Factors associated with SARS-CoV-2 infection in unvaccinated children and young adults

BACKGROUND AND OBJECTIVES

Pediatric COVID-19 cases are often mild or asymptomatic, which has complicated estimations of disease burden using existing testing practices. We aimed to determine the age-specific population seropositivity and risk factors of SARS-CoV-2 seropositivity among children and young adults during the pandemic in British Columbia (BC).

METHODS

We conducted two cross-sectional serosurveys: phase 1 enrolled children and adults < 25 years between November 2020-May 2021 and phase 2 enrolled children < 10 years between June 2021-May 2022 in BC. Participants completed electronic surveys and self-collected finger-prick dried blood spot (DBS) samples. Samples were tested for immunoglobulin G antibodies against ancestral spike protein (S). Descriptive statistics from survey data were reported and two multivariable analyses were conducted to evaluate factors associated with seropositivity.

RESULTS

A total of 2864 participants were enrolled, of which 95/2167 (4.4%) participants were S-seropositive in phase 1 across all ages, and 61/697 (8.8%) unvaccinated children aged under ten years were S-seropositive in phase 2. Overall, South Asian participants had a higher seropositivity than other ethnicities (13.5% vs. 5.2%). Of 156 seropositive participants in both phases, 120 had no prior positive SARS-CoV-2 test. Young infants and young adults had the highest reported seropositivity rates (7.0% and 7.2% respectively vs. 3.0-5.6% across other age groups).

CONCLUSIONS

SARS-CoV-2 seropositivity among unvaccinated children and young adults was low in May 2022, and South Asians were disproportionately infected. This work demonstrates the need for improved diagnostics and reporting strategies that account for age-specific differences in pandemic dynamics and acceptability of testing mechanisms.

SARS-CoV-2 cross-sectional seroprevalence study among public school staff in Metro Vancouver after the first Omicron wave in British Columbia, Canada

OBJECTIVE

To determine the SARS-CoV-2 seroprevalence among school workers within the Greater Vancouver area, British Columbia, Canada, after the first Omicron wave.

DESIGN

Cross-sectional study by online questionnaire, with blood serology testing.

SETTING

Three main school districts (Vancouver, Richmond and Delta) in the Vancouver metropolitan area.

PARTICIPANTS

Active school staff enrolled from January to April 2022, with serology testing between 27 January and 8 April 2022. Seroprevalence estimates were compared with data obtained from Canadian blood donors weighted over the same sampling period, age, sex and postal code distribution.

PRIMARY AND SECONDARY OUTCOMES

SARS-CoV-2 nucleocapsid antibody testing results adjusted for test sensitivity and specificity, and regional variation across school districts using Bayesian models.

RESULTS

Of 1850 school staff enrolled, 65.8% (1214/1845) reported close contact with a COVID-19 case outside the household. Of those close contacts, 51.5% (625/1214) were a student and 54.9% (666/1214) were a coworker. Cumulative incidence of COVID-19 positive testing by self-reported nucleic acid or rapid antigen testing since the beginning of the pandemic was 15.8% (291/1845). In a representative sample of 1620 school staff who completed serology testing (87.6%), the adjusted seroprevalence was 26.5% (95% CrI 23.9% to 29.3%), compared with 32.4% (95% CrI 30.6% to 34.5%) among 7164 blood donors.

CONCLUSION

Despite frequent COVID-19 exposures reported, SARS-CoV-2 seroprevalence among school staff in this setting remained no greater than the community reference group. Results are consistent with the premise that many infections were acquired outside the school setting, even with Omicron.

Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates

Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.

SARS-CoV-2 seroprevalence among Vancouver public school staff in British Columbia, Canada: a cross-sectional study

OBJECTIVES

Few studies reported COVID-19 cases in schools during the 2020/21 academic year in a setting of uninterrupted in-person schooling. The main objective was to determine the SARS-CoV-2 seroprevalence among school staff in Vancouver public schools.

DESIGN

Cumulative incident COVID-19 cases among all students and school staff based on public health data, with an embedded cross-sectional serosurvey among a school staff sample that was compared to period, age, sex and geographical location-weighted data from blood donors.

SETTING

Vancouver School District (British Columbia, Canada) from kindergarten to grade 12.

PARTICIPANTS

Active school staff enrolled from 3 February to 23 April 2021 with serology testing from 10 February to 15 May 2021.

MAIN OUTCOME MEASURES

SARS-CoV-2 seroprevalence among school staff, based on spike (S)-based (unvaccinated staff) or N-based serology testing (vaccinated staff).

RESULTS

Public health data showed the cumulative incidence of COVID-19 among students attending in-person was 9.8 per 1000 students (n=47 280), and 13 per 1000 among school staff (n=7071). In a representative sample of 1689 school staff, 78.2% had classroom responsibilities, and spent a median of 17.6 hours in class per week (IQR: 5.0-25 hours). Although 21.5% (363/1686) of surveyed staff self-reported close contact with a COVID-19 case outside of their household (16.5% contacts were school-based), 5 cases likely acquired the infection at school based on viral testing. Sensitivity/Specificity-adjusted seroprevalence in 1556/1689 staff (92.1%) was 2.3% (95% CI: 1.6% to 3.2%), comparable to a sex, age, date and residency area-weighted seroprevalence of 2.6% (95% CI: 2.2% to 3.1%) among 5417 blood donors.

CONCLUSION

Seroprevalence among staff was comparable to a reference group of blood donors from the same community. These data show that in-person schooling could be safely maintained during the 2020/21 school year with mitigation measures, in a large school district in Vancouver, Canada.

Estimating naloxone need in the USA across fentanyl, heroin, and prescription opioid epidemics: a modelling study

BACKGROUND

The US overdose crisis is driven by fentanyl, heroin, and prescription opioids. One evidence-based policy response has been to broaden naloxone distribution, but how much naloxone a community would need to reduce the incidence of fatal overdose is unclear. We aimed to estimate state-level US naloxone need in 2017 across three main naloxone access points (community-based programmes, provider prescription, and pharmacy-initiated distribution) and by dominant opioid epidemic type (fentanyl, heroin, and prescription opioid).

