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1 Introduction

Epigenetic responses mediate the interaction between the environment and the genome to promote homeostasis in the face of external stressors. The environment, including the internal and external microbiota, is the classic trigger of epigenetic changes. The stress response is a prime example of a physiological process that allows an organism to respond to stress, and when the stress is relieved, restore homeostasis [1]. Although, best studied in vertebrates, all organisms, including arthropods, are equipped with a stress response system [2]. Epigenetic processes have long been known to potentiate stress responses by influencing cellular physiology, organismal physiology, differentiation, morphogenesis, variability, both within the lifetime of an organism and between generations, aiding in organismal adaptability [3–5].

Classical examples of the microbiome’s ability to modulate multiple host physiological functions are baculovirus-mediated control of caterpillar behavior [6], Ophiocordyceps manipulation of ant behavior [7,8], through direct metabolite influence on host gene expression and the complex manipulation of host reproduction by Wolbachia spp. bacteria by epigenetic modulation [9,10]. These studies demonstrate the influence of microorganisms on arthropod behaviors, often by inducing epigenetic modifications of the host.

Epigenetic control relies on complex inter-dependent processes of histone modification, and DNA modification, orchestrated by non-coding RNAs [3,4,11,12]. The euchromatic histone lysine methyltransferase 2 [EHMT2) gene is recognized as a master epigenetic regulatory gene that is involved in multiple biological processes, including restoring homeostasis following physiological stress [1,13–15]. Mammals have two subunits: GLP and G9a, whereas invertebrates have been found to have only one [16]. EHMTs are characterized by their SET domains, domains that typically have methyltransferase activity that tend to promote transcriptional repression through the mono- and dimethylation of histone H3 at H3K9 and H3K27 [15,16]. In Drosophila, EHMT2 promotes survival when the organism is subjected to oxidative stress or viral infection, positioning EHMT2 as a key epigenetic regulator of stress responses [13,14]. Interestingly, EHMT2 has also been shown to have an analogous role in mammalian stress responses by modulating the interferon-mediated immune response in mice embryonic fibroblasts [1,17]. EHMT2 expression attenuates the stress response pathways, allowing pathogen tolerance that prevents succumbing to the overconsumption of energy and resources by the immune response [13,14].

Ticks are hematophagous arthropods and are the vectors of the greatest diversity of pathogens among arthropod vectors, indicating appreciable tolerance for diverse microbiota [18]. Ticks vector multiple pathogens, including Borrelia burgdorferi, a prominent pathogen for human and veterinary health [18–22]. Ticks acquire pathogens upon blood feeding from an infected animal, and then may transmit the pathogen to other hosts upon their next blood meal [23]. Once B. burgdorferi is transmitted to non-adapted mammals such as humans or dogs, it can cause Lyme disease. As the spirochetes disseminate throughout the host’s body, it can have far-reaching effects with a broad range of multi-system clinical symptoms [24,25]. The manifold effects of climate change affect interactions between the environment, animals and humans. Ectothermic arthropod vectors such as ticks are particularly influenced, as are the distribution of their reservoir hosts, all leading to increased infections of humans, companion and agricultural animals [26–29]; human sero-reactivity to Borrelia has been estimated to be approximately 14.5%, depending on location, detection methodology and risk factors [30].

Borrelia burgdorferi and related members of the Borrelia genus have adapted to use ticks as vectors [18,31,32]. Borrelia spirochetes are able to adapt to the dramatically different environments of the arthropod vector and the vertebrate host by sophisticated regulation of its own gene expression [18] as well as modulating the gene expression of its vertebrate host to promote its survival [18,19,33,34]. In the host, changes in gene expression are often seen in genes related to the extra-cellular matrix, the stress response, actin interactions, and the immune response, increasing the probability of the pathogen establishing in the host [34]. Manipulation of its tick vectors is much less studied. As the vector of the pathogen, natural selection would act against a deleterious effect of Borrelia on the tick. That consideration does not, however, preclude other changes to gene expression in the vector, in particular those that promote pathogen maintenance in the tick, tick fitness and transfer to the host.

Physiological, behavioral and underlying genetic changes have been detected in Ixodes ricinus species complex ticks [18,20] of which Ixodes scapularis and Ixodes persulcatus are members. I. scapularis, the most common member of this group in North America, has a genome 89% homologous to I. ricinus [35]. Studies on I. ricinus found that B. burgdorferi presence in the tick was correlated with increased tick fitness [36]. Ticks infected with Borrelia spp. had a greater reserve of fat stores than those uninfected, which reduced mortality from desiccation [37]. Infected ticks in this species complex also show reduced locomotion, and the resulting questing behavior, that conserve energy [38,39]. Although these physiological changes did not correlate with increased pathogen prevalence in one natural setting [40], the lag between tick population establishment and pathogen prevalence [41] would be a plausible explanation for this discrepancy. These physiological changes are underpinned by changes in the expression of genes responsible for nutrient accumulation, lipid production, carbon use, and metabolism, the expression of which are altered in Borrelia-infected ticks compared with uninfected ticks [42]. One clear example of manipulation of the tick genome is evidenced by the tick-encoded protein Tick Receptor for Outer Surface Protein A (TROSPA). This receptor binds B. burgdorferi outer surface protein A (OspA) to allow the bacteria to colonize the tick. The receptor is more highly expressed in Borrelia-infected than uninfected ticks, indicating control of tick gene expression to the benefit of the bacteria [43]. Borrelia burgdorferi manipulates the expression of other tick genes to enhance vector fitness. In I. scapularis, B. burgdorferi infection correlates with increased expression of a tick salivary protein, tick histamine release factor, which leads to enhanced tick feeding, and potentially also enhanced B. burgdorferi transmission [44]. Borrelia burgdorferi also appears able to manipulate the tick vector to aid the bacteria in establishing infection in the host. Tick salivary proteins Salp15 binds to the Borrelia protein Outer surface protein C (OspC) when the bacteria is introduced into the host and protects the pathogen from the host immune system [18,45,46]. These gene expression changes reflect changes in tick gene transcription coupled to the presence of Borrelia, suggesting multiple manipulations of the vector genome to promote pathogen transmission.

