Ecological Niche Modelling of the Bacillus anthracis A1.a sub-lineage in Kazakhstan

<p>Abstract</p> <p>Background</p> <p><it>Bacillus anthracis</it>, the causative agent of anthrax, is a globally distributed zoonotic pathogen that continues to be a veterinary and human health problem in Central Asia. We used a database of anthrax outbreak locations in Kazakhstan and a subset of genotyped isolates to model the geographic distribution and ecological associations of <it>B. anthracis </it>in Kazakhstan. The aims of the study were to test the influence of soil variables on a previous ecological niche based prediction of <it>B. anthracis </it>in Kazakhstan and to determine if a single sub-lineage of <it>B. anthracis </it>occupies a unique ecological niche.</p> <p>Results</p> <p>The addition of soil variables to the previously developed ecological niche model did not appreciably alter the limits of the predicted geographic or ecological distribution of <it>B. anthracis </it>in Kazakhstan. The A1.a experiment predicted the sub-lineage to be present over a larger geographic area than did the outbreak based experiment containing multiple lineages. Within the geographic area predicted to be suitable for <it>B. anthracis </it>by all ten best subset models, the A1.a sub-lineage was associated with a wider range of ecological tolerances than the outbreak-soil experiment. Analysis of rule types showed that logit rules predominate in the outbreak-soil experiment and range rules in the A1.a sub-lineage experiment. Random sub-setting of locality points suggests that models of <it>B. anthracis </it>distribution may be sensitive to sample size.</p> <p>Conclusions</p> <p>Our analysis supports careful consideration of the taxonomic resolution of data used to create ecological niche models. Further investigations into the environmental affinities of individual lineages and sub-lineages of <it>B. anthracis </it>will be useful in understanding the ecology of the disease at large and small scales. With model based predictions serving as approximations of disease risk, these efforts will improve the efficacy of public health interventions for anthrax prevention and control.</p

Modeling the Potential Distribution of Bacillus anthracis under Multiple Climate Change Scenarios for Kazakhstan

Anthrax, caused by the bacterium Bacillus anthracis, is a zoonotic disease that persists throughout much of the world in livestock, wildlife, and secondarily infects humans. This is true across much of Central Asia, and particularly the Steppe region, including Kazakhstan. This study employed the Genetic Algorithm for Rule-set Prediction (GARP) to model the current and future geographic distribution of Bacillus anthracis in Kazakhstan based on the A2 and B2 IPCC SRES climate change scenarios using a 5-variable data set at 55 km2 and 8 km2 and a 6-variable BioClim data set at 8 km2. Future models suggest large areas predicted under current conditions may be reduced by 2050 with the A2 model predicting ∼14–16% loss across the three spatial resolutions. There was greater variability in the B2 models across scenarios predicting ∼15% loss at 55 km2, ∼34% loss at 8 km2, and ∼30% loss with the BioClim variables. Only very small areas of habitat expansion into new areas were predicted by either A2 or B2 in any models. Greater areas of habitat loss are predicted in the southern regions of Kazakhstan by A2 and B2 models, while moderate habitat loss is also predicted in the northern regions by either B2 model at 8 km2. Anthrax disease control relies mainly on livestock vaccination and proper carcass disposal, both of which require adequate surveillance. In many situations, including that of Kazakhstan, vaccine resources are limited, and understanding the geographic distribution of the organism, in tandem with current data on livestock population dynamics, can aid in properly allocating doses. While speculative, contemplating future changes in livestock distributions and B. anthracis spore promoting environments can be useful for establishing future surveillance priorities. This study may also have broader applications to global public health surveillance relating to other diseases in addition to B. anthracis

Detection of influenza virus and pathogens of acute respiratory viral infections in population of Kazakhstan during 2018-2019 epidemic season

