From Trait-Structured Within-Host Dynamics to SIR Models: A Multiscale Framework With Re-Exposure

Gastrointestinal (GI) parasites cause significant production losses in grazing ruminants which can be mitigated by breeding animals resistant to disease. Lymphocyte cytokine production and parasite-specific Immunoglobulin A (IgA) are adaptive immune traits associated with immunity to GI parasites. To explore the utility of these traits for selective breeding purposes, this study estimated the genetic parameters of the immune traits in sheep and assessed their relationship with disease and productivity traits. Whole blood stimulation assays were performed on 1 040 Scottish Blackface lambs at two months of age in 2016–2017. Blood was stimulated with either pokeweed mitogen (PWM), a non-specific activator of lymphocytes, and Teladorsagia circumcincta (T-ci) larval antigen to activate parasite-specific T lymphocytes. The type of adaptive immune response was determined by quantifying production of cytokines interferon-gamma (IFN-γ), interleukin (IL)-4, and IL-10, which relate to T-helper type (Th) 1, Th2 and regulatory T cell responses, respectively. Serum T-ci specific IgA was also quantified. Heritabilities were estimated for each immune trait by univariate analyses. Genetic and phenotypic correlations were estimated between different immune traits, and between immune traits vs. disease and productivity traits that were recorded at three months of age. Disease phenotypes were expressed as faecal egg counts (FEC) of nematode parasites (Strongyles and Nematodirus), faecal oocyst counts (FOC) of coccidian parasites, and faecal soiling score; production was measured as lamb live weight. Significant genetic variation was observed in all immune response traits. Heritabilities of cytokine production varied from low (0.14 ± 0.06) to very high (0.77 ± 0.09) and were always significantly greater than zero (P < 0.05). IgA heritability was found to be moderate (0.41 ± 0.09). Negative associations previously identified between IFN-γ production and FOC, and IL-4 production and strongyle FEC, were not evident in this study, potentially due to the time-lag between immune and parasitology measures. Instead, a positive genetic correlation was found between FOC and PWM-induced IFN-γ production, while a negative genetic correlation was found between FOC and T-ci induced IL-10. Live weight was negatively genetically correlated with IFN-γ responses. Overall, IFN-γ and IL-4 responses were positively correlated, providing little evidence of cross-regulation of Th1 and Th2 immunity within individual sheep. Furthermore, T-ci specific IgA was highly positively correlated with PWM-induced IL-10, indicating a possible role for this cytokine in IgA production. Our results suggest that while genetic selection for adaptive immune response traits is possible and may be beneficial for parasite control, selection of high IFN-γ responsiveness may negatively affect productivity.

Host age alters disease life history. A case study in zooplankton and a castrating pathogen

Global analysis of an environmental disease transmission model linking within-host and between-host dynamics

This paper seeks to critically review the occurrence and variety of waterborne pathogens in aquaculture systems and examine their effects on the immunological status of commercially significant aquatic-based organisms through secondary source material. A systematic secondary data analysis was carried out in 86 publications, released in 2020-25, and found in databases (PubMed, Scopus, and Web of Science). The data entailed over 52,000 observations of fish in different geographical locations. The relationships between the pathogen load and immune response were determined using standardized methods to estimate the prevalence of pathogens, Immunological Response Index (IRI), correlation, regression, and meta-analysis. The overall prevalence of the pathogens was 41.3% (12.4% to 68.7%). The most common were bacterial infections (52%), parasitic (31%) infections, and viral (17%) infections. The prevalence was higher in the region of Asia (48.6%) and Africa (44.1%) than in Europe (32.8%) and the Americas (36.5%). The average IRI was 1.12, which represented an average immune response. A strong positive correlation (r = 0.68, p &lt; 0.01) and regression model (R² = 0.46) confirmed that pathogen prevalence significantly influences immune response. The effect size (Cohen’s d = 0.74) was significant due to meta-analysis. The study observes that the abundance of waterborne pathogens has a tremendous effect on the immune health of fish and is influenced by the environment and management. These results highlight the importance of better disease surveillance, early diagnostic biomarkers, and sustainable aquaculture.

Cholera, caused by the pathogenic Vibrio cholerae bacteria, remains a severe public health threat. Although a lot of emphasis has been placed on the population-level spread of the disease, the infection itself starts within the body. As such, we formulated a multi-scale model that explicitly connects the within-host and between-host dynamics of the disease. To model the within-host dynamics, we assigned each susceptible individual with a pathogen load that increases through the uptake of contaminated food and water (booster event). We introduced minimal and maximal times when the booster events happen and defined a time since the last booster event. We then scaled the within-host dynamics to the population where we structured the susceptible population using the two variables (pathogen load and time since the last booster event). We analyzed the pathogen load’s invariant distribution and utilized the results and time scale assumptions to reduce the dimension of the multi-scale model. The resulting model is an SIR model whose incidence function has terms derived from the multi-scale model. We finally conducted numerical simulations to investigate the long-term behavior of the SIR model. The simulations revealed parameter regions where either no cholera cases happen, where cholera is present at a low prevalence, and where a full-blown cholera epidemic takes off.

