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Infection prevalence and ecotypes of Anaplasma phagocytophilum in moose Alces alces, red deer Cervus elaphus, roe deer Capreolus capreolus and Ixodes ricinus ticks from Norway
research-article
Author(s):
Vetle M. Stigum
1 ,
Ryanne I. Jaarsma
2 ,
Hein Sprong
2 ,
Christer M. Rolandsen
3 ,
Atle Mysterud
1
,
4
,
Publication date (Electronic):
3 January 2019
Journal:
Parasites & Vectors
Publisher:
BioMed Central
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Abstract
Background
The geographical expansion of the tick Ixodes ricinus in northern Europe is a serious concern for animal and human health. The pathogen Anaplasma phagocytophilum is transmitted by ticks and causes emergences of tick-borne fever (anaplasmosis) in livestock. The transmission dynamics of the different ecotypes of A. phagocytophilum in the ecosystems is only partly determined. Red deer and roe deer contribute to circulation of different ecotypes of A. phagocytophilum in continental Europe, while the role of moose for circulation of different ecotypes is not fully established but an important issue in northern Europe.
Methods
We determined infection prevalence and ecotypes of A. phagocytophilum in moose ( n = 111), red deer ( n = 141), roe deer ( n = 28) and questing ticks ( n = 9241) in Norway.
Results
As previously described, red deer was exclusively linked to circulation of ecotype I, while roe deer was exclusively linked to circulation of ecotype II. Surprisingly, we found 58% ecotype I ( n = 19) and 42% of ecotype II ( n = 14) in moose. Both ecotypes were found in questing ticks in areas with multiple cervid species present, while only ecotype I was found in ticks in a region with only red deer present. Hence, the geographical distribution of ecotypes in ticks followed the distribution of cervid species present in a given region and their link to ecotype I and II.
Conclusions
Moose probably function as reservoirs for both ecotype I and II, indicating that the ecotypes of A. phagocytophilum are not entirely host-specific and have overlapping niches. The disease hazard depends also on both host abundance and the number of immature ticks fed by each host. Our study provides novel insights in the northern distribution and expansion of tick-borne fever.
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Most cited references36
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Multiplex real-time PCR for detection of anaplasma phagocytophilum and Borrelia burgdorferi.
A multiplex real-time PCR assay was developed for the simultaneous detection of Anaplasma phagocytophilum and Borrelia burgdorferi. The assay was tested on various Anaplasma, Borrelia, Erhlichia, and Rickettsia species, as well as on Bartonella henselae and Escherichia coli, and the assay was found to be highly specific for A. phagocytophilum and the Borrelia species tested (B. burgdorferi, B. parkeri, B. andersonii, and B. bissettii). The analytical sensitivity of the assay is comparable to that of previously described nested PCR assays (A. phagocytophilum, 16S rRNA; B. burgdorferi, fla gene), amplifying the equivalent of one-eighth of an A. phagocytophilum-infected cell and 50 borrelia spirochetes. The dynamic range of the assay for both A. phagocytophilum and B. burgdorferi was >/=4 logs of magnitude. Purified DNA from A. phagocytophilum and B. burgdorferi was spiked into DNA extracted from uninfected ticks and from negative control mouse and human bloods, and these background DNAs were shown to have no significant effect on sensitivity or specificity of the assay. The assay was tested on field-collected Ixodes scapularis ticks and shown to have 100% concordance compared to previously described non-probe-based PCR assays. To our knowledge, this is the first report of a real-time multiplex PCR assay that can be used for the simultaneous and rapid screening of samples for A. phagocytophilum and Borrelia species, two of the most common tick-borne infectious agents in the United States.
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Climate, Deer, Rodents, and Acorns as Determinants of Variation in Lyme-Disease Risk
Richard Ostfeld, Charles D. Canham, Kelly Oggenfuss … (2006)
Introduction Many emerging and re-emerging infectious diseases of humans are zoonoses transmitted by vectors. Examples include West Nile virus, ehrlichiosis, anaplasmosis, tick-borne encephalitis and Lyme disease. In each case, the vector—usually a mosquito or tick—acquires the pathogen from a vertebrate host during a blood meal taken early in the life cycle and becomes capable of transmitting it to humans during a later blood meal. Risk of human exposure to the disease increases with increasing abundance and infection prevalence of the vectors [ 1, 2]. For virtually all vector-borne zoonoses, disease incidence in humans varies substantially from year to year [ 3– 5]. Determining the causes of interannual variation in entomological risk would facilitate the development and deployment of preventative measures, potentially reducing the burden of disease. Lyme disease is the most frequently reported vector-borne disease in the US [ 6]. Lyme disease is most prevalent in northeastern and north-central regions where suburban and exurban development encroaches on deciduous forest ecosystems that support the pathogen, vector, and their vertebrate hosts [ 7]. The etiological agent is a spirochete bacterium, Borrelia burgdorferi, which is transmitted by ticks in the Ixodes ricinus complex. In the eastern and central US, the vector is the blacklegged tick, Ixodes scapularis. I. scapularis is a three-host tick, requiring three blood meals, one each as a larva, nymph, and adult, to fulfill its life cycle. Larval ticks hatch in midsummer, typically uninfected with B. burgdorferi, and begin seeking a host for their initial blood meal. After feeding on a vertebrate host for several days, the larvae drop off the host and molt into the nymphal stage, which undergoes diapause for almost a year before becoming active and seeking a host the following late spring or early summer. Both larvae and nymphs are highly nonspecific in their choice of hosts, parasitizing dozens of species of mammal, bird, and lizard [ 8]. After feeding to repletion, nymphs drop off the host and molt into the adult stage, which seeks a medium- or large-mammal host in midautumn of the same year. Infection with B. burgdorferi can be acquired from the host during either the larval or nymphal blood meal, and both nymphs and adults are capable of transmitting infection to a vertebrate host, including humans. Owing to its tiny size, potentially high abundance, and summer feeding, the nymphal stage is most likely to transmit B. burgdorferi to people and hence is responsible for the great majority of Lyme-disease cases [ 9]. Risk of human exposure to Lyme disease, given entry into habitats where ticks occur (mainly forests; [ 10]), is a function of the density of infected nymphal ticks, which in turn is the product of the total density of nymphs (DON) and the nymphal infection prevalence (NIP) [ 11]. Determining the causes of variation in density of infected nymphs (DIN) and its component parts is an important goal with both ecological and epidemiological implications. Human behavioral patterns, such as time spent in forest habitat and protective measures taken against exposure to ticks, also influence Lyme-disease risk but are beyond the scope of this study. Prior studies of the factors influencing DIN and DON have focused largely on variation in climate and in the abundance and distribution of white-tailed deer (Odocoileus virginianus). Because ticks spend greater than 95% of their lives on the forest floor either digesting the blood meal, undergoing diapause, or seeking a host, exposure to ambient temperature and humidity could be important to survival rates and population dynamics [ 12– 14]. In