METHODS

In this modelling study, we developed, parameterised, and applied a mechanistic model of risk of opioid overdose and used it to estimate the expected reduction in opioid overdose mortality after deployment of a given number of two-dose naloxone kits. We performed a literature review and used a modified-Delphi panel to inform parameter definitions. We refined an established model of the population at risk of overdose by incorporating changes in the toxicity of the illicit drug supply and in the naloxone access point, then calibrated the model to 2017 using data obtained from proprietary data sources, state health departments, and national surveys for 12 US states that were representative of each epidemic type. We used counterfactual modelling to project the effect of increased naloxone distribution on the estimated number of opioid overdose deaths averted with naloxone and the number of naloxone kits needed to be available for at least 80% of witnessed opioid overdoses, by US state and access point.

FINDINGS

Need for naloxone differed by epidemic type, with fentanyl epidemics having the consistently highest probability of naloxone use during witnessed overdose events (range 58-76% across the three modelled states in this category) and prescription opioid-dominated epidemics having the lowest (range 0-20%). Overall, in 2017, community-based and pharmacy-initiated naloxone access points had higher probability of naloxone use in witnessed overdose and higher numbers of deaths averted per 100 000 people in state-specific results with these two access points than with provider-prescribed access only. To achieve a target of naloxone use in 80% of witnessed overdoses, need varied from no additional kits (estimated as sufficient) to 1270 kits needed per 100 000 population across the 12 modelled states annually. In 2017, only Arizona had sufficient kits to meet this target.

INTERPRETATION

Opioid epidemic type and how naloxone is accessed have large effects on the number of naloxone kits that need to be distributed, the probability of naloxone use, and the number of deaths due to overdose averted. The extent of naloxone distribution, especially through community-based programmes and pharmacy-initiated access points, warrants substantial expansion in nearly every US state.

FUNDING

National Institute of Health, National Institute on Drug Abuse.

Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities

Estimates of the basic reproduction number (R ₀) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R ₀ across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R ₀ in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R ₀ between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%-86%. The Bayesian analysis provided an overall estimate of R ₀ = 2.51 (90% credible interval 0.47-9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%-69%) of all potential cases being averted within the LTHC facilities, or 75% (68%-79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

How much leeway is there to relax COVID-19 control measures?

Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, and therefore many locations began to see resurgence in COVID-19 cases. We present a Bayesian method to estimate the leeway to reopen, or alternatively the strength of change required to re-establish COVID-19 control, in a range of jurisdictions experiencing different COVID-19 epidemics. We estimated the timing and strength of initial control measures such as widespread distancing and compared the leeway jurisdictions had to reopen immediately after NPI measures to later estimates of leeway. Finally, we quantified risks associated with reopening and the likely burden of new cases due to introductions from other jurisdictions. We found widely varying leeway to reopen. After initial NPI measures took effect, some jurisdictions had substantial leeway (e.g., Japan, New Zealand, Germany) with > 0.99 probability that contact rates were below 80% of the threshold for epidemic growth. Others had little leeway (e.g., the United Kingdom, Washington State) and some had none (e.g., Sweden, California). For most such regions, increases in contact rate of 1.5-2 fold would have had high (> 0.7) probability of exceeding past peak sizes. Most jurisdictions experienced June-August trajectories consistent with our projections of contact rate increases of 1-2-fold. Under such relaxation scenarios for some regions, we projected up to ∼100 additional cases if just one case were imported per week over six weeks, even between jurisdictions with comparable COVID-19 risk. We provide an R package covidseir to enable jurisdictions to estimate leeway and forecast cases under different future contact patterns. Estimates of leeway can establish a quantitative basis for decisions about reopening. We recommend a cautious approach to reopening economies and borders, coupled with strong monitoring for changes in transmission.

Examining the dynamics of Epstein-Barr virus shedding in the tonsils and the impact of HIV-1 coinfection on daily saliva viral loads

Epstein-Barr virus (EBV) is transmitted by saliva and is a major cause of cancer, particularly in people living with HIV/AIDS. Here, we describe the frequency and quantity of EBV detection in the saliva of Ugandan adults with and without HIV-1 infection and use these data to develop a novel mathematical model of EBV infection in the tonsils. Eligible cohort participants were not taking antiviral medications, and those with HIV-1 infection had a CD4 count >200 cells/mm3. Over a 4-week period, participants provided daily oral swabs that we analysed for the presence and quantity of EBV. Compared with HIV-1 uninfected participants, HIV-1 coinfected participants had an increased risk of EBV detection in their saliva (IRR = 1.27, 95% CI = 1.10-1.47) and higher viral loads in positive samples. We used these data to develop a stochastic, mechanistic mathematical model that describes the dynamics of EBV, infected cells, and immune response within the tonsillar epithelium to analyse potential factors that may cause EBV infection to be more severe in HIV-1 coinfected participants. The model, fit using Approximate Bayesian Computation, showed high fidelity to daily oral shedding data and matched key summary statistics. When evaluating how model parameters differed among participants with and without HIV-1 coinfection, results suggest HIV-1 coinfected individuals have higher rates of B cell reactivation, which can seed new infection in the tonsils and lower rates of an EBV-specific immune response. Subsequently, both these traits may explain higher and more frequent EBV detection in the saliva of HIV-1 coinfected individuals.

Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the "contact ratio" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the "contact ratio" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

Modeling COVID-19 and Its Impacts on U.S. Immigration and Customs Enforcement (ICE) Detention Facilities, 2020

U.S. Immigration and Customs Enforcement (ICE) facilities house thousands of undocumented immigrants in environments discordant with the public health recommendations to reduce the transmission of 2019 novel coronavirus (COVID-19). Using ICE detainee population data obtained from the ICE Enforcement and Removal Operations (ERO) website as of March 2, 2020, we implemented a simple stochastic susceptible-exposed-infected-recovered model to estimate the rate of COVID-19 transmission within 111 ICE detention facilities and then examined impacts on regional hospital intensive care unit (ICU) capacity. Models considered three scenarios of transmission (optimistic, moderate, pessimistic) over 30-, 60-, and 90-day time horizons across a range of facility sizes. We found that 72% of individuals are expected to be infected by day 90 under the optimistic scenario (R0 = 2.5), while nearly 100% of individuals are expected to be infected by day 90 under a more pessimistic (R0 = 7) scenario. Although asynchronous outbreaks are more likely, day 90 estimates provide an approximation of total positive cases after all ICE facility outbreaks. We determined that, in the most optimistic scenario, coronavirus outbreaks among a minimum of 65 ICE facilities (59%) would overwhelm ICU beds within a 10-mile radius and outbreaks among a minimum of 8 ICE facilities (7%) would overwhelm local ICU beds within a 50-mile radius over a 90-day period, provided every ICU bed was made available for sick detainees. As policymakers seek to rapidly implement interventions that ensure the continued availability of life-saving medical resources across the USA, they may be overlooking the pressing need to slow the spread of COVID-19 infection in ICE's detention facilities. Preventing the rapid spread necessitates intervention measures such as granting ICE detainees widespread release from an unsafe environment by returning them to the community.

