scholarly journals Combining Next-Generation Sequencing and Immune Assays: A Novel Method for Identification of Antigen-Specific T Cells

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74231 ◽  
Author(s):  
Mark Klinger ◽  
Katherine Kong ◽  
Martin Moorhead ◽  
Li Weng ◽  
Jianbiao Zheng ◽  
...  
2020 ◽  
Vol 94 (9) ◽  
Author(s):  
Marilia Rita Pinzone ◽  
Maria Paola Bertuccio ◽  
D. Jake VanBelzen ◽  
Ryan Zurakowski ◽  
Una O’Doherty

ABSTRACT Next-generation sequencing (NGS) represents a powerful tool to unravel the genetic make-up of the HIV reservoir, but limited data exist on its use in vitro. Moreover, most NGS studies do not separate integrated from unintegrated DNA, even though selection pressures on these two forms should be distinct. We reasoned we could use NGS to compare the infection of resting and activated CD4 T cells in vitro to address how the metabolic state affects reservoir formation and dynamics. To address these questions, we obtained HIV sequences 2, 4, and 8 days after NL4-3 infection of metabolically activated and quiescent CD4 T cells (cultured with 2 ng/ml interleukin-7). We compared the composition of integrated and total HIV DNA by isolating integrated HIV DNA using pulsed-field electrophoresis before performing sequencing. After a single-round infection, the majority of integrated HIV DNA was intact in both resting and activated T cells. The decay of integrated intact proviruses was rapid and similar in both quiescent and activated T cells. Defective forms accumulated relative to intact ones analogously to what is observed in vivo. Massively deleted viral sequences formed more frequently in resting cells, likely due to lower deoxynucleoside triphosphate (dNTP) levels and the presence of multiple restriction factors. To our surprise, the majority of these deleted sequences did not integrate into the human genome. The use of NGS to study reservoir dynamics in vitro provides a model that recapitulates important aspects of reservoir dynamics. Moreover, separating integrated from unintegrated HIV DNA is important in some clinical settings to properly study selection pressures. IMPORTANCE The major implication of our work is that the decay of intact proviruses in vitro is extremely rapid, perhaps as a result of enhanced expression. Gaining a better understanding of why intact proviruses decay faster in vitro might help the field identify strategies to purge the reservoir in vivo. When used wisely, in vitro models are a powerful tool to study the selective pressures shaping the viral landscape. Our finding that massively deleted sequences rarely succeed in integrating has several ramifications. It demonstrates that the total HIV DNA can differ substantially in character from the integrated HIV DNA under certain circumstances. The presence of unintegrated HIV DNA has the potential to obscure selection pressures and confound the interpretation of clinical studies, especially in the case of trials involving treatment interruptions.


2014 ◽  
Vol 193 (10) ◽  
pp. 5338-5344 ◽  
Author(s):  
Barbera van Schaik ◽  
Paul Klarenbeek ◽  
Marieke Doorenspleet ◽  
Antoine van Kampen ◽  
D. Branch Moody ◽  
...  

DNA Repair ◽  
2015 ◽  
Vol 26 ◽  
pp. 44-53 ◽  
Author(s):  
Chen-Pang Soong ◽  
Gregory A. Breuer ◽  
Ryan A. Hannon ◽  
Savina D. Kim ◽  
Ahmed F. Salem ◽  
...  

2018 ◽  
Author(s):  
Timothy G. Johnstone ◽  
Rajagopal Chari ◽  
David Koppstein ◽  
Ronald J. Hause ◽  
Rafael Ponce ◽  
...  

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shao-Wu Zhang ◽  
Xiang-Yang Jin ◽  
Teng Zhang

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.


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