Tracking Resilience to Infections by Mapping Disease Space

Disease tolerance as immune defense strategy in bats: One size fits all?

Bats are natural reservoirs for zoonotic pathogens, yet the determinants of microbial persistence as well as the specific functionality of their immune system remain largely enigmatic. Their propensity to harbor viruses lethal to humans and/or livestock, mostly in absence of clinical disease, makes bats stand out among mammals. Defending against pathogens relies on avoidance, resistance, and/or tolerance strategies. In bats, disease tolerance has recently gained increasing attention as a prevailing host defense paradigm. We here summarize the current knowledge on immune responses in bats in the context of infection with zoonotic agents and discuss concepts related to disease tolerance. Acknowledging the wide diversity of bats, the broad spectrum of bat-associated microbial species, and immune-related knowledge gaps, we identify research priorities necessary to provide evidence-based proofs for disease tolerance in bats. Since disease tolerance relies on networks of biological processes, we emphasize that investigations beyond the immune system, using novel technologies and computational biology, could jointly advance our knowledge about mechanisms conferring bats reservoir abilities. Although disease tolerance may not be the "one fit all" defense strategy, deciphering disease tolerance in bats could translate into novel therapies and inform prevention of spillover infections to humans and livestock.

Gene expression signatures in blood from a West African sepsis cohort define host response phenotypes

Our limited understanding of the pathophysiological mechanisms that operate during sepsis is an obstacle to rational treatment and clinical trial design. There is a critical lack of data from low- and middle-income countries where the sepsis burden is increased which inhibits generalized strategies for therapeutic intervention. Here we perform RNA sequencing of whole blood to investigate longitudinal host response to sepsis in a Ghanaian cohort. Data dimensional reduction reveals dynamic gene expression patterns that describe cell type-specific molecular phenotypes including a dysregulated myeloid compartment shared between sepsis and COVID-19. The gene expression signatures reported here define a landscape of host response to sepsis that supports interventions via targeting immunophenotypes to improve outcomes.

Exploring dose-response relationships in Aedes aegypti survival upon bacteria and arbovirus infection

A detailed understanding of how host fitness changes in response to variations in microbe density (an ecological measure of disease tolerance) is an important aim of infection biology. Here, we applied dose-response curves to study Aedes aegypti survival upon exposure to different microbes. We challenged female mosquitoes with Listeria monocytogenes, a model bacterial pathogen, Dengue 4 virus and Zika virus, two medically relevant arboviruses, to understand the distribution of mosquito survival following microbe exposure. By correlating microbe loads and host health, we found that a blood meal promotes disease tolerance in our systemic bacterial infection model and that mosquitoes orally infected with bacteria had an enhanced defensive capacity than insects infected through injection. We also showed that Aedes aegypti displays a higher survival profile following arbovirus infection when compared to bacterial infections. Here, we applied a framework for investigating microbe-induced mosquito mortality and details how the lifespan of Aedes aegypti varies with different inoculum sizes of bacteria and arboviruses.

Plasma proteomic profiling of bacterial cold water disease-resistant and -susceptible rainbow trout lines and biomarker discovery

Genetic variation for disease resistance is present in salmonid fish; however, the molecular basis is poorly understood, and biomarkers of disease susceptibility/resistance are unavailable. Previously, we selected a line of rainbow trout for high survival following standardized challenge with Flavobacterium psychrophilum (Fp), the causative agent of bacterial cold water disease. The resistant line (ARS-Fp-R) exhibits over 60 percentage points higher survival compared to a reference susceptible line (ARS-Fp-S). To gain insight into the differential host response between genetic lines, we compared the plasma proteomes from day 6 after intramuscular challenge. Pooled plasma from unhandled, PBS-injected, and Fp-injected groups were simultaneously analyzed using a TMT 6-plex label, and the relative abundance of 513 proteins was determined. Data are available via ProteomeXchange, with identifier PXD041308, and the relative protein abundance values were compared to mRNA measured from a prior, whole-body RNA-seq dataset. Our results identified a subset of differentially abundant intracellular proteins was identified, including troponin and myosin, which were not transcriptionally regulated, suggesting that these proteins were released into plasma following pathogen-induced tissue damage. A separate subset of high-abundance, secreted proteins were transcriptionally regulated in infected fish. The highest differentially expressed protein was a C1q family member (designated complement C1q-like protein 3; C1q-LP3) that was upregulated over 20-fold in the infected susceptible line while only modestly upregulated, 1.8-fold, in the infected resistant line. Validation of biomarkers was performed using immunoassays and C1q-LP3, skeletal muscle troponin C, cathelcidin 2, haptoglobin, leptin, and growth and differentiation factor 15 exhibited elevated concentration in susceptible line plasma. Complement factor H-like 1 exhibited higher abundance in the resistant line compared to the susceptible line in both control and challenged fish and thus was a baseline differentiator between lines. C1q-LP3 and STNC were elevated in Atlantic salmon plasma following experimental challenge with Fp. In summary, these findings further the understanding of the differential host response to Fp and identifies salmonid biomarkers that may have use for genetic line evaluation and on-farm health monitoring.