The role of mathematical modelling in aiding public health policy decision-making: A case study of the BC opioid overdose emergency

The province of British Columbia is currently experiencing the highest rate of apparent opioid-related deaths within Canada. This dramatic increase in overdose deaths has been primarily driven by the increase of fentanyl and fentanyl-analogues within the unregulated, highly unpredictable and toxic street drug supply. A public health emergency was declared in B.C. in April 2016. After the emergency was declared, overdose-related death rates continued to rise, reaching unprecedented levels. In the context of enhanced collaboration between government organizations and researchers, a series of mathematical studies improved the ability of government and service providers to understand the impact of scaled-up strategies, including harm reduction and treatment services. In this commentary we describe how government agencies collaborated with researchers and other experts to use modelling results, and describe lessons learned for enhancing these collaborations. Mathematical modelling provides a viable and timely approach to the generation of intelligence, combining disparate data to assess the on-going impact of a comprehensive package of interventions during a public health emergency, and enhancing accountability for investments.

Modelling the combined impact of interventions in averting deaths during a synthetic-opioid overdose epidemic

BACKGROUND AND AIMS

The province of British Columbia (BC) Canada has experienced a rapid increase in illicit drug overdoses and deaths during the last 4 years, with a provincial emergency declared in April 2016. These deaths have been driven primarily by the introduction of synthetic opioids into the illicit opioid supply. This study aimed to measure the combined impact of large-scale opioid overdose interventions implemented in BC between April 2016 and December 2017 on the number of deaths averted.

DESIGN

We expanded on the mathematical modelling methodology of our previous study to construct a Bayesian hierarchical latent Markov process model to estimate monthly overdose and overdose-death risk, along with the impact of interventions.

SETTING AND CASES

Overdose events and overdose-related deaths in BC from January 2012 to December 2017.

INTERVENTIONS

The interventions considered were take-home naloxone kits, overdose prevention/supervised consumption sites and opioid agonist therapy MEASUREMENTS: Counterfactual simulations were performed with the fitted model to estimate the number of death events averted for each intervention and in combination.

FINDINGS

Between April 2016 and December 2017, BC observed 2177 overdose deaths (77% fentanyl-detected). During the same period, an estimated 3030 (2900-3240) death events were averted by all interventions combined. In isolation, 1580 (1480-1740) were averted by take-home naloxone, 230 (160-350) by overdose prevention services and 590 (510-720) were averted by opioid agonist therapy.

CONCLUSIONS

A combined intervention approach has been effective in averting overdose deaths during British Columbia's opioid overdose crisis in the period since declaration of a public health emergency (April 2016-December 2017). However, the absolute numbers of overdose deaths have not changed.

Predicting the impact of clustered risk and testing behaviour patterns on the population-level effectiveness of pre-exposure prophylaxis against HIV among gay, bisexual and other men who have sex with men in Greater Vancouver, Canada

Pre-exposure prophylaxis (PrEP) has the potential to greatly reduce transmission of HIV. However, significant questions remain around how behavioural factors may influence its impact within target populations. We used a 2014 sexual behaviour survey to modify and recalibrate a mathematical model of HIV infection dynamics within the population of gay, bisexual and other men who have sex with men (GBMSM) in the Greater Vancouver area of British Columbia, Canada. We performed a clustering analysis on the survey data to divide the population into categories associated with their reported risk of HIV exposure as well as their reported testing habits and attitudes towards PrEP. We found a positive association between reported risk and testing behaviour and level of awareness/interest in PrEP. Using the cluster groups to structure the population, we then estimated the impact of PrEP on HIV transmission in our study population. We found that the association between behaviour and interest in PrEP substantially boosted the population-level effectiveness of PrEP. Within our model, if PrEP adoption was unrelated to risk and testing, an additional 206 (95% credible interval 5-261), new infections representing 15% of total infections are predicted to occur among GBMSM over ten years, compared to where PrEP is adopted by individuals according to their level of interest. Our results underscore the importance of incorporating behavioural data into models when predicting the impact of future public health interventions.

A novel Bayesian approach to predicting reductions in HIV incidence following increased testing interventions among gay, bisexual and other men who have sex with men in Vancouver, Canada

Increasing HIV testing rates among high-risk groups should lead to increased numbers of cases being detected. Coupled with effective treatment and behavioural change among individuals with detected infection, increased testing should also reduce onward incidence of HIV in the population. However, it can be difficult to predict the strengths of these effects and thus the overall impact of testing. We construct a mathematical model of an ongoing HIV epidemic in a population of gay, bisexual and other men who have sex with men. The model incorporates different levels of infection risk, testing habits and awareness of HIV status among members of the population. We introduce a novel Bayesian analysis that is able to incorporate potentially unreliable sexual health survey data along with firm clinical diagnosis data. We parameterize the model using survey and diagnostic data drawn from a population of men in Vancouver, Canada. We predict that increasing testing frequency will yield a small-scale but long-term impact on the epidemic in terms of new infections averted, as well as a large short-term impact on numbers of detected cases. These effects are predicted to occur even when a testing intervention is short-lived. We show that a short-lived but intensive testing campaign can potentially produce many of the same benefits as a campaign that is less intensive but of longer duration.

Arp2/3 complex-driven spatial patterning of the BCR enhances immune synapse formation, BCR signaling and B cell activation

When B cells encounter antigens on the surface of an antigen-presenting cell (APC), B cell receptors (BCRs) are gathered into microclusters that recruit signaling enzymes. These microclusters then move centripetally and coalesce into the central supramolecular activation cluster of an immune synapse. The mechanisms controlling BCR organization during immune synapse formation, and how this impacts BCR signaling, are not fully understood. We show that this coalescence of BCR microclusters depends on the actin-related protein 2/3 (Arp2/3) complex, which nucleates branched actin networks. Moreover, in murine B cells, this dynamic spatial reorganization of BCR microclusters amplifies proximal BCR signaling reactions and enhances the ability of membrane-associated antigens to induce transcriptional responses and proliferation. Our finding that Arp2/3 complex activity is important for B cell responses to spatially restricted membrane-bound antigens, but not for soluble antigens, highlights a critical role for Arp2/3 complex-dependent actin remodeling in B cell responses to APC-bound antigens.

