Intraspecific diversity of the marine fish pathogen Tenacibaculum maritimum as determined by randomly amplified polymorphic DNA-PCR

AIM: The aim of the present study was to evaluate the intraspecific genetic variability within Tenacibaculum maritimum strains isolated from different species of marine fish. METHODS AND RESULTS: Twenty-nine strains isolated from five different fish species and three reference strains were characterized by randomly amplified polymorphic DNA (RAPD) method. Cluster analysis of RAPD-PCR profiles showed that the strains, regardless of the oligonucleotide primer employed (P2 and P6), were separated into two main groups that strongly correlated with the host species and/or O-serotypes described for this pathogen. One group composed all strains isolated from sole (Solea senegalensis and S. solea) and gilthead seabream (Sparus aurata), and the other compiled the T. maritimum isolates from yellowtail (Seriola quinqueradiata), Atlantic salmon (Salmo salar) and turbot (Scophthalmus maximus). An important exception was observed in the RAPD patterns of the reference strains, which were included in different genetic groups depending on the primer employed. CONCLUSIONS: The results obtained demonstrated genetic variability within the T. maritimum isolated from different marine fish. Such genetic variability proved to be strongly associated with the host and/or serogroups described for this pathogen. SIGNIFICANCE AND IMPACT OF THE STUDY: The RAPD analysis constitutes a valuable molecular technique for epidemiological studies of T. maritimum. Interestingly, this is the first report of intraspecific differentiation and characterization of T. maritimum strains isolated from cultured fish.     

Two optimal mutation rates in obligate pathogens subject to deleterious mutation

Pathogen species with high mutation rates are likely to accumulate deleterious mutations that reduce their reproductive potential within the host. By altering the within-host growth rate of the pathogen, the deleterious mutation load has the potential to affect epidemiological properties such as prevalence, mean pathogen load, and the mean duration of infections. Here, I examine an epidemiological model that allows for multiple segregating mutations that affect within-host replication efficiency. The model demonstrates a complex range of outcomes depending on pathogen mutation rate, including two distinct, widely separated mutation rates associated with high pathogen prevalence. The low mutation rate prevalence peak is associated with small amounts of genetic diversity within the pathogen population, relatively stable prevalence and infection dynamics, and genetic variation partitioned between hosts. The high mutation rate peak is characterized by considerable genetic diversity both within and between hosts, relatively frequent invasions by more virulent types, and is qualitatively similar to an RNA virus quasispecies. The two prevalence peaks are separated by a valley where natural selection favors evolution toward the optimal within-host state, which is associated with high virulence and relatively rapid host mortality. Both chronic and acute infections are examined using stochastic forward simulations.     

Regression-Based Ranking of Pathogen Strains with Respect to Their Contribution to Natural Epidemics

Genetic variation in pathogen populations may be an important factor driving heterogeneity in disease dynamics within their host populations. However, to date, we understand poorly how genetic diversity in diseases impact on epidemiological dynamics because data and tools required to answer this questions are lacking. Here, we combine pathogen genetic data with epidemiological monitoring of disease progression, and introduce a statistical exploratory method to investigate differences among pathogen strains in their performance in the field. The method exploits epidemiological data providing a measure of disease progress in time and space, and genetic data indicating the relative spatial patterns of the sampled pathogen strains. Applying this method allows to assign ranks to the pathogen strains with respect to their contributions to natural epidemics and to assess the significance of the ranking. This method was first tested on simulated data, including data obtained from an original, stochastic, multi-strain epidemic model. It was then applied to epidemiological and genetic data collected during one natural epidemic of powdery mildew occurring in its wild host population. Based on the simulation study, we conclude that the method can achieve its aim of ranking pathogen strains if the sampling effort is sufficient. For powdery mildew data, the method indicated that one of the sampled strains tends to have a higher fitness than the four other sampled strains, highlighting the importance of strain diversity for disease dynamics. Our approach allowing the comparison of pathogen strains in natural epidemic is complementary to the classical practice of using experimental infections in controlled conditions to estimate fitness of different pathogen strains. Our statistical tool, implemented in the R package StrainRanking, is mainly based on regression and does not rely on mechanistic assumptions on the pathogen dynamics. Thus, the method can be applied to a wide range of pathogens.     