The adaptive microbiome hypothesis and immune interactions in amphibian mucus

The microbiome is known to provide benefits to hosts, including extension of immune function. Amphibians are a powerful immunological model for examining mucosal defenses because of an accessible epithelial mucosome throughout their developmental trajectory, their responsiveness to experimental treatments, and direct interactions with emerging infectious pathogens. We review amphibian skin mucus components and describe the adaptive microbiome as a novel process of disease resilience where competitive microbial interactions couple with host immune responses to select for functions beneficial to the host. We demonstrate microbiome diversity, specificity of function, and mechanisms for memory characteristic of an adaptive immune response. At a time when industrialization has been linked to losses in microbiota important for host health, applications of microbial therapies such as probiotics may contribute to immunotherapeutics and to conservation efforts for species currently threatened by emerging diseases.

Genetic variation and microbiota in bumble bees cross-infected by different strains of C. bombi

The bumblebee Bombus terrestris is commonly infected by a trypanosomatid gut parasite Crithidia bombi. This system shows a striking degree of genetic specificity where host genotypes are susceptible to different genotypes of parasite. To a degree, variation in host gene expression underlies these differences, however, the effects of standing genetic variation has not yet been explored. Here we report on an extensive experiment where workers of twenty colonies of B. terrestris were each infected by one of twenty strains of C. bombi. To elucidate the host's genetic bases of susceptibility to infection (measured as infection intensity), we used a low-coverage (~2 x) genome-wide association study (GWAS), based on angsd, and a standard high-coverage (~15x) GWAS (with a reduced set from a 8 x 8 interaction matrix, selected from the full set of twenty). The results from the low-coverage approach remained ambiguous. The high-coverage approach suggested potentially relevant genetic variation in cell surface and adhesion processes. In particular, mucin, a surface mucoglycoprotein, potentially affecting parasite binding to the host gut epithelia, emerged as a candidate. Sequencing the gut microbial community of the same bees showed that the abundance of bacterial taxa, such as Gilliamella, Snodgrassella, or Lactobacillus, differed between 'susceptible' and 'resistant' microbiota, in line with earlier studies. Our study suggests that the constitutive microbiota and binding processes at the cell surface are candidates to affect infection intensity after the first response (captured by gene expression) has run its course. We also note that a low-coverage approach may not be powerful enough to analyse such complex traits. Furthermore, testing large interactions matrices (as with the full 20 x 20 combinations) for the effect of interaction terms on infection intensity seems to blur the specific host x parasite interaction effects, likely because the outcome of an infection is a highly non-linear process dominated by variation in individually different pathways of host defence (immune) responses.

The buried gems of disease tolerance in animals: Evolutionary and interspecies comparative approaches: Interspecies comparative approaches are valuable tools for exploring potential new mechanisms of disease tolerance in animals: Interspecies comparative approaches are valuable tools for exploring potential new mechanisms of disease tolerance in animals

Host defense mechanisms are categorized into different strategies, namely, avoidance, resistance and tolerance. Resistance encompasses mechanisms that directly kill the pathogen while tolerance is mainly concerned with alleviating the harsh consequences of the infection regardless of the pathogen burden. Resistance is well-known strategy in immunology while tolerance is relatively new. Studies addressed tolerance mainly using mouse models revealing a wide range of interesting tolerance mechanisms. Herein, we aim to emphasize on the interspecies comparative approaches to explore potential new mechanisms of disease tolerance. We will discuss mechanisms of tolerance with focus on those that were revealed using comparative study designs of mammals followed by summarizing the reasons for adopting comparative approaches on disease tolerance studies. Disease tolerance is a relatively new concept in immunology, we believe combining comparative studies with model organism study designs will enhance our understanding to tolerance and unveil new mechanisms of tolerance.

Systems redox biology in health and disease

Living organisms need to be able to cope with environmental challenges and other stressors and mount adequate responses that are as varied as the spectrum of those challenges. Understanding how the multi-layered biological stress responses become integrated across and between different levels of organization within an organism can provide a different perspective on the nature and inter-relationship of complex systems in health and disease. We here compare two concepts which have been very influential in stress research: Selye's 'General Adaptation Syndrome' and Sies's 'Oxidative Stress' paradigm. We show that both can be embraced within a more general framework of 'change and response'. The 'Reactive Species Interactome' allows each of these to be considered as distinct but complementary aspects of the same system, representative of roles at different levels of organization within a functional hierarchy. The versatile chemistry of sulfur - exemplified by hydrogen sulfide, glutathione and proteinous cysteine thiols - enriched by its interactions with reactive oxygen, nitrogen and sulfur species, would seem to sit at the heart of the 'Redox Code' and underpin the ability of complex organisms to cope with stress.

