Tracking Resilience to Infections by Mapping Disease Space

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.

Transcriptomic Studies of Malaria: a Paradigm for Investigation of Systemic Host-Pathogen Interactions

Transcriptomics, the analysis of genome-wide RNA expression, is a common approach to investigate host and pathogen processes in infectious diseases. Technical and bioinformatic advances have permitted increasingly thorough analyses of the association of RNA expression with fundamental biology, immunity, pathogenesis, diagnosis, and prognosis. Transcriptomic approaches can now be used to realize a previously unattainable goal, the simultaneous study of RNA expression in host and pathogen, in order to better understand their interactions. This exciting prospect is not without challenges, especially as focus moves from interactions in vitro under tightly controlled conditions to tissue- and systems-level interactions in animal models and natural and experimental infections in humans. Here we review the contribution of transcriptomic studies to the understanding of malaria, a parasitic disease which has exerted a major influence on human evolution and continues to cause a huge global burden of disease. We consider malaria a paradigm for the transcriptomic assessment of systemic host-pathogen interactions in humans, because much of the direct host-pathogen interaction occurs within the blood, a readily sampled compartment of the body. We illustrate lessons learned from transcriptomic studies of malaria and how these lessons may guide studies of host-pathogen interactions in other infectious diseases. We propose that the potential of transcriptomic studies to improve the understanding of malaria as a disease remains partly untapped because of limitations in study design rather than as a consequence of technological constraints. Further advances will require the integration of transcriptomic data with analytical approaches from other scientific disciplines, including epidemiology and mathematical modeling.

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.

The Predictive Value of Inflammation-Related Peripheral Blood Measurements in Cancer Staging and Prognosis

In this review, we discuss the interaction between cancer and markers of inflammation (such as levels of inflammatory cells and proteins) in the circulation, and the potential benefits of routinely monitoring these markers in peripheral blood measurement assays. Next, we discuss the prognostic value and limitations of using inflammatory markers such as neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios and C-reactive protein measurements. Furthermore, the review discusses the benefits of combining multiple types of measurements and longitudinal tracking to improve staging and prognosis prediction of patients with cancer, and the ability of novel in silico frameworks to leverage this high-dimensional data.

A nutrient mediates intraspecific competition between rodent malaria parasites in vivo

Hosts are often infected with multiple strains of a single parasite species. Within-host competition between parasite strains can be intense and has implications for the evolution of traits that impact patient health, such as drug resistance and virulence. Yet the mechanistic basis of within-host competition is poorly understood. Here, we demonstrate that a parasite nutrient, para-aminobenzoic acid (pABA), mediates competition between a drug resistant and drug susceptible strain of the malaria parasite, Plasmodium chabaudi. We further show that increasing pABA supply to hosts infected with the resistant strain worsens disease and changes the relationship between parasite burden and pathology. Our experiments demonstrate that, even when there is profound top-down regulation (immunity), bottom-up regulation of pathogen populations can occur and that its importance may vary during an infection. The identification of resources that can be experimentally controlled opens up the opportunity to manipulate competitive interactions between parasites and hence their evolution.

Tolerance to bovine clinical mastitis: Total, direct, and indirect milk losses

The objectives of this paper were to estimate direct and indirect milk losses associated with mastitis. Indirect losses, linked to indirect tolerance, are mediated by the increase in milk somatic cell count (SCC) in response to bacterial infection. Direct losses, linked to weak direct tolerance, are not mediated by the increase in SCC. So far, studies have evaluated milk loss associated with clinical mastitis without considering both components, which may lead to biased estimates of their sum; that is, the total loss in milk. A total of 43,903 test-day records on milk and SCC from 3,716 cows and 5,858 lactations were analyzed with mediation mixed models and health trajectories to estimate the amount of direct, indirect, and total milk losses after adjustment for known and potentially unmeasured (sensitivity analyses) confounding factors. Estimates were formalized under the counterfactual causal theory of causation. In this study, milk losses were mostly mediated by an increase in SCC. They were highest in the first month of lactation, when SCC were highest. Milk losses were estimated at 0.5, 0.8, and 1.1 kg/d in first, second, and third and greater parity, respectively. Two phases described how changes in milk were associated with changes in SCC: on average, one occurred before and one after the day preceding the clinical diagnosis. In both phases, changes in milk were estimated at 1 mg/d per 10³ cells/mL. After adjusting for known confounders, cow effect accounted for 20.7 and 64.2% of the variation in milk in the first and second phases, respectively. This suggests that deviations from the resilient path were highest during the second phase of inflammation and that selection for cows more tolerant to mastitis is feasible. As discussed herein, epigenetic regulation of macrophage polarization may contribute to the variation in milk observed in the second phase.

Topological data analysis (TDA) applied to reveal pedogenetic principles of European topsoil system

Recent developments in applied mathematics are bringing new tools that are capable to synthesize knowledge in various disciplines, and help in finding hidden relationships between variables. One such technique is topological data analysis (TDA), a fusion of classical exploration techniques such as principal component analysis (PCA), and a topological point of view applied to clustering of results. Various phenomena have already received new interpretations thanks to TDA, from the proper choice of sport teams to cancer treatments. For the first time, this technique has been applied in soil science, to show the interaction between physical and chemical soil attributes and main soil-forming factors, such as climate and land use. The topsoil data set of the Land Use/Land Cover Area Frame survey (LUCAS) was used as a comprehensive database that consists of approximately 20,000 samples, each described by 12 physical and chemical parameters. After the application of TDA, results obtained were cross-checked against known grouping parameters including five types of land cover, nine types of climate and the organic carbon content of soil. Some of the grouping characteristics observed using standard approaches were confirmed by TDA (e.g., organic carbon content) but novel subtle relationships (e.g., magnitude of anthropogenic effect in soil formation), were discovered as well. The importance of this finding is that TDA is a unique mathematical technique capable of extracting complex relations hidden in soil science data sets, giving the opportunity to see the influence of physicochemical, biotic and abiotic factors on topsoil formation through fresh eyes.

