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Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Background: HIV-1 set point viral load (SPVL) is a highly variable trait that influences disease progression and transmission risk. Men who are exclusively insertive (EI) during anal intercourse require more sexual contacts to become infected than exclusively receptive (ER) men. Thus, we hypothesize that EIs are more likely to acquire their viruses from highly infectious partners (i.e., with high SPVLs) and to have higher SPVLs than infected ERs. Methods: We used a one-generation Bernoulli model, a dynamic network model, and data from the Multicenter AIDS Cohort Study (MACS) to examine whether and under what circumstances MSM differ in SPVL by sexual role. Results: Both models predicted higher SPVLs in EIs than role versatile (RV) or ER men, but only in scenarios where longer-term relationships predominated. ER and RV men displayed similar SPVLs. EI men remained far less likely than ER men to become infected, however. When the MACS data were limited by some estimates of lower sex partner counts (a proxy for longer relationships), EI men had higher SPVLs; these differences were clinically relevant (>0.3 log10 copies/mL) and statistically significant (p

Set-point viral load (SPVL) correlates with the age at which people acquire HIV. Although immunosenescence may seem like a parsimonious explanation for this, it does not easily explain the observation that the relationship between age and SPVL attenuates when accounting for source partner SPVL. Here we propose an alternative explanation that encompasses this latter finding: that decreasing risk of acquisition with older age generates a selection bottleneck that selects for more virulent strains with age.We adapted a previously published model of HIV transmission and evolution (EvoNetHIV), parameterized here for men who have sex with men (MSM). We conducted a series of simulation experiments that vary seven behavioral or clinical parameters that affect exposure risk as people age. We conducted regressions to determine the mean increase in SPVL per 10-year increase in seroconversion age, with and without source SPVL in the model.All runs generated significant relationships between seroconversion age and SPVL when not including source SPVL. All saw attenuated relationships, most to near 0, with source SPVL included. Four of our behavioral measures (relational duration, age-related homophily, coital frequency, and mean age at relationship formation) had clear effects on this relationship, all in the hypothesized direction. Combining multiple forms of behavioral heterogeneity yielded an increase of 0.056 log10 copies/mL SPVL per 10-year increase in seroconversion age, nearly as large as that seen in two empirical studies of age-SPVL correlations in MSM.The higher virulence of HIV among those infected later in life may be partly explained by a combination of selective bottlenecks and behavioral heterogeneity by age. Variation in the strength of this effect across populations may be in part due to different behavioral, epidemiological and clinical conditions, and not require assumptions about differences in patterns of immunosenescence among populations.

HIV set point viral load (SPVL), the viral load established shortly after initial infection, is a proxy for HIV virulence: higher SPVLs lead to higher risk of transmission and faster disease progression. Three models of test-and-treat scenarios, mainly in heterosexual populations, found that increasing treatment coverage selected for more virulent viruses. We modeled virulence evolution in a population of men who have sex with men (MSM) with increasing test-and-treat coverage. We extended a stochastic, dynamic network model (EvoNetHIV). We varied relationship patterns (MSM vs. heterosexual), HIV transmission models (increasing vs. plateauing probability of transmission at very high viral loads), and treatment roll-out (with explicit testing or fixed intervals between infection and treatment). In scenarios most similar to previous models (longer relational durations and the plateauing transmission function), we replicated trends previously found: increasing treatment coverage led to increased virulence (0.12 log10 increase in mean population SPVL between 20% and 100% treatment coverage). In scenarios reflecting MSM behavioral data using the increasing transmission function, increasing treatment coverage selected for viruses with lower virulence (0.16 log10 decrease in mean population SPVL between 20% and 100% treatment coverage). These findings emphasize the impact of sexual network conditions and transmission function details on predicted epidemiological and evolutionary outcomes. Varying these features creates very different evolutionary environments, which in turn lead to opposite effects in mean population SPVL evolution. Our results suggest that, under some realistic conditions, effective test-and-treat strategies may not face the previously reported tradeoff in which increasing coverage leads to evolution of greater virulence. This suggests instead that a virtuous cycle of increasing treatment coverage and diminishing virulence is possible.

Pathogen populations can evolve in response to selective pressure from vaccine-induced immune responses. For HIV, models predict that viral adaptation, either via strain replacement or selection on de novo mutation, may rapidly reduce the effectiveness of an HIV vaccine. We hypothesized that behavioral risk compensation after vaccination may accelerate the transmission of vaccine resistant strains, increasing the rate of viral adaptation and leading to a more rapid decline in vaccine effectiveness. To test our hypothesis, we modeled: a) the impact of risk compensation on rates of HIV adaptation via strain replacement in response to a partially effective vaccine; and b) the combined impact of risk compensation and viral adaptation on vaccine-mediated epidemic control. We used an agent-based epidemic model that was calibrated to HIV-1 trends in South Africa, and includes demographics, sexual network structure and behavior, and within-host disease dynamics. Our model predicts that risk compensation can increase the rate of HIV viral adaptation in response to a vaccine. In combination, risk compensation and viral adaptation can, under certain scenarios, reverse initial declines in prevalence due to vaccination, and result in HIV prevalence at 15 years equal to or greater than prevalence without a vaccine.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Pathogen evolution is a potential threat to the long-term benefits provided by public health vaccination campaigns. Mathematical modeling can be a powerful tool to examine the forces responsible for the development of vaccine resistance and to predict its public health implications. We conducted a systematic review of existing literature to understand the construction and application of vaccine resistance models. We identified 26 studies that modeled the public health impact of vaccine resistance for 12 different pathogens. Most models predicted that vaccines would reduce overall disease burden in spite of evolution of vaccine resistance. Relatively few pathogens and populations for which vaccine resistance may be problematic were covered in the reviewed studies, with low- and middle-income countries particularly under-represented. We discuss the key components of model design, as well as patterns of model predictions.