Stochastic Dynamics of the Latently Infected Cell Reservoir During HIV Infection

The presence of cells latently infected with HIV is currently considered to be a major barrier to viral eradication within a patient. Here, we consider birth-death-immigration models for the latent cell population in a single patient, and present analytical results for the size of this population in the absence of treatment. We provide results both at steady state (viral set point), and during the non-equilibrium setting of early infection. We obtain semi-analytic results showing how latency-reversing drugs might be expected to affect the size of the latent pool over time. We also analyze the probability of rare mutant viral strains joining the latent cell population, allowing for steady-state and dynamic viral populations within the host.

Sustained Reduction in Sexual Behavior that May Pose a Risk of HIV Transmission Following Diagnosis During Early HIV Infection Among Gay Men in Vancouver, British Columbia

Increased viral load during early HIV infection (EHI) disproportionately contributes to HIV transmission among gay men. We examined changes in sexual behavior that may pose a risk of HIV transmission (condomless anal sex (AS) with a serodiscordant or unknown status partner, CAS-SDU) in a cohort of 25 gay men newly diagnosed during EHI who provided information on 241 sexual partners at six time points following diagnosis. Twenty-two (88%) participants reported ≥1 AS partner (median time to first AS 80 days) and 12 (55%) reported ≥1 partnership involving CAS-SDU (median 116 days). In hierarchical generalized linear mixed effects models, AS was significantly less likely in all time periods following diagnosis and more likely with serodiscordant partners. The likelihood of CAS-SDU decreased three months after diagnosis and was higher in recently versus acutely infected participants. Most men in our study abstained from sex immediately after diagnosis with sustained longer-term reduction in CAS-SDU, confirming the importance of timely diagnosis during EHI.

Effects of spatiotemporal HSV-2 lesion dynamics and antiviral treatment on the risk of HIV-1 acquisition

Patients with Herpes Simplex Virus-2 (HSV-2) infection face a significantly higher risk of contracting HIV-1. This is thought to be due to herpetic lesions serving as entry points for HIV-1 and tissue-resident CD4+ T cell counts increasing during HSV-2 lesional events. We have created a stochastic and spatial mathematical model describing the dynamics of HSV-2 infection and immune response in the genital mucosa. Using our model, we first study the dynamics of a developing HSV-2 lesion. We then use our model to quantify the risk of infection with HIV-1 following sexual exposure in HSV-2 positive women. Untreated, we find that HSV-2 infected women are up to 8.6 times more likely to acquire HIV-1 than healthy patients. However, when including the effects of the HSV-2 antiviral drug, pritelivir, the risk of HIV-1 infection is predicted to decrease by up to 35%, depending on drug dosage. We estimate the relative importance of decreased tissue damage versus decreased CD4+ cell presence in determining the effectiveness of pritelivir in reducing HIV-1 infection. Our results suggest that clinical trials should be performed to evaluate the effectiveness of pritelivir or similar agents in preventing HIV-1 infection in HSV-2 positive women.

The risk of contracting HIV-1 is significantly higher in people who have genital HSV-2 infections. Here, we put forward a new mathematical model to describe HSV-2 infection and the process of HIV-1 infection in the genital mucosa surrounding HSV-2 lesions. We determine how the characteristics of HSV-2 infection affect the risk of HIV-1 infection, and determine whether reducing the severity of HSV-2 symptoms with antiviral drugs can be expected to decrease the risk of HIV-1 infection. We find that the risk of HIV-1 infection is dependent on three factors: the amount of HIV-1 the patient is exposed to, the severity of HSV-2 lesions, and the number of CD4+ T immune cells in the genital mucosa. Our model predicts that antiviral drugs targeting HSV-2 can cause a therapeutic decrease in lesion severity and CD4+ T cell count in the genital mucosa. This furthermore causes a significant decrease in the risk of HIV-1 infection but the dose of HSV-2 antiviral drug must be sufficiently high. Our results support further development and testing of new HSV-2 antiviral drugs to help decrease the world-wide burden of HIV-1.

Distribution of take-home opioid antagonist kits during a synthetic opioid epidemic in British Columbia, Canada: a modelling study

BACKGROUND

Illicit use of high-potency synthetic opioids has become a global issue over the past decade. This misuse is particularly pronounced in British Columbia, Canada, where a rapid increase in availability of fentanyl and other synthetic opioids in the local illicit drug supply during 2016 led to a substantial increase in overdoses and deaths. In response, distribution of take-home naloxone (THN) overdose prevention kits was scaled up (6·4-fold increase) throughout the province. The aim of this study was to estimate the impact of the THN programme in terms of the number of deaths averted over the study period.

METHODS

We estimated the impact of THN kits on the ongoing epidemic among people who use illicit opioids in British Columbia and explored counterfactual scenarios for the provincial response. A Markov chain model was constructed explicitly including opioid-related deaths, fentanyl-related deaths, ambulance-attended overdoses, and uses of THN kits. The model was calibrated in a Bayesian framework incorporating population data between Jan 1, 2012, and Oct 31, 2016.

FINDINGS

22 499 ambulance-attended overdoses and 2121 illicit drug-related deaths (677 [32%] deaths related to fentanyl) were recorded in the study period, mostly since January, 2016. In the same period, 19 074 THN kits were distributed. We estimate that 298 deaths (95% credible interval [CrI] 91-474) were averted by the THN programme. Of these deaths, 226 (95% CrI 125-340) were averted in 2016, following a rapid scale-up in distribution of kits. We infer a rapid increase in fentanyl adulterant at the beginning of 2016, with an estimated 2·3 times (95% CrI 2·0-2·9) increase from 2015 to 2016. Counterfactual modelling indicated that an earlier scale-up of the programme would have averted an additional 118 deaths (95% CrI 64-207). Our model also indicated that the increase in deaths could parsimoniously be explained through a change in the fentanyl-related overdose rate alone.

