Principles Governing Establishment versus Collapse of HIV-1 Cellular Spread
Data files
Nov 19, 2019 version files 309.02 MB
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code-data-HatayeJ.zip
309.02 MB
Abstract
A population at low census might go extinct, or instead transition into exponential growth to become firmly established. Whether this pivotal event occurs for a within-host pathogen can be the difference between health and illness. Here we define the principles governing whether HIV-1 spread among cells fails or becomes established, by coupling stochastic modeling with laboratory experiments. Following ex vivo activation of latently-infected CD4 T cells without de novo infection, stochastic cell division and death contributes to high variability in the magnitude of initial virus release. Transition to exponential HIV-1 spread often fails due to release of an insufficient amount of replication-competent virus. Establishment of exponential growth occurs when virus produced from multiple infected cells exceeds a critical population size. We quantitatively define the crucial transition to exponential viral spread. Thwarting this process would prevent HIV transmission or rebound from the latent reservoir.
The file "code-data-HatayeJ.zip" contains two experimental data tables and script code in R, details are in the file "README.txt".
These files were generated for this research publication:
Principles Governing Establishment versus Collapse of HIV-1 Cellular Spread
Hataye JM et al. Cell Host & Microbe, 2019
https://doi.org/10.1016/j.chom.2019.10.006
See this publication for details. It has an extensive methods section. The HIV env sequencing for this study was deposited at Genbank (https://www.ncbi.nlm.nih.gov/genbank/) with accession numbers MN515491-MN516420.
There are two experimental data files in the directory /expData
HIVrna1.txt, HIV RNA detection data (RT-PCR) from ex vivo culture supernatants using CD4+ T cells isolated from the peripheral blood of human donors with HIV-1 infection on combination antiretroviral therapy (ART). These data are organized into a table with columns with the following headings:
well: culture well index
set: experiment set index
pid: patient ID (anonymized)
cd4: clinical CD4+ T cell count on day of donation
vl: clinical HIV viral load on day of donation
outg: If TRUE then cultures had exogenous target cells plus IL-2 added for outgrowth conditions
efv: If TRUE then cultures had efavirenz (reverse-transcriptase inhibitor) added for viral inhibition conditions
tcab: If TRUE then cultures had T cell activation beads (containing anti-CD3, anti-CD2, anti-CD28) added
days: days of culture
cells: number of CD4+ T cells added to culture on day 0 (day of sorting)
rcas: fraction of RCAS retroviral RNA recovered (range 0-1).
dna: HIV DNA copies detected from entire culture supernatant following DNAse treatment
rna: HIV RNA copies in entire culture supernatant (based on gag RT-PCR reaction, dilution factors and RCAS recovery)
rnac: HIV RNA copies in entire culture supernatant, corrected for residual HIV DNA if present
HIVrna2.txt, HIV RNA detection data (RT-PCR) from secondary (virus transfer from primary culture) and tertiary HIV culture supernatants.
The remaining directories contain simulation results or R code to perform data analysis, plotting, fitting of ordinary differential equation models, stochastic simulation, and Bayesian inference in Stan.
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