Viral Dynamics during Structured Treatment Interruptions of Chronic Human Immunodeficiency Virus Type 1 Infection
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ABSTRACT Although antiviral agents which block human immunodeficiency virus (HIV) replication can result in long-term suppression of viral loads to undetectable levels in plasma, long-term therapy fails to eradicate virus, which generally rebounds after a single treatment interruption. Multiple structured treatment interruptions (STIs) have been suggested as a possible strategy that may boost HIV-specific immune responses and control viral replication. We analyze viral dynamics during four consecutive STI cycles in 12 chronically infected patients with a history (>2 years) of viral suppression under highly active antiretroviral therapy. We fitted a simple model of viral rebound to the viral load data from each patient by using a novel statistical approach that allows us to overcome problems of estimating viral dynamics parameters when there are many viral load measurements below the limit of detection. There is an approximate halving of the average viral growth rate between the first and fourth STI cycles, yet the average time between treatment interruption and detection of viral loads in the plasma is approximately the same in the first and fourth interruptions. We hypothesize that reseeding of viral reservoirs during treatment interruptions can account for this discrepancy, although factors such as stochastic effects and the strength of HIV-specific immune responses may also affect the time to viral rebound. We also demonstrate spontaneous drops in viral load in later STIs, which reflect fluctuations in the rates of viral production and/or clearance that may be caused by a complex interaction between virus and target cells and/or immune responses.Keywords:
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Patterns of influenza molecular viral shedding following influenza infection have been well established; predictors of viral shedding however remain uncertain.We sought to determine factors associated with peak molecular viral load, duration of shedding, and viral area under the curve (AUC) in children and adult Hutterite colony members with laboratory-confirmed influenza.A cohort study was conducted in Hutterite colonies in Alberta, Canada. Flocked nasal swabs were collected during three influenza seasons (2007-2008 to 2009-2010) from both symptomatic and asymptomatic individuals infected with influenza. Samples were tested by real-time reverse-transcription polymerase chain reaction for influenza A and influenza B, and the viral load was determined for influenza A-positive samples.For seasonal H1N1, younger age was associated with a larger AUC, female sex was associated with decreased peak viral load and reduced viral shedding duration, while the presence of comorbidity was associated with increased peak viral load. For H3N2, younger age was associated with increased peak viral load and increased AUC. For pandemic H1N1, younger age was associated with increased peak viral load and increased viral AUC, female sex was associated with reduced peak viral load, while inapparent infection was associated with reduced peak viral load, reduced viral shedding duration, and reduced viral AUC.Patterns of molecular viral shedding vary by age, sex, comorbidity, and the presence of symptoms. Predictor variables vary by influenza A subtype.
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1. A high viral load in nasopharyngeal aspirate (with or without a high viral load in serum) is a useful prognostic indicator of respiratory failure or mortality. The presence of viral RNA in multiple body sites is also indicative of poor prognosis. 2. Early treatment with an effective antiviral agent before day 10 may decrease the peak viral load, and thus ameliorate the clinical symptoms and mortality, and reduce viral shedding and the risk of transmission
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In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the following hypotheses: 1) in contrast to nasal passages, viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur because of a blunting of peak viral load that decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lungs.