Pseudomonas viridiflava, a multi host plant pathogen with significant genetic variation at the molecular level

The pectinolytic species Pseudomonas viridiflava has a wide host range among plants, causing foliar and stem necrotic lesions and basal stem and root rots. However, little is known about the molecular evolution of this species. In this study we investigated the intraspecies genetic variation of P. viridiflava amongst local (Cretan), as well as international isolates of the pathogen. The genetic and phenotypic variability were investigated by molecular fingerprinting (rep-PCR) and partial sequencing of three housekeeping genes (gyrB, rpoD and rpoB), and by biochemical and pathogenicity profiling. The biochemical tests and pathogenicity profiling did not reveal any variability among the isolates studied. However, the molecular fingerprinting patterns and housekeeping gene sequences clearly differentiated them. In a broader phylogenetic comparison of housekeeping gene sequences deposited in GenBank, significant genetic variability at the molecular level was found between isolates of P. viridiflava originated from different host species as well as among isolates from the same host. Our results provide a basis for more comprehensive understanding of the biology, sources and shifts in genetic diversity and evolution of P. viridiflava populations and should support the development of molecular identification tools and epidemiological studies in diseases caused by this species.     

Complete Genome Sequence of Brucella abortus A13334, a New Strain Isolated from the Fetal Gastric Fluid of Dairy Cattle

Brucella abortus is a major pathogen that infects livestock and humans. A new strain of B. abortus (A13334) was isolated from the fetal gastric fluid of a dairy cow, with the aim of using it to compare genetic properties, analyze virulence factor, and survey the epidemiological relationship to other Brucella species. Here, we report the complete and annotated genome sequence of B. abortus A13334.     

Evolution of Pathogen Specialisation in a Host Metapopulation: Joint Effects of Host and Pathogen Dispersal

Metapopulation processes are important determinants of epidemiological and evolutionary dynamics in host-pathogen systems, and are therefore central to explaining observed patterns of disease or genetic diversity. In particular, the spatial scale of interactions between pathogens and their hosts is of primary importance because migration rates of one species can affect both spatial and temporal heterogeneity of selection on the other. In this study we developed a stochastic and discrete time simulation model to specifically examine the joint effects of host and pathogen dispersal on the evolution of pathogen specialisation in a spatially explicit metapopulation. We consider a plant-pathogen system in which the host metapopulation is composed of two plant genotypes. The pathogen is dispersed by air-borne spores on the host metapopulation. The pathogen population is characterised by a single life-history trait under selection, the infection efficacy. We found that restricted host dispersal can lead to high amount of pathogen diversity and that the extent of pathogen specialisation varied according to the spatial scale of host-pathogen dispersal. We also discuss the role of population asynchrony in determining pathogen evolutionary outcomes.     

Culture-free genome-wide locus sequence typing (GLST) provides new perspectives on Trypanosoma cruzi dispersal and infection complexity

Analysis of genetic polymorphism is a powerful tool for epidemiological surveillance and research. Powerful inference from pathogen genetic variation, however, is often restrained by limited access to representative target DNA, especially in the study of obligate parasitic species for which ex vivo culture is resource-intensive or bias-prone. Modern sequence capture methods enable pathogen genetic variation to be analyzed directly from host/vector material but are often too complex and expensive for resource-poor settings where infectious diseases prevail. This study proposes a simple, cost-effective ‘genome-wide locus sequence typing’ (GLST) tool based on massive parallel amplification of information hotspots throughout the target pathogen genome. The multiplexed polymerase chain reaction amplifies hundreds of different, user-defined genetic targets in a single reaction tube, and subsequent agarose gel-based clean-up and barcoding completes library preparation at under 4 USD per sample. Our study generates a flexible GLST primer panel design workflow for Trypanosoma cruzi, the parasitic agent of Chagas disease. We successfully apply our 203-target GLST panel to direct, culture-free metagenomic extracts from triatomine vectors containing a minimum of 3.69 pg/μl T. cruzi DNA and further elaborate on method performance by sequencing GLST libraries from T. cruzi reference clones representing discrete typing units (DTUs) TcI, TcIII, TcIV, TcV and TcVI. The 780 SNP sites we identify in the sample set repeatably distinguish parasites infecting sympatric vectors and detect correlations between genetic and geographic distances at regional (< 150 km) as well as continental scales. The markers also clearly separate TcI, TcIII, TcIV and TcV + TcVI and appear to distinguish multiclonal infections within TcI. We discuss the advantages, limitations and prospects of our method across a spectrum of epidemiological research.     

Population genetics of a transformable bacterium: The influence of horizontal genetic exchange on the biology of Neisseria meningitidis

Abstract Information on the biochemistry and genetics of bacterial species, usually obtained by the study of single isolates, is enhanced by studies of populations of bacteria. Recent advances in molecular technology, particularly polymerase chain reaction-based nucleotide sequence analysis, provide powerful for the study of population genetics. Data obtained by such techniques indicate that, while some bacterial species have a clonal population structure, others are non-clonal or panmictic. Clonal populations are a consequence of asexual reproduction by binary fission; panmictic population structures results from 'horizontal' exchange of genetic material between clones. A consequence of horizontal genetic exchange is mosaic gene structures, recognisable by comparisons of nucleotide sequences. In transformable bacteria, for example the human pathogen Neisseria meningitidis , several different genes, including the gene encoding the class 1 outer membrane protein, a major surface antigen, are mosaics. This genetic process has implications both for vaccine design and in the interpretation of epidemiological data.     