INTERPRETATION

The THN programme substantially reduced the number of overdose deaths during a period of rapid increase in the number of illicit drug overdoses due to fentanyl in British Columbia. However, earlier adoption and distribution of the THN intervention might have had an even greater impact on overdose deaths. Our findings show the value of a fast and effective response at the start of a synthetic opioid epidemic. We also believe that multiple interventions are needed to achieve an optimal impact.

FUNDING

Canadian Institutes of Health Research Partnerships for Health Systems Improvement programme (grant 318068) and Natural Science and Engineering Research Council of Canada (grant 04611).

On the duration of the period between exposure to HIV and detectable infection

HIV infection cannot be detected immediately after exposure because plasma viral loads are too small initially. The duration of this phase of infection (the "eclipse period") is difficult to estimate because precise dates of exposure are rarely known. Therefore, the reliability of clinical HIV testing during the first few weeks of infection is unknown, creating anxiety among HIV-exposed individuals and their physicians. We address this by fitting stochastic models of early HIV infection to detailed viral load records for 78 plasma donors, taken during the period of exposure and infection. We first show that the classic stochastic birth-death model does not satisfactorily describe early infection. We therefore apply a different stochastic model that includes infected cells and virions separately. Since every plasma donor in our data eventually becomes infected, we must condition the model to reflect this bias, before fitting to the data. Applying our best estimates of unknown parameter values, we estimate the mean eclipse period to be 8-10 days. We further estimate the reliability of a negative test t days after potential exposure.

In vivo regulation of integrin turnover by outside-in activation

The development of three-dimensional tissue architecture requires precise control over the attachment of cells to the extracellular matrix (ECM). Integrins, the main ECM-binding receptors in animals, are regulated in multiple ways to modulate cell-ECM adhesion. One example is the conformational activation of integrins by extracellular signals ('outside-in activation') or by intracellular signals ('inside-out activation'), whereas another is the modulation of integrin turnover. We demonstrate that outside-in activation regulates integrin turnover to stabilize tissue architecture in vivo Treating Drosophila embryos with Mg(2+) and Mn(2+), known to induce outside-in activation, resulted in decreased integrin turnover. Mathematical modeling combined with mutational analysis provides mechanistic insight into the stabilization of integrins at the membrane. We show that as tissues mature, outside-in activation is crucial for regulating the stabilization of integrin-mediated adhesions. This data identifies a new in vivo role for outside-in activation and sheds light on the key transition between tissue morphogenesis and maintenance.

Applied stretch initiates directional invasion through the action of Rap1 GTPase as a tension sensor

Although it is known that a stiffening of the stroma and the rearrangement of collagen fibers within the extracellular matrix facilitate the movement of tumor cells away from the primary lesion, the underlying mechanisms responsible are not fully understood. We now show that this invasion, which can be initiated by applying tensional loads to a three-dimensional collagen gel matrix in culture, is dependent on the Rap1 GTPases (Rap1a and Rap1b, referred to collectively as Rap1). Under these conditions Rap1 activity stimulates the formation of focal adhesion structures that align with the tensional axis as single tumor cells move into the matrix. These effects are mediated by the ability of Rap1 to induce the polarized polymerization and retrograde flow of actin, which stabilizes integrins and recruits vinculin to preformed adhesions, particularly those near the leading edge of invasive cells. Rap1 activity also contributes to the tension-induced collective invasive elongation of tumor cell clusters and it enhances tumor cell growth in vivo Thus, Rap1 mediates the effects of increased extracellular tension in multiple ways that are capable of contributing to tumor progression when dysregulated.

Design Parameters for Granzyme-Mediated Cytotoxic Lymphocyte Target-Cell Killing and Specificity

Cytotoxic lymphocytes are key elements of the immune system that are primarily responsible for targeting cells infected with intracellular pathogens, or cells that have become malignantly transformed. Target cells are killed mainly via lymphocyte exocytosis of specialized lysosomes containing perforin, a pore-forming protein, and granzymes, which are proteases that induce apoptosis. Due to its central role in lymphocyte biology, as well as its implication in a host of pathologies from cancer to autoimmunity, the granzyme-perforin pathway has been the subject of extensive investigation. Nevertheless, the details of exactly how granzyme and perforin cooperate to induce target-cell death remain controversial. To further investigate this system, we developed a biophysical model of the immunological synapse between a cytotoxic lymphocyte and a target cell using a spatial stochastic simulation algorithm. We used this model to calculate the spatiotemporal evolution of granzyme B and perforin from the time of their exocytosis to granzyme internalization by the target cell. We used a metric of granzyme internalization to delineate which biological processes were critical for successful target-cell lysis. We found that the high aspect ratio of the immunological synapse was insufficient in this regard, and that molecular crowding within the synapse is critical to preserve sufficient concentrations of perforin and granzyme for consistent pore formation and granzyme transfer to target cells. However, even when pore formation occurs in our model, a large amount of both granzyme and perforin still escape from the synapse. We argue that a tight seal between the cytotoxic lymphocyte and its target cell is not required to avoid bystander killing. Instead, we propose that the requirement for spatiotemporal colocalization of granzyme and perforin acts as an effective bimolecular filter to ensure target specificity.

Conditions for eradicating hepatitis C in people who inject drugs: A fibrosis aware model of hepatitis C virus transmission

It is estimated that 80% of new hepatitis C virus (HCV) infections occur among people who inject drugs (PWID). Eradicating HCV from this population is key for the complete eradication of the disease, and the advent of simple to use, high efficacy treatments could conceivably make this scenario possible. This paper presents a mathematical model where transmission of HCV is studied in a simulated population of PWID where fibrosis progression is explicitly tracked. The stability thresholds that determine whether HCV will remain endemic or become eradicated were established numerically, and analytically on a reduced version of the model. Conditions on testing and treatment rates for eradication to occur were determined, within the context of the new high efficacy therapies. The results show that HCV eradication in the PWID population of the Vancouver, BC test scenario is achievable, but testing and especially treatment rates will need to increase significantly from current rates. Parameter estimates were drawn from published data.

In vivo quantitative analysis of Talin turnover in response to force

Cell–ECM adhesion is regulated by mechanical force. Quantitative imaging and mathematical modeling are used to elucidate how the intracellular adhesion complex of integrin-based adhesions responds to force, revealing the molecular mechanisms that allow the adhesion complex to respond to force to stabilize cell–ECM adhesion over development.