There are global increases in the use of HIV antiretroviral therapy (ART), guided by clinical benefits of early ART initiation, and the efficacy of treatment as prevention of transmission. UNAIDS has set forth an ambitious 90-90-90 strategy for 2020. Separately, it has been shown theoretically and empirically that HIV virulence can evolve over time. Observed virulence levels may reflect an adaptive balance between infected lifespan and per-contact transmission rate. Critically, the potential effects of widespread ART usage on HIV virulence are unknown. To predict these effects, we used an agent-based stochastic model to simulate changes in HIV virulence, using set point viral load as a virulence proxy. We calibrated our epidemic model to the prevalence and incidence trends of South Africa. We also repeated our analysis using a separate model calibration, as a sensitivity analysis, and found that predicted impact of ART on virulence was relatively insensitive to calibration. We explored two distinct ART scenarios, at increasing coverage levels: ART initiation based on patients reaching a CD4+ T cell count threshold, or based on time elapsed since infection (a scenario that mimics the "universal testing and treatment" aspirations in the 90-90-90 UNAIDS targets). We found that HIV virulence is generally unchanged in scenarios of CD4-based initiation. However, with ART initiation based on time since infection (universal test and treat), virulence can increase moderately within several years of ART rollout, under high coverage levels and early treatment initiation albeit within the context of epidemics that are rapidly decreasing in size. We also show that, compared to non-evolutionary HIV epidemic models, allowing for evolving virulence predicts greater declines in HIV incidence after ART rollout. Our modeling study suggests that increasing virulence driven by universal test and treat is likely not a major public health concern, but could be monitored in sentinel surveillance, in a manner similar to transmitted drug resistance.

HIV viral load (VL) predicts both transmission potential and rate of disease progression. For reasons that are still not fully understood, the set point viral load (SPVL) established after acute infection varies across individuals and populations. Previous studies have suggested that population mean SPVL (MSPVL) has evolved near an optimum that reflects a trade-off between transmissibility and host survival. Sexual network structures affect rates of potential exposure during different within-host phases of infection marked by different transmission probabilities, and thus affect the number and timing of transmission events. These structures include relational concurrency, which has been argued to explain key differences in HIV burden across populations. We hypothesize that concurrency will alter the fitness landscape for SPVL in ways that differ from other network features whose impacts accrue at other times during infection. To quantitatively test this hypothesis, we developed a dynamic, stochastic, data-driven network model of HIV transmission, and evolution to assess the impact of key sexual network phenomena on MSPVL evolution. Experiments were repeated in sensitivity runs that made different assumptions about transmissibility during acute infection, SPVL heritability, and the functional form of the relationship between VL and transmissibility. For our main transmission model, scenarios yielded MSPVLs ranging from 4.4 to 4.75 log10 copies/ml, covering much of the observed empirical range. MSPVL evolved to be higher in populations with high concurrency and shorter relational durations, with values varying over a clinically significant range. In linear regression analyses on these and other predictors, main effects were significant (P < 0.05), as were interaction terms, indicating that effects are interdependent. We also noted a strong correlation between two key emergent properties measured at the end of the simulations—MSPVL and HIV prevalence—most clearly for phenomena that affect transmission networks early in infection. Controlling for prevalence, high concurrency yielded higher MSPVL than other network phenomena. Interestingly, we observed lower prevalence in runs in which SPVL heritability was zero, indicating the potential for viral evolution to exacerbate disease burden over time. Future efforts to understand empirical variation in MSPVL should consider local HIV burden and basic sexual behavioral and network structure.

SummaryBacterial lineages that chronically infect cystic fibrosis (CF) patients genetically diversify during infection. However, the mechanisms driving diversification are unknown. By dissecting ten CF lung pairs and studying ∼12,000 regional isolates, we were able to investigate whether clonally related Pseudomonas aeruginosa inhabiting different lung regions evolve independently and differ functionally. Phylogenetic analysis of genome sequences showed that regional isolation of P. aeruginosa drives divergent evolution. We investigated the consequences of regional evolution by studying isolates from mildly and severely diseased lung regions and found evolved differences in bacterial nutritional requirements, host defense and antibiotic resistance, and virulence due to hyperactivity of the type 3 secretion system. These findings suggest that bacterial intermixing is limited in CF lungs and that regional selective pressures may markedly differ. The findings also may explain how specialized bacterial variants arise during infection and raise the possibility that pathogen diversification occurs in other chronic infections characterized by spatially heterogeneous conditions.

