The Tick Microbiome: Why Non-Pathogenic Microorganisms Matter in Tick Biology and Pathogen Transmission

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CONTENTS SUMMARY ...... 1 CHAPTER I. General introduction ...... 7 The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission ...... 9 Abstract ...... 11 Introducction ...... 11 The effect of non-pathogenic microorganisms on tick biology ...... 13 Non-pathogenic microorganisms interact with TBP in different ways ...... 16 Tick symbionts can be opportunistic vertebrate pathogens ...... 17 Pathogens and tick symbionts are often phylogenetically related ...... 18 Conclusion and perspectives ...... 20 Author contributions ...... 20 Acknowledgments ...... 20 References ...... 21 HYPOTHESIS AND OBJECTIVES ...... 27 CHAPTER II. Microbiome composition and pathogen genetic diversity in ticks using omics approaches...... 31 Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus ...... 33 Abstract ...... 35 Introducction ...... 35 Materials and methods ...... 36 Tick samples and processing ...... 36 Integrated metaomics experimental design ...... 36 Metatranscriptomics for the identification of bacterial species in the tick microbiome ..... 37 Metaproteomics for bacterial protein identification ...... 38 Phylogenetic and taxonomic abundance analyses ...... 40 Validation of tick bacterial microbiota identifications by real-time PCR ...... 40 Results and discussion ...... 40 Metatranscriptomics bacteria identification in I. ricinus microbiota ...... 40 Integration of metatranscriptomics and metaproteomics approaches ...... 41 Putative functional implications of integrated metaomics results ...... 42 Conclusions ...... 43 Conflict of interest statement ...... 43 Acknowledgments ...... 43 Appendix A. Supplementary data ...... 43 References ...... 43 Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi ...... 47 Abstract ...... 49 Introduction ...... 49 Material and Methods ...... 50 Sample collection and study model ...... 50 Performance of whole-genome shotgun metagenomics sequencing ...... 50 Sequencing data analysis ...... 50 Results and Discussion ...... 52 Conclusion ...... 53 Availability of data ...... 55 Conflict of interest statement ...... 55 Acknowledgements ...... 55 Appendix A. Supplementary data ...... 55 References ...... 55 Combination of RT-PCR and proteomics for the identification of Crimean-Congo hemorrhagic fever virus in ticks ...... 59 Abstract ...... 61 Introducction ...... 62 Materials and methods ...... 63 Results and discussion ...... 66 Declarations ...... 69 Author contribution statement ...... 69 Funding statement ...... 70 Competing interest statement ...... 70 Additional information ...... 70 References ...... 70 Draft genome sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis Isolates from different hosts ...... 75 Abstract ...... 77 Acknowledgments ...... 78 References ...... 78 Anaplasma phagocytophilum strain NY18, whole genome shotgun sequencing project ...... 79 Anaplasma marginale strain Oklahoma-2, Anaplasma phagocytophilum strain NY18, whole genome shotgun sequencing project ...... 80 Anaplasma ovis strain Idaho, whole genome shotgun sequencing project ...... 81 BioProyect ...... 82 CHAPTER III. Effect of abiotic and biotic factors in mosquito microbiome composition 85 Biotic and abiotic factors shape the microbiota of wild caught populations of the arbovirus vector Culicoides imicola ...... 87 Abstract ...... 89 Introduction ...... 89 Results ...... 90 Characterization of C. imicola microbiome revealed similarities and differences between different wild-caught populations ...... 90 Characterization of host DNA and proteins in C. imicola revealed differences in host preferences between the two populations ...... 90 Characterization of abiotic factors revealed differences between C. imicola locations ...... 91 The biotic and abiotic factors’ impact on the microbiome composition of C. imicola ...... 92 Discussion ...... 92 Conclusions ...... 96 Experimental procedures ...... 97 Study design and sample collection ...... 97 Whole-genome shotgun metagenomic sequencing and analysis ...... 97 Metagenome dataset validation by qPCR ...... 98 Biotic factors: characterization of C. imicola host preferences ...... 98 Characterization of the impact of biotic and abiotic factors on C. imicola microbiome ...... 99 Availability of data ...... 99 Acknowledgements ...... 100 References ...... 100 Supporting Information ...... 103 Additional file 1 ...... 104 CHAPTER IV. General discussion ...... 107 Conclusions ...... 123 Glossary...... 129

SUMMARY

1 2 SUMMARY

In the current world, mosquitoes and ticks are the most important vectors of a wide variety of pathogens that affect humans and animals. Microbes are part of the hologenome of vectors, which participate in many biological processes implicated in eco-epidemiological dynamics of the vector. Microbial communities might confer attributes to their host, firstly, affecting the ability of vectors to adapt to new environments and vertebrate hosts; and secondly the capacity to acquire, maintain and transmit pathogens. Providing3 answers to central questions regarding vector-pathogen-microbiota interactions is a challenge for current research, ie: who is there, what biological processes are occurring, and where are these processes happening?. In this sense, metaomics approaches applied to the study of the vector microbiome (ticks and mosquitoes), have the power to integrate network-based tools metagenomics, transcriptomics and proteomics to describe the complexity of the microbiota and the biological processes involved in host/vector-pathogen and host/vector-microbiota interactions. The success of the combining proteomics and genomics methods implemented for the identification and characterization of microbiota and tick-borne pathogens paves the way for future comparative genomic studies that will help to identify differences in the proteome/genome that could be involved in the tropism by the vector. Also, it will let us know if those differences are related to the functionality and therefore the pathogenesis and virulence. Furthermore, on the base of meatomics approaches a key priority is to identify microbial targets and to generate solid genomic annotations for better design of prevention strategies against vector-borne diseases. Ultimately, the database collection of microorganisms that compose the microbiome of vectors, in combination with genome editing, reconstruction and identification of vector-borne pathogens genomic traits, are the most promising directions for the surveillance and control of vector-borne diseases.

Chapter I. General introduction Content: Chapter I is structured as a review paper [Bonnet, S. I., Binetruy, F., Hernández- Jarguín, A. M., & Duron, O. (2017). The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Frontiers in Cellular and Infection Microbiology, 7, 236]. This paper contributes to the introduction of the current thesis. In this publication, four fundamental biological aspects are reviewed to better understand the composition and complexity of the vector-microbiome interactions. First, the role of commensal microorganisms and symbionts in tick's biology; second, the effect of tick microbiota in the acquisition and transmission of pathogens; third, the microbial interactions, especially those between symbionts and closely related pathogens, and finally, the role of symbionts as potential vertebrate pathogens. In addition, this review includes a brief description of a wide diversity of microbial communities associated with different families and species of ticks with complex effects on tick's biology. A special emphasis on the effect of symbiotic microorganisms on survival, reproduction and their impact on the colonization and transmission of pathogens was also reviewed. Nevertheless, factors that shape the composition of the microbiome are still under investigation and is clear that host genetics, the environment and geography, and pathogens eco-epidemiological cycle are important to shape the diversity of tick microbial communities.

Chapter II. Microbiome composition and pathogen genetic diversity in ticks using omics approaches

Content: In Chapter II we addressed the identification of the microbiota composition of Ixodes ricinus and Ixodes ventalloi, tick vector-borne arthropods within the family Ixodidae. To exhaustively explore vector-microbiota-pathogen interactions, a first fundamental requirement is to include a methodological approach that yields accurate microbial taxonomic profiles.

3 Hence, we explored the bacterial microbiota of the laboratory-reared I. ricinus using a metaomic analysis strategy combining metaproteomic and metatranscriptomic analysis. The workflow for the study, was designed in order to reuse the available data of RNA-seq, and the construction of a target bacterial database for the metaomic analysis. An important contribution of this approach was the validation at the protein level of the bacterial sequences identified by RNA-seq and the identification of metabolically active bacterial communities that provided the metaproteomics. One promising result of this approach was to generate further knowledge on the mechanisms occurring between pathogenic bacteria and tick gut microbiota that facilitate infection and proliferation [Hernández-Jarguín, A., Díaz-Sánchez, S., Villar, M., de la Fuente, J. (2018). Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus. Ticks Tick Borne Diseases. 9(5):1241-1251]. A contrasting approach, termed whole-genome shotgun-metagenomic sequencing consisted in the use of supervised clustering to group shotgun reads into bacterial gene families. We used this method to first describe the microbiota composition of the wild-caught I. ventalloi microbiota. The major microbiota was structured by members of the genus Anaplasma, Borrelia and Rickettsia. Other cohabiting members such as symbionts, endosymbiont, ubiquitous and some pathogenic genera for animals and humans, were identified at low rates as well. [Díaz-Sánchez, S., Hernández-Jarguín, A., Torina, A., Fernández de Mera, I. G., Blanda, V., Caracappa, S., Gortázar, C., de la Fuente, J. (2019). Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi. Ticks and Tick-Borne Diseases. 10(2):336-343.]. In summary, characterization of the microbiota of laboratory and wild-caught tick populations is necessary to enhance our knowledge of putative microbiota of specific tick species. The exploration of both biological contexts would allow to speculate with the functional roles of microbial putative members and their biological implications in the eco-epidemiological role of this vectors. Additionally, we characterized virus and bacteria vector-borne pathogens using different -omic analysis. Conventional amplification-based assays used to target pathogens remain difficult and unspecific, due to the low level of infection and/or the presence of closely related species. Herein, we proposed a metaproteomic pipeline that provide support and validate at protein level the identification of the Crimean-Congo hemorrhagic fever virus (CCHFV) in ticks by RT-PCR. Following this pipeline, we demonstrated that including a metaproteomic analysis the identification of CCHFV pipeline is more accurate as it is possible to obtain the genotypic differentiation between CCHFV virus and other Nairovirus. [Fernández de Mera, I. G., Chaligiannis, I., Hernández-Jarguín, A., Villar, M., Mateos-Hernández, L., Papa, A., de la Fuente, J. et al. (2017). Combination of RT-PCR and proteomics for the identification of CCHV virus in ticks. Heliyon, 3(7)]. Based on the implantation of genomics and the rapid progression of genome editing and mapping tools, we reconstructed the draft genome of different isolates of the tick-borne bacteria pathogen Anaplasma spp, including the strains A. phagocytophilum NY18, A. marginale Oklahoma-2, and the first report of A. ovis Idaho, using whole-genome sequencing and de novo assembly. [Díaz-Sánchez, S., Hernández-Jarguín, A., Fernández de Mera, I. G., Alberdi, P., Zweygarth, E., Gortázar, C., de la Fuente, J. (2018). Draft genome sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis isolates from different hosts. Genome Announcements, 6:5]. In this study, SNPs, substitutions, insertions and deletions in different regions of the genome of these isolates were identified. In the future, genomic data available from different vector-borne pathogens will allow the design of comprehensive comparative genomic studies to answer central questions for the control of tick-borne diseases.

Chapter III. Effect of abiotic and biotic factors in mosquito microbiome composition

Content: Chapter III is a contribution to the study of the microbiota composition of wild-caught Culicoides imicola biting midges [Díaz-Sánchez, S., Hernández-Jarguín, A., Torina, A., Fernández de Mera, IG, Estrada-Peña, A., Villar, M., La Russa, F., Blanda, V., Vicente, J., Caracappa, S., Gortazar, C., de la Fuente, J. (2018). Biotic and abiotic factors shape the microbiota of wild populations of the arbovirus Culicoides imicola vector. Insect Molecular Biology 27(6):847-861]. To study the insect-associated microbiome we utilized a de novo 4 whole-genome shotgun metagenomic sequencing pipeline. In addition, we included in the study the identification of the preferred host-feeding source (biotic factors) of C. imicola, using together a proteomic assay, and DNA-analysis based on the amplification of the cytochrome b gene. In parallel, we collected data related to the temperature and soil moisture for each wild- caught mosquito collection site (abiotic factors). Then, the combined effect of the biotic and abiotic factors over the microbiota composition of C. imicola was also reported. A core microbiome together with unique microbial taxa among C. imicola populations composed of virus, bacteria and fungus was observed. Interestingly, the combined effect of biotic and abiotic features composing the mosquito habitat could contribute to those differences observed in the microbiome composition. To this end, this research, apart from contributing to characterize the microbiome of C. imicola, has found unique microbial features implicated in C. imicola adaption and expansion to new environments. Importantly, it will be valuable data for comprehensive eco-epidemiological studies and successful control of vector-borne diseases strategies.

5 6 Chapter I. General introduction

Bonnet, S. I., Binetruy, F., Hernández-Jarguín, A. M., & Duron, O. (2017). The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Frontiers in Cellular and Infection Microbiology, 7, 236

7 8 The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission

9 10 &KDSWHU, REVIEW published: 08 June 2017 doi: 10.3389/fcimb.2017.00236

The Tick Microbiome: Why Non-pathogenic Microorganisms Matter in Tick Biology and Pathogen Transmission

Sarah I. Bonnet 1*, Florian Binetruy 2, Angelica M. Hernández-Jarguín 3 and Olivier Duron 2

1 UMR BIPAR INRA-ENVA-ANSES, Maisons-Alfort, France, 2 Laboratoire MIVEGEC (Maladies Infectieuses et Vecteurs: Écologie, Génétique, Évolution et Contrôle), Centre National de la Recherche Scientiﬁque (UMR5290), IRD (UMR224), Université de Montpellier, Montpellier, France, 3 SaBio Instituto de Investigación en Recursos Cinegéticos CSIC-UCLM-JCCM, Ciudad Real, Spain

Ticks are among the most important vectors of pathogens affecting humans and other animals worldwide. They do not only carry pathogens however, as a diverse group of commensal and symbiotic microorganisms are also present in ticks. Unlike pathogens, their biology and their effect on ticks remain largely unexplored, and are in fact often neglected. Nonetheless, they can confer multiple detrimental, neutral, or beneﬁcial effects to their tick hosts, and can play various roles in ﬁtness, nutritional adaptation, development, reproduction, defense against environmental stress, and immunity. Non-pathogenic microorganisms may also play a role in driving transmission of tick-borne pathogens (TBP), with many potential implications for both human and animal health. In addition, the genetic proximity of some pathogens to mutualistic symbionts hosted by ticks is evident when studying phylogenies of several bacterial Edited by: Lorenza Putignani, genera. The best examples are found within members of the Rickettsia, Francisella, and Bambino Gesù Ospedale Pediatrico Coxiella genera: while in medical and veterinary research these bacteria are traditionally (IRCCS), Italy recognized as highly virulent vertebrate pathogens, it is now clear to evolutionary Reviewed by: ecologists that many (if not most) Coxiella, Francisella, and Rickettsia bacteria are Ulrike G. Munderloh, University of Minnesota, United States actually non-pathogenic microorganisms exhibiting alternative lifestyles as mutualistic Edward Shaw, ticks symbionts. Consequently, ticks represent a compelling yet challenging system Oklahoma State University, United States in which to study microbiomes and microbial interactions, and to investigate the *Correspondence: composition, functional, and ecological implications of bacterial communities. Ultimately, Sarah I. Bonnet deciphering the relationships between tick microorganisms as well as tick symbiont [email protected] interactions will garner invaluable information, which may aid in the future development

Received: 31 March 2017 of arthropod pest and vector-borne pathogen transmission control strategies. Accepted: 22 May 2017 Keywords: tick, tick symbionts, tick borne pathogens, microbiome, microbial interactions Published: 08 June 2017 Citation: Bonnet SI, Binetruy F, INTRODUCTION Hernández-Jarguín AM and Duron O (2017) The Tick Microbiome: Why Over the last few decades, considerable research eﬀorts have focused on the diversity, distribution, Non-pathogenic Microorganisms Matter in Tick Biology and Pathogen and impact of tick-borne pathogens (TBP). The list of known or potential TBP is constantly Transmission. evolving, and includes a variety of viruses, bacteria, and parasites aﬄicting humans and many Front. Cell. Infect. Microbiol. 7:236. other animals worldwide (de la Fuente et al., 2008; Heyman et al., 2010; Dantas-Torres et al., 2012; doi: 10.3389/fcimb.2017.00236 Rizzoli et al., 2014). Less well studied or understood are whole microbial communities hosted by

11 &KDSWHU, Bonnet et al. Tick-Microbiota-Pathogen Interactions ticks, which attract much less attention, yet are equally important. clearly illustrated by the large panel of lifestyle strategies These communities include TBP, but also non-pathogenic that microorganisms use to infect and persist within tick microorganisms such as commensal and mutualistic microbes populations (Figure 1). As vertebrate pathogens, TBP normally that are also abundant in ticks (Andreotti et al., 2011; Carpi et al., spread via infectious (horizontal) transmission through tick 2011; Williams-Newkirk et al., 2014; Duron et al., 2015a, 2017). bite and blood feeding. A few TBP can also be vertically Until recently, these non-pathogenic microorganisms were transmitted in ticks, and thus be maintained throughout generally overlooked by scientists working with ticks and TBP. each generation as observed for Babesia species (Chauvin Before 1990, their existence was largely ignored and all bacteria et al., 2009), Rickettsia rickettsii (Burgdorfer et al., 1981), found in ticks were usually considered to be potential TBP or viruses (Xia et al., 2016). Other tick microorganisms are without necessarily undergoing rigorous health risk assessment. highly specialized intracellular symbionts depending almost Toward the end of the 1990s, the advent of simple PCR assays exclusively on maternal (transovarial) transmission to ensure led to a growing understanding that a few intracellular bacteria, their persistence in tick populations (Niebylski et al., 1997b; Lo such as the Coxiella-like endosymbiont and the Francisella- et al., 2006; Sassera et al., 2006; Klyachko et al., 2007; Felsheim like endosymbiont (Coxiella-LE and Francisella-LE hereafter), et al., 2009; Machado-Ferreira et al., 2011; Lalzar et al., 2014; are actually non-pathogenic microorganisms hosted by ticks Duron et al., 2015a; Kurtti et al., 2015). Tick microorganism (Niebylski et al., 1997a; Noda et al., 1997). Deeper investigation diversity is further augmented due to the fact that environmental of microbial biodiversity through high-throughput sequencing microorganisms can also colonize ticks: microbes present on and DNA barcoding led to another leap in understanding: vertebrate skin surfaces may colonize ticks during blood feeding, non-pathogenic microorganisms from many different clades are while those present in the soil or vegetation can colonize ticks present in ticks, and can generally coexist with TBP (Clay et al., on the ground, once they have dropped off their vertebrate hosts 2008; Andreotti et al., 2011; Carpi et al., 2011; Lalzar et al., 2012; (Narasimhan and Fikrig, 2015). Overall, the diverse range of Vayssier-Taussat et al., 2013; Qiu et al., 2014; Williams-Newkirk microbial lifestyle strategies creates a complex web of interactions et al., 2014; Narasimhan and Fikrig, 2015; Abraham et al., 2017). offering excellent opportunities to tackle questions about the In addition, current available data on tick microbiomes suggest impact of whole microbial communities on tick biology and TBP that non-pathogenic microorganisms exhibit higher taxonomic transmission. In spite of this, the direct effects of pathogens diversity than TBP since they encompass most major bacterial and other microbes on tick physiology and activity has received and Archaea groups (Andreotti et al., 2011; Carpi et al., 2011; much less attention than their effects on vertebrate hosts. In most Nakao et al., 2013; Qiu et al., 2014; Williams-Newkirk et al., 2014). cases, the function of tick endosymbionts in relation to their host Altogether, it is now clear that ticks carry complex microbial has not been determined. Many of these endosymbionts have communities that are largely dominated by non-pathogenic obligate intracellular life cycles or are difficult to cultivate, which microorganisms. Most importantly, this implies that both ticks may explain the gaps in current knowledge (Tully et al., 1995; and TBP are commonly engaged in interactions with non- Kurtti et al., 1996; Niebylski et al., 1997a; Duron et al., 2015a). pathogenic microorganisms. However, for some bacteria, tissue-specific localization has been The composition of these microbial communities is highly defined, which may aid us to understand bacterial impact on variable: environmental constraints are key drivers of their both tick biology and pathogen transmission (Noda et al., 1997; structure as shown by differences in bacterial diversity observed Klyachko et al., 2007; Lalzar et al., 2014; Narasimhan and Fikrig, between laboratory-reared and wild ticks (Heise et al., 2010; 2015). Similarly, the use of microarray or RNASeq technologies Zolnik et al., 2016). It was further reported that bacterial to analyze induced tick microbiome expression patterns and community structures could vary depending on the examined varying composition following a variety of conditions, may also tick species (Lalzar et al., 2012), the season during which ticks further elucidate their roles (Rodriguez-Valle et al., 2010). This were collected (Lalzar et al., 2012), the examined geographical regions (van Overbeek et al., 2008; Carpi et al., 2011; Williams- Newkirk et al., 2014), the examined tick life stage (Moreno et al., 2006; Clay et al., 2008; Williams-Newkirk et al., 2014; Zolnik et al., 2016), and between different feeding statuses (Heise et al., 2010; Menchaca et al., 2013; Zhang et al., 2014). Furthermore, bacterial community structures may differ depending of the presence of pathogens (Steiner et al., 2008; Abraham et al., 2017). Overall, the quantity of potential variations highlights the lability of microbial communities hosted by ticks, and future studies should focus on understanding how these variations impact tick biology. Below, we will discuss the interesting hypothesis that the inherent flexibility of microbial communities may help ticks adapt to environmental stresses, such as TBP FIGURE 1 | Origin and acquisition of tick microorganisms. Red arrows: presence. vertebrate pathogens acquired from tick bites; blue arrows: maternally Microorganisms inhabiting ticks are not only taxonomically inherited tick symbionts acquired via transovarial and transtadial transmission; green arrows: microorganisms acquired from the environment. diverse, they are also ecologically diverse. This diversity is

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Bonnet et al. Tick-Microbiota-Pathogen Interactions knowledge is of both medical and veterinary interest since it may demonstrates that several infectious agents have close genetic enable the reassessment of tick-associated health risks, but also proximity with mutualistic tick symbionts. This indicates that of ecological and evolutionary importance by highlighting co- some bacterial genera (eg. Rickettsia, Francisella, and Coxiella) evolutionary processes acting between ticks and their microbes. have the capacity to frequently undergo evolutionary shifts Indeed, some symbionts, but not all (Weller et al., 1998), have a between pathogenic and non-pathogenic forms, a process that joint evolutionary history of several million years with their tick may lead to the emergence of novel infectious diseases. hosts (Almeida et al., 2012; Duron et al., 2017), suggesting that complex interactions may have evolved in these associations. If biologists aim to fully understand the ecological and evolutionary THE EFFECT OF NON-PATHOGENIC processes involved in tick biology and the emergence of tick- MICROORGANISMS ON TICK BIOLOGY borne diseases, a thorough examination of non-pathogenic microorganisms is also required. Perhaps the most remarkable observation of recent times is the Maternally-inherited symbionts are well-known to use specific pivotal role of symbiotic interactions in normal tick biology, adaptive strategies to spread and persist within arthropod including ecological specialization to an exclusive blood diet. populations, either providing fitness benefits to female hosts or Symbionts—i.e., microorganisms engaged in close and long-term manipulating host reproduction (Moran et al., 2008; Ferrari and interactions with their tick hosts—are exceptionally diverse in Vavre, 2011). Two categories of widespread endosymbionts are ticks: at least 10 distinct genera of maternally-inherited bacteria usually recognized in arthropods, although intermediates and have been reported in ticks over the last decade (listed in Table 1 transition forms are also frequent: and Figure 2)(Duron et al., 2017). Three of these symbionts are only found in ticks: Coxiella-LE, which infects at least two thirds – The first category consists of obligate (primary) mutualistic of tick species, Midichloria, which inhabits the mitochondria symbionts required to support normal host development, thus of some tick species, and Francisella-LE, which has only been assisting their host in various essential functions. This includes reported in a few tick species (Table 1). The seven remaining nutritional diet upgrades by providing biosynthetic pathways symbiont genera are more- or less-frequently found in other absent from their hosts (Moran et al., 2008; Wernegreen, arthropod groups, including several well-studied insects. Five 2012). Indeed, most blood-feeding insects such as bed bugs, symbionts, Wolbachia, Cardinium, Arsenophonus, Spiroplasma, lice, and tsetse flies harbor obligate symbionts that provide and Rickettsia, are commonly identified in some arthropod B vitamins and cofactors not readily obtainable in sufficient groups, while two others, Rickettsiella and Lariskella, have only quantities from a uniquely blood-based diet (Akman et al., been reported in a few other arthropod taxa in addition to ticks 2002; Hosokawa et al., 2010; Boyd et al., 2013; Nikoh et al., (Table 1). 2014). Coxiella-LE has been reported as essential for tick survival – The second category consists of facultative (secondary) and reproduction in the Amblyomma americanum lone star symbionts not required for host survival. Some are defensive tick (Zhong et al., 2007). As an obligate symbiont, Coxiella- symbionts conferring protection against natural enemies or LE is, by definition, present in most individuals of a given tick heat (Oliver et al., 2010; Ferrari and Vavre, 2011), while others species (Clay et al., 2008; Machado-Ferreira et al., 2011; Lalzar are reproductive parasites that manipulate host reproduction et al., 2012; Duron et al., 2015b, 2017): thus their mutualistic through the induction of parthenogenesis, feminization of relationship is required for the survival of both organisms. genetic males, male-killing, and cytoplasmic incompatibility Remarkably, some Coxiella-LE may form evolutionarily stable (conditional sterility between infected and uninfected associations with their tick hosts that last for millions of years specimens) (Engelstadter and Hurst, 2009; Cordaux et al., (Duron et al., 2017). These associations typically exhibit strict co- 2011). cladogenesis, resulting in congruent host-symbiont phylogenies In this article we review four major biological aspects where our as recently observed between members of the Rhipicephalus views on tick microbes have undergone substantial change over genus and their associated Coxiella-LE (Duron et al., 2017). the last decade. Firstly, we must emphasize that non-pathogenic The discovery of Coxiella-LE in numerous other tick groups microorganisms have much more complex effects on ticks (Jasinskas et al., 2007; Clay et al., 2008; Machado-Ferreira et al., than previously thought. Indeed, it is now evident that several 2011; Almeida et al., 2012; Lalzar et al., 2012; Duron et al., maternally-inherited symbionts are required for tick survival and 2014, 2015a, 2017), indicates that it is the most widespread and reproduction, while other symbionts can have multiple effects biologically relevant tick symbiont. An examination of Coxiella- on tick life history traits. Secondly, whilst tick TBP transmission LE intra-host localization revealed pronounced tissue tropism modes have been studied for decades, we now understand that in all examined tick species. This symbiont typically infects the certain non-pathogenic microorganisms may also interfere with ovaries (to ensure maternal transmission) and the distal part TBP transmission. Thirdly, although microorganisms are often of Malpighian tubules, suggesting a possible role in nutrition, categorized as “TBP,” “commensals,” or “maternally-inherited osmoregulation, or excretion (Klyachko et al., 2007; Machado- symbionts,” both intermediate and transitional states frequently Ferreira et al., 2011; Lalzar et al., 2014). Examination of eggs from occur. In this context, it thus appears vital to not overlook several tick species confirmed that Coxiella-LE is transmitted to the full range of potential effects, as have been recently >99% of tick progeny, demonstrating highly efficient maternal described in microbiome studies. And finally, bacterial phylogeny transmission (Machado-Ferreira et al., 2011; Lalzar et al., 2014;

Frontiers in Cellular and Infection Microbiology | www.frontiersin.org 3 June 2017 | Volume 7 | Article 236

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Bonnet et al. Tick-Microbiota-Pathogen Interactions

FIGURE 2 | Simpliﬁed eubacterial phylogeny showing the evolutionary relationships between the ten genera containing maternally inherited tick symbionts (labeled 1–10, as detailed in Table 1).

Duron et al., 2015a). Remarkably, the Coxiella-LE genome was recent bacterial genome data suggest that these bacteria have shown to encode major B vitamin synthesizing pathways such highly-evolved adaptive mechanisms enabling tick survival. as biotin (B7 vitamin), folic acid (B9), riboflavin (B2), and their Indeed, their genomes encode functions suggesting that they cofactors, that are not usually obtainable in sufficient quantities have—at least partially as for Coxiella-LE—a genetic capability from a uniquely blood-based diet (Gottlieb et al., 2015; Smith for de novo B vitamin synthesis. Indeed, the Francisella-LE et al., 2015). By ensuring nutritional upgrading of the blood diet, genomes from the fowl tick Argas persicus and the Gulf Coxiella-LE has enabled ticks to utilize an unbalanced dietary Coast tick Amblyomma maculatum contain complete genetic resource and thus become hematophagy specialists. pathways for biotin, folic acid, and riboflavin biosynthesis (Sjodin Recent studies have suggested that alternative obligate et al., 2012; Gerhart et al., 2016). Similarly, recent metabolic symbionts other than Coxiella-LE may also exist. Around one reconstructions of Rickettsia genomes indicated that all genes third of examined tick species lack Coxiella-LE or harbor required for folic acid biosynthesis are present in Rickettsia Coxiella-LE at much lower frequencies than expected for an symbiont genomes obtained from both the black-legged tick obligate symbiont (Duron et al., 2014, 2015a, 2017). A large Ixodes scapularis and the Western black-legged tick I. pacificus survey of 81 tick species showed that in almost all tick species (Hunter et al., 2015). Worthy of note here is that laboratory without Coxiella-LE infection, another maternally-inherited findings directly corroborate the existence of beneficial Rickettsia symbiont was usually present (Duron et al., 2017). Among these symbionts since they exert a significant effect on larval motility alternative obligate symbionts were Francisella-LE, Rickettsia, of A. americanum, Dermacentor variabilis, and I. scapularis ticks and Rickettsiella, which are often present in all specimens of (Kagemann and Clay, 2013). Overall, these maternally-inherited the infected tick species (Duron et al., 2017). Although formal symbionts are thus of ecological and evolutionary importance testing with nutritional and physiological experiments is now to the tick species they infect, and potentially mediate tick required to validate their role as alternative obligate symbionts, adaptation to hematophagy. In addition, it should be notice

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TABLE 1 | List of the ten maternally inherited bacteria found in ticks and relevant (non-exhaustive) references.