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ABSTRACT Background Viral load kinetics and the duration of viral shedding are important determinants for disease transmission. We aim i) to characterize viral load dynamics, duration of viral RNA, and viable virus shedding of SARS-CoV-2 in various body fluids and ii) to compare SARS-CoV-2 viral dynamics with SARS-CoV-1 and MERS-CoV. Methods Medline, EMBASE, Europe PMC, preprint servers and grey literature were searched to retrieve all articles reporting viral dynamics and duration of SARS-CoV-2, SARS-CoV-1 and MERS-CoV shedding. We excluded case reports and case series with < 5 patients, or studies that did not report shedding duration from symptom onset. PROSPERO registration: CRD42020181914. Findings Seventy-nine studies on SARS-CoV-2, 8 on SARS-CoV-1, and 11 on MERS-CoV were included. Mean SARS-CoV-2 RNA shedding duration in upper respiratory tract, lower respiratory tract, stool and serum were 17.0, 14.6, 17.2 and 16.6 days, respectively. Maximum duration of SARS-CoV-2 RNA shedding reported in URT, LRT, stool and serum were 83, 59, 35 and 60 days, respectively. Pooled mean duration of SARS-CoV-2 RNA shedding was positively associated with age (p=0.002), but not gender (p = 0.277). No study to date has cultured live virus beyond day nine of illness despite persistently high viral loads. SARS-CoV-2 viral load in the upper respiratory tract appears to peak in the first week of illness, while SARS-CoV-1 and MERS-CoV peak later. Conclusion Although SARS-CoV-2 RNA shedding in respiratory and stool can be prolonged, duration of viable virus is relatively short-lived. Thus, detection of viral RNA cannot be used to infer infectiousness. High SARS-CoV-2 titers are detectable in the first week of illness with an early peak observed at symptom onset to day 5 of illness. This review underscores the importance of early case finding and isolation, as well as public education on the spectrum of illness. However, given potential delays in the isolation of patients, effective containment of SARS-CoV-2 may be challenging even with an early detection and isolation strategy. Funding No funding was received.
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Viral load and shedding duration are highly associated with the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. However, limited studies have reported on viral load or shedding in children and adolescents infected with sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2).This study aimed to investigate the natural course of viral load in asymptomatic or mild pediatric cases.Thirty-one children (<18 years) with confirmed SARS-CoV-2 infection were hospitalized and enrolled in this study. Viral loads were evaluated in nasopharyngeal swab samples using real-time reverse transcription polymerase chain reaction (E, RdRp, N genes). cycle threshold (Ct) values were measured when patients met the clinical criteria to be released from quarantine.The mean age of the patients was 9.8 years, 18 (58%) had mild disease, and 13 (42%) were asymptomatic. Most children were infected by adult family members, most commonly by their mothers. The most common symptoms were fever and sputum (26%), followed by cough and runny nose. Nine patients (29%) had a high or intermediate viral load (Ct value≤30) when they had no clinical symptoms. Viral load showed no difference between symptomatic and asymptomatic patients. Viral rebounds were found in 15 cases (48%), which contributed to prolonged viral detection. The mean duration of viral detection was 25.6 days. Viral loads were significantly lower in patients with viral rebounds than in those with no rebound (E, P=0.003; RdRp, P=0.01; N, P=0.02).Our study showed that many pediatric patients with coronavirus disease 2019 (COVID-19) experienced viral rebound and showed viral detection for more than 3 weeks. Further studies are needed to investigate the relationship between viral rebound and infectiousness in COVID-19.
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Abstract Background Since the emergence of COVID-19, tens of millions of people have been infected, and the global death toll approached 1 million by September 2020. Understanding the transmission dynamics of emerging pathogens, such as SARS-CoV-2 and other novel human coronaviruses is imperative in designing effective control measures. Viral load contributes to the transmission potential of the virus, but findings around the temporal viral load dynamics, particularly the peak of transmission potential, remain inconsistent across studies due to limited sample sizes. Methods We searched PubMed through June 8th 2020 and collated unique individual-patient data (IPD) from papers reporting temporal viral load and shedding data from coronaviruses in adherence with the PRISMA-IPD guidelines. We analyzed viral load trajectories using a series of generalized additive models and analyzed the duration of viral shedding by fitting log-normal models accounting for interval censoring. Results We identified 115 relevant papers and obtained data from 66 (57.4%) – representing a total of 1198 patients across 14 countries. SARS-CoV-2 viral load peaks prior to symptom onset and remains elevated for up to three weeks, while MERS-CoV and SARS-CoV viral loads peak after symptom onset. SARS-CoV-2, MERS-CoV, and SARS-CoV had median viral shedding durations of 4.8, 4.2, and 1.2 days after symptom onset. Disease severity, age, and specimen type all have an effect on viral load, but sex does not. Discussion Using a pooled analysis of the largest collection of IPD on viral load to date, we are the first to report that SARS-CoV-2 viral load peaks prior to – not at – symptom onset. Detailed estimation of the trajectories of viral load and virus shedding can inform the transmission, mathematical modeling, and clinical implications of SARS-CoV-2, MERS-CoV, and SARS-CoV infection.