The innate immune response plays an important but unknown role in host defense against Mycobacterium tuberculosis . To define the function of innate immunity during tuberculosis, we evaluated M. tuberculosis replication dynamics during murine infection. Our data show that the early pulmonary innate immune response limits M. tuberculosis replication in a MyD88-dependent manner. Strikingly, we found that little M. tuberculosis cell death occurs during the first 2 weeks of infection. In contrast, M. tuberculosis cells deficient in the surface lipid phthiocerol dimycocerosate (PDIM) exhibited significant death rates, and consequently, total bacterial numbers were reduced. Host restriction of PDIM-deficient M. tuberculosis was not alleviated by the absence of interferon gamma (IFN-γ), inducible nitric oxide synthase (iNOS), or the phagocyte oxidase subunit p47. Taken together, these data indicate that PDIM protects M. tuberculosis from an early innate host response that is independent of IFN-γ, reactive nitrogen intermediates, and reactive oxygen species. By employing a pathogen replication tracking tool to evaluate M. tuberculosis replication and death during infection, we identify both host and pathogen factors affecting the outcome of infection.

We used an agent-based network model to examine the effect of targeting different risk groups with unsuppressed HIV viral load for linkage or re-linkage to HIV-related treatment services in a heterosexual population with annual testing. Our model identifies prevention strategies that can reduce incidence to negligible levels (i.e., less than 0.1 infections per 100 person-years) 20 years after a targeted Treatment-as-Prevention (TasP) campaign. The model assumes that most (default 95%) of the population is reachable (i.e., could, in principle, be linked to effective care) and a modest (default 5% per year) probability of a treated person dropping out of care. Under random allocation or CD4-based targeting, the default version of our model predicts that the TasP campaign would need to suppress viral replication in ~80% of infected people to halt the epidemic. Under age-based strategies, by contrast, this percentage drops to 50% to 60% (for strategies targeting those

Background Development of an HIV vaccine might be essential to ending the HIV/AIDS pandemic. However, vaccines can result in the emergence and spread of vaccine-resistant strains. Indeed, analyses of breakthrough infections in the HIV phase 3 vaccine trial RV144 identified HIV genotypes with differential rates of transmission in vaccine and placebo recipients. We hypothesized that, for HIV vaccination programs based on partially effective vaccines similar to RV144, HIV adaptation will rapidly diminish the expected vaccine impact. Methods and findings Using two HIV epidemic models, we simulated large-scale vaccination programs and, critically, included HIV strain diversity with respect to the vaccine response. We show here that rapid population-level viral adaptation can lead to decreased overall vaccine efficacy and substantially fewer infections averted by vaccination, when comparing scenarios with and without viral evolution (with outcomes depending on vaccination coverage, vaccine efficacy against the sensitive allele, and the initial resistant allele frequency). Translating this to the epidemic in South Africa, a scenario with 70% vaccination coverage may result in 250,000 infections (non-averted by vaccination) within 10 years of vaccine rollout that are due solely to HIV adaptation, all else being equal. Conclusions These findings suggest that approaches to HIV vaccine development, program implementation, and epidemic modeling may require attention to viral adaptation in response to vaccination.

Conjugative plasmids of Gram-negative bacteria have both vertical and horizontal modes of transmission: they are segregated to daughter cells during division, and transferred between hosts by plasmid-encoded conjugative machinery. Despite maintaining horizontal mobility, many plasmids carry fertility inhibition (fin) systems that repress their own conjugative transfer. To assess the ecological basis of self-transfer repression, we compared the invasion of bacterial populations by fin(+) and fin(-) variants of the plasmid R1 using a computational model and co-culture competitions. We observed that the fin(+) variant had a modest cost to the host (measured by reduction in growth rate), while the fin(-) variant incurred a larger cost. In simulations and empirical competitions the fin(-) plasmid invaded cultures quickly, but was subsequently displaced by the fin(+) plasmid. This indicated a competitive advantage to reducing horizontal transmission and allowing increased host replication. Computational simulations predicted that the advantage associated with reduced cost to the host would be maintained over a wide range of environmental conditions and plasmid costs. We infer that vertical transmission in concert with competitive exclusion favour decreased horizontal mobility of plasmids. Similar dynamics may exert evolutionary pressure on parasites, such as temperate bacteriophages and vertically transmitted animal viruses, to limit their rates of horizontal transfer.

Does Mycobacterium tuberculosis replicate in vivo, or does it persist in the host in a nonreplicating latent state? David Sherman and his colleagues have developed a technique to answer this question in mice and find that the mycobacteria do replicate in vivo. It is unknown whether these findings will hold true in other animals, particularly nonhuman primates, but this technique could be applied to study the in vivo replication of other persistent pathogens responsible for chronic infections. Few tools exist to assess replication of chronic pathogens during infection. This has been a considerable barrier to understanding latent tuberculosis, and efforts to develop new therapies generally assume that the bacteria are very slowly replicating or nonreplicating during latency1,2,3. To monitor Mycobacterium tuberculosis replication within hosts, we exploit an unstable plasmid that is lost at a steady, quantifiable rate from dividing cells in the absence of antibiotic selection. By applying a mathematical model, we calculate bacterial growth and death rates during infection of mice. We show that during chronic infection, the cumulative bacterial burden—enumerating total live, dead and removed organisms encountered by the mouse lung—is substantially higher than estimates from colony-forming units. Our data show that M. tuberculosis replicates throughout the course of chronic infection of mice and is restrained by the host immune system. This approach may also shed light on the replication dynamics of other chronic pathogens.

