Deficiency

We have previously shown that CD36 is a membrane protein that facilitates long chain fatty acid (FA) transport by muscle tissues. We also documented the significant impact of muscle CD36 expression on heart function, skeletal muscle insulin sensitivity as well as on overall metabolism. To identify a comprehensive set of genes that are differentially regulated by CD36 expression in the heart, we used two microarray technologies (Affymetrix and Agilent) to compare gene expression in heart tissues from CD36 KnocK-Out (KO-CD36) versus wild type (WT-CD36) mice. The obtained results using the two technologies were similar with around 35 genes differentially expressed using both technologies. Absence of CD36 led to down-regulation of the expression of three groups of genes involved in pathways of FA metabolism, angiogenesis/apoptosis and structure. These data are consistent with the fact that the CD36 protein binds FA and thrombospondin 1 invoved respectively in lipid metabolism and anti-angiogenic activities. In conclusion, our findings led to validate our data analysis workflow and identify specific pathways, possibly underlying the phenotypic abnormalities in CD36 Knock -Out hearts.     

CD8alpha+ and CD11b+ dendritic cell-restricted MHC class II controls Th1 CD4+ T cell immunity

The activation, proliferation, differentiation, and trafficking of CD4 T cells is central to the development of type I immune responses. MHC class II (MHCII)-bearing dendritic cells (DCs) initiate CD4(+) T cell priming, but the relative contributions of other MHCII(+) APCs to the complete Th1 immune response is less clear. To address this question, we examined Th1 immunity in a mouse model in which I-A(beta)(b) expression was targeted specifically to the DCs of I-A(beta)b-/- mice. MHCII expression is reconstituted in CD11b(+) and CD8alpha(+) DCs, but other DC subtypes, macrophages, B cells, and parenchymal cells lack of expression of the I-A(beta)(b) chain. Presentation of both peptide and protein Ags by these DC subsets is sufficient for Th1 differentiation of Ag-specific CD4(+) T cells in vivo. Thus, Ag-specific CD4(+) T cells are primed to produce Th1 cytokines IL-2 and IFN-gamma. Additionally, proliferation, migration out of lymphoid organs, and the number of effector CD4(+) T cells are appropriately regulated. However, class II-negative B cells cannot receive help and Ag-specific IgG is not produced, confirming the critical MHCII requirement at this stage. These findings indicate that DCs are not only key initiators of the primary response, but provide all of the necessary cognate interactions to control CD4(+) T cell fate during the primary immune response.     

Ecoenzymatic stoichiometry and microbial processing of organic matter in northern bogs and fens reveals a common P-limitation between peatland types

We compared carbon (C), nitrogen (N), and phosphorus (P) concentrations in atmospheric deposition, runoff, and soils with microbial respiration [dehydrogenase (DHA)] and ecoenzyme activity (EEA) in an ombrotrophic bog and a minerotrophic fen to investigate the environmental drivers of biogeochemical cycling in peatlands at the Marcell Experimental Forest in northern Minnesota, USA. Ecoenzymatic stoichiometry was used to construct models for C use efficiency (CUE) and decomposition (M), and these were used to model respiration (R_m). Our goals were to determine the relative C, N, and P limitations on microbial processes and organic matter decomposition, and to identify environmental constraints on ecoenzymatic processes. Mean annual water, C, and P yields were greater in the fen, while N yields were similar in both the bog and fen. Soil chemistry differed between the bog and fen, and both watersheds exhibited significant differences among soil horizons. DHA and EEA differed by watersheds and soil horizons, CUE, M, and R_m differed only by soil horizons. C, N, or P limitations indicated by EEA stoichiometry were confirmed with orthogonal regressions of ecoenzyme pairs and enzyme vector analyses, and indicated greater N and P limitation in the bog than in the fen, with an overall tendency toward P-limitation in both the bog and fen. Ecoenzymatic stoichiometry, microbial respiration, and organic matter decomposition were responsive to resource availability and the environmental drivers of microbial metabolism, including those related to global climate changes.     

Forecasting youth adjustment at age 15 from school readiness profiles at 54 months

ABSTRACT

A person-oriented approach examined the extent to which patterns of school readiness across social and cognitive domains in 944 typically-developing 54-month-old children forecast academic achievement, social-emotional development, risk taking, and executive functioning at age 15. Prior work identified six distinct profiles of school readiness at 54 months that predicted group differences in achievement in first grade, as well as achievement and social-emotional outcomes in fifth grade. After controlling for demographics, early language skills, and home and school factors, the 54-month readiness profiles demonstrated different performance on risk-taking and executive function behaviors assessed at age 15. Children with attention problems at 54 months were most likely to believe that peers were engaging in risky behaviors and to have smoked more than 2 cigarettes by age 15. Children with low working memory and low to average social skills at 54 months were outperformed by their peers on working memory and executive function tasks at age 15. Results are discussed in terms of continuity in forms of developmental function.     

Lead resistance and sensitivity in Staphylococcus aureus

Abstract Five lead-resistant strains of Staphylococcus aureus were isolated. Plasmid-free lead-sensitive variants were obtained from the three plasmid-bearing strains. Lead-resistant strains tolerated an approximately 600 × higher Pb(NO₃)₂ concentration than lead-sensitive strains. Both types of strains initially bound lead, but only the resistant strains accumulated the metal as an intracellular lead-phosphate.     

Microarray profile of differentially expressed genes in a monkey model of allergic asthma

Background

Inhalation of Ascaris suum antigen by allergic monkeys causes an immediate bronchoconstriction and delayed allergic reaction, including a pulmonary inflammatory infiltrate. To identify genes involved in this process, the gene-expression pattern of allergic monkey lungs was profiled by microarrays. Monkeys were challenged by inhalation of A. suum antigen or given interleukin-4 (IL-4) treatment; lung tissue was collected at 4, 18 or 24 h after antigen challenge or 24 h after IL-4. Each challenged monkey lung was compared to a pool of normal, unchallenged monkey lungs.

Results

Of the approximately 40,000 cDNAs represented on the microarray, expression levels of 169 changed by more than 2.5-fold in at least one of the pairwise probe comparisons; these cDNAs encoded 149 genes, of which two thirds are known genes. The largest number of regulated genes was observed 4 h after challenge. Confirmation of differential expression in the original tissue was obtained for 95% of a set of these genes using real-time PCR. Cluster analysis revealed at least five groups of genes with unique expression patterns. One cluster contained genes for several chemokine mediators including eotaxin, PARC, MCP-1 and MCP-3. Genes involved in tissue remodeling and antioxidant responses were also identified as regulated by antigen and IL-4 or by antigen only.

Conclusion

This study provides a large-scale profile of gene expression in the primate lung following allergen or IL-4 challenge. It shows that microarrays, with real-time PCR, are a powerful tool for identifying and validating differentially expressed genes in a disease model.