Maternally inherited Distribution in ticks and other arthropods Major properties (non-exhaustive references) bacteria

1-Coxiella-LE Very common in ticks, not found in other arthropods (Noda et al., Obligate symbiont in most tick species (Zhong et al., 2007; 1997; Jasinskas et al., 2007; Clay et al., 2008; Carpi et al., 2011; Gottlieb et al., 2015; Smith et al., 2015). Closely related to the Machado-Ferreira et al., 2011; Almeida et al., 2012; Lalzar et al., agent of Q fever, C. burnetii (Duron et al., 2015a) 2012; Duron et al., 2014, 2015a, 2017)

2-Rickettsiella Scattered distribution in arthropods (Tsuchida et al., 2010, 2014; Unknown effect in ticks. Facultative mutualist in aphids (Tsuchida Bouchon et al., 2012; Iasur-Kruh et al., 2013), common in ticks et al., 2010, 2014) and likely in other insects (Iasur-Kruh et al., (Kurtti et al., 2002; Vilcins et al., 2009; Anstead and Chilton, 2014; 2013). Some strains are entomopathogenic (Bouchon et al., 2012) Duron et al., 2015a, 2016, 2017)

3-Arsenophonus Common in arthropods (Duron et al., 2008a; Novakova et al., Male-killer in parasitoid wasps (Werren et al., 1986; Duron et al., 2009), present in ticks (Clay et al., 2008; Dergousoff and Chilton, 2010), putative obligate symbiont in bat ﬂies and louse ﬂies (Duron 2010; Reis et al., 2011; Clayton et al., 2015; Duron et al., 2017) et al., 2014), facultative symbiont in other insects (Novakova et al., 2009; Jousselin et al., 2013). In the sheep tick, Ixodes ricinus, detection of Arsenophonus may be due to contamination by a hymenopteran parasitoid (Bohacsova et al., 2016)

4-Francisella-LE Rare in ticks, not found in other arthropods (Niebylski et al., 1997a; Unknown effect in most cases but alternative obligate symbiont in Noda et al., 1997; Scoles, 2004; Goethert and Telford, 2005; some tick species (Gerhart et al., 2016; Duron et al., 2017); closely Clayton et al., 2015; Gerhart et al., 2016; Duron et al., 2017) related to the agent of tularaemia (F. tularensis)(Sjodin et al., 2012)

5-Cardinium Common in arthropods (Zchori-Fein and Perlman, 2004; Duron Unknown effect in ticks. Reproductive manipulator in diverse et al., 2008a,b), present in ticks (Kurtti et al., 1996; Benson et al., insect species (Engelstadter and Hurst, 2009) 2004; Duron et al., 2017)

6-Spiroplasma Common in arthropods (Weinert et al., 2007; Duron et al., 2008a), Unknown effect in ticks. Male-killer in diverse insect species present in ticks (Tully et al., 1981, 1995; Henning et al., 2006; (Engelstadter and Hurst, 2009) Duron et al., 2017)

7-Lariskella Rare and with a scattered distribution in arthropods (Matsuura Unknown effect et al., 2012; Toju et al., 2013), rarely reported in ticks (Qiu et al., 2014; Duron et al., 2017)

8-Midichloria Present in ticks, not found in other arthropods (Lo et al., 2006; Unknown effect; inhabits tick mitochondria (Epis et al., 2013) Epis et al., 2008; Venzal et al., 2008; Reis et al., 2011; Najm et al., 2012; Qiu et al., 2014; Williams-Newkirk et al., 2014; Paul et al., 2016; Bonnet et al., 2017; Duron et al., 2017)

9-Rickettsia Common in arthropods (Perlman et al., 2006; Weinert et al., Unknown effect in ticks. Reproductive manipulator in diverse 2009), present in ticks (Niebylski et al., 1997b; Clayton et al., insect species (Engelstadter and Hurst, 2009) and defensive 2015; Kurtti et al., 2015; Duron et al., 2017) symbiont in other insects (Lukasik et al., 2013) closely related to pathogenic strains, often tick-borne, infecting vertebrates (Perlman et al., 2006; Darby et al., 2007; Weinert et al., 2009)

10-Wolbachia Very common in arthropods (Duron et al., 2008b; Zug and Unknown effect in ticks. Reproductive manipulation in many Hammerstein, 2012), present in ticks (Andreotti et al., 2011; Carpi arthropods (Engelstadter and Hurst, 2009), facultative mutualist et al., 2011; Reis et al., 2011; Duron et al., 2017) (defensive symbiosis) in others such as mosquitoes (Brownlie and Johnson, 2009; Hamilton and Perlman, 2013), obligate symbiont in bed bugs (Hosokawa et al., 2010; Nikoh et al., 2014). At least in the case of the sheep tick, Ixodes ricinus, it has been demonstrated that the detection of Wolbachia was due to a contamination by a hymenopteran parasitoid (Plantard et al., 2012)

Adapted from Duron et al. (2017). that some tick-borne pathogenic Anaplasmataceae bacteria Rickettsia genus is appropriate. Indeed, most of the novel (including Anaplasma phagocytophilum, Ehrlichia chaffeensis, Rickettsia species or strains discovered in recent years are found and Neorickettsia sennetsu) are also able to synthetize all major exclusively in arthropods and never in vertebrates (Perlman vitamins, suggesting that they may also confer a beneficial role in et al., 2006; Darby et al., 2007; Weinert et al., 2009). In ticks, as ticks when present (Dunning Hotopp et al., 2006). for many other arthropods, these Rickettsia are not pathogenic Many Rickettsia species are well-known TBP, therefore a but are actually maternally-inherited symbionts with poorly comment on the true ecological diversity existing within the known effects on tick biology. This is the case for the Rickettsia

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Bonnet et al. Tick-Microbiota-Pathogen Interactions species identified in the black-legged tick I. scapularis (Rickettsia to date the only demonstration of sex ratio distortion in ticks buchneri, formerly known as Rickettsia REIS; Kurtti et al., possibly due to a symbiont. 2015), the American dog tick Dermacentor variabilis (Rickettsia High symbiont infection frequency is rarely observed within peacockii; Felsheim et al., 2009), and likely the tree-hole tick each tick species, and an intermediate infection frequency is Ixodes arboricola (Rickettsia vini; Duron et al., 2017). The fact much more common (Noda et al., 1997; Clay et al., 2008; that ticks carry both pathogenic and non-pathogenic Rickettsia Lalzar et al., 2012; Duron et al., 2017). Interestingly, infection that may interact (as early reported by Burgdorfer et al., 1981), frequencies of each maternally-inherited symbiont are often underscores the need to be able to clearly distinguish between the variable between geographic populations of a given tick species two in further studies on these bacteria. (Clay et al., 2008; Lalzar et al., 2012; Duron et al., 2017). Along with obligate symbionts, ticks commonly harbor This is the case for the soft tick Ornithodoros sonrai, with facultative symbionts belonging to a variety of bacterial genera Midichloria and Rickettsia reaching high infection frequencies (listed in Table 1). Examination of a representative collection of in some populations, but remaining absent from others (Duron 81 tick species (i.e., approximately 10% of known tick species et al., 2017). Similar geographical patterns were observed for and including both soft and hard ticks) illustrated facultative many other tick species and for several symbionts such as symbiont diversity, since it revealed the presence of maternally- Rickettsiella and Spiroplasma in the tree-hole tick I. arboricola inherited bacteria in almost all species (79 of 81) (Duron and the polar seabird tick I. uriae (Duron et al., 2016, 2017). These et al., 2017). Remarkably, many of these tick species (44) patterns strongly suggest that maternally-inherited symbiont hosted more than one symbiont. In multi-infected tick species, infection dynamics are variable across tick populations and symbionts assembled in communities which could reach high species. Such infection frequency variations may be driven by levels of complexity. Indeed, six distinct genera of maternally- costs and benefits associated with harboring maternally-inherited inherited symbionts co-exist in sheep tick I. ricinus populations symbionts, and may maintain intermediate frequencies in tick (Midichloria, Spiroplasma, Coxiella-LE, Rickettsia, Wolbachia, populations, as is commonly observed in other arthropod species and Rickettsiella) and in African blue tick Rhipicephalus (Oliver et al., 2010). However, even if the nature of these costs decoloratus populations (Midichloria, Coxiella-LE, Francisella- and benefits has been well-studied in insects, they remain to LE, Rickettsia, Cardinium, and Spiroplasma)(Duron et al., 2017). be determined in ticks. Another important parameter in our It should be noted that detecting a heritable bacterium can understanding of this infection dynamics may be variation of sometimes be due to cross-contamination as for several I. ricinus biological features between tick males and females: indeed, studies. Some recorded Wolbachia and Arsenophonus infections adult males from Ixodes species do not blood-feed, contrary were actually due to the cryptic presence of a Wolbachia- to females, which may imply that adult males do not need and Arsenophonus-infected endoparasitoid wasp, Ixodiphagus nutritional symbionts. The importance of this “sex” parameter to hookeri, within tick tissues (Plantard et al., 2012; Bohacsova et al., symbiont infection dynamics remains also to be demonstrated. 2016). As a result, the presence of at least some of these tick Interestingly, this pattern was observed for Midichloria with symbionts must be treated with caution. males less commonly infected than females in I. ricinus (Lo Although the role of these facultative symbionts is not yet et al., 2006). This suggests that Midichloria may be an important clearly elucidated, one study suggested that Arsenophonus sp. nutritional symbiont for I. ricinus, as recently proposed (Duron can affect host-seeking success by decreasing A. americanum, et al., 2017). I. scapularis, and D. variabilis tick motility when Rickettsia Along with maternally-inherited symbionts, other non- symbionts increased such mobility (Kagemann and Clay, 2013). pathogenic microbes are present in ticks (Andreotti et al., 2011; Recent sequencing and analysis of the Midichloria mitochondrii Carpi et al., 2011; Narasimhan et al., 2014; Qiu et al., 2014; genome led to the hypothesis that the bacteria could serve as an Williams-Newkirk et al., 2014; Abraham et al., 2017). Most are additional ATP source for the host cell during oogenesis (Sassera likely to inhabit the tick gut, while others may also colonize et al., 2011). In addition, this symbiont has been ascribed a the tick surface cuticle. Overall, the biological effects of these possible helper role in tick molting processes (Zchori-Fein and non-pathogenic microbes on ticks remain entirely unknown, but Bourtzis, 2011). As mentioned above, there is no evidence to it is likely that those inhabiting the tick gut could participate date showing that the Wolbachia detected in ticks are “true” in blood meal digestion (Narasimhan and Fikrig, 2015) and tick symbionts (Plantard et al., 2012). Interestingly, in insects, complex interactions with TBP as we further detailed. Wolbachia is known to act as defensive endosymbionts (reviewed by Brownlie and Johnson, 2009), or as manipulator of host reproduction (review in Engelstadter and Hurst, 2009; Cordaux NON-PATHOGENIC MICROORGANISMS et al., 2011), suggesting that similar effect may exist in ticks. INTERACT WITH TBP IN DIFFERENT WAYS The same questioning are required regarding Arsenophonus nasoniae: known to be responsible for sex-ratio distortion in An alternative fascinating aspect is that non-pathogenic diverse arthropod species (Werren et al., 1986; Duron et al., microorganisms can also interfere with TBP replication and 2010) but of unknown effect in ticks. Finally and interestingly, transmission by influencing TBP abundance and diversity in tick a Rickettsiella symbiont has been observed in a parthenogenetic populations, as well as their transmission to vertebrate hosts. laboratory colony of the tick I. woodi (Kurtti et al., 2002). This All aforementioned non-pathogenic bacteria present in ticks tick species is generally known to be bisexual, suggesting that could have the potential to impact TBP infection processes in Rickettsiella infection may induce asexuality which represents different ways. One can assume that TBP and non-pathogenic

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Bonnet et al. Tick-Microbiota-Pathogen Interactions microorganisms may neutralize each other because they are in the emergence of defensive symbionts in insects, including direct competition for limited resources, such as a particular maternally-inherited symbionts that protect their insect host nutrient or tissue, or because they stimulate the same immune against a variety of pathogens (reviewed by Haine, 2008; Brownlie system function. Alternatively, non-pathogenic microorganisms and Johnson, 2009). For instance, some maternally-inherited may excrete molecules directly inhibiting the growth of a symbionts such as Wolbachia may interfere with the replication TBP competitor, or, inversely, facilitate TBP development by and transmission of a wide range of pathogens (including immunosuppressing the tick host. As a result, non-pathogenic viruses, bacteria, protozoa, nematodes, and parasitoids), and microorganisms may facilitate, limit, or block TBP transmission, protect insects from parasite-induced mortality, possibly by up- depending on the nature of tick microbial interactions. regulating the insect’s immune system (Brownlie and Johnson, Equally pertinent in this regard is the role of the microbiota 2009; Gross et al., 2009). Recently, some of these findings inhabiting the tick gut (Narasimhan et al., 2014; Abraham have been applied to the development of parasite control et al., 2017). The tick gut represents the TBP entry point, methods, where Wolbachia infection has been used to limit therefore gut microbiota can directly mediate TBP colonization the vector competence of mosquitoes (Hoffmann et al., 2011). and influence their early survival (Narasimhan and Fikrig, 2015). In comparison, very little was known about the existence This finding has been perfectly illustrated in a recent study of defensive symbionts in ticks. However, Burgdorfer et al. which manipulated the gut microbiota of the black-legged tick were the first to report the presence of defensive symbionts I. scapularis: specimens reared in sterile containers (i.e., thus in the Rocky Mountain wood tick Dermacentor andersoni.In preventing preventing external bacterial contamination) showed this tick, a maternally-inherited symbiont, Rickettsia peacockii, increased engorgement weights, and decreased colonization by hampered the multiplication and transovarial transmission of the causative agent of Lyme disease, Borrelia burgdorferi, when the spotted fever agent, Rickettsia rickettsii (Burgdorfer et al., compared to normal specimens (Narasimhan et al., 2014). 1981). Similarly, resistance of the ovaries of D. variabilis to Similarly, ticks fed on antibiotic-treated mice exhibited modified co-infection with Ricketisia montana and Rickettsia rhipicephali gut microbiota that also resulted in increased feeding and low has been reported (Macaluso et al., 2002). Other experiments B. burgdorferi colonization rates (Narasimhan et al., 2014). on D. andersoni further showed that A. marginale infection Altering the gut microbiota was actually found to decrease density was negatively correlated to the infection density of production of a glycoprotein from the tick peritrophic matrix, another maternally-inherited symbiont, Rickettsia belli (Gall which separates the gut lumen from the epithelium. This et al., 2016). In I. scapularis, it has also been reported that male peritrophic matrix is pivotal for Borrelia colonization success, as ticks infected by the maternally-inherited symbionts Rickettsia it protects B. burgdorferi colonizing the gut epithelial cells from buchneri have significantly lower rates of B. burgdorferi infection toxic gut lumen compounds. Compromised peritrophic matrix than symbiont-free males (Steiner et al., 2008). Overall, these due to altered gut microbiota will thus impede B. burgdorferi observations suggested that the maternally-inherited Rickettsia colonization. However, the reverse is true for another TBP, the symbionts may be major defensive symbionts protecting ticks anaplasmosis agent, A. phagocytophilum (Abraham et al., 2017). against TBP colonization. As a result, Rickettsia symbionts may Remarkably, this bacterium manipulates the gut microbiota be a key factor influencing TBP abundance and diversity in tick of I. scapularis to favor its establishment. By inducing tick populations. glycoprotein production, A. phagocytophilum partially blocks Conversely, maternally-inherited symbionts may not always bacterial biofilm formation, and thus reduces peritrophic matrix protect ticks against pathogens: the presence of one maternally- integrity, rendering the tick more susceptible to infection inherited symbiont, Francisella-LE, in D. andersoni was positively (Abraham et al., 2017). Altogether, these observations have associated with pathogenic Francisella novicida infection (Gall uncovered a “Dr. Jekyll and Mr. Hyde”-like role of the tick et al., 2016). Because these results were only obtained following gut microbiota, so that an unaltered gut microbiota will favor laboratory manipulations, they should be treated with caution colonization by some TBP, such as Borrelia, whereas it may as F. novicida is not considered to be a TBP, as the majority also block colonization by other TBP, such as Anaplasma. This of people infected with F. novicida contract the pathogen after antagonistic effect of tick gut microbiota on TBP may explain the ingesting infected water or ice, and not via tick bites. This study rarity of Borrelia-Anaplasma co-infections in ticks collected from thus relies on an artificial F. novicida tick infection that is unlikely the field (Abraham et al., 2017). to happen in the field, and most importantly, using a pathogen Other interaction mechanisms may also exist. In well- that has not co-evolved with tick symbionts. This naturally studied animals, such as insects, antagonistic interactions raises the question of whether Francisella-LE can actually protect arise when horizontally-transmitted parasites and vertically- D. andersoni against TBPs that naturally occur in this tick species. transmitted microorganisms co-infecting the same host have conflicting evolutionary interests (Haine et al., 2005; reviewed by Haine, 2008; Ben-Ami et al., 2011; Hamilton and Perlman, 2013). TICK SYMBIONTS CAN BE Vertically-transmitted microorganisms, such as maternally- OPPORTUNISTIC VERTEBRATE inherited symbionts, are under strong selection pressure to PATHOGENS enhance the reproductive success of the hosts they infect (Moran et al., 2008). Conversely, parasites are typically transmitted Although biologists often classify host-microbe relationships as between unrelated hosts and are therefore not directly affected either “mutualism,” “commensalism,” or “parasitism,” there are by altered host fecundity. This conflict of interest has favored difficulties in defining the boundaries of these definitions. Rather,

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Bonnet et al. Tick-Microbiota-Pathogen Interactions host-microbe relationships should be best described as a broad a higher rate than TBP. However, despite this, Coxiella-LE continuum, as intermediate states and transitions between states infections are very rare in vertebrates, and most strains described occur frequently. Several maternally-inherited tick symbionts to date have only been identified from ticks (Duron et al., 2015a). are remarkable examples of this continuum, as recent literature It is thus thought that these bacteria pose a low infection risk to has reported that certain symbionts may be transmitted to vertebrates because their genome seems to be extremely reduced vertebrates following tick bite, as will be detailed further in and is devoid of known virulence genes (Gottlieb et al., 2015; this section (Shivaprasad et al., 2008; Woc-Colburn et al., 2008; Smith et al., 2015). Nonetheless, Coxiella-LE have the potential Vapniarsky et al., 2012; Bazzocchi et al., 2013; Edouard et al., to cause rare infections in vertebrates and should always be 2013; Angelakis et al., 2016; Seo et al., 2016a; Bonnet et al., 2017). considered in future studies on tick-borne diseases. Most importantly, some of these symbionts have the potential to In comparison, vertebrate infections by symbionts other opportunistically infect vertebrate hosts, including humans. than Coxiella-LE are clearly less common. This includes Maternally-inherited arthropod symbionts are commonly the maternally-inherited symbiont Arsenophonus, present in thought to be exclusively domesticated by their arthropod approximately 5% of terrestrial arthropods, including some tick hosts: they cannot invade naïve hosts and have evolved to be species (Duron et al., 2008a, 2017). Arsenophonus is actually dependent on arthropod-based transmission mechanisms unique among maternally-inherited symbionts because it is able through transovarial inheritance (Moran et al., 2008; to grow outside arthropod cells, in extracellular environments Wernegreen, 2012). However, some tick symbionts, such as (Huger et al., 1985; Werren et al., 1986). This ability enhances the certain Coxiella, Midichloria and Arsenophonus strains, are not likelihood of successful opportunistic Arsenophonus infection, actually completely dependent on ticks. Rather than strictly as was recently observed in a woman who was bitten by a tick maternal, their transmission may be partially horizontal, during a trip to Southeast Asia. This patient presenting with a i.e., infectious, thus presenting a substantial infection risk rash and an eschar developed a co-infection with Arsenophonus to vertebrates (Shivaprasad et al., 2008; Woc-Colburn et al., and Orientia tsutsugamushi (the causative agent of scrub typhus) 2008; Vapniarsky et al., 2012; Bazzocchi et al., 2013; Edouard (Edouard et al., 2013). In this context, it is likely that rash et al., 2013; Angelakis et al., 2016; Seo et al., 2016a). Among and eschar development following Orientia infection may have these symbionts, Coxiella-LE are the most commonly found favored a secondary, opportunistic, Arsenophonus infection. microorganisms in vertebrates. Indeed, tick-transmitted In other cases, the identification of symbionts as opportunistic Coxiella-LE has recently been reported to cause mild infectious vertebrate pathogens is more difficult and remains speculative. symptoms in humans from Europe (Angelakis et al., 2016). This is the case for Midichloria, an intra-mitochondrial symbiont These microorganisms were notably detected in human skin of the sheep tick I. ricinus and a few other tick species. Several biopsy samples and may be a common causative agent of scalp lines of evidence have recently suggested that vertebrate hosts eschar and neck lymphadenopathy. Coxiella-LE infections have can be inoculated with Midichloria during a tick bite. Indeed, also been occasionally reported in pet birds such as psittacines most Midichloria are localized in the tick ovaries, where they are and toucans reared in North America (Shivaprasad et al., 2008; transmitted to the progeny, but some have also been detected Woc-Colburn et al., 2008; Vapniarsky et al., 2012). These latter in the salivary glands and saliva of I. ricinus (Di Venere et al., Coxiella-LE can cause fatal disease: infected birds exhibited 2015). In addition, Midichloria DNA, as well as antibodies against lethargy, weakness, emaciation, and progressive neurologic signs a Midichloria antigen, were detected in the blood of vertebrates for several days prior to death. Conversely, another Coxiella-LE exposed to tick bites (Bazzocchi et al., 2013). However, whether was identified in several South Korean horse blood samples, but Midichloria can cause a true infection and pathological alteration none of the horses showed apparent symptoms of infection (Seo in mammalian hosts remains to be determined. et al., 2016b). The ability of Coxiella-LE to infect vertebrates through tick biting is at least partially explained by their tissue tropism within PATHOGENS AND TICK SYMBIONTS ARE the tick body. Aside from tick ovaries and Malpighian tubules, OFTEN PHYLOGENETICALLY RELATED examination of tick internal organs also revealed substantial Coxiella-LE concentrations within the salivary glands of some The vast range of intracellular bacteria in ticks is particularly tick species (Klyachko et al., 2007; Machado-Ferreira et al., 2011; illustrative of their propensity to evolve extreme and contrasting Qiu et al., 2014) but not in others (Liu et al., 2013; Lalzar et al., phenotypes. Certain species, such as Rickettsia spp. and Coxiella 2014). This tissue tropism may enable Coxiella-LE release into the spp., have taken eukaryote associations to the extreme by vertebrate during tick bite, thus favoring opportunistic infections completely abandoning any semblance of a free-living phase and (Duron et al., 2015a). The overall likelihood of such Coxiella-LE replicating solely within the host cell. However, they do use a tick-to-vertebrate transfers seems high since (i) ticks are found large panel of lifestyle strategies to spread and persist within host worldwide and feed on many different vertebrate species, (ii) populations: while some are extremely virulent pathogens, others at least two thirds of tick species are infected by Coxiella-LE, behave as subtle mutualistic symbionts (Perlman et al., 2006; and (iii) when present in a given tick species, Coxiella-LE are Darby et al., 2007; Weinert et al., 2009; Sjodin et al., 2012; Duron usually present in almost all specimens (Duron et al., 2015a). et al., 2015a, 2017; Gerhart et al., 2016). Although both strategies Overall, these observations suggest that, through tick parasitism, require high degrees of lifestyle specialization, they are not fixed vertebrates are often exposed to Coxiella-LE, and probably at endpoints along the bacterium-eukaryote interaction spectrum;

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Bonnet et al. Tick-Microbiota-Pathogen Interactions rather, parasitism and mutualism may shift through repeated evolutionary transitions. This explains why both pathogenic and mutualistic forms of several major bacterial genera commonly hosted by ticks are abundantly represented. The foremost examples of these transitions are found in three major intracellular bacteria genera: Coxiella, Francisella, and Rickettsia (Figure 2), which are all commonly identified in ticks. In medical and veterinary research, these intracellular bacteria are traditionally recognized as highly virulent vertebrate pathogens, as they have evolved specific mechanisms to penetrate into the host cytosol, appropriate nutrients for replication, subvert host immune responses, and ultimately enable infectious transmission to a new host individual (Darby et al., 2007; Celli and Zahrt, 2013; van Schaik et al., 2013; Jones et al., 2014). In humans, major intracellular pathogens have been identified from these bacterial groups, as exemplified by the agent of Q fever, Coxiella burnetii, the agent of tularaemia, Francisella tularensis, the agent of epidemic typhus, Rickettsia prowazekii, the agent of Rocky Mountain spotted fever, Rickettsia rickettsia,orthe causative agent of Mediterranean spotted fever, Rickettsia conorii (Figure 2). All of these organisms are extremely infectious and some are currently classified as potential weapons for biological warfare (Darby et al., 2007; Celli and Zahrt, 2013; van Schaik et al., 2013; Jones et al., 2014). In addition, several species of tick-borne bacteria as typified by rickettsiae that were considered non-pathogenic for decades are now associated with human infections (Parola et al., 2013; Bonnet et al., 2017). However, as we have detailed above, novel intracellular bacteria engaged in endosymbiotic associations with arthropod hosts have also recently been discovered within each of these groups (Figure 2). Phylogenetic investigations have revealed rapid and repeated FIGURE 3 | Evolutionary relationships between pathogenic and non-pathogenic (symbiotic) forms within the Francisella, Coxiellai, and evolutionary shifts within these three genera between pathogenic Rickettsia bacterial genera. (A–C) Simplified phylogenies of Coxiella, (associated with vertebrates and, in some cases, vectored by Francisella, and Rickettsia, respectively, adapted from Perlman et al. (2006), arthropods) and endosymbiotic forms (specifically linked to Weinert et al. (2009), Duron et al. (2015a), and Sjodin et al. (2012). Red: arthropods). However, the evolutionary shifting pattern varies pathogenic forms; blue: endosymbiotic forms associated with arthropods among genera (Figure 2). In Coxiella, complementary lines of (ticks for Francisella and Coxiella; ticks and other arthropods for Rickettsia); black: bacterial outgroups. Colored circles on tree branches indicate major argument indicate a recent emergence of the Q fever agent, C. evolutionary transitions from symbiotic ancestors to pathogenic descendants burnetii, from a Coxiella-LE strain hosted by soft ticks (Duron (red circles) and from pathogenic ancestors to symbiotic descendants (blue et al., 2015a). The Coxiella genus displays extensive genetic circles). diversity, with at least four highly divergent clades (Duron et al., 2015a). While Coxiella-LE strains hosted by ticks are found in all these clades, all C. burnetii strains cluster within one of these clades, delineating an embedded group among soft tick the entire Rickettsial diversity (i.e., including pathogenic and Coxiella-LE (Figure 3A). This phylogenetic pattern indicates that non-pathogenic forms) clearly indicate that switching between the ancestor of C. burnetii was a tick-associated Coxiella which hosts (invertebrates, vertebrates, and even plants) has been succeeded in infecting vertebrate cells (Duron et al., 2015a). a common feature of Rickettsia evolution (Perlman et al., The remarkably low genetic diversity of C. burnetii indicates 2006; Darby et al., 2007; Weinert et al., 2009). Based on unique and recent emergence of this highly infectious vertebrate current data, it is difficult to estimate how often vertebrate pathogen (Duron et al., 2015a). Interestingly, this hypothesis was pathogenesis has evolved within Rickettsia. But as intracellular initially raised a decade ago from observations of the profound adaptation to arthropods is a feature of all current Rickettsia, differences in C. burnetii genome content relative to other it suggests that their most common recent ancestor was pathogenic intracellular bacteria (Seshadri et al., 2003). adapted to arthropod endosymbiosis. Surprisingly, comparing Similarly, in Rickettsia spp., recent evidence revealed that human pathogens with closely related non-pathogens showed no human pathogens—vectored by blood feeding arthropods such relationships between pathogenicity and the acquisition of novel as ticks—emerged relatively late in the evolution of this genus virulence genes: vertebrate virulence seems to occur rather as (Figure 3C; Perlman et al., 2006; Darby et al., 2007; Weinert result of lost or malfunctioning replication systems (Darby et al., et al., 2009). Phylogenetic investigations taking into account 2007).

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Conversely, in Francisella, the evolutionary pattern is of evolutionary change in ticks, as clearly shown by their role substantially different, since most of the diversity found in this in driving their tick hosts to adapt to a strict hematophagous genus is due to pathogenic or opportunistic species (Sjodin diet (Gottlieb et al., 2015; Smith et al., 2015; Duron et al., 2017). et al., 2012). Very little is known about the evolution and Other non-pathogenic microorganisms such as maternally- origin of tick Francisella-LE (Michelet et al., 2013; Gerhart inherited Rickettsia symbionts and gut microbiota are also likely et al., 2016; Duron et al., 2017). However, the few Francisella- to substantially contribute to the acquisition of ecologically LE species identified to date delineate a unique monophyletic important traits, such as TBP resistance (Burgdorfer et al., clade that clearly originates from pathogenic forms (Figure 3B; 1981; Steiner et al., 2008; Narasimhan et al., 2014; Abraham Duron et al., 2017). Interestingly, the Francisella-LE genome is et al., 2017). It is therefore vital to establish the nature of the similar in size to pathogenic Francisella species’ genomes, but interactions between non-pathogenic microorganisms, their about one-third of the protein-coding genes are pseudogenized tick hosts, and co-infecting TBP. To achieve this goal, it is and are likely non-functional (Gerhart et al., 2016). This essential to understand how ticks acquire their microbiota, suggests that Francisella-LE is undergoing a global process and how microbial community structures are shaped by of genome reduction, an evolutionary development typically various environmental and host factors, and also by microbial observed in maternally-inherited symbionts (Moran et al., 2008). interactions within these communities. This knowledge is a Interestingly, Francisella-LE has conserved intact most of its key step toward using non-pathogenic microorganisms to genes involved in B vitamin biosynthesis, highlighting the pivotal limit TBP transmission and persistence. Similarly, whether tick role these genes play in adaption to its current endosymbiotic symbionts have the potential to opportunistically infect humans lifestyle (Gerhart et al., 2016). and other vertebrates should be investigated in depth. Lastly, we Overall, these two very different phenotypes (symbiosis would like to emphasize that the study of some non-pathogenic vs. pathogenesis), along with two contrasting transmission microorganisms, such as members of the Coxiella, Francisella, modes (vertical vs. horizontal), and variable host specificity and Rickettsia genera, can advance our understanding of (ticks vs. vertebrates), make the Coxiella, Francisella, and many infectious diseases including Q fever, tularemia, and Rickettsia genera especially fascinating. They thus offer an rickettsial diseases. The broad phenotypic diversity present unusual opportunity to answer questions about the origins and in these three bacterial genera make them perfect models mechanisms of symbiosis and pathogenesis. Further studies to study the evolutionary emergence of pathogenicity and characterizing host range and infectivity of different genera adaptations to living in vertebrate cells. Owing to their medical members would be invaluable to obtaining such results, as importance, the pathogenic species of these genera have been would the characterization of tick symbiotic strain genomes. the targets of several genome sequencing projects, which have However, research efforts to date have invariably tended to provided insights into the mechanisms and consequences of concentrate on their medically important relatives, and so we their specialized lifestyles (Darby et al., 2007; van Schaik et al., know comparatively little about the biology of maternally- 2013). Conversely, the symbiotic forms adapted to tick hosts inherited symbionts. This neglect is unfortunate because fully have received much less attention and a lot of things remain to understanding the whole scope of Coxiella, Francisella, and be elucidated (but see Gillespie et al., 2012, 2015; Clark et al., Rickettsia phenotypes linked to genome sequences, will provide 2015 for genomics insights about Rickettsia). In this context, an excellent system to test hypotheses on the importance comparative genomic approaches will be highly valuable in of genome content and plasticity in the emergence and enhancing our understanding of the evolutionary ecology of reversibility of extreme phenotypes such as symbiosis and both pathogenic and non-pathogenic intracellular bacteria, and pathogenesis. in identifying novel candidate genes contributing to virulence and persistence in vertebrate cells. CONCLUSION AND PERSPECTIVES AUTHOR CONTRIBUTIONS Extensive literature studies have now made it clear that TBP are not alone: an appreciable range of diverse non-pathogenic SB, AH, FB, and OD conducted the literature research, wrote the microorganisms has also been detected in almost all tick species paper and prepared the figures and tables. All authors provided examined so far. Perhaps the most important consideration critical review and revisions. for the future is not the incidence of these non-pathogenic microorganisms, but their phenotypes. The varied collection ACKNOWLEDGMENTS of non-pathogenic microorganisms includes intracellular maternally-inherited symbionts and microbes inhabiting the We thank members of our laboratories for fruitful discussions tick gut, and each could strongly influence—in very different and especially the Tiques et Maladies à Tiques group ways—the biology of their tick hosts as well as TBP infection (REID-Réseau Ecologie des Interactions Durables). We also dynamics. Recent findings have shown that several maternally- acknowledge an Investissement d’Avenir grant of the Agence inherited symbionts such as Coxiella-LE are important drivers Nationale de la Recherche (CEBA: ANR-10-LABX-25-01).