Function and mechanisms in defence strategies

A useful discussion of defence strategies cannot do without linking defence mechanisms to their function, that is, their contributions to fitness. Whereas the former is the domain of immunology, the latter is the subject of evolutionary ecology. For this, the concepts of the defence chart and the disease space can be used to connect the two domains and to sharpen the focus. These use different approaches but converge to the same end, that is, to understand what fitness costs and benefits are associated with existing mechanisms and how to identify the best defence strategy in a given environment.

Breeding for disease resilience: opportunities to manage polymicrobial challenge and improve commercial performance in the pig industry

Disease resilience, defined as an animal's ability to maintain productive performance in the face of infection, provides opportunities to manage the polymicrobial challenge common in pig production. Disease resilience can deliver a number of benefits, including more sustainable production as well as improved animal health and the potential for reduced antimicrobial use. However, little progress has been made to date in the application of disease resilience in breeding programs due to a number of factors, including (1) confusion around definitions of disease resilience and its component traits disease resistance and tolerance, and (2) the difficulty in characterizing such a complex trait consisting of multiple biological functions and dynamic elements of rates of response and recovery from infection. Accordingly, this review refines the definitions of disease resistance, tolerance, and resilience based on previous studies to help improve the understanding and application of these breeding goals and traits under different scenarios. We also describe and summarize results from a "natural disease challenge model" designed to provide inputs for selection of disease resilience. The next steps for managing polymicrobial challenges faced by the pig industry will include the development of large-scale multi-omics data, new phenotyping technologies, and mathematical and statistical methods adapted to these data. Genome editing to produce pigs resistant to major diseases may complement selection for disease resilience along with continued efforts in the more traditional areas of biosecurity, vaccination and treatment. Altogether genomic approaches provide exciting opportunities for the pig industry to overcome the challenges provided by hard-to-manage diseases as well as new environmental challenges associated with climate change.

Topological analysis of the latent geometry of a complex network

Most real-world networks are embedded in latent geometries. If a node in a network is found in the vicinity of another node in the latent geometry, the two nodes have a disproportionately high probability of being connected by a link. The latent geometry of a complex network is a central topic of research in network science, which has an expansive range of practical applications, such as efficient navigation, missing link prediction, and brain mapping. Despite the important role of topology in the structures and functions of complex systems, little to no study has been conducted to develop a method to estimate the general unknown latent geometry of complex networks. Topological data analysis, which has attracted extensive attention in the research community owing to its convincing performance, can be directly implemented into complex networks; however, even a small fraction (0.1%) of long-range links can completely erase the topological signature of the latent geometry. Inspired by the fact that long-range links in a network have disproportionately high loads, we develop a set of methods that can analyze the latent geometry of a complex network: the modified persistent homology diagram and the map of the latent geometry. These methods successfully reveal the topological properties of the synthetic and empirical networks used to validate the proposed methods.

COVID-19: A Redox Disease-What a Stress Pandemic Can Teach Us About Resilience and What We May Learn from the Reactive Species Interactome About Its Treatment

Significance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19), affects every aspect of human life by challenging bodily, socioeconomic, and political systems at unprecedented levels. As vaccines become available, their distribution, safety, and efficacy against emerging variants remain uncertain, and specific treatments are lacking. Recent Advances: Initially affecting the lungs, COVID-19 is a complex multisystems disease that disturbs the whole-body redox balance and can be long-lasting (Long-COVID). Numerous risk factors have been identified, but the reasons for variations in susceptibility to infection, disease severity, and outcome are poorly understood. The reactive species interactome (RSI) was recently introduced as a framework to conceptualize how cells and whole organisms sense, integrate, and accommodate stress. Critical Issues: We here consider COVID-19 as a redox disease, offering a holistic perspective of its effects on the human body, considering the vulnerability of complex interconnected systems with multiorgan/multilevel interdependencies. Host/viral glycan interactions underpin SARS-CoV-2's extraordinary efficiency in gaining cellular access, crossing the epithelial/endothelial barrier to spread along the vascular/lymphatic endothelium, and evading antiviral/antioxidant defences. An inflammation-driven "oxidative storm" alters the redox landscape, eliciting epithelial, endothelial, mitochondrial, metabolic, and immune dysfunction, and coagulopathy. Concomitantly reduced nitric oxide availability renders the sulfur-based redox circuitry vulnerable to oxidation, with eventual catastrophic failure in redox communication/regulation. Host nutrient limitations are crucial determinants of resilience at the individual and population level. Future Directions: While inflicting considerable damage to health and well-being, COVID-19 may provide the ultimate testing ground to improve the diagnosis and treatment of redox-related stress diseases. "Redox phenotyping" of patients to characterize whole-body RSI status as the disease progresses may inform new therapeutic approaches to regain redox balance, reduce mortality in COVID-19 and other redox diseases, and provide opportunities to tackle Long-COVID. Antioxid. Redox Signal. 35, 1226-1268.