Estimating the basic reproductive number from viral sequence data

Epidemiological processes leave a fingerprint in the pattern of genetic structure of virus populations. Here, we provide a new method to infer epidemiological parameters directly from viral sequence data. The method is based on phylogenetic analysis using a birth-death model (BDM) rather than the commonly used coalescent as the model for the epidemiological transmission of the pathogen. Using the BDM has the advantage that transmission and death rates are estimated independently and therefore enables for the first time the estimation of the basic reproductive number of the pathogen using only sequence data, without further assumptions like the average duration of infection. We apply the method to genetic data of the HIV-1 epidemic in Switzerland.     

Polymorphic microsatellite markers for the human oomycete pathogen Pythium insidiosum

We describe the development and characterization of microsatellite loci from the human oomycete pathogen Pythium insidiosum. Nine of 15 microsatellite loci were shown to be appropriate for population genetic study. All loci were polymorphic with observed heterozygosities ranging from 0.241 to 0.912 and from five to 18 alleles per locus among 65 individuals in Thailand. These markers are being used to ascertain multilocus genotypes for molecular epidemiological and population genetic analyses of this little known human pathogen.     

A new phylodynamic model of Mycobacterium bovis transmission in a multi-host system uncovers the role of the unobserved reservoir

Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems.     

The evolution of epidemic influenza

Recent developments in complete-genome sequencing, antigenic mapping and epidemiological modelling are greatly improving our knowledge of the evolution of human influenza virus at the epidemiological scale. In particular, recent studies have revealed a more complex relationship between antigenic evolution, natural selection and reassortment than previously realized. Despite these advances, there is much that remains to be understood about the epidemiology of influenza virus, particularly the processes that determine the virus's strong seasonality. We argue that a complete understanding of the evolutionary biology of this important human pathogen will require a genomic view of genetic diversity, including the acquisition of polymorphism data from within individual hosts and from geographical regions, particularly the tropics, which have been poorly surveyed to date.     

<Emphasis Type="Italic">Candida dubliniensis</Emphasis>: Epidemiology and Phenotypic Methods for Identification

Candida dubliniensis is an emerging pathogen first described in 1995, which shares many phenotypic features with Candida albicans and therefore may be misidentified in microbial laboratories. Despite various phenotypic techniques described in the literature to differentiate the two species, the correct identification of C. dubliniensis remains problematic due to phenotypic similarities between these species. Thus, as the differences between both are best characterized at genetic levels, several molecular methods have been proposed to provide a specific and rapid identification of this species. Epidemiological studies have shown that C. dubliniensis is prevalent throughout the world and it is primarily associated with oral carriage and oropharyngeal infections in patients infected with human immunodeficiency virus (HIV). However, data acquired from its isolation from other healthy and immunocompromised patients are variable, and there is still no real consensus on the epidemiological relevance of this species. In this article, we review the various phenotypic methods used in the identification of C. dubliniensis and the epidemiological impact of this new species.     

Inferring the Source of Transmission with Phylogenetic Data

Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which has made it difficult to develop statistical tests to either confirm or deny putative transmission pairs with genetic data. We present a method to incorporate additional information about an infectious disease epidemic, such as incidence and prevalence of infection over time, to inform estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy. These methods enable forensic applications, such as source-case attribution, for infectious disease epidemics with incomplete sampling, which is usually the case for high-morbidity community-acquired pathogens like HIV, Influenza and Dengue virus. These methods also enable epidemiological applications such as the identification of factors that increase the risk of transmission. We demonstrate these methods in the context of the HIV epidemic in Detroit, Michigan, and we evaluate the suitability of current sequence databases for forensic and epidemiological investigations. We find that currently available sequences collected for drug resistance testing of HIV are unlikely to be useful in most forensic investigations, but are useful for identifying transmission risk factors.     

Landscape simplification shapes pathogen prevalence in plant‐pollinator networks

Species interaction networks, which play an important role in determining pathogen transmission and spread in ecological communities, can shift in response to agricultural landscape simplification. However, we know surprisingly little about how landscape simplification‐driven changes in network structure impact epidemiological patterns. Here, we combine mathematical modelling and data from eleven bipartite plant‐pollinator networks observed along a landscape simplification gradient to elucidate how changes in network structure shape disease dynamics. Our empirical data show that landscape simplification reduces pathogen prevalence in bee communities via increased diet breadth of the dominant species. Furthermore, our empirical data and theoretical model indicate that increased connectance reduces the likelihood of a disease outbreak and decreases variance in prevalence among bee species in the community, resulting in a dilution effect. Because infectious diseases are implicated in pollinator declines worldwide, a better understanding of how land use change impacts species interactions is therefore critical for conserving pollinator health.     

