989 resultados para Gene isolation


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Two fractions showing proteolytic enzymes have been obtained from the latex of Synadenium grantii Hook, 'f', using gel-filtration and anion-exchange chromatographic techniques. Both these proteases have the same molecular mass of 76+/-2 kDa each. They exhibit maximal activity at pH 7.0 and at a temperature of 60 degreesC. They display stability over a pH range from 5-10 and are also highly thermostable. Irreversible inhibition by PMSF indicates that they are serine proteases. In addition, histidine residues also appear to play an important role in catalysis as evidenced by inhibition with DEPC. They also exhibit similarity with respect to pH and temperature optima, kinetic properties and thermal stability. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

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The positive element (PE) (-69 to -98 bp) within the 5'-proximal region of the CYP2B1B2 gene (+1 to -179 bp) of rat liver is essential for phenobarbitone (PB) response and gives a single major complex with the rat liver cytosol in gel shift analysis. This complex corresponds to complex I (top) of the three complexes given by the nuclear extracts. PB treatment of rats leads to a decrease in complex I formation with the cytosol and PE and an increase in the same with the nuclear extract in gel shift analysis. Both the changes are counteracted by simultaneous okadaic acid administration. The nuclear protein giving rise to complex I has been isolated and has an M-r of 26 kDa. The cytosolic counterpart consists of two species, 26 and 28 kDa, as revealed by Southwestern blot analysis using labeled PE. It is concluded that PB treatment leads to the translocation accompanied by processing of the cytosolic protein species into the nucleus that requires protein dephosphorylation. It is suggested that PB may exert a global regulation on the transcription of many genes by modulating the phosphorylation status of different protein factors involved in transcriptional regulation. (C) 2002 Elsevier Science (USA).

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We describe here the characterization of the gene gp64 encoding the envelope fusion protein GP64 (open reading frame) ORF 105 from Bombyx mori nucleopolyhedrovirus (BmNPV). gp64 was transcribed from the early to late stages of infection and the transcripts were seen from 6 to 72 h post infection (hpi). The early transcripts initiated from a consensus CAGT motif while the late transcripts arose from three conserved TAAG motifs, all of which were located in the near upstream region of the coding sequence. Both early and late transcripts terminated at a run of T residues following the second polyadenylation signal located 31 nt downstream of the translation termination codon. BmGP64 protein was detectable from 6 hpi and was present in larger quantities throughout the infection process from 12 hpi, in BmNPV-infected BmN cells. The persistent presence of GP64 in BmN cells differed from the protein expression pattern of GP64 in Autographa californica multinucleocapsid nucleopolyhedrovirus infection, where the protein levels decreased significantly by late times (48 hpi). BmGP64 was located in the membrane and cytoplasm of the infected host cells and as a component of the budded virions. The production of infectious budded virus and the fusion activity were reduced when glycosylation of GP64 was inhibited. (C) 2003 Elsevier Science B.V. All rights reserved.

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This study examines the population genetic structure of Asian elephants (Elephas maximus) across India, which harbours over half the world's population of this endangered species. Mitochondrial DNA control region sequences and allele frequencies at six nuclear DNA microsatellite markers obtained from the dung of free-ranging elephants reveal low mtDNA and typical microsatellite diversity. Both known divergent clades of mtDNA haplotypes in the Asian elephant are present in India, with southern and central India exhibiting exclusively the β clade of Fernando et al. (2000), northern India exhibiting exclusively the α clade and northeastern India exhibiting both, but predominantly the α clade. A nested clade analysis revealed isolation by distance as the principal mechanism responsible for the observed haplotype distributions within the α and β clades. Analyses of molecular variance and pairwise population FST tests based on both mitochondrial and microsatellite DNA suggest that northern-northeastern India, central India, Nilgiris (in southern India) and Anamalai-Periyar (in southern India) are four demographically autonomous population units and should be managed separately. In addition, evidence for female philopatry, male-mediated gene flow and two possible historical biogeographical barriers is described.

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Tuberous sclerosis complex (TSC) is an autosomal dominant disorder with loci on chromosome 9q34.12 (TSC1) and chromosome 16p13.3 (TSC2). Genes for both loci have been isolated and characterized. The promoters of both genes have not been characterized so far and little is known about the regulation of these genes. This study reports the characterization of the human TSC1 promoter region for the first time. We have identified a novel alternative isoform in the 5' untranslated region (UTR) of the TSC1 gene transcript involving exon 1. Alternative isoforms in the 5' UTR of the mouse Tsc1 gene transcript involving exon I and exon 2 have also been identified. We have identified three upstream open reading frames (uORFs) in the 5' UTR of the TSC1/Tsc1 gene. A comparative study of the 5' UTR of TSC1/Tsc1 gene has revealed that there is a high degree of similarity not only in the sequence but also in the splicing pattern of both human and mouse TSC1 genes. We have used PCR methodology to isolate approximately 1.6 kb genomic DNA 5' to the TSC1 cDNA. This sequence has directed a high level of expression of luciferase activity in both HeLa and HepG2 cells. Successive 5' and 3' deletion analysis has suggested that a -587 bp region, from position +77 to -510 from the transcription start site (TSS), contains the promoter activity. Interestingly, this region contains no consensus TATA box or CAAT box. However, a 521-bp fragment surrounding the TSS exhibits the characteristics of a CpG island which overlaps with the promoter region. The identification of the TSC1 promoter region will help in designing a suitable strategy to identify mutations in this region in patients who do not show any mutations in the coding regions. It will also help to study the regulation of the TSC1 gene and its role in tumorigenesis. (C) 2003 Elsevier B.V. All rights reserved.

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Background: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. Results: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. Conclusions: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems. The source code for NETGEM is available from https://github.com/vjethava/NETGEM