These physiological changes and the underlying changes in gene expression promote increase tick fitness, and so foster improved transmission of the Borrelia bacteria. However, the underlying epigenetic mechanisms of this process remain largely unexplored for Borrelia. In contrast, epigenetic control of the tick vector by Anaplasma and related tick-borne intracellular bacteria has been investigated [47–49]. Selection for vector manipulation in obligate tick-transmitted bacteria, such as B. burgdorferi, would be expected to be strong. Thus, demonstrating an epigenetic change in response to B. burgdorferi infection is an important first step in a more complete understanding of how tick pathogens modulate physiological changes in their vector to increase the likelihood of pathogen transmission.

DNA methylation has been identified in diverse arachnids, including ticks, although it has been found to be reduced in many parasitiformes [50]. Unlike classical vertebrate systems, DNA methylation in invertebrates tends to be found in gene bodies rather than regulatory regions and may serve to promote rather than repress transcription [50,51]. As is true of vertebrates, in invertebrates the extent, targets and, to some extent, mechanism of DNA methylation, varies between species and the variability of methylation in invertebrates suggests that it also serves non-transcriptional functions [52,53]. I. ricinus has transcripts encoding DNA methyltransferase [DNMT) enzymes and methylation occurs at the 5-carbon position of cytosine bases [54]. Ixodes scapularis has the corresponding DNMT sequences [55]. Ixodes scapularis also contains pericentromeric tandem repeats, called Ixodes scapularis repeats (ISRs), which were found to be methylated by both methylation-sensitive restriction enzyme digests [55] and binding of antibodies against 5-methylcytosine to tick DNA [54]. These repeats appear to be non-coding and their function is currently unknown [55]. The I. scapularis genome has robust representation of families of tandem repeats, constituting 40% of the genome, with ISR-2 repeat family being the most common, constituting approximately 7% of the genome [23]. The limited DNA methylation present in I. scapularis may have discouraged research interest into epigenetic regulation in this species. Nevertheless, [56] found modest changes in DNA methylation in response to temperature in the Asian longhorned tick, Haemaphysalis longicornis, so DNA methylation may be a relevant epigenetic mechanism in ticks.

Histone modifications and non-coding RNA (ncRNA) regulation are the most complex players in epigenetic regulation and orchestrate complex epigenetic responses to the environment in many species, including ticks [20,49,57,58]. The requirement of the histone methyltransferase DOT1L for development in the soft tick Ornithodoros moubata exemplifies the essential nature of epigenetic gene regulation [59]. I. scapularis has 34 histone modifying enzymes and shows a complex epigenetic response to Anaplasma phagocytophilum infection [49]. Additionally, [23] identified 4439 ncRNA genes in the I. scapularis genome. Although the function of each ncRNA encoded by these genes remains to be elucidated, [60] found that A. phagocytophilum modulates the miRNA profile of ticks during infection.

The goal of this work was to investigate the epigenetic impact of B. burgdorferi on DNA methylation and histone modifications in the tick I. scapularis. The epigenetic effects of this pathogen on gene expression in the vertebrate host has been documented, but appreciably less work exists on the pathogen’s effect on the tick vector. Investigation of altered histone modifications in I. scapularis in response to B. burgdorferi infection is hampered by the absence of known gene targets of Borrelia genome manipulation. Consequently, we examined expression of the key epigenetic regulator, EHMT2. This is a gene with both a well-defined role as an epigenetic regulator and a known role in the stress response and pathogen tolerance, a key physiological response expected in a vector organism.

2 Materials and methods

2.1 Tick samples

Ticks used for methylation analysis were sourced from archived samples at the Mount Allison Tick Bank (Mount Allison’s Animal Care Committee, #102550). These ticks had been donated by members of the public. Upon receipt, half of each tick was used for DNA testing for tick-borne pathogens as described by [61] and the other half archived at -20^oC. For this study, 26 engorged adult female archived half-ticks, 6 donated in 2018 as part of a pilot study and 20 donated in 2020 were selected (Table 1). Ticks used for the EHMT2/G9a expression analysis needed to be collected alive to preserve RNA integrity. These ticks were collected in October 2022, from the Annapolis Valley, Nova Scotia, by collaborator Glen Parsons. Ticks were collected by flagging, placed in a urine specimen container with a moist towel and sent by personal courier to the lab. In the lab, ticks were stored in a 10^oC incubator for no more than 7 days. Living ticks were manually killed by dismembering and half ticks were placed in RNALater (Sigma R091) and stored at -80^oC to reduce RNA degradation. Both female and male ticks were used for the EHMT2/G9a expression analysis.

[Figure omitted. See PDF.]

2.2 Screening of tick samples for tick-borne pathogens

Screening for pathogens common in the region, including Borrelia, was conducted. DNA was extracted from the archived half-tick sample or the half tick that was not used for RNA isolation. DNA extraction was as described by [61] with the exception that the DNeasy® Blood & Tissue Kit (Qiagen 74104) was used, following the manufacturer’s recommended protocol for tissue extraction with incubation of the tick samples overnight at 56°C with proteinase K. The concentration of the DNA samples was determined using a Nanodrop®ND-1000 UV-Vis Spectrophotometer (Thermo Fisher Scientific). Tick DNA samples used for methylation testing were selected based on high DNA concentration, a 260/280 ratio between 1.8 and 2.0 and the 260/230 ratio greater than or equal to 2.0.

