The Nature of Host–Pathogen Interactions

2021 ◽  
pp. 7-28
Author(s):  
Jennifer C. Owen ◽  
James S. Adelman ◽  
Amberleigh E. Henschen
Keyword(s):  
Infectious Disease ◽  
Disease Ecology ◽  
Population Level ◽  
Host Pathogen Interactions ◽  
Epidemiological Modeling ◽  
Individual Host ◽  
Host Pathogen ◽  
Multiple Meanings ◽  
The Individual ◽  
In The Beginning

The dynamics of infectious disease are driven by the fundamental processes that mediate host–pathogen interactions. A basic understanding of the mechanisms underlying these interactions is essential for disease ecologists regardless of their scale of inquiry. This chapter covers the terms and concepts commonly used in ecological studies of infectious disease across levels of organization and scales of inquiry, from the individual host organism to host populations and multispecies communities. When applicable, aspects that are unique to birds and their biology are highlighted. The between-host processes discussed in the beginning of the chapter arise from the within-host processes between the pathogen and the host’s immune system. These processes are then used as a framework to introduce the basics of epidemiological modeling and the population-level disease dynamics. The chapter is not meant to be exhaustive but, instead, to provide a common foundation for readers approaching this topic from unique backgrounds. Given the transdisciplinary nature of avian infectious disease ecology, many of the terms used have multiple meanings assigned to them that are taxon- or discipline-specific. Such variation in key terminology is, in large part, a consequence of the transdisciplinary and multiscaled approaches inherent in studying host–pathogen–vector–environment interactions.

scholarly journals Next-generation technologies for studying host–pathogen interactions: a focus on dual transcriptomics, CRISPR/Cas9 screening and organs-on-chips

2019 ◽  
Vol 77 (6) ◽  
Author(s):  
Buket Baddal
Keyword(s):  
Infectious Disease ◽  
Drug Targets ◽  
Genome Mining ◽  
Three Dimensional ◽  
Local Environment ◽  
Rna Seq ◽  
Host Pathogen Interactions ◽  
Host Pathogen ◽  
On Chip ◽  
Organ Models

ABSTRACT Pathogens constantly interact with their hosts and the environment, and therefore have evolved unique virulence mechanisms to target and breach host defense barriers and manipulate host immune response to establish an infection. Advances in technologies that allow genome mining, gene editing such as CRISPR/Cas9, genomic, epigenomic and transcriptomic studies such as dual RNA-seq, coupled with bioinformatics, have accelerated the field of host–pathogen interactions within a broad range of infection models. Underpinning of the molecular changes that accompany invasion of eukaryotic cells with pathogenic microorganisms at the intersection of host, pathogen and their local environment has provided a better understanding of infectious disease mechanisms and antimicrobial strategies. The recent evolution of physiologically relevant three-dimensional (3-D) tissue/organ models and microfluidic organ-on-chip devices also provided a window to a more predictive framework of infectious disease processes. These approaches combined hold the potential to highly impact discovery of novel drug targets and vaccine candidates of the future. Here, we review three of the available and emerging technologies—dual RNA-seq, CRISPR/Cas9 screening and organs-on-chips, applicable to the high throughput study and deciphering of interaction networks between pathogens and their hosts that are critical for the development of novel therapeutics.

scholarly journals A HYBRID MODEL FOR STUDYING SPATIAL ASPECTS OF INFECTIOUS DISEASES

2012 ◽  
Vol 54 (1-2) ◽  
pp. 37-49 ◽  
Author(s):  
BENJAMIN J. BINDER ◽  
JOSHUA V. ROSS ◽  
MATTHEW J. SIMPSON
Keyword(s):  
Infectious Disease ◽  
Spatial Analysis ◽  
Infectious Diseases ◽  
Hybrid Model ◽  
Population Level ◽  
Infectious Agents ◽  
Agent Based ◽  
Individual Level ◽  
The Individual ◽  
Spread Of Infection

AbstractWe consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

scholarly journals Spatial dynamics and genetics of infectious diseases on heterogeneous landscapes

2007 ◽  
Vol 4 (16) ◽  
pp. 935-948 ◽  
Author(s):  
Leslie A Real ◽  
Roman Biek
Keyword(s):  
Infectious Disease ◽  
Spatial Genetic Structure ◽  
Management Strategies ◽  
Spatial Dynamics ◽  
Disease Process ◽  
Disease Spread ◽  
Host Pathogen Interactions ◽  
Host Pathogen ◽  
Control And Management ◽  
And Control

