**1. Introduction**

Wine fermentation is a complicated biochemical process in which yeasts play an active role in the production of ethanol, CO2, and other metabolites from glucose and fructose of grapes [1]. Wine fermentation spontaneously takes place by yeast strains that are present on the grape surface or winery equipment. By today's technology, to achieve complete fermentation, good oenological properties and high production yield commercially produced yeast strains, and mostly *Saccharomyces cerevisiae* are used for wine fermentation as starter microorganisms [2].

*S. cerevisiae* strains derived from industrial wine have hexose transporters (HXT 1–7) that are responsible for wine fermentation. It is mentioned that there is no growth or fermentation when HXT 1–7 are deleted from the genes of this yeast [3]. Ethanol formation in the wine medium causes a change in the affinities of hexose transporters and the change in the affinities of hexose transporters causes stuck fermentation. In this study, stuck fermentation due to ethanol formation was discussed and the experiments were conducted to prevent stuck fermentation.

The hexose sugars, glucose and fructose, are the main reducing monosaccharides present in grapes or grape musts. The amounts of total sugars in grapes or grape musts change between 160 and 300 g/L that consist of almost equal amounts of glucose and fructose before fermentation [4]. During wine fermentation, yeasts, especially *S. cerevisiae*, coferment these monosaccharides and produce wine components [3–5]. Since yeasts have glucophilic character, which is the preference of fermenting glucose to fructose [5], the utilization rate of glucose is higher than that of fructose during fermentation [4]. The glucophilic character of yeasts may be due to transportation across the plasma membrane of yeast by hexose transporters or phosphorylation inside the cell of yeast by hexose kinases has different affinities through glucose and fructose [4]. These different utilization rates result in glucose/ fructose discrepancy (GFD) and residual fructose amount higher than 2 g/L [6] when the fermentation process is completed [4]. Since the sweetness of fructose is approximately twice than that of glucose [7], it affects the final sweetness of wine and the wine fermentation results in higher sweetness, which is undesirable in the wine industry. Also, high residual fructose increases the risk of microbial spoilage [8–10] and decreases the ethanol yield in wine [4–6]. This has been informed that sluggish (incomplete) or stuck (depleted) fermentation in the literature [6].

Although the exact reason for stuck or sluggish fermentations has not been determined yet, there are more than 15 reported reasons such as nitrogen deficiency [3, 4, 6, 8, 9, 11], limitation or excess amount of oxygen [8, 9], too much clarification [8, 9], formation of by-products due to fermentation [9], high ethanol accumulation [4, 6, 8, 9, 12], vitamin and mineral deficiency [8, 9], toxic residues for yeasts from fermentation [5, 8, 9], deprivation of nutrients for yeasts [10], too high or low temperatures [10], environment with high acidity [10], the formation of inhibitors like phenols [10], change in the equation of ionic components [10], higher sulfite content [8], and so on.

There are some known reasons for stuck fermentation and also, there are some possible ways and improvement methods against stuck fermentation such as nitrogen supplementation, controlling the oxygen amount, controlling the temperature of the environment, selecting the yeast according to process, controlling nutrients for yeast growth, and so on. Although many techniques and improvements are developed, stuck fermentation is still a major problem for the wine industry since it causes product losses [9].

Usage of an enzyme in wine media for preventing or restarting the stuck fermentation was not studied before. Therefore, using glucose isomerase to prevent or restart the stuck fermentation was studied as a novel approach.


**4. Discussion**

#### *4.1. System Analysis*

The mathematical model of the system is given by Equations (3) and (4) in the Laplace domain. The unit step response of the system is shown in Figure 6. Besides the gain and time constant, other essential characteristics of the system are the rise time, transient time, settling time, and steady-state. These system characteristics are calculated from the transfer function or are found by plotting the step response in MATLAB.

**Figure 6.** Unit step response of the system *G*(*s*) with confidence region.

In Figure 6, the rise time is 0.104 s, the transient time is 0.184 s, and the final value corresponds to a gain of 0.174 nA; thus, the system does not have an overshoot. Experimental output data and the mathematical model indicate that stability is enforced. The same conclusion was drawn by conducting a pole location analysis. The model has one pole with a negative real part, and the system was determined to be stable [22,24,25].

The model in the Laplace domain corresponds to the differential equation under the assumption that all initial conditions are zero [22]. The inverse Laplace transformation gives a solution of a differential equation in the time domain, and the system described with the solution to Equation (4) in the time domain is

$$\log(t) = 0.1738 \left( 1 - e^{\frac{t}{0.0777}} \right) h(t) \tag{5}$$

where *g*(*t*) corresponds to *G*(*s*), representing the unit step response of the system *G*(*s*) (see Figure 6) when all initial conditions are equal to zero, and *h*(*t*) is the input function, which corresponds to the unit step or Heaviside function *h*(*t*).

We simulated outputs of the model using Equation (4) with inactivation and deactivation stimuli. The compared results of the measured outputs and simulated outputs are shown in Figure 7. The model satisfies the dynamic behavior of the Kv10.1 voltage-gated ion channel based on the adopted assumptions.

Since different cellular mechanisms can affect the opening behavior of ion channels, we believe that time-dependent transfer functions can be used to provide a more accurate description of the dynamic characteristics of ion channels. This hypothesis is supported by the fact that all mathematical systems are non-stationary and that the mathematical origin of non-stationarity can be proved [31].

#### *4.2. Control Algorithm*

The model (Equation (3)) is stable, observable, and controllable because the rank of the controllability and observability matrix is equal to *n*, where *n* = 1, and the system is a first-order system [22,24,25]. We considered a classical control algorithm for the Kv10.1 model. The controller is designed using the Sisotool in MATLAB. We chose an automated tuning method for a PID compensator in a feedback loop, the tuning method is a robust time response, and the controller type is PID. The controller is given by

$$C(s) = 316.85 \left( \frac{1 + 0.0026s}{s} \right) \tag{6}$$

**Figure 7.** Measured and simulated output macroscopic current for the inactivation and deactivation input stimuli (grey lines). Input stimulus inactivation: measured output (**a**), simulated output (**b**); input stimulus deactivation: measured output (**c**), simulated output (**d**).

The unit step response of the controlled Kv10.1 voltage-gated ion channel in a closedloop, also called a feedback loop, is shown in Figure 8. The Kv10.1 in the feedback loop displays a rise time of 0.043 s, a peak amplitude of 1.06 nA, an overshoot of 6.08%, a transient time of 0.149 s, and a final value of 1 nA. The controlled model has a faster rise time and transient response, and the final value corresponds to the desired value.

**Figure 8.** Unit step response of the Kv10.1 model in a control loop.

The feedback control system with the Kv10.1 model, *G*(*s*), and controller *C*(*s*) in a loop was implemented in Simulink (Figure 9a). A sine waveform with unit amplitude and frequency of 1 Hz was chosen as a simulation input. The simulated output is shown in Figure 9b. Nevertheless, using this approach of ion channel modeling, the ion channel could be simulated as a single system or a system in a control loop with any randomly chosen input, enabling the obtained output to be analyzed.