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.

Linking functional and molecular mechanisms of host resilience to malaria infection

It remains challenging to understand why some hosts suffer severe illnesses, while others are unscathed by the same infection. We fitted a mathematical model to longitudinal measurements of parasite and red blood cell density in murine hosts from diverse genetic backgrounds to identify aspects of within-host interactions that explain variation in host resilience and survival during acute malaria infection. Among eight mouse strains that collectively span 90% of the common genetic diversity of laboratory mice, we found that high host mortality was associated with either weak parasite clearance, or a strong, yet imprecise response that inadvertently removes uninfected cells in excess. Subsequent cross-sectional cytokine assays revealed that the two distinct functional mechanisms of poor survival were underpinned by low expression of either pro- or anti-inflammatory cytokines, respectively. By combining mathematical modelling and molecular immunology assays, our study uncovered proximate mechanisms of diverse infection outcomes across multiple host strains and biological scales.

The topology of data: Opportunities for cancer research

Motivation

Topological methods have recently emerged as a reliable and interpretable framework for extracting information from high-dimensional data, leading to the creation of a branch of applied mathematics called Topological Data Analysis (TDA). Since then, TDA has been progressively adopted in biomedical research. Biological data collection can result in enormous datasets, comprising thousands of features and spanning diverse datatypes. This presents a barrier to initial data analysis as the fundamental structure of the dataset becomes hidden, obstructing the discovery of important features and patterns. TDA provides a solution to obtain the underlying shape of datasets over continuous resolutions, corresponding to key topological features independent of noise. TDA has the potential to support future developments in healthcare as biomedical datasets rise in complexity and dimensionality. Previous applications extend across the fields of neuroscience, oncology, immunology and medical image analysis. TDA has been used to reveal hidden subgroups of cancer patients, construct organizational maps of brain activity and classify abnormal patterns in medical images. The utility of TDA is broad and to understand where current achievements lie, we have evaluated the present state of TDA in cancer data analysis.

Results

This article aims to provide an overview of TDA in Cancer Research. A brief introduction to the main concepts of TDA is provided to ensure that the article is accessible to readers who are not familiar with this field. Following this, a focussed literature review on the field is presented, discussing how TDA has been applied across heterogeneous datatypes for cancer research.

USING MULTIVARIATE ANALYSES TO EXPLORE DISEASE PROGRESSION OF FINCH MYCOPLASMOSIS

Lesion severity scales have been developed for a number of wildlife diseases causing external pathology. Perhaps the best known and most widely used scoring system has been developed for finch mycoplasmosis in which observers measure conjunctival pathology along a four-point scale of increasing severity. We developed novel techniques to characterize variation in host phenotype based on occupancy of multidimensional trait space (disease space). First, we used shape analysis to track distortions of the inner and outer eye rims, defined by 16 anatomical landmarks. Then, we used community analysis to evaluate pathology based on the presence or absence of a unique set of binary descriptors. We applied these techniques to experimental infection data to relate differences in conjunctival pathology to stage of infection. Specifically, by comparing specimens that received the same severity score at different time points in infection, we asked if shape or community analyses could distinguish between individuals in early infection versus those in recovery. We found that individual eyes followed predictable loops through disease space, tracking further from their origin with more severe pathology. Also, certain pathological descriptors were more likely to appear earlier versus later in infection. Our results indicated that leveraging differences in pathology captured in complex trait space could complement severity scores by better resolving the time course of infection from limited data points.

Can aging research generate a theory of health?

While aging research and policy aim to promote 'health' at all ages, there remains no convincing explanation of what this 'health' is. In this paper, I investigate whether we can find, implicit within the sciences of aging, a way to know what health is and how to measure it, i.e. a theory of health. To answer this, I start from scientific descriptions of aging and its modulators and then try to develop some generalizations about 'health' implicit within this research. After discussing some of the core aspects of aging and the ways in which certain models describe spatial and temporal features specific to both aging and healthy phenotypes, I then extract, explicate, and evaluate one potential construct of health in these models. This suggests a theory of health based on the landscape of optimized phenotypic trajectories. I conclude by considering why it matters for more candidate theories to be proposed and evaluated by philosophers and scientists alike.

Quo vadis? Central Rules of Pathogen and Disease Tropism

Understanding why certain people get sick and die while others recover or never become ill is a fundamental question in biomedical research. A key determinant of this process is pathogen and disease tropism: the locations that become infected (pathogen tropism), and the locations that become damaged (disease tropism). Identifying the factors that regulate tropism is essential to understand disease processes, but also to drive the development of new interventions. This review intersects research from across infectious diseases to define the central mediators of disease and pathogen tropism. This review also highlights methods of study, and translational implications. Overall, tropism is a central but under-appreciated aspect of infection pathogenesis which should be at the forefront when considering the development of new methods of intervention.