Nested Polymerase Chain Reaction (nPCR) was performed to detect DNA from B. burgdorferi, B. miyamotoi, Anaplasma spp. and Babesia spp in the tick DNA. The primers used for pathogen detection and annealing temperatures are shown in Table 2 and 25µl reactions using GoTaq Green (Promega M7122) were performed. All reactions were done in UV-treated dead air PCR cabinets and DNA extraction, PCR and gel electrophoresis were done in separate areas with unconnected air flow. No template controls (using 2 µL of nuclease-free water (nfH₂O) instead of template DNA) were done at the start and end of all PCR manipulations. Amplification involved an initial denaturation step for four minutes at 94°C, denaturation occurred at 94°C for one minute, annealing at the temperatures shown in Table 2 for one minute, and extension at 72°C for one minute, for 35 cycles. The final elongation step was at 72°C for ten minutes. Samples were held at 4°C until removal from the thermocycler and stored at −20°C until further use. The second round of the nPCR was performed similarly using 2 µL of the first-round PCR samples as DNA input. Amplicons were visualized using 1.2% agarose gel electrophoresis in sodium borate (SB) buffer with Eco-stain (Bio Basic DT81413), imaged using a UV transilluminator (Labnet).

[Figure omitted. See PDF.]

2.3 Primer design

ISR primer design: Primers were designed to amplify three families of heterochromatic tandem repeats, Ixodes scapularis repeats (ISR): ISR1, ISR2A, ISR2B, ISR2C, ISR2D, and ISR3 previously identified as being methylated by [55]. The full nucleotide sequence of each repeat was identified in the NCBI GenBank (Table 3) from the consensus sequences reported in [55], using the basic local alignment search tool (nBLAST). Because of the complexity of designing primers for tandem repeats with several internal repeat motifs, each of the six target genomic region primers were manually designed with suitable length (18–24 nucleotides), GC content of 50 ± 4%, melting temperature (T_m) of 60°C ± 3°C and a target amplicon length of 200–300 bp, as required for the MeDIP reactions. Primer sets were assessed using an Oligonucleotide properties calculator to screen for hairpins, self-complementarity, and self-annealing sites [62] and in silico PCR using Amplify4. As flaws in the primers were identified, this process was reiterated. Primer sets were then tested with tick DNA and produced amplicons of the expected sizes, albeit with laddering and smearing as would be expected for repeat DNA (S1A-C Fig).

[Figure omitted. See PDF.]

Primer design for I. scapularis EHMT2/G9a and reference gene: Several primers were designed for this study. The sequence of Drosophila melanogaster EHMT2 [13] was used to search I. scapularis genome sequences to find the most similar I. scapularis gene (accession number: GHJT01005725), which was presumed to be I. scapularis EHMT2. The NIH Primer BLAST tool was used to generate primers for the I. scapularis predicted EHMT2 mRNA, of which, two were chosen (Table 2).

2.4 PCR optimization

ISR primers: The optimal annealing temperature for each primer pair was determined empirically through conventional end point gradient PCR using GoTaq Green (Promega). Gradient temperatures tested for ISR1 were: 51, 51.9, 53.4, 55.6, 58.7, 60.9, 62.3, 63, 63.4, 65, 67.2, 68 °C. Gradient temperatures tested for ISR2A, ISR2B, and ISR2C were: 47.4, 48.6, 50.4, 51, 51.9, 53.4, 55.6, 58.7, 60.9, 62.3, 63, 63.4, 65, 67.2, 68 °C. ISR3 gradient temperatures were: 51, 51.9, 53.4, 55.6, 58.7, 60.9, 62.3, 63 °C. Amplification conditions were: a 3-minute denaturing period at 95°C, followed by 40 cycles of 30 second denaturation at 95°C, a 60 second annealing period and a 30 second elongation period at 72°C followed by a final elongation step at 72°C for 10 minutes. Amplification results were assessed by agarose gel electrophoresis as described above and the optimal amplification temperature is shown in Table 2. While a single clear amplicon is optimal, repeat sequences can generate ladders or smears upon amplification. The predicted amplicon size of ISR1 is 215 bp, and a faint band at this size is seen (S1A Fig). During subsequent qPCR reactions, however, amplification was found to be superior at 51^oC, so this annealing temperature was used. An empirically determined annealing temperature for ISR 2A and 2B primers was not obtained; both consistently produced a smear (S1A, B Fig) so the predicted annealing temperature of 55°C was used for qPCR. The predicted amplicon sizes of ISR2C and ISR2D are 242 and 213 bp, respectively; a faint band consistent with these sizes, embedded in a smear of different length fragments, was seen (S1B Fig). ISR3 produced multiple bands, the most prominent of which was consistent with the expected 211 bp amplicon size (S1C Fig). This band remained prominent at 55^oC so this temperature was used for convenience.

Optimization of EHMT2 primers: The I. scapularis rps4 and l13a are effective reference genes with constant expression through development and external environmental perturbations [63], so primers for these genes (Table 2) were used for normalization of EHMT2 gene expression. Amplification consistent with the expected products are shown in S2A and B. The l13a primer set was the most sensitive (lowest Ct; cycle threshold values) so that primer set was used for this study (S1 Table in Supplemental Tables). Amplification conditions for the control genes were described by [63] and since the annealing temperature of both designed EHMT2 primers were only 5^oC higher than the annealing temperatures of the reference genes, this PCR program was used for the EHMT2 primers as well. Amplification conditions consisted of 95^oC for 2 minutes followed by 40 cycles of denaturation at 95^oC for 15 seconds, annealing at 55^oC for 30 seconds and elongation at 72^oC for 30 seconds. If gel electrophoresis did not immediately follow qPCR, qPCR products were stored at −20°C.