**Figure 9.** Simulink Kv10.1 model in a control loop (**a**); simulated output for sine input (**b**).

Microelectromechanical (MEMS) devices and nanoelectromechanical (NEMS) devices can be designed as MEMS PID controllers and NEMS PID controllers. In a clinical context, nanorobots are already used for the mechanical opening of a cell membrane [32], microscopic surgical procedures, drug delivery, gene therapy, or selective destruction of cancer cells [4]. Therefore, our vision is that free-floating and guided nanorobots could have promising applications in cancer detection and therapy. Research has shown that the up-regulation of the selected channel Kv10.1 can promote cell proliferation and thus tumor development in different organs and human body systems [18], whereas the downregulation of Kv10.1 could potentially have the opposite effect, providing a potential oncological target. However, since the same ion channels are also expressed in normal, healthy cells, albeit to minor expression levels, one of the biggest challenges is to specifically block or manipulate them only in cancer cells. Nanorobots could be programmed to target certain cell clusters, tissues or organs (e.g., prostate, stomach, or breast) and specifically control the activity of selected ion channels in cancer cells without affecting the same channels expressed in surrounding healthy cells or other tissues and thus impairing their function. A controlled down-regulation of the ion channel current in specific cells only would also allow for a systemic application, such as the use of traveling nanorobots for the treatment of cancer in various regions of the body or targeted tissues and cell clusters, as shown in Figure 10, for example.

#### *4.3. Potential Use Case for Treating Breast Cancer Using a Kv10.1 Controlled Nanorobot*

Our hypothesis for the use of swimming nanorobots is to control ion-channel activity to specifically target cancer cells in breast tumors. We know that Kv10.1 channels in the nervous system contribute to the control of neuronal excitability. However, since activation requires strong depolarization, a single action potential would not be sufficient for activation [33]. In contrast, cancer cells are usually depolarized so that the channels are predominantly open, providing a potential target for pinpoint destruction by nanorobots. For example, overexpression of Kv10.1 channels is significantly up-regulated in invasive breast carcinoma, whereas healthy breast tissue shows hardly any expression or activity of the Kv10.1 channel [30,33–36]. Floating nanorobots applied via the blood circulation can be guided to the targeted sites and measure and control the Kv10.1 channel activity of the cells in the area of interest. Since nanorobots act as sensors and actuators, if the sensor interface detects an altered behavior of Kv10.1 channel activity, as is the case in breast cancer cells, the channels could be controlled accordingly by the Kv10.1 feedback control system of the robot (see Figures 9 and 10). Alternatively, after detecting a cancer cell based on aberrant or

abnormal channel activity, such MEMS/NEMS-based nanorobot devices may also act as a nano-scalpel that mechanically destroys the targeted cell.

**Figure 10.** Illustration of possible application of Kv10.1-controlled nanorobots in breast cancer detection and treatment. The cancerous area will be reached by the nanorobots through the blood vessel system. Created with BioRender.

Further examples of possible applications are discussed in [34,37–39]. However, although the practical implementation of this use case has not been performed and validated, this work demonstrates a potential new, forward-looking direction for cancer detection and therapy.

#### **5. Conclusions**

In this work, voltage-gated ion channels were modeled as control objects or systems by applying the control system theory and a system identification method. The Kv10.1 voltage-gated ion channel exhibits dynamic behavior as a first-order dynamic system. We presented a classical control algorithm and described how this algorithm could theoretically be used to control the response of the Kv10.1 voltage-gated ion channel. Other control algorithms, such as robust control or fuzzy control, could also be used. Research has shown that controlling the Kv10.1 ion channel under up-regulated conditions plays a vital role in cancer prevention and treatment. Nanoelectromechanical systems, such as nanorobots, could be used in the treatment of cancer as they are able to travel through the blood vessel system to the target cells and bind to the ion channels. The robot's control unit can then be activated based on the implemented control algorithm, and the desired ion channel regulation can be performed to treat channel activity by the down or upregulation of the targeted channels.

It is, therefore, expected that such models, which could also describe the activity of multiple ion channels and their interactions, e.g., described by a MIMO system, will enable the use of nanoelectromechanical systems and nanorobots, as demonstrated for the Kv10.1 voltage-gated ion channel, and for other ion channel families, with the goal of establishing new tools for cancer detection and treatment at the single-cell level.

**Author Contributions:** Conceptualization, J.L.Š.; methodology, J.L.Š.; validation, J.L.Š.; formal analysis, J.L.Š.; investigation, J.L.Š. and S.L.; resources, C.B.; data curation, J.L.Š. and S.L.; writing—original draft preparation, J.L.Š.; writing—review and editing, J.L.Š., S.L. and C.B.; visualization, J.L.Š.; supervision, J.L.Š. and C.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** Open Access Funding by the Graz University of Technology.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** https://channelpedia.epfl.ch/ionchannels/ (accessed on 15 February 2022).

**Acknowledgments:** We acknowledge Open Access Funding by Graz University of Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**5.2 Positioning of the One Health concept in relation to human and animal health**

Climate change and environmental change are part of the set of changes that affect ecosystems and promote the emergence and re-emergence of animal diseases. In recent years, more than 70% of these emerging infectious zoonosis have their source in wildlife [Black and Nunn, 2009] and about 60% of emerging infectious diseases are classified as zoonotic, i.e. transmitted from animals to humans [20].

Global warming contributes to the emergence of infectious diseases in the animal and plant world by exerting a marked effect on arthropod insects (mosquitoes, aphids, sandflies, fleas), on ticks that can harbor and transport viruses, bacteria and protozoa. In addition, it causes a thermal increase that increases the risk of extending the current geographical range of these species with the risk of transmission to animals and humans of diseases against which there is no natural immunity [28]. There are a number of meteorological and climatic parameters that can affect human and animal health. In addition to heat, cold, water, ozone, air, allergens and ultraviolet rays, these include heat, cold, water, ozone, air, allergens and ultraviolet rays.

#### *5.2.1 In men*

Some authors argue that climate change is the most significant threat to human health in the 21st century, associated with an increase in chronic diseases, health problems caused by extreme heat and floods, food shortages caused by drought or floods, and various diseases including respiratory problems. The effects of climate change on human health can be both direct and indirect. They are considered direct when they are related to the physiological effects of heat and cold and indirect when they result, for example, from changes in human behavior following forced migration, major natural disasters such as floods and earthquakes, or major outbreaks of food-borne or vector-borne diseases. Not all climate-induced changes are necessarily negative for human health, especially those leading to a decrease in extreme temperatures [42].

#### **a. Impact of heat and cold**

In temperate countries, periods of high heat are sometimes accompanied by relatively high morbidity and mortality. It is estimated, for example, that in Europe, for a one-degree increase in temperature, heat-related mortality would increase by 1–4%. Heat is an immediate health risk to be considered because it has been shown that nearly three-quarters of the hot days observed since 1850 are attributable to climate change [43]. On the other hand, cold can also cause illness and death among the most vulnerable, especially the elderly and homeless. Variations in temperature, especially those that are upwardly oriented, can be the cause of some foodborne and vector-borne zoonotic diseases.