Prior research has indicated a link between seborrheic dermatitis (SD) and dysregulated immune responses; however, the causal linkage between this association and certain immune cell features has yet to be thoroughly clarified. This study seeks to examine the potential causal influence of immune cell characteristics on the pathogenesis of seborrheic dermatitis (SD) and to evaluate if these characteristics can function as biomarkers for early diagnosis and therapy. Data mining was performed using the Genome-Wide Association Study (GWAS) Catalog database to select 731 immune cell features for exposure variables and seborrheic dermatitis for the outcome variable in a forward Mendelian Randomization (MR) study. In a reverse MR analysis, seborrheic dermatitis was designated as the exposure factor, while the significant immune cell traits recognized by a forward MR analysis were used as outcome factors to exclude potential reverse causality. Five MR analyses were carried out by utilizing methods such as inverse variance weighting (IVW). Additionally, false discovery rate (FDR) correction, heterogeneity testing, level-dependent pleiotropy analysis, and sensitivity analysis were conducted. This study identified 11 immune traits causally associated with seborrheic dermatitis (SD). Specifically, five immune phenotypes were found to have been linking to a lower risk of SD: CD24on transitional, CD28 - DN(CD4 - CD8-)%T cell, CD45RA - CD28 - CD8br AC, Resting Treg%CD4, SSC - A on NKT. Conversely, six immunized traits have been shown with a greater risk of SD: CD127 - CD8br AC, CD20on CD24 + CD27+, CD27on IgD - CD38br, CD27on unsw mem, CD28 + DN(CD4 - CD8-)%T cell, HLA DR on CD33br HLA DR + CD14dim. The sensitivity analysis showed an absence of heterogeneity and level-dependent pleiotropy.This study demonstrates that specific immune cell traits not only correlate with SD but also provide valuable mechanistic insights into its pathogenesis. These immune traits also hold promise as biomarkers for early diagnosis, risk stratification, and personalized therapy. In addition, the findings provide new insights into immune mechanisms, highlighting the potential of targeted immunotherapies in SD treatment.

Protective immunity induced by concurrent intradermal injection of porcine circovirus type 2 and Mycoplasma hyopneumoniae inactivated vaccines in pigs

Understanding how quickly pathogens replicate and how quickly the immune system responds is important for predicting the epidemic spread of emerging pathogens. Host body size, through its correlation with metabolic rates, is theoretically predicted to impact pathogen replication rates and immune system response rates. Here, we use mathematical models of viral time courses from multiple species of birds infected by a generalist pathogen (West Nile Virus; WNV) to test more thoroughly how disease progression and immune response depend on mass and host phylogeny. We use hierarchical Bayesian models coupled with nonlinear dynamical models of disease dynamics to incorporate the hierarchical nature of host phylogeny. Our analysis suggests an important role for both host phylogeny and species mass in determining factors important for viral spread such as the basic reproductive number, WNV production rate, peak viraemia in blood and competency of a host to infect mosquitoes. Our model is based on a principled analysis and gives a quantitative prediction for key epidemiological determinants and how they vary with species mass and phylogeny. This leads to new hypotheses about the mechanisms that cause certain taxonomic groups to have higher viraemia. For example, our models suggest that higher viral burst sizes cause corvids to have higher levels of viraemia and that the cellular rate of virus production is lower in larger species. We derive a metric of competency of a host to infect disease vectors and thereby sustain the disease between hosts. This suggests that smaller passerine species are highly competent at spreading the disease compared with larger non-passerine species. Our models lend mechanistic insight into why some species (smaller passerine species) are pathogen reservoirs and some (larger non-passerine species) are potentially dead-end hosts for WNV. Our techniques give insights into the role of body mass and host phylogeny in the spread of WNV and potentially other zoonotic diseases. The major contribution of this work is a computational framework for infectious disease modelling at the within-host level that leverages data from multiple species. This is likely to be of interest to modellers of infectious diseases that jump species barriers and infect multiple species. Our method can be used to computationally determine the competency of a host to infect mosquitoes that will sustain WNV and other zoonotic diseases. We find that smaller passerine species are more competent in spreading the disease than larger non-passerine species. This suggests the role of host phylogeny as an important determinant of within-host pathogen replication. Ultimately, we view our work as an important step in linking within-host viral dynamics models to between-host models that determine spread of infectious disease between different hosts.