Recent clinical trials have renewed interest in treating HIV-1 infection through the use of induction–maintenance regimens, a strategy commonly used for the treatment of TB and certain hematopoeitic malignancies. In these conditions, the common element underlying success has been the ability of induction regimens to reduce therapy-resistant pathogens to levels that can be controlled using a maintenance regimen. The success of recent clinical trials of induction–maintenance therapy for HIV-1 suggests that this concept could have a variety of applications, such as reducing toxicities, reducing treatment costs and improving the treatment of salvage patients. However, current induction–maintenance protocols have not fully capitalized on available quantitative data concerning pharmacokinetics and pharmacodynamics, viral replication dynamics, viral latency and the evolution of drug resistance. In this review, we reason that it should be possible to improve success rates of induction–maintenance and other innovative, therapeutic strategies using mathematical models that account for this information.

The effects that granulocyte-monocyte colony-stimulating factor (GM-CSF) has on HIV-1 replication in monocyte-derived macrophage are controversial. We noted that groups reporting that GM-CSF inhibits HIV-1 replication performed their experiments at relatively high cell densities. To address this issue, we performed experiments at different macrophage densities. In cultures seeded at low cell densities, we find that adding GM-CSF during the first week of culture (ie, before infection, during maturation) increased viral replication compared with that in untreated controls in 10 of 11 donors with quantifiable HIV-1 replication. (No effects were observed if GM-CSF was added after the first week of culture.) In cultures seeded at the higher cell densities representative of those in some previous studies, adding GM-CSF during the first week reduced subsequent viral replication in 8 of 12 donors. In all cases in which GM-CSF reduced viral replication, however, the pH in the wells containing GM-CSF-treated cells dropped dramatically. Macrophages in these acidified cultures had numerous dark granules, suggesting that they were under stress. We conclude, contrary to previous reports, that GM-CSF usually enhances viral replication when cells are grown at low densities in which excessive medium acidification can be prevented. Our results illustrate the dramatic effects that in vitro tissue culture conditions can have when studying the effect of cytokines on HIV-1 replication.

Rapid evolution and high sequence diversity enable Human Immunodeficiency Virus (HIV) populations to acquire mutations to escape antiretroviral drugs and host immune responses, and thus are major obstacles for the control of the pandemic. One strategy to overcome this problem is to focus drugs and vaccines on regions of the viral genome in which mutations are likely to cripple function through destabilization of viral proteins. Studies relying on sequence conservation alone have had only limited success in determining critically important regions. We tested the ability of two structure-based computational models to assign sites in the HIV-1 capsid protein (CA) that would be refractory to mutational change. The destabilizing mutations predicted by these models were rarely found in a database of 5811 HIV-1 CA coding sequences, with none being present at a frequency greater than 2%. Furthermore, 90% of variants with the low predicted stability (from a set of 184 CA variants whose replication fitness or infectivity has been studied in vitro) had aberrant capsid structures and reduced viral infectivity. Based on the predicted stability, we identified 45 CA sites prone to destabilizing mutations. More than half of these sites are targets of one or more known CA inhibitors. The CA regions enriched with these sites also overlap with peptides shown to induce cellular immune responses associated with lower viral loads in infected individuals. Lastly, a joint scoring metric that takes into account both sequence conservation and protein structure stability performed better at identifying deleterious mutations than sequence conservation or structure stability information alone. The computational sequence–structure stability approach proposed here might therefore be useful for identifying immutable sites in a protein for experimental validation as potential targets for drug and vaccine development.

The failure of HIV-1 to escape at some cytotoxic T-lymphocyte (CTL) epitopes has generally been explained in terms of viral fitness costs or ineffective or attenuated CTL responses. Relatively little attention has been paid to the evolutionary time required for escape mutants to be detected. This time is significantly affected by selection, mutation rates, the presence of other advantageous mutations, and the effective population size of HIV-1 in vivo (typically estimated to be {approx}10{sup 3} in chronically infected patients, though one study has estimated it to be {approx}10{sup 5}). Here, we use a forward simulator with experimentally estimated HIV-1 parameters to show that these delays can be substantial. For an effective population size of 10{sup 3}, even highly advantageous mutants (s = 0.5) may not be detected for a couple of years in chronically infected patients, while moderately advantageous escape mutants (s = 0.1) may not be detected for up to 10 years. Even with an effective population size of 10{sup 5}, a moderately advantageous escape mutant (s = 0.1) may not be detected in the population within 2 years if it has to compete with other selectively advantageous mutants. Stochastic evolutionary forces, therefore, in addition to viral fitness costsmore » and ineffective or attenuated CTL responses, must be taken into account when assessing the selection of CTL escape mutations.« less