Food-borne bacterial infectious diseases such as salmonellosis and colibacillosis are generally sensitive to temperature and are becoming increasingly important every day even in developed countries such as Europe and the United States.


*3.1. Surgery*

BMs from DTC tend to be highly destructive, resulting in pathological fractures and spinal cord compression from lesions in the spine. These SREs, including intractable pain and neurological symptoms, severely compromise the PS and QOL of patients. Local tumor control without SREs is important for patients with DTC-BMs because the prognosis of these patients is more favorable compared with that of patients with BMs from other cancers. Therefore, surgery for BMs is indicated more often than that for other organ metastases. For BMs, there are palliative and excisional surgery (metastasectomy) categories. Palliative surgery is performed to prevent symptomatic SREs, including pathological fractures and spinal cord compression, or to alleviate symptoms due to SREs. Metastasectomy is the complete excision of the tumor, aimed at achieving long-term local tumor control.


2.2.2. Electrospinning Process

The polymer blends were electrospun using an electrospinning apparatus (STKIT-40, Linari Engineering, Pisa, Italy), equipped with a high voltage generator (5–40 kV), a glass syringe of 10 mL with a stainless steel needle (0.8 mm), a volumetric pump (Razel R99-E) and a planar collector. The following parameters were used to obtain N loaded scaffolds: DV (voltage) = 22 kV, needle-to-collector distance = 24 cm, flow = 0.379 mL/h, to obtain H loaded scaffolds: DV (voltage) = 24 kV, needle-to-collector distance = 22 cm, flow = 0.379 mL/h, relative humidity: 40%, environmental temperature: 25 ◦C. All the scaffolds were then crosslinked by heating at 150 ◦C for 1 h in a tight container protected from light; the process was also reported as being able to dry sterilize the products [18]. Preliminarily, N stability in the heating process was assessed using a diode array detector (DAD) HPLC (see Section <IP_ADDRESS>). For this purpose, the active ingredient was subjected to the heating treatment in the same conditions as the scaffolds (150 ◦C for 1 h) (in a tight container protected from light). Chromatograms and UV/visible spectra (200–700 nm) at the maximum of the corresponding chromatographic peaks were compared.


**Author details**

Rodger K. Bufford\*, Jessica Cantley, Jaycee Hallford, Yadira Vega and Jessica Wilbur Graduate School of Clinical Psychology at George Fox University, Newberg, Oregon, United States of America

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**Appendix A**

The analytical hierarchy process (AHP) proposed by Thomas L. Saaty (1980) is a method to express the relative importance or dominance of individual element(s) over an assessment, such as criteria or characteristics useful, for example, for decision-making [42]. This technique organizes the evaluation elements into a hierarchy level, assigning numerical values, which gives mathematical support to the organization. The AHP uses pairwise comparison or paired ranking to compare the various elements of the analysis. This comparison is based on a numerical qualification, considering a prime number subjacent scale from 1 to 9 (Saaty scale). Data are entered into a squared matrix to give qualifications; therefore, the level of importance (Table A1).

#### **Table A1.** Saaty scale used in paired ranking.


Virulence Factors of *Salmonella Typhi*

*Noor S.K. Al-Khafaji, Ali M.K. Al-Bayati and Hussein O.M. Al-Dahmoshi*

### **Abstract**

S. Typhi is an enteric bacillus which belongs,to the genus *Salmonella* in the family Enterobacteriacaea and it is a multi–organs pathogen which inhibits the lymphatic tissues of the small intestine, liver, spleen, and blood stream of infected humans. S. Typhi has a mixture of features that make it an efficient pathogen. This species contains an endotoxin that is characteristic of Gram-negative organisms, as well as the virulence-enhancing Vi antigen. Many of the S. Typhi virulence factors are clustered in some areas of the chromosome known as *Salmonella* pathogenicity islands (SPI), such as adhesion, invasion, and toxin genes. A protein known as invasin that permits non-phagocytic cells is also produced and excreted by the bacterium., Where it is capable of intracellular living. The oxidative burst of leukocytes may also be inhibited, making innate immune reaction ineffective.

**Keywords:** S. Typhi-virulence factors -endotoxin, enterotoxin, cytotoxin

### **1. Introduction**

It was quite a long time before typhoid fever was differentiated from other febrile disorders. Pierre Louis was the first who used the word " typhoid " and give the classical picture of typhoid in 1829 and described in detail post-mortem finding, especially the enlargement and ulceration of peyer's patches. However, he did not clearly differentiate between typhoid and typhus. In 1837, Gerhard was the first who clearly differentiate typhoid from typhus fever and William Budd described the contagious nature of the disease and Incriminated transmission of facially polluted water supplies in 1873 [1].

In 1873, William Budd, a physician in Bristol who was interested in cholera and intestinal fever, showed that typhoid fever could be transmitted by a particular toxin found in the excrement and that this propagation was responsible for the contamination of water by the feces of patients. Each case was linked to another anterior case, according to Budd. A significant number of doctors and scientists have attempted to discover the nature of the disease-causing microorganism and have experienced considerable difficulty in isolating the bacillus. It was Karl Joseph Eberth, Rudolf Virchow's doctor and pupil, who discovered the bacillus in the abdominal lymph nodes and the spleen in 1879. In 1880 and 1881, he reported his findings. The genus '*Salmonella*' was named after Daniel Elmer Salmon, an American veterinary pathologist, who was the administrator of the USDA (United States Department of Agriculture) research program. His discovery was then tested and confirmed by German and English bacteriologists, including Robert Koch., Thus, despite the fact

that a number of scientists had contributed to the quest [2, 3], the organism was named after him. *Salmonella* has thus become new scientific knowledge and thus the mechanisms of infection and the presence of healthy carriers have been relatively nascent [4]. Recent reports suggest that there are approximately 20 million cases of typhoid each year, resulting in deaths of 100,000-200,000 [5]. Karl J. Eberth, who isolated the bacterium from spleen parts and lymph nodes from a patient who died of typhoid fever and discovered the typhoid agglutinins and their diagnostic application, first isolated S. Typhi in 1880. In 1881, Robert Koch succeeded in cultivating the bacterium. However the isolation of typhoid bacillus from other enteric bacteria was unclear due to the lack of differential characters [6, 7].

*Salmonella* is a genus of rod shaped (bacillus) gram negative bacteria related to family Enterobacteriaceae. They have two species which are *Salmonella enterica* and *Salmonella bongori*.  *Salmonella* species are non spore forming, predominantly motile enterobacteria for cell diameters between on 0.7 and 1.5 μm, lengths for 2 to 5 μm, and peritrichous flagella (all concerning the cell body [10]. exceptions *S. Gallinarum* and *S. Pullorum* [11, 12]. The bacterial strain was named after the American pathologist, Dr. Daniel Elmer Salmon, who collaborated with Smith. The *Salmonella* nomenclature is controversial and still changing. The Centers for Disease Control and Prevention (CDC) is currently using the *Salmonella* nomenclature system suggested by the World Health Organization (WHO) Collaborating Centre as a nomenclature system.: Species: *Salmonella enterica* serotype Typhi. [13].