A framework for understanding gene expression plasticity and its influence on stress tolerance

Phenotypic plasticity can serve as a stepping stone towards adaptation. Recently, studies have shown that gene expression contributes to emergent stress responses such as thermal tolerance, with tolerant and susceptible populations showing distinct transcriptional profiles. However, given the dynamic nature of gene expression, interpreting transcriptomic results in a way that elucidates the functional connection between gene expression and the observed stress response is challenging. Here, we present a conceptual framework to guide interpretation of gene expression reaction norms in the context of stress tolerance. We consider the evolutionary and adaptive potential of gene expression reaction norms and discuss the influence of sampling timing, transcriptomic resilience, as well as complexities related to life history when interpreting gene expression dynamics and how these patterns relate to host tolerance. We highlight corals as a case study to demonstrate the value of this framework for non-model systems. As species face rapidly changing environmental conditions, modulating gene expression can serve as a mechanistic link from genetic and cellular processes to the physiological responses that allow organisms to thrive under novel conditions. Interpreting how or whether a species can employ gene expression plasticity to ensure short-term survival will be critical for understanding the global impacts of climate change across diverse taxa.

Topological Data Analysis of Coronary Plaques Demonstrates the Natural History of Coronary Atherosclerosis

OBJECTIVES

This study sought to identify distinct patient groups and their association with outcome based on the patient similarity network using quantitative coronary plaque characteristics from coronary computed tomography angiography (CTA).

BACKGROUND

Coronary CTA can noninvasively assess coronary plaques quantitatively.

METHODS

Patients who underwent 2 coronary CTAs at a minimum of 24 months' interval were analyzed (n = 1,264). A similarity Mapper network of patients was built by topological data analysis (TDA) based on the whole-heart quantitative coronary plaque analysis on coronary CTA to identify distinct patient groups and their association with outcome.

RESULTS

Three distinct patient groups were identified by TDA, and the patient similarity network by TDA showed a closed loop, demonstrating a continuous trend of coronary plaque progression. Group A had the least coronary plaque amount (median 12.4 mm³ [interquartile range (IQR): 0.0 to 39.6 mm³]) in the entire coronary tree. Group B had a moderate coronary plaque amount (31.7 mm³ [IQR: 0.0 to 127.4 mm³]) with relative enrichment of fibrofatty and necrotic core (32.6% [IQR: 16.7% to 46.2%] and 2.7% [IQR: 0.1% to 6.9%] of the total plaque, respectively) components. Group C had the largest coronary plaque amount (187.0 mm³ [IQR: 96.7 to 306.4 mm³]) and was enriched for dense calcium component (46.8% [IQR: 32.0% to 63.7%] of the total plaque). At follow-up, total plaque volume, fibrous, and dense calcium volumes increased in all groups, but the proportion of fibrofatty component decreased in groups B and C, whereas the necrotic core portion decreased in only group B (all p < 0.05). Group B showed a higher acute coronary syndrome incidence than other groups (0.3% vs. 2.6% vs. 0.6%; p = 0.009) but both group B and C had a higher revascularization incidence than group A (3.1% vs. 15.5% vs. 17.8%; p < 0.001). Incorporating group information from TDA demonstrated increase of model fitness for predicting acute coronary syndrome or revascularization compared with that incorporating clinical risk factors, percentage diameter stenosis, and high-risk plaque features.

CONCLUSIONS

The TDA of quantitative whole-heart coronary plaque characteristics on coronary CTA identified distinct patient groups with different plaque dynamics and clinical outcomes. (Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging [PARADIGM]; NCT02803411).

Fighting microbial pathogens by integrating host ecosystem interactions and evolution

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation

Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic valve implantation and identifying patient groups with similar clinical outcome. Using the Transcatheter Valve Therapy registry data, we divided 344 patients into 2 sequential cohorts (cohort 1, n = 211, cohort 2, n = 143). We investigated patient similarity analysis to identify unique phenogroups of patients in the first cohort. We subsequently applied the semisupervised AutoML to the second cohort for developing automatic phenogroup labels. The patient similarity network identified 5 patient phenogroups with substantial variations in clinical comorbidities and in-hospital and 30-day outcomes. Cumulative assessment of patients from both cohorts revealed lowest rates of procedural complications in Group 1. In comparison, Group 5 was associated with higher rates of in-hospital cardiovascular mortality (odds ratio [OR] 35, 95% confidence interval [CI] 4 to 309, p = 0.001), in-hospital all-cause mortality (OR 9, 95% CI 2 to 33, p = 0.002), 30-day cardiovascular mortality (OR 18, 95% CI 3 to 94, p <0.001), and 30-day all-cause mortality (OR 3, 95% CI 1.2 to 9, p = 0.02) . For 30-day cardiovascular mortality, using phenogroup data in conjunction with the Society of Thoracic Surgeon score improved the overall prediction of mortality versus using the Society of Thoracic Surgeon scores alone (AUC 0.96 vs AUC 0.8, p = 0.02). In conclusion, we illustrate that semisupervised AutoML platforms identifies unique patient phenogroups who have similar clinical characteristics and overall risk of adverse events post-transcatheter aortic valve implantation.