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ABSTRACT Although antiviral agents which block human immunodeficiency virus (HIV) replication can result in long-term suppression of viral loads to undetectable levels in plasma, long-term therapy fails to eradicate virus, which generally rebounds after a single treatment interruption. Multiple structured treatment interruptions (STIs) have been suggested as a possible strategy that may boost HIV-specific immune responses and control viral replication. We analyze viral dynamics during four consecutive STI cycles in 12 chronically infected patients with a history (>2 years) of viral suppression under highly active antiretroviral therapy. We fitted a simple model of viral rebound to the viral load data from each patient by using a novel statistical approach that allows us to overcome problems of estimating viral dynamics parameters when there are many viral load measurements below the limit of detection. There is an approximate halving of the average viral growth rate between the first and fourth STI cycles, yet the average time between treatment interruption and detection of viral loads in the plasma is approximately the same in the first and fourth interruptions. We hypothesize that reseeding of viral reservoirs during treatment interruptions can account for this discrepancy, although factors such as stochastic effects and the strength of HIV-specific immune responses may also affect the time to viral rebound. We also demonstrate spontaneous drops in viral load in later STIs, which reflect fluctuations in the rates of viral production and/or clearance that may be caused by a complex interaction between virus and target cells and/or immune responses.
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Abstract Prediction and management of zoonotic pathogen spillover requires an understanding of infection dynamics within reservoir host populations. Transmission risk is often assessed using prevalence of infected hosts, with infection status based on the presence of genomic material. However, detection of viral genomic material alone does not necessarily indicate the presence of infectious virus, which could decouple prevalence from transmission risk. We undertook a multi-faceted investigation of Hendra virus shedding in Pteropus bats, combining insights from virus isolation, viral load proxies, viral prevalence, and longitudinal patterns of shedding, from 6,151 samples. In addition to seasonal and interannual fluctuation in prevalence, we found evidence for periodic shifts in the distribution of viral loads. The proportion of bats shedding high viral loads was higher during peak prevalence periods during which spillover events were observed, and lower during non-peak periods when there were no spillovers. We suggest that prolonged periods of low viral load and low prevalence reflect prolonged shedding of non-infectious RNA, or viral loads that are insufficient or unlikely to overcome dose barriers to spillover infection. These findings show that incorporating viral load (or proxies of viral load) into longitudinal studies of virus excretion will better inform predictions of spillover risk than prevalence alone. Significance statement We present a comprehensive analysis of a high-profile bat-virus system (Hendra virus in Australian flying-foxes) to demonstrate that both prevalence and viral loads can shift systematically over time, resulting in concentrated periods of increased spillover risk when prevalence and viral loads are high. We further suggest that prolonged periods of low-prevalence, low-load shedding may not reflect excretion of infectious virus, resolving the outstanding puzzle of why spillovers have not been observed during periods of low off-season prevalence in subtropical Australia, or more frequently in tropical Australia despite consistent low-prevalence shedding. The consideration of viral loads (or proxies of viral load) along with prevalence may improve risk inference from longitudinal surveys of zoonotic viruses across wildlife reservoir hosts.