*Article* **Ecotyping of** *Anaplasma phagocytophilum* **from Wild Ungulates and Ticks Shows Circulation of Zoonotic Strains in Northeastern Italy**

**Laura Grassi, Giovanni Franzo , Marco Martini , Alessandra Mondin, Rudi Cassini , Michele Drigo , Daniela Pasotto , Elena Vidorin and Maria Luisa Menandro \***

> Department of Animal Medicine, Production and Health (MAPS), University of Padova, 35020 Legnaro, Padova, Italy<EMAIL_ADDRESS>(L.G<EMAIL_ADDRESS>(G.F<EMAIL_ADDRESS>(M.M<EMAIL_ADDRESS>(A.M<EMAIL_ADDRESS>(R.C<EMAIL_ADDRESS>(M.D<EMAIL_ADDRESS>(D.P<EMAIL_ADDRESS>(E.V.)

**\*** Correspondence<EMAIL_ADDRESS>
**Simple Summary:** Tick-borne infectious diseases represent a rising threat both for human and animal health, since they are emerging worldwide. Among the bacterial infections, Anaplasma phagocytophilum has been largely neglected in Europe. Despite its diffusion in ticks and animals, the ecoepidemiology of its genetic variants is not well understood. The latest studies identify four ecotypes of Anaplasma phagocytophilum in Europe, and only ecotype I has shown zoonotic potential. The aim of the present study was to investigate the genetic variants of Anaplasma phagocytophilum in wild ungulates, the leading reservoir species, and in feeding ticks, the main vector of infection. The analyzed samples were collected in northeastern Italy, the same area where the first Italian human cases of anaplasmosis in the country were reported. Using biomolecular tools and phylogenetic analysis, ecotypes I and II were detected in both ticks (Ixodes ricinus species) and wild ungulates. Specifically, ecotype II was mainly detected in roe deer and related ticks; and ecotype I, the potentially zoonotic variant, was detected in Ixodes ricinus ticks and also in roe deer, red deer, chamois, mouflon, and wild boar. These findings reveal not only the wide diffusion of Anaplasma phagocytophilum, but also the presence of zoonotic variants.

**Abstract:** *Anaplasma phagocytophilum* (*A. phagocytophilum*) is a tick-borne pathogen causing disease in both humans and animals. Human granulocytic anaplasmosis (HGA) is an emerging disease, but despite the remarkable prevalence in European ticks and wild animals, human infection appears underdiagnosed. Several genetic variants are circulating in Europe, including the zoonotic ecotype I. This study investigated *A. phagocytophilum* occurrence in wild ungulates and their ectoparasites in an area where HGA has been reported. Blood samples from wild ungulates and ectoparasites were screened by biomolecular methods targeting the *mps2* gene. The *groEL* gene was amplified and sequenced to perform genetic characterization and phylogenetic analysis. A total of 188 blood samples were collected from different wild ungulates species showing an overall prevalence of 63.8% (88.7% in wild ruminants and 3.6% in wild boars). The prevalence of *A. phagocytophilum* DNA in ticks (manly *Ixodes ricinus*), and keds collected from wild ruminants was high, reflecting the high infection rates obtained in their hosts. Among ticks collected from wild boars (*Hyalomma marginatum* and *Dermacentor marginatus*) no DNA was detected. Phylogenetic analysis demonstrated the presence of ecotype I and II. To date, this is the first Italian report of ecotype I in alpine chamois, mouflon, and wild boar species. These findings suggest their role in HGA epidemiology, and the high prevalence detected in this study highlights that this human tick-borne disease deserves further attention.

**Keywords:** *Anaplasma phagocytophilum*; zoonosis; tick; wild ungulates; phylogenesis; molecular epidemiology

**Citation:** Grassi, L.; Franzo, G.; Martini, M.; Mondin, A.; Cassini, R.; Drigo, M.; Pasotto, D.; Vidorin, E.; Menandro, M.L. Ecotyping of *Anaplasma phagocytophilum* from Wild Ungulates and Ticks Shows Circulation of Zoonotic Strains in Northeastern Italy. *Animals* **2021**, *11*, 310. https://doi.org/10.3390/ ani11020310

Academic Editor: David González-Barrio Received: 4 December 2020 Accepted: 22 January 2021 Published: 26 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

*Anaplasma phagocytophilum* (*A. phagocytophilum*) is a small, Gram-negative, obligate intracellular bacterium belonging to the Anaplasmataceae family, order Rickettsiales [1]. It infects white blood cells, mainly neutrophilic granulocytes, and is the causal agent of human granulocytic anaplasmosis (HGA), tick-borne fever (TBF), or pasture fever of domestic ruminants and granulocytic anaplasmosis of horses (equine granulocytic anaplasmosis, EGA), dogs (canine granulocytic anaplasmosis, CGA) and cats (feline anaplasmosis, FA) [2,3].

*Anaplasma phagocytophilum* is a tick-borne pathogen mainly transmitted, in Europe, by the *Ixodes* genus, especially *I. ricinus*. This bacterium has been detected in other hard tick species, but their vectorial efficiency seems negligible [3–5]. Occasionally, HGA can be caused by blood transfusion, transplacental transmission, or direct contact with infected blood of wild ungulates at butchering [6]. However, tick bites represent the main route of transmission, and once infection is established, ticks maintain *A. phagocytophilum* through trans-stadial transmission, while the transovarial route seems not to be efficient [7]. Accordingly, mainly nymphal and adult stages are involved in bacterial transmission [8]. *Ixodes ricinus* is therefore considered an efficient vector but not a reservoir [4], thus vertebrate species, especially wildlife, seem to be pivotal in maintaining *A. phagocytophilum* circulation [9,10].

Prevalence in *I. ricinus* ticks is reported to increase according to the number of blood meals. Adult ticks therefore show higher infection rates than nymphs and larvae [4], reaching even 100% in adult stages collected from wild ungulates [11]. Wild ungulates harbor relevant amounts of all tick stages [11] and play a critical role in the transmission cycle of anaplasma, as they act as both tick hosts and reservoir species of *A. phagocytophilum* [12]. In Europe, roe deer (*Capreolus capreolus*) and red deer (*Cervus elaphus*) in particular show very high *A. phagocytophilum* prevalence [4]. Although other wild ruminants may display a non-negligible infection frequency, their role has not yet been established [8]. In wild boar, *A. phagocytophilum* infection is less common, but this ungulate has been suggested as a reservoir of zoonotic strains [11,13]. Indeed, the genetic diversity of *A. phagocytophilum* strains has been demonstrated, and the genetic variants seem to display a host preference, showing different epidemiological and clinical pictures [8,10]. The genes commonly used to genotype and distinguish these variants are 16S *rRNA*, *groEL*, *ankA*, and *msp4*. Among these, the *groEL* gene, belonging to *groESL* heat shock operon, seems to be a good marker of *A. phagocytophilum* genetic diversity. It is mostly studied in Europe, where the genetic variability is greater than that observed in other geographical areas, such as North America [3,10].