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24 25 26 HYPOTHESIS AND OBJECTIVES

27 28 HYPOTHESIS

Genomics and combined metaomics-based strategies for the taxonomic and functional profiling of vector microbiota allow the characterization of vector-borne pathogens and its genetic variants, and reveal factors related to vector capacity and competence.

OBJECTIVES

Main objective:

Characterization of commensal, symbiotic and pathogenic microbiota, and the impact of biotic and abiotic factors combining genomics and metaomics approaches in different vector models.

Specific objectives:

1. To characterize the bacterial microbiota of Ixodes ricinus and potential biological processes implicated in tick-microbiota-pathogen interactions using metaomics approaches.

2. To characterize the wild-caught Ixodes ventalloi metagenome using a de novo assembly approach and targeted whole-genome shotgun-metagenomics sequencing to explore the bacterial composition and putative tick-borne pathogens.

3. To identify and characterize the genetic variants of Crimean-Congo hemorrhagic fever virus in the tick vectors Dermacentor marginatus and Haemaphysalis parva combining genomics and proteomics approaches.

4. To characterize the draft genome of the tick-borne pathogens Anaplasma spp. using whole-genome sequencing analysis and assembly.

5. To characterize wild-caught Culicoides imicola microbiome, and to study the impact of biotic and abiotic factors on microbiome composition.

29 30 Chapter II. Microbiome composition and pathogen genetic diversity in ticks using omics approaches

Hernández-Jarguín, A., Díaz-Sánchez, S., Villar, M., de la Fuente, J. (2018). Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus. Ticks and Tick-Borne Disease. 9(5):1241-1251.

Díaz-Sánchez, S., Hernández-Jarguín, A., Torina, A., Fernández de Mera, I. G., Blanda, V., Caracappa, S., Gortázar, C., de la Fuente, J. (2019). Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi. Ticks and Tick-Borne Diseases. 10(2):336-343.

Fernández de Mera, I. G., Chaligiannis, I., Hernández-Jarguín, A., Villar, M., Mateos- Hernández, L., Papa, A., Sotiraki, S., Ruiz-Fons, F., Cabezas-Cruz, A., Gortázar, C., de la Fuente, J. et al. (2017). Combination of RT-PCR and proteomics for the identification of Crimean-Congo hemorrhagic fever virus in ticks. Heliyon, 3(7):353.

Díaz-Sánchez, S., Hernández-Jarguín, A., Fernández de Mera, I. G., Alberdi, P., Zweygarth,E., Gortazar, C., de la Fuente, J. (2018). Draft genome sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis isolates from different hosts. Genome Announcements, 6(5):1503-17.

31 32 Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus

33 34 &KDSWHU,,

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Contents lists available at ScienceDirect

Ticks and Tick-borne Diseases

journal homepage: www.elsevier.com/locate/ttbdis

Original article Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus ⁎ Angélica Hernández-Jarguína,1, Sandra Díaz-Sáncheza,1, Margarita Villara, José de la Fuentea,b, a SaBio, Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo s/n, 13005, Ciudad Real, Spain b Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, 74078, USA

ARTICLE INFO ABSTRACT

Keywords: An innovative metaomics approach integrating metatranscriptomics and metaproteomics was used to char- Metatranscriptomics acterize bacterial communities in the microbiota of the Lyme borreliosis spirochete vector, Ixodes ricinus (Acari: Metaproteomics Ixodidae). Whole internal tissues and salivary glands from unfed larvae and female ticks, respectively were used. Metaomics Reused I. ricinus RNA-sequencing data for metranscriptomics analysis together with metaproteomics provided a Tick better characterization of tick bacterial microbiota by increasing bacteria identification and support for iden- Microbiota tified bacteria with putative functional implications. The results showed the presence of symbiotic, commensal, Biofilm soil, environmental, and pathogenic bacteria in the I. ricinus microbiota, including previously unrecognized commensal and soil microorganisms. The results of the metaomics approach may have implications in the characterization of putative mechanisms by which pathogen infection manipulates tick microbiota to facilitate infection. Metaomics approaches integrating different omics datasets would provide a better description of tick microbiota compositions, and insights into tick interactions with microbiota, pathogens and hosts.

1. Introduction approaches (Tanca et al., 2013, 2014; Franzosa et al., 2015; Aguiar- Pulido et al., 2016; Cheng et al., 2017). Furthermore, metaomics or the The microbiota plays an important role in several processes af- integration of different omics approaches allows network-based ana- fecting human and animal health, agriculture, environment, and host- lyses to describe the complexity and function of different biological pathogen interactions (Kau et al., 2011; Schwabe and Jobin, 2013; processes involved in host/tick-pathogen and host/tick-microbiome Philippot et al., 2013; Bouchez et al., 2016). Next-generation sequen- interactions (Franzosa et al., 2015; Villar et al., 2015; Narasimhan and cing or omics technologies can be used for microbiota characterization Fikrig, 2015), and the discovery of new targets for prevention and under different experimental and natural conditions. Metagenomics control of tick-borne diseases (Abraham et al., 2017; Narasimhan et al., have been used to characterize the microbiota in different hosts in- 2017; Xiang et al., 2017). cluding both model and nonmodel organisms such as humans and tick Ixodes ricinus (Linnaeus 1758) (Acari: Ixodidae) are obligate hema- vectors (Clay et al., 2008; Andreotti et al., 2011; Carpi et al., 2011; tophagous ectoparasites and vectors of multiple pathogens such as Vayssier-Taussat et al., 2015; Qiu et al., 2014; Williams-Newkirk et al., Borrelia spp. (Lyme borreliosis and hard tick-borne relapsing fever), 2014; Van Treuren et al., 2015; Narasimhan and Fikrig, 2015; Yoon Anaplasma phagocytophilum (human granulocytic anaplasmosis), tick- et al., 2015; Abraham et al., 2017; Heintz-Buschart and Wilmes, 2017; borne encephalitis virus (TBE), and Babesia spp. (babesiosis) (de la Greay et al., 2018; Varela-Stokes et al., 2017; Xiang et al., 2017). Dif- Fuente et al., 2008, 2017). Additionally, I. ricinus have a diverse com- ferent metatranscriptomics approaches have been also applied to the munity of commensal and symbiotic microorganisms which exert study of microbial communities in arthropod vectors and vertebrate multiple effects on tick fitness, nutrition, development, reproduction, hosts (Mäder et al., 2011; Johansson et al., 2013; Vayssier-Taussat defense against environmental stress, immunity and transmission of et al., 2013; Razzauti et al., 2015; Luo et al., 2017). Recently, meta- tick-borne pathogens (Bonnet et al., 2017; de la Fuente et al., 2017). proteomics and metabolomics have emerged as powerful tools for the The I. ricinus microbiome was first characterized using a metagenomics characterization of dynamic host-microbiome interactions, particularly approach (Carpi et al., 2011; Nakao et al., 2013; Bonnet et al., 2014). in combination with metagenomics and metatranscriptomics Vayssier-Taussat et al. (2013) characterized the bacterial community of

⁎ Corresponding author at: SaBio, Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo s/n, 13005, Ciudad Real, Spain.

E-mailaddress:[email protected](J.delaFuente). 1Theseauthorscontributedequallytotheworkreportedinthispaper. https://doi.org/10.1016/j.ttbdis.2018.04.020 Received18January2018;Receivedinrevisedform28April2018;Accepted29April2018

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35 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 1. Metaomics experimental design. An integrated metatranscriptomics and metaproteomics approach was developed for the characterization of I. ricinus bacterial microbiota. Reused I. ricinus RNA-seq data was the basis for metatranscriptomics analysis that resulted in the database of identiﬁed bacterial genera. This database was then use to generate the Uniprot protein database used in metaproteomics analysis.

I. ricinus using a whole transcriptomics approach, resulting in a better Czech Republic. Whole internal tissues and salivary glands from 300 identification of previously unknown bacteria and accurate identifica- larvae and 30 female ticks, respectively were combined and used for tion of potential pathogens. This method also provides a better under- RNA-seq. All ticks were washed with a series of solutions composed of standing of the tick-microbiome interactions when compared to meta- tap water, 3% hydrogen peroxide, two washes of distilled water, 70% genomics. Additionally, reusing RNA sequencing (RNA-seq) data has ethanol and two more washes with distilled water prior to dissection for been also used as an efficient strategy for the screening of pathogens in DNA, RNA and protein extraction. Total DNA, RNA and proteins were ticks (Zhuang et al., 2014a). extracted using Tri Reagent (Sigma-Aldrich, St. Louis, MO, USA) ac- In this study, we used the integration of metatranscriptomics and cording to manufacturer instructions. RNA was further purified with metaproteomics for the characterization of the tick bacterial microbiota the RNeasy MinElute Cleanup Kit (Qiagen, Valencia, CA, USA) and in unfed I. ricinus. Reused I. ricinus RNA-seq data for metranscriptomics characterized using the Agilent 2100 Bioanalyzer (Santa Clara, CA, analysis together with metaproteomics provided a better characteriza- USA) in order to evaluate the quality and integrity of RNA preparations. tion of tick microbiome by increasing bacterial identification and sup- DNA and RNA concentrations were determined using the Nanodrop ND- port for identified bacteria with putative functional implications. 1000 (NanoDrop Technologies Wilmington, Delaware USA). Proteins were resuspended in 20 mM Tris-HCl pH 7.5 with 4% SDS and protein 2. Materials and methods concentration was determined using the BCA Protein Assay kit (Thermo Scientific, Rockford, IL, USA) with bovine serum albumin (BSA) as 2.1. Tick samples and processing standard.

Tick samples were obtained and processed as previously described 2.2. Integrated metaomics experimental design (Genomic Resources Development Consortium et al., 2014). Brieﬂy, I. ricinus unfed larvae and adult females were obtained from the reference An integrated metatranscriptomics and metaproteomics approach laboratory colony maintained at the tick rearing facility of the Institute was developed for the characterization of I. ricinus bacterial microbiota of Parasitology of the Biology Centre of the Academy of Sciences of the (Fig. 1). Reused I. ricinus RNA-seq data (Genomic Resources

36 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Table 1 Oligonucleotide primers and real-time PCR conditions. .

Organism Gene Primer sequences (5-′3′) Annealing References temperature

Ixodidae 16S rRNA GACAAGAAGACCCTA 42 °C Zivkovic et al. (2009) ATCCAACATCGAGGT Ixodes spp. rps 4 GGTGAAGAAGATTGTCAAGCAGAG 54 °C Koči et al. (2013) TGAAGCCAGCAGGGTAGTTTG Rickettsia spp. 16S rRNA AGAGTTTGATCCTGGCTCAG 54 °C Fernández de Mera et al. AACGTCATTATCTTCCTTGC (2013) SFG Rickettsia ompA ATGGCGAATATTTCTCCAAAA 52 °C Oteo et al. (2006) AGTGCAGCATTCGCTCCCCCT Anaplasma spp. 16S rRNA CAGAGTTTGATCCTGGCTCAGAACG 42 °C Ruiz-Fons et al. (2012) GAGTTTGCCGGGACTTCTTCTGTA Anaplasma phagocytophilum msp2 ATGGAAGGTAGTGTTGGTTATGGTATT 60 °C Courtney et al. (2004) TTGGTCTTGAAGCGCTCGTA Ehrlichia spp. 16S rRNA GGTACCYACAGAAGAAGTCC 54 °C Martin et al. (2005) TAGCACTCATCGTTTACAGC Pseudomonas putida dmpN ATCACCGACTGGGACAAGTGGGAAGACC 50 °C Selvaratnam et al. (1995) TGGTATTCCAGCGGTGAAACGGCGG Wolbachia spp. wsp GGGTCCAATAAGTGATGAAGAAAC 55 °C Kondo et al. (2002) TTAAAACGCTACTCCAGCTTCTGC Candidatus Midichloria 16S rRNA CAAAAGTGAAAGCCTTGGGC 58 °C Caﬁso et al. (2016) mitochondrii TGAGACTTAAAYCCCAACATC Borrelia spp. 16S rRNA TAGATGAGTCTGCGTCTTATTA 58 °C Noda et al. (2013) CTTACACCAGGAATTCTAACTT

Table 2 Development Consortium et al., 2014) derived from combined female Relative bacteria IDs and PSMs obtained at genus level. salivary glands and larvae were the basis for metatranscriptomics fi Phylum Genus IDs PSMs analysis that resulted in the database of identi ed bacterial genera. This database was then used to generate the Uniprot protein database for Proteobacteria Acinetobacter 0.22 0.16 application as a variant of the proteomics informed by transcriptomics Anaplasma 10.67 0.73 (PIT) approach (Evans et al., 2012) in the metaproteomics analysis. Bradyrhizobium 2.44 11.07 Brevibacterium 1.78 0.77 Brucella 0.22 0.38 2.3. Metatranscriptomics for the identification of bacterial species in the tick Candidatus Midichloria 15.55 0.05 microbiome Candidatus Neoehrlichia 8.22 0.00 Ehrlichia 12.89 0.63 Escherichia 0.89 4.67 A metatranscriptomics pipeline was developed based on the reused I. Francisella 0.22 0.00 ricinus RNA-seq data (Fig. 1). Tick RNA-seq analysis was conducted as Gilliamella 0.22 1.23 previously described (Genomic Resources Development Consortium et al., Klebsiella 0.22 4.24 2014). The I. ricinus transcriptome, raw reads and assembly results can be Lysobacter 0.22 0.00 − Mesorhizobium 0.22 1.34 accessed at dryad entries doi: 10.5061/dryad.9js92/1 doi: 10.5061/ Neorickettsia 0.67 0.06 dryad.9js92/8. The metatranscriptomics database was then generated Pseudomonas 0.67 14.72 from the 19,831,942 I. ricinus unaligned reads that did not match to the I. Rickettsia 0.44 0.18 scapularis reference genome (assembly JCVI_ISG_i3_1.0; http://www.ncbi. Rickettsiella 1.78 0.15 Sphingomonas 0.22 9.55 nlm.nih.gov/nuccore/NZ_ABJB000000000)(Genomic Resources Variovorax 0.22 3.92 Development Consortium et al., 2014). The unaligned reads were ex- Wolbachia 0.67 0.00 tracted from the BAM files (the binary version of the SAM file, a tab- Actinobacteria Actinomyces 1.33 2.90 delimited text file that contains sequence alignment data) that resulted Arthrobacter 0.44 4.44 after the assembly of I. ricinus transcriptome using the SAMtools (Li et al., Amycolatopsis 0.22 0.23 Bifidobacterium 0.44 0.68 2009; Li, 2011; http://samtools.sourceforge.net). Then, the unaligned Corynebacterium 1.55 5.70 reads were searched against a bacterial sequence database constructed Propionibacterium 0.89 0.31 with genome and/or species-specific ribosomal RNA (rRNA) sequences Rhodococcus 0.22 7.17 downloaded from the NCBI (https://www.ncbi.nlm.nih.gov) (Supple- Firmicutes Enterococcus 0.44 5.06 fi – Kurthia 0.22 0.15 mentary le 1 Table 1). The bioinformatics approach to identify bacterial Staphylococcus 0.44 0.48 sequences was done in two steps. First, the LAST genome-scale sequence Streptococcus 0.89 12.20 comparison tool (http://last.cbrc.jp) was used to search against the bac- Spirochaetes Borrelia 20.67 1.23 terial database previously constructed. The reads containing poly-A tails Treponema 0.22 0.00 were discarded. As cut-off criteria for genome-scale sequence comparison Bacteroidetes Mucilaginibacter 0.22 1.72 Fusobacteria Leptotrichia 0.22 0.27 we applied minimum alignment length of 100 nucleotides, e-value 0.001, Tenericutes Spiroplasma 10.67 1.03 with a word size of 11, and a minimum of 70% sequence identity. Then, Uncultured bacteria Uncultured bacteria 2.22 2.63 the putative bacterial reads detected with LAST were further confirmed by BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE= Normalized data is shown as the number of count reads or identifications/ BlastSearch) peptide spectrum matches (IDs/PSMs) divided by the total number of IDs/PSMs ł x 100. (Frith et al., 2010a, 2010b; Kie basa et al., 2011). BLAST assignments were done by using the 10 best BLAST hits (BBH) for each putative bacteria previously assigned by LAST. The sequences with hits matching to bacteria were confirmed as identified bacterial sequences,

37 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 2. Phylogenetic and taxonomic abundance analyses. Phylogenetic pruned tree and associated heatmap showing the relative abundance of bacterial genomes (number of count reads or identifications, IDs) and the correspondent peptide assignments (peptide spectrum matches, PSMs) at genus level. The analyses were done using a Heatmap Tool associated with a pruned phylogenetic tree generated with the platform phyloT (http://phylot.biobyte.de) based on NCBI taxonomy, and visualized using the Interactive Tree of Life software v3.4.3 (http://itol.embl.de). discarding the rest. Manual filtering was applied to remove those se- concentration of 1% and the peptides were finally desalted onto OMIX quences with similarity to functional domains. The metatranscriptomics Pipette tips C18 (Agilent Technologies, Santa Clara, CA, USA), dried- database was constructed taking the number of count reads or identi- down and stored at −20 °C until mass spectrometry analysis. The de- fications (IDs) assigned to each identified bacterial sequences, and salted protein digests were resuspended in 0.1% formic acid and ana- normalized against the total number of IDs (Supplementary file 2–Da- lyzed by reverse phase (RP)-liquid chromatography (LC)-mass spec- taset 1). trometry (MS)/MS (RP-LC–MS/MS) using an Easy-nLC II system coupled to an ion trap LCQ Fleet mass spectrometer (Thermo Scien- 2.4. Metaproteomics for bacterial protein identification tific). The peptides were concentrated (on-line) by reverse phase chromatography using a 0.1 × 20 mm C18 RP pre-column (Thermo The metaproteomics pipeline included de novo identification using Scientific), and then separated using a 0.075 × 100 mm C18 RP column a protein database constructed based on the bacterial genera identified (Thermo Scientific) operating at 0.3 ml/min. Peptides were eluted using by metatrascriptomics, a variant of the PIT approach (Evans et al., a 180-min gradient from 5 to 35% solvent B in solvent A (solvent A: 2012)(Fig. 1). Protein extracts (150 μg per sample) were on-gel con- 0.1% formic acid in water, solvent B: 0.1% formic acid in acetonitrile). centrated by SDS-PAGE as previously described (Villar et al., 2015). Electrospray ionization (ESI) was done using a Fused-silica PicoTip The unseparated protein band was visualized by staining with GelCode Emitter ID 10 mm (New Objective, Woburn, MA, USA) interface. Pep- Blue Stain Reagent (Thermo Scientific), excised, cut into 2 × 2 mm tides were detected in survey scans from 400 to 1600 amu (1 mscan), cubes and digested overnight at 37 °C with 60 ng/μl sequencing grade followed by three data dependent MS/MS scans (Top 3), using an iso- trypsin (Promega, Madison, WI, USA) at 5:1 protein:trypsin (w/w) ratio lation width of 2 mass-to-charge ratio units, normalized collision en- in 50 mM ammonium bicarbonate, pH 8.8 containing 10% (v/v) acet- ergy of 35%, and dynamic exclusion applied during 30 s periods. The onitrile (Schevchenko et al., 2006). The resulting tryptic peptides from MS/MS raw files were searched against a compiled database containing the gel band were extracted by 30 min-incubation in 12 mM ammonium the Uniprot Ixodidae taxonomy (134,957 entries in February 2017) bicarbonate, pH 8.8. Trifluoroacetic acid was added to a final together with a database created from the bacterial genera identified by

38 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Table 3 Biological information about bacterial genera identiﬁed by metatranscriptomics in I. ricinus, and previously reported in diﬀerent tick species.

Bacterial genera Biological information Tick spp. References

Actinomyces Commensals of the caecum gut flora and Data not found oral cavities in human. Responsible of abscesses formation in the mouth, lungs and gastrointestinal tract Acinetobacter Soil organisms. Some species are I. scapularis ricinus ovatus persulcatus Benson et al. (2004); Moreno et al. (2006); van opportunistic pathogens causing human Haemaphysalis flava Amblyomma americanum Overbeek et al. (2008); Qiu et al. (2014); Clay et al. infections Dermacentor niveus (2008); Zhuang et al. (2014b); Narasimhanet al. (2014, 2017); Abraham et al. (2017) Amycolatopsis Soil bacteria with antibiotic and anti- Data not found inflammatory properties Anaplasma Tick-borne intracellular bacterial I. scapularis persulcatus pavlovskyi ricinus Benson et al. (2004); Moreno et al. (2006); pathogens causing diseases in humans and Kurilshikov et al. (2015); van Overbeek et al. animals (2008) Arthrobacter Soil bacteria associated to bioremediation D. niveus Zhuang et al. (2014b) processes Bidifidobacterium Commensal and symbiotic bacteria in the Data not found human body. Produce lactic acid that modulates the intestinal pH Borrelia Vector-borne bacteria responsible for Lyme Rhipicephalus microplus ricinus scapularis Andreotti et al. (2011); Carpi et al. (2011); disease and relapsing fever in mammals persulcatus pavlovskyi A. americanum Vayssier-Taussat et al. (2013); van Overbeek et al. (2008); Schabereiter-Gurtner et al. (2003); Moreno et al. (2006); Kurilshikov et al. (2015); Clay et al. (2008) Bradyrhizobium Soil bacteria, present in the roots of plant I. ovatus persulcatus H. flava Qiu et al., 2014

fixing N2 Brevibacterium Soil bacteria, some species can be found in I. ovatus scapularis Qiu et al. (2014); Narasimhan et al. (2014, 2017); human skin Abraham et al. (2017) Brucella Pathogenic bacteria causing disease in I. ricinus Carpi et al. (2011) human and animals Candidatus Midichloria Tick endosymbiotic bacteria A. americanum ricinus Ponnusamy et al. (2014); Trout Fryxell and DeBruyn (2016); van Overbeek et al. (2008) Candidatus Neoehrlichia Tick endosymbiotic bacteria I. ricinus pavlovskyi Carpi et al. (2011); Vayssier-Taussat et al. (2013); van Overbeek et al. (2008); Kurilshikov et al. (2015) *Corynebacterium Saprophytes, some species are pathogenic R. microplus ovatus persulcatus H. flava Andreotti et al. (2011); Qiu et al. (2014) for plants and animals Ehrlichia Tick-borne intracellular bacterial I. scapularis persulcatus ovatus H. flava D. Benson et al. (2004); Kurilshikov et al. (2015); Qiu pathogens causing diseases in humans and reticulatus R. microplus A. americanum et al. (2014); Xu et al. (2015); Clay et al. (2008) animals Enterococcus Commensals of digestive tract, R. microplus ovatus persulcatus A. americanum Andreotti et al. (2011); Qiu et al. (2014); Clay et al. opportunistic pathogens causing septicemia (2008) and urinary tract infection in mammals Escherichia Commensals of digestive and urinary tracts, R. microplus D. silvarum D. niveus persulcatus Andreotti et al. (2011); Liu et al. (2016); Zhuang opportunistic pathogens causing diarrhea et al. (2014b); Qiu et al. (2014) to dysentery in mammals Francisella/Francisella- Intracellular bacterial pathogens D. reticulatus D. andersoni ricinus ovatus persulcatus Kurilshikov et al. (2015); Gall et al. (2016); like endosymbiont transmitted by vectors (ticks, mosquitoes, A. maculatum Vayssier-Taussat et al. (2013); Nakao et al. (2013); flies), and causing tularemia Budachetri et al. (2014) Giliamella Bee endosymbiotic bacteria Data not found Klebsiella Saprophytes in soil and water, commensals R. microplus Andreotti et al. (2011) of gastrointestinal tract, opportunistic pathogens responsible for septicemia and pneumonia in mammals Kurthia Environmental bacteria, present in Data not found mammal feces, soil and water. Opportunistic pathogens for humans causing endocarditis Leptotrichia Natural flora in humans, some species cause I. persulcatus Qiu et al., 2014 opportunistic infections Mesorhizobium Soil bacteria, present in the roots of plant Data not found

fixing N2 Mucilaginibacter Environmental bacteria H. longicornis Liu et al. (2016) Neorickettsia/ Intracellular bacteria, transmitted by I. scapularis ovatus I. affinis persulcatus ricinus Benson et al. (2004); Moreno et al. (2006); Van Rickettsia vectors (ticks, fleas, chiggers, lice), pavlovskyi D. andersoni D. reticulatus D. niveus H. Treuren et al. (2015); Qiu et al. (2014); Nakao et al. responsible for human diseases such as longicornis H. formosensis H. flava A.testudinarium (2013); Van Treuren et al. (2015); Kurilshikov et al. spotted fever and typhus A. americanum A. maculatum R. microplus (2015); Zhuang et al. (2014b); Williams-Newkirk et al. (2014); Carpi et al. (2011); van Overbeek et al. (2008); Vayssier-Taussat et al. (2013); Schabereiter-Gurtner et al. (2003); Liu et al. (2016); Gall et al. (2016); Ponnusamy et al. (2014); Trout Fryxell and DeBruyn (2016); Clay et al. (2008); Budachetri et al. (2014); Xiang et al. (2017); Xu et al. (2015) Propionibacterium Commensals of human gut and skin I. ricinus ovatus persulcatus H. flava Carpi et al. (2011); Qiu et al. (2014) (continued on next page)

39 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Table 3 (continued)

Bacterial genera Biological information Tick spp. References

Pseudomonas Saprophytes in soil, opportunistic R. microplus ricinus scapularis persulcatus pavlovsky Andreotti et al. (2011); Xu et al. (2015); pathogens for humans and plants, plant ovatus H. longicornis D. niveus A. americanum Schabereiter-Gurtner et al. (2003); Carpi et al. growth promoters (2011); Moreno et al. (2006); Qiu et al. (2014); Kurilshikov et al. (2015); Liu et al. (2016); Zhuang et al. (2014b); Clay et al. (2008) *Rhodococcus Saprophytes in soil and water, one species R. microplus ricinus persulcatus ovatus pavlovsky Andreotti et al. (2011); Carpi et al. (2011); is pathogenic for animals causing scapularis Schabereiter-Gurtner et al. (2003); Kurilshikov pneumonia et al. (2015); Qiu et al., (2014); Moreno et al. (2006) Rickettsiella Tick endosymbiotic bacteria I. pavlovsky A. variegatum Kurilshikov et al. (2015); Nakao et al. (2013) endosymbiont Sphingomonas Environmental bacteria and bioremediation I. scapularis ovatus persulcatus H. longicornis H. Benson et al. (2004); Qiu et al. (2014); Liu et al. agents, some specimens cause clinical flava (2016) infections in humans Sphingobium Commonly isolated from soil D. niveus ovatus Zhuang et al. (2014b); Qiu et al. (2014) Spiroplasma Symbionts in the gut hemolymph, few I. ovatus persulcatus H. flava Qiu et al. (2014) species are pathogenic for mice (cataracts and neurological damage) Staphylococcus Saprophytes in soil, commensals of skin and R. microplus D. nievus ricinus ovatus persulcatus H. Andreotti et al. (2011); Xu et al. (2015); Zhuang mucosal surfaces, opportunistic pathogens flava et al. (2014b); Schabereiter-Gurtner et al. (2003); (septicemia, food poisoning) Qiu et al. (2014) Streptococcus Saprophytes in soil and water, commensals R. microplus scapularis Andreotti et al. (2011); Benson et al. (2004) of skin and mucosal surfaces, opportunistic pathogens (septicemia, meningitis, pneumonia) Variovorax Soil bacterium associated with Data not found bioremediation processes Wolbachia Mutualistic bacteria of many insects and R. microplus scapularis ricinus Andreotti et al. (2011); Benson et al. (2004); Carpi nematodes et al. (2011); van Overbeek et al. (2008)