the laboratory, ticks experience high mortality when exposed to low humidity and high temperatures [ 15]. Consequently, hot and/or dry springs and summers have been postulated to reduce subsequent nymphal tick densities and Lyme-disease risk [ 16– 18]. Because adult I. scapularis feed predominantly on white-tailed deer [ 19], much research has evaluated the impact of variation in abundance of deer on abundance of ticks. When deer are eliminated from some habitats by hunting or fencing, the abundance of ticks typically is strongly reduced [ 20– 22]. Studies comparing natural variation in deer abundance with that in tick abundance are less conclusive; some have shown strong associations [ 23– 25], whereas others have not [ 26– 28]. Less attention has been paid to the potential effects of variation in abundance of hosts for larval ticks in influencing variation in DIN, DON, and NIP. This is perhaps a consequence of the lack of specialization by larvae on any particular host species. However, larval I. scapularis feed abundantly on white-footed mice (Peromyscus leucopus), and this host is the most competent natural reservoir for B. burgdorferi [ 29, 30]. High feeding success on mice combined with high reservoir competence has led some researchers to postulate that Lyme-disease risk will vary with mouse abundance [ 31, 32]. Although eastern chipmunks (Tamias striatus) host many larval ticks and are competent B. burgdorferi reservoirs [ 33], the impacts of variation in chipmunk abundance on Lyme-disease risk have been even less thoroughly explored (but see [ 34, 35]). The food resources for tick hosts might also be important to Lyme-disease risk. Oak trees ( Quercus spp.) that dominate many forests in the US Lyme-endemic zone are known to produce highly variable acorn crops, a phenomenon known as masting. Acorns comprise a crucial resource for several vertebrate species, including white-footed mice, eastern chipmunks, and white-tailed deer, and can influence population density of the rodents [ 32, 36– 38] as well as space use by deer [ 39]. Jones et al. [ 40] described a dramatic increase in abundance of larval I. scapularis following experimental simulation of a masting event, but the scale of the experiments was insufficient to assess longer-term impacts on abundance of nymphs. In a follow-up, Ostfeld et al. [ 41] described a positive correlation between metrics of Lyme-disease risk and both prior-year mouse abundance and acorn abundance 2 y previously. No prior study has assessed simultaneously the effects of variation in temperature, precipitation, deer, mice, chipmunks, and acorns, on variation in entomological risk of exposure to Lyme disease ( Figure 1). Assessments of subsets of these putative causal variables are characterized by relatively short time series, which have limited power to assess the influence of each factor separate from the others. Here we use long-term monitoring of these parameters combined with model comparison approaches to address the causes of variable risk in an area of high Lyme-disease incidence. Results The principal entomological Lyme-disease risk factor, DIN, varied by an order of magnitude among years, ranging from 1.07 infected nymphs × 100 m −2 in 1997 (averaged across all six plots) to 10.00 × 100 m −2 in 1996. This variation was due primarily to variation in DON, which varied 6-fold among years (3.59 to 21.07 × 100 m −2). In contrast, NIP varied less than 2-fold among years, from 0.24 in 2005 to 0.45 in 1999. Models for Total DON Effect of growing degree days in the previous year (GDD t −1) on DON in the current year was positive but very weak in both the magnitude (estimated slope of the regression) and the strength of evidence for the effect ( Table 1). Total growing season precipitation in the current year (PPT t ) also weakly influenced DON, but the effect was Gaussian with a peak at 223 mm. A model in which these two weather terms are combined multiplicatively was stronger than either of the univariate models, but explained only 15% of the variance ( Table 1). None of the other climate variables, deer variables, or prior year's density of larvae (DOL t −1) had any effect on DON; i.e., Akaike's information criterion corrected for small sample size (AIC corr) values were much higher than those of the means model. Abundance of acorns t −2, mice t −1, chipmunks t −1, and rodents t −1 (sum of mice and chipmunks) all independently influenced DON, and in all cases linear models were superior to exponential or power functions. The best univariate model for DON was a simple linear model of chipmunks t −1, which explained 40% of the variance ( Table 2; Figure 2). In no case was a multiple regression model superior to the chipmunk model ( Table 2). Models for NIP Among all univariate models, NIP responded only to the density of acorns in year t−2 ( Figure 3), and this relationship explained only 16% of the variance in NIP. Models incorporating the climate variables, deer variables, DOL, mice, chipmunks, and total rodents performed no better than the means model ( Tables 3 and 4). Because none of the independent variables other than acorns t −2 produced an improvement over the means model, there was no justification for testing multiple regression models. Models for DIN Similar to the results for DON, the effect of GDD t −1 on DIN in year t was positive but very weak in both the magnitude and the strength of evidence for the effect ( Table 5). Total PPT t also weakly influenced DIN, and again the effect was Gaussian with a peak at 213 mm of rainfall. A model in which these two weather terms are combined multiplicatively was stronger than either of the univariate models, but explained only 11% of the variance ( Table 5). None of the other weather variables, deer variables, nor DOL t −1 had any effect on DIN, with AIC corr values higher than those of the means model. Abundance of acorns t −2, mice t −1, chipmunks t −1, and rodents t −1 all independently influenced DIN. For acorns t −2, mice t −1, and rodents t −1, the best models were nonlinear, but the difference in AIC corr (ΔAIC corr) between the best model and corresponding linear model was always less than 1 (i.e., they had equivalent levels of support in the data), and the shapes of all nonlinear models were very close to linear ( Table 5). Consequently, we tested linear combinations of these predictor variables in multiple regressions. A model combining mice t −1 and acorns t −2 multiplicatively was the best model of all multiple regressions combining predictor variables that were supported by univariate analyses, and it explained 57% of the variance in DIN ( Table 6; Figure 4A). The model with multiplicative effects of chipmunks t −1 and acorns t −2 was nearly as good, with ΔAIC corr = 1.05, and a slightly higher R 2 (0.61) ( Figure 4B). Host Responses to Acorns Densities of both mice and chipmunks responded strongly to the prior year's acorn abundance, and in both cases the relationship was best described by saturating power functions ( Figure 5A and 5B). As a result of their similar responses to acorn abundance, the abundance of mice and chipmunks was strongly correlated among years ( r = 0.62). A linear model of the mouse–chipmunk correlation was superior to any of the nonlinear models, with a slope of 0.25 indicating that mice are consistently about four times as abundant as chipmunks ( Figure 5C). As a consequence of acorns influencing rodent abundances, and rodents influencing nymphal abundances, we observed a general pattern in which DON tracked rodents, and rodents tracked acorns, each effect displaying a 1-y lag ( Figure 6). Given their relatively long generation times and low reproductive potential, it is unrealistic to expect deer population abundance to track annual variation in acorn production. However, the potential exists for deer to be attracted from nonoak to oak habitats by the presence of abundant acorns [ 