Based on the *groEL* classification, four ecotypes, subdivided into eight clusters, have been identified in different tick and vertebrate species [10,14]. Ecotype I has the widest host range, and is found in livestock, dogs, cats, horses, and wild species, including carnivores, small mammals, wild boars, and other ungulates. Also, human infections are driven by ecotype I, which is of primary importance in the epidemiology of HGA [3,14]. Other ecotypes seem to be preferentially associated with certain species. Ecotype II is detected mainly in roe deer and only sporadically in other species, ecotype III is linked to small mammals, especially rodents, and ecotype IV is found mainly in birds. This distribution seems to also be related to the host preference of different species of ticks. Ecotypes I and II are mainly detected in *I. ricinus* [10]. Both ecotypes have also been identified in other ectoparasites such as deer keds (*Lipoptena cervi*, *L. cervi*) [14]. The role of this species in the transmission of anaplasmosis has been suggested but not demonstrated [15]. Ecotypes III and IV have been detected in *Ixodes trianguliceps* and *Ixodes frontalis*, which usually feed on rodents and birds, respectively [14]. The distribution of European ecotypes thus seems to be affected by vertebrate host and vector species, their interaction and, consequently, geographic origin [3,10]. Although ecotypes seem more represented in some preferential animal species, many studies show their overlap within the same mammal species, suggesting that *A. phagocytophilum* variants may not be host-specific at all [3,16].

Human granulocytic anaplasmosis has been reported worldwide, but is mainly detected in the northern hemisphere [17]. The first human cases in the USA and Europe were identified in 1990 and 1996, respectively [18,19]. In Italy, the first HGA cases were reported in the Friuli–Venezia–Giulia region in 2000 [20], followed by other cases reported in Sicily and Sardinia [21,22]. Despite high infection rates of *A. phagocytophilum* in ticks and in different wild and domestic species, HGA seems to be rare at present. In Italy, 16 different tick species have been reported to bite humans, in Northeastern Italy, almost all cases are attributed to *I. ricinus* [23–25]. A possible reason for the low HGA prevalence in Italy is that it is not a notifiable disease, which could lead to underestimation of disease incidence [24]. In addition, the subclinical or poorly specific nature of *A. phagocytophilum*-induced symptoms further decreases the recognition and reporting infections. However, if not treated, it could evolve into more severe and even fatal syndromes. Immunocompromised patients are at higher risk of high morbidity and mortality [26]. Based on these findings, many authors agree that HGA is neglected and underdiagnosed, and thus underestimated [3,9,26].

Although the studied area, northeastern Italy, is the same place where the first Italian cases of HGA were reported [20,27], to date, there are few epidemiological and no phylogenetic studies on this pathogen in wild ungulates, which may act as an important reservoir of *A. phagocytophilum* variants related to HGA. Thus, this study aims to fill this gap, evaluating the presence and distribution of zoonotic *A. phagocytophilum* ecotypes in wildlife and in their associated ectoparasites.

#### **2. Materials and Methods**

#### *2.1. Sampling Sites and Specimens Collection*

The area under investigation comprises several different sites in northeastern Italy. The first comprises pre-alpine and alpine localities in the province of Udine in the Friuli– Venezia–Giulia region. The other two, both located in the Veneto region, included pre-alpine and alpine areas in Belluno province, and hilly areas in Euganean Hills Regional Park. The highest mountain peak of alpine areas reaches an elevation of 2500 m above sea level (a.s.l.), while Euganean hills are characterized by a maximum elevation of 600 m a.s.l. The hunting reserves are all characterized by the presence of wild ungulate populations, that increased remarkably in the last decades as a consequence of animal protection and reintroduction policies or illegal release of gaming animals [28]. In particular, free-ranging wild ruminants are particularly present in pre-alpine and alpine areas, where the most abundant are roe deer and red deer, followed by chamois and mouflon; these areas are also colonized by free-ranging wild boar populations [29,30]. In contrast, only free-ranging wild boars and fallow deer are present in the hilly areas of Euganean Hills Regional Park [31].

Blood and ectoparasites were collected from culled wild ungulates by hunters during the hunting seasons of 2017–2018 and 2018–2019, lasting from spring to winter. Hunters were instructed and trained on sampling procedures and data collection, reporting the features of killed animals (species, sex, and estimated age), and the place and the date of hunting. To avoid cross-contamination linked to field conditions, a selection of samples was carried out, i.e., collected when only one animal was killed per day for each hunter. Wholeblood samples were retrieved by cutting the jugular vein or main vessels of the chest cavity and collected in 9 mL Vacumed® with K3EDTA tubes (FL Medical s.r.l., Torreglia, Padova, Italy), refrigerated as soon as possible, and brought to the veterinary infectious disease laboratory (Department of Animal Medicine, Production and Health, Padua University, Italy) within 4 days. The blood samples were divided into 200 µL aliquots and stored at −80 ◦C until analysis.

When present, the matching ectoparasites were also collected before animal exsanguination and brought to the laboratory in sterile plastic tubes. The identification of tick species was performed with stereomicroscope and microscope, following the identification keys described by Cringoli et al., then the parasites were stored at −80 ◦C until analysis [32].

A total of 188 blood samples were collected from wild ungulates: 17 from Euganean Hills Regional Park, 62 from an alpine area of the Veneto region, and 109 from an alpine

area of the Friuli– Venezia–Giulia region. Six species were sampled: Alpine chamois (*Rupicapra rupicapra*; n = 9), roe deer (*Capreolus capreolus*; n = 74), red deer (*Cervus elaphus*; n = 39), mouflon (*Ovis musimon*; n = 8), fallow deer (*Dama dama*; n = 3), and wild boar (*Sus scrofa*; n = 55). A subsample of ticks (up to a maximum of 10) was collected from all infested ungulates. With the exception of alpine chamois, wild ruminant species had a higher tick burden compared to wild boar. In total, 277 ticks were collected and analyzed. The following tick species were identified: *I. ricinus* (n = 258), *Dermacentor marginatus* (*D. marginatus*) (n = 18) and *Hyalomma marginatum* (*H. marginatum*) (n = 1). In addition, also 15 deer keds (*L. cervi*) were found. When more than one tick was collected from the same host, ticks were analyzed in pools composed of a maximum of 2 specimens characterized by the same species, gender, and stage. Thus, a total of 216 samples were tested for *A. phagocytophilum*: 201 tick samples (pooled ticks = 76; individual ticks = 125) and 15 deer keds.