Multiple factors can influence the immune response of ectothermic vertebrates, including body temperature (Tb), gonadal steroids, and seasonality, in ways that are thought to reflect trade-offs between energetic investment in immunity versus reproduction. Hibernating tegu lizards (Salvator merianae) are a unique model to investigate how immunocompetence might be influenced by different factors during their annual cycle. We assessed immunological measures (plasma bacterial killing ability, total and differential leukocyte count), plasma hormone levels (testosterone in males, estradiol and progesterone in females, and corticosterone [CORT] in both sexes), Tb, and body condition from adult tegus during each stage of their annual cycle: reproduction, post-reproduction/preparation for hibernation, and hibernation. Our hypothesis that immune traits present higher values during the reproductive phase, and a sharp decrease during hibernation, was partially supported. Immune variables did not change between life history stages, except for total number of leukocytes, which was higher at the beginning of the reproductive season (September) in both males and females. Average Tb of the week prior to sampling was positively correlated with number of eosinophils, basophils, monocytes, and azurophils, corroborating other studies showing that when animals maintain a high Tb, there is an increase in immune activity. Surprisingly, no clear relationship between immune traits and gonadal steroids or CORT levels was observed, even when including life history stage in the model. When gonadal hormones peaked in males and females, heterophil: lymphocyte ratio (which often elevates during physiological stress) also increased. Additionally, we did not observe any trade-off between reproduction and immunity traits, sex differences in immune traits, or a correlation between body condition and immune response. Our results suggest that variation in patterns of immune response and correlations with body condition and hormone secretion across the year can depend upon the specific hormone and immune trait, and that experienced Tb is an important variable determining immune response in ectotherms.

It is widely accepted that infection and immune response incur significant metabolic demands, yet the respective demands of specific immune responses to live pathogens have not been well delineated. It is also established that upon activation, metabolic pathways undergo shifts at the cellular level. However, most studies exploring these issues at the systemic or cellular level have utilized pathogen associated molecular patterns (PAMPs) that model sepsis, or model antigens at isolated time points. Thus, the dynamics of pathogenesis and immune response to a live infection remain largely undocumented. To better quantitate the metabolic demands induced by infection, we utilized a live pathogenic infection model. Mice infected with Listeria monocytogenes were monitored longitudinally over the course of infection through clearance. We measured systemic metabolic phenotype, bacterial load, innate and adaptive immune responses, and cellular metabolic pathways. To further delineate the role of adaptive immunity in the metabolic phenotype, we utilized two doses of bacteria, one that induced both sickness behavior and protective (T cell mediated) immunity, and the other protective immunity alone. We determined that the greatest impact to systemic metabolism occurred during the early immune response, which coincided with the greatest shift in innate cellular metabolism. In contrast, during the time of maximal T cell expansion, systemic metabolism returned to resting state. Taken together, our findings demonstrate that the timing of maximal metabolic demand overlaps with the innate immune response and that when the adaptive response is maximal, the host has returned to relative metabolic homeostasis.

Given an endemic infectious disease and a budget, how do we optimally allocate interventions to control the disease? This paper shows that the optimal strategy varies depending on the budget, the type of intervention, the trajectory of pathogen load, and the objective.Using a model with explicit within- and between-host dynamics, we model isolation, supportive treatment, and specific treatment. Isolation and supportive treatment affect the transmission coefficient and the disease-induced mortality rate, respectively, in the between-host dynamics. Specific treatment affects the clearance rate of pathogens in the within-host dynamics.We study the optimisation of the three interventions for various budget levels via evaluating isolation and supportive treatment at the population level and specific treatment at both the population and individual levels. At the population level, we consider the risk of transmission, the burden of illness, and the survival probability, and to that end, we choose the population-level infection rate, the population density of infected individuals, and the total disease-induced mortality rate as objective functions. At the individual level, we consider the length of infection and the pathogen load, and to that end, we choose the maximum infection-age and the maximum pathogen load as objective functions. The objective is to minimise these functions through varying two variables that refer to when the intervention starts and when it stops for an infected individual and also indicate what kind of individuals can get the intervention from the population perspective.We find that the optimal strategy of isolation is to isolate individuals with a higher pathogen load, given a lower budget. The optimal strategy of supportive treatment can be the same as isolation or simply no treatment. The optimal strategy of specific treatment is complicated, and it can be to treat individuals with pathogen loads above a particular level until they recover or until the pathogens can decrease when treatment stops, or it can be another scenario.

Intranasal DNA Vaccination Induces Potent Mucosal and Systemic Immune Responses and Cross-protective Immunity Against Influenza Viruses

Benefits of immune protection versus immunopathology costs: A synthesis from cytokine KO models

Pathogen within-host dynamics and disease outcome: what can we learn from insect studies?

The regulation of IL-12: Its role in infectious, autoimmune, and allergic diseases