Abstract Introduction Influenza infections differ greatly among individuals, with controlled human infection studies showing wide variation in viral load, immune activation, and symptom profiles. However, the biological and quantitative mechanisms underlying these differences remain unclear. Understanding how host and viral factors interact to drive this heterogeneity could provide insights relevant to both natural and experimental infections. Methods We used a data-driven, mechanistic modeling framework that integrates viral replication dynamics with immune responses in participants infected with influenza. The approach used digital twin and dimensionality reduction techniques to identify and simulate patterns of infection and immune control across individuals. Results The analysis identified distinct infection clusters arising from multivariate influences, and quantified the contribution of variability in inoculum size, virus infectivity, and baseline immunity. Important tradeoffs, such as between cell efficacy and expansion, were revealed, and host—pathogen interactions remained relatively consistent between primary infection and reinfection scenarios. Additional analyses of symptom data illustrated the model’s predictive value and revealed potential subjectivity that was independent of viral strain. Conclusion These findings illustrate the importance of mechanistic modeling in disentangling the complex determinants of influenza infection outcomes and suggest that individual-level variability may lead to shared patterns of disease resolution. Funding Source NIH NIAID Topic Categories Computational and Systems Immunology (COMP)

Choice of immunoassay influences population seroprevalence estimates. Post hoc adjustments for assay performance could improve comparability of estimates across studies and enable pooled analyses. We assessed post hoc adjustment methods using data from 2021 to 2023 SARS-CoV-2 serosurveillance studies in Alberta, Canada: one that tested 124 008 blood donations using Roche immunoassays (SARS-CoV-2 nucleocapsid total antibody and anti-SARS-CoV-2 S) and another that tested 214 780 patient samples using Abbott immunoassays (SARS-CoV-2 IgG and anti-SARS-CoV-2 S). Comparing datasets, seropositivity for antibodies against nucleocapsid (anti-N) diverged after May 2022 due to differential loss of sensitivity as a function of time since infection. The commonly used Rogan-Gladen adjustment did not reduce this divergence. Regression-based adjustments using the assays' semiquantitative results produced more similar estimates of anti-N seroprevalence and rolling incidence proportion (proportion of individuals infected in recent months). Seropositivity for antibodies targeting SARS-CoV-2 spike protein was similar without adjustment, and concordance was not improved when applying an alternative, functional threshold. These findings suggest that assay performance substantially impacted population inferences from SARS-CoV-2 serosurveillance studies in the Omicron period. Unlike methods that ignore time-varying assay sensitivity, regression-based methods using the semiquantitative assay resulted in increased concordance in estimated anti-N seropositivity and rolling incidence between cohorts using different assays.

BACKGROUND:Respiratory syncytial virus (RSV) is a major cause of severe respiratory infections in infants, especially under 5 years of age. Several studies have reported that interactions between RSV and bacteria like Streptococcus pneumoniae (SP), Moraxella catarrhalis (MC), Haemophilus influenzae (HI) and Staphylococcus aureus (SA) may influence their pathogenicity and the clinical outcome of infection. However, existing knowledge about RSV-bacterial interactions in infants comes primarily from cross-sectional studies, which cannot evaluate the influence of infection sequence on these interactions. METHODS:We conducted a nested prospective study of the Zambian mother-infant birth cohort, followed for the first 14 weeks of life. Nasopharyngeal samples were collected from both infants and mothers biweekly. We analyzed the sequence of RSV and bacterial detection, and its correlation with RSV and bacterial density, and with severity of symptoms. RESULTS:SP acquisition in infants following RSV infection is associated with lower bacterial densities compared to SP alone. These effects were not observed with Moraxella catarrhalis , Staphylococcus aureus or Haemophilus influenzae , or in the mothers. Additionally, prior RSV did not result in earlier bacterial acquisition. Our data suggests that prior bacterial infection with any of the species did not significantly affect RSV viral loads. Finally, antibiotic exposure in infants did not reduce bacterial density overall, but infants with SP-only infections who received antibiotics showed increased bacterial levels. CONCLUSION:The observed effect of prior RSV on SP density, which was not seen in other bacteria, suggests a specific interaction between RSV and SP that may influence bacterial colonization dynamics differently than other bacterial species, highlighting the need for further investigation into these mechanisms.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in millions of deaths globally. Adults with immunosuppression (e.g., solid organ transplant recipients) and those undergoing active cancer treatments experience worse infections and more severe COVID-19. It is difficult to conduct clinical studies in these populations, resulting in a restricted amount of data that can be used to relate mechanisms of immune dysfunction to COVID-19 outcomes in these vulnerable groups. To study immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in individuals with cancer and immunosuppression, we adapted our mathematical model of the immune response during COVID-19 and generated virtual patient cohorts of cancer and immunosuppressed patients. The cohorts of plausible patients recapitulated available longitudinal clinical data collected from patients in Montréal, Canada area hospitals. Our model predicted that both cancer and immunosuppressed virtual patients with severe COVID-19 had decreased CD8+ T cells, elevated interleukin-6 concentrations, and delayed type I interferon peaks compared to those with mild COVID-19 outcomes. Additionally, our results suggest that cancer patients experience higher viral loads (however, with no direct relation with severity), likely because of decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side effect of anti-cancer therapy. Furthermore, severe cancer and immunosuppressed virtual patients suffered a high degree of tissue damage associated with elevated neutrophils. Lastly, parameter values associated with monocyte recruitment by infected cells were found to be elevated in severe cancer and immunosuppressed patients with respect to the COVID-19 reference group. Together, our study highlights that dysfunction in type I interferon and CD8+ T cells are key drivers of immune dysregulation in COVID-19, particularly in cancer patients and immunosuppressed individuals.

