2005
DOI: 10.1016/j.cell.2005.06.006
|View full text |Cite
|
Sign up to set email alerts
|

Stochastic Gene Expression in a Lentiviral Positive-Feedback Loop: HIV-1 Tat Fluctuations Drive Phenotypic Diversity

Abstract: HIV-1 Tat transactivation is vital for completion of the viral life cycle and has been implicated in determining proviral latency. We present an extensive experimental/computational study of an HIV-1 model vector (LTR-GFP-IRES-Tat) and show that stochastic fluctuations in Tat influence the viral latency decision. Low GFP/Tat expression was found to generate bifurcating phenotypes with clonal populations derived from single proviral integrations simultaneously exhibiting very high and near zero GFP expression. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

31
711
1
2

Year Published

2006
2006
2024
2024

Publication Types

Select...
6
2

Relationship

0
8

Authors

Journals

citations
Cited by 599 publications
(763 citation statements)
references
References 74 publications
31
711
1
2
Order By: Relevance
“…However, a recent study using a similar Tat/LTR promoter suggested that in this system gene expression over the longer term is essentially stochastic. 34 The results presented here may differ from studies using fluorescent markers partly because highlevel gene expression may be required for the cell to manifest and maintain puromycin resistance. The system Figure 4 Vectors persisted in the variegated clones in which the majority lacked puromycin resistance.…”
Section: Discussioncontrasting
confidence: 49%
See 1 more Smart Citation
“…However, a recent study using a similar Tat/LTR promoter suggested that in this system gene expression over the longer term is essentially stochastic. 34 The results presented here may differ from studies using fluorescent markers partly because highlevel gene expression may be required for the cell to manifest and maintain puromycin resistance. The system Figure 4 Vectors persisted in the variegated clones in which the majority lacked puromycin resistance.…”
Section: Discussioncontrasting
confidence: 49%
“…44,45 In actively expressing cells, this may be due to a positive feedback axis involving Tat transactivating the viral LTR. 34 A second form of silencing correlates with epigenetic changes and in this system probably involves the collapse of the Tat-TAR positive feedback axis. If this is disabled temporarily, the vulnerability of the vector to epigenetic silencing is probably similar to that observed in MLV and other gene therapy vectors.…”
Section: Gene Expression Of Integrated Hiv-1 Vector Hp Mok Et Almentioning
confidence: 99%
“…Therefore, instead of a smooth deterministic course, the fundamentally random nature of chemical reactions results in "noisy" reaction trajectories in individual cells. The resulting heterogeneous response of an ensemble of cells to a particular external signal 8 necessitates going beyond chemical kinetics, relying instead on the theory of stochastic processes 9,10,11,12 to describe signaling dynamics.…”
Section: Introductionmentioning
confidence: 99%
“…Recent research has demonstrated that, at the single-cell level, regulatory proteins are often produced in bursts [8][9][10][11][12]. Such proteins can be further involved in autoregulation (e.g., the Tat regulatory protein which controls the latency switch of HIV-1 viral infections) [13][14][15][16][17][18][19] or in downstream regulation of biochemical switches (e.g., switching of flagellar rotation states in bacterial chemotaxis) [4][5][6][7]. Some interesting questions arise from these observations: How does feedback from proteins produced in bursts regulate noise in gene expression and biochemical switching?…”
mentioning
confidence: 99%
“…Regulation of protein noise.-There has been considerable focus in previous work on analyzing the effects of feedback on the noise η ¼ hn 2 i=hni 2 − 1 characterizing the protein steady-state distribution [13][14][15][16]25]. To analyze the impact of feedback, we first compare the noise for the case with feedback (α > 0) to the case without feedback (α ¼ 0) in Figs.…”
mentioning
confidence: 99%