Resilience integrates concepts in aging research.

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

Metabolomic Analysis of Diverse Mice Reveals Hepatic Arginase-1 as Source of Plasma Arginase in Plasmodium chabaudi Infection.

Infections disrupt host metabolism, but the factors that dictate the nature and magnitude of metabolic change are incompletely characterized. To determine how host metabolism changes in relation to disease severity in murine malaria, we performed plasma metabolomics on eight Plasmodium chabaudi-infected mouse strains with diverse disease phenotypes. We identified plasma metabolic biomarkers for both the nature and severity of different malarial pathologies. A subset of metabolic changes, including plasma arginine depletion, match the plasma metabolomes of human malaria patients, suggesting new connections between pathology and metabolism in human malaria. In our malarial mice, liver damage, which releases hepatic arginase-1 (Arg1) into circulation, correlated with plasma arginine depletion. We confirmed that hepatic Arg1 was the primary source of increased plasma arginase activity in our model, which motivates further investigation of liver damage in human malaria patients. More broadly, our approach shows how leveraging phenotypic diversity can identify and validate relationships between metabolism and the pathophysiology of infectious disease. IMPORTANCE Malaria is a severe and sometimes fatal infectious disease endemic to tropical and subtropical regions. Effective vaccines against malaria-causing Plasmodium parasites remain elusive, and malaria treatments often fail to prevent severe disease. Small molecules that target host metabolism have recently emerged as candidates for therapeutics in malaria and other diseases. However, our limited understanding of how metabolites affect pathophysiology limits our ability to develop new metabolite therapies. By providing a rich data set of metabolite-pathology correlations and by validating one of those correlations, our work is an important step toward harnessing metabolism to mitigate disease. Specifically, we showed that liver damage in P. chabaudi-infected mice releases hepatic arginase-1 into circulation, where it may deplete plasma arginine, a candidate malaria therapeutic that mitigates vascular stress. Our data suggest that liver damage may confound efforts to increase levels of arginine in human malaria patients.

Multi-start Evolutionary Nonlinear OpTimizeR (MENOTR): A hybrid parameter optimization toolbox.

Parameter optimization or "data fitting" is a computational process that identifies a set of parameter values that best describe an experimental data set. Parameter optimization is commonly carried out using a computer program utilizing a non-linear least squares (NLLS) algorithm. These algorithms work by continuously refining a user supplied initial guess resulting in a systematic increase in the goodness of fit. A well-understood problem with this class of algorithms is that in the case of models with correlated parameters the optimized output parameters are initial guess dependent. This dependency can potentially introduce user bias into the resultant analysis. While many optimization programs exist, few address this dilemma. Here we present a data analysis tool, MENOTR, that is capable of overcoming the initial guess dependence in parameter optimization. Several case studies with published experimental data are presented to demonstrate the capabilities of this tool. The results presented here demonstrate how to effectively overcome the initial guess dependence of NLLS leading to greater confidence that the resultant optimized parameters are the best possible set of parameters to describe an experimental data set. While the optimization strategies implemented within MENOTR are not entirely novel, the application of these strategies to optimize parameters in kinetic and thermodynamic biochemical models is uncommon. MENOTR was designed to require minimal modification to accommodate a new model making it immediately accessible to researchers with a limited programming background. We anticipate that this toolbox can be used in a wide variety of data analysis applications. Prototype versions of this toolbox have been used in a number of published investigations already, as well as ongoing work with chemical-quenched flow, stopped-flow, and molecular tweezers data sets. STATEMENT OF SIGNIFICANCE: Non-linear least squares (NLLS) is a common form of parameter optimization in biochemistry kinetic and thermodynamic investigations These algorithms are used to fit experimental data sets and report corresponding parameter values. The algorithms are fast and able to provide good quality solutions for models involving few parameters. However, initial guess dependence is a well-known drawback of this optimization strategy that can introduce user bias. An alternative method of parameter optimization are genetic algorithms (GA). Genetic algorithms do not have an initial guess dependence but are slow at arriving at the best set of fit parameters. Here, we present MENOTR, a parameter optimization toolbox utilizing a hybrid GA/NLLS algorithm. The toolbox maximizes the strength of each strategy while minimizing the inherent drawbacks.

Uncovering drivers of dose-dependence and individual variation in malaria infection outcomes.

