Recommendations for control of pathogens and infectious diseases in fish research facilities

Concerns about infectious diseases in fish used for research have risen along with the dramatic increase in the use of fish as models in biomedical research. In addition to acute diseases causing severe morbidity and mortality, underlying chronic conditions that cause low-grade or subclinical infections may confound research results. Here we present recommendations and strategies to avoid or minimize the impacts of infectious agents in fishes maintained in the research setting. There are distinct differences in strategies for control of pathogens in fish used for research compared to fishes reared as pets or in aquaculture. Also, much can be learned from strategies and protocols for control of diseases in rodents used in research, but there are differences. This is due, in part, the unique aquatic environment that is modified by the source and quality of the water provided and the design of facilities. The process of control of pathogens and infectious diseases in fish research facilities is relatively new, and will be an evolving process over time. Nevertheless, the goal of documenting, detecting, and excluding pathogens in fish is just as important as in mammalian research models.     

Evolution of airborne infectious diseases according to changes in characteristics of the host population

The authors predicted evolutionary changes in airborne infectious diseases according to changes in the characteristics of the host population. The predictions were based upon a mathematical model of infectious diseases and the validity of the predictions was verified against the history of man and pathogens. The feature of this model is that it involves a density of pathogens in the environment as an additional variable which can be regarded as more suitable to airborne infectious diseases. In spite of this modification, this study reached a similar conclusion to the threshold density theory: that is, susceptible host density in the absence of the pathogen must be larger than that in the presence of the pathogen, for the pathogen to be persistent. Moreover the authors concluded that one type of pathogen cannot be replaced by another type of pathogen as long as the susceptible host density of the former type is the mininum one. The predictions were considered to be valid for a wide range of infectous diseases. Making use of these principles, the authors predicted that the variety of infectious diseases should increase as host density increases and that pathogens should evolve to be less virulent as the host life-span increases. The finalidea discussed is whether or nor the history of man and pathogen can be verified by the predictions.     

Infectomics: genomics and proteomics of microbial infections

The completion of genomic sequences is the greatest triumph of molecular reductionism since the discovery of the DNA double helix in 1953. However, the utility of reductionism is becoming limited and holistic approaches, including theories and techniques, are desperately needed in the postgenomic era. In the field of infectious diseases there is an urgent need for global approaches that can efficiently, precisely and integratively study structural and functional genomics and proteomics of microbial infections (infectomics). The combination of new (e.g. DNA and protein microarrays) and traditional approaches (e.g. cloning, PCR, gene knockout and knockin, and antisense) will help overcome the challenges we are facing today. We assume that the global phenotypic changes (infectomes) in microbes and their host during infections are encoded by the genomes of microbial pathogens and their hosts, expressed in certain environmental conditions devoted to specific microbe-host interactions. Global drug responses (pharmacomes) in microbes and their host can be detected by genomic and proteomic approaches. Genome-wide approaches to genotyping and phenotyping or expression profiling will eventually lead to global dissection of microbial pathogenesis, efficient and rapid diagnosis of infectious diseases, and the development of novel strategies to control infections. The key fundamental issue of infectious diseases is how to globally and integratively understand the interactions between microbial pathogens and their hosts by using infectomics. In this review, we focus on the events that are considered important in infectomics. Electronic Publication     

Translating innate immunity into immunological memory: implications for vaccine development

Vaccination is the most effective means of preventing infectious diseases. Despite the success of many vaccines, there is presently little knowledge of the immunological mechanisms that mediate their efficacy. Such information will be critical in the design of future vaccines against old and new infectious diseases. Recent advances in immunology are beginning to provide an intellectual framework with which to address fundamental questions about how the innate immune system shapes adaptive immunity. In this review, we summarize current knowledge about how the innate immune system modulates the quantity and quality of long-term T and B cell memory and protective immune responses to pathogens. In addition, we point out unanswered questions and identify critical challenges, the solution of which, we believe, will greatly facilitate the rational design of novel vaccines against a multitude of emerging infections.     

Directly transmitted viral diseases: modeling the dynamics of transmission

A key hurdle in understanding the spread and control of infectious diseases is to capture appropriately the dynamics of pathogen transmission. As people and goods travel increasingly rapidly around the world, so do pathogens; we must be prepared to understand their spread, in terms of the contact network between hosts, viral life history and within-host dynamics. This will require collaborative work that takes into account viral life history, strategy and evolution, and host genetics, demographics and immunodynamics. Mathematical models are a useful tool for integrating the data and analyses from diverse fields that contribute to our understanding of viral transmission dynamics in heterogeneous host populations.     