Cell adhesion to the extracellular matrix (ECM) allows cells to form and maintain three-dimensional tissue architecture. Cell–ECM adhesions are stabilized upon exposure to mechanical force. In this study, we used quantitative imaging and mathematical modeling to gain mechanistic insight into how integrin-based adhesions respond to increased and decreased mechanical forces. A critical means of regulating integrin-based adhesion is provided by modulating the turnover of integrin and its adhesion complex (integrin adhesion complex [IAC]). The turnover of the IAC component Talin, a known mechanosensor, was analyzed using fluorescence recovery after photobleaching. Experiments were carried out in live, intact flies in genetic backgrounds that increased or decreased the force applied on sites of adhesion. This analysis showed that when force is elevated, the rate of assembly of new adhesions increases such that cell–ECM adhesion is stabilized. Moreover, under conditions of decreased force, the overall rate of turnover, but not the proportion of adhesion complex components undergoing turnover, increases. Using point mutations, we identify the key functional domains of Talin that mediate its response to force. Finally, by fitting a mathematical model to the data, we uncover the mechanisms that mediate the stabilization of ECM-based adhesion during development.

Toll-like receptor ligands sensitize B-cell receptor signalling by reducing actin-dependent spatial confinement of the receptor

Integrating signals from multiple receptors allows cells to interpret the physiological context in which a signal is received. Here we describe a mechanism for receptor crosstalk in which receptor-induced increases in actin dynamics lower the threshold for signalling by another receptor. We show that the Toll-like receptor ligands lipopolysaccharide and CpG DNA, which are conserved microbial molecules, enhance signalling by the B-cell antigen receptor (BCR) by activating the actin-severing protein cofilin. Single-particle tracking reveals that increased severing of actin filaments reduces the spatial confinement of the BCR within the plasma membrane and increases BCR mobility. This allows more frequent collisions between BCRs and greater signalling in response to low densities of membrane-bound antigen. These findings implicate actin dynamics as a means of tuning receptor signalling and as a mechanism by which B cells distinguish inert antigens from those that are accompanied by indicators of microbial infection.

Microbial pathogens can activate both innate and adaptive receptors, and integration of these signals may enhance the sensitivity of the immune response. Freeman et al. show that innate microbial cues sensitize B cells to antigen by increasing actin dynamics and reducing the actin-dependent confinement of the B-cell receptor.

Probability of a false-negative HIV antibody test result during the window period: a tool for pre- and post-test counselling

Failure to understand the risk of false-negative HIV test results during the window period results in anxiety. Patients typically want accurate test results as soon as possible while clinicians prefer to wait until the probability of a false-negative is virtually nil. This review summarizes the median window periods for third-generation antibody and fourth-generation HIV tests and provides the probability of a false-negative result for various days post-exposure. Data were extracted from published seroconversion panels. A 10-day eclipse period was used to estimate days from infection to first detection of HIV RNA. Median (interquartile range) days to seroconversion were calculated and probabilities of a false-negative result at various time periods post-exposure are reported. The median (interquartile range) window period for third-generation tests was 22 days (19-25) and 18 days (16-24) for fourth-generation tests. The probability of a false-negative result is 0.01 at 80 days' post-exposure for third-generation tests and at 42 days for fourth-generation tests. The table of probabilities of falsely-negative HIV test results may be useful during pre- and post-test HIV counselling to inform co-decision making regarding the ideal time to test for HIV.

Assessing the optimal virulence of malaria-targeting mosquito pathogens: a mathematical study of engineered Metarhizium anisopliae

Background

Metarhizium anisopliae is a naturally occurring fungal pathogen of mosquitoes. Recently, Metarhizium has been engineered to act against malaria by directly killing the disease agent within mosquito vectors and also effectively blocking onward transmission. It has been proposed that efforts should be made to minimize the virulence of the fungal pathogen, in order to slow the development of resistant mosquitoes following an actual deployment.

Results

Two mathematical models were developed and analysed to examine the efficacy of the fungal pathogen. It was found that, in many plausible scenarios, the best effects are achieved with a reduced or minimal pathogen virulence, even if the likelihood of resistance to the fungus is negligible. The results for both models depend on the interplay between two main effects: the ability of the fungus to reduce the mosquito population, and the ability of fungus‐infected mosquitoes to compete for resources with non‐fungus‐infected mosquitoes.

Conclusions

The results indicate that there is no obvious choice of virulence for engineered Metarhizium or similar pathogens, and that all available information regarding the population ecology of the combined mosquito‐fungus system should be carefully considered. The models provide a basic framework for examination of anti‐malarial mosquito pathogens that should be extended and improved as new laboratory and field data become available.

Mechanical modulation of receptor-ligand interactions at cell-cell interfaces

Cell surface receptors have been extensively studied because they initiate and regulate signal transduction cascades leading to a variety of functional cellular outcomes. An important class of immune receptors (e.g., T-cell antigen receptors) whose ligands are anchored to the surfaces of other cells remain poorly understood. The mechanism by which ligand binding initiates receptor phosphorylation, a process termed "receptor triggering", remains controversial. Recently, direct measurements of the (two-dimensional) receptor-ligand complex lifetimes at cell-cell interface were found to be smaller than (three-dimensional) lifetimes in solution but the underlying mechanism is unknown. At the cell-cell interface, the receptor-ligand complex spans a short intermembrane distance (15 nm) compared to long surface molecules (LSMs) whose ectodomains span >40 nm and these LSMs include phosphatases (e.g., CD45) that dephosphorylate the receptor. It has been proposed that size-based segregation of LSMs from a receptor-ligand complex is a mechanism of receptor triggering but it is unclear whether the mechanochemistry supports such small-scale segregation. Here we present a nanometer-scale mathematical model that couples membrane elasticity with the compressional stiffness and lateral mobility of LSMs. We find robust supradiffusive segregation of LSMs from a single receptor-ligand complex. The model predicts that LSM redistribution will result in a time-dependent tension on the complex leading to a decreased two-dimensional lifetime. Interestingly, the model predicts a nonlinear relationship between the three- and two-dimensional lifetimes, which can enhance the ability of receptors to discriminate between similar ligands.

Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything

BACKGROUND

Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.

METHODS

We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.

RESULTS

The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.

CONCLUSION

Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.