Host immune responses play a pivotal role in defending against influenza viruses. The activation of various immune components, such as interferon, macrophages, and CD8+ T cells, works to limit viral spread while maintaining lung integrity. Recent mathematical modeling studies have investigated these responses, describing their regulation, efficacy, and movement within the lung. Here, we discuss these studies and their emphasis on identifying nonlinearities and multifaceted roles of different cell phenotypes that could be responsible for spatially heterogeneous infection patterns.

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific—and practical–medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

IntroductionInfluenza virus infections are a major global health problem. Influenza can result in mild/moderate disease or progress to more severe disease, leading to high morbidity and mortality. Severity is thought to be primarily driven by immunopathology, but predicting which individuals are at a higher risk of being hospitalized warrants investigation into host genetics and the molecular signatures of the host response during influenza infections.MethodsHere, we performed transcriptome and genotype analysis in healthy controls and patients exhibiting mild/moderate or severe influenza (ICU patients). A unique aspect of our study was the genotyping of all participants, which allowed us to assign ethnicities based on genetic variation and assess whether the variation was correlated with expression levels. ResultsWe identified 169 differentially expressed genes and related molecular pathways between patients in the ICU and those who were not in the ICU. The transcriptome/genotype association analysis identified 871 genes associated to a genetic variant and 39 genes distinct between African-Americans and Caucasians. We also investigated the effects of age and sex and found only a few discernible gene effects in our cohort. DiscussionTogether, our results highlight select risk factors that may contribute to an increased risk of ICU admission for influenza-infected patients. This should help to develop better diagnostic tools based on molecular signatures, in addition to a better understanding of the biological processes in the host response to influenza.

Custom-built mathematical models make the immune response more predictable and offer mechanistic insights into fundamental immunology.

BACKGROUND: Breathlessness is a disabling symptom, with complexity that is often under-recognized and undertreated in asthma. OBJECTIVE: To highlight the burden of breathlessness in people with severe compared with mild-to-moderate asthma and identify psychophysiological correlates of breathlessness. METHODS: This was a cross-sectional study of people with mild-to-severe asthma, who attended 2 in-person visits to complete a multidimensional assessment. The proportion of people with mild-to-moderate versus severe asthma who reported physically limiting breathlessness (modified Medical Research Council [mMRC] dyspnea score >= 2) was compared. Psychophysiological factors associated with breathlessness in people with asthma were identified via a directed acyclic graph and explored with multivariate logistic regression to predict breathlessness. RESULTS: A total of 144 participants were included, of whom, 74 (51%) had mild-to-moderate asthma and 70 (49%) severe asthma. Participants were predominantly female (n[ 103, 72%) with a median (quartile 1, quartile 3) age of 63.4 (50.5, 69.5) years and body mass index (BMI) of 31.3 (26.2, 36.0) kg/m(2). The proportion of people reporting mMRC >= 2 was significantly higher in those with severe- (n = 37, 53%) than those with mild-to-moderate (n = 21, 31%) asthma (P = .013). Dyspnoea-12 Total (8.00 = 4.75, 17.00] vs 5.00 [2.00, 11.00], P = .037) score was also significantly higher in the severe asthma group. Significant predictors of physically limiting breathlessness were BMI, asthma control, exercise capacity, and hyperventilation symptoms. Airflow limitation and type 2 inflammation were poor breathlessness predictors. CONCLUSIONS: Over half of people with severe asthma experience physically limiting breathlessness despite treatment. Targeting psychophysiological factors, or traits, associated with breathlessness may help relieve this distressing symptom, which is of high priority to people with asthma. (C) 2024 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