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In a typical HIV-1-infected patient, plasma viral load (pVL) increases steeply in the first week after acute infection, then decreases as the immune system becomes activated, resulting in antibody seroconversion 3–13 days after infection and a full western blot pattern approximately 3 months later [1–3]. The so-called viral set point or steady-state viral load is reached after approximately 40–276 days from the acute infection moment [1]. Especially in the first few weeks of infection, differences are obvious in patients, especially with regard to time to peak load and time to viral load drop from peak to nadir [1], but also in the absolute viral RNA count. The viral set point is thought to represent a trade-off between viral replication capacity and repression of the virus by the host immune system. HIV-1 RNA levels vary considerably between individuals and also throughout the infection course in a particular individual. The viral load at set point is an important parameter, as it is strongly predictive of clinical progression [4,5]. Both the innate replicating capacity (fitness) of the virus strain and the strength of the host immune system would intuitively be the most obvious contributors, but it has been suggested that age, sex, shared human leukocyte antigen (HLA) alleles and duration of infection also contribute to the phenomenon [6]. The involvement of virus characteristics could easily be measured by analyzing the HIV replication capacity in donor–recipient pairs, wherein the viral load should be similar if viral replication fitness is the main determinant of pVL. A cohort of transmission pairs, necessary to study comparative HIV-1 viral load dynamics, is not easy to establish. Viral relationships indicative of transmission should first be determined by phylogenetic analysis. Then, an acute phase plasma sample (to minimize the effect of immune pressure) of the recipient and a matching sample from the donor should be available. Hecht et al. [7] have analyzed early plasma samples from 24 such transmission pairs, all comprising men having sex with men (MSM), and reported a significant correlation between the HIV-1 RNA levels within the transmission pairs. However, they cautioned that these results should be reproduced in other cohorts to validate the finding. We here report a similar analysis in early samples from 56 sequence-verified HIV-1 transmission pairs, 60% MSM and 40% heterosexual, from The Netherlands. Recipients were sampled during primary infection, 20 recipients were in Fiebig et al. [8] stages III–IV (viral RNA+/− or indeterminate western blot) and 36 recipients were in Fiebig et al. [8] stages V (viral RNA+/western blot p31−) and VI (viral RNA+/western blot fully developed). HIV-1 blood pVL measurements were done using the Versant HIV-1 RNA 3.0 assay (Bayer Diagnostics Division, Tarrytown, New York, USA), NucliSens HIV-1 RNA (bioMérieux, Boxtel, The Netherlands) or m2000rt (Abbott Molecular Inc., Des Plaines, Illinois, USA). Viral loads of all couples were measured using the same assay. Samples from donors matched the time point when recipient samples were taken. Linear regression analysis was done with GraphPad Prism, version 5.01 (GraphPad Software, San Diego, California, USA) and correlation coefficients were calculated. In contrast to Hecht et al. [7], we do not find a strong correlation between plasma viral RNA levels within the pairs (Fig. 1). The Pearson coefficient of correlation (r) in our cohort was 0.25 for all 56 transmission pairs, 0.29 (range −0.17 to 0.65) for pairs when the recipients were in Fiebig et al. [8] stages III–IV and 0.06 (range −0.27 to 0.39) for pairs when the recipients were in Fiebig et al. [8] stages V–VI, suggesting that the correlation is completely lost when the infection progresses. The correlation coefficient (r) between viral RNA levels in donors and recipients was 0.55 in the 24 pairs studied by Hecht et al. [7], which were in similar early stages of HIV infection. A correlation coefficient (r) above 0.8 is usually denoted as strong and below 0.5 as weak, whereas r is equal to 1 represents a perfect correlation. So, in our transmission pairs, we detect only a weak correlation between viral RNA levels in acutely infected recipients and donors. Similar results were obtained for a transmission cohort [6] in Zambia where the viral RNA levels between 115 donor and seroconverting recipient pairs had a correlation coefficient (r) of 0.21 (P = 0.03). In this study, factors such as sex, age, HLA markers and duration of infection were also shown to contribute.Fig. 1: Relationship of HIV-1 RNA levels in 56 transmission pairs. Viral RNA levels in blood plasma from source individuals were correlated with viral RNA levels in recipients in the acute or early stages of infection. Correlations are shown for all 56 transmission pairs or for sources and recipients when the latter are separated according to the primary infection stage criteria of Fiebig et al. [8].The low correlation between pVL in donors and recipients suggests that viral traits do contribute to pVL early in infection, but that other factors are equally or more important.
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