39, 42]. Because deer are an important host for adult ticks, and adult ticks lay eggs where they drop off their hosts, we expected that DOL in any given year would reflect deer space use the prior fall and should be positively correlated with acorn abundance. We found a weak, linear relationship between deer t −2 and DOL t −1, with this model being only a slight improvement over the means model (ΔAIC corr = 0.39; R 2 = 0.04). In addition, no relationship existed between acorns t−2 and DOL t −1; all models, whether linear or nonlinear, were worse than the means model. Discussion Climate, deer, and acorns each have been proposed as primary determinants of temporal variation in risk of human exposure to Lyme disease, as measured by abundance and Borrelia-infection prevalence in nymphal Ixodes ticks. Using a model comparison approach and a 13-y dataset, we found weak support for climate variables, no support for deer, and strong support for an effect of acorns, mediated by acorn effects on white-footed mice and eastern chipmunks, which host many larval ticks and are competent reservoirs for B. burgdorferi. Climate Variables Of the four climate variables, two influenced DON and DIN, but they did so in unanticipated ways. Both DON and DIN increased linearly, albeit weakly, with increases in the prior year's temperature (GDD t −1). This result conflicts with the expectation that heat-caused mortality is an important regulator of tick abundance [ 15, 43], but is consistent with the finding that (detrended) incidence of Lyme disease in people is positively correlated with summer temperatures in the prior year [ 44]. Both DON and DIN were influenced, again weakly, by precipitation in the current but not prior year, but intermediate levels of precipitation favored highest nymphal abundances. Again, this result conflicts with the expectation that tick survival increases linearly with ambient moisture [ 18]. Low DON and DIN in years of high precipitation could be caused by either flood-induced mortality or that caused by natural enemies (e.g., fungi) that are facilitated by high moisture [ 45]. As expected, none of the climate variables influenced NIP. NIP is determined by the proportions of larval tick meals taken from the various host species, which vary strongly in their reservoir competence. It seems unlikely that climate variables will influence the choice of hosts by larval ticks or relative abundances of different hosts. It remains possible that climate variables other than the ones we examined influence stage-specific tick survival and abundance and that the weak or absent effects we observed are a result of not including more important variables. Our analytical approach could easily support assessments of other variables for which there is some a priori expectation of an effect. Deer Variables The assertion that variable deer abundance is responsible for variable abundance of blacklegged ticks and hence Lyme-disease risk has become almost axiomatic [ 46, 47]. This seems to be based largely on repeated observations that removal of deer by fencing or shooting causes dramatic declines in tick abundance. However, some evidence suggests that the effects of deer on ticks are nonlinear, weak, and variable with tick life stage [ 26– 28]. An effect of deer abundance in year t−2 on nymphs in year t would be expected if (1) the number of autumn blood meals taken by adult ticks is correlated with the abundance of deer, causing (2) density of larval ticks in year t−1 to be correlated with deer in year t−2, and if (3) abundance of nymphs in year t is correlated with that of larvae in year t−1. Here, we found that more than 3-fold variation in our indices of deer abundance did not affect subsequent nymph abundance. This observation supports the assertion that, once deer abundance exceeds a low threshold value, further increases in deer density have little if any effect on nymphal densities [ 48]. We did observe a weak effect of deer on subsequent larval abundance, but larval abundance in any given year had no impact on next year's abundance of nymphs. This clear but surprising result indicates a decoupling of stage-specific abundances and suggests instead that abundance of nymphs depends on larval feeding opportunities the prior year (see below). The lack of demographic forcing from larval to nymphal stage each year also suggests that the effect of larval host abundance in any given year should penetrate only to the next year and not beyond. Acorn and Rodent Variables Previous research [ 32, 40] supported an effect of acorns in year t−2 on nymphs in year t via two pathways, one in which abundant acorns t −2 boosted larvae t −1 by enhancing deer t −2, and the other in which acorns t−2 boosted rodents t −1, which in turn elevated nymphs t ( Figure 1). Our results strongly support the second, rodent-driven pathway ( Figure 6) and refute the first, deer-driven one. Although univariate models with GDD t −1, PPT t , acorns t −2, mice t −1, and total rodents t −1 all had support, the best model had chipmunks in the previous year as the sole predictor of DON. A stronger role for chipmunks than for mice was somewhat surprising given the lower population densities of chipmunks ( Figure 4) and only modestly higher average larval burdens on chipmunks [ 33] at our sites. However, less intense grooming of larval ticks by chipmunks [ 49] could facilitate larval survival to the nymphal stage. Elsewhere in the North American range of Lyme disease, chipmunks have been postulated to play a critical role in the enzootic cycle [ 34, 35, 50]. In contrast to previous results based on a shorter time series [ 41] and to expectations based on the high reservoir competence of mice and chipmunks [ 33], neither mice t −1 nor chipmunks t −1 influenced NIP. Instead, we found that the univariate model of acorns t−2 was the only one supported by the data. Empirically based models [ 51] indicate that NIP varies strongly with variable species composition in the community of hosts for larval ticks. Consequently, an impact of acorns but not of single host species would be expected if acorns alter the species composition in the host community. In addition to mice and chipmunks, acorns are likely to influence abundance and space use by gray squirrels (Sciurus carolinensis), raccoons (Procyon lotor), and turkeys (Meleagris gallopavo). The former two species are incompetent reservoirs [ 33], and the latter is unlikely to be a competent reservoir [ 52]. Therefore, by influencing a suite of hosts that both stimulate and depress NIP, acorns might be expected to explain more of the variation in NIP than would any single host species. Because DIN is the product of DON and NIP, one might expect that the best explanatory model would be more complex than those for its component parts. Indeed, we found that the model of multiplicative effects of mice t −1 (the second best predictor of DON) and acorns t−2 (the best predictor of NIP) had the most support. The model with multiplicative effects of chipmunks t −1 (the best predictor of DON) and acorns t−2 was almost as strongly supported. Tick abundance and Lyme-disease risk can be high in habitats with few or no oaks [ 21]; therefore, we do not expect that acorn abundance will be a universal predictor of risk [ 44]. However, small rodent hosts are almost always involved in risk as both permissive tick hosts and competent Borrelia reservoirs. Population densities of mice and chipmunks vary dramatically among years in both oak-dominated and nonoak-dominated forests [ 53], and we expect that the increase in Lyme-disease risk that accompanies high rodent densities should be widespread, no matter the causes of rodent fluctuations. Acorns provide a convenient leading indicator of rodent abundance, and seeds of other tree species [ 53] or predators [ 54, 55] might act similarly. Previous studies of the determinants of variable Lyme-disease