[This corrects the article DOI: 10.1371/journal.pcbi.1009480.].

The endpoint dilution assay’s output, the 50% infectious dose (ID50), is calculated using the Reed-Muench or Spearman-Kärber mathematical approximations, which are biased and often miscalculated. We introduce a replacement for the ID50 that we call Specific INfection (SIN) along with a free and open-source web-application, midSIN (https://midsin.physics.ryerson.ca) to calculate it. midSIN computes a virus sample’s SIN concentration using Bayesian inference based on the results of a standard endpoint dilution assay, and requires no changes to current experimental protocols. We analyzed influenza and respiratory syncytial virus samples using midSIN and demonstrated that the SIN/mL reliably corresponds to the number of infections a sample will cause per mL. It can therefore be used directly to achieve a desired multiplicity of infection, similarly to how plaque or focus forming units (PFU, FFU) are used. midSIN’s estimates are shown to be more accurate and robust than the Reed-Muench and Spearman-Kärber approximations. The impact of endpoint dilution plate design choices (dilution factor, replicates per dilution) on measurement accuracy is also explored. The simplicity of SIN as a measure and the greater accuracy provided by midSIN make them an easy and superior replacement for the TCID50 and other in vitro culture ID50 measures. We hope to see their universal adoption to measure the infectivity of virus samples.

The emergence and availability of closely related clinical isolates of SARS-CoV-2 offers a unique opportunity to identify novel nonsynonymous mutations that may impact phenotype. Global sequencing efforts show that SARS-CoV-2 variants have emerged and then been replaced since the beginning of the pandemic, yet we have limited information regarding the breadth of variant-specific host responses. Using primary cell cultures and the K18-hACE2 mouse, we investigated the replication, innate immune response, and pathology of closely related, clinical variants circulating during the first wave of the pandemic. Mathematical modeling of the lung viral replication of four clinical isolates showed a dichotomy between two B.1. isolates with significantly faster and slower infected cell clearance rates, respectively. While isolates induced several common immune host responses to infection, one B.1 isolate was unique in the promotion of eosinophil-associated proteins IL-5 and CCL11. Moreover, its mortality rate was significantly slower. Lung microscopic histopathology suggested further phenotypic divergence among the five isolates showing three distinct sets of phenotypes: (i) consolidation, alveolar hemorrhage, and inflammation, (ii) interstitial inflammation/septal thickening and peribronchiolar/perivascular lymphoid cells, and (iii) consolidation, alveolar involvement, and endothelial hypertrophy/margination. Together these findings show divergence in the phenotypic outcomes of these clinical isolates and reveal the potential importance of nonsynonymous mutations in nsp2 and ORF8.

Respiratory viral infections are a leading global cause of disease with multiple viruses detected in 20–30% of cases, and several viruses simultaneously circulating. Some infections with unique viral copathogens result in reduced pathogenicity, while other viral pairings can worsen disease. The mechanisms driving these dichotomous outcomes are likely variable and have only begun to be examined in the laboratory and clinic. To better understand viral–viral coinfections and predict potential mechanisms that result in distinct disease outcomes, we first systematically fit mathematical models to viral load data from ferrets infected with respiratory syncytial virus (RSV), followed by influenza A virus (IAV) after 3 days. The results suggest that IAV reduced the rate of RSV production, while RSV reduced the rate of IAV infected cell clearance. We then explored the realm of possible dynamics for scenarios that had not been examined experimentally, including a different infection order, coinfection timing, interaction mechanisms, and viral pairings. IAV coinfection with rhinovirus (RV) or SARS-CoV-2 (CoV2) was examined by using human viral load data from single infections together with murine weight-loss data from IAV-RV, RV-IAV, and IAV-CoV2 coinfections to guide the interpretation of the model results. Similar to the results with RSV-IAV coinfection, this analysis shows that the increased disease severity observed during murine IAV-RV or IAV-CoV2 coinfection was likely due to the slower clearance of IAV-infected cells by the other viruses. The improved outcome when IAV followed RV, on the other hand, could be replicated when the rate of RV infected cell clearance was reduced by IAV. Simulating viral–viral coinfections in this way provides new insights about how viral–viral interactions can regulate disease severity during coinfection and yields testable hypotheses ripe for experimental evaluation.