The Antivirogram and PhenoSense assays are widely used phenotypic tests for HIV drug resistance. There are limited data on the reproducibility of each assay, and little is known about the correlation between the two. Using data from the Stanford HIV drug resistance database, we performed a comprehensive analysis of the reproducibility of each assay, and calculated the correlation and concordance of the two assays using both general IC50fold change cutoff values and drug-specific cutoff values. Although the within-assay correlations were high (rank correlation coefficients r=0.94 and r=0.95 for the Antivirogram and PhenoSense assays, respectively), the between-assay correlation was considerably lower ( r=0.36). Using drug-specific cutoff values for viruses classified as resistant by the Antivirogram or PhenoSense assays, respectively, only 71.4% [95% confidence intervals (95% CI): 58.7–82.1%] and 57.0% (95% CI: 45.3–68.1%) of the samples were classified as resistant using the other assay. The poor agreement between the assays was primarily due to the extremely poor correlation between these assays for samples with low resistance values ( r=0.02 and r=0.61 for samples with the Antivirogram measurements lower or higher than 2.0, respectively). Since the cutoff values for both assays are relatively low, our analysis suggests that one should be very careful when interpreting measurements that are near the cutoff values for drug resistance.

Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model out-performed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.

Although human immunodeficiency virus type 1 (HIV-1) recombinants have been found with high frequency, little is known about the forces that select for these viruses or their importance to pathogenesis. Here we document the emergence and dynamics of 11 distinct HIV-1 recombinants in a man who was infected with two subtype B HIV-1 strains and progressed rapidly to AIDS without developing substantial cellular or humoral immune responses. Although numerous frequency oscillations were observed, a single recombinant lineage eventually came to dominate the population. Numerical simulations indicate that the successive recombinant forms displaced each other too rapidly to be explained by any simple model of random genetic drift or sampling variation. All of the recombinants, including several resulting from independent recombination events, possessed the same sequence motif in the V3 loop, suggesting intense selection on this segment of the viral envelope protein. The outgrowth of the predominant V3 loop recombinants was not, however, associated with changes in coreceptor utilization. The final variant was instead notable for having lost 3 of 14 potential glycosylation sites. We also observed high ratios of synonymous-to-nonsynonymous nucleotide changes—suggestive of purifying selection—in all viral populations, with particularly high ratios in newly arising recombinants. Our study, therefore, illustrates the unusual and important patterns of viral adaptation that can occur in a patient with weak immune responses. Although it is hard to tease apart cause and effect in a single patient, the correlation with disease progression in this patient suggests that recombination between divergent viruses, with its ability to create chimeras with increased fitness, can accelerate progression to AIDS.

A latent pool of HIV-1 is established early in memory CD4+ T lymphocytes and persists during antiretroviral therapy. Also, viral replication may continue in subjects despite undetectable viremia. However, it remains unclear whether this residual replication results in any significant sequence evolution. We were therefore interested in studying the viral evolution and HIV-1 DNA dynamics in subjects with primary infection receiving or not receiving early potent antiretroviral therapy. In 16 subjects, HIV-1 DNA load was monitored from 1 to 23 days, up to 1253 days, after onset of symptoms. Extensive sequential cloning and sequence analysis of the V3 region was performed in four subjects. In the treated subjects a continuous decline in the proviral load was found, corresponding to a half-life of about 6 months. As expected in newly infected individuals the founder virus populations showed high intrasubject sequence similarity. Also, a limited increase in the viral divergence was detected during the first 6 months in three treated subjects. Thereafter, no significant sequence changes were found despite analysis of a large number of clones. Our data thus suggest that early and successful therapy in compliant subjects with primary HIV-1 infection results in a highly restricted viral evolution and a decline in the proviral load close to the decay rate of human memory T lymphocytes.

Objectives: To study the natural course of viremia during primary HIV infection (PHI). Method: Eight patients were followed from a median of 5 days from the onset of PHI illness. Plasma HIV-1 RNA levels were measured frequently and the results were fitted to mathematical models. HIV-1 RNA levels were also monitored in nine patients given two reverse transcriptase inhibitors and a protease inhibitor after a median of 7 days from the onset of PHI illness. Results: HIV-1 RNA appeared in the blood during the week preceding onset of PHI illness and increased rapidly during the first viremic phase, reaching a peak at a mean of 7 days after onset of illness. This was followed by a phase of rapidly decreasing levels of HIV-1 RNA to an average of 21 days after onset. Viral density continued to decline thereafter but at a 5- to 50-fold lower rate; a steady-state level was reached at a median of 2 months after onset of PHI. Peak viral density levels correlated significantly with levels measured between days 50 and 600. Initiation of antiretroviral treatment during PHI resulted in rapidly declining levels to below 50 copies/ml. Conclusions: This study demonstrates the kinetic phases of viremia during PHI and indicates two new contributions to the natural history of HIV-1 infection: PHI peak levels correlate with steady-state levels and HIV-1 RNA declines biphasically; an initial rapid decay is usually followed by a slow decay, which is similar to the initial changes seen with antiviral treatment.