#### *2.2. Biomolecular Analysis*

DNA was extracted from 200 µL of whole blood and from tick homogenates using the DNeasy Blood & Tissue Kit (QIAGEN, Hilden, Germany), according to the manufacturer's instructions. A canine blood sample, certified as negative to *A. phagocytophilum* infection by the Italian authority and research organization for animal health and food safety (Istituto Zooprofilattico delle Venezie, IZSVe), was included in each extraction run as an extraction negative control and thereafter tested together with the diagnostic samples. Prior to extraction, an exogenous DNA internal control (supplied by Quanti-Nova Pathogen + IC kit, QIAGEN) was spiked in all blood and ectoparasites specimens. To exclude false negative results due to the presence of polymerase inhibitors, all DNA samples were checked in duplicate for the presence of the internal control DNA, according to the manufacturer's instructions. DNA samples showing no inhibition were then screened for the presence of *A. phagocytophilum* using real-time PCR targeting a 77 bp portion of the *msp2* gene (Table 1) [33].

**Table 1.** List of primers and probes used for *Anaplasma phagocytophilum* detection and sequencing.

All real-time PCR reactions were performed on a LightCycler96 instrument (Roche, Basel, Switzerland) using QuantiNova Pathogen + IC Kit reagents (QIAGEN, Hilden, Germany) in a final reaction volume of 7 µL, consisting of 0.8 µM of each primer, 0.25 µM of probe and 2 µL of DNA sample. The following thermal protocol was used: Preincubation at 95 ◦C for 120 s, followed by 50 cycles of denaturation at 95 ◦C for 5 s, annealing at 60 ◦C for 5 s, and extension at 72 ◦C for 30 s. Internal, positive, and negative controls were included in each run. Samples were considered positive if both replicates showed an amplification curve with a mean Cq value lower to 40.00 or equal.

To genetically characterize *A. phagocytophilum* strains, positive samples were tested by conventional PCR using primers targeting a 600 bp portion of *groEL* gene (Table 1) [34]. As a target, 5 µL of DNA was used in a 25 µL reaction mixture containing 0.3 µM of each primer and 1× Phire Hot Start II PCR Master Mix (Thermo Fischer Scientific, Milano, Italy). Amplifications were carried out in a TGradient thermal cycler (Biometra, Analytic Jena GmbH, Jena, Germany) in the following conditions: Initial denaturation at 98 ◦C for 60 s, followed by 50 cycles of denaturation at 98 ◦C for 5 s, annealing at 60 ◦C for 5 s, and extension at 72 ◦C for

7 s. Negative and positive controls were included in each run. PCR products were visualized by 2% agarose gel electrophoresis containing SybrSafe DNA (Thermo Fischer Scientific, Milano, Italy) gel stain in Tris-borate-EDTA (TBE) buffer (Merk KGaA, Darmstadt, Germany) using GelDoc EZ Imager (Bio–Rad Laboratories, Milano, Italy). Amplicons were enzymatically purified with ExoSap-IT Express PCR Product Cleanup (Thermo Fischer Scientific, Milano, Italy) and delivered to an external laboratory (StarSEQ®, Mainz, Germany) to be sequenced in both directions using the same PCR primers.

#### *2.3. Statistical Analysis*

Logistic regression models were fitted to evaluate the association between *A. phagocytophilum* infection and host features. The following categorical variables were considered in univariable and multivariable models: Area (Euganean Hills Regional Park, Friuli–Venezia– Giulia Alps, Veneto Alps), season (spring, summer, autumn, winter), gender (female, male), age (<1 year, ≥1 year), and species (chamois, red deer, roe deer, wild boar). Due to the small number of tested individuals, fallow deer and mouflon were not included among the considered species. The analysis was performed also considering the variable species as dichotomous (ruminant, wild boar), also including in that case fallow deer and mouflons. A model was preferred over a simpler one when a significant improvement on the overall fit was demonstrated by likelihood ratio test. All analyses were performed in SPSS v23.0 (IBM Corporation, Armonk, NY, USA): The significance level was set to *p* < 0.05.

#### *2.4. Sequence Analysis*

Consensus sequences were generated using ChromasPro Software v.2.1.8 (Technelysium Pty Ltd., South Brisbane, QLD, Australia) and compared with representative sequences available in the National Center for Biotechnology GenBank database with the BLASTn tool [35]. The genetic analysis was performed according to the classification proposed by Jahafari et al. and implemented by Jaarsma et al., based on *groEL* gene sequencing [10,14].

DNA sequences corresponding to the region between nucleotide positions 533 and 1033 of the CP015376 *groEL* open reading frame obtained in the present study, were aligned to 1998 sequences representative of the 4 ecotypes and 8 clusters previously described [10] using MAFFT v7.450 [36]. Additionally, the Italian sequences reported by Di Domenico et al., were also included in the study [37]. To reduce the tree complexity and computational burden, only one sequence representative of all identical ones was identified using CD-HIT and included in the phylogenetic tree (Table S1) [38]. The sequence suitability for phylogenetic analysis was assessed by likelihood mapping analysis, performed using IQ-TREE, and a phylogenetic tree was reconstructed with the same software, selecting as a substitution model the one with the lowest Akaike information criterion (AIC), value calculated using JmodelTest [39,40]. The reliability of inferred clades was investigated by performing 10,000 ultrafast bootstrap replicates.

#### **3. Results**

#### *3.1. Wild Ungulates*

The overall prevalence of *A. phagocytophilum* in wild ungulates was 63.8% (95% CI: 56.7–70.3%; 120 out of 188). However, the prevalence was above 75% in all ruminant species and significantly higher (overall ruminants prevalence = 88.7% (95% CI: 83.4–94.1; 118 out of 133) than that observed in wild boar (3.6%; 95% CI: 0–8.6; 2 out of 55). Wild ruminants thus have 208 times higher odds of being infected (95% CI: 57.1–504.1; *p* < 0.001) (Table 2). No significant differences were identified among wild ruminant species (*p* = 0.454).

**Table 2.** Prevalence of *Anaplasma phagocytophilum* detected by real-time PCR in ungulate species grouped according to different variables. EHR, Euganean Hills Regional Park; FVG, Friuli–Venezia–Giulia.

\* Ages of four positive animals (2 roe deer, 1 red deer, and 1 wild boar) and one negative wild boar were not available.

Other statistically significant differences in *A. phagocytophilum* infection prevalence were observed in univariable analysis among the three different areas. Particularly, the odds ratios were 8.4 (95% CI: 2.5–37.9; *p* = 0.001) and 14.6 (95% CI: 4.1–69.9; *p* < 0.001) higher in Friuli– Venezia–Giulia and the Veneto Alps than in the Euganean Hills Regional Park, respectively. These differences should be evaluated considering the species distribution, which can act as confounder. In particular, wild boar samples were collected especially from the Euganean Hills Regional Park during spring. In fact, taking into account host distribution in the multivariable analysis, the differences among geographic areas became non-significant. A significant difference was observed among seasons, with summer, autumn, and winter showing odds of 4.7 (95% CI: 1.8–12.8; *p* = 0.001), 5.6 (95% CI: 2.2–15.7; *p* < 0.001) and 4.0 (95% CI: 1.1–17.2; *p* = 0.046) higher than spring. Also in this case, seasonal patterns of wild species culling can be considered a confounder, and no significant difference in seasonality was observed when the host species was included in the multivariable model. No significant differences were observed according to gender (*p* = 0.871) or age (*p* = 0.639).

