scholarly journals Discovery and description of the first human Retro-Giant virus

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1005 ◽  
Author(s):  
Elena Angela Lusi ◽  
Federico Caicci

Background:Robert Gallo reported the first human retrovirus HLTV in 1980. What we report here is the first human giant virus, Mimivirus-like, with a retroviral core.Methods:  The isolation of human giant viruses from human T cells  Leukaemia was performed on 25% sucrose gradient. The purified viral pellet was examined using electron microscopy (EM), after immunolabelling with anti-FeLV gag p27 moAb, used for its ability to bind conserved epitopes among different mammalian retroviruses. These human giant viruses were tested for reverse transcriptase activity. RNA extracted from the viral  particles was initially amplified with the Pan Retrovirus PCR technique.  In  addition,a shotgun whole genome sequence was performed. Results:EM showed the presence of ~400 nm giant viruses, mimivirus-like, specifically labelled by anti-FeLV gag p27 Ab. The giant viruses had the reverse transcribing property. Whole genome sequence showed the presence of transforming retroviral genes in the large viral genome confirming that the Retro-Giant viruses are a distinct branch, missing from the current classification of retroviruses. Conclusions:Although sharing some of the morphological features with Mimiviruses, this human giant virus differs substantially from environmental DNA-giant viruses isolated so far, in that it manifests a unique mammalian transforming retroviral core and T cell tropism. The virus should not be confused with a classic human retrovirus nor even a large human retrovirus, but an ancestral human giant virus, mimivirus-like, with a mammalian retroviral core. Certainly, the oncogenic potential of the viral particle and its T cell tropism is of concern and further studies are needed to clarify the role of this giant virus in human diseases and evolution of archetypal retroviruses.

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1005
Author(s):  
Elena Angela Lusi ◽  
Federico Caicci

Background:Robert Gallo reported the first human retrovirus HLTV in 1980. What we report here is the first human giant virus, Mimivirus-like, with a retroviral core.Methods:  The isolation of human giant viruses from human T cells  Leukaemia was performed on 25% sucrose gradient. The purified viral pellet was examined using electron microscopy (EM), after immunolabelling with anti-FeLV gag p27 moAb, used for its ability to bind conserved epitopes among different mammalian retroviruses. RNA extracted from the viral particles was amplified with the Pan Retrovirus PCR technique that targets the most conserved VLPQG and YMDD in the Pol region of different retroviruses. The amplified genes were sequenced and analyzed with molecular phylogenetic tests.Results:EM showed the presence of ~400 nm giant viruses, mimivirus-like, specifically labelled by anti-FeLV gag p27 Ab. RNA extracted from the particles contained retroviral genes. Molecular phylogenetic analyses of 150 bp amplicon product, compared with the same size amplicons of the Pol gene of diverse retroviruses, showed that the retro-giant viruses are a distinct branch, missing from the current classification of retroviruses.Conclusions:Although sharing some of the morphological features with Mimiviruses, this human giant virus differs substantially from environmental DNA-giant viruses isolated so far, in that it manifests a unique mammalian transforming retroviral core and T cell tropism. The virus should not be confused with a classic human retrovirus nor even a large human retrovirus, but an ancestral human giant virus, mimivirus-like, with a mammalian retroviral core. Certainly, the oncogenic potential of the viral particle and its T cell tropism is of concern and further studies are needed to clarify the role of this giant virus in human diseases and evolution of archetypal retroviruses.


2010 ◽  
Vol 36 (4) ◽  
pp. 688-694
Author(s):  
Yi-Jun WANG ◽  
Yan-Ping LÜ ◽  
Qin XIE ◽  
De-Xiang DENG ◽  
Yun-Long BIAN

2014 ◽  
Vol 40 (12) ◽  
pp. 2059
Author(s):  
Lin-Yi QIAO ◽  
Xin LI ◽  
Zhi-Jian CHANG ◽  
Xiao-Jun ZHANG ◽  
Hai-Xian ZHAN ◽  
...  

IDCases ◽  
2020 ◽  
pp. e01034
Author(s):  
Charlie Tan ◽  
Fang-I Lu ◽  
Patryk Aftanas ◽  
Kara Tsang ◽  
Samira Mubareka ◽  
...  

Author(s):  
Amnon Koren ◽  
Dashiell J Massey ◽  
Alexa N Bracci

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Theo Meuwissen ◽  
Irene van den Berg ◽  
Mike Goddard

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.


Sign in / Sign up

Export Citation Format

Share Document