Explicit spatial analysis of infectious disease processes recognizes that host–pathogen interactions occur in specific locations at specific times and that often the nature, direction, intensity and outcome of these interactions depend upon the particular location and identity of both host and pathogen. Spatial context and geographical landscape contribute to the probability of initial disease establishment, direction and velocity of disease spread, the genetic organization of resistance and susceptibility, and the design of appropriate control and management strategies. In this paper, we review the manner in which the physical organization of the landscape has been shown to influence the population dynamics and spatial genetic structure of host–pathogen interactions, and how we might incorporate landscape architecture into spatially explicit population models of the infectious disease process to increase our ability to predict patterns of disease occurrence and optimally design vaccination and control policies.

scholarly journals Modeling Host-Pathogen Interactions in the Context of the Microenvironment: Three-Dimensional Cell Culture Comes of Age

2018 ◽  
Vol 86 (11) ◽  
Author(s):  
Jennifer Barrila ◽  
Aurélie Crabbé ◽  
Jiseon Yang ◽  
Karla Franco ◽  
Seth D. Nydam ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Historical Context ◽  
Three Dimensional ◽  
Microbial Pathogens ◽  
Host Pathogen Interactions ◽  
Commensal Microbiota ◽  
Disease Mechanisms ◽  
Host Pathogen ◽  
Single Cell Type ◽  
Biomechanical Forces

ABSTRACTTissues and organs provide the structural and biochemical landscapes upon which microbial pathogens and commensals function to regulate health and disease. While flat two-dimensional (2-D) monolayers composed of a single cell type have provided important insight into understanding host-pathogen interactions and infectious disease mechanisms, these reductionist models lack many essential features present in the native host microenvironment that are known to regulate infection, including three-dimensional (3-D) architecture, multicellular complexity, commensal microbiota, gas exchange and nutrient gradients, and physiologically relevant biomechanical forces (e.g., fluid shear, stretch, compression). A major challenge in tissue engineering for infectious disease research is recreating this dynamic 3-D microenvironment (biological, chemical, and physical/mechanical) to more accurately model the initiation and progression of host-pathogen interactions in the laboratory. Here we review selected 3-D models of human intestinal mucosa, which represent a major portal of entry for infectious pathogens and an important niche for commensal microbiota. We highlight seminal studies that have used these models to interrogate host-pathogen interactions and infectious disease mechanisms, and we present this literature in the appropriate historical context. Models discussed include 3-D organotypic cultures engineered in the rotating wall vessel (RWV) bioreactor, extracellular matrix (ECM)-embedded/organoid models, and organ-on-a-chip (OAC) models. Collectively, these technologies provide a more physiologically relevant and predictive framework for investigating infectious disease mechanisms and antimicrobial therapies at the intersection of the host, microbe, and their local microenvironments.

Optimality analysis of Th1/Th2 immune responses during microparasite-macroparasite co-infection, with epidemiological feedbacks

Parasitology ◽  
2008 ◽  
Vol 135 (7) ◽  
pp. 841-853 ◽  
Author(s):  
ANDY FENTON ◽  
TRACEY LAMB ◽  
ANDREA L. GRAHAM
Keyword(s):  
Immune Responses ◽  
Population Level ◽  
Parasite Abundance ◽  
Individual Level ◽  
Host Immune Responses ◽  
Individual Host ◽  
Diverse Community ◽  
The Individual ◽  
Optimality Models

SUMMARYIndividuals are typically co-infected by a diverse community of microparasites (e.g. viruses or protozoa) and macroparasites (e.g. helminths). Vertebrates respond to these parasites differently, typically mounting T helper type 1 (Th1) responses against microparasites and Th2 responses against macroparasites. These two responses may be antagonistic such that hosts face a ‘decision’ of how to allocate potentially limiting resources. Such decisions at the individual host level will influence parasite abundance at the population level which, in turn, will feed back upon the individual level. We take a first step towards a complete theoretical framework by placing an analysis of optimal immune responses under microparasite-macroparasite co-infection within an epidemiological framework. We show that the optimal immune allocation is quantitatively sensitive to the shape of the trade-off curve and qualitatively sensitive to life-history traits of the host, microparasite and macroparasite. This model represents an important first step in placing optimality models of the immune response to co-infection into an epidemiological framework. Ultimately, however, a more complete framework is needed to bring together the optimal strategy at the individual level and the population-level consequences of those responses, before we can truly understand the evolution of host immune responses under parasite co-infection.