Table modified from Razzauti et al. (2015). *These genera were previously identified as contamination of DNA extraction kits reagents and ultrapure water systems, which may lead to erroneous identifications in bacterial assignments (Salter et al., 2014). metatranscriptomics (4,185,346 Uniprot entries in February 2017) 2.6. Validation of tick bacterial microbiota identifications by real-time PCR using the SEQUEST algorithm (Proteome Discoverer 1.4, Thermo Sci- entific). The following constraints were used for the searches: tryptic DNA from the same unfed larvae and female salivary glands samples cleavage after Arg and Lys, up to two missed cleavage sites, and tol- were used for validation of tick bacterial microbiota identification by erances of 1 Da for precursor ions and 0.8 Da for MS/MS fragment ions real-time PCR. Specific oliginucleotide primers for the bacteria and the searches were performed allowing optional Met oxidation and Rickettsia spp., Ehrlichia spp., Spotted Fever Group (SFG) Rickettsia, Cys carbamidomethylation. A false discovery rate (FDR) < 0.01 was Anaplasma spp., Anaplasma phagocytophilum, Borrelia spp., Wolbachia considered as condition for successful peptide assignments and at least spp., Candidatus Midichloria mitochondrii and Pseudomonas putida were two peptides per protein were the necessary condition for protein used (Table 1). The iScript One-Step was used to perform the real-time identification. After discarding Ixodidae assignations, peptides corre- PCR with SYBR Green and the iQ5 thermal cycler (Bio-Rad, Hercules, sponding to bacterial genera were grouped and the total number of CA, USA) following manufacturer's recommendations. A dissociation peptide spectrum matches (PSMs) for each bacterial genera were nor- curve was run at the end of the reaction to ensure that only one am- malized against the total number of PSMs (Supplementary file 3–Da- plicon was formed and that the amplicons denatured consistently in the taset 2). The gene ontology (GO) annotations for biological process (BP) same temperature range for every sample (Ririe et al., 1997). DNA le- were done in proteins identified in tick-borne pathogens (TBPs; Ana- vels were normalized against tick 16S rRNA and Ixodes rps4 genes fol- plasma, Borrelia, Ehrlichia and Rickettsia genera) according to Uniprot lowing the conditions previously reported by Zivkovic et al. (2009) and (http://www.uniprot.org) (Supplementary file 3–Dataset 2). Koči et al. (2013). Normalization was performed using the genNorm method (ddCT method as implemented by Bio-Rad iQ5 Standard Edi- tion, Version 2.0) (Livak and Schmittgen, 2001). 2.5. Phylogenetic and taxonomic abundance analyses 3. Results and discussion The metatrasncriptomics and metaproteomics bacteria relative abundance and taxonomic analyses were done using a Heatmap Tool 3.1. Metatranscriptomics bacteria identification in I. ricinus microbiota associated with a pruned phylogenetic tree generated with the platform phyloT (http://phylot.biobyte.de) based on NCBI taxonomy, and vi- The metatranscriptomics analysis identified a total of 450 reads that sualized using the Interactive Tree of Life software v3.4.3 (http://itol. matched with specific bacterial genomes distributed among 8 phyla and embl.de)(Letunic and Bork, 2011). A correlation analysis was con- 38 genera, including uncultured bacteria (Table 2, Fig. 2 and Supple- ducted in Microsoft Excel (version 12.0) between bacterial genera mentary file 2–Dataset 1). The most represented phyla identified were fi identi cation by metatranscriptomics (IDs) and metaproteomics Proteobacteria with 21 genera, followed by Actinobacteria and Firmi- ffi (PSMs). The Pearson's correlation coe cient was calculated using the cutes represented by 7 and 4 genera, respectively (Table 2). Other phyla ffi Pearson's Correlation Coe cient Calculator (http://www. such as Tenericutes, Spirochaetes, Fusobacteria and Bacteroidetes were socscistatistics.com/tests/pearson/Default2.aspx)(r = 0.5). also identified but with lower diversity (Table 2 and Fig. 2). As expected, most of the bacteria identified by metatranscriptomics have been previously described as apart of the microbiota in different tick

40 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 3. Comparative analysis of metranscriptomics and metaproteomics results. (A) Representation of the bacterial phyla with the corresponding number of genera (species) identified at RNA/protein levels. (B) Validation of integrated metatranscriptomics and metaproteomics results by PCR. Bacterial DNA levels were determined by real-time PCR and normalized against tick 16S rRNA and Ixodes rps 4. Oligonucleotide primers and real-time PCR conditions are described in Table 2. DNA levels are shown in arbitrary units as normalized Ct values. Two experiments were conducted with similar results. (C) Correlation analysis was conducted between normalized number of count reads or identifications (IDs; metatranscriptomics) and peptide spectrum matches (PSMs; metaproteomics) results (Table 2) corresponding to all bacterial genera (N = 38), Proteobacteria (N = 21), Actinobacteria (N = 7) and Firmicutes (N = 4) phyla represented by 4 genera or more, and TBPs (genera Anaplasma, Ehrlichia, Rickettsia, and Borrelia). The linear correlation coefficients (R2) and Pearson correlation coefficients (*r > +0.5) are shown. For reference, Streptococcus and Anaplasma genera are shown in black and red rhombuses, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) species (Table 3). However, other genera such as Actinomyces, Amyco- (Supplementary file 3–Dataset 2). Metaproteomics not only provided latopsis, Bidifidobacterium, Giliamella, Kurthia, Mesorhizobium and Var- support to metatranscriptomics results by identifying 87% of the iovorax have not been previously reported in ticks (Table 3). Identified identified bacterial genera (Table 2), but also increased bacteria iden- bacteria included tick endosymbionts such as Candidatus Midichloria, tification for different genera (Fig. 3A, Supplementary file 2–Dataset 1 Wolbachia, Francisella, Spiroplasma and Rickettsiella, commensals such as and Supplementary file 3–Dataset 2). However, metaproteomics may Escherichia, Staphylococcus and Streptococcus, soil and environmental results in some peptide assignments that could match to several related microorganisms such as Acinetobacter, Arthrobacter, Bradyrhizobium, species, which requires further analyses with amino acid sequences of Sphingomonas and Pseudomonas, human pathogens such as Brucella and peptides used for protein identity for better definition at the species Enterococcus, and TBPs such as Anaplasma, Ehrlichia, Rickettsia, Neor- level (Tanca et al., 2013, 2014; Fernández de Mera et al., 2017). ickettsia and Borrelia (Tables 2 and 3 and Fig. 2). Among the newly Therefore, the integration of metaproteomics with metatranscriptomics identified bacteria in tick microbiota, commensal (Actinomyces, Bidifi- provides a better resolution at the species level. For example, although dobacterium, Giliamella) and soil (Amycolatopsis, Kurthia, Mesorhizobium several Rickettsiella spp. were identified at metatranscriptomics and and Variovorax) microorganisms were present (Tables 2 and 3 and metaproteomics levels, only a Rickettsiella endosymbiont of Ixodes spp. Fig. 2). was identified by both analyses (Supplementary file 2–Dataset 1 and Supplementary file 3–Dataset 2). These bacteria are closely related to pathogenic Rickettsiella spp. (Cordaux et al., 2007), and the metaomics 3.2. Integration of metatranscriptomics and metaproteomics approaches approach provided a better support for the presence of Rickettsiella endosymbionts in the I. ricinus microbiota. Differences in bacterial The metatranscriptomics and metaproteomics results were in- identification by metranscriptomics and metaproteomics may be also tegrated to provide a metaomics approach to bacteria identifications in due to differences in RNA and protein levels (Fig. 2 and Table 2), which the I. ricinus microbiota. A total of 10,845 PSMs were assigned to dif- could be affected by post-transcriptional and post-translational ferent bacterial genera present in the metatranscriptomics database

41 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 4. Protein annotation in identified tick-borne pathogens. The GO analysis for BP was conducted on proteins identified in TBPs (Anaplasma, Borrelia, Ehrlichia and Rickettsia genera). Quantitative representation of BP abundance (%) was done using the total number of PSMs represented on each BP. modifications (Fan et al., 2013; Ayllón et al., 2015; Villar et al., 2015). in unfed I. ricinus. These commensal and environmental bacteria (Fir- This suggestion was supported by the multiple bacterial proteins that micutes) and TBPs may interact to affect multiple processes in the tick were identified by metaproteomics when compared to results of meta- such as tick fitness, nutrition, development, reproduction, defense transcriptomics analysis (Supplementary file 2–Dataset 1 and Supple- against environmental stress, immunity and vector competence mentary file 3–Dataset 2). Additional support to integrated metatran- (Narasimhan and Fikrig, 2015; Bonnet et al., 2017; de la Fuente et al., scriptomics and metaproteomics results was provided at the DNA level 2017; Abraham et al., 2017). by PCR(Fig. 3B). The GO analysis for BP was conducted on proteins identified in TBPs (Anaplasma, Borrelia, Ehrlichia and Rickettsia genera) (Supplementary file 3–Dataset 2) that showed a positive correlation between meta- 3.3. Putative functional implications of integrated metaomics results transcriptomics and metaproteomics results (Fig. 3C). The results showed that excluding unknown proteins, energy metabolism was the This study is a “proof-of-concept” for the metaomics approach to most abundant BP in all bacterial genera (Fig. 4). Another BP re- tick microbiome characterization. Integrated metatranscriptomics and presented in all bacteria was protein synthesis (Fig. 4). Finally, BPs metaproteomics results were functionally more relevant than those represented in some but not all bacterial genera included regulation of obtained by metranscriptomics alone, suggesting that identified bac- tick host gene expression, bacteria-tick interactions, DNA replication teria might form part of the active microbial community in unfed I. and transcription, DNA repair, nucleoside metabolism, cell wall bio- ricinus.Differences in bacterial microbiota composition have been at- synthetic process, cell division, and motility (Fig. 4). These results tributed to variations between tick species, collection sites, sex and further supported that some of the identified bacteria may be meta- developmental stages, feeding status, and pathogen infection (Williams- bolically active, and involved in tick-bacteria interactions. Newkirk et al., 2014; Van Treuren et al., 2015; Bonnet et al., 2017; The I. ricinus ticks used in this study were obtained from an unin- Abraham et al., 2017; Xiang et al., 2017). A correlation analysis be- fected reference laboratory colony. Then, why TBPs such as A. phago- tween metatranscriptomics and metaproteomics results revealed the cytophilum and Borrelia spp. were identified by integrated metatran- absence of correlation for the entire bacterial microbiome, and for scriptomics and metaproteomics analysis? Two possible responses to certain phyla such as Proteobacteria and Actinobacteria (Fig. 3C). this question are (a) that the colony may be infected with transovarially However, for Firmicutes and TBPs a positive correlation was obtained transmitted TBPs or (b) that although these bacteria were identified as between normalized RNA IDs and protein PSMs (Fig. 3C). Most of the TBPs, they may represent non-pathogenic genetic variants of these metatranscriptomics data corresponded to rRNA, which is the pre- pathogens. In support to the last suggestion, it has been shown the dominant material in the ribosome and essential for protein synthesis presence of non-pathogenic species and/or variants in both A. phago- (Cole et al., 2003). Therefore, a positive correlation between rRNA and cytophilum and Borrelia spp. (Anderson et al., 1990; Massung et al., protein levels may reflect that these bacteria were metabolically active

42 A. Hernández-Jarguín et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

2002, 2003 ; Portillo et al., 2005; Al-Khedery and Barbet, 2014; Stokes Appendix A. Supplementary data et al., 2016). Nevertheless, their role as commensals or pathogenic potential is unknown. Supplementary data associated with this article can be found, in the Recently, Abraham et al. (2017) demonstrated that A. phagocyto- online version, at https://doi.org/10.1016/j.ttbdis.2018.04.020. philum manipulates tick microbiota through induction of I. scapularis antifreeze glycoprotein (IAFGP) that results in alteration of bacterial References biofilm formation to facilitate infection. Additionally, they showed that A. phagocytophilum alters the composition of the tick microbiota after Abraham, N.M., Liu, L., Jutras, B.L., Yadav, A.K., Narasimhan, S., Gopalakrishnan, V., midgut infection (Abraham et al., 2017). Based on our results and using Ansari, J.M., Jefferson, K.K., Cava, F., Jacobs-Wagner, C., Fikrig, E., 2017. 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The authors declare that there are no con icts of interest. de la Fuente, J., Antunes, S., Bonnet, S., Cabezas-Cruz, A., Domingos, A.G., Estrada-Peña, A., Johnson, N., Kocan, K.M., Mansfield, K.L., Nijhof, A.M., Papa, A., Rudenko, N., Villar, M., Alberdi, P., Torina, A., Ayllón, N., Vancova, M., Golovchenko, M., Grubhoffer, L., Caracappa, S., Fooks, A.R., Gortazar, C., Rego, R.O.M., 2017. Tick- Acknowledgments pathogen interactions and vector competence: identification of molecular drivers for tick-borne diseases. Front. Cell. Infect. Microbiol. 7, 114. We thank Raquel Tobes and Marina Manrique (Oh no sequences! Evans, V.C., Barker, G., Heesom, K.J., Fan, J., Bessant, C., Matthews, D.A., 2012. De novo derivation of proteomes from transcriptomes for transcript and protein identification. Research group, Era7 Bioinformatics, Granada, Spain) for technical Nat. Methods 9, 1207–1211. assistance with the metatranscriptomics analysis. This work was fi- Fan, Y., Thompson, J.W., Dubois, L.G., Moseley, M.A., Wernegreen, J.J., 2013. Proteomic nancially supported by the H2020 COllaborative Management Platform analysis of an unculturable bacterial endosymbiont (Blochmannia) reveals high – for detection and Analyses of (Re-) emerging and foodborne outbreaks abundance of chaperonins and biosynthetic enzymes. J. Proteome Res. 12, 704 718. Fernández de Mera, I.G., Ruiz-Fons, F., Mangold, K.J., Gortázar, C., de la Fuente, J., 2013. in Europe (COMPARE) Grant 643476. MV was supported by the Spotted Fever Group rickettsiae in questing ticks, Central Spain. Emerg. Infect. Dis. Research Plan of the University of Castilla- La Mancha(UCLM), Spain. 19, 1163–1165.

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45 46 Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi

47 48 &KDSWHU,,

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Contents lists available at ScienceDirect

Ticks and Tick-borne Diseases

journal homepage: www.elsevier.com/locate/ttbdis

Original article Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi ⁎ Sandra Díaz-Sáncheza, ,1, Angélica Hernández-Jarguína,1, Alessandra Torinab, Isabel G. Fernández de Meraa, Valeria Blandab, Santo Caracappab, Christian Gortazara, José de la Fuentea,c a SaBio, Instituto de Investigación en Recursos Cinegéticos IREC-CSIC-UCLM-JCCM, Ronda de Toledo s/n, 13005, Ciudad Real, Spain b Intituto Zooproﬁlattico Sperimentale della Sicilia, Via G. Marinuzzi no3, 90129, Palermo, Italy c Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, OK, 74078, USA

ARTICLE INFO ABSTRACT

Keywords: Exploring the microbial diversity of ticks is crucial to understand geographical dispersion and pathogen trans- Microbiota mission. Tick microbes participate in many biological processes implicated in the acquisition, maintenance, and Tick transmission of pathogens, and actively promote host phenotypic changes, and adaptation to new environments. Shotgun-metagenomics The microbial community of Ixodes ventalloi still remains unexplored. In this study, the bacterial microbiota of Vector wild-caught I. ventalloi was characterized using shotgun-metagenomic sequencing in samples from unfed adults collected during December 2013-January 2014 in two locations from Sicily, Italy. The microbiota identiﬁed in I. ventalloi was mainly composed of symbiotic, commensal, and environmental bacteria. Interestingly, we identi- ﬁed the genera Anaplasma and Borrelia as members of the microbiota of I. ventalloi. These results advance our information on I. ventalloi microbiota composition, with potential implications in tick-host adaptation, geographic expansion, and vector competence.

1. Introduction Traditionally, tick-endosymbionts and their cooperative relationship have received most of the researchers´ attention (Cowdry, 1925; Ticks and tick-borne diseases represent a major health issue Ahantarig et al., 2013; de la Fuente et al., 2016; Duron et al., 2017). (Jongejan and Uilenberg, 2004). Scientists around the world are con- Nevertheless, the diversity of the tick-associated microbiota is high, and cerned about how the increase of human activities, fragmentation of microbes acquired from the environment likely have an impact in di- wildlife habitat and global warming have incremented the opportu- verse metabolic networks, stimulate microbial alliances, and host nities for ticks to expand, colonizing new territories, and increase the functions (Zolnik et al., 2016; de la Fuente et al., 2016, 2017). In recent contacts with new hosts (Allen et al., 2003; Dantas-Torres, 2015; years, the interest on “who” of the tick microbes and “how” they are Estrada-Peña et al., 2014; Narasimham and Fikring, 2015). Microbes implicated in vector competence (i.e. a component of vectorial capacity form part of the tick hologenome, and thus participate in many biolo- that depends on genetic factors aﬀecting the ability of a vector to gical processes directly linked to vector eco-epidemiological dynamics transmit a pathogen) is growing to better design tick-borne diseases (Ahantarig et al., 2013; Clay and Fuqua, 2010; Narasimhan et al., 2014; management strategies (Greay et al., 2018). Bonnet et al., 2017). One of the key aspects for researchers is to deci- So far, the tick-associated microbiota of the genus Ixodes (Linnaeus, pher how the tick microbial community assists and modulates the ac- 1758) has only been described in the species I. scapularis, I. ricinus, I. quisition, maintenance, and transmission of pathogens (Carpi et al., persulcatus, I. pavlovskyi and I. ovatus by 16S amplicon sequencing stu- 2011; de la Fuente et al., 2017; Abraham et al., 2017). Recent trends in dies (Qiu et al., 2014; Eshoo et al., 2015; Kurilshikov et al., 2015; metagenomics have permitted to explore the implication of tick-asso- Rynkiewicz et al., 2015; van Treuren et al., 2015; Sui et al., 2017; Clow ciated microbiota in the development of adaptive skills. One of the keys et al., 2018; Greay et al., 2018; Hernández-Jarguín et al., 2018). is the rapid adaptation of microbes to new environments, accelerating However, the tick-associated microbiota of many Ixodes species has not host phenotypic changes, which in turn facilitates adaptation (Brucker been described yet. and Bordenstein, 2013; de la Fuente et al., 2016; Sevellec et al., 2018). The rabbit tick, I. ventalloi (Gil Collado, 1936) is one of those Ixodes

⁎ Corresponding autor. E-mail address: [email protected] (S. Díaz-Sánchez). 1 These authors contributed equally to the work reported in this paper. https://doi.org/10.1016/j.ttbdis.2018.11.014 Received 25 June 2018; Received in revised form 10 October 2018; Accepted 15 November 2018 $YDLODEOHRQOLQH1RYHPEHU ;(OVHYLHU*PE+$OOULJKWVUHVHUYHG

49 S. Díaz-Sánchez et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ² species whose associated microbiota is still unexplored. From decades, DNA was extracted using the NucleoSpin TriPrep kit (Macherey-Nagel, medical relevance of I. ventalloi has been questioned as originally this Duren, Germany) according to the manufacturer’s instructions and tick was restricted to parasitize the European wild rabbit (Oryctolagus quantified using PicoGreen (Invitrogen, Carlsbad, CA, USA) for tem- cuniculus). However, recent reports have warned about the potential plate ranges of 4 ng to 100 ng. An aliquot of each pooled replicate was vector competence of I. ventalloi to transmit pathogens (Santos et al., adjusted to the same DNA concentration and exhaustively fragmented 2004; Latrofa et al., 2017; Huber et al., 2017), adapt to new hosts using a BioRuptor (Diagenode, Inc, NJ, USA). Libraries were prepared (Santos Dias and Santos-Reis, 1989; Otranto et al., 2014; Pennisi et al., using the Ultra DNA library preparation kit (New England Biolabs, 2015; Latrofa et al., 2017), and geographic expansion (Chastel et al., Ipswich, MA, USA) according to the manufacturer´s instructions. 1984; Petney et al., 1996; Jameson and Medlock, 2011; Santos-Silva Pooled samples were indexed using a unique combination of primers et al., 2006 2011). Novel whole-genome shotgun metagenomics can be provided by the manufacturer. After library preparation, DNA library used to characterize the microbiota composition of tick vectors by se- was purified in 1.5% agarose gels to select fragments with sizes within quencing the genome of all microorganisms present in the sample 150–400 bp, quantified with the Bioanalyzer TM 2100 (Agilent Tech- (Carpi et al., 2011). nologies, Palo Alto, CA, USA) and then pooled in equimolar con- Herein, we present the first characterization of the bacterial mi- centrations. In addition, to prepare for cluster generation and sequen- crobiota of adult wild-caught I. ventalloi. Specifically, we used a cing, equal volumes of normalized libraries were combined, diluted in methodology based on Illumina-technology as an attempt to broad hybridization buffer and heat denatured, according to Illumina protocol screen the bacterial microbiota composition and the load of common (Illumina, Inc, San Diego, CA, USA). Finally, pair-end sequencing was tick-borne pathogens of I. ventalloi. Finally, the tick bacterial microbial- performed on the HiSeq2000 platform (Illumina, San Diego, CA, USA) rich lineages that compose the putative microbiota of I. ventalloi will using the TruSeq reagent kit (2 × 100 bp) according to the manu- provide insights into potential host-microbiome interactions partici- facturer´s instructions (Fig. 1). pating in vector competence. 2.3. Sequencing data analysis 2. Material and methods For assembly of the metagenomics reads, raw data files from 2.1. Sample collection and study model shotgun sequencing were de-multiplexed, filtered by quality and converted to fastq using Casava v.1.8.2 (Illumina). High-quality reads were Unfed host-seeking adult female I. ventalloi ticks were collected processed using a metagenomic de novo assembly approach. First, a during December 2013 and January 2014 from two localities in Sicily filtering analysis was performed in order to remove the arthropod (Italy), within the Natural Area of Monte Pellegrino. Monte Pellegrino is reads. a natural reserve that extends over an area of 1300 ha bordering Raw reads from I. ventalloi were mapped using Bowtie2 software Palermo and extending to the Mediterranean Sea, characterized by a (http://bowtie-bio.sourceforge.net/index.shtml) (Langmead et al., Mediterranean climate and a diverse flora and fauna (Raimondo and 2009) against Ixodes scapularis complete genome assembled Venturella, 1993a,b;Surano et al., 1993). The two localities selected for (VectorBase, https://www.vectorbase.org). All the unmapped reads tick collection, Boschetto Airoldi (Location 1; Long 13.35141°, Lat remaining after vector filtering (14 to 22 million reads per sample re- 38.14946°, 35 m a.s.l.) and Castello Utveggio (Location 2; Lon presenting 24–28% of the total; Supplementary file 1: TS2) were as- 13.35469,º Lat 38.15640°, 280 m a.s.l.), can be described as an artificial sembled using SPADES with a minimum contig length of 200 bp forest of pine and eucalyptus and the ubiquitous presence of rabbits (http://bioinf.spbau.ru/spades). The quality of genome assemblies (Oryctolagus cuniculus, Linnaeus, 1758), rodents (Rattus norvegicus, corresponding to the I. ventalloi metagenome was further evaluated Berkenhout, 1769) and some species of Canidae (Torina et al., 2018). using QUAST (http://bioinf.spbau.ru/quast)” (Supplementary file 1: One thousand and twenty-five total ticks were collected from these FS1). And finally were searched and annotated against a microbial two locations. Collection of host-seeking ticks was conducted from ve- database previously constructed with bacterial sequences of species- getation using a 1 m2 flannel drag cloth that was dragged along the specific ribosomal RNA (rRNA) sequences downloaded from the NCBI floor and surrounding vegetation. Ticks were identified to the genus (https://www.ncbi.nlm.nih.gov) (Supplementary file 1: TS1). The and species levels by using a steromicroscope, according to the standard bioinformatics approach to identify bacterial sequences was done in keys given by Manilla (1998). A total of 125 ticks were finally washed two steps. First, we used LAST genome-scale sequence comparison tool in 70% ethanol and sterile distilled water to reduce background con- (http://last.cbrc.jp) to search against the bacterial database previously tamination. Whole ticks were then cut and crushed with a sterile constructed. As cut off criteria for LAST we applied minimum alignment scalpel, combined with lysis buffer and shaken on a vortex with glass length of 100 nt, e-value 0.001, with a word size of 11, and a minimum beads. Pooling individual I. ventalloi ticks was required to obtain good of 70% sequence identity. Then, putative bacterial reads detected with DNA extraction yields. Finally, ticks were grouped according to location LAST were further confirmed by BLAST (https://blast.ncbi.nlm.nih. for subsequent analysis. In this project, we aim to identify unique and gov/Blast.cgi?PAGE_TYPE = BlastSearch) (Frith et al., 2010a, b; common taxa in a particular tick species, so two biological replicates Kiełbasa et al., 2011). BLAST assignments were done by using the 10 were taken on each location for consistently obtain biological in- best BLAST hits (BBH) for each putative bacteria previously assigned by formation (Fig. 1). LAST. The sequences with hits matching to bacteria were confirmed as identified bacterial sequences, discarding the rest (Fig. 1). 2.2. Performance of whole-genome shotgun metagenomics sequencing The microbiota composition was reported in terms of relative abundance of the taxa identified, and defined by the proportion of reads Genomic DNA was extracted from two biological replicates of mapping to the microbial bacteria database previously constructed pooled samples with a total of 100 tick specimens for Location 1, and 25 (Supplementary file 1: TS1). Taxonomic classification was based on tick specimens for Location 2. Finally two biological replicates with 50 multigene assignments, 16S rRNA and intergenic regions. The taxo- pooled specimens each for Location 1, and 12 and 13 pooled specimens nomic relative abundance was calculated as the total number of reads each for Location 2 were used for further analysis (Fig. 1). Whole ticks mapping to each bacteria genome, and normalized against the total were ground and pulverized in liquid nitrogen and homogenized using number of reads. The taxonomic relative abundance was assessed se- a glass homogenizer (20 strokes) in 4 ml buffer (0.25 sucrose, 1 mM parately for each biological replicate and for each location. Finally,

MgCl2, 10 mM Tris−HCl, pH 7.4), supplemented with 4% SDS and bacterial taxonomic assignments at phyla and genus level were ranked complete mini protease inhibitor cocktail (Roche, Basel, Switzerland). based on the ratio of the average relative abundance across biological

50 S. Díaz-Sánchez et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 1. Workﬂow scheme for I. ventalloi microbiota identiﬁcation.

51 S. Díaz-Sánchez et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ² replicates for each location (Fig. 1; Supplementary file 2: Dataset S1). Table 1 Further classification of bacterial taxa at genus level was done as fol- Bacterial microbiota composition of I. ventalloi. Genus level composition is re- lows: genera were classified as major members of the microbiota when ported in terms of average percentage of relative abundance. the ratio of taxonomic relative abundance was ≥ 1.00%. As well, MAJOR MICROBIOTA* genera were classified as minor members of the microbiota when the ratio of taxonomic relative abundance was between 0.10 and 1.00%. Phylum Family Genus Relative The bacterial genera that represented less than a 0.10% in the micro- abundance (%) biota can be found as well, in Table 1. Actinobacteria Corynebacterineae Rhodococcus 9.89 To test differential abundance of the taxonomic assignments within Geodermatophilaceae Blastococcus 1.29 each location, the biological replicates were compared on the basis of Mycobacteriaceae Mycobacterium 10.68 univariate statistical comparisons by a Mann-Whitney/Kruskal-Wallis Nocardioidaceae Aeromicrobium 2.60 Propionibacteriaceae Cutibacterium 14.76 analysis (adjusted p-value cut-off 0.05). Finally, the putative microbiota Pseudonocardiaceae Amycolatopsis 2.02 to both locations were compared by ANOSIM analysis and Bray-Curtis Actinosynema 1.00 dissimilarity that were performed at genus level using the software R Pseudonocardia 1.21 v.1.1.38, and the anosim function within the vegan package. Tick-as- Tsukamurellacea Tsukamurella 2.07 sociated microbiota composition was visually assayed with pruned Bacteroidetes Bacteroidaceae Bacteroides 2.52 Proteobacteria Anaplasmataceae Anaplasma 7.48 cladograms displayed using the open source platforms PhyloT based on Caulobacteraceae Brevundimonas 2.15 the NCBI taxonomy names (http://phylot.biobyte.de)andiTOL Comamonadaceae Variovorax 2.10 (https://itol.embl.de) open source platforms. Enterobacteriaceae Escherichia 7.75 Moraxellaceae Moraxella 2.67 Pseudomonadaceae Pseudomonas 2.82 3. Results and discussion Rickettsiaceae Rickettsia 9.29 Sphingomonadaceae Sphingomonas 2.13 All the data obtained from the shotgun metagenomic sequencing of Spirochaetes Spirochaetaceae Borrelia 1.64 whole tick bodies contributed to structure the microbiota composition MINOR MICROBIOTA** of I. ventalloi including pathogenic symbiotic and non-pathogenic bac- Phylum Family Genus Relative abundance (%) teria. Actinobacteria Conexibacteraceae Conexibacter 0.36 The bacterial microbiota analysis of the pooled samples represented Dermabacteraceae Brachybacterium 0.29 a total of 125 I. ventalloi ticks, which were further processed and se- Dietziaceae Dietzia 0.46 quenced. After the run, sequences were demultiplexed and filtered by Microbacteriaceae Clavibacter 0.07 Micrococcaceae Arthrobacter 0.21 quality, which finally rendered about 250 millions pair-end reads per Rothia 0.26 sample. Overall, 73,976,098 million pair-end reads remained after Micromonosporaceae Actinoplanes 0.97 vector filtering, cleaning and quality processing. Unidentified reads Micromonospora 0.43 were not included in the analysis, and 1–6704 reads (average ± SE, Propionibacteriaceae Propionibacterium 0.23 124 ± 32 Location 1, 229 ± 71 Location 2) were used for genera Firmicutes Bacillaceae Bacillus 0.21 Erysipelotrichaceae Erysipelothrix 0.10 classification (Supplementary file 2: Dataset S1). Finally, the metage- Lachnospiraceae Roseburia 0.14 nomics sequencing revealed that the bacteria taxonomic composition of Peptoniphilaceae Anaerococcus 0.06 all dataset included a total of 6 phyla, 71 families and 107 genera Finegoldia 0.12 (Supplementary file 2: Dataset S1). The phyla Actinobacteria (48.95%) Streptococcaceae Streptococcus 0.53 and Proteobacteria (44.83%) were predominant over the phyla Veillonellaceae Veillonella 0.10 Tenericutes Spiroplasmataceae Spiroplasma 0.41 Bacteroidetes (2.59%), Firmicutes (1.92%) and Spirochaetes (1.64%), Proteobacteria Acetobacteraceae Roseomonas 0.30 meanwhile the phyla Tenericutes (0.41%) was scarcely represented. Aeromonadaceae Aeromonas 0.14 The differential abundance analysis showed no statistical differ- Alcaligenaceae Achromobacter 0.39 ences between the biological replicates composing each location Bordetella 0.10 Bradyrhizobiaceae Afipia 0.18 (Mann-Whitney/Kruskal-Wallis, p > 0.05). This result was supported Bradyrhizobium 0.44 by the ANOSIM and Bray-Curtis dissimilarities analysis performed at Rhodopseudomonas 0.24 the genus level (R= -0; p = 0.66) which revealed a similar microbiota Burkholderiaceae Burkholderia 0.19 composition, leading us to speculate with the existence of a putative Ralstonia 0.16 microbiota present in wild-caught I. ventalloi. Whether or not geo- C. Midichloriaceae C. Midichloria 0.22 Caulobacteraceae Caulobacter 0.80 graphic location and/or environment are important drivers of differ- Comamonadaceae Acidovorax 0.85 ences in the microbiota of I. ventalloi needs to be further addressed. Enterobacteriaceae Enterobacter 0.17 Within the putative microbiota observed, the major members of the Klebsiella 0.24 microbiota included 17 families and 19 genera (Table 1; Fig. 2A). And a Salmonella 0.11 Moraxellaceae Acinetobacter 0.66 total of 31 families and 43 genera composed the so-called minor Pasteurellaceae Mannheimia 0.23 members of the microbiota (Table 1; Fig. 2B). Phyllobacteriaceae Mesorhizobium 0.31 Among all microbiota members of I. ventalloi, the bacteria genera Rhizobiaceae Agrobacterium 0.25 Anaplasma and Borrelia were detected within the major microbiota Rhizobium 0.23 (Table 1; Fig. 2A). These dominant genera includes from species that Sinorhizobium 0.31 Rhodobacteraceae Rhodobacter 0.14 are the causative agents of diseases such as human granulocytic ana- Rhodocyclaceae Thauera 0.11 plasmosis (A. phagocytophilum), Lyme borreliosis (B. burgdorferi s.l.), Novosphingobium 0.43 and tick-borne relapsing fever (e.g. B. miyamotoi)(Estrada-Peña et al., Xanthomonadaceae Pseudoxanthomonas 0.49 2012; Breuner et al., 2017), to non-pathogenic species and species of Stenotrophomonas 0.30 LESS REPRESENTED BACTERIA*** undetermined pathogenicity (Anderson et al., 1990; Portillo et al., Phylum Family Genus Relative 2005; Margos et al., 2011;2014). I. ventalloi is not the classic vector of abundance Anaplasma and Borrelia, despite previous studies suggest that I. ventalloi (%) might be a suitable host and somehow takes part in their eco-epide- (continued on next page) miological cycles, or even cooperate for a mutual benefit (Santos et al.,