We present and analyze a model for the interaction of human immunodeficiency virus type 1 (HIV-1) with target cells that includes a time delay between initial infection and the formation of productively infected cells. Assuming that the variation among cells with respect to this 'intracellular' delay can be approximated by a gamma distribution, a high flexible distribution that can mimic a variety of biologically plausible delays, we provide analytical solutions for the expected decline in plasma virus concentration after the initiation of antiretroviral therapy with one or more protease inhibitors. We then use the model to investigate whether the parameters that characterize viral dynamics can be identified from biological data. Using non-linear least-squares regression to fit the model to simulated data in which the delays conform to a gamma distribution, we show that good estimates for free viral clearance rates, infected cell death rates, and parameters characterizing the gamma distribution can be obtained. For simulated data sets in which the delays were generated using other biologically plausible distributions, reasonably good estimates for viral clearance rates, infected cell death rates, and mean delay times can be obtained using the gamma-delay model. For simulated data sets that include added simulated noise, viral clearance rate estimates are not as reliable. If the mean intracellular delay is known, however, we show that reasonable estimates for the viral clearance rate can be obtained by taking the harmonic mean of viral clearance rate estimates from a group of patients. These results demonstrate that it is possible to incorporate distributed intracellular delays into existing models for HIV dynamics and to use these refined models to estimate the half-life of free virus from data on the decline in HIV-1 RNA following treatment.

Ten subjects received 600 to 1,200 mg of the human immunodeficiency virus type 1 (HIV-1) protease inhibitor ritonavir per day. Following 2 weeks of therapy, plasma HIV RNA levels decreased by a mean of 1.57 (range, 0.89 to 1.96) log units. With continued therapy, HIV RNA levels began to rise in eight subjects. The initial rise in plasma RNA levels was temporally associated with the development and quantitative increase in the V82 resistance mutation. Doubling times of the V82A mutant virus were estimated to be 2.4 to 4.8 days. An L63P/A mutation was commonly present at baseline even in subjects with a durable virologic response. The concomitant acquisition of an L63P/A mutation with the V82A/F mutation at the time when plasma RNA levels rebounded suggests a role for the L63P/A mutation in improving the fitness of the V82A/F mutation. Subsequent additional genotypic changes at codons 54 and 84 were often associated with further increases in plasma RNA levels. Ongoing viral replication in the presence of drugs resulted in the appearance of additional genotypic changes, including the L90M saquinavir resistance mutation, and decreased phenotypic susceptibility. The relative fitness of the protease V82A ritonavir resistance mutation and reverse transcriptase T215Y/F zidovudine resistance mutation following drug withdrawal were estimated to be 96 to 98% that of the wild type. Durability of the virologic response was associated with plasma RNA levels at the nadir. A virologic response beyond 60 days was not observed unless plasma HIV RNA levels were suppressed below 2,000 copies/ml, consistent with estimates from V82A doubling times for selection of a single resistance mutation to dominate the replicating population.

Somatic cells, including immune cells such as T-cells have a limited capacity for proliferation and can only replicate for a finite number of generations (known as the Hayflick limit) before dying. In this paper we use mathematical models to investigate the consequences of introducing a Hayflick limit on the dynamics of T-cells stimulated with specific antigen. We show that while the Hayflick limit does not alter the dynamics of T-cell response to antigen over the short term, it may have a profound effect on the long-term immune response. In particular we show that over the long term the Hayflick limit may be important in determining whether an immune response can be maintained to a persistent antigen (or parasite). The eventual outcome is determined by the magnitude of the Hayflick limit, the extent to which antigen reduces the input of T-cells from the thymus, and the rate of antigen-induced proliferation of T-cells. Counter to what might be expected we show that the persistence of an immune response (immune memory) requires the density of persistent antigen to be less than a defined threshold value. If the amount of persistent antigen (or parasite) is greater than this threshold value then immune memory will be relatively short lived. The consequences of this threshold for persistent mycobacterial and HIV infections and for the generation of vaccines are discussed.

We manipulated a bacteria-phage model system to investigate empirical and theoretical conditions allowing for the coexistence of an Escherichia coli host with each of two virulent phage species, a T1-like phage (T1X) and λvir. In minimal medium in the laboratory, bacteria coexisted with each phage species in continuous (chemostat) culture; however, in serial culture, T1X rapidly became extinct in all cases and λvir became extinct in most cases. When we refined a previously developed mechanistic model of bacteria-phage interactions in continuous culture, this model failed to predict our laboratory observations of long-term stability of bacteria and phage in chemostats. A serial transfer version of this model, however, came much closer to predicting bacteria-phage dynamics. To investigate why models of continuous culture failed, we tested hypotheses for phage persistence by manipulating experimental culture conditions. We found that wall populations of bacteria strongly influenced the stability of both phag...

Many Proteobacteria use acyl-homoserine lactone (AHL)-mediated quorum sensing to activate the production of antibiotics at high cell density. Extracellular factors like antibiotics can be considered public goods shared by individuals within a group. Quorum-sensing control of antibiotic production may be important for protecting a niche or competing for limited resources in mixed bacterial communities. To begin to investigate the role of quorum sensing in interspecies competition, we developed a dual-species co-culture model using the soil saprophytes Burkholderia thailandensis (Bt) and Chromobacterium violaceum (Cv). These bacteria require quorum sensing to activate the production of antimicrobial factors that inhibit growth of the other species. We demonstrate that quorum-sensing-dependent antimicrobials can provide a competitive advantage to either Bt or Cv by inhibiting growth of the other species in co-culture. Although the quorum-sensing signals differ for each species, we show that the promiscuous signal receptor encoded by Cv can sense signals produced by Bt, and that this ability to eavesdrop on Bt can provide Cv an advantage in certain situations. We use an in silico approach to investigate the effect of eavesdropping in competition, and show conditions where early activation of antibiotic production resulting from eavesdropping can promote competitiveness. Our work supports the idea that quorum sensing is important for interspecies competition and that promiscuous signal receptors allow eavesdropping on competitors in mixed microbial habitats.