Influenza and other acute respiratory viral infections are the most common infectious diseases of our time, causing a significant harm to human health as well as great economic damage. At least five groups of viruses, including more than 300 subtypes, are currently related to ARVI pathogens. Such infectious agents are characterized by a high degree of variability resulting in replaced virus antigenic characteristics augmenting their contagiousness, immunoevasion, and resistance to chemotherapeutic drugs. Of relevance, influenza and other ARVIs also pose a threat due to subsequent rapid formation of bacterially-associated respiratory diseases as well as their continuous variability and emergence of new pathogenic species. In recent years, subtype A (H1N1) and A (H3N2) with predominance of pandemic strain, as well as type B influenza viruses have been simultaneously found in circulation. Most common among the causative agents of noninfluenza ARVIs, are respiratory syncytial virus, rhino- and adenoviruses, as well as I/III parainfluenza viruses. Here we present the results of virological and serological studies of clinical samples collected during the 2018—2019 epidemic season in the territory of the Republic of Kazakhstan after analyzing 2794 clinical samples (2530 nasopharyngeal swabs and 264 blood serum samples) of patients diagnosed with ARVI, ARI, bronchitis, and pneumonia. Examining nasopharyngeal swabs by using RT-PCR showed that the mixed etiology influenza viruses with predominant A/H1N1pdm virus circulated in Kazakhstan. In particular, influenza virus genetic material was found in 511 swabs (20.20% of total examined samples), so that influenza A virus RNA was detected in 508 biological samples such as A/H1N1 — in 289, A/H3N2 — 209, unverified virus subtype — 10 samples. Type B influenza virus was detected in 3 samples. Analyzing 264 blood serum samples by the HAI assay and ELISA showed the presence of antibodies specific to influenza A/H1N1, A/H3N2, and B viruses in the population of various regions of Kazakhstan, thereby indirectly confirming their co-circulation. 42 influenza virus strains were isolated in chicken embryos, of which 28 were assigned to A/H1N1pdm virus, 13 — A/H3N2 virus, and one isolate was identified as influenza B virus. The laboratory diagnostics of clinical samples for ARVIs revealed that respiratory syncytial virus prevailed among identified non-influenza agents, whereas rhino- and adenoviruses were less common. Metapneumoviruses, bocaviruses, coronaviruses, and type I parainfluenza viruses were detected in few cases. Comparison of our study data with the data on 2017—2018 circulation of influenza pathogens showed that in Kazakhstan influenza A and B viruses continued to circulate, with the dominance of A/H1N1pdm virus as it was in the previous epidemic season. Identification of non-influenza viruses, the causative agents of 2018—2019 respiratory infections, showed the predominance of respiratory syncytial virus that correlated with the aforementioned results

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics

We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (Gi*) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1st, 2nd, and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, Gi* revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and Mann-Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predicted larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation

Assays for Identification and Differentiation of Brucella Species: A Review

Brucellosis is one of the most important and widespread bacterial zoonoses worldwide. Cases are reported annually across the range of known infectious species of the genus Brucella. Globally, Brucella melitensis, primarily hosted by domestic sheep and goats, affects large proportions of livestock herds, and frequently spills over into humans. While some species, such as Brucella abortus, are well controlled in livestock in areas of North America, the Greater Yellowstone Ecosystem supports the species in native wild ungulates with occasional spillover to livestock. Elsewhere in North America, other Brucella species still infect domestic dogs and feral swine, with some associated human cases. Brucella spp. patterns vary across space globally with B. abortus and B. melitensis the most important for livestock control. A myriad of other species within the genus infect a wide range of marine mammals, wildlife, rodents, and even frogs. Infection in humans from these others varies with geography and bacterial species. Control in humans is primarily achieved through livestock vaccination and culling and requires accurate and rapid species confirmation; vaccination is Brucella spp.-specific and typically targets single livestock species for distribution. Traditional bacteriology methods are slow (some media can take up to 21 days for bacterial growth) and often lack the specificity of molecular techniques. Here, we summarize the molecular techniques for confirming and identifying specific Brucella species and provide recommendations for selecting the appropriate methods based on need, sensitivity, and laboratory capabilities/technology. As vaccination/culling approaches are costly and logistically challenging, proper diagnostics and species identification are critical tools for targeting surveillance and control

Current and future geographic distribution of <i>Bacillus anthracis</i> using climate data at 55 km².

<p>(A) current geographic distribution, (B) A2 future climate scenario, (C) B2 future climate scenario. Color ramp indicates model agreement, with darker areas representing areas with high model agreement or greater confidence in the GARP prediction.</p

Accuracy Metrics for the current predicted distributions from each GARP experiment.

<p>* AUC  =  area under curve.</p><p>† <i>n</i> was divided into 50% training/50% testing at each model iteration.</p><p>§ p<0.001.</p><p><i>Note: Independent data used for accuracy metrics appear in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009596#pone-0009596-g001" target="_blank">figure 1</a> (yellow points)</i>.</p