Establishment and stability of the latent HIV-1 DNA reservoir

Brodin, Johanna; Zanini, Fabio; Thebo, Lina; Lanz, Christa; Bratt, Göran; Neher, Richard A.; Albert, Jan

doi:10.1101/053983

Cited by 3 publications

(4 citation statements)

References 33 publications

(53 reference statements)

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“…Brodin et al 12 suggested that the decay of labile populations may produce false signals of evolution during treatment, even in the absence of viral replication. We used computer simulations of viral populations during acute infection and treatment to confirm this hypothesis.…”

mentioning

confidence: 99%

Re-evaluating evolution in the HIV reservoir

Rosenbloom

Hill

Laskey

et al. 2017

Nature

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mentioning

confidence: 99%

Re-evaluating evolution in the HIV reservoir

Rosenbloom

Hill

Laskey

et al. 2017

Nature

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“…Depending on template input, minor variation at frequencies down to 0.3% could be detected and frequencies could be reliably measured down to about 1% (see Zanini et al (2016) and Methods below). For some of the analyses below, we include one additional patient (p7) described in (Brodin et al, 2016), for a total of 82 plasma samples. Eight of the ten patients were infected with subtype B, one with subtype C, and one with subtype CRF01 AE.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, the functional latent reservoir of HIV-1 is unlikely to bias our estimates of mutations rates. In a recent study of proviral DNA in the same patients, we found that the latent reservoir is an accurate snapshot of the HIV-1 diversity circulating in the year prior to the sample (Brodin et al, 2016). Hence we don't expect that the accumulation of diversity is delayed in substantial ways by contributions from reactivated latent virus.…”

Section: Neutral Mutation Rate Matrixmentioning

confidence: 91%

In-vivo mutation rates and fitness landscape of HIV-1

Zanini

Puller

Brodin

et al. 2016

Preprint

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Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of evolution. Using whole genome deep sequencing data from longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and fitness costs in HIV-1 from the temporal dynamics of genetic variation. At approximately neutral sites, mutations accumulate with a rate of 1.2 × 10 −5 per site per day, in agreement with the rate measured in cell cultures. The rate from G to A is largest, followed by the other transitions C to T, T to C, and A to G, while transversions are more rare. At non-neutral sites, most mutations reduce virus replication; using a model of mutation selection balance, we estimated the fitness cost of mutations at every site in the HIV-1 genome. About half of all nonsynonymous mutations have large fitness costs (greater than 10%), while most synonymous mutations have costs below 1%. The cost of synonymous mutations is especially low in most of gag and pol, while much higher costs are observed in important RNA structures and regulatory regions. The intrapatient fitness cost estimates are consistent across multiple patients, suggesting that the deleterious part of the fitness landscape is universal and explains a large fraction of global HIV-1 group M diversity. IntroductionHIV-1 evolves rapidly within individual hosts: mutations allow it to evade immune predation but can also impair viral replication. Genetic changes arise during reverse transcription, during forward transcription by the human RNA polymerase II, or are caused by the innate immune system (Abram et al., 2010;Cuevas et al., 2015;Malim, 2009;Mansky and Temin, 1995). These changes are the source of genetic diversity, from which selection amplifies beneficial variants and filters deleterious mutations. Characterization of the mutation rate matrix and the genome wide landscape of fitness effects is a prerequisite a quantitative understanding of the evolutionary dynamics of HIV and for rational design of both vaccines and resistance proof drugs.The majority of mutations are deleterious, some mutations are neutral and have little or no effect, and a minority of mutations are beneficial. While beneficial mutations rapidly spread through the virus population within a patient, deleterious mutations stay at low frequency in a balance between mutation and selection. Beneficial mutations are often patient-specific and mediate escape from cytotoxic T-lymphocytes (CTL) and neutralizing antibodies (Bar et al., 2012;Goonetilleke et al., 2009;Walker and McMichael, 2012). At the same time, substitutions in response to immune selection are expected to lower intrinsic viral fitness; host-specific adaptation is a tradeoff between immune evasion and fitness costs of escape mutations.Since HIV-1 proteins serve the same function in different hosts, the landscape of fitness costs might be expected to be similar in different hosts. However, the effect of a particular mutation can depend on other sites in the...

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“…The formation of a latently infected reservoir in CD4 T cells is a key barrier to an HIV cure (Chun et al, 1997;Finzi et al, 1997). The reservoir is highly stable, long lived, and resistant to current antiretroviral therapy (Brodin et al, 2016;Elsheikh et al, 2019;Ismail et al, 2021). Additionally, clonal expansion in vivo counteracts gradual erosion of infected cells (De Scheerder et al, 2019;Lau et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation

Ashokkumar

Peterson

Harigaya

et al. 2022

Preprint

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Despite the success of antiretroviral therapy, HIV cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA-seq/ATAC-seq approach to simultaneously profile the transcriptomic and epigenomic characteristics of ~4000 latently infected cells after reactivation using three different latency-reversing agents (LRAs). Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identify cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrate that a machine learning model trained on these data was 68% accurate at predicting viral reactivation. Finally, we validate the role of a new candidate HIV-regulating factor, GATA3, in the viral response to prostratin stimulation. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

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Establishment and stability of the latent HIV-1 DNA reservoir

Cited by 3 publications

References 33 publications

Re-evaluating evolution in the HIV reservoir

Re-evaluating evolution in the HIV reservoir

In-vivo mutation rates and fitness landscape of HIV-1

Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation

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