Purified silymarin-derived natural products from the milk thistle plant (Silybum marianum) block hepatitis C virus (HCV) infection and inhibit T cell proliferation in vitro. An intravenous formulation of silibinin (SIL), a major component of silymarin, displays anti-HCV effects in humans and also inhibits T-cell proliferation in vitro. We show that SIL inhibited replication of HIV-1 in TZM-bl cells, PBMCs, and CEM cells in vitro. SIL suppression of HIV-1 coincided with dose-dependent reductions in actively proliferating CD19+, CD4+, and CD8+ cells, resulting in fewer CD4+ T cells expressing the HIV-1 co-receptors CXCR4 and CCR5. SIL inhibition of T-cell growth was not due to cytotoxicity measured by cell cycle arrest, apoptosis, or necrosis. SIL also blocked induction of the activation markers CD38, HLA-DR, Ki67, and CCR5 on CD4+ T cells. The data suggest that SIL attenuated cellular functions involved in T-cell activation, proliferation, and HIV-1 infection. Silymarin-derived compounds provide cytoprotection by suppressing virus infection, immune activation, and inflammation, and as such may be relevant for both HIV mono-infected and HIV/HCV co-infected subjects.

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene–gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

According to neo-Darwinian theory, random mutation produces genetic differences among organisms whereas natural selection tends to increase the frequency of advantageous alleles. However, several recent papers claim that certain mutations in bacteria and yeast occur at much higher rates specifically when the mutant phenotypes are advantageous. Various molecular models have been proposed that might explain these directed mutations, but the models have not been confirmed. Critics contend that studies purporting to demonstrate directed mutation lack certain controls and fail to account adequately for population dynamics. Further experiments that address these criticisms do not support the existence of directed mutations.

We used bacteria to study experimentally the process of genetic adaptation to environmental temperature. Replicate lines of Escherichia coli, founded from a common ancestor, were propagated for 2,000 generations in 4 different thermal regimes as 4 experimental groups: constant 32, 37, or 42°C (thermal specialists), or a daily alternation between 32 and 42°C (32/42°C: thermal generalists). The ancestor had previously been propagated at 37°C for 2,000 generations. Adaptation of the groups to temperature was measured by improvement in fitness relative to the ancestor, as estimated by competition experiments. All four experimental groups showed improved relative fitness in their own thermal environment (direct response of fitness). However, rates of fitness improvement varied greatly among temperature groups. The 42°C group responded most rapidly and extensively, followed by the 32 and 32/42°C groups, whose fitness improvements were indistinguishable. The 37°C group, which experienced the ancestral temperature, had the slowest and least extensive fitness improvement. The correlated fitness responses of each group, again relative to the common ancestor, were measured over the entire experimental range of temperatures. No necessary tradeoff between direct and correlated responses of fitness was apparent: for example, the improved fitness of the 42°C group at 42°C was not accompanied by a loss of fitness at 37°C or 32°C. However, the direct fitness responses were usually greater than the correlated responses, judged both by comparing direct and correlated responses of a single group at different temperatures and by comparing direct and correlated responses of different groups at a single temperature. These comparisons indicate that the observed adaptation was, in fact, largely temperature specific. Also, the fitness responses of the generalist group across a range of temperatures were less variable than those of the thermal specialist groups considered as whole.

Background HIV-1 is frequently detected in the lungs of infected individuals and is likely important in the development of pulmonary opportunistic infections. The unique environment of the lung, rich in alveolar macrophages and with specialized local immune responses, may contribute to differential evolution or selection of HIV-1. Methodology and Findings We characterized HIV-1 in the lung in relation to contemporaneous viral populations in the blood. The C2-V5 region of HIV-1 env was sequenced from paired lung (induced sputum or bronchoalveolar lavage) and blood (plasma RNA and proviral DNA from sorted or unsorted PBMC) from 18 subjects. Compartmentalization between tissue pairs was assessed using 5 established tree or distance-based methods, including permutation tests to determine statistical significance. We found statistical evidence of compartmentalization between lung and blood in 10/18 subjects, although lung and blood sequences were intermingled on phylogenetic trees in all subjects. The subject showing the greatest compartmentalization contained many nearly identical sequences in BAL sample, suggesting clonal expansion may contribute to reduced viral diversity in the lung in some cases. However, HIV-1 sequences in lung were not more homogeneous overall, nor were we able to find a lung-specific genotype associated with macrophage tropism in V3. In all four subjects in whom predicted X4 genotypes were found in blood, predicted X4 genotypes were also found in lung. Conclusions Our results support a picture of continuous migration of HIV-1 between circulating blood and lung tissue, with perhaps a very limited degree of localized evolution or clonal replication.