Identification of Topological Network Modules in Perturbed Protein Interaction Networks

Biological networks consist of functional modules, however detecting and characterizing such modules in networks remains challenging. Perturbing networks is one strategy for identifying modules. Here we used an advanced mathematical approach named topological data analysis (TDA) to interrogate two perturbed networks. In one, we disrupted the S. cerevisiae INO80 protein interaction network by isolating complexes after protein complex components were deleted from the genome. In the second, we reanalyzed previously published data demonstrating the disruption of the human Sin3 network with a histone deacetylase inhibitor. Here we show that disrupted networks contained topological network modules (TNMs) with shared properties that mapped onto distinct locations in networks. We define TMNs as proteins that occupy close network positions depending on their coordinates in a topological space. TNMs provide new insight into networks by capturing proteins from different categories including proteins within a complex, proteins with shared biological functions, and proteins disrupted across networks.

Topological methods for genomics: present and future directions

Topological methods are emerging as a new set of tools for the analysis of large genomic datasets. They are mathematically grounded methods that extract information from the geometric structure of data. In the last few years, applications to evolutionary biology, cancer genomics, and the analysis of complex diseases have uncovered significant biological results, highlighting their utility for fulfilling some of the current analytic needs of genomics. In this review, the state of the art in the application of topological methods to genomics is summarized, and some of the present limitations and possible future developments are reviewed.

Microbes Dress for Success: Tolerance or Resistance?

The intestinal microbiota performs essential functions for host physiology, but the specific constituents and the microbial factors required to promote host health remain largely unknown. A study by Rangan et al. suggests that members of the microbiota can modify microbial associated molecular patterns to promote host defense against invading pathogens.

Role of GATA transcription factor ELT-2 and p38 MAPK PMK-1 in recovery from acute P. aeruginosa infection in C. elegans

Infectious diseases caused by bacterial pathogens reduce the fitness of their associated host but are generally limited in duration. In order for the diseased host to regain any lost fitness upon recovery, a variety of molecular, cellular, and physiological processes must be employed. To better understand mechanisms underlying the recovery process, we have modeled an acute Pseudomonas aeruginosa infection in C. elegans using brief exposures to this pathogen and subsequent antibiotic treatment. To identify host genes altered during recovery from P. aeruginosa infection, we performed whole genome expression profiling. The analysis of this dataset indicated that the activity of the host immune system is down-regulated upon recovery and revealed shared and pathogen-specific host responses during recovery. We determined that the GATA transcription factor ELT-2 and the p38 MAP kinase PMK-1 are necessary for animals to successfully recover from an acute P. aeruginosa infection. In addition, we found that ELT-2 plays a more prominent and earlier role than PMK-1 during recovery. Our data sheds further light on the molecular mechanisms and transcriptional programs involved in recovery from an acute bacterial infection, which provides a better understanding of the entire infectious disease process.

Understanding Disease Tolerance and Resilience

Our first ever Open Highlights explores recent Open Access research into the complex relationship between host and pathogen during the course of an infection, and the factors that determine its eventual outcome.

Host resilience to emerging coronaviruses

Recently, two coronaviruses, severe acute respiratory syndrome coronavirus and Middle East respiratory syndrome coronavirus, have emerged to cause unusually severe respiratory disease in humans. Currently, there is a lack of effective antiviral treatment options or vaccine available. Given the severity of these outbreaks, and the possibility of additional zoonotic coronaviruses emerging in the near future, the exploration of different treatment strategies is necessary. Disease resilience is the ability of a given host to tolerate an infection, and to return to a state of health. This review focuses on exploring various host resilience mechanisms that could be exploited for treatment of severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus and other respiratory viruses that cause acute lung injury and acute respiratory distress syndrome.

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.

Experiments using fruit flies infected with Listeria monocytogenes show that changes in the shape of a disease tolerance curve can reveal the source of pathology for an infectious system.

It is an intuitive assumption that the severity of symptoms suffered during an infection must be linked to pathogen loads. However, the dose–response relationship explaining how health varies with respect to pathogen load is non-linear and can be described as a “disease tolerance curve;” this relationship can vary in response to the genetic properties of the host or pathogen as well as environmental conditions. We studied what changes in the shape of this curve can teach us about the underlying circuitry of the immune response. Using a model system in which we infected fruit flies with the bacterial pathogen Listeria monocytogenes, we observed an S-shaped disease tolerance curve. This type of curve can be described by three or four parameters in a standard manner, which allowed us to develop a simple mathematical model to explain how the curve is expected to change shape as the immune response changes. After observing the variation in curve shape due to host and pathogen genetic variation, we conclude that the damage caused by Listeria infection does not result from an over-exuberant immune response but rather is caused more directly by the pathogen.