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Table 1 (continued) ticks (Qiu et al., 2014; Narasimhan et al., 2015; Van Treuren et al., 2015; Sui et al., 2017; Clow et al., 2018). Members of the genus Pseu- * MAJOR MICROBIOTA domonas have been linked to the maintenance of the midgut redox Actinobacteria Actinomycetaceae Actinomyces 0.02 homeostasis in blood-feeding insects (Alvarez et al., 2012). Following Bifidobacteriaceae Bifidobacterium 0.05 these abundant symbionts, we detected much lower abundance of the Micrococcaceae Haematomicrobium 0.05 genera Acinetobacter and Spiroplasma,(Table 1; Fig. 2B). Both genera Bacteroidetes Flavobacteriaceae Flavobacterium 0.03 are frequently reported in Ixodes i.e., I. ovatus, I. persulcatus, I. ricinus Chitinophagaceae Niastella 0.01 Sphingobacteriaceae Pedobacter 0.02 and I. pavlovskyi (Tully et al., 1981; Taylor et al., 2015; Qiu et al., 2014; Prevotellaceae Prevotella 0.01 Bell-Sakyi et al., 2015; Kurilshikov et al., 2015), as facultative sym- Cytophagaceae Runella 0.01 biotic bacteria (Lysyk et al., 1999; Qiu et al., 2014; Moutailler et al., Deinococcus- Deinococcaceae Deinococcus 0.03 2016). However their lifestyle is controversial and switches from Thermus commensals and symbiotic lifestyle to become in pathogenic to human Firmicutes Bacillaceae Anoxybacillus 0.04 Clostridiaceae Clostridium 0.01 and animals (Henning et al., 2006; Lo et al., 2015). Lachnospiraceae Dorea 0.01 Interestingly, we also observed the obligate intracellular tick en- Enterococcaceae Enterococcus 0.06 dosymbiont Candidatus Midichloria mitochondrii but represented by Listeriaceae Listeria 0.01 very low rates within the minor members of the microbiota (Table 1; Veillonellaceae Megasphaera 0.02 Fig. 2B). This intracellular symbiont is well-described in I. ricinus Staphylococcaceae Staphylococcus 0.09 Leuconostocacceae Weissella 0.01 (Sassera et al., 2006). However, in other hard ticks the presence of this Ruminococcaceae Faecalibacterium 0.02 symbiotic bacterium is very variable or absent (Epis et al., 2008; Fusobacteria Fusobacteriaceae Fusobacterium 0.01 Beninati et al., 2009). In field-collected I. ricinus females a 100% pre- Proteobacteria Campylobacteraceae Arcobacter 0.02 valence of C. Midichloria mitochondrii was found in ovarian tissues, Acetobacteraceae Asaia 0.01 Caulobacteraceae Asticcacaulis 0.04 however, in males the prevalence decrease notably across maturation Bartonellaceae Bartonella 0.01 (Lo et al., 2006; Njam et al., 2012). Therefore, the low presence of C. Brucellaceae Brucella 0.01 Midichloria mitochondrii observed in our study might be explained by Pseudomonadaceae Cellvibrio 0.001 the effect of pooling male and female ticks, which likely reduced the Enterobacteriaceae Citrobacter 0.02 chances to identify this bacterium. Nevertheless, the association of this Coxiellaceae Coxiella 0.01 Anaplasmataceae Ehrlichia 0.04 bacterium with I. ventalloi should be further explored before conjecture Erwiniaceae Erwinia 0.04 any symbiotic interaction. Francisellaceae Francisella 0.002 Other genera composing the minor microbiota included some pa- Acetobacteraceae Gluconobacter 0.01 thogenic species for animals and humans, including Bordetella, Legionellaceae Legionellaceae 0.01 Myxococcaceae Myxococcus 0.04 Klebsiella, Salmonella, Erysiphelotrix, Mannheimia,andStreptococcus Pasteurellaceae Pasteurella 0.003 (Table 1; Fig. 2B). Most of these genera are common members of adult Morganellaceae Proteus 0.004 tick´s midgut microbiota, and their presence is coherent with co-in- Providencia 0.01 fections likely acquired from the environment and/or from their on- Yersiniaceae Serratia 0.07 host feeding habits (Carpi et al., 2011; Otranto et al., 2014; Pennisi Enterobacteriaceae Shimwella 0.001 Polyangiaceae Sorangium 0.07 et al., 2015). But at the same time, tick´s co-infection can be linked to Vibrionaceae Vibrio 0.003 co-transmission of these pathogens to both human and animals, which Rickettsiaceae Wolbachia 0.004 might result in disease severity and/or interfere in diagnosis (Moutailler Xanthomonadales Xylella 0.01 et al., 2016). Yersiniaceae Yersinia 0.01 Sphingomonadaceae Zymomonas 0.01 We detected the dominant genus Cutibacterium (formerly Spirochaetes Spirochaetaceae Treponema 0.05 Propionibacterium) at high rates within the major microbiota (Table 1; Fig. 2B). Colonization of ticks by skin bacteria from humans is a * Major Microbiota composition of I. ventalloi. Genus level composition is common finding in the microbiota of blood-feeding arthropods. In reported in terms of average relative abundance with values of ≥ 1.00%. particular, Cutibacterium resides in the skin sebaceous glands skin re- ** Minor Microbiota composition of I. ventalloi. Genus level composition is leasing volatile molecules that result attractive to blood-feeding ar- reported in terms of average relative abundance with values of 0.10%- 1.00%. thropods, that might conditionate host-seeking behavior (Niels et al., *** Less Represented Microbiota found in I. ventalloi. Genus level com- 2018). position is reported in terms of relative abundance with values of less than 0.10%. Besides pathogenic bacteria, naturally-occurring bacteria were detected within the minor microbiota, including the genera Bacillus, 2004; Tomassone et al., 2013; Bonnet et al., 2017). However, it should Bradyrhizobium, Novosphingobium, Agrobacterium, and Stenotrophomonas be noted that our analysis is not a reflection of the bacterial microbiota (Table 1; Fig. 2B). All these genera are frequent members of the soil and at species level. The major microbiota contained the genus Rickettsia as water microbial communities that colonize tick´s breeding sites, but well (Table 1; Fig. 2A). This genus is a common member of the Ixodes also these bacteria are described from contamination of DNA extraction midgut microbiota (Nakao et al., 2013; Vayssier-Taussat et al., 2013; kits (Narasimhan et al., 2014; Salter et al., 2014; Carpi et al., 2011). Naramsiham et al., 2014; Qiu et al., 2014; Hernández-Jarguín et al., However, the notion of this bacterium as true microbial partners and its 2018), and as a result of co-evolution it can show a dual behavior as colonization pathways is not well understood, as they can be selectively pathogen or endosymbiont (Carpi et al., 2011; Hunter et al., 2015; acquired commensals or occasional colonizers of tick´s midguts. Bonnet et al., 2017). It is evident that the presence of this bacterium within the microbiota might influence vector competence. Whether or 4. Conclusion not I. ventalloi is capacitated to maintain and transmit these pathogens it is a matter of debate. In this study we characterized the composition of bacterial micro- Other cohabiting members of the major microbiota detected, were biota in I. ventalloi field collected from two locations in Sicily, Italy. the ubiquitous genera, Sphingomonas and Pseudomonas (Table 1; Additionally, the putative microbiota of I. ventalloi was described using Fig. 2A). These genera have been previously catalogued with the status shotgun metagenomic methodology detecting pathogenic, symbiotic “symbiont”, due to the high rates of prevalence frequently observed in and non-pathogenic agents. The first identification of the tick bacterial microbiota is a fundamental step for further explore microbial

53 S. Díaz-Sánchez et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ²

Fig. 2. Unrooted phylogenetic tree showing the major (A) and the minor (B) microbiota identiﬁed in I. ventalloi. The phylogenetic tree was generated with PhyloT and visualized with iTOL.

54 S. Díaz-Sánchez et al. &KDSWHU,, 7LFNVDQG7LFNERUQH'LVHDVHV ² functionality and interactions. From the bacterial microbiota observed Washington DC, USA (Accessed 7 March 2018). http://www.iom.edu/∼/media/ in this study, many biological and epidemiological questions have Files/Activity%20Files/Disease/TickBorne/08-The-Tick-Microbiome.pdf. Cowdry, E.V., 1925. A group of microorganisms transmitted hereditarily in ticks and arisen, especially regarding “how” and “who” are implicated in I. ven- apparently unassociated with disease. J. Exp. Med. 41, 817–830. talloi capacity and vector competence, host adaptation and geographic Clow, K.M., Weese, J.S., Rousseau, J., Jardine, C.M., 2018. Microbiota of field-collected expansion, that will be interesting to approach in future tick micro- Ixodes scapularis and Dermacentor variabilis from eastern and southern Ontario, Canada. .Ticks Tick Borne Dis. 9, 235–244. biome research studies. Dantas-Torres, F., 2015. 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Microbiol. 7, 114. Duron, O., Binetruy, F., Noël, V., Cremaschi, J., McCoy, K.D., Arnathau, C., Plantard, O., Goolsby, J., Pérez de León, A.A., Heylen, D.J.A., Van Oosten, A.R., Gottlieb, Y., The authors declare that there are no conflicts of interest. Baneth, G., Guglielmone, A.A., Estrada-Peña, A., Opara, M.N., Zenner, L., Vavre, F., Chevillon, C., 2017. Evolutionary changes in symbiont community structure in ticks. Acknowledgements Mol. Ecol. 26, 2905–2921. Epis, S., Sassera, D., Beninati, T., Lo, N., Beati, L., Piesman, J., Rinaldi, L., McCoy, K.D., Torina, A., Sacchi, L., Clementi, E., Genchi, M., Magnino, S., Bandi, C., 2008. We thank Raquel Tobes and Marina Manrique (Oh no sequences! Midichloria mitochondrii is widespread in hard ticks (Ixodidae) and resides in the Research group, Era 7 Bioinformatics, Granada, Spain) for technical mitochondria of phylogenetically diverse species. Parasitol 135, 485–494. Eshoo, M.W., Carolanm, H.E., Massire, C., Chou, D.M., Crowder, C.D., Rounds, M.A., assistance with the metagenomics data analysis. This work was finan- Phillipson, C.A., Schutzer, S.E., Ecker, D.J., 2015. Survey of Ixodes pacificus ticks in cially supported by the H2020 Collaborative Management Platform for California reveals a diversity of microorganisms and novel and widespread detection and Analyses of (Re-) emerging and foodborne outbreaks in Anaplasmataceae species. PLoS One 10, e0135828. Europe (COMPARE) Grant 643476. We thank the Juan de la Cierva Estrada-Peña, A., Ayllón, N., de la Fuente, J., 2012. Impact of climate trends on tick-borne pathogen transmission. Front. Physiol. 3, 64. incorporación grants funded by Spanish Ministry of Economy, and the Estrada-Peña, A., Ostfeld, R.S., Peterson, A.T., Poulin, R., de la Fuente, J., 2014. Effects of Program for Teacher Development for the Superior Type (PRODEP, environmental change on zoonotic disease risk: an ecological primer. 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56 57 58 Combination of RT-PCR and proteomics for the identification of Crimean-Congo hemorrhagic fever virus in ticks

Fernández de Mera, I. G., Chaligiannis, I., Hernández-Jarguín, A., Villar, M., Mateos- Hernández, L., Papa, A., Sotiraki, S., Ruiz-Fons, F., Cabezas-Cruz, A., Gortázar, C., de la Fuente, J. (2017). Combination of RT-PCR and proteomics for the identification of Crimean-Congo hemorrhagic fever virus in ticks. Heliyon, 3(7):353.

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Received: 22 May 2017 Combination of RT-PCR and Revised: 1 July 2017 Accepted: proteomics for the 5 July 2017 Cite as: identification of Isabel G. Fernández de Mera, Ilias Chaligiannis, Angélica Hernández-Jarguín, Crimean-Congo hemorrhagic Margarita Villar, Lourdes Mateos-Hernández, Anna Papa, Smaragda Sotiraki, fever virus in ticks Francisco Ruiz-Fons, Alejandro Cabezas-Cruz, Christian Gortázar, José de la Fuente. Combination of RT-PCR and proteomics for a,1, b,1 the identification of Crimean- Isabel G. Fernández de Mera *, Ilias Chaligiannis , Congo hemorrhagic fever virus Angélica Hernández-Jarguín a,1, Margarita Villar a, Lourdes Mateos-Hernández a, in ticks. Heliyon 3 (2017) e00353. Anna Papa b, Smaragda Sotiraki c, Francisco Ruiz-Fons a, Alejandro Cabezas-Cruz d,e, doi: 10.1016/j.heliyon.2017. a a,f, e00353 Christian Gortázar , José de la Fuente * a SaBio. Instituto de Investigación de Recursos Cinegéticos, IREC-CSIC-UCLM-JCCM, 13005 Ciudad Real, Spain b Department of Microbiology, Medical School, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece c Veterinary Research Institute, Hellenic Agricultural Organisation-Demeter, NAGREF Campus, 57001 Thermi, Thessaloniki, Greece d Institute of Parasitology, Biology Center of the Academy of Sciences of the Czech Republic, 37005 České Budějovice, Czech Republic e Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic f Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, Oklahoma, USA

* Corresponding authors at: Instituto de Investigación de Recursos Cinegéticos, IREC-CSIC-UCLM-JCCM, 13005 Ciudad Real, Spain. E-mail addresses: [email protected] (I.G. Fernández de Mera), [email protected] (J. de la Fuente). 1 The first three authors contributed equally to the work reported in this paper.

Abstract

Crimean-Congo hemorrhagic fever (CCHF) is an emerging tick-borne zoonotic disease caused by the CCHF virus (CCHFV). In this study, an experimental approach combining RT-PCR and proteomics was used for the identification and characterization of CCHFV in 106 ticks from 7 species that were collected from small ruminants in Greece. The methodological approach included an initial

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Article No~e00353 screening for CCHFV by RT-PCR followed by proteomics analysis of positive and control negative tick samples. This novel approach allowed the identification of CCHFV-positive ticks and provided additional information to corroborate the RT- PCR findings using a different approach. Two ticks, Dermacentor marginatus and Haemaphysalis parva collected from a goat and a sheep, respectively were positive for CCHFV. The sequences for CCHFV RNA segments S and L were characterized by RT-PCR and proteomics analysis of tick samples, respectively. These results showed the possibility of combining analyses at the RNA and protein levels using RT-PCR and proteomics for the characterization of CCHFV in ticks. The results supported that the CCHFV identified in ticks are genetic variants of the AP92 strain. Although the AP92-like strains probably do not represent a high risk of CCHF to the population, the circulation of genetically diverse CCHFV strains could potentially result in the appearance of novel viral genotypes with increased pathogenicity and fitness.

Keywords: Infectious disease, Public health, Veterinary science, Evolution, Genetics, Virology

1. Introduction

Crimean-Congo hemorrhagic fever (CCHF) is an emerging tick-borne zoonotic disease causing sporadic cases or outbreaks of severe illness in humans (Ergonul, 2006; Bente et al., 2013; Papa et al., 2015a; Martina et al., 2017). CCHF is caused by the CCHF virus (CCHFV), which is distributed across a large geographic area from western China to the Middle East and Southeastern Europe and throughout most of Africa (Ergonul, 2006; Bente et al., 2013).

CCHFV is maintained in vertical and horizontal transmission cycles involving ixodid ticks and a variety of wild and domestic vertebrates, which do not show signs of illness but serve as reservoirs (Bente et al., 2013). The maintenance of active foci of CCHFV in the field may depend on tick survival, requiring favorable climatic conditions and high numbers of suitable hosts for adult ticks (Estrada-Peña et al., 2013). The virus circulates in a number of tick genera, but Hyalomma species are considered the principal source of human infection (Bente et al., 2013; Papa et al., 2015a). Humans could also be infected by contact with body fluids from infected viremic animals or patients (Bente et al., 2013; Papa et al., 2015a). CCHFV is considered the most genetically diverse of the arboviruses because it shows differences among genotypes ranging from 20% for the viral S segment to 31% for the M segment nucleotide sequences (Bente et al., 2013). Furthermore, different genotypes can be found within the same geographic area, while closely related genotypes have been isolated in geographically distant regions (Bente et al., 2013). Phylogenetic analysis of CCHFV from different locations suggests that the virus dispersed a long time ago, possibly by ticks carried on migratory birds or

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Article No~e00353 through the international livestock trade (Anagnostou and Papa, 2009; Bente et al., 2013; Papa et al., 2015a).

Currently, CCHFV is diagnosed by virus isolation, serology and molecular-based techniques. Virus isolation requires laboratory biosafety level (BSL-3/4), which is not available in many institutions. Serology is useful in epidemiological studies of infected hosts by detection of CCHFV-specific IgM or IgG antibodies, but detection of anti-CCHFV IgM in infected patients requires at least 3–5 days post infection, which may account for false negative results during early acute phase of infection (Aradaib et al., 2011). Molecular techniques based on RT-PCR are used for the amplification of viral genome segments (Midilli et al., 2009; Osman et al., 2013; Bente et al., 2013; Papa et al., 2014; 2015a). Most of the RT-PCR assays use a secondary nested amplification or nucleic acid hybridization to increase the sensitivity and to confirm the identity of the amplified PCR product (Aradaib et al., 2011; Osman et al., 2013).

However, despite virus amplification after multiplication in ticks feeding on a susceptible host (Dickson and Turell, 1992; Bente et al., 2013; de la Fuente et al., 2017), CCHFV identification in individual ticks may be difficult due to low infection levels and the presence of other Nairoviruses (Walker et al., 2015). In this regard, the proteomics approach using matrix assisted laser desorption ionization- time of flight mass spectrometry (MALDI-TOF MS) would provide additional information to corroborate the RT-PCR findings using a different approach, and assist in the differentiation between different CCHFV genotypes and Nairoviruses (Singhal et al., 2015) and the characterization of tick-virus molecular interactions (Papa et al., 2017).

In this study, a novel approach using the combination of RT-PCR and MALDI- TOF MS was used for the molecular identification and characterization of CCHFV in ticks collected from domestic ruminants in endemic areas of Greece.

2. Materials and methods

Partially fed ticks from 7 species (N = 106) were collected in 10 Greek counties from sheep and goats, which are considered the most suitable indicator animals for the circulation of CCHFV (Schuster et al., 2016)(Fig. 1 and Table 1). Collected ticks were classified (Manilla, 1998) and frozen until dissection of internal organs for analysis. Ticks were divided in two similar vertical halves to dissect internal organs for RNA and protein studies. Total RNA was extracted from one half ticks using TriReagent (Sigma, St. Louis, MO, USA) and following manufacturer recommendations. CCHFV RNA was amplified using a nested RT-PCR targeting virus S segment as previously described (Midilli et al., 2009). The amplicons were cloned and at least 3 clones were sequenced for each amplicon.

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Fig. 1. Tick sampling. Map of Greece showing counties where ticks were collected from sheep and goats. The sites where CCHFV-positive ticks were collected are shown in green.

For proteomics analysis, internal tissues were dissected from the another half of CCHV-infected (n = 2) and uninfected (n = 4) ticks, lysed with phosphate buffered saline (PBS) supplemented with 1% Triton X–100 and complete protease inhibitor cocktail (Roche, Basel, Switzerland), and homogenized by passing through a needle (27G). Samples were sonicated for 1 min in an ultrasonic cooled bath, followed by vortexing for 10 sec. After three cycles of sonication-vortex, tick lysates were centrifuged at 200 x g for 5 min to remove cell debris. The supernatants were collected and protein concentration was determined using the BCA Protein Assay (Life Technologies, Carlsbad, CA) with BSA as standard. Protein extracts (10 μg) from each CCHV-infected and uninfected ticks were on- gel concentrated by SDS-PAGE and trypsin digested as described previously (Villar et al., 2015). The desalted protein digest was resuspended in 0.1% formic acid and analyzed by RP-LC-MS/MS using an Easy-nLC II system coupled to an ion trap LTQ mass spectrometer (Thermo Scientific, Waltham, MA, USA). The peptides were concentrated (on-line) by reverse phase chromatography using a 0.1 × 20 mm C18 RP precolumn (Thermo Scientific), and then separated using a 0.075 × 100 mm C18 RP column (Thermo Scientific) operating at 0.3 ml/min. Peptides were eluted using a 60-min gradient from 5 to 40% solvent B (solvent A: 0,1%

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Table 1. Tick species collected from sheep and goats in Greece and used in the study.

Tick species N (female, male) Tick hosts (N) County (N)

Dermacentor marginatus 40 (20, 20) sheep (17), goat (23) Arta (3) Etoloakarnania (3) Evritania (6) Fokida (11) Ftiotida (1) Korinthos (14)† Preveza (2) Haemaphysalis parva 24 (20, 4) sheep (16), goat (8) Arta (3) Etoloakarnania (1) Evritania (4) Fokida (9)† Ftiotida (2) Preveza (3) Thessaloniki (2) Haemaphysalis punctata 1 (0, 1) sheep (1) Arta (1) Haemaphysalis sulcata 22 (19, 3) sheep (15), goat (7) Arta (4) Etoloakarnania (2) Fokida (11) Preveza (4) Thessaloniki (1) Haemaphysalis sp. 1 (1, 0) sheep (1) Ftiotida (1) Ixodes gibbosus 13 (13, 0) sheep (13) Limnos (13) Rhipicephalus sanguineus 4 (4, 0) sheep (1), goat (3) Limnos (1) Rodopi (3) Rhipicephalus bursa 1 (0, 1) goat (1) Rodopi (1)

† Ticks were collected from sheep and goats in Greece and identified to species level. Tick samples positive for CCHFV by RT-PCR and sequence analysis of the S segment, and by proteomics analysis of the L segment. formic acid in water, solvent B: 0,1% formic acid in acetonitrile). ESI ionization was done using a Fused-silica PicoTip Emitter ID 10 mm (New Objective, Woburn, MA, USA) interface. Peptides were detected in survey scans from 400 to 1600 amu (1 mscan), followed by fifteen data dependent MS/MS scans (Top 15), using an isolation width of 2 mass-to-charge ratio units, normalized collision energy of 35%, and dynamic exclusion applied during 30 sec periods. The MS/MS raw files were searched against a compiled database containing all sequences from Ixodida (81,241 entries in March 2016), Ruminantia (87,986 entries in March

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2016) and Nairovirus (847 entries in March 2016) Uniprot taxonomies (http:// www.uniprot.org) using the SEQUEST algorithm (Proteome Discoverer 1.4, Thermo Scientific). The following constraints were used for the searches: tryptic cleavage after Arg and Lys, up to two missed cleavage sites, and tolerances of 1 Da for precursor ions and 0.8 Da for MS/MS fragment ions and the searches were performed allowing optional Met oxidation and Cys carbamidomethylation. Data are available via PeptideAtlas with identifier PASS00907. A false discovery rate (FDR) < 0.01 was considered as condition for successful peptide assignments and at least 2 peptides per protein were used for protein identification.

3. Results and discussion

The results showed that 2 of the 106 sampled ticks were positive for CCHFV by RT-PCR (Table 1). The positive ticks corresponded to Dermacentor marginatus and Haemaphysalis parva collected from a goat and a sheep at Korinthos and Fokida counties, respectively (Fig. 1 and Table 1). These tick species were previously reported to be infected with CCHFV (Albayrak et al., 2010; Hekimoglu et al., 2012). The sequences of the S segment (GenBank accession numbers KU365757 and KU365758) were 99% identical between them at the nucleotide and amino acid levels. Furthermore, these sequences showed a 98% and 99–100% identity at the nucleotide and amino acid levels, respectively to CCHFV strain AP92 (DQ211638) previously isolated from a Rhipicephalus bursa tick. To confirm the identity of amplified and sequenced PCR products, a proteomics analysis was conducted using tissues from infected and control uninfected ticks. Tick- and host-derived proteins were identified in both infected and uninfected ticks, and were excluded from the analysis. Nairovirus-derived peptides corresponding to the L segment containing a putative RNA-dependent RNA polymerase (Bente et al., 2013), and unique to CCHFV were identified in infected D. marginatus and H. parva ticks (Table 2). The results confirmed CCHFV infection in these ticks with an alternative method and a different genome region (L segment) from that targeted by RT-PCR (S segment). The proteomics analysis was targeted at the L segment because this segment showed a higher peptide identification among different Nairoviruses in this study. The sequences for L segment peptides No. 1, 2 and 5 (Table 2) were 100% identical to the corresponding sequence of the prototype AP92 strain (ABB30012).

The results suggested that the CCHFV identified in the present study in ticks are genetic variants of the AP92 strain, but are closer to the old AP92 strain (98% nucleotide sequence identity) than to the novel AP92-like strain (91% nucleotide sequence identity) (Fig. 2). Greece is a Mediterranean country where currently only one CCHF case has been reported (Papa et al., 2015a). The absence of CCHF cases in Greece, together with the high seroprevalence in the human population, and the fact that one of the veterinarians who isolated the AP92 strain

66 Table 2. Nairovirus-derived peptides identified in infected ticks.

No. Peptide sequence Sequence alignment Nairovirus (sequence identity among CCHFV genotypes) (GenBank accesion number)

1 TELLLNSLTLLHCFLKHAPSDAIMEVESK (100%) TELLLNSLTLLHCFLKHAP–SDAIMEVESK CCHFV (Q6TFZ8) TELLLNSLCLLHCFLKHTS–QDAIQEVESK Dugbe virus (NP_690576) DQLYLSSLSLLHCFFCHTL–TSSVMEASSK Hughes virus (AMT75407) DDVVLNSIALLHVFMHHAP–KAAILEMNSK Sakhalin virus (AMT75419) 2 IVFAKIGLSGNGYDFIWTTQMIANSNFNVCKR (97–100%) IVFAKIGLSGNGYDFIWTTQMIANSNFNVCK CCHFV (Q6TFZ8) -VFAKLGLSGNNYDFIWTLQMIANSNFNVCK Dugbe virus (NP_690576) -VFAKMGLSDKSYDFIWTVQMIANSNFNVCK Thiafora virus (ALD84355) 3 VLDCMFNCKLCVEISADTLILRPESKER (86–100%) VLDCMFNCKLCVEISADTLILRPESKER CCHFV (Q6TFZ8) VLDCLFSCEVCIEIESGIL-LLKQKTQENSKTTLSLSR Dugbe virus (NP_690576) VLHKIFNCKIAVSLDEGLLYLRPETRE Hughes virus (AMT75407) VLDTFFCNVEVSLTSKV-LYLLPEGSDDPNRVTLSKIR Sakhalin virus (AMT75419) &KDSWHU,, 4 RDDEELTNSSSLK (62–100%) RDDEELTNSSSLK CCHFV (Q6TFZ8) 5 FTWFQEVVLYGKICETFLRCCTEFNR (96–100%) FTWFQEVVLYGKICETFLRCCTEFNR CCHFV (Q6TFZ8) FKWYQKLVYYGKICETFLQCCTEFTR Dera Ghazi Khan virus (AMT75389) FGWFQEVVLYSKICETFLRCCTEFSR Dugbe virus (NP_690576) FKWYQKLVLYGKICETFLQCCTEFRR Hughes virus (AMT75407) FGWYQELVLYSKICETFLRCCTEFTR Sakhalin virus (AMT75419) FTWFQEVLLYSKICETFLRICTEFNR Thiafora virus (ALD84355) 6 FMNIHAPELMPENCLFSSEEFNELIKLKK (74–100%) FMNIHAPELMPENCLFSSEEFNELIKLKK CCHFV (Q6TFZ8)

The identified Nairovirus-derived peptides corresponding to the L segment were aligned to viral amino acid sequences available at the GanBank using BLAST. Conserved amino acids when compared to the CCHFV sequence are shown underlined. Only virus sequences for which a hit was found after alignment are shown. Sequence identity among CCHFV genotypes (in parenthesis) was obtained after alignment with all available CCHFV sequences. ril No~e00353 Article 67 &KDSWHU,,

[(Fig._2)TD$IG] Article No~e00353

Fig. 2. Phylogenetic analysis of CCHFV. A phylogenetic tree was built using S gene nucleotide sequences from CCHFV. The CCHFV sequences obtained in this study are shown with red arrows. In the tree, the different isolates were annotated as “host.country.NCBI accession number”. The country code is Burkina Faso (BF), Central African Republic (CAR), China (CHI), Democratic Republic of Congo (DRC), Greece (GRE), Iran (IRA), Kazakhstan (KAZ), Mauritania (MAU), Madagascar (MAD), Namibia (NAM), Nigeria (NIG), Pakistan (PAK), Russia (RUS), Sudan (SUD), South Africa (SAF), Senegal (SEN), Turkey (TUR), Uganda (UGA), and NA (not available). The DQ211638 and U04958 represent the same isolate corresponding to the prototype AP92 strain. Nucleotide sequences were aligned using MAFFT version 7.0 (http://mafft.cbrc.jp/alignment/software/). Non-aligned regions were removed with Gblocks (version 0.91b) implemented in Phylogeny.fr. The final cured alignments contained 396 gap-free nucleotide positions. The best-fit model of the sequence evolution was selected based on Corrected Akaike Information Criterion (cAIC) and Bayesian Information Criterion (BIC) implemented in Molecular Evolutionary Genetics Analysis (MEGA) version 6.0 (http://www.ncbi.nlm. nih.gov/pmc/articles/PMC3840312/). The Kimura-2 model, which had the lowest values of cAIC and BIC, was chosen for subsequent phylogenetic analyses. The Neighbor joining (NJ) method implemented in MEGA, was used to obtain the best tree topology. A proportion of Gamma distributed sites (+G, 0.24) was estimated in MEGA. Dugbe virus was used as outgroup. Reliability of internal branches was assessed using the bootstrapping method (1000 replicates). Graphical representation and editing of the phylogenetic tree was performed with MEGA. demonstrated very high titres of anti-CCHFV antibodies, led to the suggestion that the AP92 strain is not pathogenic for humans (Papa et al., 2015b). Besides the prototype AP92 strain, a novel AP92-like strain (KF146306) has been recently detected in Greece in R. bursa collected from sheep in an area with 6% CCHFV seroprevalence (Papa et al., 2014). Currently, one human case with mild symptoms has been associated with the AP92-like strain in Turkey (Midilli et al., 2009), and

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Article No~e00353 one fatal case due to the AP92-like strain was recently reported in Iran (Salehi- Vaziri et al., 2016).