The tradeoff between the need to suppress drug-resistant viruses and the problem of treatment toxicity has led to the development of various drug-sparing HIV-1 treatment strategies. Here we use a stochastic simulation model for viral dynamics to investigate how the timing and duration of the induction phase of induction–maintenance therapies might be optimized. Our model suggests that under a variety of biologically plausible conditions, 6–10 mo of induction therapy are needed to achieve durable suppression and maximize the probability of eradicating viruses resistant to the maintenance regimen. For induction regimens of more limited duration, a delayed-induction or -intensification period initiated sometime after the start of maintenance therapy appears to be optimal. The optimal delay length depends on the fitness of resistant viruses and the rate at which target-cell populations recover after therapy is initiated. These observations have implications for both the timing and the kinds of drugs selected for induction–maintenance and therapy-intensification strategies., Author Summary Clinicians treating HIV infection must balance the need to suppress viral replication against the harmful side effects and significant cost of antiretroviral therapy. Inadequate therapy often results in the emergence of resistant viruses and treatment failure. These difficulties are especially acute in resource-poor settings, where antiretroviral agents are limited. This has prompted an interest in induction–maintenance (IM) treatment strategies, in which brief intensive therapy is used to reduce host viral levels. Induction is followed by a simplified and more easily tolerated maintenance regimen. IM approaches remain an unproven concept in HIV therapy. We have developed a mathematical model to simulate clinical responses to antiretroviral drug therapy. We account for latent infection, partial drug efficacy, cross-resistance, viral recombination, and other factors. This model accurately reflects expected outcomes under single, double, and standard three-drug antiretroviral therapy. When applied to IM therapy, we find that (1) IM is expected to be successful beyond 3 y under a variety of conditions; (2) short-term induction therapy is optimally started several days to weeks after the start of maintenance; and (3) IM therapy may eradicate some preexisting drug-resistant viral strains from the host. Our simulations may help develop new treatment strategies and optimize future clinical trials.

Bonhoeffer et al . (Reports, 26 November 2004, p. 1547) presented evidence for positive epistasis in a clinical data set of HIV-1 mutants and corresponding fitness values. We demonstrate that biases in the original and simulated data sets may lead to erroneous evidence for epistasis. More rigorous statistical tests must be used to account for such biases before one can infer epistasis.

A biologically explicit simulation model of resource competition between two species of seed-eating heteromyid rodent indicates that stable coexistence is possible on a homogeneous resource if harvested food is stored and consumers steal each other's caches. Here we explore the coexistence mechanisms involved by analyzing how consumer phenotypes and presence of a noncaching consumer affect the competitive outcome. Without cache exchange, the winning consumer is better at harvesting seeds and produces more offspring per gram of stored food. With cache exchange, coexistence is promoted by interspecific trade-offs between harvest ability, metabolic efficiency, and ability to pilfer defended caches of heterospecifics or scavenge undefended caches of dead conspecifics or heterospecifics. Cache exchange via pilferage can equalize competitor fitnesses but has little stabilizing effect and leads to stable coexistence only in the presence of a noncaching consumer. In contrast, scavenging is both equalizing and stabilizing and promotes coexistence without a third consumer. Because body size affects a heteromyid rodent's metabolic rate, seed harvest rate, caching strategy, and ability to steal caches, interspecific differences in body size should produce the trade-offs necessary for coexistence. The observation that coexisting heteromyids differ in body size therefore indicates that cache exchange may promote diversity in heteromyid communities.

We introduce a publicly available webserver, PIRSpred (http://protinfo.compbio.washington.edu/pirspred/), for accurate human immunodeficiency virus type 1 (HIV-1) genotypic resistance/susceptibility prediction. The server accepts mutant HIV-1 protease or reverse transcriptase (RT) enzyme sequences as input and predicts resistance/susceptibility to several FDA-approved inhibitors using three approaches: linear regression, docking with dynamics, and their consensus. The predictions made by the server outperform other publicly available HIV-1 genotypic interpretation algorithms, and can be used efficiently and economically in a clinical setting to guide treatment against HIV-1 infection.

Single-cell impedance spectroscopy integrated with lab-on-a-chip systems provides a direct and minimally invasive approach for monitoring and characterizing properties of individual cells in real-time. Here we investigate the theoretical potential and limitations of this technique for analyzing single membrane-bound particles as small as 100 nm in diameter. Our theoretical model suggests a lower limit of detection for single cells on the order of a few microns.Copyright © 2005 by ASME

Human immunodeficiency virus type 1 (HIV-1) has high replication and mutation rates that generate large census populations and high levels of genetic variation. We examined the roles of natural selection, population growth, random genetic drift, and recombination in shaping the variation in 1509 C2-V5 env sequences derived from nine men with chronic HIV-1 infection. These sequences were obtained from clinical visits that reflect the first 6-13.7 years of infection. Pairwise comparisons of nonsynonymous and synonymous distances, Tajima's D test, Fu and Li's D* test, and a test of recurrent mutation revealed evidence for episodes of nonneutral evolution in a total of 22 out of 145 blood samples, representing six of the nine individuals. Using three coalescent-based maximum-likelihood estimators, we found viral effective population sizes in all nine individuals to be ∼103. We also show that a previous estimate of the effective population size of ∼105 based on rare haplotype frequencies decreases to ∼103 upon correcting a biased sampling procedure. We conclude that the genetic variation in these data sets can be explained by a predominance of random genetic drift of neutral mutations with brief episodes of natural selection that were frequently masked by recombination.