To understand why some hosts get sicker than others from the same type of infection, it is essential to explain how key processes, such as host responses to infection and parasite growth, are influenced by various biotic and abiotic factors. In many disease systems, the initial infection dose impacts host morbidity and mortality. To explore drivers of dose-dependence and individual variation in infection outcomes, we devised a mathematical model of malaria infection that allowed host and parasite traits to be linear functions (reaction norms) of the initial dose. We fitted the model, using a hierarchical Bayesian approach, to experimental time-series data of acute Plasmodium chabaudi infection across doses spanning seven orders of magnitude. We found evidence for both dose-dependent facilitation and debilitation of host responses. Most importantly, increasing dose reduced the strength of activation of indiscriminate host clearance of red blood cells while increasing the half-life of that response, leading to the maximal response at an intermediate dose. We also explored the causes of diverse infection outcomes across replicate mice receiving the same dose. Besides random noise in the injected dose, we found variation in peak parasite load was due to unobserved individual variation in host responses to clear infected cells. Individual variation in anaemia was likely driven by random variation in parasite burst size, which is linked to the rate of host cells lost to malaria infection. General host vigour in the absence of infection was also correlated with host health during malaria infection. Our work demonstrates that the reaction norm approach provides a useful quantitative framework for examining the impact of a continuous external factor on within-host infection processes.

Western diet regulates immune status and the response to LPS-driven sepsis independent of diet-associated microbiome.

Sepsis is a deleterious immune response to infection that leads to organ failure and is the 11th most common cause of death worldwide. Despite plaguing humanity for thousands of years, the host factors that regulate this immunological response and subsequent sepsis severity and outcome are not fully understood. Here we describe how the Western diet (WD), a diet high in fat and sucrose and low in fiber, found rampant in industrialized countries, leads to worse disease and poorer outcomes in an LPS-driven sepsis model in WD-fed mice compared with mice fed standard fiber-rich chow (SC). We find that WD-fed mice have higher baseline inflammation (metaflammation) and signs of sepsis-associated immunoparalysis compared with SC-fed mice. WD mice also have an increased frequency of neutrophils, some with an "aged" phenotype, in the blood during sepsis compared with SC mice. Importantly, we found that the WD-dependent increase in sepsis severity and higher mortality is independent of the microbiome, suggesting that the diet may be directly regulating the innate immune system through an unknown mechanism. Strikingly, we could predict LPS-driven sepsis outcome by tracking specific WD-dependent disease factors (e.g., hypothermia and frequency of neutrophils in the blood) during disease progression and recovery. We conclude that the WD is reprogramming the basal immune status and acute response to LPS-driven sepsis and that this correlates with alternative disease paths that lead to more severe disease and poorer outcomes.

The physiological basis of disease tolerance in insects.

Immunology textbooks teach us about the ways hosts can recognize and kill microbes but leave out something important: the mechanisms used to survive infections. Survival depends on more than simply detecting and eliminating microbes; it requires that we prevent and repair the damage caused by pathogens and the immune response. Recent work in insects is helping to build our understanding of this aspect of pathology, called disease tolerance. Here we discuss papers that explore disease tolerance using theoretical, population genetics, and mechanistic approaches.

Going to Bat(s) for Studies of Disease Tolerance.

A majority of viruses that have caused recent epidemics with high lethality rates in people, are zoonoses originating from wildlife. Among them are filoviruses (e.g., Marburg, Ebola), coronaviruses (e.g., SARS, MERS), henipaviruses (e.g., Hendra, Nipah) which share the common features that they are all RNA viruses, and that a dysregulated immune response is an important contributor to the tissue damage and hence pathogenicity that results from infection in humans. Intriguingly, these viruses also all originate from bat reservoirs. Bats have been shown to have a greater mean viral richness than predicted by their phylogenetic distance from humans, their geographic range, or their presence in urban areas, suggesting other traits must explain why bats harbor a greater number of zoonotic viruses than other mammals. Bats are highly unusual among mammals in other ways as well. Not only are they the only mammals capable of powered flight, they have extraordinarily long life spans, with little detectable increases in mortality or senescence until high ages. Their physiology likely impacted their history of pathogen exposure and necessitated adaptations that may have also affected immune signaling pathways. Do our life history traits make us susceptible to generating damaging immune responses to RNA viruses or does the physiology of bats make them particularly tolerant or resistant? Understanding what immune mechanisms enable bats to coexist with RNA viruses may provide critical fundamental insights into how to achieve greater resilience in humans.

Vector Immunity and Evolutionary Ecology: The Harmonious Dissonance.

