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Asset Tag: Translocation

July 7, 2019  |  

Pseudoautosomal region 1 length polymorphism in the human population.

The human sex chromosomes differ in sequence, except for the pseudoautosomal regions (PAR) at the terminus of the short and the long arms, denoted as PAR1 and PAR2. The boundary between PAR1 and the unique X and Y sequences was established during the divergence of the great apes. During a copy number variation screen, we noted a paternally inherited chromosome X duplication in 15 independent families. Subsequent genomic analysis demonstrated that an insertional translocation of X chromosomal sequence into theMa Y chromosome generates an extended PAR. The insertion is generated by non-allelic homologous recombination between a 548 bp LTR6B repeat within the Y chromosome PAR1 and a second LTR6B repeat located 105 kb from the PAR boundary on the X chromosome. The identification of the reciprocal deletion on the X chromosome in one family and the occurrence of the variant in different chromosome Y haplogroups demonstrate this is a recurrent genomic rearrangement in the human population. This finding represents a novel mechanism shaping sex chromosomal evolution.

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July 7, 2019  |  

The impact of aminoglycosides on the dynamics of translation elongation.

Inferring antibiotic mechanisms on translation through static structures has been challenging, as biological systems are highly dynamic. Dynamic single-molecule methods are also limited to few simultaneously measurable parameters. We have circumvented these limitations with a multifaceted approach to investigate three structurally distinct aminoglycosides that bind to the aminoacyl-transfer RNA site (A site) in the prokaryotic 30S ribosomal subunit: apramycin, paromomycin, and gentamicin. Using several single-molecule fluorescence measurements combined with structural and biochemical techniques, we observed distinct changes to translational dynamics for each aminoglycoside. While all three drugs effectively inhibit translation elongation, their actions are structurally and mechanistically distinct. Apramycin does not displace A1492 and A1493 at the decoding center, as demonstrated by a solution nuclear magnetic resonance structure, causing only limited miscoding; instead, it primarily blocks translocation. Paromomycin and gentamicin, which displace A1492 and A1493, cause significant miscoding, block intersubunit rotation, and inhibit translocation. Our results show the power of combined dynamics, structural, and biochemical approaches to elucidate the complex mechanisms underlying translation and its inhibition. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

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July 7, 2019  |  

Coordinated conformational and compositional dynamics drive ribosome translocation.

During translation elongation, the ribosome compositional factors elongation factor G (EF-G; encoded by fusA) and tRNA alternately bind to the ribosome to direct protein synthesis and regulate the conformation of the ribosome. Here, we use single-molecule fluorescence with zero-mode waveguides to directly correlate ribosome conformation and composition during multiple rounds of elongation at high factor concentrations in Escherichia coli. Our results show that EF-G bound to GTP (EF-G-GTP) continuously samples both rotational states of the ribosome, binding with higher affinity to the rotated state. Upon successful accommodation into the rotated ribosome, the EF-G-ribosome complex evolves through several rate-limiting conformational changes and the hydrolysis of GTP, which results in a transition back to the nonrotated state and in turn drives translocation and facilitates release of both EF-G-GDP and E-site tRNA. These experiments highlight the power of tracking single-molecule conformation and composition simultaneously in real time.

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July 7, 2019  |  

Structural variation offers new home for disease associations and gene discovery

Following completion of the Human Genome Project, most studies of human genetic variation have centered on single nucleotide polymorphisms (SNPs). SNPs are numerous in individual genomes and serve as useful genetic markers in association studies across a population. These markers have been leveraged to identify genetic loci for disease risk and draw associations with numerous traits of interest. Despite their usefulness, SNPs do not tell the whole story. For example, most SNPs are associated with only a small increased risk of disease, and they usually cannot identify on their own which genes are causal. This has resulted in what many researchers have referred to as missing or hidden heritability.

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July 7, 2019  |  

Hunting structural variants: Population by population

Until recently, most population-scale genome sequencing studies have focused on identifying single nucleotide variants (SNVs) to explore genetic differences between individuals. Like so many SNV-based genome-wide association studies, however, these efforts have had difficulty identifying causative genetic mechanisms underlying most complex functions. More and more, the genomics community has realised that structural variation is likely responsible for many of the traits and phenotypes that scientists have not been able to attribute to SNVs. This class of variants, defined as genetic differences of 50 bp or larger, accounts for most of the DNA sequence differences between any two people. Structural variants (SVs) are also already known to cause many common and rare diseases including ALS, schizophrenia, leukemia, Carney complex, and Huntington’s disease. Despite the importance of SVs, these larger variants have been understudied and underreported compared to their single-nucleotide counterparts. One reason is that they remain difficult to detect. Their length often means they cannot be fully spanned using short sequencing reads. They also often occur in highly repetitive or GC-rich regions of the genome, making them challenging targets. As such, this class of human genetic variation has remained vastly under-explored in global populations and is now ripe for discovery.

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July 7, 2019  |  

Structural variation detection using next-generation sequencing data: A comparative technical review.

Structural variations (SVs) are mutations in the genome of size at least fifty nucleotides. They contribute to the phenotypic differences among healthy individuals, cause severe diseases and even cancers by breaking or linking genes. Thus, it is crucial to systematically profile SVs in the genome. In the past decade, many next-generation sequencing (NGS)-based SV detection methods have been proposed due to the significant cost reduction of NGS experiments and their ability to unbiasedly detect SVs to the base-pair resolution. These SV detection methods vary in both sensitivity and specificity, since they use different SV-property-dependent and library-property-dependent features. As a result, predictions from different SV callers are often inconsistent. Besides, the noises in the data (both platform-specific sequencing error and artificial chimeric reads) impede the specificity of SV detection. Poorly characterized regions in the human genome (e.g., repeat regions) greatly impact the reads mapping and in turn affect the SV calling accuracy. Calling of complex SVs requires specialized SV callers. Apart from accuracy, processing speed of SV caller is another factor deciding its usability. Knowing the pros and cons of different SV calling techniques and the objectives of the biological study are essential for biologists and bioinformaticians to make informed decisions. This paper describes different components in the SV calling pipeline and reviews the techniques used by existing SV callers. Through simulation study, we also demonstrate that library properties, especially insert size, greatly impact the sensitivity of different SV callers. We hope the community can benefit from this work both in designing new SV calling methods and in selecting the appropriate SV caller for specific biological studies. Copyright © 2016 Elsevier Inc. All rights reserved.

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