Coevolutionary immune system dynamics driving pathogen speciation

We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.Comment: main article: 16 pages, 5 figures; supporting information: 3 page

The Suppression of Immune System Disorders by Passive Attrition

Exposure to infectious diseases has an unexpected benefit of inhibiting autoimmune diseases and allergies. This is one of many fundamental fitness tradeoffs associated with immune system architecture. The immune system attacks pathogens, but also may (inappropriately) attack the host. Exposure to pathogens can suppress the deleterious response, at the price of illness and the decay of immunity to previous diseases. This “hygiene hypothesis” has been associated with several possible underlying biological mechanisms. This study focuses on physiological constraints that lead to competition for survival between immune system cell types. Competition maintains a relatively constant total number of cells within each niche. The constraint implies that adding cells conferring new immunity requires loss (passive attrition) of some cells conferring previous immunities. We consider passive attrition as a mechanism to prevent the initial proliferation of autoreactive cells, thus preventing autoimmune disease. We see that this protection is a general property of homeostatic regulation and we look specifically at both the IL-15 and IL-7 regulated niches to make quantitative predictions using a mathematical model. This mathematical model yields insight into the dynamics of the “Hygiene Hypothesis,” and makes quantitative predictions for experiments testing the ability of passive attrition to suppress immune system disorders. The model also makes a prediction of an anti-correlation between prevalence of immune system disorders and passive attrition rates

Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In each scenario, individuals are faced with a forced "go" versus "no go" evacuation decision, based on information available on competing broadcast and peer-to-peer sources. In this controlled setting, all actions and observations are recorded prior to the decision, enabling development of a quantitative decision making model that accounts for the disaster likelihood, severity, and temporal urgency, as well as competition between networked individuals for limited emergency resources. Individual differences in behavior within this social setting are correlated with individual differences in inherent risk attitudes, as measured by standard psychological assessments. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios.Comment: Approved for public release; distribution is unlimite

Robustness and Fragility in Immunosenescence

We construct a model to study tradeoffs associated with aging in the adaptive immune system, focusing on cumulative effects of replacing naive cells with memory cells. Binding affinities are characterized by a stochastic shape space model. System loss arising from an individual infection is associated with disease severity, as measured by the total antigen population over the course of an infection. We monitor evolution of cell populations on the shape space over a string of infections, and find that the distribution of losses becomes increasingly heavy-tailed with time. Initially this lowers the average loss: the memory cell population becomes tuned to the history of past exposures, reducing the loss of the system when subjected to a second, similar infection. This is accompanied by a corresponding increase in vulnerability to novel infections, which ultimately causes the expected loss to increase due to overspecialization, leading to increasing fragility with age (i.e., immunosenescence). In our model, immunosenescence is not the result of a performance degradation of some specific lymphocyte, but rather a natural consequence of the built-in mechanisms for system adaptation. This “robust, yet fragile” behavior is a key signature of Highly Optimized Tolerance

Association of Phosphate-Containing versus Phosphate-Free Solutions on Ventilator Days in Patients Requiring Continuous Kidney Replacement Therapy

Background and objectives Hypophosphatemia is commonly observed in patients receiving continuous KRT. Patients who develop hypophosphatemia may be at risk of respiratory and neuromuscular dysfunction and therefore subject to prolongation of ventilator support. We evaluated the association of phosphate-containing versus phosphate-free continuous KRT solutions with ventilator dependence in critically ill patients receiving continuous KRT. Design, setting, participants, & measurements Our study was a single-center, retrospective, pre-post cohort study of adult patients receiving continuous KRT and mechanical ventilation during their intensive care unit stay. Zeroinflated negative binomial regression with and without propensity score matching was used to model our primary outcome: ventilator-free days at 28 days. Intensive care unit and hospital lengths of stay as well as hospital mortality were analyzed with a t test or a chi-squared test, as appropriate. Results We identified 992 eligible patients, of whom 649 (65%) received phosphate-containing solutions and 343 (35%) received phosphate-free solutions. In multivariable models, patients receiving phosphate-containing continuous KRT solutions had 12% (95% confidence interval, 0.17 to 0.47) more ventilator-free days at 28 days. Patients exposed to phosphate-containing versus phosphate-free solutions had 17% (95% confidence interval, 20.08 to 20.30) fewer days in the intensive care unit and 20% (95% confidence interval, 2 0.12 to 20.32) fewer days in the hospital. Concordant results were observed for ventilator-free days at 28 days in the propensity score matched analysis. There was no difference in hospital mortality between the groups. Conclusions The use of phosphate-containing versus phosphate-free continuous KRT solutions was independently associated with fewer ventilator days and shorter stay in the intensive care unit

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group

Immune Responses to Two Sequential Inoculations by the Same Antigen

<p>Results are shown for maximum binding affinity pairs  = y→. The rapid response to the secondary inoculation (represented by the smaller size of the second peak) is due to the elevated number of memory cells. The immunological loss, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020160#pcbi-0020160-e015" target="_blank">Equation 15</a>, is defined to be the area under the antigen population peak. For the primary and secondary peaks, the values are 8,860 and 1,525, respectively. The model parameters used in this and all simulations in this paper are as follows: <i>α</i> = 1.5, <i>β</i> = 0.083, <i>f</i> = 0.38, <i>δ</i> = 0.01, <i>ρ</i> = 1, <i>ϕ</i> = 10⁻⁴, <i>H</i> = 10, <i>γ_max</i> = 0.005, <i>b</i> = 2. These parameter values give typical behavior for the model. </p

Distribution of Losses over 600 Realizations of the System

<p>Results are shown after the first infection, after 250 infections (when J is at its lowest value, corresponding to the optimal state), and after 400 infections (corresponding to immunsenescence), in blue, green, and red respectively. The infection probabilities have the same distribution as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020160#pcbi-0020160-g002" target="_blank">Figure 2</a>.</p