qPCR calibration, qPCR conditions and standard curves: The iCycler, Bio-Rad was calibrated prior to measurements according to the manufacturer’s instructions with FAM dye and external well factor solution (Bio-Rad). All qPCR preparations were performed in a PCR hood (Fisher Scientific PCR Workstation) using autoclaved and UV-irradiated equipment and consumables. qPCR reactions were prepared in triplicate or duplicate for the ISR amplifications and in duplicate for the EHMT study. Reactions were prepared in 8-strip PCR tubes or 96 well plates (BrandTech, Cat. 781320 or 781375), with 10 μL of iQTM SYBR Green Supermix (BioRad, Cat. 1708880), 1 μL forward primer and 1 μL reverse primer specific to the region being targeted, 10 μL nf-H2O and 2 μL of input DNA for the ISR amplifications, and half as much (10µl total reactions) for the EHMT samples (previously validated to perform as well as the 20µl reactions). Two “no template” negative controls lacking input DNA were included in each run for each primer set. Following calibration, standard curves were produced to allow correlation of Ct values for each primer set to known amounts of input DNA. For the ISR primers, 2-fold serial dilutions of tick DNA from 8ng/µl to 0.125ng/µl were tested using IQ™ SYBR® Green Supermix (Bio-Rad, C#64313832). For EHMT2 and l13a primers a series of standard dilutions with both infected and uninfected tick cDNA was performed. Only the lowest three dilutions (1:1, 1:2, 1:4) generated Ct values (S3A-C Fig). Standard curves, plotting the Ct values against DNA concentrations, were made for each ISR region, with the exception of ISR1 which failed to amplify reliably at 51°C and the relative amount of methylated DNA, normalized to the input DNA, was calculated based from the standard curves, the R² values of which were all above 0.9. The standard curve was used to determine the efficiency of each primer, the linear range, and the detection and quantification limit. The slope of the standard curves allows determination of the PCR efficiency by using the formula E = 10^-1/slope.

2.5 Determining DNA methylation of I. scapularis centromeric repeats by MeDIP

The Diagenode MagMeDIP qPCR kit (Diagenode, C02010021) was used to quantify the DNA methylation status of ISR centromeric repeats of ticks infected and uninfected with B. burgdorferi. This protocol involved a series of 6 steps: (1) Cell collection and lysis; (2) DNA extraction and purification; (3) DNA shearing; (4) Methylated DNA immunoprecipitation; (5); Methylated DNA isolation; (6) qPCR analysis. The manufacturer’s protocol was followed with the following parameters and exceptions. As the starting material was archived DNA, steps 1 and 2 were not needed. To determine DNA fragment length, the integrity of the archived DNA was first assessed by agarose gel electrophoresis. Samples with initial fragment sizes larger than 400 bp were diluted to 1.2 μg of DNA in 55 μL with the GenDNA TE buffer and sonicated using a sonication bath (FS30, Fisher Scientific, Serial#RTA050266358) set to 8-10^oC and sheared with 10 repetitions of 30 second pulses with return to ice for 30 seconds between pulses. Gel electrophoresis was then used to assess fragment size and sonication repeated until fragments of approximately 400 bp were produced. The methylated-DNA immunoprecipitation (IP) reactions were prepared as described by the manufacturer, using the mouse 5-methylcytosine (5-mC) monoclonal antibody 33D3 (Diagenode, Cat. No. C15200081, Antibody registry AB_2572207), diluted as recommended by the manufacturer (antibody diluted 1:1 with nuclease-free water and then diluted 1:5 with buffers, DNA and all other reagents for a final 1:10 dilution). Validation of the antibody has been performed by the manufacturer (technical validation information https://www.diagenode.com/files/products/antibodies/Datasheet_5-mC33D3_C15200081-100.pdf). IP tubes were placed on a rotating wheel (SCILOGEX SCI-RD-E Analog Tube Rotator Mixer, C#824230019999), set at 40 rpm, at 4°C for approximately 18 hours. The kit-provided meDNA spike-in and unDNA spike-in controls, sheared methylated and unmethylated DNA from Arabidopsis thaliana, were used. Aliquots of DNA (1/10^th of the IP sample) prepared in the same manner and at the same time as the experimental samples but not subjected to immunoprecipitation, were used for the input samples. Following immunoprecipitation, the controls and immunoprecipitation reactions were again treated in parallel. Borrelia-infected and non-infected samples were tested in parallel. The isolation of the methylated DNA was performed as described by the manufacturer and the resulting DNA stored at −20°C until qPCR was performed. Prior to processing the experimental samples, control reactions targeting the 2 spike-in controls were prepared to determine the efficiency of each MeDIP reaction. These reactions were performed in duplicate for each spike-in control with the provided proprietary primers sets, as described by the manufacturer. qPCR results were used to measure the percentage of DNA methylation for the immunoprecipitated or input control DNA for each of the ISR target regions.

Input DNA and IP DNA were diluted 1:2 with nf H2O and used for subsequent qPCR reactions for each of the ISR target regions as described above. The Ct values were used to calculate the percentage of recovery of methylated DNA with the equation: % recovery = E^ [(Ct (10% input) – X) -Ct (IP)] x 100, where E is the amplification efficiency of the primers and “X” is the value to which the amplification efficiency, E, must be raised to equal 10, to compensate for the 1/10^th dilution of the input sample. In cases where E has not been determined, this can be approximated by: % recovery = 2^ [Ct (10% input) – 3.32 -Ct (IP sample)] x 100. In cases where the methylation of the target region is being compared to a reference region of the genome, the enrichment status of the sample can be determined; this was not done in this study as there was no relevant reference gene.