These results showed the possibility of using this experimental approach for the identification and characterization of CCHFV in ticks, and confirmed the presence of genetically diverse CCHFV strains in different tick species collected from goats and sheep in Greece. The combination of RT-PCR with MALDI-TOF MS increases the possibility of characterizing CCHFV genetic variants circulating in different regions, and differentiating CCHFV from other Nairoviruses (Table 2). CCHFV shows a higher genetic diversity when compared to other arboviruses, a finding that has been linked to increased virulence and emergence in new geographic locations (Xia et al., 2016). These results also supported the increasing evidence that the circulation of the low or not pathogenic AP92-like strains probably does not represent a high risk of CCHF to the population. However, the circulation of genetically diverse CCHFV strains could potentially result in the appearance of novel viral genotypes with increased pathogenicity and fitness (Bente et al., 2013).

Declarations Author contribution statement

Isabel G Fernandez de Mera, Ilias Chaligianis: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Angelica Hernández Jarguin, Lourdes Mateos-Hernández, Francisco Ruiz-Fons: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Margarita Villar: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Anna Papa, Jose de la Fuente: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Smaragda Sotiraki: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Alejandro Cabezas-Cruz: Analyzed and interpreted the data; Wrote the paper.

Christian Gortazar: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

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Funding statement

This work was partially funded by the EU FP7 grant ANTIGONE (#278976) and State Scholarship Foundation IKY, NSRF 2007-2013. IGFM and MV were supported by the Research Plan of the University of Castilla-La Mancha (UCLM), Spain. LMH was supported by a fellowship from the UCLM.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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72 73 74 Draft genome sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis isolates from different hosts

Díaz-Sánchez, S., Hernández-Jarguín, A., Fernández de Mera, I. G., Alberdi, P., Zweygarth, E., Gortazar, C., de la Fuente, J. (2018). Draft genome sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis isolates from different hosts. Genome Announcements, 6(5)1503-17.

75 76 &KDSWHU,, PROKARYOTES crossm

Draft Genome Sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis Isolates from Different Hosts

Sandra Diaz-Sanchez,a AngélicaHernández-Jarguín,a Isabel G. Fernández de Mera,a Pilar Alberdi,a Erich Zweygarth,b,c Christian Gortazar,a José dela Fuentea,d aSaBio, Instituto de Investigación en Recursos Cinegéticos IREC (CSIC-UCLM-JCCM), Ciudad Real, Spain bInstitute for Parasitology and Tropical Veterinary Medicine, Free University of Berlin, Berlin, Germany cDepartment of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Onderstepoort, South Africa dDepartment of Veterinary Pathobiology, Center for Veterinary Health Sciences, Oklahoma State University, Stillwater, Oklahoma, USA

ABSTRACT Here, we report the draft genome sequences of isolates of Anaplasma phagocytophilum, Anaplasma marginale, and Anaplasma ovis. The genomes of A. phagocytophilum (human), A. marginale (cattle), and A. ovis (goat) isolates from the United States were sequenced and characterized. This is the ﬁrst report of an A. ovis genome sequence.

he genus Anaplasma (Rickettsiales: Anaplasmataceae) comprises obligatory intracel- Tlular Gram-negative bacteria that are mainly transmitted by ticks, so far including seven species, Anaplasma phagocytophilum, A. marginale, A. ovis, A. bovis, A. centrale, A. platys, and A. capra (1, 2). These pathogens cause different forms of anaplasmosis in humans and domestic and wild animals worldwide (3). Recently, several studies have reported genome sequence information for Anaplasma spp. to advance the identification of candidate protective antigens and knowledge of genetic diversity, host tropism, virulence, and tick transmissibility of these pathogens (4–9). Currently, sequence information is available for 29 and 14 genomes for A. phagocytophilum and A. marginale, respectively, and 1 genome for A. centrale. However, genome sequence information is not available for other Anaplasma spp. such as A. ovis, which was included in this study. Here, we report the draft genome sequences of the strains A. phagocytophilum NY18 (10), A. marginale Oklahoma-2 (11, 12), and A. ovis Idaho (12, 13), which were isolated in the United States from human, cow, and goat, respectively. The isolates were grown in cultured Ixodes scapularis IDE8 or ISE6 cells as previously described (11), and chromosomal DNA samples were obtained by using the DNeasy blood and tissue and MinElute PCR purification kits (Qiagen, Valencia, CA, USA) according to the manufac- Received 3 January 2018 Accepted 5 January turer’s protocols. Genomic DNA was subjected to fragmentation using Agencourt 2018 Published 1 February 2018 Citation Diaz-Sanchez S, Hernández-Jarguín A, AMPure XP (Beckman Coulter, Brea, CA, USA) to obtain DNA fragments of an average Fernández de Mera IG, Alberdi P, Zweygarth E, final size of about 500 bp. Samples were then used to prepare sequencing-amenable Gortazar C, de la Fuente J. 2018. Draft genome TruSeq libraries (NEB-Next, New England Biolabs, Ipswich, MA, USA). The libraries were sequences of Anaplasma phagocytophilum, A. marginale, and A. ovis isolates from different quantitated with quantitative PCR (qPCR), and DNA was then denatured and equili- hosts. Genome Announc 6:e01503-17. https:// brated so that a final library concentration of 10 pM was loaded onto a MiSeq version doi.org/10.1128/genomeA.01503-17. 3 flow cell (Illumina, San Diego, CA, USA) and sequenced using a 2 ϫ 250 paired-end Copyright © 2018 Diaz-Sanchez et al. This is an sequencing protocol with Ͼ74% of the bases showing a Q30 factor of Ͼ30. Genome open-access article distributed under the terms of the Creative Commons Attribution 4.0 assembly and analysis were conducted by CD Genomics (Shirley, NY, USA). After International license. processing with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) Address correspondence to José de la Fuente, for quality control, high-quality reads were assembled using the short oligonucleotide [email protected]. analysis package SOAPdenovo2 (version 2.04) (http://soap.genomics.org.cn/soapdenovo S.D.-S. and A.H.-J. contributed equally to the present work. .html). The assembled results were optimized according to the paired-end and overlap Volume 6 Issue 5 e01503-17

77 &KDSWHU,, Diaz-Sanchez et al. relations of the reads by using GapCloser (version 1.12) (http://soap.genomics.org.cn/ soapdenovo.html) to repair the results of the assembly hole and remove the redundant sequences from the final assembly. The protein-coding genes were predicted using Glimmer 3.02 (https://ccb.jhu.edu/software/glimmer/), and tRNAscan-SE (http://lowelab .ucsc.edu/tRNAscan-SE/) and RNAmmer (http://www.cbs.dtu.dk/services/RNAmmer/) were used to identify tRNA and rRNA, respectively. The genome sequences were also uploaded into Rapid Annotations using Subsystems Technology (RAST) (14)to check the annotated sequences. The assembled genomes were mapped to reference genomes (Anaplasma phagocytophilum strain HZ [GenBank accession number NC_ 007797] and Anaplasma marginale strain Florida [NC_012026]) using SOAPaligner (version 2.21) (http://soap.genomics.org.cn/soapaligner.html). The sequenced genomes consisted of 1,210 (A. phagocytophilum NY18), 1,033 (A. marginale Oklahoma-2), and 1,034 (A. ovis Idaho) genes. The availability of these genome sequences from field Anaplasma isolates will allow comparative analysis to other Anaplasma species to expand the study of the evolution and host specificity of these pathogens and to find correlates with phenotypic variation with implications for anaplasmosis disease risk assessment and control. Accession number(s). The genome sequences were deposited in GenBank under accession numbers PKOG00000000 (A. phagocytophilum NY18), PKOF00000000 (A. marginale Oklahoma-2), and PKOE00000000 (A. ovis Idaho).

ACKNOWLEDGMENTS This research was supported by the COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe (COMPARE) grant 643476. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

REFERENCES 1. Dumler JS, Barbet AF, Bekker CPJ, Dasch GA, Palmer GH, Ray SC, Rikihisa Haddad N. 2016. Draft Anaplasma phagocytophilum genome se- Y, Rurangirwa FR. 2001. Reorganization of genera in the families Rick- quences from five cows, two horses, and one roe deer collected in ettsiaceae and Anaplasmataceae in the order Rickettsiales: unification of Europe. Genome Announc 4:e00950-16. https://doi.org/10.1128/genomeA some species of Ehrlichia with Anaplasma, Cowdria with Ehrlichia and .00950-16. Ehrlichia with Neorickettsia, descriptions of six new species combinations 9. Al-Khedery B, Barbet AF. 2014. Comparative genomics identifies a po- and designation of Ehrlichia equi and “HGE agent” as subjective syn- tential marker of human-virulent Anaplasma phagocytophilum. Patho- onyms of Ehrlichia phagocytophila. Int J Syst Evol Microbiol 51: gens 3:25–35. https://doi.org/10.3390/pathogens3010025. 2145–2165. https://doi.org/10.1099/00207713-51-6-2145. 10. Asanovich KM, Bakken JS, Madigan JE, Aguero-Rosenfeld M, Wormser 2. Li H, Zheng YC, Ma L, Jia N, Jiang BG, Jiang RR, Huo QB, Wang YW, Liu GP, Dumler JS. 1997. Antigenic diversity of granulocytic Ehrlichia isolates HB, Chu YL, Song YD, Yao NN, Sun T, Zeng FY, Dumler JS, Jiang JF, Cao from humans in Wisconsin and New York and a horse in California. J WC. 2015. Human infection with a novel tick-borne Anaplasma species in Infect Dis 176:1029–1034. https://doi.org/10.1086/516529. China: a surveillance study. Lancet Infect Dis 15:663–670. https://doi 11. Blouin EF, Barbet AF, Yi J, Kocan KM, Saliki JT. 2000. Establishment and .org/10.1016/S1473-3099(15)70051-4. characterization of an Oklahoma isolate of Anaplasma marginale in 3. Kocan KM, de la Fuente J, Cabezas-Cruz A. 2015. The genus Anaplasma: cultured Ixodes scapularis cells. Vet Parasitol 87:301–313. https://doi.org/ new challenges after reclassification. Rev Sci Tech 34:577–586. https:// 10.1016/S0304-4017(99)00183-1. doi.org/10.20506/rst.34.2.2381. 12. de la Fuente J, García-García JC, Blouin EF, Saliki JT, Kocan KM. 2002. 4. Dark MJ, Al-Khedery B, Barbet AF. 2011. Multistrain genome analysis Infection of tick cells and bovine erythrocytes with one genotype of the identifies candidate vaccine antigens of Anaplasma marginale. Vaccine 29:4923–4932. https://doi.org/10.1016/j.vaccine.2011.04.131. intracellular Ehrlichia Anaplasma marginale excludes infection with other 5. Dugat T, Loux V, Marthey S, Moroldo M, Lagrée AC, Boulouis HJ, Haddad N, genotypes. Clin Diagn Lab Immunol 9:658–668. https://doi.org/10.1128/ Maillard R. 2014. Comparative genomics of first available bovine Anaplasma CDLI.9.3.658-668.2002. phagocytophilum genome obtained with targeted sequence capture. BMC 13. Ndung’u LW, Aguirre C, Rurangirwa FR, McElwain TF, McGuire TC, Genomics 15:973. https://doi.org/10.1186/1471-2164-15-973. Knowles DP, Palmer GH. 1995. Detection of Anaplasma ovis infection in 6. Battilani M, De Arcangeli S, Balboni A, Dondi F. 2017. Genetic diversity goats by the major surface protein 5 competitive inhibition enzyme- and molecular epidemiology of Anaplasma. Infect Genet Evol 49: linked immunosorbent assay. J Clin Microbiol 33:675–679. 195–211. https://doi.org/10.1016/j.meegid.2017.01.021. 14. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, 7. Lockwood S, Brayton KA, Broschat SL. 2016. Comparative genomics reveals Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, multiple pathways to mutualism for tick-borne pathogens. BMC Genomics Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, 17:481. https://doi.org/10.1186/s12864-016-2744-9. Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O. 2008. 8. Dugat T, Rossignol MN, Rué O, Loux V, Marthey S, Moroldo M, Silaghi The RAST server: rapid annotations using subsystems technology. BMC C, Höper D, Fröhlich J, Pfeffer M, Zweygarth E, Lagrée AC, Boulouis HJ, Genomics 9:75. https://doi.org/10.1186/1471-2164-9-75.

Volume 6 Issue 5 e01503-17

78 Anaplasma phagocytophilum strain NY18, whole genome shotgun sequencing project GenBank: PKOG00000000.1 This entry is the master record for a whole genome shotgun sequencing project and contains no sequence data.

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LOCUS PKOG01000000 142 rc DNA linear BCT 02-JAN-2018 DEFINITION Anaplasma phagocytophilum strain NY18, whole genome shotgun sequencing project. ACCESSION PKOG00000000 VERSION PKOG00000000.1 DBLINK BioProject: PRJNA420172 BioSample: SAMN08107862 KEYWORDS WGS. SOURCE Anaplasma phagocytophilum ORGANISM Anaplasma phagocytophilum Bacteria; Proteobacteria; Alphaproteobacteria; Rickettsiales; Anaplasmataceae; Anaplasma; phagocytophilum group. REFERENCE 1 (bases 1 to 142) AUTHORS Diaz-Sanchez,S. TITLE Draft genome sequence of Anaplasma phagocytophilum, A. marginale and A. ovis isolates with different host and geographical origin JOURNAL Unpublished REFERENCE 2 (bases 1 to 142) AUTHORS Diaz-Sanchez,S. TITLE Direct Submission JOURNAL Submitted (28-DEC-2017) Department of Animal Health, IREC-SaBiO, Ronda de Toledo s/n, Ciudad Real 13005, Spain COMMENT The Anaplasma phagocytophilum whole genome shotgun (WGS) project has the project accession PKOG00000000. This version of the project (01) has the accession number PKOG01000000, and consists of sequences PKOG01000001-PKOG01000142. Annotation was added by the NCBI Prokaryotic Genome Annotation Pipeline (released 2013). Information about the Pipeline can be found here: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

##Genome-Assembly-Data-START## Assembly Method :: SOAPdenovo v. v. 2.04 Genome Representation :: Full Expected Final Version :: Yes Reference-guided Assembly :: GCF_000013125.1 Genome Coverage :: 71.6126X Sequencing Technology :: Illumina HiSeq ##Genome-Assembly-Data-END##

##Genome-Annotation-Data-START## Annotation Provider :: NCBI Annotation Date :: 12/28/2017 09:31:52 Annotation Pipeline :: NCBI Prokaryotic Genome Annotation Pipeline Annotation Method :: Best-placed reference protein set; GeneMarkS+ Annotation Software revision :: 4.3 Features Annotated :: Gene; CDS; rRNA; tRNA; ncRNA; repeat_region Genes (total) :: 1,210 CDS (total) :: 1,167 Genes (coding) :: 1,037 CDS (coding) :: 1,037 Genes (RNA) :: 43 rRNAs :: 1, 1, 1 (5S, 16S, 23S) complete rRNAs :: 1, 1, 1 (5S, 16S, 23S) tRNAs :: 37 ncRNAs :: 3 Pseudo Genes (total) :: 130 Pseudo Genes (ambiguous residues) :: 2 of 130 Pseudo Genes (frameshifted) :: 41 of 130 Pseudo Genes (incomplete) :: 113 of 130 Pseudo Genes (internal stop) :: 18 of 130 Pseudo Genes (multiple problems) :: 36 of 130 ##Genome-Annotation-Data-END## FEATURES Location/Qualifiers source 1..142 /organism="Anaplasma phagocytophilum" /mol_type="genomic DNA" /strain="NY18" /host="Homo sapiens" /db_xref="taxon:948" /country="USA" /collection_date="2016" WGS PKOG01000001-PKOG01000142 // https://www.ncbi.nlm.nih.gov/nuccore/PKOG00000000 79 Anaplasma marginale strain Oklahoma-2, whole genome shotgun sequencing project GenBank: PKOF00000000.1 This entry is the master record for a whole genome shotgun sequencing project and contains no sequence data.

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LOCUS PKOF01000000 44 rc DNA linear BCT 02-JAN-2018 DEFINITION Anaplasma marginale strain Oklahoma-2, whole genome shotgun sequencing project. ACCESSION PKOF00000000 VERSION PKOF00000000.1 DBLINK BioProject: PRJNA420172 BioSample: SAMN08107863 KEYWORDS WGS. SOURCE Anaplasma marginale ORGANISM Anaplasma marginale Bacteria; Proteobacteria; Alphaproteobacteria; Rickettsiales; Anaplasmataceae; Anaplasma. REFERENCE 1 (bases 1 to 44) AUTHORS Diaz-Sanchez,S. TITLE Draft genome sequence of Anaplasma phagocytophilum, A. marginale and A. ovis isolates with different host and geographical origin JOURNAL Unpublished REFERENCE 2 (bases 1 to 44) AUTHORS Diaz-Sanchez,S. TITLE Direct Submission JOURNAL Submitted (28-DEC-2017) Department of Animal Health, IREC-SaBiO, Ronda de Toledo s/n, Ciudad Real 13005, Spain COMMENT The Anaplasma marginale whole genome shotgun (WGS) project has the project accession PKOF00000000. This version of the project (01) has the accession number PKOF01000000, and consists of sequences PKOF01000001-PKOF01000044. Annotation was added by the NCBI Prokaryotic Genome Annotation Pipeline (released 2013). Information about the Pipeline can be found here: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

##Genome-Assembly-Data-START## Assembly Method :: SOAPdenovo v. v. 2.04 Genome Representation :: Full Expected Final Version :: Yes Reference-guided Assembly :: GCF_000020305.1 Genome Coverage :: 71.6052X Sequencing Technology :: Illumina HiSeq ##Genome-Assembly-Data-END##

##Genome-Annotation-Data-START## Annotation Provider :: NCBI Annotation Date :: 12/28/2017 09:07:45 Annotation Pipeline :: NCBI Prokaryotic Genome Annotation Pipeline Annotation Method :: Best-placed reference protein set; GeneMarkS+ Annotation Software revision :: 4.3 Features Annotated :: Gene; CDS; rRNA; tRNA; ncRNA; repeat_region Genes (total) :: 1,033 CDS (total) :: 990 Genes (coding) :: 950 CDS (coding) :: 950 Genes (RNA) :: 43 rRNAs :: 1, 1, 1 (5S, 16S, 23S) complete rRNAs :: 1, 1, 1 (5S, 16S, 23S) tRNAs :: 37 ncRNAs :: 3 Pseudo Genes (total) :: 40 Pseudo Genes (ambiguous residues) :: 0 of 40 Pseudo Genes (frameshifted) :: 18 of 40 Pseudo Genes (incomplete) :: 24 of 40 Pseudo Genes (internal stop) :: 3 of 40 Pseudo Genes (multiple problems) :: 5 of 40 ##Genome-Annotation-Data-END## FEATURES Location/Qualifiers source 1..44 /organism="Anaplasma marginale" /mol_type="genomic DNA" /strain="Oklahoma-2" /host="Cow" /db_xref="taxon:770" /country="USA" /collection_date="2016" WGS PKOF01000001-PKOF01000044 // // 80 https://www.ncbi.nlm.nih.gov/nuccore/PKOG00000000 Anaplasma marginale strain Oklahoma-2, whole genome shotgun sequencing project GenBank: PKOF00000000.1 This entry is the master record for a whole genome shotgun sequencing project and contains no sequence data.

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https://www.ncbi.nlm.nih.gov/nuccore/PKOF00000000 81 BioProject ID 420172 - BioProject - NCBI

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Draft genome sequence of Anaplasma phagocytophilum, A. Accession: PRJNA420172 ID: 420172 marginale and A. ovis isolates with different host and geographical origin

Here we report the draft genome sequence of isolates of Anaplasma phagocytophilum, A. More...

Accession PRJNA420172

Data Type Genome sequencing and assembly

Scope Multispecies

Submission Registration date: 29-Nov-2017 IREC-SaBiO

Relevance Industrial

Project Data:

Number Resource Name of Links

SEQUENCE DATA Nucleotide (total) 232 WGS master 3 SRA Experiments 3 Protein Sequences 2937

OTHER DATASETS BioSample 3 Assembly 3

Assembly details: Download Assembly level Number of Assemblies Scaffold 3 Total 3 Assembly Level WGS BioSample Strain Taxonomy GCA_002849365.1 PKOF00000000 SAMN08107863 Oklahoma-2 Anaplasma marginale GCA_002849345.1 PKOE00000000 SAMN08107864 Idaho Anaplasma ovis GCA_002849375.1 PKOG00000000 SAMN08107862 NY18 Anaplasma phagocytophilum

SRA Data Details Parameter Value Data volume, Gbases 6 Data volume, Mbytes 3705

https://www.ncbi.nlm.nih.gov/bioproject/PRJNA420172 82 83 84 Chapter III. Effect of abiotic and biotic factors in mosquito microbiome composition

Díaz-Sánchez, S., Hernández-Jarguín, A., Torina, A., Fernández de Mera, I.G., Estrada-Peña, a., Villar, M., La Russa, F., Blanda, V., Vicente, J., Caracappa, S., Gortázar, C., de la Fuente, J. (2018). Biotic and abiotic factors shape the microbiota of wild-caught populations of the arbovirus vector Culicoides imicola. Insect Molecular Biology. 27(6):847-861.

85 86 Biotic and abiotic factors shape the microbiota of wild-caught populations of the arbovirus vector Culicoides imicola

Díaz-Sánchez, S., Hernández-Jarguín, A., Torina, A., Fernández de Mera, I.G., Estrada- Peña, a., Villar, M., La Russa, F., Blanda, V., Vicente, J., Caracappa, S., Gortázar, C., de la Fuente, J. (2018). Biotic and abiotic factors shape the microbiota of wild-caught populations of the arbovirus vector Culicoides imicola. Insect Molecular Biology. 27(6):847-861.

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Insect Molecular Biology (2018) 27(6 ), 847–861 doi: 10.1111/imb.12526

Biotic and abiotic factors shape the microbiota of wild-caught populations of the arbovirus vector Culicoides imicola

S. Díaz-Sánchez*1, A. Hernández-Jarguín*1, protein identification showed differences in host A. Torina†1, I. G. Fernández de Mera*, preferences between the two populations, with Homo A. Estrada-Peña‡, M. Villar*, F. La Russa†, sapiens and Canis lupus familiaris L. being the V. Blanda†, J. Vicente*, S. Caracappa*, C. Gortazar* preferred bloodmeal source in both locations. A and J. de la Fuente*§ principal component analysis showed that the *SaBio, Instituto de Investigación en Recursos combined effect of host preferences (H. sapiens) and Cinegéticos, IREC-CSIC-UCLM-JCCM, Ciudad local soil moisture factors shape the microbiome Real, Spain; †Intituto Zooprofilattico Sperimentale composition of wild-caught populations of C. imicola. della Sicilia, Palermo, Sicily, Italy; ‡Facultad de These results contribute to characterizing the role of Veterinaria, Universidad de Zaragoza, Zaragoza, Spain; the microbiome in insect adaptation and its utility in and §Department of Veterinary Pathobiology, Center for predicting geographic expansion of Culicoides Veterinary Health Sciences, Oklahoma State University, species with potential implications for the control of Stillwater, OK, USA vector-borne diseases.

Abstract Keywords: metagenome, Culicoides microbiota, Biting midges of the genus Culicoides are known biotic and abiotic factors, temperature and soil vectors of arboviruses affecting human and animal moisture, host blood meal source. health. However, little is known about Culicoides imicola microbiota and its influence on this insect’s Introduction biology. In this study, the impact of biotic and abiotic Biting midges of the genus Culicoides Latreille, 1809 factors on C. imicola microbiota was characterized (Diptera: Ceratopogonidae) are abundant haematopha- using shotgun-metagenomic sequencing of whole- gous insects and vectors of arbovirus affecting human body DNA samples. Wild-caught C. imicola adult (Carpenter et al., 2013) and animal (Mellor et al., 2000) nulliparous females were sampled in two locations health, including bluetongue virus (BTV) and Oropouche from Sicily, Italy. The climatic variables of temperature virus. During blood feeding, Culicoides lacerate the skin and soil moisture from both localities were recorded to ingest the effusion into this injury containing blood, together with potential host bloodmeal sources. skin cells and lymph (Pagès et al., 2014). In particular, Shared core microbiome among C. imicola populations Culicoides imicola Kieffer has been associated with the included Pseudomonas, Escherichia, Halomonas, transmission of BTV in small ruminants and African horse Candidatus Zinderia, Propionibacterium, and sickness virus, which affects equids such as horses, Schizosaccharomyces. Specific and unique taxa were mules, and donkeys (Purse et al., 2005). C. imicola’s dis- also found in C. imicola from each location, tribution was restricted to Africa and occasionally found highlighting similarities and differences in microbiome in European Mediterranean countries (Purse et al., 2005; composition between the two populations. DNA and Venter et al., 1994), but it has been hypothesized that climate change in the last decades has extended their First published online 17 September 2018. prevalence to other regions of the world (Acevedo et al., Correspondence:José de la Fuente, SaBio, Instituto de Investigación de 2010; Guichard et al., 2014; Jacquet et al., 2015). How Recursos Cinegéticos, IREC-CSIC-UCLM-JCCM, 13005 Ciudad Real, Spain e-mail: [email protected] C. imicola has adapted to spread so quickly in different 1The first three authors contributed equally to the work reported in this areas of Europe, Asia and Africa is not fully understood paper. (Guichard et al., 2014; Jacquet et al., 2015).