The Biology of Physiological Health

The ability to maintain health, or recover to a healthy state after disease, is an active process involving distinct adaptation mechanisms coordinating interactions between all physiological systems of an organism. Studies over the past several decades have assumed the mechanisms of health and disease are essentially inter-changeable, focusing on the elucidation of the mechanisms of disease pathogenesis to enhance health, treat disease, and increase healthspan. Here, I propose that the evolved mechanisms of health are distinct from disease pathogenesis mechanisms and suggest that we develop an understanding of the biology of physiological health. In this Perspective, I provide a definition of, a conceptual framework for, and proposed mechanisms of physiological health to complement our understanding of disease and its treatment.

Why breed disease-resilient livestock, and how?

BACKGROUND

Fighting and controlling epidemic and endemic diseases represents a considerable cost to livestock production. Much research is dedicated to breeding disease resilient livestock, but this is not yet a common objective in practical breeding programs. In this paper, we investigate how future breeding programs may benefit from recent research on disease resilience.

MAIN BODY

We define disease resilience in terms of its component traits resistance (R: the ability of a host animal to limit within-host pathogen load (PL)) and tolerance (T: the ability of an infected host to limit the damage caused by a given PL), and model the host's production performance as a reaction norm on PL, depending on R and T. Based on this, we derive equations for the economic values of resilience and its component traits. A case study on porcine respiratory and reproductive syndrome (PRRS) in pigs illustrates that the economic value of increasing production in infectious conditions through selection for R and T can be more than three times higher than by selection for production in disease-free conditions. Although this reaction norm model of resilience is helpful for quantifying its relationship to its component traits, its parameters are difficult and expensive to quantify. We consider the consequences of ignoring R and T in breeding programs that measure resilience as production in infectious conditions with unknown PL-particularly, the risk that the genetic correlation between R and T is unfavourable (antagonistic) and that a trade-off between them neutralizes the resilience improvement. We describe four approaches to avoid such antagonisms: (1) by producing sufficient PL records to estimate this correlation and check for antagonisms-if found, continue routine PL recording, and if not found, shift to cheaper proxies for PL; (2) by selection on quantitative trait loci (QTL) known to influence both R and T in favourable ways; (3) by rapidly modifying towards near-complete resistance or tolerance, (4) by re-defining resilience as the animal's capacity to resist (or recover from) the perturbation caused by an infection, measured as temporal deviations of production traits in within-host longitudinal data series.

CONCLUSIONS

All four alternatives offer promising options for genetic improvement of disease resilience, and most rely on technological and methodological developments and innovation in automated data generation.

Metabolic profiling during malaria reveals the role of the aryl hydrocarbon receptor in regulating kidney injury

Systemic metabolic reprogramming induced by infection exerts profound, pathogen-specific effects on infection outcome. Here, we detail the host immune and metabolic response during sickness and recovery in a mouse model of malaria. We describe extensive alterations in metabolism during acute infection, and identify increases in host-derived metabolites that signal through the aryl hydrocarbon receptor (AHR), a transcription factor with immunomodulatory functions. We find that Ahr^-/- mice are more susceptible to malaria and develop high plasma heme and acute kidney injury. This phenotype is dependent on AHR in Tek-expressing radioresistant cells. Our findings identify a role for AHR in limiting tissue damage during malaria. Furthermore, this work demonstrates the critical role of host metabolism in surviving infection.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

CovidSens: a vision on reliable social sensing for COVID-19

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

Back From the Brink: Alterations in B and T Cell Responses Modulate Recovery of Rainbow Trout From Chronic Immunopathological Tetracapsuloides bryosalmonae Infection