risk or incidence have tended to focus on one or a small number of potential independent variables, and statistically significant effects of both climate [ 16– 18] and deer [ 23– 25] have been described. When we included candidate climate variables, deer indices, and larval tick density together with densities of mice, chipmunks, and acorns, our model comparison methods never selected models incorporating climate, deer, or larvae and always selected models with either rodents, acorns, or both, as explanatory variables. Effects of variable climate or deer abundance on Lyme-disease risk might achieve statistical significance without being biologically important if statistical models fail to assess more potent independent variables. It remains possible that variable climate and deer abundance affect large-scale spatial variation in Lyme-disease risk even if their impacts on temporal variability are weak. Our long-term studies of Lyme-disease risk at the epicenter of the epidemic in North America strongly implicate a role for population density of rodent hosts and their food resources. We suggest that masting indices could be used to alert the public when years of high Lyme-disease risk are anticipated. Materials and Methods Study sites. Field studies were conducted on the property of the Institute of Ecosystem Studies (IES) in Dutchess County, southeastern New York (lat 41 °50′N, long 73 °45′W), in the center of the northeastern US endemic zone for Lyme disease. Dutchess County has had among the highest incidence rates of Lyme disease in the US during the past 10 y [ 56]. IES forests are typical of the eastern deciduous forests of New York and New England, dominated by oaks (Quercus rubra and Quercus prinus) in the overstory (57%–70% oak relative basal area; [ 42]), with oak and sugar maple (Acer saccharum) seedlings, maple-leaved viburnum (Viburnum acerifolium), witch hazel (Hamamelis virginiana), and ironwood (Ostrya virginiana) common in the understory. Two 2.25-ha plots (150 m × 150 m) were established in 1991, and four more were added in 1995 to comprise three pairs of plots with ca. 150 m separating members of a pair and more than 700 m separating pairs. Acorn sampling. Acorn abundance was monitored on each of the six plots by placing circular baskets under the canopies of mature oaks distributed throughout the plot. The original two plots had twenty 0.5-m 2 baskets, and the remaining four had twenty-five 1.0-m 2 baskets. Seed baskets were supported by monofilament line attached to nylon stakes and were resistant to seed predators. Intact, mature acorns were counted monthly during autumn of each year, and the total number of acorns from all baskets within a plot was divided by the total basket area to derive an estimate of annual acorn production on each plot. Full-season acorn data were collected from 1993 through 2004 on the original two plots and from 1999 through 2004 on the remaining four plots. Acorn density in year t−2 was used as an independent variable potentially affecting Lyme-disease risk factors. Small-mammal sampling. Each year from 1991 (the two original plots) or from 1995 (remaining plots) through 2005 we have monitored abundance of small mammals at IES using capture–mark–recapture techniques. On each plot we established an 11 × 11 point grid of Sherman live traps, with 15 m between trap stations and two traps per station, for a total of 242 traps per grid. Trapping was conducted for 2–3 consecutive days every 3–4 wk, generally from May to November of each year. Traps were baited with crimped oats (with sunflower seeds and cotton batting added during cold weather), set at 1600 hours and checked between 0800 hours and about 1200 hours the following morning. This schedule allowed us to capture both diurnal (chipmunks) and nocturnal (mice) small mammals. These two species comprised more than 90% of captures. Small mammals were marked with individually numbered metal ear tags and released after handling at the point of capture. Data on age, sex, body mass, ectoparasite burden, and trap station were recorded on each capture. Protocols for animal handling were approved annually by an Institutional Animal Care and Use Committee. We estimated population densities of white-footed mice and eastern chipmunks by inputting data from all trap sessions in a year into the Jolly-Seber open population model in program POPAN5 [ 57]. We selected the Jolly-Seber model that incorporates individual heterogeneity in capture probability. Because we were interested in assessing the importance of rodent abundance on nymphal tick abundance the following year, we focused on estimating rodent densities in midsummer, which is the time of peak activity of larval I. scapularis at our sites [ 10]. In order to create a standard metric when actual trapping sessions varied in time, we estimated mouse and chipmunk abundance for each grid and year on August 15 by linear interpolation between rodent abundance estimates for trap sessions immediately before and after August 15. Rodent densities in year t−1 were used as independent variables potentially affecting Lyme-disease risk factors. Deer abundance estimates. Deer abundance was estimated at two levels, one at the scale of the IES property and the other at the scale of individual plots. For estimating property-wide deer abundance annually, we used population estimates from the IES limited-access bow-hunting program, which has run continuously from 1987 to the present [ 58]. Between seven and 11 hunters per year hunted an average of 40 h each (range, 29–54 h) between mid-October and mid-November. All hunters were IES staff members or volunteers working with the staff wildlife biologist. Each was given exclusive access to one of 34 discrete hunting areas averaging 22 ha (range, 9.3–35.7 ha). Virtually all hunting was from tree stands. Each day, hunters reported the number of hours hunted and the number of deer sighted, and these data were converted to deer observed per hour hunted. As a validation of this method, we asked whether deer observed per hour by bow hunters were correlated with deer counts from annual autumn spotlighting surveys conducted from 1987 to 2000 (details in [ 58]) and found that the two census methods were highly correlated ( r = 0.70, df = 11, p = 0.01; [ 58]). To estimate deer distribution on a smaller scale, we used deer browse surveys conducted each spring (1983–2004) at a number of sites (range, 38–50) distributed throughout the IES property. Commonly browsed tree species or genera [ 59, 60] have served as an index to trends in browsing rates. These species/genera include red maple (Acer rubrum), sugar maple (A. saccharum), serviceberry (Amelanchier arborea), black birch (Betula lenta), black cherry (Prunus serotina), oaks ( Quercus spp.), hickories ( Carya spp.), and ashes ( Fraxinus spp.). Browsed woody stems detected in spring reflect the distribution of deer foraging activity during the previous autumn and winter. Data were collected along transects of unrestricted width spaced either 200 m or 300 m apart. Starting points were associated with corners of a 100-m grid overlay of the property. A major compass direction was randomly drawn each year for transect direction. As index species were sighted, all buds below 2 m were counted and examined for browsing by deer. At least 100 buds in total were examined for each site. The percentage of available stems browsed was calculated for each site and for each index species. We selected browse survey plots closest to each of the trapping grids (less than 100 m distant) to estimate deer activity specific to each of the plots. Deer browsing intensity in year t−2 was used as an independent variable potentially affecting Lyme-disease risk factors. Climate variables. An almost infinite number of climate variables (temperature, precipitation, minimum, maximum, variance, mean, specific to months or seasons, etc.) potentially