2.6 EHMT2 gene expression

RNA was extracted using the Qiagen RNEasy Mini Kit following the manufacturer’s directions. All work was performed in a designated sterile RNA hood and all instruments were decontaminated. The RNAlater in which the dissected ticks were stored was manually removed with a pipette. After the Qiagen RLT buffer was added, the dissected tick samples were manually homogenized with a micropestle, then centrifuged (Labnet Spectrafuge 24D) for 3 minutes at 16.3Xg to pellet the debris. The supernatant was removed and further processing was conducted according to the manufacturer’s instructions. Each sample was treated for 15 minutes with the supplied DNase solution. Samples were eluted with 30 µL of RNase free water which was applied to the column twice. 3 µL of each sample was aliquoted for quantification via NanoDrop (NanoDrop 1000) with SandWatch software and the remainder was immediately frozen at -80^oC.

For each sample, a cDNA synthesis reaction using LunaScript (New England Biolabs) was performed, as well as a “no-RT” control to ensure the removal of residual genomic DNA. All reactions were performed in a sterile Clean Prep Hood with 250ng input RNA in a 20ul reaction according using the to the manufacturer’s instructions. cDNA synthesis occurred in a themocycler (MultiGene OptiMAX Labnet International, Inc.), for 2 min at 25^oC for primer annealing, 10 minutes at 55^oC for cDNA synthesis and 95^oC for 1 minute for heat inactivation. Samples were then frozen at -20^oC until use.

2.7 Statistical and other Analyses

To assess the significance of ISR DNA methylation, both between repeats and in response to Borrelia infection, the Ct values determined by the qPCR were used for calculations of the percentage of DNA recovered as described in 2.5. R studio was used to conduct an unpaired t-test measuring DNA methylation differences by infection status and an ANOVA was used to assess the difference in DNA methylation between ISR repeats, although the repeats are not independent as the same ticks are used across primer regions.

To assess the results of EHMT2 gene expression, the Ct values generated from qPCR were normalized to the reference gene amplification via double delta Ct analysis [64]. This analysis requires that there is near equal efficiency of each primer set (within 5%), there is near 100% amplification efficacy of the reference and target genes, and that internal control genes are consistently expressed regardless of treatment [64]; efficiency of the primers sets are described above and developmental stability of expression of the reference genes is shown by [63].

3 Results

3.1 Experimental approach

To determine the epigenetic effect of B. burgdorferi on I. scapularis ticks, we assessed differences in both DNA methylation and expression of EHMT2/G9a in B. burgdorferi-infected and -uninfected ticks. Ticks were obtained as donations from the public and tested for B. burgdorferi infection by nested PCR upon receipt. DNA from these ticks were then rescreened for other common tick-borne pathogens. The methylation status of ISR repeats, previously determined to be methylated [54,55], was assessed by magnetic-methylated-DNA immunoprecipitation (MagMeDIP) reactions. Expression of EHMT2/G9a in B. burgdorferi-infected and -uninfected ticks was determined by real-time reverse transcription PCR using primers designed for I. scapularis G9a and the housekeeping gene l13a.

3.2 Analysis of DNA methylation of Ixodes scapularis centromeric repeat regions (ISR)

For the DNA methylation study, DNA was extracted from 26 female I. scapularis ticks, 10 infected with B. burgdorferi, 16 uninfected. The concentration of the extracted DNA and 260/280, and 260/230 ratios of archived tick bank samples were assessed and were considered in selecting which samples to include in the groups of tested ticks. Ticks infected with Anaplasma spp. or Babesia spp. (Table 1) were excluded. Following sonication, MeDIP was used to isolate methylated DNA and the recovered methylated DNA was quantified through qPCR targeting the ISR regions. Control qPCR reactions using the meDNA and unDNA primers, which target the methylated or unmethylated spike-in controls, provided with the MagMeDIP qPCR kit were performed to ensure success of the MeDIP in recovering methylated DNA. S4A-C Fig shows the electrophoresis of the qPCR amplicons (S4A Fig) and melt curves and amplification curves (S4B and C Fig, respectively) for these controls, and confirms successful methylated DNA recovery. Efficiency of amplification of the ISR regions ranged from 2.044 to 1.536; an efficiency value of 2 is optimal [65]. Amplification efficiencies for each of the ISR regions, ISR2A, ISR2B, ISR2C, ISR2D and ISR3 were 1.726, 1.536, 2.044, 1.706, and 1.847, respectively.

qPCR was conducted on the methyl-enriched and input DNA samples for each primer region. The PCR amplification curves and melt curves from each ISR primer sets are shown in Fig 1A-F. The melt curves show predominantly single peaks, as expected for the specific amplification of the desired targets, although ISR1 (Fig 1A) and ISR2C (Fig 1D) showed multiple peaks, suggesting amplification of multiple complex repeats occurred. The average Ct values and proportion of methylated DNA was calculated from the replicates of each IP and input samples. Fig 2 shows the relative recovery of methylated DNA from each ISR region, after normalization to the input DNA, for both Borrelia positive and negative ticks (Fig 2A) and for the different ISRs without regard to Borrelia infection status (Fig 2B). ISR1 was excluded from this analysis because of erratic amplification that precluded generation of a standard curve for the primer set for this region. Not all ticks showed amplification for each primer set so n = 24 for ISR2A, 20 for ISR2B, 22 for ISER2C, 24 for ISR2D, and 25 for ISR3.

[Figure omitted. See PDF.]

One peak in the melt curve indicates that one amplicon is being amplified, whereas multiple peaks in the melt curve indicate multiple amplicons. A: Melt curve and amplification curve of ISR1 at 51°C. B: Melt curve and amplification curve of ISR2A at 55°C. C: Melt curve and amplification curve of ISR2B at 55°C. D: Melt curve and amplification curve of ISR2C at 49°C. E: Melt curve and amplification curve of ISR2D at 55°C. F: Melt curve and amplification curve of ISR3 at 55°C.