BackgroundSJ733, a newly developed inhibitor of P. falciparum ATP4, has a favorable safety profile and rapid antiparasitic effect but insufficient duration to deliver a single-dose cure of malaria. We investigated the safety, tolerability, and pharmacokinetics of a multidose SJ733 regimen and a single-dose pharmacoboost approach using cobicistat to inhibit CYP3A4, thereby increasing exposure.MethodsTwo multidose unboosted cohorts (n = 9) (SJ733, 300 mg and 600 mg daily for 3 days) followed by three single-dose boosted cohorts combining SJ733 (n = 18) (75-, 300-, or 600-mg single dose) with cobicistat (150-mg single dose) as a pharmacokinetic booster were evaluated in healthy volunteers (ClinicalTrials.gov: NCT02661373).FindingsAll participants tolerated SJ733 well, with no serious adverse events (AEs), dose-limiting toxicity, or clinically significant electrocardiogram or laboratory test findings. All reported AEs were Grade 1, clinically insignificant, and considered unlikely or unrelated to SJ733. Compared to unboosted cohorts, the SJ733/cobicistat-boosted cohorts showed a median increase in area under the curve and maximum concentration of 3·9 × and 2·6 ×, respectively, and a median decrease in the ratio of the major CYP3A-produced metabolite SJ506 to parent drug of 4·6 × . Incorporating these data in a model of parasite dynamics indicated that a 3-day regimen of SJ733/cobicistat (600 mg/150 mg daily) relative to a single 600-mg dose ± cobicistat would increase parasite clearance from 106 to 1012 parasites/µL.InterpretationThe multidose and pharmacoboosted approaches to delivering SJ733 were well-tolerated and significantly increased drug exposure and prediction of cure. This study supports the further development of SJ733 and demonstrates an innovative pharmacoboost approach for an antimalarial.FundingGlobal Health Innovative Technology Fund, Medicines for Malaria Venture, National Institutes of Health, and American Lebanese Syrian Associated Charities.

BACKGROUND:Low-dose long-term azithromycin is recommended in clinical practice guidelines for obstructive airway diseases (OAD); however, an optimal therapeutic regimen is not yet established.AIM:To understand the patterns of azithromycin use in OAD, characterise the patients who received it and evaluate its safety and efficacy using real-world data.METHODS:We audited 91 patients who had received azithromycin for at least 4 weeks for the management of asthma, chronic obstructive pulmonary disease (COPD) or non-cystic fibrosis bronchiectasis.RESULTS:The mean age was 65 ± 18 years, 60% were female and 48% were ex-smokers. The majority had asthma (75%), either alone (50%) or in combination with COPD (12%) or bronchiectasis (13%). Most (64%) reported cough or sputum at baseline. The most common treatment regimen was azithromycin 250 mg daily (73%) for more than 1 year (57%), with only seven adverse events. There was a significant reduction in the proportions of patients requiring emergency department visits (48% vs 32%; P < 0.001) and hospital admissions (35% vs 31%; P < 0.001) after starting azithromycin. In 88% of cases, physicians favoured the use of azithromycin.CONCLUSION:Physicians are currently using low-dose azithromycin for a long duration of more than 1 year for the management of OAD. The typical case definition is an older non-smoking adult with persistent asthma, often in combination with another OAD and presenting with bothersome cough or sputum. Azithromycin was well tolerated and led to reduced healthcare utilisation. Further research is required to establish an optimal dosage regimen of azithromycin in OAD.

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate infection with the SARS-CoV-2 USA-WA1/2020 strain increased the risk of pneumococcal (type 2 strain D39) coinfection in a time-dependent, but sex-independent, manner in the transgenic K18-hACE2 mouse model of COVID-19. Bacterial coinfection increased lethality when the bacteria was initiated at 5 or 7 d post-virus infection (pvi) but not at 3 d pvi. Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.