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After mating in swarms and bloodmeal ingestion, Results Culicoides females lay eggs in muddy areas with abun- Characterization of C. imicola microbiome revealed dant organic material. The development from eggs to lar- similarities and differences between different vae, pupae and adults usually takes about 15–25 days, wild-caught populations but this can be up to 7 months during overwintering and is affected by climatic conditions (Benelli et al., 2017). The An experimental approach was developed to characterize four larval instars live as omnivores/detritivores, thus eat- the impact of biotic and abiotic factors on the C. imicola ing both animal and plant-derived material (Conte et al., microbiome under natural conditions (Fig. 1). The micro- 2007). biome in whole bodies from two field-collected C. imicola Like other insects, Culicoides spp. harbour a complex populations was characterized using a whole-genome microbiome essential to maintain their lifecycle and fit- shotgun metagenomic sequencing approach and vali- ness through interactions with microbial endosymbionts dated by quantitative polymerase chain reaction (qPCR) (Campbell et al., 2004; Lewis et al., 2014; Nayduch et al., using selected bacteria (Supplementary file 1: Fig. S1A, 2015). Most of these microbial endosymbionts are part of B; Supplementary file 2: Dataset S1). The metagenomic the core microbiome that functions by maintaining import- sequencing revealed that C. imicola harbours a micro- ant physiological processes within the host (Franzenburg biome that includes representatives from both eukary- et al., 2013; Douglas, 2015). Biotic (host, plants) and abi- ote and bacteria phyla in addition to viruses (Fig. 2 and otic (temperature, soil moisture) factors play an important Supplementary file 1: Fig. S2A–C, Table S1). role in the microbiome composition, favouring selection of Bacteria were the most represented kingdom, microbial communities acquiring specific-attributed func- including three bacterium phyla, 17 families, 20 gen- tions needed for vector survival, adaptation and patho- era and 28 species (Fig. 2 and Supplementary file 1: gen acquisition and transmission (Zouache et al., 2009; Fig. S2A–C, Table S1). A shared core microbiome in Minard et al., 2013; Villegas and Pimenta, 2016; Douglas, C. imicola populations from Collesano and Trapani was 2015). Acevedo et al. (2010) modelled the importance of composed of the genera Pseudomonas, Escherichia, biotic and abiotic factors such as spatial, topoclimatic, host Halomonas, Candidatus Zinderia, Propionibacterium and and soil factors on C. imicola distribution, showing that the Schizosaccharomyces (Fig. 3). No differences in relative effects of host and topoclimate factors, followed by soil, abundance were detected on the shared core microbiome, explain the variation in the abundance of C. imicola. except for Pseudomonas putida, whose relative abun- Limited information is available on the biotic and abiotic dance was significantly higher (P = 0.02) in Collesano factors that impact the microbiome composition in wild- (14.4%) when compared with Trapani (0.3%) (Fig. 3 and caught Culicoides spp. Most of the studies with C. imicola Supplementary file 1: Fig. S1A, B and Table S1). In the are descriptions of the microbiome composition, or solely shared core microbiome, the kingdom Eukaryota was targeting specific bacterial endosymbionts (Campbell represented by the genera Schizosaccharomyces (Fig. 3). et al., 2004; Lewis et al., 2014). For example, Campbell None of the viruses identified in this study were found in et al. (2004) concluded that the main sources of bacte- the shared core microbiome of C. imicola (Figs. 3 and 4 ). ria for Culicoides sonorensis was strongly linked to their Differences in the microbiome composition were also breeding sites, affected by factors such as soil composi- found in C. imicola populations that showed the presence tion, plants and dung from small ruminants. of unique species for each location (Fig. 4). In C. imicola Novel whole-genome shotgun metagenomics can from Collesano, the most abundant species in the micro- offers a better resolution of the microbiome composition biome belonged to Bacillus cereus with a 66.0% relative by sequencing the genome of all microorganisms present abundance (Figs. 2 and 4 ). However, in C. imicola from in the sample, including bacteria, archaea, viruses and Trapani, viruses were abundant; in particular, Musca parasites. Herein, we hypothesized that C. imicola popu- hytrovirus was the most abundant microorganism with an lations share a core metagenome, but differences in abi- 82.4% relative abundance (Figs. 2 and 4 ). otic and biotic factors such as temperature, soil moisture and host preferences may shape the microbiome, poten- Characterization of host DNA and proteins in C. imicola tially affecting insect adaptation and vector competence. revealed differences in host preferences between the To address this hypothesis, in this study a whole-genome two populations shotgun metagenomic sequencing was used to charac- Gene sequences from the mitochondrial Cytochrome b terize the composition of the microbiome of wild-caught gene (Cyt b) were used to identify potential host blood- C. imicola collected from two locations with differences meal sources at species level (Fig. 5 and Supplementary in biotic and abiotic factors to characterize the impact of file 1: Table S2). All the Cyt b sequences from C. imi- these variables on microbiome composition. cola collected in Collesano (n = 11) were assigned to

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Figure 1. Experimental design and workflow followed for the characterization of the impact of biotic and abiotic factors on the microbiome composition of Culicoides imicola collected in Collesano and Trapani. Maps were constructed using the Esri ArcMap 9.3 software. The localization of the study areas in Trapani and Collesano, Sicily, is shown. The digital elevation model was processed through the interpolation of level curves values of the Sicilian region, obtaining the elevations of study sites. The land use of the areas near to the farms was obtained from Corine Land Cover 2006 processed by the European Environmental Agency describing the coverage and, in part, the use of the soil in Europe. Spatial selection allowed deriving the different levels of the land use classes that affect the areas where the farms are placed. Land use classes: 1.1, urban fabric; 2.1.1, nonirrigated arable land; 2.2.1, vineyards; 2.2.2, fruit trees and berry plantations; 2.4.3, land principally occupied by agriculture; 3.2.1, natural grassland; 3.2.3, sclerophyllous vegetation. [Colour figure can be viewed at wileyonlinelibrary.com]

H. sapiens. However, in Trapani, the Cyt b sequences representation as host bloodmeal source between the two (n = 6) were assigned to C. lupus familiaris L. as the main populations of C. imicola (Fig. 5 and Supplementary file 3: host bloodmeal source for C. imicola (66%), followed by Dataset S2). However, as in the DNA analysis, the pro- Ovis aries L. and H. sapiens (17% each), revealing signifi- teomics analysis identified H. sapiens (38%) (Collesano) cant differences between the two populations (P ≤ 0.001). and H. sapiens (36%) and C. lupus familiaris (20%) Although the highest sequence identity for C. lupus (Trapani) as the main host bloodmeal sources for C. imi- familiaris corresponded to Cyt b pseudogenes, these cola in these locations (Fig. 5 and Supplementary file 3: sequences showed >99% query cover with >80% iden- Dataset S2). tity (E-value > 7 × 10−83) only to Canis spp. mitochondrial Cyt b sequences (Supplementary file 1: Table S2), thus Characterization of abiotic factors revealed differences providing support for species identification. In contrast, the between C. imicola locations proteomics analysis identified proteins (n = 494) belong- Climate data were collected in both Trapani and Collesano ing to all host species included in the analysis (H. sapiens, for all four seasons (Supplementary file 1: Table ). Trapani Bos taurus L., Sus scrofa L., O. aries L., Capra aegagrus and Collesano showed climate differences in terms of tem- hircus and C. lupus familiaris), without differences in their perature and normalized difference in vegetation index

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Figure 2. Relative abundance of microorganisms in Culicoides imicola populations collected from Trapani and Collesano by (A) phylum, (B) family and (C) genus. Both populations were characterized by a microbiome composed of Virus, Eukaryota and Bacteria. Metagenome relative abundance at different taxonomic levels was obtained taking the number of count reads or identifications assigned to each identified sequence, and normalized against the total number of identifications using METAPHLAN. The mean between the two replicates was used to calculate the average relative abundance shown in the figure. [Colour figure can be viewed at wileyonlinelibrary.com]

(NDVI) due to the island topography. Every season was principal components showed that within the 20 variables warmer in Trapani than in Collesano, with differences of included in the study (Supplementary file 1: Table ), the 27% (spring), 20% (summer), 17% (fall) and 26% (winter), variation in C. imicola microbiome composition was and yearly differences of 22% (P ≤ 0.01; Supplementary explained by two factors (Table 1). The first principal file 1: Table ). However, the total number of days in which component explained 75% of the variation and was com- total temperature or seasonal temperature was above posed of the variables NDVI and H. sapiens bloodmeal zero was similar in both locations. In Collesano, soil mois- source (Table 1). These results demonstrated that, under ture observed in terms of NDVI was higher than in Trapani our experimental conditions, the variations in C. imicola (up to 20%; Supplementary file 1: Table ). However, plant microbiome composition are explained by the combined coverage was more variable every season in Trapani than effect of host preferences (H. sapiens) and NDVI (as a in Collesano. proxy for local soil moisture) factors.

The biotic and abiotic factors’ impact on the microbiome Discussion composition of C. imicola In this study, the metagenome and the impact of biotic The influence of the combined effect of abiotic (tempera- (host bloodmeal source) and abiotic (temperature and ture and soil moisture) and biotic (host DNA) factors on soil moisture) factors on microbiome composition were the C. imicola microbiome was characterized by princi- characterized in wild-caught C. imicola populations. Our pal component analysis (PCA). The results of the three results provided additional information on the C. imicola

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Figure 3. Venn diagram showing the shared core metagenome and the specific metagenome of Culicoides imicola collected in Collesano and Trapani. The metagenome comparative analysis between C. imicola populations was performed using METASTATS (https://metastats.cbcb.umd.edu/detection. html; P = 0.05). Taxa within the shared core metagenome that are significantly abundant are indicated (*P < 0.05). [Colour figure can be viewed at wileyonlinelibrary.com] microbiome, and the impact of biotic and abiotic factors shared core microbiome is the result of a positive selec- on microbiome composition under natural conditions. tion over the constant invasion of transient microorgan- The microbiome of C. imicola showed low diversity taxa isms (Minard et al., 2015). How insects select and acquire composition in both populations. Similar studies in other microorganisms that become part of a semi-stable micro- insects have also reported low diversity records in the biota is not well understood. However, the acquisition of microbiome (Douglas, 2011; Broderick and Lemaitre, bacteria is strongly linked to the bacterial load in the adult 2012; Jones et al., 2013; Charan et al., 2016), and high bloodmeal sources and microorganisms they are exposed relative abundance of selected microorganisms (Osei- to in the breeding sites (Broderick et al., 2004; Chandler et Poku et al., 2012; Minard et al., 2015). However, site/ al., 2012; Schauer et al., 2012; Aharon et al., 2013; Jones sample comparisons of virus presence may be affected et al., 2013). Based on these results, we can speculate by the sampling methodology used, which is suitable for that the shared core microbiome community found in our microbiome studies but sample storage at room tempera- study might be explained by similarities in the C. imicola ture and in ethanol are not suitable for virome studies and habitat found in both locations, which may lead to the the unbiased detection of RNA viruses. acquisition of similar microorganisms (Mellor et al., 2000). It has been demonstrated that geographical adaptation The shared core microbiome identified in C. imicola of insects is strongly associated with the microbiome com- from both locations was predominantly composed of position (Minard et al., 2013), and the microbiome might Proteobacteria and Actinobacteria, which have been respond differently to abiotic and biotic challenges (Engel previously reported in mosquitoes and Diptera (Seitz et and Moran, 2013; Minard et al., 2015). Recently, Minard al., 1987; Maudlin et al., 1990; Vasanthi and Hoti, 1992; et al. (2015) reported a low diversity in the gut microbiota Demaio et al., 1996; Fouda et al., 2001). In C. imicola, of invasive mosquito species colonizing new geographi- the relative abundance of Pseudomonas was significantly cal areas. These results suggested that low diversity in higher than other microorganisms and was present in C. imicola microbiome composition might reflect the the shared core microbiome with differences between recent expansion of these midges in the Mediterranean both populations for P. putida. Pseudomonas is a com- area (Jacquet et al., 2016). mon clade ubiquitous in Culicoides breeding sites but Despite the low diversity observed in the C. imicola also found as a gut commensal (Parker et al., 1977; microbiome, a shared core microbiome was identified that Campbell et al., 2004; Erham, 2016). Pseudomonas might play a role in insect adaptation to new geographi- has been widely associated with water and humid envi- cal areas (Crotti et al., 2010; Gusmão et al., 2010). The ronments, the preferred sites for Culicoides during larval

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Figure 4. Phylogenic tree and heatmap showing the relative abundance of Culicoides imicola microbiome at species level on each location. Cladograms were displayed using PHYLOT (https://phylot.biobyte.de) and ITOL (https://itol.embl.de), and the metagenome comparative analysis between C. imicola populations was performed using METASTATS (https://metastats.cbcb.umd.edu/detection.html; *P < 0.05). [Colour figure can be viewed at wileyonlinelibrary.com] development, where these bacteria are likely acquired collected in Collesano and Trapani may correlate with and maintained in the gut until adults completely develop. higher soil moisture (as indicated by an increased NDVI) Recently, Chavshin et al. (2015) showed the persistence registered in Collesano across all seasons. of Pseudomonas during the maturation of Anopheles ste- Other microorganisms present in the C. imicola shared phensi under laboratory conditions. They observed that core microbiome have been identified in other insects with Pseudomonas is able to colonize the Malpighian tubules functional implications. Candidatus Zinderia, a symbiont and persist during larval stage to adults. The members commonly associated with the sap-feeding insect Arizona of this clade have been attributed with a great variety of spittlebug (Clastoptera arizonara) that is able to synthesize benefits for the insect, as detoxifiers of polluted environ- three specific amino acids, tryptophan, methionine and ments, protection of eggs against other bacteria, insect histidine (McCutcheon and Moran, 2010), has been never growth and habitat adaptation, and blood digestion in reported before in Culicoides spp. Propionibacterium was blood-feeding insects (Peck and Walton, 2006; Lam et previously identified in C. sonorensis Wirth and Jones al., 2007; Alvarez et al., 2012; Senderovich and Halpern, and Culicoides variipennis (Coquillette) (Campbell et al., 2013; Chavshin et al., 2015). Our results support a role 2004). Culicoides can acquire Propionobacterium during for Pseudomonas in C. imicola as part of the shared core blood feeding as it is a common commensal found in microbiome. However, the difference in the relative abun- mammalian skin that synthesizes attractive substances dance of Pseudomonas between Culicoides populations for mosquitoes, which explains the high infection rate

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Figure 5. Distribution of host (Homo sapiens, Canis lupus familiaris, Ovis aries, Capra aegagrus hircus, Sus scrofa, Bos taurus) bloodmeal sources in Culicoides imicola collected from Collesano and Trapani. Data were obtained by two complementary experimental approaches: host-DNA identification by PCR, and sequencing of the Cyt b gene and host-protein identification by proteomics. [Colour figure can be viewed at wileyonlinelibrary.com] by this bacterium (Verhulst et al., 2009). Insect associ- In addition to the shared core microbiome, C. imicola ations with yeast are very specific and play a symbiotic also harboured a population-specific microbiome in this role (Vega and Dowd, 2005). Yeast has been frequently study. This population-specific microbiome could reflect isolated from the external parts of mosquitoes and nonbit- the impact of abiotic and biotic factors defining the habitat ing Diptera such as Drosophila (Frants and Mertvetsova, of C. imicola that modulate the microbial composition of 1986; Coluccio et al., 2008), but none of these associa- the environment, and the presence of symbiotic, patho- tions has been previously reported in C. imicola in which genic or commensal microorganisms in the microbiome Schizosaccharomyces was identified in the present study. (Minard et al., 2013; 2015) (Supplementary file 1: Table ). Most of the insect–yeast symbiotic relationships are nutri- Furthermore, the analysis reported here was conducted tional, as yeasts provide specific enzymatic machinery with whole body samples, and therefore the microbiome components for digestion, and synthesis of amino acids, identified may contain microorganisms associated with vitamins and sterols. In turn, yeasts are maintained in different insect tissues, such as midgut, salivary gland an adequate environment, transported and dispersed and exoskeleton. Some of these microorganisms may (Morais et al., 1994). Furthermore, insect–yeast associ- be pathogenic in plants (ie Potyvirus) and animals (ie ations exert an important effect on shaping the microbial Alpharetrovirus) for which C. imicola may serve as vector. community (Jones et al., 2013), which could also explain However, despite the outbreaks of bluetongue reported the low diversity of bacterial taxa in the microbiome of in Sicily (Torina et al., 2004), BTV was not identified in both C. imicola populations. C. imicola from Collesano and Trapani. The relatively low

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Table 1. Principal component analysis (PCA) to characterize the were the main host bloodmeal sources for C. imicola, and influence of the combined effect of abiotic (temperature and soil these hosts were present on both farms. Taken together, moisture) and biotic (host DNA) factors on Culicoides imicola microbiome. the results of these methods for the characterization of host bloodmeal sources identified all hosts described Variable Component matrix in both farms except horses, which were present in low numbers. The farm located in Collesano raised cattle, PC1 PC2 PC3 goats and sheep, and horses and pigs are also present. LSTD (temperature) −0.995 −0.097 −0.017 NDVI (soil moisture) 0.995* 0.097 0.017 In Trapani, the farm is devoted to sheep, but cattle and Homo sapiens blood 0.995* 0.097 0.017 pigs are also present. Dogs are present on both farms, but source they are more abundant on the farm located in Trapani. Canis lupus familiaris −0.995 −0.097 −0.017 blood source C. imicola is considered to be a mammalophilic oppor- Capra aegagrus hircus −0.995 −0.097 −0.017 tunist, with preference for livestock species (Meiswinkel blood source et al., 2004; Lassen et al., 2012). In Senegal, C. imicola Propionibacterium −0.508 0.758 0.410 acnes was found to have a preference for horses when compared Bacillus cereus 0.987 0.156 −0.043 with sheep, and to be mostly nocturnal with peak activity thurigiensis after sunset (Fall et al., 2015). In Tunisia, C. imicola sam- Bacillus megaterium 0.994 0.111 0.003 Bacillus mojavensis 0.840 −0.332 0.43 ples collected near human habitats and analysed for Cyt b Bacillus pumilus 0.920 0.324 −0.22 and prepronociceptin genes showed feeding preferences Bacillus subtilis 0.993 0.117 −0.003 Candidatus Zinderia 0.167 0.953 −0.255 for humans, goats and sheep (Slama et al., 2015). insecticola Although the results using the two methods supported Aeromonas −0.648 0.515 0.560 that H. sapiens and C. lupus familiaris were the main host unclassified Enterobacter cloacae −0.988 −0.141 −0.060 bloodmeal sources for C. imicola, differences between the Escherichia coli −0.814 0.385 0.434 methods were evident. In general, the host diversity iden- Escherichia 0.565 −0.801 0.200 tified in C. imicola was higher at the protein than at DNA unclassified Pseudomonas putida 0.991 0.129 −0.043 levels. These results could be explained be several fac- Pseudomonas −0.821 −0.481 −0.308 tors, such as the higher resolution of mass spectrometry unclassified Propionibacterium 0.621 −0.516 0.590 (MS) when compared with PCR (Niare et al., 2016), and phage P100D the longer stability for proteins than DNA in blood-feed- Musca hytrovirus −0.995 −0.102 −0.021 ing arthropods (Martinez-de la Puente et al., 2013; Villar

Total variance explained PC1 PC2 PC3 et al., 2015). These results supported the combination of Total eigenvalues 15.146 3.394 1.46 PCR and proteomics-based methods for a better char- Percentage variance 75.732 16.968 7.30 acterization of host bloodmeal sources in blood-feeding Percentage cumulative 75.732 92.700 100.0 insects. variance The results showed that NDVI (as a proxy for soil mois- *Highest loading components. ture) and the presence of humans as bloodmeal source LSTD, averaged land surface temperature; NDVI, normalized difference for C. imicola constitute strong factors to explain the vari- in vegetation index. ations in the microbiome composition. NDVI is an indi- The table shows variable loadings on the first three principal components (PC) of the PCA to characterize the influence of the combined ef- cator of photosynthetic activity, and thus a proxy of soil fect of abiotic (temperature and soil moisture) and biotic (host DNA) moisture that favours the presence of certain microbial factors on C. imicola metagenome. Eigenvalues, percentage variance communities, as we observed for Pseudomonas, showing and percentage cumulative variance extracted from the full database for each PC are shown. higher relative abundance in C. imicola from Collesano. Other studies have found differences in the gut microbial communities of insects associated to bloodmeal and spe- proportion of BTV-infected C. imicola in vector populations cific environmental conditions (Toft and Andersson, 2010; may affect these results, together with the factors dis- Morag et al., 2012; Osei-Poku et al., 2012; Nayduch et al., cussed earlier of sampling methodology and sample pres- 2015; Charan et al., 2016), which supports the impact of ervation that may influence virus detection. Nevertheless, biotic and abiotic factors on the microbiome. additional studies are required to characterize the role of symbiotic, commensal and pathogenic microorganisms in Conclusions the C. imicola microbiome. These results expanded the information available on Host preferences for C. imicola were characterized C. imicola microbiome composition, including the iden- using two complementary experimental approaches for tification of eukaryotic microorganisms and viruses. A the identification of host DNA and proteins. The results of shared core microbiome was characterized in C. imicola this study showed that H. sapiens and C. lupus familiaris wild-caught populations from Collesano and Trapani.

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Additionally, C. imicola also harboured a population-spe- with 100 pooled specimens each were used for further cific microbiome, which could reflect the impact of abiotic analysis. Two replicates were used for DNA analyses, and biotic factors defining the habitat of C. imicola that and the remaining three replicates were used for host modulate the microbiome composition. Further studies protein identification. should be directed to characterizing the functional role of symbiotic, commensal and pathogenic microorganisms Whole-genome shotgun metagenomic sequencing and in the C. imicola microbiome, and their effect on vector analysis competence for pathogens. The results of this study Genomic DNA was extracted from whole bodies of two also showed the impact of biotic (H. sapiens bloodmeal biological replicates of pooled samples with 100 spec- source) and abiotic (NDVI as a proxy for soil moisture) imens each that were ground and pulverized in liquid factors on the C. imicola microbiome, which constitutes nitrogen and homogenized using a glass homogenizer important information to characterize insect adapta- (20 strokes) in 4 ml buffer (0.25 M sucrose, 1 mM magne- tion and predict geographic expansion of these vectors sium chloride, 10 mM tris(hydroxymethyl)aminomethane with potential implications for the control of vector-borne hydrochloride, pH 7.4) supplemented with 4% sodium diseases. dodecyl sulphate (SDS) and complete mini protease inhibitor cocktail (Roche, Basel, Switzerland). DNA was Experimental procedures extracted using the NucleoSpin TriPrep kit (Macherey- Study design and sample collection Nagel, Düren, Germany) according to the manufacturer’s C. imicola adult nulliparous females were sampled during instructions and quantified using PicoGreen (Invitrogen, September–October 2013 in two localities of Sicily, Italy Carlsbad, CA, USA) for template ranges of 4–100 ng. An (Fig. 1). The first farm is located in Collesano, a small aliquot of each pooled replicate was adjusted to the same village of 108 km2 and around 4000 inhabitants at 266 m DNA concentration and fragmented using a BioRuptor above sea level in Palermo province (latitude 37.94547°, (Diagenode, Inc., Denville, NJ, USA). Libraries were pre- longitude 13.88009°). This farm raises cattle, goats and pared using the Ultra DNA library preparation kit (New sheep, and horses and pigs are also present. The second England Biolabs, Ipswich, MA, USA) according to the farm is located in Trapani province, Locogrande district, manufacturer’s instructions. Samples were indexed using near the Trapani–Birgi Airport (latitude 37.898199°, lon- a unique combination of primers provided by the manu- gitude 12.510562°) at 26 m above sea level. Sheep are facturer. After library preparation, DNA library was puri- the main livestock host at this farm, but cattle and pigs fied in 1.5% agarose gels to select fragments with sizes are also present. Dogs are present on both farms, but within 150–400 bp, quantified with a Bioanalyzer (Agilent they are more abundant on the farm located in Trapani. Technologies, Palo Alto, CA, USA) and then pooled in Both farms are located in agricultural areas, including equimolar concentrations. Finally, 2 × 100 bp pair-end heterogeneous areas with annual crops associated with sequencing was performed in the HiSeq2000 platform permanent crops with olive groves and vineyards, and (Illumina, San Diego, CA, USA) using a 2 × 100 program near natural grassland. In both cases, sampling was car- according to the manufacturer’s instructions. The reads ried out using ultraviolet suction traps of Onderstepoort obtained were demultiplexed and filtered by quality, type (ARC-Institute for Agricultural Engineering, Pretoria, which finally rendered 250 million pair-end reads passing South Africa) (Venter and Meiswinkel, 2005; Venter et filter per sample. al., 2006). Traps were hung at a height of 1.5 m above For assembly of the metagenomic reads, raw data ground. Distances to stables and paddock with animals files from shotgun sequencing were de-multiplexed, fil- were less than 20 m, and traps were active from sunset tered by quality and converted into fastq using CASAVA (07:00 p.m.) to sunrise (08:00 a.m.). The insects, attracted v.1.8.2 (Illumina). High-quality reads were processed by the ultraviolet light, were sucked into plastic contain- using a metagenomic de novo assembly approach. ers containing 200–300 ml of water in which a few drops First, a filtering analysis was performed in order to of detergent (Hederol, Procter & Gamble Professional, remove insect reads. Raw insect reads were mapped Weybridge, UK) were added to reduce surface tension. using BOWTIE software (https://bowtie-bio.source- Collected insects were placed in vials with 70% ethanol forge.net/index.shtml; Langmead et al., 2009) against and stored at room temperature. C. imicola specimens Chironomus tentans genome (NCBI accession number were identified and separated from other insects by SAMEA3158483, https://www.ncbi.nlm.nih.gov/assem- using a stereomicroscope to characterize wing patterns, bly/GCA_000786525.1/) and C. sonorensis annotated as previously described (Rawlings, 1996). Five hundred reference transcriptome PRJNA238338 (https://www. specimens of C. imicola from different trap catches were ncbi.nlm.nih.gov/bioproject/PRJNA238338/) accessed in selected from each location and five biological replicates April 2016. All the unmapped reads (61–69 million

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S. Díaz-Sánchez et al. reads per sample, representing 24–28% of the total; implemented by Bio-Rad iQ5 Standard Edition, Version Supplementary file 1: Table ) were assembled using 2.0 (Livak and Schmittgen, 2001; Pfaffl 2001). Normalized SPADES with a minimum contig length of 200 bp (https://bio- Ct values for C. imicola collected in Collesano and inf.spbau.ru/spades). The quality of genome assemblies Trapani were compared by Student’s t-test for samples corresponding to the C. imicola metagenome was further with unequal variance (P ≤ 0.05). evaluated using QUAST (https://bioinf.spbau.ru/quast). All the assembly metrics are provided in the Supplementary Biotic factors: characterization of C. imicola host file 1: Table . Metagenome relative abundance at different preferences taxonomic levels was obtained by taking the number of Two experimental approaches were used to characterize count reads or identifications assigned to each identi- the potential bloodmeal sources of collected C. imicola fied sequence, and normalized against the total number populations: (a) host-DNA identification by PCR and (b) of identifications (Hernández-Jarguín et al., 2018) using host-protein identification by proteomics. METAPHLAN (Segata et al., 2012; Truong et al., 2015), and coverage analysis with SAM tools (https://samtools.source- Host-DNA identification by PCR. The same C. imicola forge.net) (Supplementary file 2: Dataset S1). METAPHLAN DNA samples used for DNA sequencing were used for was used to assign sequences to particular taxa using host-DNA identification by PCR. The PCR and sequence default parameters that included BLASTN default e-value analysis of Cyt b gene was done as previously described −6 threshold of 1 × 10 , UCLUST nucleotide identity threshold (Slama et al., 2015). The PCR was performed in a vol- of 75%, and rejected sequences as core genes if its homol- ume of 50 μl using 1 μl of each primer cyt b 1: 5′-CCA ogy pattern in the clade deviated from the expected base- TCC AAC ATC TCA GCA TGA TGA AA-3′ and cyt b 2: line misannotation error rate with confidence greater than 5′-GCC CCT CAG AAT GAT ATT TGT CCT CA-3′ and the 95%. The same procedure was applied to all taxonomic 5PRIME Hot Master Mix (www.5Prime.com) following the levels (from phyla to genera to species). Cladograms were manufacturer’s instructions. The PCR conditions included displayed using the PHYLOT (https://phylot.biobyte.de) and a denaturation step at 95 °C for 10 min, 40 amplifi- ITOL (https://itol.embl.de) open-source software platforms. cation cycles (94 °C for 30 s, 52 °C for 30 s, 72 °C for Finally, the metagenome comparative analysis between 45 s) and a final incubation at 72 °C for 5 min. The PCR C. imicola populations was performed using METASTATS products corresponding to the Cyt b gene were cloned ≤ (https://metastats.cbcb.umd.edu/detection.html; P 0.05) according to the manufacturer’s instructions using the (White et al., 2014; Paulson et al., 2011). TOPO TA cloning kit (Invitrogen, Carlsbad, CA, USA) and sequenced by Secugen, Madrid, Spain. A total of 11 and Metagenome dataset validation by qPCR six Cytb b nucleotide sequences from samples collected The same C. imicola DNA samples used for DNA at Collesano and Trapani respectively were edited using sequencing were also analysed by qPCR to validate the the SNAPGENE viewer (https://www.snapgene.com/prod- metagenomic data. Specific primers for Acinetobacter ucts/snapgene_viewer/; GSL Biotech, Chicago, IL, USA) spp., Pseudomonas spp. and P. putida genes were used and aligned to the GenBank DNA sequence database as previously described (Spilker et al., 2004; Minard et al., using the nucleotide–nucleotide Basic Local Alignment 2013) (Supplementary file 1: Table ). The iScript One-Step Search Tool (BLAST; https://blast.ncbi.nlm.nih.gov/Blast. was used to perform the qPCR with SYBR Green and the cgi) to assign a vertebrate host species (Supplementary iQ5 thermal cycler (Bio-Rad, Hercules, CA, USA) follow- file 1: Table S2). Host species assignment was com- ing manufacturer’s recommendations. The qPCR SYBR pleted when a match of 95% or more was found between Green reactions were carried out in 20 μl volume with 1 μl our sequences and those in the GenBank. The number DNA (15–18 ng/μl), 10 μM of each of forward and reverse of sequences assigned to each host was compared primers, and 10 μl of One-Step SYBR reaction mix. PCR between the Collesano and Trapani populations by chi- conditions are shown in the Supplementary file 1: Table . squared test (P ≤ 0.05). All sequences with BLAST identity For negative controls, genomic DNA was replaced by <100% were deposited in the GenBank with accession molecular-grade water. A dissociation curve was run at numbers MG182861–MG182873 (Supplementary file 1: the end of the reaction to ensure that only one amplicon Table S2). was formed and that the amplicons denatured consistently in the same temperature range for every sample Host-protein identification by proteomics. For protein (Ririe et al., 1997). DNA levels were normalized against extraction, three biological replicates of pooled samples C. imicola Elongation factor 1b gene, following the con- with 100 specimens each were ground and pulverized ditions reported previously by Anbazhagan et al. (2011). in liquid nitrogen and proteins were extracted using the Normalization was performed using the ddCT method as AllPrep DNA/RNA/Protein Mini Kit (Qiagen Inc., Valencia,

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CA, USA) according to the manufacturer’s instructions. false discovery rate ≤0.01 was considered as a condition Precipitated proteins were resuspended in 20 mM tris(hy- for successful peptide assignments and considering that droxymethyl)aminomethane hydrochloride pH 7.5 with at least two peptides per protein were required for pro- 4% SDS, and protein concentration was determined tein identification. Finally, a total of 494 proteins were using the BCA Protein Assay (Thermo Scientific, San assigned to vertebrate hosts and grouped by host spe- Jose, CA, USA) using bovine serum albumin as standard. cies (Supplementary file 3: Dataset S2), and the average Protein extracts (100 μg per sample) were on-gel con- number of peptide spectrum matches for each species centrated by SDS polyacrylamide gel electrophoresis as were normalized against the average number of peptide previously described (Villar et al., 2010). The unseparated spectrum matches of Culicoides spp. proteins (n = 3) and protein bands were visualized by staining with GelCode compared between the Collesano and Trapani populations Blue Stain Reagent (Thermo Scientific), excised, cut into by chi-squared test (P ≤ 0.05). 2 × 2 ×2 mm3 cubes and digested overnight at 37 °C with 60 ng/μl sequencing-grade trypsin (Promega, Madison, Abiotic factors: characterization of temperature and soil WI, USA) at 5 : 1 protein : trypsin (w/w) ratio in 50 mM moisture in C. imicola collection sites. The study area ammonium bicarbonate, pH 8.8, containing 10% (v/v) ace- is under a Mediterranean climate, mainly featuring hot tonitrile (Shevchenko et al., 2006). The resulting tryptic pep- and dry summers, and warm and rainy winters. However, tides from each band were extracted by 30 min incubation Trapani and Collesano showed climate differences in in 12 mM ammonium bicarbonate, pH 8.8. Trifluoroacetic terms of temperature and soil moisture due to the island acid was added to a final concentration of 1% and the topography (Supplementary file 1: Table ). Climate data peptides were finally desalted onto OMIX pipette tips C18 included in models were the averaged land surface tem- (Agilent Technologies, Santa Clara, CA, USA), dried-down perature and the NDVI, a proxy of photosynthetic activity and stored at −20 °C until MS analysis. The desalted proof the plant canopy, which relates to the type of vegetation tein digests were resuspended in 0.1% formic acid and and soil moisture (Carlson et al., 2009). Both datasets analysed by reverse-phase liquid chromatography MS/ were obtained at a resolution of 0.05° from the MODIS MS using an Easy-nLC II system coupled to a linear ion web site (https://modis.gsfc.nasa.gov/data/dataprod/) trap (LTQ) mass spectrometer (Thermo Scientific). The (Remer et al., 2005) and compared between locations by peptides were concentrated (on-line) by reverse-phase analysis of variance (ANOVA test, P ≤ 0.05) (Estrada-Peña chromatography using a 0.1 mm × 20 mm C18 RP pre- and de la Fuente, 2016). Other abiotic factors included column (Thermo Scientific), and then separated using a were the land use and land cover, using the standard 0.075 mm × 100 mm C18 RP column (Thermo Scientific) European classifications of CORINE3 (https://www.eea. operating at 0.3 ml/min. Peptides were eluted using a europa.eu/data-and-maps/data/clc-2000-vector-6). 120 min gradient from 5% to 40% solvent B in solvent A (solvent A: 0.1% formic acid in water; solvent B: 0.1% Characterization of the impact of biotic and abiotic formic acid in acetonitrile). Electrospray ionization was factors on C. imicola microbiome done using a fused-silica Pico-Tip emitter ID 10 mm (New The combined effect of biotic and abiotic factors on the Objective, Woburn, MA, USA) interface. Peptides were C. imicola microbiome was characterized using PCA detected in survey scans from 400 to 1600 amu (1 mscan), (Supplementary file 1: Table ). Linear combinations of the followed by 15 data-dependent MS/MS scans (Top 15), biotic and abiotic variables were used to build the PCA to using an isolation width of two mass-to-charge ratio units, finally obtain the new variable that best explained variations normalized collision energy of 35% and dynamic exclusion in the microbiome. The Statistical Package for the Social applied during 30 s periods. Three technical replicates Science (SPSS; IBM Analytics, Armonk, NY, USA; https:// per sample were analysed. The MS/MS raw files were www.ibm.com/analytics/us/en/) was used for analysis, searched against a compiled database containing all the including the biotic and abiotic variables and the microbi- proteins for B. taurus, O. aries, S. scrofa, C. lupus famil- ome components detected at the species level at a relative iaris, H. sapiens, C. aegagrus hircus and Culicoides spp. abundance higher than 1% (Supplementary file 1: Table ). Uniprot (https://www.uniprot.org) entries (18348, 23112, 26104, 25491, 70946, 3101 and 1909 entries respectively in March 2016) using the SEQUEST algorithm (Proteome Availability of data Discoverer 1.4, Thermo Scientific). The following con- Raw metagenomic reads for the C. imicola popula- straints were used for the searches: tryptic cleavage after tions collected at Collesano and Trapani are deposited Arg and Lys, up to two missed cleavage sites, and toler- in the [Dryad repository doi:10.5061/dryad.mr401g7]. ances of 1 Da for precursor ions and 0.8 Da for MS/MS Proteomics data on host bloodmeal identification are fragment ions and the searches were performed allowing deposited in the [Dryad repository doi:10.5061/dryad. optional Met oxidation and Cys carbamidomethylation. A mr401g7].