[Figure omitted. See PDF.]

A: ISR regions ISR2A, ISR2B, ISR2C, ISR2D and ISR3 (left to right), Borrelia-infected (dark grey) and uninfected (light grey). B: Relative percent DNA methylation of each ISR region with Borrelia-infected and uninfected tick values combined.

Visual inspection of the methylated DNA recovery from Borrelia-infected (Borrelia (+)) ticks and uninfected (Borrelia (-)), (Fig 2A), supported by unpaired t-tests, for each ISR region indicates that there was no significant difference between the infection statuses for any of the ISR regions (p = 0.96, 0.70, 0.93, 0.30, and 0.86 for ISR2A, ISR2B, ISR2C, ISR2D and ISR3, respectively). As there was no significant difference between Borrelia (+) and Borrelia (-) samples, these values were combined to assess any differences in DNA methylation between the different ISR regions. Fig 2B, supported by an ANOVA, shows that the ISRs exhibits different levels of DNA methylation with ISR2A, ISR2C and ISR3 showing higher relative levels of DNA methylation than ISR2B and ISR2D. The f-ratio value was 4.56247 and p-value 0.002103. Post-hoc Turkey t-tests showed there was a significant difference between ISR2A and ISR2B (p = 0.039), ISR2A and ISR2D (p = 0.004) and ISR2D and ISR3 (p = 0.49). These results indicate that the centromeric repeats are methylated and show that the different repeats are methylated to different extents. When normalized to the recovery of methylated DNA, there is no evidence that the presence or absence of Borrelia influences the DNA methylation of either highly, or modestly, methylated ISR centromeric repeats.

3.3 Histone modification – Euchromatic histone lysine methyltransferase 2 (EHMT2)

We next sought to investigate whether the presence of Borrelia would affect histone methylation in ticks. In the absence of existing knowledge of which histone modifications might be relevant, rather than targeting specific histone modifications at specific genes, we started by investigating whether the presence of Borrelia altered the transcription of the epigenetic master gene, EHMT2. Wild-captured non-engorged ticks were screened for common pathogens and RNA was extracted from 10 of Borrelia-infected and 10 uninfected ticks (Table 1, S2 Table in Supplemental Tables), followed by cDNA synthesis and qPCR for both the EHMT2 gene and l13a, a housekeeping gene, used as a control gene.

Both sets of EHMT2 primers, EHMT2–6 and EHMT2–8, and candidate reference genes, l13a and rps4 amplified tick-derived cDNA well, based on both Ct values (S3 Table in Supplemental Tables) and confirmatory gel electrophoresis (S5A-D Fig). The l13a primer set was more sensitive than the rps4 primer set with the l13a primers consistently yielding lower Ct values than rps4 (S1 Table in Supplemental Tables, S5C-D Fig), so l13a was used as the reference gene. The DNase-treated no-RT controls of each tick also underwent qPCR with the EHMT2–6, EHMT2–8, and l13a primer sets. With the exception of one sample, NS038, which was removed from the study, no samples showed amplification, indicating successful cDNA synthesis without contaminating genomic DNA (S4 Table in Supplemental Tables, S6A-D Fig).

To assess differences between EHMT2 transcription in infected and uninfected ticks, the Ct values from each tick (excluding NS038) were plotted for EHMT2–6 (Fig 3A) and EHMT2–8 (Fig 3B), along with the Ct values for the control gene, l13a (Fig 3C). The plot for each EHMT2 primer set, EHMT2–6 and EHMT2–8, (Fig 3A and 3B) suggests that the EHMT2–6 primers are more sensitive than EHMT2–8 primers, as evidenced by lower Ct values and clearer banding when the amplification products are visualized by electrophoresis (S7A vs 7B Fig). Thus, the ensuing analysis was conducted with only the EHMT2–6 data.

[Figure omitted. See PDF.]

A: Plot generated with the Ct values produced from qPCR with EHMT2 6 primers. B: Plot generated with Ct values produced from EHMT2 8 primers. C: Plot generated with the Ct values with the control gene, l13a, primers.

Double delta Ct analysis was used to normalize the EHMT2 results to the housekeeping gene, l13a. This analysis calculates the difference between the expressions of the test gene and the housekeeping gene in each sample, then assesses the difference in that differential between the two experimental conditions – infected and uninfected. The double delta Ct analysis operates under assumptions that there is equal primer efficiency between primer sets and that there is near 100% amplification efficacy of both experimental and control genes and that the internal control gene, l13a in this case, is constantly expressed regardless of treatment [64]. Amplification efficiency was calculated for each EHMT2 primer set (S5A-C Fig) and yielded 1.88 for EHMT2 6 and 1.92 for l13a. These efficacies satisfy the assumption that there is near equal primer efficiency between both genes and are within 10% of the optimal amplification value of 2. These conditions were met. Fig 3C shows the Ct values for the l13a primers. The lower Ct values from the uninfected tick samples indicates a greater abundance of amplifiable cDNA, and presumably mRNA, in the uninfected tick samples versus the infected tick samples.

The difference in EHMT2 expression, once normalized against the cDNA abundance, shows that infected ticks have 826X higher EHMT2 expression than uninfected ticks (Table 4). Applying this analysis to individual infected ticks (Fig 4) allows visualization of the variability in EHMT2 expression. This variability did not correspond in an obvious manner to the (known) suite of tick pathogens other than B. burgdorferi or to tick sex (Table 1).

[Figure omitted. See PDF.]

Chart shows EHMT2 expression normalized to the expression of the housekeeping gene l13a by subtracting the l13a Ct value from the EHMT2 value; lower differential values are indicative of lower EHMT2 Ct values representing greater EHMT2 expression.