could influence tick survival and densities of nymphs. Consequently, the probability of uncovering spurious relationships between climate and tick abundance is quite high if many explanatory variables are included without clear a priori justification [ 61]. To avoid this problem, we selected climate variables that have been reported to influence either changes in the abundance of immature blacklegged ticks or in human Lyme-disease incidence [ 16, 17]. The climate variables selected represent temperature and precipitation conditions in either the current year (year t) or the prior year (year t−1). Values for year t reflect potential impacts of climate on survival of the current year's nymphal stage, whereas those for year t−1 represent possible effects of climate on survival of the prior year's larval stage. Following Jones and Kitron [ 16], for temperature data, we included only those months from the beginning of the growing season through the end of the activity season for the appropriate life stage (July for nymphs, September for larvae; [ 16, 41]). Specifically, we used cumulative growing degree days (GDD) for March 1–June 30 of year t, and cumulative GDD for March 1–September 30 of year t−1. For precipitation, we included only those months from the onset of potential soil moisture limitation (May) to the end of the appropriate activity period [ 16]; i.e., PPT (in mm) for May 1–June 30 of year t; and PPT for May 1–September 30 of year t−1 [ 16]. All climate data came from the IES environmental monitoring station ( http://www.ecostudies.org/emp_purp.html, located less than 1.5 km from our field plots Tick and Borrelia sampling. Estimates of the abundance and infection prevalence of nymphal ticks comprised the response variables of interest. In addition, estimates of larval abundance in year t−1 were used as an independent variable. We monitored the abundance of larval and nymphal ticks in each plot and year by dragging 1-m 2 white corduroy drag cloths [ 62] along 450 m of transects approximately every 3 wk from April through November. Drag cloths were examined and all ticks counted and removed every 30 m. Frequent sampling in the early 1990s revealed that peak host-seeking activity for larvae occurred in mid- to late August, and for nymphs in mid- to late June, and we timed our annual sampling to coincide with these peaks. For each plot and year, we estimated larval and nymphal abundance as the peak density (ticks per 100 m 2). Peak densities were highly correlated with cumulative seasonal densities (correlation coefficients typically greater than 0.80; R. Ostfeld, unpublished data) on each plot. Infection of individual ticks with B. burgdorferi was determined using direct immunofluorescence assay (DIA). Ticks were washed once in 70% ethanol and twice in deionized water and ground in phosphate-buffered saline (PBS). Three 5-ml aliquots of tick suspension were placed in separate wells in a multiwell slide, air-dried, and fixed in cold acetone for 10 min. Fluorescein rabbit anti- B. burgdorferi conjugate was incubated in wells at 37 °C for 45 min, after which slides were washed in PBS, dried, and mounted with a coverslip. Slides were examined systematically to categorize each tick as either infected or uninfected. On average, 378 nymphs (range, 146–660) were examined for infection each year. Our estimates of tick infection based on DIA have been verified by virtually identical infection prevalence estimates (within 1.6 percentage points) for IES ticks based on PCR and reverse-line blotting [ 63, 64]. Statistical methods. Our goal was to evaluate the strength of evidence for effects of a series of plausible independent variables on temporal variation in entomological risk of human exposure to Lyme disease ( Figure 1). Risk was measured by annual estimates at individual sample locations for the DON, NIP, and their product, the DIN. For each of the three response variables (DON, NIP, DIN) we compared the strength of evidence for a series of alternative, competing regression models using AIC corr. We created both linear and nonlinear (exponential, power function, Gaussian) models, as appropriate. We first compared evidence for each of the 11 independent variables separately by comparing the AIC corr of their regression models to the AIC corr value of an intercept-only (i.e., mean) model. The 11 independent variables included two temperature variables, two precipitation variables, two indices of deer abundance, abundance of larval ticks, acorn abundance, and three measures of small mammal abundance. We checked for colinearity among the independent variables using correlation coefficients. Because there were missing values for some variables in some years and plots, the univariate models were compared against mean models estimated with the same subset of (nonmissing) observations. We then tested series of increasingly complex models by combining sets of independent variables for which there was evidence (as measured by AIC) of univariate effects. Our choices for the forms of the multiple regression models were guided by the dictates of parsimony: while our dataset represents an enormous sampling effort over a 13-y period, the actual sample sizes (a given plot in a given year) were still relatively small, ranging from 42 to 58 observations for the various models. We used simulated annealing (a global optimization algorithm) to find the maximum likelihood estimates for the parameters of each model. The observations were assumed to be normally distributed with a homogeneous variance. Examination of the residuals indicates that this assumption was appropriate for all three of the response variables. The specific annealing algorithm we used (based on Goffe et al. [ 65]) allows a bounded search in cases where a range of parameter values is either mathematically inconsistent or biologically unreasonable (i.e., negative densities or variance estimates). The sample locations were widely enough distributed that we are confident in assuming that the error terms in our models are spatially independent, and there was no evidence of temporal autocorrelation in the residuals. The simulated annealing and all of the statistical analyses were done using R ( http://www.r-project.org).
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Human Granulocytic Anaplasmosis and Anaplasma phagocytophilum
J. Dumler, Kyoung-Seong Choi, Jose Garcia-Garcia … (2005)
Human granulocytic anaplasmosis (HGA) was first identified in 1990 in a Wisconsin patient who died with a severe febrile illness 2 weeks after a tick bite ( 1 ). During the terminal phases of the infection, clusters of small bacteria were noted within neutrophils in the peripheral blood (Figure 1), assumed to be phagocytosed gram-positive cocci. A careful review of the blood smear suggested the possibility of human ehrlichiosis, an emerging infection with similar bacterial clusters in peripheral blood monocytes among infected patients in the southeast and south-central United States. All blood cultures were unrevealing, and specific serologic and immunohistochemical tests for Ehrlichia chaffeensis, the causative agent of human monocytic ehrlichiosis (HME) were negative. Over the ensuing 2 years, 13 cases with similar intraneutrophilic inclusions were identified in the same region of northwestern Wisconsin and eastern Minnesota ( 2 ). Aside from the bacterial clusters, common features among these persons included fever, headache, myalgia, malaise, absence of skin rash, leukopenia, thrombocytopenia, and mild injury to the liver. Figure 1 Anaplasma phagocytophilum in human peripheral blood band neutrophil (A. Wright stain, original magnification ×1,000), in THP-1 myelomonocytic cell culture (B, LeukoStat stain, original magnification, ×400), in neutrophils infiltrating human spleen (C, immunohistochemistry with hematoxylin counterstain; original magnification ×100), and