Reverse engineering an amyloid aggregation pathway with dimensional analysis and scaling

Human islet amyloid polypeptide (hIAPP) is a cytotoxic protein that aggregates into oligomers and fibrils that kill pancreatic β-cells. Here we analyze hIAPP aggregation in vitro, measured via thioflavin-T fluorescence. We use mass-action kinetics and scaling analysis to reconstruct the aggregation pathway, and find that the initiation step requires four hIAPP monomers. After this step, monomers join the nucleus in pairs, until the first stable nucleus (of size approximately 20 monomers) is formed. This nucleus then elongates by successive addition of single monomers. We find that the best-fit of our data is achieved when we include a secondary fibril-dependent nucleation pathway in the reaction scheme. We predict how interventions that change rates of fibril elongation or nucleation rates affect the accumulation of potentially cytotoxic oligomer species. Our results demonstrate the power of scaling analysis in reverse engineering biochemical aggregation pathways.

Antigen potency and maximal efficacy reveal a mechanism of efficient T cell activation

T cell activation, a critical event in adaptive immune responses, depends on productive interactions between T cell receptors (TCRs) and antigens presented as peptide-bound major histocompatibility complexes (pMHCs). Activated T cells lyse infected cells, secrete cytokines, and perform other effector functions with various efficiencies, which depend on the binding parameters of the TCR-pMHC complex. The mechanism through which binding parameters are translated to the efficiency of T cell activation, however, remains controversial. The "affinity model" suggests that the dissociation constant (KD) of the TCR-pMHC complex determines the response, whereas the "productive hit rate model" suggests that the off-rate (koff) is critical. Here, we used mathematical modeling to show that antigen potency, as determined by the EC50 (half-maximal effective concentration), which is used to support KD-based models, could not discriminate between the affinity and the productive hit rate models. Both models predicted a correlation between EC50 and KD, but only the productive hit rate model predicted a correlation between maximal efficacy (Emax), the maximal T cell response induced by pMHC, and koff. We confirmed the predictions made by the productive hit rate model in experiments with cytotoxic T cell clones and a panel of pMHC variants. Thus, we propose that the activity of an antigen is determined by both its potency (EC50) and maximal efficacy (Emax).

A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies

A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density, nonspecific adhesion forces, and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data, although discrepancies remain. We also quantitatively describe the parameter space in which binding occurs. Our model elaborates substantially on previous work, and our results offer guidance for the refinement of therapeutic immunoadhesins. Furthermore, our comparison with data from Jurkat T cells also points toward mechanisms relating epitope immobility to cell adhesion.

A stochastic model of latently infected cell reactivation and viral blip generation in treated HIV patients

Motivated by viral persistence in HIV+ patients on long-term anti-retroviral treatment (ART), we present a stochastic model of HIV viral dynamics in the blood stream. We consider the hypothesis that the residual viremia in patients on ART can be explained principally by the activation of cells latently infected by HIV before the initiation of ART and that viral blips (clinically-observed short periods of detectable viral load) represent large deviations from the mean. We model the system as a continuous-time, multi-type branching process. Deriving equations for the probability generating function we use a novel numerical approach to extract the probability distributions for latent reservoir sizes and viral loads. We find that latent reservoir extinction-time distributions underscore the importance of considering reservoir dynamics beyond simply the half-life. We calculate blip amplitudes and frequencies by computing complete viral load probability distributions, and study the duration of viral blips via direct numerical simulation. We find that our model qualitatively reproduces short small-amplitude blips detected in clinical studies of treated HIV infection. Stochastic models of this type provide insight into treatment-outcome variability that cannot be found from deterministic models.

The space and time frames of T cell activation at the immunological synapse

The selective recognition of antigenic peptides by T cells requires the spatio/temporal integration of a panoply of molecular triggers. The space frame of T cell antigen receptors (TCR) interaction with peptide/MHC complexes (pMHC) displayed by antigen presenting cells is delineated by the micrometer-scale area of the immunological synapse. The time frame of T cell stimulation is governed by a series of short TCR-pMHC interactions that are integrated into sustained signaling leading to productive activation. We discuss here how approaching antigen recognition from the time and space angles is key to the comprehension of the puzzling process of T cell activation.

Dynamic regulation of CD45 lateral mobility by the spectrin-ankyrin cytoskeleton of T cells

The leukocyte common antigen, CD45, is a critical immune regulator whose activity is modulated by cytoskeletal interactions. Components of the spectrin-ankyrin cytoskeleton have been implicated in the trafficking and signaling of CD45. We have examined the lateral mobility of CD45 in resting and activated T lymphocytes using single-particle tracking and found that the receptor has decreased mobility caused by increased cytoskeletal contacts in activated cells. Experiments with cells that have disrupted betaI spectrin interactions show decreased cytoskeletal contacts in resting cells and attenuation of receptor immobilization in activated cells. Applying two types of population analyses to single-particle tracking trajectories, we find good agreement between the diffusion coefficients obtained using either a mean squared displacement analysis or a hidden Markov model analysis. Hidden Markov model analysis also reveals the rate of association and dissociation of CD45-cytoskeleton contacts, demonstrating the importance of this analysis for measuring cytoskeleton binding events in live cells. Our findings are consistent with a model in which multiple cytoskeletal contacts, including those with spectrin and ankyrin, participate in the regulation of CD45 lateral mobility. These interactions are a major factor in CD45 immobilization in activated cells. Furthermore, cellular activation leads to CD45 immobilization by reduction of the CD45-cytoskeleton dissociation rate. Short peptides that mimic spectrin repeat domains alter the association rate of CD45 to the cytoskeleton and cause an apparent decrease in dissociation rates. We propose a model for CD45-cytoskeleton interactions and conclude that the spectrin-ankyrin-actin network is an essential determinant of immunoreceptor mobility.

Dependence of T cell antigen recognition on T cell receptor-peptide MHC confinement time

T cell receptor (TCR) binding to diverse peptide-major histocompatibility complex (pMHC) ligands results in various degrees of T cell activation. Here we analyze which binding properties of the TCR-pMHC interaction are responsible for this variation in pMHC activation potency. We have analyzed activation of the 1G4 cytotoxic T lymphocyte clone by cognate pMHC variants and performed thorough correlation analysis of T cell activation with 1G4 TCR-pMHC binding properties measured in solution. We found that both the on rate (k_on) and off rate (k_off) contribute to activation potency. Based on our results, we propose a model in which rapid TCR rebinding to the same pMHC after chemical dissociation increases the effective half-life or “confinement time” of a TCR-pMHC interaction. This confinement time model clarifies the role of k_on in T cell activation and reconciles apparently contradictory reports on the role of TCR-pMHC binding kinetics and affinity in T cell activation.

