Quantifying the relationship between human Lyme disease and Borrelia burgdorferi exposure in domestic dogs

Lyme disease (LD) is the most common vector-borne disease in the United States. Early confirmatory diagnosis remains a challenge, while the disease can be debilitating if left untreated. Further, the decision to test is complicated by under-reporting, low positive predictive values of testing in non-endemic areas and travel, which together exacerbate the difficulty in identification of newly endemic areas or areas of emerging concern. Spatio-temporal analyses at the national scale are critical to establishing a baseline human LD risk assessment tool that would allow for the detection of changes in these areas. A well-established surrogate for human LD incidence is canine LD seroprevalence, making it a strong candidate covariate for use in such analyses. In this paper, Bayesian statistical methods were used to fit a spatio-temporal spline regression model to estimate the relationship between human LD incidence and canine seroprevalence, treating the latter as an explanatory covariate. A strong non-linear monotonically increasing association was found. That is, this analysis suggests that mean incidence in humans increases with canine seroprevalence until the seroprevalence in dogs reaches approximately 30%. This finding reinforces the use of canines as sentinels for human LD risk, especially with respect to identifying geographic areas of concern for potential human exposure

2013 AAFP Feline Vaccination Advisory Panel Report

Rationale: This Report was developed by the Feline Vaccination Advisory Panel of the American Association of Feline Practitioners (AAFP) to provide practical recommendations to help clinicians select appropriate vaccination schedules for their feline patients based on risk assessment. The recommendations rely on published data as much as possible, as well as consensus of a multidisciplinary panel of experts in immunology, infectious disease, internal medicine and clinical practice

A novel canine model of immune thrombocytopenia: has immune thrombocytopenia (ITP) gone to the dogs?

Canine immune thrombocytopenia (ITP) is analogous to human ITP, with similar platelet counts and heterogeneity in bleeding phenotype among affected individuals. With a goal of ultimately investigating this bleeding heterogeneity, a canine model of antibody-mediated ITP was developed. Infusion of healthy dogs with 2F9, a murine IgG2a monoclonal antibody to the canine platelet glycoprotein GPIIb (a common target of autoantibodies in ITP) resulted in profound, dose-dependent thrombocytopenia. Model dogs developed variable bleeding phenotypes, e.g. petechiae and haematuria, despite similar degrees of thrombocytopenia. 2F9 infusion was not associated with systemic inflammation, consumptive coagulopathy, or impairment of platelet function. Unexpectedly however, evaluation of cytokine profiles led to the identification of platelets as a potential source of serum interleukin-8 (IL8) in dogs. This finding was confirmed in humans with ITP, suggesting that platelet IL8 may be a previously unrecognized modulator of platelet-neutrophil crosstalk. The utility of this model will allow future study of bleeding phenotypic heterogeneity including the role of neutrophils and endothelial cells in ITP

Local and regional temporal trends (2013–2019) of canine Ehrlichia spp. seroprevalence in the USA

Abstract Background In the USA, there are several Ehrlichia spp. of concern including Ehrlichia canis, Ehrlichia ewingii, Ehrlichia chaffeensis, Ehrlichia muris eauclarensis, and “Panola Mountain Ehrlichia”. Of these, E. canis is considered the most clinically relevant for domestic dogs, with infection capable of causing acute, subclinical, and chronic stages of disease. Changes in climate, land use, habitats, and wildlife reservoir populations, and increasing contact between both human and dog populations with natural areas have resulted in the increased risk of vector-borne disease throughout the world. Methods A Bayesian spatio-temporal binomial regression model was applied to serological test results collected from veterinarians throughout the contiguous USA between January 2013 and November 2019. The model was used to quantify both regional and local temporal trends of canine Ehrlichia spp. seroprevalence and identify areas that experienced significant increases in seroprevalence. Results Regionally, increasing seroprevalence occurred within several states throughout the central and southeastern states, including Missouri, Arkansas, Mississippi, Alabama, Virginia, North Carolina, Georgia and Texas. The underlying local trends revealed increasing seroprevalence at a finer scale. Clusters of locally increasing seroprevalence were seen from the western Appalachian region into the southern Midwest, along the Atlantic coast in New England, parts of Florida, Illinois, Wisconsin and Minnesota, and in a couple areas of the Mountain region. Clusters of locally decreasing seroprevalence were seen throughout the USA including New York and the mid-Atlantic states, Texas, the Midwest, and California. Conclusions Canine Ehrlichia spp. seroprevalence is increasing in both endemic and non-endemic areas of the USA. The findings from this study indicate that dogs across a wide area of the USA are at risk of exposure and these results should provide veterinarians and pet owners with the information they need to make informed decisions about prevention of tick exposure

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge

Local and regional temporal trends (2013–2019) of canine Ehrlichia spp. seroprevalence in the USA

Abstract Background In the USA, there are several Ehrlichia spp. of concern including Ehrlichia canis, Ehrlichia ewingii, Ehrlichia chaffeensis, Ehrlichia muris eauclarensis, and “Panola Mountain Ehrlichia”. Of these, E. canis is considered the most clinically relevant for domestic dogs, with infection capable of causing acute, subclinical, and chronic stages of disease. Changes in climate, land use, habitats, and wildlife reservoir populations, and increasing contact between both human and dog populations with natural areas have resulted in the increased risk of vector-borne disease throughout the world. Methods A Bayesian spatio-temporal binomial regression model was applied to serological test results collected from veterinarians throughout the contiguous USA between January 2013 and November 2019. The model was used to quantify both regional and local temporal trends of canine Ehrlichia spp. seroprevalence and identify areas that experienced significant increases in seroprevalence. Results Regionally, increasing seroprevalence occurred within several states throughout the central and southeastern states, including Missouri, Arkansas, Mississippi, Alabama, Virginia, North Carolina, Georgia and Texas. The underlying local trends revealed increasing seroprevalence at a finer scale. Clusters of locally increasing seroprevalence were seen from the western Appalachian region into the southern Midwest, along the Atlantic coast in New England, parts of Florida, Illinois, Wisconsin and Minnesota, and in a couple areas of the Mountain region. Clusters of locally decreasing seroprevalence were seen throughout the USA including New York and the mid-Atlantic states, Texas, the Midwest, and California. Conclusions Canine Ehrlichia spp. seroprevalence is increasing in both endemic and non-endemic areas of the USA. The findings from this study indicate that dogs across a wide area of the USA are at risk of exposure and these results should provide veterinarians and pet owners with the information they need to make informed decisions about prevention of tick exposure

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases

Development and Characterization of Anti-Nitr9 Antibodies

The novel immune-type receptors (NITRs), which have been described in numerous bony fish species, are encoded by multigene families of inhibitory and activating receptors and are predicted to be functional orthologs to the mammalian natural killer cell receptors (NKRs). Within the zebrafish NITR family, nitr9 is the only gene predicted to encode an activating receptor. However, alternative RNA splicing generates three distinct nitr9 transcripts, each of which encodes a different isoform. Although nitr9 transcripts have been detected in zebrafish lymphocytes, the specific hematopoietic lineage(s) that expresses Nitr9 remains to be determined. In an effort to better understand the role of NITRs in zebrafish immunity, anti-Nitr9 monoclonal antibodies were generated and evaluated for the ability to recognize the three Nitr9 isoforms. The application of these antibodies to flow cytometry should prove to be useful for identifying the specific lymphocyte lineages that express Nitr9 and may permit the isolation of Nitr9-expressing cells that can be directly assessed for cytotoxic (e.g., NK) function