Recent scientific breakthroughs have significantly expanded our understanding of arthropod vector immunity. Insights in the laboratory have demonstrated how the immune system provides resistance to infection, and in what manner innate defenses protect against a microbial assault. Less understood, however, is the effect of biotic and abiotic factors on microbial-vector interactions and the impact of the immune system on arthropod populations in nature. Furthermore, the influence of genetic plasticity on the immune response against vector-borne pathogens remains mostly elusive. Herein, we discuss evolutionary forces that shape arthropod vector immunity. We focus on resistance, pathogenicity and tolerance to infection. We posit that novel scientific paradigms should emerge when molecular immunologists and evolutionary ecologists work together.

Predicting position along a looping immune response trajectory.

When we get sick, we want to be resilient and recover our original health. To measure resilience, we need to quantify a host's position along its disease trajectory. Here we present Looper, a computational method to analyze longitudinally gathered datasets and identify gene pairs that form looping trajectories when plotted in the space described by these phases. These loops enable us to track where patients lie on a typical trajectory back to health. We analyzed two publicly available, longitudinal human microarray datasets that describe self-resolving immune responses. Looper identified looping gene pairs expressed by human donor monocytes stimulated by immune elicitors, and in YF17D-vaccinated individuals. Using loops derived from training data, we found that we could predict the time of perturbation in withheld test samples with accuracies of 94% in the human monocyte data, and 65-83% within the same cohort and in two independent cohorts of YF17D vaccinated individuals. We suggest that Looper will be useful in building maps of resilient immune processes across organisms.

Host Energy Source Is Important for Disease Tolerance to Malaria.

Pathologic infections are accompanied by a collection of short-term behavioral perturbations collectively termed sickness behaviors [1, 2]. These include changes in body temperature, reduced eating and drinking, and lethargy and mimic behaviors of animals in torpor and hibernation [1, 3-6]. Sickness behaviors are important, pathogen-specific components of the host response to infection [1, 3, 7-9]. In particular, host anorexia has been shown to be beneficial or detrimental depending on the infection [7, 8]. While these studies have illuminated the effects of anorexia on infection, they consider this behavior in isolation from other behaviors and from its effects on host metabolism and energy. Here, we explored the temporal dynamics of multiple sickness behaviors and their effect on host energy and metabolism throughout infection. We used the Plasmodium chabaudi AJ murine model of malaria as it causes severe pathology from which most animals recover. We found that infected animals did become anorexic, skewing their metabolism toward fatty acid oxidation and ketosis. Metabolism of fats requires oxygen for the production of ATP. In this model, animals also suffer severe anemia, limiting their ability to carry oxygen concurrent with their switch toward fatty acid metabolism. We reasoned that the combination of anorexia and anemia would increase pressure on glycolysis as a critical energy pathway because it does not require oxygen. Treating infected mice when anorexic with the glycolytic inhibitor 2-deoxyglucose (2DG) reduced survival; treating animals with glucose improved survival. Peak parasite loads were unchanged, demonstrating changes in disease tolerance. Parasite clearance was reduced with 2DG treatment, suggesting altered resistance.

A Macrophage Colony-Stimulating-Factor-Producing γδ T Cell Subset Prevents Malarial Parasitemic Recurrence.

Despite evidence that γδ T cells play an important role during malaria, their precise role remains unclear. During murine malaria induced by Plasmodium chabaudi infection and in human P. falciparum infection, we found that γδ T cells expanded rapidly after resolution of acute parasitemia, in contrast to αß T cells that expanded at the acute stage and then declined. Single-cell sequencing showed that TRAV15N-1 (Vδ6.3) γδ T cells were clonally expanded in mice and had convergent complementarity-determining region 3 sequences. These γδ T cells expressed specific cytokines, M-CSF, CCL5, CCL3, which are known to act on myeloid cells, indicating that this γδ T cell subset might have distinct functions. Both γδ T cells and M-CSF were necessary for preventing parasitemic recurrence. These findings point to an M-CSF-producing γδ T cell subset that fulfills a specialized protective role in the later stage of malaria infection when αß T cells have declined.

How Many Parameters Does It Take to Describe Disease Tolerance?

The study of infectious disease has been aided by model organisms, which have helped to elucidate molecular mechanisms and contributed to the development of new treatments; however, the lack of a conceptual framework for unifying findings across models, combined with host variability, has impeded progress and translation. Here, we fill this gap with a simple graphical and mathematical framework to study disease tolerance, the dose response curve relating health to microbe load; this approach helped uncover parameters that were previously overlooked. Using a model experimental system in which we challenged Drosophila melanogaster with the pathogen Listeria monocytogenes, we tested this framework, finding that microbe growth, the immune response, and disease tolerance were all well represented by sigmoid models. As we altered the system by varying host or pathogen genetics, disease tolerance varied, as we would expect if it was indeed governed by parameters controlling the sensitivity of the system (the number of bacteria required to trigger a response) and maximal effect size according to a logistic equation. Though either the pathogen or host immune response or both together could theoretically be the proximal cause of pathology that killed the flies, we found that the pathogen, but not the immune response, drove damage in this model. With this new understanding of the circuitry controlling disease tolerance, we can now propose better ways of choosing, combining, and developing treatments.