#### *3.2. Ectoparasites*

Out of 201 tick samples, 122 were *I. ricinus* adult ticks, mainly female (142/182); adult male (38/182) and nymphal stages (2/182) were less frequent. All *D. marginatus* (10/18 females and 8/18 males) and *H. marginatum* (1 male) were collected from wild boars at Euganean Hills Regional Park. *L. cervi* keds were found on roe deer (9/15), red deer (3/15), and wild boars (3/15).

Real-time PCR positive results were obtained in 63.7% of ticks (95% CI: 56.5–70.2; 128 out of 201) of ticks and in 40.0% of deer keds (95% CI: 17.4–67.1; 6 out of 15). Bacterial DNA was not detected in *D. marginatus* and *H. marginatum* that were collected from negative wild boars. One out of two nymphal pools tested positive.

Results of real-time PCR analysis of tick *A. phagocytophilum* were grouped according to several variables (Table 3). Twelve host–parasite pairs displayed at least one missing feature (e.g., sex, engorgement, etc.). Therefore, a total of 189 records were included in this final analysis. Tick positivity to *Anaplasma* infection was differently distributed according to host species, host infectious status, and tick sex (Table 3).

**Table 3.** Real-time PCR positivity of *Anaplasma phagocytophilum* in ticks according to different host and vector variables. Only records with complete data for both hosts and ectoparasites are included.

#### *3.3. Ecotypes and Clusters*

Only samples positive with real-time PCR screening (120 blood samples, 128 tick samples, and 6 keds) were further analyzed by conventional PCR, targeting a portion of the *groEL* gene to ecotype them. Fifty-seven good quality sequences were obtained when mean Cq values between the two replicates in real-time PCR were below 33.00. Of these 57 sequences, 48 were from blood samples and 9 from *I. ricinus* feeding ticks (GenBank accession numbers from MT473440 to MT 473496) and were used for bioinformatics analysis (Figure 1 and Figure S1 and Table S2).

Two *A. phagocytophilum* from ticks and 28 from animals were classified as ecotype I, cluster 1 (n = 30). These were derived from wild boar (n = 1), alpine chamois (n = 1), mouflon (n = 3), roe deer (n = 6), and red deer (n = 17). The ticks infected with ecotype I were hosted by one roe deer and one red deer. All other *A. phagocytophilum* sequences (n = 27) were classified as ecotype 2, and all were grouped in cluster 3, sampled from roe deer (n = 20) and related ticks (n = 7). Of note, a tick infected with *A. phagocytophilum* belonging to ecotype I was feeding on an ecotype II positive roe deer. Further details are shown in Supplementary Files (Figure S1 and Table S2).

**Figure 1.** Maximum likelihood phylogenetic tree based on *groEL* gene of Italian strains collected from wild ungulates (dots) and their *Ixodes ricinus* ticks (triangles). Species of origin are color-coded. Ecotypes and respective clusters are highlighted with different colors. For graphical reasons, only a subset of reference sequences [10] is included.

#### **4. Discussion**

In European countries *A. phagocytophilum* infection has been reported in a wide range of wild animals, including several species of rodents, hares, wild carnivores, birds, reptiles, and especially wild ungulates species [4,8]. This research investigated the epidemiology and phylogenesis of *A. phagocytophilum* infection in wild ungulates and related ectoparasites. We report widespread circulation of *A. phagocytophilum* in sylvatic cycles in northeastern Italy.

– The overall *A. phagocytophilum* prevalence detected in wild ungulates was 63.8%. A high prevalence was reported in roe deer (91.9%) and red deer (87.2%), irrespective of the age (Table 2). This evidence supports their reservoir role [16,41]. Previous Italian studies reported both similar [37,42,43] and much lower prevalence data in wild ungulates [44–48]. *A. phagocytophilum* was also frequently detected in the other wild ruminant species, i.e., mouflon (75%), chamois (77.8%), and fallow deer (100%). Although only a limited number of samples of these species were analyzed in the present study, which were not representative of the target population, our data are in line with other studies [12,37,49], suggesting that more attention should be given to their impact on the anaplasmosis infectious cycle. An opposite role emerged for wild boars, with a much lower prevalence (3.6%) observed. This finding seems related to the lower quantity of ticks harbored by wild boars [11,50], thus decreasing the probability of wild boar infection.

No statistical association has been observed regarding the host prevalence and sex, age and, interestingly, seasonality. Actually, ticks have been collected from wild ruminants throughout the year, at similar frequency. This finding could be due to an increased tick survival and activity even during the winter months because of global warming [51,52].

Our research confirms the pivotal role of *I. ricinus* as the main vector of *A. phagocytophilum*, compared to other hard tick species, as previously reported [8,53]. The high prevalence (63.7%) of *A. phagocytophilum* detected in ticks collected from wild ungulates in northeastern is related to the reservoir role of these animals and the engorgement status of ticks [11]. Previous studies on *I. ricinus* removed from wild ungulates in other Italian regions, however, reported a lower prevalence, ranging from 5.1 to 31.2% [37,44,54–56], even when the same analytical methods was used. This evidence highlights that the prevalence of this bacterium depends not only on the presence of *I. ricinus* and wild ungulates, but on complex interactions involving the entire ecosystem, resulting in different enzootic cycles depending on the studied area, even within the same country [14,57]. Twelve positive ticks were collected from negative animals (Table 3) and three were non-engorged males. Therefore, infection may have been acquired in a previous stages and maintained through trans-stadial transmission, as previously reported by other authors [3,14]. However, the higher percentage of *A. phagocytophilum* infected ticks found on positive wild ungulates confirms the efficient transmission between these reservoir species and the vector. In this study mainly adult ticks were collected from ungulates, but larvae and nymphs can also parasitize these animals and some of them could maintain the infection through transstadial transmission [11]. Previous studies conducted on questing ticks in the same study area showed a percentage of *A. phagocytophilum* infection up to 9% in *I. ricinus* ticks from Belluno province [58,59] and up to 9.9% in ticks from a neighboring area [60]. A lower prevalence (1.5%) was found when a more extensive territory, including sites near the plain, was investigated [61]. On the other hand, the lack of *A. phagocytophilum* detection in *Dermacentor* and *Hyalomma* ticks supports a secondary or negligible role in anaplasmosis transmission [3,8]. Alternatively, they could be involved in the anaplasma infectious cycle only in areas where *I. ricinus* populations are particularly rare or absent, which is not the case of the considered region [5,57]. Moreover, *Dermacentor* and *Hyalomma* ticks have been found only on wild boars in the Euganean Hills Regional Park, an area characterized by a maximum altitude of 600 m a.s.l. where wild boars are more represented than ruminants, consisting mainly of fallow deer. Ecological conditions thus may be poorly comparable with other wilder areas where several wild ruminant populations share the same habitat and where mainly *I. ricinus* is detected. Therefore, a potential biasing effect of the host species and ecological niche cannot be excluded.