ultrastructure by transmission electron microscopy in HL-60 cell culture (D; courtesy of V. Popov; original magnification ×21,960). In 1994, through application of broad range molecular amplification and DNA sequencing, the causative agent was recognized as distinct from E. chaffeensis. The agent was initially named HGE agent ( 1 , 2 ), although morphologic and serologic studies indicated a close or identical relationship to the veterinary pathogens of neutrophils, E. equi and E. (Cytoecetes) phagocytophila. During the process of classification of the human agent, phylogenetic studies showed taxonomic disarray among organisms broadly referred to as ehrlichiae, and a careful reorganization now places those bacteria previously classified as E. phagocytophila, E. equi, and the HGE agent into a different genus as a single species, A. phagocytophilum (Figure 2) ( 1 , 3 ). The fallout from the reclassification of these organisms is the proposal for a complete revision of the families Rickettsiaceae and Anaplasmataceae. Under the proposed revision, the tribe structure of the Rickettsiaceae would be abolished, and species in the Ehrlichieae tribe would be assigned to the family Anaplasmataceae, with several placed into the genera Ehrlichia (Cowdria ruminantium), Anaplasma (E. equi, E. phagocytophila, HGE agent, E. platys, E. bovis), and Neorickettsia (E. sennetsu and E. risticii). The genera Ehrlichia and Anaplasma possess all pathogens in the family that are transmissible by ticks and that generally infect peripheral blood cellular elements, including leukocytes, platelets, and erythrocytes. Figure 2 Current phylogeny and taxonomic classification of genera in the family Anaplasmataceae. The distance bar represents substitutions per 1,000 basepairs. E. coli, Escerichia coli. HGA is increasingly recognized as an important and frequent cause of fever after tick bite in the Upper Midwest, New England, parts of the mid-Atlantic states, northern California, and many parts of Europe, all areas where Ixodes ticks bite humans ( 4 – 6 ). The ecology of A. phagocytophilum is increasingly understood. The bacterium is maintained in a transmission cycle with Ixodes persulcatus complex ticks, including I. scapularis in the eastern United States, I. pacificus in the western United States, I. ricinus in Europe, and probably I. persulcatus in parts of Asia. Tick infection is established after an infectious blood meal, and the bacterium is transstadially but not transovarially passed ( 3 ). The major mammalian reservoir for A. phagocytophilum in the eastern United States is the white-footed mouse, Peromyscus leucopus, although other small mammals and white-tailed deer (Odocoileus virginianus) can also be infected. White-footed mice have transient (1–4 weeks) bacteremia; deer are persistently and subclinically infected. Human infection occurs when humans impinge on tick–small mammal habitats ( 4 – 7 ). HGA is clinically variable, but most patients have a moderately severe febrile illness with headache, myalgia, and malaise. Among 10 clinical studies that describe the findings in HGA across North America and Europe and that comprise up to 685 patients (Table), the most frequent manifestations are malaise (94%), fever (92%), myalgia (77%), and headache (75%); a minority have arthralgia or involvement of the gastrointestinal tract (nausea, vomiting, diarrhea), respiratory tract (cough, pulmonary infiltrates, acute respiratory distress syndrome [ARDS]), liver, or central nervous system ( 4 – 7 ). Rash is observed in 6%, although no specific rash has been associated with HGA and co-infection with Borrelia burgdorferi, which can cause simultaneous erythema migrans, is not infrequent. Frequent laboratory abnormalities identified in up to 329 patients include thrombocytopenia (71%), leukopenia (49%), anemia (37%), and elevated hepatic transaminase levels (71%). Table Metaanalysis of clinical manifestations and laboratory abnormalities in patients with human granulocytic anaplasmosis* Characteristics All North America Europe Median %† Mean % n‡ Mean % n Mean % n Symptom or sign Fever 100 92 480 92 448 98 66 Myalgia 74 77 514 79 448 65 66 Headache 89 75 378 73 289 89 66 Malaise 93 94 90 96 271 47 15 Nausea 44 38 256 36 207 47 49 Vomiting 20 26 90 34 41 19 49 Diarrhea 13 16 90 22 41 10 49 Cough 13 19 260 22 207 10 49 Arthralgias 58 46 497 47 448 37 49 Rash 3 6 685 6 289 4 53 Stiff neck 11 18 22 22 18 0 4 Confusion 9 17 211 17 207 0 4 Laboratory abnormality Leukopenia 38 49 329 50 282 47 47 Thrombocytopenia 71 71 329 72 282 64 47 Elevated serum AST or ALT§ 74 71 170 79 123 51 47 Elevated serum creatinine 15 43 72 49 59 0 13 *Data from references 5, 6, 8–15. †Median percentage of patients with feature among all reports. ‡Number of patients with data available for metaanalysis. §AST, aspartate aminotransferase; ALT, alanine aminotransferase. Recent seroepidemiologic data suggest that many infections go unrecognized, and in endemic areas as much as 15% to 36% of the population has been infected ( 16 , 17 ). In Wisconsin, the yearly incidence of HGA from 1990 to 1995 was as high as 58 cases/100,000 in 1 county (Lyme disease incidence in the same region was 110 cases/100,000) ( 5 ). The overall yearly Connecticut incidence rate from 1997 to 1999 was 24 to 51 cases/100,000 population ( 18 ). Symptomatic infection in Europe appears to be rare; 66 cases have been reported, despite a median seroprevalence rate of 6.2% among 35 published reports, with rates as high as 21% in some European studies. Similarly, the median infection prevalence in European I. ricinus ticks is 3% (45 publications), a figure close to that observed among North American I. scapularis and I. pacificus ticks (median 4.7% among 42 publications). What is unclear from these data is whether the discrepancy between the seroprevalence and symptomatic rate results from underdiagnosis of infection, asymptomatic serologic reactions, or even infections that produce cross-reactive serologic responses. In any case, symptomatic infection can occur often in tick-endemic regions and varies in severity from mild, self-limited fever to death. Severity sufficient for hospitalization is observed in half of symptomatic patients and is associated with older age, higher neutrophil counts, lower lymphocyte counts, anemia, the presence of morulae in leukocytes, or underlying immune suppression ( 5 ). Approximately 5%–7% of patients require intensive care, and at least 7 deaths have been identified ( 2 , 4 , 5 , 7 , 19 ), in which delayed diagnosis and treatment were risk factors. Severe complications include a septic or toxic shock–like syndrome, coagulopathy, atypical pneumonitis/acute respiratory distress syndrome (ARDS), acute abdominal syndrome, rhabdomyolysis, myocarditis, acute renal failure, hemorrhage, brachial plexopathy, demyelinating polyneuropathy, cranial nerve palsies, and opportunistic infections. At least 3 of the deaths resulted from opportunistic fungal or viral infections or hemorrhage that occurred immediately after HGA. In 2 cases, the patients had reasons for preexisting immunocompromise, which suggests that an intact immune system is important for recovery and that HGA further antagonizes immune dysfunction ( 2 , 4 , 5 , 7 ). Unlike results of animal observations ( 20 ), no evidence has shown A. phagocytophilum persistence in humans. Pathology of A. phagocytophilum Infections in Humans Few histopathologic studies of HGA have been conducted. Of 7 patients with fatal cases, 3 died from opportunistic infections ( 2 , 4 , 5 , 7 ), including exsanguination after ulcerative Candida esophagitis, ulcerative herpes simplex virus esophagitis with cryptococcal pneumonia, and invasive pulmonary aspergillosis. In 2 other deaths, the patients experienced myocarditis (likely viral) or generalized lymphadenopathy and mononuclear phagocyte system activation. The pathologic changes in humans include perivascular