Tracking Resilience to Infections by Mapping Disease Space.

Infected hosts differ in their responses to pathogens; some hosts are resilient and recover their original health, whereas others follow a divergent path and die. To quantitate these differences, we propose mapping the routes infected individuals take through "disease space." We find that when plotting physiological parameters against each other, many pairs have hysteretic relationships that identify the current location of the host and predict the future route of the infection. These maps can readily be constructed from experimental longitudinal data, and we provide two methods to generate the maps from the cross-sectional data that is commonly gathered in field trials. We hypothesize that resilient hosts tend to take small loops through disease space, whereas nonresilient individuals take large loops. We support this hypothesis with experimental data in mice infected with Plasmodium chabaudi, finding that dying mice trace a large arc in red blood cells (RBCs) by reticulocyte space as compared to surviving mice. We find that human malaria patients who are heterozygous for sickle cell hemoglobin occupy a small area of RBCs by reticulocyte space, suggesting this approach can be used to distinguish resilience in human populations. This technique should be broadly useful in describing the in-host dynamics of infections in both model hosts and patients at both population and individual levels.

What Can Vampires Teach Us about Immunology?

Speculative fiction examines the leading edge of science and can be used to introduce ideas into the classroom. For example, most students are already familiar with the fictional infectious diseases responsible for vampire and zombie outbreaks. The disease dynamics of these imaginary ailments follow the same rules we see for real diseases and can be used to remind students that they already understand the basic rules of disease ecology and immunology. By engaging writers of this sort of fiction in an effort to solve problems in immunology we may be able to perform a directed evolution experiment where we follow the evolution of plots rather than genetic traits.

Defining Resistance and Tolerance to Cancer.

There are two ways to maintain fitness in the face of infection: resistance is a host's ability to reduce microbe load and disease tolerance is the ability of the host to endure the negative health effects of infection. Resistance and disease tolerance should be applicable to any insult to the host and have been explored in depth with regards to infection but have not been examined in the context of cancer. Here, we establish a framework for measuring and separating resistance and disease tolerance to cancer in Drosophila melanogaster. We plot a disease tolerance curve to cancer in wild-type flies and then compare this to natural variants, identifying a line with reduced cancer resistance. Quantitation of these two traits opens an additional dimension for analysis of cancer biology.

Drosophila melanogaster Natural Variation Affects Growth Dynamics of Infecting Listeria monocytogenes.

We find that in a Listeria monocytogenes/Drosophila melanogaster infection model, L. monocytogenes grows according to logistic kinetics, which means we can measure both a maximal growth rate and growth plateau for the microbe. Genetic variation of the host affects both of the pathogen growth parameters, and they can vary independently. Because growth rates and ceilings both correlate with host survival, both properties could drive evolution of the host. We find that growth rates and ceilings are sensitive to the initial infectious dose in a host genotype-dependent manner, implying that experimental results differ as we change the original challenge dose within a single strain of host.

The genetics of immunity.

In this commentary, Brian P. Lazzaro and David S. Schneider examine the topic of the Genetics of Immunity as explored in this month's issues of GENETICS and G3: Genes|Genomes|Genetics. These inaugural articles are part of a joint Genetics of Immunity collection (ongoing) in the GSA journals.

The Drosophila deubiquitinating enzyme dUSP36 acts in the hemocytes for tolerance to Listeria monocytogenes infections.

Listeria monocytogenes is a facultative intracellular pathogen which can infect Drosophila melanogaster. Upon infection, Drosophila mounts an immune response including antimicrobial peptide production and autophagy activation. A set of previously published results prompted us to study the role of the deubiquitinating enzyme dUSP36 in response to L. monocytogenes infections. We show in this report that flies with dUsp36-specific inactivation in hemocytes are susceptible to L. monocytogenes infections (as are flies with autophagy-deficient hemocytes) but are still able to control bacterial growth. Interestingly, flies with dUsp36-depleted hemocytes are not sensitized to infection by other pathogens. We conclude that dUsp36 plays a major role in hemocytes for tolerance to L. monocytogenes.