Analysis of total DNA by minisatellite and simple-sequence repeat primers for the use of population studies in Plasmopara halstedii

Total DNA of Plasmopara halstedii isolates from Germany was analysed for polymorphisms potentially useful for the differentiation of field isolates with respect to epidemiological studies or pathotype characterization. The isolation of the DNA started from mitotically formed zoosporangia, which is the only cellular structure of the biotrophic pathogen accessible independently from its host. The total DNA of the pathogen was used to perform DNA fingerprints with minisatellite and simple-sequence repeat primers. Polymorphisms were found that allowed differentiation on the level of single field isolates; however, they were not correlated with either physiological races or the geographic origin of the isolates. Using such differentiating primers, single spore strains of three pathogen isolates were also analysed with respect to genetic homogeneity. Minor variation was visible in the mitotically derived offspring, but the overall appearance of these patterns was mostly uniform with those of the respective parental isolate.     

Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data

Knowledge on the transmission tree of an epidemic can provide valuable insights into disease dynamics. The transmission tree can be reconstructed by analysing either detailed epidemiological data (e.g. contact tracing) or, if sufficient genetic diversity accumulates over the course of the epidemic, genetic data of the pathogen. We present a likelihood-based framework to integrate these two data types, estimating probabilities of infection by taking weighted averages over the set of possible transmission trees. We test the approach by applying it to temporal, geographical and genetic data on the 241 poultry farms infected in an epidemic of avian influenza A (H7N7) in The Netherlands in 2003. We show that the combined approach estimates the transmission tree with higher correctness and resolution than analyses based on genetic or epidemiological data alone. Furthermore, the estimated tree reveals the relative infectiousness of farms of different types and sizes.     

A Review Molecular Typing Methods for <Emphasis Type="Italic">Aspergillus flavus</Emphasis> Isolates

Aspergillus flavus is the second most important Aspergillus species causing human infections. The importance of this fungus increases in regions with a dry and hot climate. Small phylogenetic studies in Aspergillus flavus indicate that the morphological species contains several genetically isolated species. Different genotyping methods have been developed and employed in order to better understand the genetic and epidemiological relationships between environmental and clinical isolates. Understanding pathogen distribution and relatedness is essential for determining the epidemiology of nosocomial infections and aiding in the design of rational pathogen control methods. Typing techniques can also give us a deeper understanding of the colonization pattern in patients. Most of these studies focused on Aspergillus fumigatus because it is medically the most isolated species. To date, there has not been any publication exclusively reviewing the molecular typing techniques for Aspergillus flavus in the literature. This article reviews all these different available methods for this organism.     

Applying population-genetic models in theoretical evolutionary epidemiology

Much of the existing theory for the evolutionary biology of infectious diseases uses an invasion analysis approach. In this Ideas and Perspectives article, we suggest that techniques from theoretical population genetics can also be profitably used to study the evolutionary epidemiology of infectious diseases. We highlight four ways in which population-genetic models provide benefits beyond those provided by most invasion analyses: (i) they can make predictions about the rate of pathogen evolution; (ii) they explicitly draw out the mechanistic way in which the epidemiological dynamics feed into evolutionary change, and thereby provide new insights into pathogen evolution; (iii) they can make predictions about the evolutionary consequences of non-equilibrium epidemiological dynamics; (iv) they can readily incorporate the effects of multiple host dynamics, and thereby account for phenomena such as immunological history and/or host co-evolution.     

Anopheles gambiae pathogen susceptibility: the intersection of genetics, immunity and ecology

Mosquitoes are the major arthropod vectors of human diseases such as malaria and viral encephalitis. However, each mosquito species does not transmit every pathogen, owing to reasons that include specific evolutionary histories, mosquito immune system structure, and ecology. Even a competent vector species for a pathogen displays a wide range of variation between individuals for pathogen susceptibility, and therefore efficiency of disease transmission. Understanding the molecular and genetic mechanisms that determine heterogeneities in transmission efficiency within a vector species could help elaborate new vector control strategies. This review discusses mechanisms of host-defense in Anopheles gambiae, and sources of genetic and ecological variation in the operation of these protective factors. Comparison is made between functional studies using Plasmodium or fungus, and we call attention to the limitations of generalizing gene phenotypes from experiments done in a single genetically simple colony.