A role for rebinding in rapid and reliable T cell responses to antigen

Experimental work has shown that T cells of the immune system rapidly and specifically respond to antigenic molecules presented on the surface of antigen-presenting-cells and are able to discriminate between potential stimuli based on the kinetic parameters of the T cell receptor-antigen bond. These antigenic molecules are presented among thousands of chemically similar endogenous peptides, raising the question of how T cells can reliably make a decision to respond to certain antigens but not others within minutes of encountering an antigen presenting cell. In this theoretical study, we investigate the role of localized rebinding between a T cell receptor and an antigen. We show that by allowing the signaling state of individual receptors to persist during brief unbinding events, T cells are able to discriminate antigens based on both their unbinding and rebinding rates. We demonstrate that T cell receptor coreceptors, but not receptor clustering, are important in promoting localized rebinding, and show that requiring rebinding for productive signaling reduces signals from a high concentration of endogenous pMHC. In developing our main results, we use a relatively simple model based on kinetic proofreading. However, we additionally show that all our results are recapitulated when we use a detailed T cell receptor signaling model. We discuss our results in the context of existing models and recent experimental work and propose new experiments to test our findings.

A hidden Markov model for single particle tracks quantifies dynamic interactions between LFA-1 and the actin cytoskeleton

The extraction of hidden information from complex trajectories is a continuing problem in single-particle and single-molecule experiments. Particle trajectories are the result of multiple phenomena, and new methods for revealing changes in molecular processes are needed. We have developed a practical technique that is capable of identifying multiple states of diffusion within experimental trajectories. We model single particle tracks for a membrane-associated protein interacting with a homogeneously distributed binding partner and show that, with certain simplifying assumptions, particle trajectories can be regarded as the outcome of a two-state hidden Markov model. Using simulated trajectories, we demonstrate that this model can be used to identify the key biophysical parameters for such a system, namely the diffusion coefficients of the underlying states, and the rates of transition between them. We use a stochastic optimization scheme to compute maximum likelihood estimates of these parameters. We have applied this analysis to single-particle trajectories of the integrin receptor lymphocyte function-associated antigen-1 (LFA-1) on live T cells. Our analysis reveals that the diffusion of LFA-1 is indeed approximately two-state, and is characterized by large changes in cytoskeletal interactions upon cellular activation.

Calculations show that T cell antigen discrimination requires multiple localized binding events (35.3)

T cells of the adaptive immune system rapidly scan the surface of antigen-presenting-cells (APC) for foreign material, in the form of peptide-MHC, using their T cell receptors (TCR). T cell activation via TCR-pMHC binding is extremely sensitive (a single presented pMHC can elicit a transient response), specific (a single amino acid substitution on a presented peptide can dramatically alter the response), and the decision to respond (e.g. by forming an immune synapse) occurs rapidly. Mathematical modeling based on statistical distributions reveals that pMHC must be repeatedly sampled (by binding TCR) in order for the T cell to make the decision to respond (or not) reliably. We find that TCR clustering and coreceptors act to immobilize pMHC which ensures that multiple binding events arise from a given pMHC. Functional assays of 1G4 T cells binding to a panel of 17 altered-peptide-ligands (NY-ESO) are used to support predictions from the model. We propose that sensitive, specific, and rapid antigen discrimination that is reliable can take place between a single TCR/pMHC.

A novel analysis of single particle trajectories quantifies the dynamic interactions between LFA-1 and the actin cytoskeleton (94.13)

Dynamics of cell-surface proteins are strongly influenced by their localized interactions with cytoskeletal attachments or intracellular signaling molecules. We developed a technique to analyze single particle trajectories to quantify these interactions. We modeled trajectories of a cell membrane-associated protein interacting with a homogeneously distributed binding partner, and applied this model to analyze the motion of the integrin receptor LFA-1 on the surface of T cells. Our analysis reveals that the diffusion of LFA-1 is two-state, with distinct diffusion coefficients in its free and cytoskeleton-bound forms. We also quantify the rates of transition between the two states, and we show that cellular activation leads to large changes in the LFA-1-cytoskeleton interactions.

Kinetic proofreading model

Kinetic proofreading is an intrinsic property of the cell signaling process. It arises as a consequence of the multiple interactions that occur after a ligand triggers a receptor to initiate a ignaling cascade and it ensures that false signals do not propagate to completion. In order for an active signaling complex to form after a ligand binds to a cell surface receptor, a sequence of binding and phosphorylation events must occur that are rapidly reversed if the ligand dissociates from the receptor. This gives rise to a mechanism by which cells can discriminate among ligands that bind to the same receptor but form ligand-receptor complexes with different lifetimes. We review experiments designed to test for kinetic proofreading and models that exhibit kinetic proofreading.

Effects of intracellular calcium and actin cytoskeleton on TCR mobility measured by fluorescence recovery

BACKGROUND

The activation of T lymphocytes by specific antigen is accompanied by the formation of a specialized signaling region termed the immunological synapse, characterized by the clustering and segregation of surface molecules and, in particular, by T cell receptor (TCR) clustering.

METHODOLOGY/PRINCIPAL FINDINGS

To better understand TCR motion during cellular activation, we used confocal microscopy and photo-bleaching recovery techniques to investigate the lateral mobility of TCR on the surface of human T lymphocytes under various pharmacological treatments. Using drugs that cause an increase in intracellular calcium, we observed a decrease in TCR mobility that was dependent on a functional actin cytoskeleton. In parallel experiments measurement of filamentous actin by FACS analysis showed that raising intracellular calcium also causes increased polymerization of the actin cytoskeleton. These in vitro results were analyzed using a mathematical model that revealed effective binding parameters between TCR and the actin cytoskeleton.

CONCLUSION/SIGNIFICANCE

We propose, based on our results, that increase in intracellular calcium levels leads to actin polymerization and increases TCR/cytoskeleton interactions that reduce the overall mobility of the TCR. In a physiological setting, this may contribute to TCR re-positioning at the immunological synapse.