How the Cluster-randomized Trial “Works”

2019 ◽  
Vol 70 (2) ◽  
pp. 341-346 ◽  
Author(s):  
James C Hurley
Keyword(s):  
Infectious Disease ◽  
Infection Prevention ◽  
Population Level ◽  
Cluster Randomized Trial ◽  
Prevention Interventions ◽  
Research Questions ◽  
Cluster Randomized ◽  
The Individual ◽  
The Impact ◽  
Infectious Disease Dynamics

AbstractCluster-randomized trials (CRTs) are able to address research questions that randomized controlled trials (RCTs) of individual patients cannot answer. Of great interest for infectious disease physicians and infection control practitioners are research questions relating to the impact of interventions on infectious disease dynamics at the whole-of-population level. However, there are important conceptual differences between CRTs and RCTs relating to design, analysis, and inference. These differences can be illustrated by the adage “peas in a pod.” Does the question of interest relate to the “peas” (the individual patients) or the “pods” (the clusters)? Several examples of recent CRTs of community and intensive care unit infection prevention interventions are used to illustrate these key concepts. Examples of differences between the results of RCTs and CRTs on the same topic are given.

scholarly journals Behavioural ecology and infectious disease: implications for conservation of biodiversity

2019 ◽  
Vol 374 (1781) ◽  
pp. 20180054 ◽  
Author(s):  
James Herrera ◽  
Charles L. Nunn
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Disease Ecology ◽  
Scale Up ◽  
Species Conservation ◽  
Population Level ◽  
Behavioural Ecology ◽  
Parasite Transmission ◽  
Transmission Potential ◽  
Increased Risk

Behaviour underpins interactions among conspecifics and between species, with consequences for the transmission of disease-causing parasites. Because many parasites lead to declines in population size and increased risk of extinction for threatened species, understanding the link between host behaviour and disease transmission is particularly important for conservation management. Here, we consider the intersection of behaviour, ecology and parasite transmission, broadly encompassing micro- and macroparasites. We focus on behaviours that have direct impacts on transmission, as well as the behaviours that result from infection. Given the important role of parasites in host survival and reproduction, the effects of behaviour on parasitism can scale up to population-level processes, thus affecting species conservation. Understanding how conservation and infectious disease control strategies actually affect transmission potential can therefore often only be understood through a behavioural lens. We highlight how behavioural perspectives of disease ecology apply to conservation by reviewing the different ways that behavioural ecology influences parasite transmission and conservation goals. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

scholarly journals Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains

2019 ◽  
Author(s):  
Abdou Moutalab Fofana ◽  
Amy Hurford
Keyword(s):  
Rapid Evolution ◽  
Population Level ◽  
Disease Spread ◽  
Medical Interventions ◽  
Individual Host ◽  
High Virulence ◽  
Human Immunodeficiency Viruses ◽  
The Individual ◽  
Host Parasite ◽  
Host Movement

AbstractMany parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the transient coexistence of strains with different virulence, which may explain the coexistence of low- and high-pathogenic strains of avian influenza and human immunodeficiency viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population-level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host-level.

A multi-scale cholera model linking between-host and within-host dynamics

2018 ◽  
Vol 11 (03) ◽  
pp. 1850034
Author(s):  
Chayu Yang ◽  
Drew Posny ◽  
Feng Bao ◽  
Jin Wang
Keyword(s):  
Human Body ◽  
Population Level ◽  
Sir Model ◽  
Disease Dynamics ◽  
Modeling Framework ◽  
Multi Scale ◽  
Scale Modeling ◽  
Individual Host ◽  
Multi Scale Modeling ◽  
The Individual

We propose a multi-scale modeling framework to investigate the transmission dynamics of cholera. At the population level, we employ a SIR model for the between-host transmission of the disease. At the individual host level, we describe the evolution of the pathogen within the human body. The between-host and within-host dynamics are connected through an environmental equation that characterizes the growth of the pathogen and its interaction with the hosts outside the human body. We put a special emphasis on the within-host dynamics by making a distinction for each individual host. We conduct both mathematical analysis and numerical simulation for our model in order to explore various scenarios associated with cholera transmission and to better understand the complex, multi-scale disease dynamics.

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