4 Discussion

4.1 Epigenetic regulation in ticks

Epigenetic regulation in ticks has been poorly explored in general, and the role of B. burgdorferi in manipulating the epigenome of its I. scapularis tick vector remains unexplored [58]. Ticks can vector more diverse pathogens than any other arthropod [23] and in nature ticks frequently transmit multiple pathogens, simultaneously or sequentially [66,67]. Each of these pathogens interact with each other as well as with the host in a battle to establish infection and find their niche to survive and propagate [66]. Manipulation of the tick vectors via modulation of histone modification enzymes have been documented for Anaplasma phagocytophilum [49]. Similarly, tick-vectored Apicomplexa parasites are successful in altering host metabolic activity and signaling pathways [68]. Among these strategies are multiple epigenetic processes: the activation of enzymes regulating epigenetic processes, secretion of proteins that affect chromatin regulatory complexes to repress host genes, and the production of regulatory microRNAs [68]. B. burgdorferi has been shown to influence its vector’s transcriptome, physiology and behaviour [37,69], all of which has implications for human and animal medicine. Despite this, the epigenetic cross talk between the tick vector and this bacterial species remains largely unexplored.

The purpose of this study was to explore the epigenetic effects of B. burgdorferi on both DNA modification and histone modifications. Ixodes scapularis has DNA methyltransferase enzymes [49] and extensive repeat regions including non-coding pericentromeric tandem repeats of DNA, termed Ixodes scapularis repeats, ISRs [23]. ISRs have been shown to be methylated based both on differential restriction with methylation-sensitive and methylation-insensitive restriction enzymes [55] and by binding of antibodies against 5-methylcytosine (5mC) to the DNA of the closely related I. ricinus tick [54]. We confirmed the presence of 5mC DNA in I. scapularis using the sensitive approach of immunoprecipitation of methylated DNA. The detection of DNA methylation using this approach, in addition to those previously performed [54,55], adds to the robustness of the conclusion that there is DNA methylation in I. scapularis. Further, we show that different ISR regions are differentially methylated; ISR2A, ISR2C, and ISR3 are more heavily methylated than ISR2B and ISR2D. There was, however, no obvious or statistically significant impact of B. burgdorferi on DNA methylation of either high, moderate or low methylation ISRs. Although not previously detected, low level DNA methylation could be present in coding regions, and our results do not exclude modification of such possible low abundance DNA methylation by the tick microbiome. In contrast, B. burgdorferi had a pronounced effect on the transcription of EHMT2, a histone modifying “master epigenetic” regulator. In other species this gene has been shown to mediate the organism’s response to physiological stress resulting from infection [1,13–17]. Our results showed that ticks infected with B. burgdorferi had significantly higher EHMT2 transcription than uninfected individuals. Interestingly, there was considerable individual variation between ticks.

In many species, DNA methylation is crucial for the stability of the genome, a process encompassing DNA repair and replication, as well as modulating gene expression [70]. However, DNA methylation is variably used in different species and DNA methylation has been found to be generally low across arthropod species [71–73].

DNA methylation patterns in response to infection by bacteria have been investigated in insects, and to a lesser extent, in arachnids. In some insects, bacterial pathogens have been found to alter the expression of the DNA methylation machinery, promoting bacterial persistence and replication [74–78]. Similarly, histone modifications and the activity of histone modifying enzymes in the host and vector have been found to be altered in response to bacterial infection [12,79,80]. These findings supports a role for epigenetics in regulating the interaction between host or vector and pathogen [81]. Research on the role of DNA methylation in pathogen response in arachnids is more limited than in insects. Abudukadier et al., [82] found that linyphiid spiders, Hylyphantes graminicola, infected with Wolbachia had decreased DNA methylation relative to uninfected spiders, suggesting a potential relationship between bacterial infection and DNA methylation in at least one arachnid. As I. scapularis ticks seem to primarily methylate non-coding centromeric repeats [54,55], a direct role of DNA methylation in controlling the transcription of genes involved in the immune response is unlikely in this species. However, in Arabidopsis thaliana, centromeric heterochromatin, which includes pericentromeric tandem repeats, becomes hypomethylated in response to infection by Pseudomonas syringae [83]. This results in cell death and morphological abnormalities, defects attributed to disrupted transcriptional activity as genome-wide methylation dynamics [83,84]. Further, the repeats might control methylation-responsive transcription of non-coding RNAs with regulatory roles. DNA methylation is also fundamentally involved in chromosome stability [70]. In this study, we documented differential DNA methylation between the different I. scapularis pericentromeric repeats. While the function of the ISR family of non-coding peri-centromeric tandem repeats remains unknown [54,55], a role in chromosome stability and segregation is plausible [85], which might implicate DNA methylation as having a structural role in the genome. Nevertheless, regardless of the biological role of DNA methylation in the ISR repeats, this methylation was not influenced by the presence of B. burgdorferi. This led to an expansion of the study into an investigation of differential histone modification in response to pathogens.

4.2 EHMT2 expression in ticks

To investigate an epigenetic effect of B. burgdorferi infection upon histone modifications in the tick I. scapularis we examined the transcription of the EHMT2 epigenetic regulator gene. Although an indirect approach, this was the most feasible approach to this question in the absence of prior knowledge of specific histone modifications and gene targets. Microbes such as Borrelia, Babesia and Anaplasma, all of which were present in at least some of the ticks used in this experiment (Table 1), have been shown to increase tick fitness [86,87]. EHMT2 expression is postulated to modulate stress response to promote viability and/or pathogen tolerance [1,13,15–17]. Based on the known role of EHMT2 in tolerance and the coevolutionary relationship between ticks and the microbes they vector, we had hypothesized that there would be either no effect on EHMT2 expression if the pathogens did not provoke any physiological stress in the tick vector, or if there was an effect, EHMT2 expression would be higher in infected ticks than uninfected ticks. Indeed, we found that infected ticks have EHMT2 expression levels an average of 826 times higher than uninfected ticks.