lymphohistiocytic inflammatory infiltrates in multiple organs, hepatitis with infrequent apoptoses, normocellular bone marrow, mild lymphoid depletion, mononuclear phagocyte hyperplasia in spleen and lymph nodes, and, rarely, splenic necrosis. Hemophagocytosis is observed in bone marrow, liver, and spleen. Vasculitis has not been observed ( 4 ). By immunohistochemical tests, A. phagocytophilum is rarely identified; organisms were abundant in only 1 patient who died, rare in 2 patients, and not identified in 2 patients ( 2 , 4 , 5 , 19 ). Infected neutrophils are not generally associated with pathologic lesions, which suggests alternative mechanisms that do not involve direct bacteria-mediated injury. Opportunistic infections and inflammatory changes in humans are not unexpected because similar findings occur in animals ( 19 , 21 ). In fact, tickborne fever (ruminant granulocytic anaplasmosis) induces diminished CD4 and CD8 peripheral blood counts, impaired mitogenic responses, impaired antibody responses, impaired neutrophil emigration, and defective phagocytosis and intracellular killing. Such in vitro findings are supported by clinical observations, which document that bacterial, fungal, and viral infections are frequent and generally worse in animals with tickborne fever ( 20 ). Disseminated staphylococcal infections that occur with tickborne fever kill ≈2% of field-raised sheep in the United Kingdom ( 20 ); louping ill, a tickborne viral encephalitis of goats is self-limited unless it occurs in conjunction with tickborne fever, when it is often fatal ( 20 ); bacterial and fungal secondary infections are more frequent in A. phagocytophilum–infected horses ( 21 ). A likely interpretation is that A. phagocytophilum is associated with perturbations in host inflammatory and immune system function. Impaired early inflammatory responses that might be induced by A. phagocytophilum could contribute to the pathogenesis of HGA, and early initiation of proinflammatory and immune responses depend on functionally competent neutrophils and mononuclear phagocytes. Pathogenesis of A. phagocytophilum Infections Anaplasma species are small (0.2–1.0 μm in diameter) obligate intracellular bacteria with a gram-negative cell wall ( 4 ), but lack lipopolysaccharide biosynthetic machinery ( 22 ). The bacteria reside in an early endosome, where they obtain nutrients for binary fission and grow into a cluster called a morula (Figure 1). Recent genomic studies demonstrated a type IV secretion apparatus, which could facilitate transfer of molecules between the bacterium and the host ( 23 , 24 ). A. phagocytophilum prefers to grow in myeloid or granulocytic cells and has been propagated in human HL-60 and KG-1 promyelocytic leukemia cells, THP-1 myelomonocytic cells, endothelial cell cultures, and tick cell cultures ( 3 ). HL-60 cells induced to differentiate into neutrophil-like cells cease to divide but enhance A. phagocytophilum growth. When differentiated into monocytic cells, HL-60 cells no longer support A. phagocytophilum growth. A. phagocytophilum binds to fucosylated and sialylated scaffold proteins on neutrophil and granulocyte surfaces ( 25 ). The most studied ligand is PSGL-1 (CD162) to which the bacterium adheres at least in part through 44-kDa major surface protein-2 (Msp2) ( 26 ). Msp2 is probably part of an "adhesin complex" involving Msp2 oligomers with other membrane proteins. After internalization of bacteria, the endosome ceases to mature and does not accumulate markers of late endosomes or phagolysosomes ( 27 ). As a result, the vacuole does not become acidified or fuse to lysosomes. A. phagocytophilum divides until cell lysis or bacteria are discharged to infect other cells. The range of described A. phagocytophilum proteins is limited, although the genome sequence should assist in defining bacterial structure and function. The most abundant protein in A. phagocytophilum is Msp2, encoded by a multigene family of at least 22 paralogs in the Webster strain genome and 52 or more paralogs in the HZ strain genome ( 28 ). Antigenic diversity among A. phagocytophilum strains from different regions is increased by msp2 gene conversion. Diversity is assumed to be driven by immune selection and may play an important role in persistence among reservoir hosts, but restricted msp2 transcription and Msp2 expression over many passages and in tick cells suggest selection by fitness for new niches, a finding underscored by Msp2's role as an adhesin ( 26 , 28 ). Aside from msp2, ankA is the most actively studied A. phagocytophilum component ( 24 , 29 ). This gene encodes a 153–160 kDa protein with at least 11 N-terminal ankyrin repeats and a C-terminus with several tandem repeats but no homology with other proteins. AnkA sequences are diverse according to geographic origin, with relative conservation among North American strains and diversity among European bacteria. Whether ankA diversity relates to severity is not known. An interesting observation regarding AnkA is its localization, where it forms a complex with chromatin in the infected granulocyte cell nucleus. Although little is known about whether AnkA affects A. phagocytophilum survival or pathogenesis, it is currently the only protein of A. phagocytophilum known to be secreted by the bacterium, that passes through the bacterial and vacuolar membrane (presumably by the A. phagocytophilum type IV secretion mechanism [23]), through the cytoplasm and nuclear membrane, to find a nuclear target. Within the nucleus of infected neutrophils or HL-60 cells, AnkA binds nuclear proteins and complexes to AT-rich nuclear DNA that lacks specific conserved sequences ( 29 ). Its mere presence in the nucleus of a cell in which gene transcription appears to be altered by infection compels further investigation of a direct pathogenetic role in regulation of eukaryotic gene expression. Animal Models and Immunopathogenicity The discrepancy between bacterial load and histopathologic changes with HGA suggests that disease relates to immune effectors that inadvertently damage tissues. In vivo human cytokine responses are dominated by interferon-γ (IFNγ) and interleukin-10 (IL-10), but lack tumor necrosis factor α (TNFα), IL-1β, and IL-4 ( 30 ), which suggests a role for macrophage activation in recovery and disease. A murine model shows a cytokine profile similar to that in humans and reproduces histopathologic lesions in infected humans, horses, and dogs ( 19 ). In this model, bacterial load peaks at day 7 and rapidly declines; IFNγ peaks at day 10 and also declines in parallel. However, histopathologic injury, minimal at days 7–10, peaks by day 14, and then resolves. This pattern suggests a role for IFNγ in histopathology and restriction of infection, which is confirmed since histopathologic lesions do not develop in IFNγ knockout mice, but the mice have a 5- to 8-fold increase in bacteremia levels ( 31 ). In contrast, IL-10 knockout mice, which poorly restrict INFγ production, do not have increased bacteremia levels, yet histopathologic lesions are significantly worse than controls. The mechanisms of bacterial growth restriction seem clearly related to INFγ production, but the role of NOS2 (iNOS) in this process is unresolved. Activation of innate immune responses through TLR2, TLR4, MyD88, TNFα, and CYBB does not contribute to control of A. phagocytophilum. Several murine models show no correlation between histopathologic injury and bacterial load. Likewise, infection of TLR2-, TLR4-, MyD88-, TNFα-, and CYBB-knockout mice does not affect bacterial burden, yet abrogates inflammatory tissue lesions. Such findings support an immune triggering role for A. phagocytophilum as a mechanism for disease. While IFNγ and IL-10 are key markers or effectors of injury with A. phagocytophilum infection, their source is unclear. Infection of neutrophils and HL-60 cells differentiated into neutrophil-like