感染组学 (infectomics) ———对感染性疾病的总体性和综合性研究

在感染性疾病的范畴内,目前急需一个能有效地、精确地和综合性地研究微生物感染的结构性和功能性基因组学和蛋白质组学 ( 感染组学 ) 的全面方法. 新的方法 ( 如 DNA 和蛋白质微阵列 ) 和传统方法 ( 如分子克隆、 PCR 、基因敲除,加进 (knockin) 和反义术等 ) 的结合将有助于克服今天的困难. 在感染时,微生物及其宿主的全部表型改变 ( 感染组 ) 均由微生物病原体及其宿主的基因组所编码,并在特异的微生物 - 宿主相互作用时的某些环境条件下表达. 微生物及其宿主的全部药物反应 ( 药理组 ) 可用基因组或蛋白质组的方法检出. 分析基因型和表型或表达形式的全基因组方法将最终导致对微生物的发病机理、感染性疾病的快速诊断和控制感染的新策略的全面研究. 感染性疾病中最基本的问题是,如何全面地和综合性地应用感染组学,来了解微生物病原体及其宿主的相互作用.     

Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.     

Modelling infectious disease - time to think outside the box?

Models occupy an essential position in the study of infectious disease as a result of the ethical problems of exposing humans to potentially lethal agents. Deliberately induced infections in well-defined animal models provide much useful information about disease processes in an approximation of their natural context. Despite this, animal models are not the natural disease process, and recent experimental advances show, perhaps not unsurprisingly, that there are large differences between natural infections and animal models. Focusing on mouse models of bacterial pathogens, we discuss some of these discrepancies and suggest ways of improving model systems in the future.     

Molecular analysis of ancient microbial infections

The detection of ancient microbial DNA offers a new approach for the study of infectious diseases, their occurrence, frequency and host-pathogen interaction in historic times and populations. Moreover, data obtained from skeletal and mummified tissue may represent an important completion of contemporary phylogenetic analyses of pathogens. In the last few years, a variety of bacterial, protozoal and viral infections have been detected in ancient tissue samples by amplification and characterization of specific DNA fragments. This holds particularly true for the identification of the Mycobacterium tuberculosis complex, which seems to be more robust than other microbes due to its waxy, hydrophobic and lipid-rich cell wall. These observations provided useful information about the occurrence, but also the frequency of tuberculosis in former populations. Moreover, these studies suggest new evolutionary models and indicate the route of transmission between human and animals. Until now, other pathogens, such as Mycobacterium leprae, Yersinia pestis, Plasmodium falciparum and others, have occasionally been identified - mostly in single case studies or small sample sizes - as well, although much less information is available on these pathogens in ancient settings. The main reason therefore seems to be the degradation and modification of ancient DNA by progressive oxidative damage. Furthermore, the constant risk of contamination by recent DNA forces to take time and cost effective measures and renders the analysis of ancient microbes difficult. Nevertheless, the study of microbial ancient DNA significantly contributes to the understanding of transmission and spread of infectious diseases, and potentially to the evolution and phylogenetic pathways of pathogens.     

Organ-on-a-chip models for elucidating the cellular biology of infectious diseases

Infectious diseases are caused by the invasion of pathogens into a host. To explore the mechanisms of pathogen infections and cellular responses, human models that can accurately recapitulate human pathophysiology are needed. Organ-on-a-chip is a type of advanced in vitro model system that cultures cells in microfluidic devices to replicate physiologically relevant microenvironments such as 3D structures, shear stress, and mechanical stimulation. Recently, organ-on-a-chips have been widely adopted to examine the pathophysiology of infectious diseases in detail. Here, we will summarize recent advances in infectious disease research of visceral organs such as the lung, intestine, liver, and kidneys, using organ-on-a-chips.     