Proliferative kidney disease (PKD) caused by the myxozoan parasite Tetracapsuloides bryosalmonae is one of the most serious infectious diseases negatively impacting farmed and wild salmonids throughout Europe and North America. PKD pathogenesis results in a massive B cell proliferation and dysregulation with aberrant immunoglobulin production and plasma cell differentiation along with a decrease in myeloid cells and inhibition of innate pathways. Despite the huge immunopathological reaction in the kidney during infection, under specific conditions, fish can survive and return to full fitness. Fish are unique in this ability to recover renal structure and functionality from extensive tissue damage in contrast to mammals. However, only limited knowledge exists regarding the host immune response coinciding with PKD recovery. Moreover, almost no studies of the immune response during disease recovery exist in fish. We utilized the rainbow trout-T. bryosalmonae system as an immunological model of disease recovery. Our results demonstrated that recovery is preceded by an intense immune response at the transcript level, decreasing parasite burden, and an increased degree of kidney inflammation. Later in the recovery phase, the immune response transpired with a significant decrease in lymphocytes and an increase in myeloid cells. These lymphocytes populations contained lower levels of B cells comparative to the control in the anterior and posterior kidney. Additionally, there was downregulation of several transcripts used as markers for plasma cells (blimp1, igt sec, igm sec, igd sec, and cd38) and T cell subsets (cd4, cd8α, cd8β, and tcrβ). The decrease in these T cell transcripts significantly correlated with decreasing parasite intensity. Alternatively, there was strong upregulation of pax-5 and igt mem. This suggests a change in B cell processes during the recovery phase relative to clinical PKD may be necessary for the host to re-establish homeostasis in terms of an arrest in the dominant antibody like response transitioning to a transcriptional profile associated with resting B cells. The knowledge generated here in combination with earlier studies illuminates the full power of analyzing the entire trajectory of disease from the normal healthy state to recovery enabling the measurement of an immune response to pinpoint a specific disease stage.

Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Topological data analysis to model the shape of immune responses during co-infections

Co-infections by multiple pathogens have important implications in many aspects of health, epidemiology and evolution. However, how to disentangle the non-linear dynamics of the immune response when two infections take place at the same time is largely unexplored. Using data sets of the immune response during influenza-pneumococcal co-infection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets. We identified persistent shapes of the simplicial complexes of the data in the three infection scenarios: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and we uncovered that the immune response during the co-infection has three phases and two transition points. During the first phase, its dynamics is inherited from its response to the primary (viral) infection. The immune response has an early shift (few hours post co-infection) and then modulates its response to react against the secondary (bacterial) infection. Between 18 and 26 h post co-infection the nature of the immune response changes again and does no longer resembles either of the single infection scenarios.

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine

OBJECTIVES

The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient.

BACKGROUND

Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features.

METHODS

A retrospective cohort of 866 patients was used to develop a network architecture using 9 echocardiographic features of LV structure and function. The data for 468 patients from 2 prospective cohort registries were then added to test the model's generalizability.

RESULTS

The map of cross-sectional data in the retrospective cohort resulted in a looped patient network that persisted even after the addition of data from the prospective cohort registries. After subdividing the loop into 4 regions, patients in each region showed unique differences in LV function, with Kaplan-Meier curves demonstrating significant differences in MACE-related rehospitalization and death (both p < 0.001). Addition of network information to clinical risk predictors resulted in significant improvements in net reclassification, integrated discrimination, and median risk scores for predicting MACE (p < 0.05 for all). Furthermore, the network predicted the cardiac disease cycle in each of the 96 patients who had second echocardiographic evaluations. An improvement or remaining in low-risk regions was associated with lower MACE-related rehospitalization rates than worsening or remaining in high-risk regions (3% vs. 37%; p < 0.001).

CONCLUSIONS

Patient similarity analysis integrates multiple features of cardiac function to develop a phenotypic network in which patients can be mapped to specific locations associated with specific disease stage and clinical outcomes. The use of patient similarity analysis may have relevance for automated staging of cardiac disease severity, personalized prediction of prognosis, and monitoring progression or response to therapies.

Biomarkers for Detecting Resilience against Mycobacterial Disease in Animals

Paratuberculosis and bovine tuberculosis are two mycobacterial diseases of ruminants which have a considerable impact on livestock health, welfare, and production. These are chronic "iceberg" diseases which take years to manifest and in which many subclinical cases remain undetected. Suggested biomarkers to detect infected or diseased animals are numerous and include cytokines, peptides, and expression of specific genes; however, these do not provide a strong correlation to disease. Despite these advances, disease detection still relies heavily on dated methods such as detection of pathogen shedding, skin tests, or serology. Here we review the evidence for suitable biomarkers and their mechanisms of action, with a focus on identifying animals that are resilient to disease. A better understanding of these factors will help establish new strategies to control the spread of these diseases.

Antibiotic resilience: a necessary concept to complement antibiotic resistance?

Resilience is the capacity of systems to recover their initial state or functions after a disturbance. The concepts of resilience and resistance are complementary in ecology and both represent different aspects of the stability of ecosystems. However, antibiotic resilience is not used in clinical bacteriology whereas antibiotic resistance is a recognized major problem. To join the fields of ecology and clinical bacteriology, we first review the resilience concept from ecology, socio-ecological systems and microbiology where it is widely developed. We then review resilience-related concepts in microbiology, including bacterial tolerance and persistence, phenotypic heterogeneity and collective tolerance and resistance. We discuss how antibiotic resilience could be defined and argue that the use of this concept largely relies on its experimental measure and its clinical relevance. We review indicators in microbiology which could be used to reflect antibiotic resilience and used as valuable indicators to anticipate the capacity of bacteria to recover from antibiotic treatments.