cells produces striking quantities of CXC and CC chemokines, including IL-8, RANTES, MIP1α, MIP1β, and MCP-1, but not IFNγ, IL-10, TNFα, IL-1β, or IL-4 ( 32 ), suggesting that A. phagocytophilum infection partially activates neutrophils. Akkoyunlu et al. demonstrated a decreased bacteremia with antibody blockade of chemokine receptors (CXCR2) and in CXCR2 knockout mice ( 33 ). This presumably provides a survival advantage to the bacterium by recruitment of new neutrophil host cells, increasing the blood concentrations of infected cells that can be acquired by tick bite. In spite of the increased bacteremia, no increase in histopathologic lesions is noted, confirming previous studies. The disadvantage of chemokine production to the host is that recruitment of inflammatory cells that are activated could produce IFNγ-induced inflammation, leading to damage to tissues. Neutrophil Functional Changes with A. phagocytophilum Infection Other notable alterations of neutrophil function and physiology are observed with A. phagocytophilum infection. A. phagocytophilum survives its initial encounter by detoxifying superoxide produced by neutrophil phagocyte oxidase assembly, perhaps by virtue of bacterial superoxide dismutase ( 23 , 34 ). Although not yet shown in infected neutrophils, infected HL-60 cells are unable to generate respiratory bursts because of reduced transcription of components of phagocyte oxidase, including gp91phox and Rac2 ( 35 , 36 ). Although this defect seems limited to the infected neutrophils and is a major mechanism that permits intracellular infection, the reduction in phagocyte oxidase may have other effects, including a reduction in local regulation of inflammation. This results from the inability of phagocyte oxidase to degrade inflammatory mediators such as leukotrienes, complement, and perhaps other components. Another normal function of neutrophils is apoptosis, which regulates inflammation by programmed cell death of activated neutrophils usually within 24 to 48 hours. The induction of apoptosis by A. phagocytophilum–infected neutrophils is delayed ≈24 hours ( 37 ) and also relates to maintained transcription of bcl2 family genes and stabilization of the mitochondrial pathway that ultimately prevents procaspase 3 processing ( 37 ). Infection by A. phagocytophilum results in significant disruption of normal neutrophil function, including endothelial cell adhesion and transmigration, motility, degranulation, respiratory burst, and phagocytosis. A. phagocytophilum–infected neutrophils and HL-60 cells are inhibited from binding to systemic and brain microvascular endothelial cells, even under conditions of low shear force ( 38 ). The adhesion defect results from the shedding of neutrophil PSGL-1 and L-selectin, which mediate the critical first step in inflammatory cell recruitment. This inhibited recruitment occurs despite the rapid mobilization of surface β2-integrins (CD11b/CD18) and ICAM-1 (CD54), which ordinarily mediate the second phase of tight endothelial-cell binding. Thus, A. phagocytophilum–infected neutrophils are inhibited from transmigrating endothelial cell barriers in spite of stimulated motility. Selectin "shedding" occurs because infected cells degranulate, including an EDTA-inhibitable sheddase (metalloprotease), β2-integrins, CD66b, and other inflammatory components such as matrix metalloproteases, which includes gelatinase (MMP9) ( 38 , 39 ). Engagement of opsonophagocytosis receptors and degranulation are usually accompanied by rapid cell death (apoptosis), but with A. phagocytophilum, degranulation occurs over a prolonged period, potentially exacerbating inflammation, especially with delayed apoptosis of infected neutrophils ( 36 , 39 , 40 ). After recruitment, chemotactic migration, and activation for respiratory burst, neutrophils are then activated for phagocytosis; however, this function is inhibited in vivo and in vitro, perhaps in part resulting from alterations of rac2 expression and loss of important surface receptors ( 40 ). Altogether, the activated-deactivated phenotype of the A. phagocytophilum–infected neutrophil may benefit the bacterium by increasing concentrations of infected cells in the peripheral blood that are unresponsive to tissue recruitment and may have a prolonged lifespan. However, the cost to the host includes activation of neutrophils to participate in proinflammatory reactions while they are unable to act as microbicidal effectors or regulators of inflammation. Conclusions Investigators of novel intracellular bacteria often address unanswered questions by investigating processes shared with other bacteria or bacterial processes, or by investigating differences that have allowed the unique niche to become occupied. Since A. phagocytophilum, along with E. ruminantium, E. ewingii, and Chlamydophila pneumoniae are the only known bacteria to survive and propagate within neutrophils, it seems most relevant that investigation should focus on adaptations permissive for neutrophil infection. What is clear is that this new tickborne infection has a great capacity to infect and cause disease in humans while maintaining a persistent subclinical state in animal reservoirs. The disease processes appear to be immune and inflammatory in nature, not directly related to pathogen burden, and result by the triggering of a detrimental and poorly regulated host response. Recent investigations have provided important phenotypic data on the range of functional changes among A. phagocytophilum–infected neutrophils and identified several compelling targets for study of fundamental pathogenetic processes. Important areas that still need intense study include the bacterial triggers of host innate and inflammatory response and the molecular and cellular mechanisms by which A. phagocytophilum influences cell function and ultimately causes injury to host cells, tissues, and organs. Perhaps by developing a more comprehensive understanding of the basic mechanisms underlying A. phagocytophilum–neutrophil/host interactions, we can appropriately target strategies for control and management.
Author and article information
Contributors
Vetle M. Stigum: vetlems@student.matnat.uio.no
Ryanne I. Jaarsma: ryanne.jaarsma@rivm.nl
Hein Sprong: hein.sprong@rivm.nl
Christer M. Rolandsen: christer.rolandsen@nina.no
Atle Mysterud:
ORCID: http://orcid.org/0000-0001-8993-7382
atle.mysterud@ibv.uio.no
Journal
Journal ID (nlm-ta): Parasit Vectors
Journal ID (iso-abbrev): Parasit Vectors
Title:
Parasites & Vectors
Publisher:
BioMed Central
(London
)
ISSN
(Electronic):
1756-3305
Publication date
(Electronic):
3
January
2019
Publication date PMC-release: 3
January
2019
Publication date Collection: 2019
Volume: 12
Electronic Location Identifier: 1
Affiliations
[1
]ISNI 0000 0004 1936 8921, GRID grid.5510.1, Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences,
, University of Oslo, ; P.O. Box 1066 Blindern, NO-0316 Oslo, Norway
[2
]ISNI 0000 0001 2208 0118, GRID grid.31147.30, Centre for Infectious Disease Control (CIb), National Institute for Public Health
and the Environment (RIVM), ; Bilthoven, the Netherlands
Author information
Atle Mysterud
http://orcid.org/0000-0001-8993-7382
Article
Publisher ID: 3256DOI: 10.1186/s13071-018-3256-z
PMC ID: 6318929
PubMed ID: 30606222
SO-VID: 05b69d21-c4ce-4908-a657-cec70ad36b90
Copyright © © The Author(s). 2019
License:
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
History
Date
received
: 10
September
2018
Date
accepted
: 4
December
2018
Funding
Funded by: FundRef http://dx.doi.org/10.13039/501100005416, Norges Forskningsråd;
Award ID: 254694
Award Recipient
:
Atle Mysterud
Funded by: FundRef http://dx.doi.org/10.13039/501100008776, Miljødirektoratet;
Award ID: 16010287
Award Recipient
:
Atle Mysterud
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Subject:
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ScienceOpen disciplines: Parasitology