Of interest is the 40% *A. phagocytophilum* positivity in *L. cervi*. These ectoparasites were collected from positive red and roe deer. Although their role in the anaplasmosis infectious cycle is still under debate, other authors report deer keds as potential carriers of *A. phagocytophilum*, raising questions about their possible vectorial capability [15,62].

The genetic analysis of the 57 sequences performed, following the classification proposed by Jahafari et al. and Jaarsma et al., showed different distributions of ecotypes and clusters [10,14]. Of the 26 sequences from roe deer, 20 belonged to ecotype II/cluster 3. Ecotype II thus seems strictly related to this species [14,16]. On the other hand, ecotype I/cluster 1 showed the widest host range, having been found in roe deer (6/26), red deer (17/17), chamois (1/1), mouflon (3/3), and wild boar (1/1) species. Interestingly, the same clade includes genetic sequences of *A. phagocytophilum* strains from human cases in Europe; sequences MT473492, MT473484, MT473481, MT473458, MT473457, MT473453, MT473452, and MT473443 from red deer and sequence MT473485 from roe deer obtained in the present study were genetically identical to strains previously sampled from humans in Belgium (BelgiumPatient2014) and the Netherlands (LT06052), respectively (Table S1). Our finding suggests that these species could be a reservoir for zoonotic *A. phagocytophilum* variants. Even if an intense debate is still going on among authors on the actual role of red deer in ecotype I ecology and zoonotic risk [16], the high prevalence and association with this

ecotype observed in our study favors their relevance and should thus be considered when evaluating the epidemiological risk for HGA [9,11,14]. The detection of ecotype I in wild boar highlights that it might be involved in anaplasmosis epidemiology, with the ability to harbor zoonotic variants [13,63]. However, the role of this species in anaplasmosis epidemiology needs further investigation, as it quickly eliminates *A. phagocytophylum* infection and has a lower tick burden [8].

Although chamois and mouflon were not considered to be pivotal in anaplasmosis diffusion, the remarkable prevalence herein reported and the demonstration that they can harbor ecotype I deserve more attention. To the best of the authors' knowledge, this is the first Italian report of ecotype I in wild boar, alpine chamois, and mouflon.

Most of the sequences obtained from *I. ricinus* ticks belonged to ecotype II/cluster 3 (7/9) and were collected from roe deer, while the remaining (2/9) were associated with ecotype I/cluster 1. In detail, one of these two ecotype I positive ticks was collected from a red deer and, interestingly, the other one was collected from a roe deer that tested positive to ecotype II, suggesting the potential simultaneous presence of different *A. phagocytophilum* variants.

In line with these results, some authors reported that *I. ricinus* ticks do not play a role in the host specificity of *A. phagocytophilum* variants, as it has been demonstrated that they may harbor all *A. phagocytophilum* ecotypes [10]. Furthermore, the generalist and teletropic feeding behavior of *I. ricinus* could facilitate the continuous exchange of ecotypes, and this behavior could explain the wide diffusion of ecotype I in almost all tested ungulates [5]. At the same time, our findings support that host specificity appears to be driven mainly by the variants themselves rather than tick feeding habits and host infection susceptibility. The presence of an overlap in ecotype host distribution further complicates the estimation of circulating ecotypes. In the present study, this emerged clearly in roe deer in which both ecotypes have been detected, accordingly to the findings of Remesar et al. [64]. Since the simultaneous presence of more than one variant can occur [65], it cannot be under-emphasized that the routinely applied Sanger sequencing did not allow identification of multiple strains, just the most abundant one, which may have resulted in an underreporting of such co-infections.

The findings of this study highlight the concrete risk of humans acquiring *A. phagocytophilum* infection from tick bites in the investigated area. Moreover, other transmission routes should be also considered. Contact with animal infected blood has been suggested as a potential infection source, particularly for people manipulating carcasses of ungulates or their meat [6]. Because of the subclinical infection in immunocompetent people, HGA should also be taken in account in blood transfusions. Unfortunately, currently HGA occurrence has rarely been investigated, especially through adequate genetic characterization, and further studies will be necessary to estimate its actual prevalence and relevance.

#### **5. Conclusions**

*A. phagocytophilum* was detected in all tested wild ungulates, with a higher prevalence in wild ruminants. Wild ungulates are involved in the *A. phagocytophilum* infectious cycle as the pivotal hosts for *I. ricinus* development and reproduction, harboring different *A. phagocytophilum* genetic variants. Of the detected variants, ecotype II/cluster 3 showed a host specificity for roe deer, while a broader tropism and the presence of overlapping niches emerged for ecotype I. Some sequences of ecotype I/cluster 1 from red deer and roe deer were identical to sequences from European human cases, highlighting their zoonotic potential. HGA itself may not cause severe disease, but it has been detected in co-infection cases with other tick-borne pathogens, leading to more severe symptomatology or atypical clinical presentations [27,66]. Due to the presence of zoonotic variants in the investigated area, the risk of infection should be communicated to categories of people at risk, such as hunters, veterinarians, and forest rangers, among others. Moreover, the often neglected HGA deserves more attention and should be considered in the differential diagnosis of human tick-borne diseases.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2076-2 615/11/2/310/s1, Figure S1: Phylogenetic tree of *Anaplasma phagocytophilum* strains. Table S1: List of representative sequence used for phylogenetic tree. Table S2: Accession numbers of new Italian *Anaplasma phagocytophilum* sequences deposited into GeneBank.

**Author Contributions:** Conceptualization, M.L.M.; methodology, A.M. and G.F.; formal analysis, M.M. and G.F.; investigation, L.G., E.V., and R.C.; resources, M.L.M., L.G., and E.V.; data curation, M.L.M. and G.F.; writing—draft preparation, review, and editing L.G., M.L.M., and G.F.; visualization, D.P. and M.D.; supervision, M.L.M.; funding acquisition, M.L.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Integrated Budget for Interdepartmental Research BIRD177004/17, "Emerging Infectious Diseases of Wildlife in Northeastern Italy", and by grant DOR1949799/19 from the Department of Animal Medicine, Productions and Health, University of Padova.

**Institutional Review Board Statement:** Game animals were hunted during licensed hunting seasons and not culled specifically for the purpose of this study. Therefore, approval of the ethics committee was not required.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in the text and in Tables 2 and 3 and Figure 1 of this article and in the Figure S1, Tables S1 and S2.

**Acknowledgments:** The authors are grateful to the hunting reserve directors, hunters, and the practitioner of Euganean Hills Regional Park for their kind collaboration and efforts in collecting the samples. Particularly, we thank Valerio Pituelli, Claudio Sabbadini, and Franco Micoli, presidents of hunting districts Tarvisiano, Valli del Natisone, and Colline Moreniche, respectively; and we thank the director of Euganean Hills Regional Park, Michele Gallo, whose coordination efforts made this work possible, Renato Sinigaglia, and Roberto Trevisan.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Archaeology and Ancient Israelite Religion**

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**Avraham Faust**

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