TMEM2 Modulates ER Stress in a Non-canonical Manner

Cells utilize multiple mechanisms to support endoplasmic reticulum (ER) function. The unfolded protein response, UPR^ER, is engaged during proteotoxic challenges to either mitigate ER stress or promote apoptosis. In a CRISPR-based genetic screen, Schinzel et al. (2019) identified TMEM2 as a mediator of ER stress tolerance independent of the individual branches of the canonical UPR^ER and linked this path to nematode longevity.

Partitioning the influence of ecology across scales on parasite evolution

Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Host circadian rhythms are disrupted during malaria infection in parasite genotype-specific manners

Infection can dramatically alter behavioural and physiological traits as hosts become sick and subsequently return to health. Such “sickness behaviours” include disrupted circadian rhythms in both locomotor activity and body temperature. Host sickness behaviours vary in pathogen species-specific manners but the influence of pathogen intraspecific variation is rarely studied. We examine how infection with the murine malaria parasite, Plasmodium chabaudi, shapes sickness in terms of parasite genotype-specific effects on host circadian rhythms. We reveal that circadian rhythms in host locomotor activity patterns and body temperature become differentially disrupted and in parasite genotype-specific manners. Locomotor activity and body temperature in combination provide more sensitive measures of health than commonly used virulence metrics for malaria (e.g. anaemia). Moreover, patterns of host disruption cannot be explained simply by variation in replication rate across parasite genotypes or the severity of anaemia each parasite genotype causes. It is well known that disruption to circadian rhythms is associated with non-infectious diseases, including cancer, type 2 diabetes, and obesity. Our results reveal that disruption of host circadian rhythms is a genetically variable virulence trait of pathogens with implications for host health and disease tolerance.

A Primer on Persistent Homology of Finite Metric Spaces

Topological data analysis (TDA) is a relatively new area of research related to importing classical ideas from topology into the realm of data analysis. Under the umbrella term TDA, there falls, in particular, the notion of persistent homology PH, which can be described in a nutshell, as the study of scale-dependent homological invariants of datasets. In these notes, we provide a terse self-contained description of the main ideas behind the construction of persistent homology as an invariant feature of datasets, and its stability to perturbations.

Artificial Intelligence in Cardiovascular Medicine

PURPOSE OF REVIEW

The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year.

RECENT FINDINGS

In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.

Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.

Energetic Trade-Offs and Hypometabolic States Promote Disease Tolerance

Host defenses against pathogens are energetically expensive, leading ecological immunologists to postulate that they might participate in energetic trade-offs with other maintenance programs. However, the metabolic costs of immunity and the nature of physiologic trade-offs it engages are largely unknown. We report here that activation of immunity causes an energetic trade-off with the homeothermy (the stable maintenance of core temperature), resulting in hypometabolism and hypothermia. This immunity-induced physiologic trade-off was independent of sickness behaviors but required hematopoietic sensing of lipopolysaccharide (LPS) via the toll-like receptor 4 (TLR4). Metabolomics and genome-wide expression profiling revealed that distinct metabolic programs supported entry and recovery from the energy-conserving hypometabolic state. During bacterial infections, hypometabolic states, which could be elicited by competition for energy between maintenance programs or energy restriction, promoted disease tolerance. Together, our findings suggest that energy-conserving hypometabolic states, such as dormancy, might have evolved as a mechanism of tissue tolerance.

How Individual Variation in Host Tolerance Affects Competence to Transmit Parasites

Tolerance, or the maintenance of host health or fitness at a given parasite burden, has often been studied in evolutionary and medical contexts, particularly with respect to effects on the evolution of parasite virulence and individual patient outcomes. These bodies of work have provided insight about tolerance for evolutionary phenomena (e.g., virulence) and individual health (e.g., recovering from an infection). However, due to the specific motivations of that work, few studies have considered the ecological ramifications of variation in tolerance, namely, how variation in forms of tolerance could mediate parasite movement through populations and even community-level disease dynamics. Tolerance is most commonly regarded as the relationship between host fitness and parasite burden. However, few if any studies have actually quantified host fitness, instead utilizing proxies of fitness as the response variables to be regressed against parasite burden. Here, we address how attention to the effects of parasite burden on traits that are relevant to host competence (i.e., the ability to amplify parasites to levels transmissible to other hosts/vectors) will enhance our understanding of disease dynamics in nature. We also provide several forms of guidance for how to overcome the challenges of quantifying tolerance in wild organisms.

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.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.