The structure and function of drug pumps

Resistance to drugs has emerged in biological systems as diverse as cancer cells undergoing chemotherapy and microbial pathogens undergoing treatment with antimicrobials. This medical problem is escalating and there is an urgent need for the development of new classes of drugs. In the case of pathogenic bacteria, we are rapidly approaching a scenario where there will be no effective antibiotics in the armoury of drugs available for treating the infectious diseases that these bacteria cause, returning us to the pre-antibiotic era when infectious diseases were rife because they were untreatable. One of the most frequently employed resistance strategies in both prokaryotes and eukaryotes is the transmembrane-protein-catalysed extrusion of drugs from the cell, with these proteins acting like bilge pumps, reducing the intracellular drug concentration to subtoxic levels. There is currently much scientific interest in understanding how these pumps operate, so that we might design transport inhibitors that would block them, allowing a renaissance for drugs that are no longer effective owing to their efflux.     

Control Strategies in Multigroup Models: The Case of the Star Network Topology

In this paper, we propose control strategies for multigroup epidemic models. We use compartmental \({\textit{SIRS}}\) models to study the dynamics of n host groups sharing the same source of infection in addition to the transmission among members of the same group. In particular, we consider a model for infectious diseases with free-living pathogens in the environment and a metapopulation model with a central patch. We give the detailed derivation of the target reproduction number under three public health interventions and provide the corresponding biological insights. Moreover, using the next-generation approach, we calculate the basic reproduction numbers associated with subsystems of our models and determine algebraic connections to the target reproduction number of the complete model. The analysis presented here illustrates that understanding the topological structure of the infection process and partitioning it into simple cycles is useful to design and evaluate the control strategies.     

Multi-locus sequence typing: a tool for global epidemiology

The characterization of pathogenic isolates plays a pivotal role in the epidemiology of infectious diseases, generating the information necessary for identifying, tracking, and intervening against disease outbreaks. In 1998 multi-locus sequence typing (MLST) was proposed as a nucleotide sequence-based approach that could be applied to many bacterial pathogens. It combined developments in high-throughput sequencing and bioinformatics with established population genetics techniques to provide a portable, reproducible, and scalable typing system that reflected the population and evolutionary biology of bacterial pathogens. MLST schemes have been developed for a variety of procaryotic and eucaryotic pathogens and the data generated have contributed to both epidemiological surveillance and fundamental studies of pathogen biology.     

多位点测序分型技术在病原微生物分型鉴定中的应用

多位点测序分型(Multilocus sequence typing,MLST)技术是一种以核苷酸序列为基础的病原菌分型方法,它是高通量测序技术与成熟的群体遗传学相结合的产物。该方法简单易行,重复性强,可以通过国际互联网对某一致病菌株在全球范围内的传播分布情况进行追踪监控。目前,MLST技术已被广泛应用于原核病原菌及一些真核病原菌(如真菌)的分型鉴定中。主要对MLST技术的原理及其在一些常见病原菌分型鉴定中的应用进行了简要的阐述。     

Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing

In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic susceptible-infectious-removed (SIR) epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an Appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations.     

Poverty,Disease, and the Ecology of Complex Systems

Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development.     

Natural products and their semi-synthetic derivatives against antimicrobial-resistant human pathogenic bacteria and fungi

Human infectious diseases caused by various microbial pathogens, in general, impact a large population of individuals every year. These microbial diseases that spread quickly remain to be a big issue in various health-related domains and to withstand the negative drug impacts, the antimicrobial-resistant pathogenic microbial organisms (pathogenic bacteria and pathogenic fungi) have developed a variety of resistance processes against many antimicrobial drug classes. During the COVID-19 outbreak, there seems to be an upsurge in drug and multidrug resistant-associated pathogenic microbial species. The preponderance of existing antimicrobials isn’t completely effective, which limits their application in clinical settings. Several naturally occurring chemicals produced from bacteria, plants, animals, marine species, and other sources are now being studied for antimicrobial characteristics. These natural antimicrobial compounds extracted from different sources have been demonstrated to be effective against a variety of diseases, although plants remain the most abundant source. These compounds have shown promise in reducing the microbial diseases linked to the development of drug tolerance and resistance. This paper offers a detailed review of some of the most vital and promising natural compounds and their derivatives against various human infectious microbial organisms. The inhibitory action of different natural antimicrobial compounds, and their possible mechanism of antimicrobial action against a range of pathogenic fungal and bacterial organisms, is provided. The review will be useful in refining current antimicrobial (antifungal and antibacterial) medicines as well as establishing new treatment strategies to tackle the rising number of human bacterial and fungal-associated infections.