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Acknowledgements Chandler, J.A., Eisen, J.A. and Kopp, A. (2012) Yeast communities of diverse Drosophila species: comparison of two sym- We thank Nicola Galati, Pippo Bono, Gaspare Lo Bue and biont groups in the same hosts. Applied and Environmental Rossella Scimeca (Intituto Zooprofilattico Sperimentale Microbiology, 78: 7327–7336. della Sicilia) for assistance with Culicoides sample col- Charan, S.S., Pawar, K.D., Gavhale, S.D., Tickhe, C.V., Charan, lection and classification. This work was financially N.S., Angel, B., et al. (2016) Comparative analysis of mid- supported by the H2020 COllaborative Management gut bacterial communities in three aedine mosquito species Platform for detection and Analyses of (Re-) emerging from dengue-endemic and non-endemic areas of Rajasthan, and foodborne outbreaks in Europe (COMPARE) grant India. 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Supporting Information Table S2. BLAST identity obtained for the host DNA sequenced clones of Cyt b from C. imicola collected in Collesano and Trapani. Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Table S3. Climate data of the averaged land surface temperature (LSTD) and the normalized difference vegetation index (NDVI), an indicator of pho- Figure S1. Metagenome dataset validation by qPCR. (A) Bacterial DNA tosynthetic activity and thus a proxy of soil moisture. levels were determined by Cyt b qPCR and normalized against Culicoides Ef1 b and shown as average + S.D. normalized Ct values. Normalized Ct Table S4. Variables included in the Principal Component Analysis (PCA). values for C. imicola collected in Collesano and Trapani were compared by Table S5. Characterization of selected microorganisms found in the C. imi- Student’s t-test for samples with unequal variance (*P < 0.05; n = 2 bio- cola population-specific microbiome. logical replicates). (B) The ratio between Collesano and Trapani C. imicola population values was calculated for qPCR and metagenomics data using Table S6. Assembly metrics for the C. imicola metagenome. the average normalized Ct values and percent relative abundance, respectively. Significant differences between both populations were obtained only Table S7. qPCR oligonucleotide primers and conditions used to validate for P. putida using both methods (*P < 0.05). the C. imicola metagenome dataset.

Figure S2. Relative abundance of microorganisms in C. imicola popula- Dataset S1. Metagenome relative abundance at different taxonomic levels tions collected from Trapani and Collesano by most abundant (A) phyla, was obtained using MetaPhlAn. (B) families and (C) genera. Dataset S2. Proteomics analysis of vector and host derived proteins in C. Table S1. Taxonomic composition of the microbiome in two different pop- imicola collected from Collesano and Trapani. ulations of C. imicola.

103 Additional file 1

Figure S1. Metagenome dataset validation by qPCR. (A) Bacterial DNA levels were determined by Cyt b qPCR and normalized against Culicoides Ef1 b and shown as average + S.D. normalized Ct values. Normalized Ct values for C. imicola collected in Collesano and Trapani were compared by Student’s t-test for samples with unequal variance (*P < 0.05; N=2 biological replicates). (B) The ratio between Collesano and Trapani C. imicola population values was calculated for qPCR and metagenomics data using the average normalized Ct values and percent relative abundance, respectively. Significant differences between both populations were obtained only for P. putida using both methods (*P < 0.05).

104 Figure S2. Relative abundance of microorganisms in C. imicola populations collected from Trapani and Collesano by most abundant (A) phyla, (B) families and (C) genera.

105 106 GENERAL DISCUSSION

107 108 General Discussion

Mosquitoes and ticks have been identified as the main vectors of a wide variety of pathogens for humans and animals (Jongejan and Uilenberg, 2004, Colwell et al., 2011). Nowadays, the incidence of vector-borne diseases and the risk of transmission is increasing worldwide, this fact is associated with some environmental factors, such as the displacement caused by global warming, fragmentation of wildlife habitat, the migration of birds, and with factors specific to human activity like the movements of man and domestic animals (Parola et al., 2008; Dantas- Torres et al., 2015; Estrada-Peña et al., 2014; Parham et al., 2015). Nonetheless, pathogens are not alone, as they coexist, within the microbiome of vectors, with a wide variety of endosymbionts, symbionts and commensals microorganisms (Bonnet et al., 2017). The rapid advance of high-throughput sequencing have greatly contributed to increase the knowledge on the complexity and diversity of vector microbiome composition (Clay et al., 2008; Andreotti et al., 2011; Carpi et al., 2011; Lalzar et al., 2012; Vayssier-Taussat et al., 2013; Qiu et al., 2014; Nakao et al., 2013; Williams-Newkirk et al., 2014; Narasimhan and Fikrig, 2015; Abraham et al., 2017; Díaz-Sánchez et al., 2018b, 2019). Metagenomics methods for the study of the taxonomic and functional profiles of the microbial communities have been applied to determine, the presence and function of these microorganisms. However, current challenges on the study of the vector microbiome focuses on two broad aspects: i) to characterize the microbiome that is representative of a given vector species, and ii) to acknowledge the nature of vector-microbiome-pathogen interactions. Characterization of the vector microbiota generates fundamental knowledge about the functioning of this “micro-ecosystem” and increases our understanding of the microbiota role in the epidemiology of vector-borne diseases. Using shotgun-metagenomics sequencing, we identified commensal, symbiotic and pathogenic microbiota in wild-caught Ixodes ventalloi (Díaz-Sánchez et al., 2019). In this study, previously uncatalogued bacteria genera within the microbiota of I. ventalloi was identified, for example the dominant genera Anaplasma and Borrellia; but also common Ixodes tick endosymbionts such as Rickettsia, (Carpi et al., 2011; Hunter et al., 2015; Bonnet et al., 2017); and commensals like Pseudomonas, Sphingomonas and Cutibacterium (Alvarez et al., 2012; Niels et al., 2018). It is well known that tick microbes participate in many biological processes (Brucker and Bordenstein, 2013; de la Fuente et al., 2016; Sevellec et al., 2018). Traditionally tick-endosymbionts have been associated with the nutritional status and a cooperative relationship with their hosts (Ahantarig et al., 2013; de la Fuente et al., 2016; Duron et al., 2017, 2018). Recently, the frequent identification of environmental microbe ssuggest it´s likely they have an impact on diverse metabolic networks, stimulating microbial alliances, and host functions (Zolnik et al., 2016; de la Fuente et al., 2016, 2017). Even though metagenomics approach allows to monitor changes in the composition and function of the microbes, it cannot

109 directly measure the functional activity of the community under a given set of conditions. Similarly, metatranscriptomics approaches have been recently applied to the study of microbial communities in arthropod vectors and vertebrate hosts (Mäder et al., 2011; Johansson et al., 2013; Razzauti et al., 2015; Luo et al., 2017). Metatranscriptomics analyses includes all RNA produced by a microbial community and uses it to profile the structure, function and diversity of the community. This method is so sensitive that also allows the detections of organisms with a number of copies that cannot be observed by DNA sequencing (Cole et al., 2003; Franzosa et al., 2015). However, even the combination of metagenomics and metatranscriptomics does not provide a direct measure of the functional activity of the community in a particular condition or at a specific time. In this regard, high-throughput proteomics allows measuring the abundance of proteins which in turn provides a more direct measure of the functional activity of a cell or a community. The advantage of using metaproteomics is that this method requires exhaustive analyses with amino acid sequences of peptides for protein identification resulting in better definition at the species level (Tanca et al., 2013, 2014; Fernández de Mera et al., 2017). Applying these concepts we integrated metatranscriptomics together with metaproteomics pipelines to characterize symbiotic, commensal, soil, environmental, and pathogenic bacteria in the Ixodes ricinus microbiota, and we identified previously unknown commensal and soil microorganisms (Hernández-Jarguín et al., 2018). Using this pipeline we observed a better efficiency combining metaproteomics and metatranscriptomics that resulted in a better support for bacterial microbiome identification at the species level and support for functionality. We demonstrated a positive correlation between RNA IDs and protein PSMs for Firmicutes (commensal and environmental bacteria) and tick-borne pathogens (TBPs) suggesting these bacteria were metabolically active in unfed I. ricinus (Hernández-Jarguín et al., 2018). Through the GO analysis for Biological Processes, we found further support for proteins identified in TBPs (Anaplasma, Borrelia, Ehrlichia and Rickettsia genera) concluding that these identified bacteria may be involved in tick-bacteria interactions as well (Hernández-Jarguin et al., 2018). Recently, Abraham and colleagues (2017), demonstrated one of the mechanisms behind tick-bacteria interactions, and reported that the bacterial pathogen Anaplasma phagocytophilum manipulates the tick microbiota through induction of Ixodes scapularis antifreeze glycoprotein (IAFGP). These finally results in the alteration of bacterial biofilm formation to facilitate the infection with Anaplasma. Interestingly from our microbiota data of unfed I. ricinus, we hypothesized that Anaplasma infection may be also facilitated by interfering with biofilm formation through reduction of the levels of biofilm matrix binding proteins (MBPs) and/or the presence of bacteria producing MBPs such as Streptococcus. To date, several studies have reported the usefulness of proteomics analysis for the diagnosis of tick-borne diseases and to allow exploratory and/or directed studies of host proteins and microbial functions (Karger et al, 2012; Yssouf et al, 2013, 2015; Fotso et al, 2014; Singhal et al, 2016).

110 However, we consider the metaomics approaches integrating different omics datasets from metagenomics, metatranscriptomics and metaproteomics studies provide (i) a better description of vector microbiota composition, (ii) insights into functional interactions occurring in the interface host-microbiota-pathogen in blood-feeding arthropods (Franzosa et al., 2015; Villar et al., 2015a; Narasimhan and Fikrig, 2015), (iii) and the discovery of new targets for prevention and control of tick-borne diseases (Abraham et al., 2017; Narasimhan et al., 2017; Xiang et al., 2017). Important advantages have resulted from the combination of genomics and proteomics, as together they can increase the resolution to identify and characterize genetic variants of tick-borne pathogens. We used this methodological approximation to characterize the Crimean-Congo hemorragic fever virus (CCHFV) in ticks (Fernández de Mera et al., 2017). CCHFV is considered the most genetically diverse of the arboviruses because it shows differences among genotypes, from the viral S segment to the M segment nucleotide sequences (Bente et al., 2013). Currently, CCHFV is diagnosed by molecular-based techniques with the amplification of viral genome segments (Midilli et al., 2009; Aradaib et al., 2011; Osman et al., 2013; Bente et al., 2013; Papa et al., 2014; 2015). However, even though virus amplification occurs after its multiplication in ticks during feeding on a susceptible host (Dickson and Turell, 1992; Bente et al., 2013; de la Fuente et al., 2017), CCHFV identification in individual ticks is difficult due to low infection levels and the presence of other Nairoviruses (Walker et al., 2015). Using genomics analyses we identified the S segment of CCHFV and with proteomics analyses was directed the L segment that contain an RNA polymerase supposedly dependent on RNA (Bente et al., 2013). Finally, using a combination of genomics and proteomics we were able to characterize the AP92 prototype strain of CCHFV in two species of ticks, Dermacentor marginatus and Haemaphysalis parva with a high resolution. The capacity to combine tools that permit rapid and better detection of genetically diverse viruses such as CCHFV is of high importance when has been linked to increased virulence and emergence in new geographic locations (Xia et al., 2016). Although the AP92 like strains probably do not represent a high risk of CCHF to the population, the circulation of genetically diverse CCHFV strains could potentially result in the appearance of novel viral genotypes within increased pathogenicity and fitness. One major objective in this area is to identify specific genotypic characteristics that can explain differences on the pathogenicity and epidemiological behavior of different tick-borne bacterial strains, as well as to reveal new genetic markers and candidate vaccine antigens. To date biological questions regarding to the transmission, pathogenicity and host-specificity of tick- borne pathogens are relatively unknown. Whole-genome sequencing and comparative genomics analysis are powerful tools that combined can add valuable information on the genotypic variations and potential implications for vectors, disease risk assessment and control. In other words, different epidemiological contexts are associated with considerable strain variations, and

111 pathogenic variants distribute geographically different. A good example, is A. phagocytophilum, a tick-borne zoonotic bacteria with a highly variable genome, a marked host tropism and marked geographical differences associated to the pathogenesis. The genus Anaplasma (Rickettsiales: Anaplasmataceae) comprises obligatory intracellular Gram-negative bacteria, so far including seven species, Anaplasma phagocytophilum, A. marginale, A. ovis, A. bovis, A. centrale, A. platys, and A. capra (Dumler et al., 2001; Li et al., 2015). These pathogens cause different forms of anaplasmosis in humans, domestic and wild animals worldwide (Kocan at al., 2015). Recently, several studies have reported genome sequence information for Anaplasma spp. to advance the identification of candidate protective antigens and our knowledge on genetic diversity, host tropism, virulence, and tick transmissibility of these pathogens (Dark et al., 2011; Dugat et al., 2014,2016; Battilani at el., 2017; Lockwood et al., 2016; Al-Khedery et al., 2014). However, there are still unresolved questions for example, which differences in the genome could be involved in host tropism?, and how these genetic differences are related to functionality, pathogenesis and virulence will help to develop genetic markers and candidate vaccine antigens. Collection of genomic data and the generation of genomic repositories is crucial to further achieve these questions. In this context, we contributed to genomic data generation by using whole-genome sequencing and de novo assembly methods to reconstruct the draft genome of different isolates of the tick-borne pathogen Anaplasma spp, including the strains A. phagocytophilum NY18, A. marginale Oklahoma-2, with the report of the first characterization of the strain A. ovis Idaho (Díaz-Sánchez et al., 2018a). To design strategies directed to reduce and control diseases caused by vector-borne pathogens it is necessary the integration of data generated from metagenomics, metaproteomics and metatranscriptomics. Nonetheless, adopting a systems biology approach to characterize vector- microbiome-pathogen interactions will need to enable the dot connection between the metadata, and metagenomic information available in the field in order to infer reliable microbiome profiles and functionality. One of the approaches is to screen the microbiome of vectors to reveal interesting features and biomarkers that can be potentially targeted and considered to develop novel vector control methods of vector-borne pathogens. For example, we can identify the microbiome responsible of key biological pathways and molecules that correlates with pathogen transmission and disease. One of the challenges in the study of the origin of the vector microbiome is to acknowledge how the microbial diversity varies and which are the related intrinsic and extrinsic vector factors that modulate microbial communities. Several studies have attributed the diversity of microbial communities on mosquitoes and ticks to factors related to the biology of the vector, including differences between species, sex, development stage and nutritional status (Lalzar et al., 2012; van Overbeek et al., 2008; Carpi et al., 2011; Williams-Newkirk et al., 2014; Moreno et al., 2006; Clay et al., 2008; Zolnik et a., 2016; Heise et al., 2010; Menchaca et al., 2013; Zhang et al., 2014);

112 environmental factors, associated with available sources of blood meal, season and collection sites, differences between laboratory-reared and wild populations (Zouache et al., 2009; Acevedo et al., 2010; Heise et al., 2010; Minard et al., 2013; Jones et al., 2013; Coon et al., 2014, Charan et al., 2016, Williams-Newkirk et al., 2014, Van Treuren et al., 2015, Douglas, 2015; Zolnik et al., 2016, Villegas and Pimenta, 2014; Bonnet et al., 2017, Xiang et al., 2017) and infection by pathogens (Steiner et al., 2008; Abraham et al., 2017). In this framework, we characterized the Culicoides imicola microbiota from two populations using shotgun-metagenomic sequencing, and studied the potential impact of the abiotic factors (measurements related to the environment) and the biotic factors (host bloodmeal sources) on its structure (Díaz Sánchez et al., 2018b). From the results, we observed that both populations of C. imicola shared a core microbiome that could be the result of a positive selection over the constant invasion of transient microorganisms (Minard et al., 2015); or the result of similar habitats with similar conditions in both locations, and thus exposure to similar microorganisms (Mellor et al., 2000). Interestingly, we determined that C. imicola microbiota was structured by a population-specific microbiome as well, and that could reflect the impact of abiotic and biotic factors and modulate microbiome composition. However, how much of the population-specific microbiota or the shared microbiota participates on the vector adaptive traits, the geographical expansion and vector competence is not clear yet. It´s becoming clearer within the scientific community that the geographic adaptation of insects is strongly associated with the composition of the microbiome (Minard et al., 2013), and that microbes acquired from the environment could enhance the mosquito adaptive skills (Engel and Moran, 2013, Minard et al., 2015, Toft and Andersson, 2010, Morag et al., 2012). In this study we also combined DNA and proteomic based methods for the characterization of host blood meal sources to finally identify the species Homo sapiens and Cannis lupus familiaris as the preferred host blood meal for C. imicola. Finally, our observations suggested that the presence of these preferred hosts in conjunction with a suitable environmental determined by the NDVI (a proxy for soil moisture) could have a potential impact in the structure of C. imicola microbiome in both populations (Díaz-Sánchez et al., 2018b). Our research is supported by various studies thath also observed differences in the insect gut microbial communities associated to bloodmeal sources and specific environmental conditions (Toft and Andersson, 2010; Morag et al., 2012; OseiǦPoku et al., 2012; Nayduch et al., 2015; Charan et al., 2016). The use of omics approaches combining genomics, metagenomics, metatranscriptomics, and metaproteomics allowed (i) to catalogue the microbiome composition of vectors (ticks and mosquitos) with a high resolution, (ii) to infer potential interactions among microbiome and pathogens, and (iii) to identify and characterize vector-borne pathogens with robustness. On the whole, the results presented in this thesis hint at exciting opportunities for future investigations on the interface vector-microbiota-pathogen, and further contribute to develop new strategies to control vectors and prevent the transmission of tick-borne pathogens.

113 References

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120 121 122 CONCLUSIONS

123 124 CONCLUSIONS

1. Integration of metatranscriptomics and metaproteomics analyses is an improve strategy for the characterization of tick bacterial microbiome communities by increasing the identification of bacterial proteins and support for identified bacteria with putative functional implications. Metaomics approaches increase our knowledge about tick microbiota and allow us to explore microbiome-pathogen interactions.

2. The findings of the combined metatranscriptomics and metaproteomics approaches suggest that Anaplasma infection may be facilitated by interfering with biofilm formation through reduction in the levels of biofilm matrix binding proteins (MBPs) and/or the presence of bacteria producing MBPs.

3. Whole-genome shotgun-metagenomics sequencing analysis is a powerful tool to characterize the tick microbial community by identifying symbiotic, commensal, environmental bacteria and potential tick-borne pathogens such as Anaplasma and Borrelia species. The generation of tick microbial community datasets is a fundamental step for resolving microbial dynamics, functionality and interactions implicated in vector competence, host adaptation and geographic expansion.

4. The combination of genomics with a metaproteomics approaches improve the identification of Crimean-Congo hemorrhagic fever virus (CCHFV) and allow differentiation between CCHFV and other Nairovirus. The metaproteomics approach provides support and validates at the protein level the identification of the CCHFV.

5. The availability of genome sequences of A. phagocytophilum, A. marginale and A. ovis isolates constitute a valuable resource for the study of tick-pathogen-vertebrate host interactions. This is the first report of an A. ovis genome sequence. The release of these three genome sequences will allow comparative genomics analysis against other Anaplasma species to study the genomic differences implicated in host tropism and virulence with implications for anaplasmosis disease risk assessment and control.

6. Whole-genome shotgun metagenomics analysis is effective to expand the information on mosquito microbiome composition, identifying also eukaryotic microorganisms and viruses. Furthermore, the combined effect of H. sapiens host preference (biotic factor) and local soil moisture (abiotic factor) shape the microbiome composition resulting in a population-specific microbiome.

7. Combining vector ecological data and whole-genome shotgun metagenomics pipelines contribute to understand and predict geographic expansion of mosquitoes with possible implications in the prevalence of vector-borne diseases.

125 126 127 128 GLOSSARY

Microbiome: collective genomes and metabolites of the microorganisms that reside in an environmental niche or colonize host animal, their interactions with each other and their interactions with the host.

Microbiota: collective dynamic communities of microorganisms, including viruses, bacteria, and fungi that establish structured interactions with their host. This interspecies relationship can range from mutualistic to commensal or pathogenic, depending on the inherent composition of the microbiota or the immune status of the host. Deregulation of host microbiota interactions impacts on host homeostasis and can have pathogenic effects.

Metagenome: is comprised of all the genetic elements of the host and all those of all the microorg-anisms (microbiome) that live in or on that host.

Metatranscriptome: refers to the total content of gene transcripts (RNA copies of the genes) in a community, considered as a unique entity, at a specific moment of sampling. Varies with time and environmental changes.

Metaproteome: refers to the total proteins of the microorganisms of a community at a specific moment of sampling. Varies with time and environmental changes.

Metagenomics: This term has been defined as “the application of modern genomics techniques to the study of communities of microbial organisms directly in their natural environments, by passing the need for isolation and lab cultivation of individual species”. Its main purpose is to infer the taxonomic profile of a microbial community.

Metatranscriptomics: The techniques applied to obtain the whole expression profile in a community (total RNA transcribed) and to follow the dynamics of gene expression patterns over time or environmental parameters.

Metaproteomics: The techniques applied to obtain the protein profile of a community at a specific moment of sampling.

Vector: an organism that is capable of transmitting a specific pathogen.

Vector competence: component of vectorial capacity that depends on genetic factors affecting the ability of a vector to transmit a pathogen.

Mutualistic interactions (i.e., commensalism) when organisms from both species benefit from their interaction.

Pathogenic interaction: where one species (i.e., the pathogen) benefits from the interaction in detriment of the other (i.e., the host).

Commensal (bacteria): refers to a neutral relationship between the bacteria and the host. In this kind of relationship, either benefits from the other or provokes any harm.

Pathogen: an infectious biological agent that can produce a disease in its host. The term is most used to describe microorganisms like virus, bacteria or fungi, among others. These agents can disrupt the normal physiology of plants, animals and humans.

129 Symbiont: an organism living in symbiosis. The intimate living together of two dissimilar organisms in a mutually beneficial relationship.

Obligate (primary) mutualistic symbionts: required to support normal host development, thus assisting their host in various essential functions. This includes nutritional diet upgrades by providing biosynthetic pathways absent from their hosts; provide B vitamins and cofactors not readily obtainable in sufficient quantities from a uniquely blood-based diet.

Facultative (secondary) symbionts: not required for host survival. Some are defensive symbionts conferring protection against natural enemies or heat; others are reproductive parasites that manipulate host reproduction.

Peritrophic matrix (PM): an extracellular matrix secreted by the gut of insects formed by chitin and glycoproteins (mucins and peritrophins). The PM involves the blood bolus, thus preventing direct contact of blood content with the midgut epithelium.

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AGRADECIMIENTOS

Quedarse en lo conocido por miedo a lo desconocido, equivale a mantenerse en vida, pero no vivir. Siempre he sido consciente de mis limitaciones, pero eso nunca ha impedido que deje de luchar por aprender y mejorar. Sin embargo, ni todos mis esfuerzos ni sacrificios habrían dado frutos gracias a la ayuda de muchas personas durante todo el proceso de la tesis doctoral.

Por lo anterior, quiero agradecer a mi director, José de la Fuente García, porque aun conociendo mi inexperiencia me brindó la oportunidad de formar parte de su equipo, de aprender a hacer ciencia y con ello mejorar notablemente mi carrera profesional. De igual forma quiero agradecer a mi codirector Christian Gortázar Smith por la oportunidad de formar parte de su equipo de monterías, por permitirme ver desde primera fila los esfuerzos que se hacen por controlar la Tuberculosis animal y también por el apoyo brindado en la realización de esta tesis. Gracias a mi codirectora Sandra Díaz Sánchez, por todo el esfuerzo, la paciencia y dedicación brindada en el desarrollo de cada apartado de esta tesis. Debo decir que ha sido un camino largo y difícil; Albert Einstein dijo alguna vez que el aprendizaje es experiencia y, con total agradecimiento hoy yo puedo decir que me llevo una maleta llena de experiencias y aprendizajes.

No cabe duda de que, a demás de mis asesores, todas las post doc del grupo me han dado grandes lecciones; Pilar Alberdi e Isabel García han sido dos personas que en todos estos años siempre mostraron disposición para enseñarme, resolverme dudas, darme una opinión científica e incluso para brindarme apoyo moral. No puedo dejar de mencionar a Margarita Villar, que además de los aprendizajes en protéomica ha sido un ejemplo de carácter y fortaleza, dos cualidades importantes para continuar en esta profesión.

A propósito, los más grandes logros en esta profesión son aquellos que se consiguen además de con otros, con trabajo en equipo. Cada experimento requiere conocimiento, tiempo y esfuerzos y, en el camino el IREC se vuelve tu segunda casa, gracias a mis compañeros Lourdes Mateos, Marinela Contreras, Iratxe Díez, a todo el equipo de Sanidad y a esas mañanas de procesado de muestras orquestado por Cristina, a Peter Pharma, Alberto Moraga, Sara Artigas, Almudena, a la querida Emilia y a todos los que forman parte del IREC, reiteradas gracias por todas las experiencias. La logística y procesos administrativos son igualmente importantes para que todos los trámites y experimentos salgan adelante; gracias a Encarnación Delgado y David Fernández por todo su trabajo administrativo y principalmente a David, gracias por tu empatía, sinceridad y amistad. Debo agradecer a Alejandro Cabezas por la oportunidad de realizar una estancia en ANSES formando parte de su equipo y, por todos los aprendizajes durante la misma. Gracias a la Dra. Consuelo Almazán, Sara Moutailler, Simo Ladislav, Sarah Bonnet, Lourdes Mateos, Clemons, Martin, Cleotilde Rouxel y Lisa Fourniol, por todas las experiencias y los detalles que hicieron mas placentera mi estancia.

Gracias a todos mis compañeros de la FMVZ-UAT en especial a Irma Herrera, por enviarme ánimos y buenos deseos y, a todos los que hicieron posible que pudiera obtener mi beca PRODEP, en especial a los doctores Julio Martínez Burnes y Hugo Barrios por confiar en mi y apoyarme para continuar formándome. Gracias a mis amigas Edna Gallardo, Elizabeth Hernández, Thalia Torres y Karla Castillo por su amistad y muestras de cariño y apoyo. En el camino de la tesis, algunas experiencias son sacrificios y dificultades que uno como estudiante debe sortear en la vida personal. Si alguien ha estado conmigo en todo momento han sido ellos, mi familia biológica, a mis padres Raúl Hernandez y Angélica Jarguín, mis enanos Rocío, Jesús y David y mis sobrinos Luka y Fernanda, y a mi mejor amiga Ceshia Guzmán y hermana por convicción, gracias por el cariño y por apoyar y respetar mis decisiones, aunque eso implique estar lejos de ustedes. Gracias a mi segunda familia, mis padres canarios Francisco Martínez y Teresa Rodríguez, por ser un apoyo moral, por todo el cariño y por abrirme siempre las puertas de su casa y hacerme sentir parte de su familia.

Hay quienes piensan que las dificultades preparan a personas comunes para destinos extraordinarios. Finalmente, agradezco al destino y la fórmula matemática de: (1+1)/2=juntos, o algo así, que formuló Luka, porque lo más extraordinario de todo esto ha sido encontrar a la persona que me complementa y que fomenta que sea un mejor ser humano. Gracias infinitas a Ariday Martínez Rodríguez, por ser mi amigo, confidente, mi compañero de piso, mi compañero de laboratorio más de una madrugada y durante el día, mi conciencia, el amor de mi vida y mi todo. Gracias por alentarme en cada desafío, gracias por preocuparte porque mantuviera una salud mental y física, gracias por acompañarme en cada etapa y darme fuerzas para seguir adelante, gracias por todo el amor que ha hecho que la añoranza de mi familia, costumbres y tradiciones sean mas llevadera y, gracias por permanecer a mi lado al mismo tiempo que yo hacía mi doctorado, siendo esto último algo complicado de sobrellevar para nuestras familias y/o para las personas que más nos quieren, por el tiempo y dedicación que requiere obtener un doctorado.

Gracias a todos y cada uno de ustedes por ayudarme a llevar a termino mi tesis doctoral, gracias a la vida por poner en mi camino a mi compañero de vida y gracias a Ariday por mantenerse a mi lado y ser un soporte fundamental en todo este proceso. GRACIAS!!