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Find video protocols related to scientific articles indexed in Pubmed.
Identification of highly-disrupted tRNA genes in nuclear genome of the red alga, Cyanidioschyzon merolae 10D.
Sci Rep
PUBLISHED: 02-27-2013
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The limited locations of tRNA introns are crucial for eukaryal tRNA-splicing endonuclease recognition. However, our analysis of the nuclear genome of an early-diverged red alga, Cyanidioschyzon merolae, demonstrated the first evidence of nuclear-encoded tRNA genes that contain ectopic and/or multiple introns. Some genes exhibited both intronic and permuted structures in which the 3-half of the tRNA coding sequence lies upstream of the 5-half, and an intron is inserted into either half. These highly disrupted tRNA genes, which account for 63% of all nuclear tRNA genes, are expressed via the orderly and sequential processing of bulge-helix-bulge (BHB) motifs at intron-exon junctions and termini of permuted tRNA precursors, probably by a C. merolae tRNA-splicing endonuclease with an unidentified subunit architecture. The results revealed a considerable diversity in eukaryal tRNA intron properties and endonuclease architectures, which will help to elucidate the acquisition mechanism of the BHB-mediated disrupted tRNA genes.
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Tight associations between transcription promoter type and epigenetic variation in histone positioning and modification.
BMC Genomics
PUBLISHED: 08-17-2011
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Transcription promoters are fundamental genomic cis-elements controlling gene expression. They can be classified into two types by the degree of imprecision of their transcription start sites: peak promoters, which initiate transcription from a narrow genomic region; and broad promoters, which initiate transcription from a wide-ranging region. Eukaryotic transcription initiation is suggested to be associated with the genomic positions and modifications of nucleosomes. For instance, it has been recently shown that histone with H3K9 acetylation (H3K9ac) is more likely to be distributed around broad promoters rather than peak promoters; it can thus be inferred that there is an association between histone H3K9 and promoter architecture.
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Computational analysis suggests a highly bendable, fragile structure for nucleosomal DNA.
Gene
PUBLISHED: 02-05-2011
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Eukaryotic chromosomal DNA coils around histones to form nucleosomes. Although histone affinity for DNA depends on DNA sequence patterns, how nucleosome positioning is determined by them remains unknown. Here, we show relationships between nucleosome positioning and two structural characteristics of DNA conferred by DNA sequence. Analysis of bendability and hydroxyl radical cleavage intensity of nucleosomal DNA sequences indicated that nucleosomal DNA is bendable and fragile and that nucleosome positional stability was correlated with characteristics of DNA. This result explains how histone positioning is partially determined by nucleosomal DNA structure, illuminating the optimization of chromosomal DNA packaging that controls cellular dynamics.
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Integrative features of the yeast phosphoproteome and protein-protein interaction map.
PLoS Comput. Biol.
PUBLISHED: 01-27-2011
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Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ?6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.
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Towards the systematic discovery of signal transduction networks using phosphorylation dynamics data.
BMC Bioinformatics
PUBLISHED: 05-07-2010
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Phosphorylation is a ubiquitous and fundamental regulatory mechanism that controls signal transduction in living cells. The number of identified phosphoproteins and their phosphosites is rapidly increasing as a result of recent mass spectrometry-based approaches.
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In silico analysis of phosphoproteome data suggests a rich-get-richer process of phosphosite accumulation over evolution.
Mol. Cell Proteomics
PUBLISHED: 01-09-2009
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Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.
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The green monster process for the generation of yeast strains carrying multiple gene deletions.
J Vis Exp
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Phenotypes for a gene deletion are often revealed only when the mutation is tested in a particular genetic background or environmental condition(1,2). There are examples where many genes need to be deleted to unmask hidden gene functions(3,4). Despite the potential for important discoveries, genetic interactions involving three or more genes are largely unexplored. Exhaustive searches of multi-mutant interactions would be impractical due to the sheer number of possible combinations of deletions. However, studies of selected sets of genes, such as sets of paralogs with a greater a priori chance of sharing a common function, would be informative. In the yeast Saccharomyces cerevisiae, gene knockout is accomplished by replacing a gene with a selectable marker via homologous recombination. Because the number of markers is limited, methods have been developed for removing and reusing the same marker(5,6,7,8,9,10). However, sequentially engineering multiple mutations using these methods is time-consuming because the time required scales linearly with the number of deletions to be generated. Here we describe the Green Monster method for routinely engineering multiple deletions in yeast(11). In this method, a green fluorescent protein (GFP) reporter integrated into deletions is used to quantitatively label strains according to the number of deletions contained in each strain (Figure 1). Repeated rounds of assortment of GFP-marked deletions via yeast mating and meiosis coupled with flow-cytometric enrichment of strains carrying more of these deletions lead to the accumulation of deletions in strains (Figure 2). Performing multiple processes in parallel, with each process incorporating one or more deletions per round, reduces the time required for strain construction. The first step is to prepare haploid single-mutants termed ProMonsters, each of which carries a GFP reporter in a deleted locus and one of the toolkit loci-either Green Monster GMToolkit-a or GMToolkit-? at the can1? locus (Figure 3). Using strains from the yeast deletion collection(12), GFP-marked deletions can be conveniently generated by replacing the common KanMX4 cassette existing in these strains with a universal GFP-URA3 fragment. Each GMToolkit contains: either the a- or ?-mating-type-specific haploid selection marker(1) and exactly one of the two markers that, when both GMToolkits are present, collectively allow for selection of diploids. The second step is to carry out the sexual cycling through which deletion loci can be combined within a single cell by the random assortment and/or meiotic recombination that accompanies each cycle of mating and sporulation.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.