The Jak-Stat (Janus Kinase-Signal transducers and activators of transcription) pathway is an evolutionarily conserved signalling pathway that is central to cell communication and function, including homeostatic processes like immunity [88]; deficiencies of the pathway are related to increased susceptibility to infection and decreased immune function [89]. EHMT2 regulates Drosophila responses to viral infections via the Jak-Stat pathway and that EHMT2 deficient mutants had decreased survival after infection [13]. This implies that increased EHMT2 is protective. Ticks, too, have a functional Jak-Stat signalling pathway that functions in antibacterial responses [90]. That we found that Borrelia-infected ticks had significantly greater EHMT2 expression than uninfected ticks implies that the entire stress response and pathogen tolerance pathway may be conserved between insects and arachnids.

The most obvious limitation of this study is that while we detected a pronounced difference in the expression of EHMT2 in B. burgdorferi-infected versus -uninfected ticks, we did not confirm that this change in the transcription of a histone modifying enzyme results in differential histone modifications. This work will require a list of differentially expressed genes in B. burgdorferi-infected vs -uninfected ticks. Another consideration is that the ticks used for this study were obtained from the natural environment, either after feeding from a host, or wild-caught while questing. As is true of wild populations, these ticks carried a variety of pathogens, some of which may also alter gene expression. The rewiring of tick metabolic networks by Anaplasma phagocytophilum has been compellingly documented [91]. Additionally, these ticks were exposed to different, and largely unknown, environmental influences, which could have influenced expression of EHMT2. Indeed, the magnitude alterations in EHMT2 transcription was highly variable between ticks. While using lab-reared ticks with controlled infections could reduce variability, such results would be less applicable to ticks in nature, the more interesting and important question. As epigenetic modifications are inter-related [3,4,11,12], future work will need to examining non-coding RNAs that may be differentially expressed under these conditions. The immune response is altered in ticks infected with B. burgdorferi [18], however, the mechanisms involved in mediating the organismal response to pathogens, and the role of epigenetics, remains to be explored.

4.3 Significance and conclusions

Infection by pathogenic Borrelia bacteria is an increasing concern for human and animal health. Investigation of the effect of these spirochete bacteria on their mammalian hosts are increasing, nevertheless, this understanding needs to be coupled to an understanding of the interaction between the tick vector and the bacteria to understand the epigenetic negotiations between pathogen and vector. An influence of Borrelia bacteria on the physiology and behavior of Ixodes scapularis ticks has been documented [38,44,86,87]. The missing link, however, is the mechanisms that Borrelia uses to modulate these changes in vector physiology and behavior. Given the pivotal role of epigenetics in mediating environmental influences on the genome, it seemed biologically important to investigate the epigenetic effects of Borrelia burgdorferi on I. scapularis.

We found that the pericentric repeats of I. scapularis are methylated, as previously reported. In addition to confirming this finding, we detected different levels of DNA methylation in individual repeats. The level of DNA methylation, was not, however, significantly responsive to the presence or absence of B. burgdorferi in the ticks. In contrast, our results clearly demonstrate that EHMT2 is significantly over-expressed in infected ticks. To our knowledge, this study is the first to document epigenetic effects of B. burgdorferi on I. scapularis. This will open an avenue to further understanding of the epigenetic mechanisms through which ticks maintain and transmit pathogens. Further, epigenetic changes can be modified by chemical means, providing a potential avenue to decrease the risk of tick-vectored infections.

Supporting information

S1 Tables. This file contains the supplementary tables 1-4.

https://doi.org/10.1371/journal.pone.0324546.s001

(DOCX)

S1 Fig. Agarose gel of Gradient PCR products for ISR primers.

All primer optimizations steps were tested on tick DNA samples #590 and #592 from 2019. “– con” = no template negative control using water instead of DNA template. The 200–2000 base pair DNA ladder is to the left in each gel image. A: Gradient PCR products targeting the ISR1 region. B: Gradient PCR products targeting the ISR2C and ISR2D regions. C: Gradient PCR products targeting the ISR3 region. While primers ideally produce a single amplicon, as these are repeat regions primers ISR2C, ISR2D, and ISR3 produced multiple bands.

https://doi.org/10.1371/journal.pone.0324546.s002

(DOCX)

S2 Fig.

Gel electrophoresis of qPCR amplification of cDNA from samples NS021, 026, 034, 035, 037, 038, 041, 043, 048 negative ticks. A) rps4 primers, with an amplicon consistent with the predicted size of 80 bp B) l13a primers, with an amplicon consistent with the predicted size of ~280 bp.

https://doi.org/10.1371/journal.pone.0324546.s003

(DOCX)

S3 Fig.

EHMT2 6 and l13a standard curves. A) Scatterplot with trendline for a three series dilution of EHMT2 6 primers. The slope of the trendline was calculated to be −2.175, with an R² value of 0.8183. The amplification efficiency was thus calculated to be 1.88, or 188%. B) Scatterplot with trendline for a three series dilution of l13a. The slope of the trendline was calculated to be −2.15, with an R² value of 0.9197. The amplification efficiency was thus calculated to be 1.92, or 192%. C) qPCR results.

https://doi.org/10.1371/journal.pone.0324546.s004

(DOCX)

S4 Fig. Control qPCR reaction to determine MeDIP efficiency using the spike-in control DNA and primers.

The 200–2000 bp ladder was used for A as a DNA size reference. A: Methylated and unmethylated spike-in control qPCR gel results. Water was used as the negative control. IP = immunoprecipitated, input = total input DNA. B: qPCR melt curve and amplification curve for the unmethylated spike-in control. C: qPCR melt curve and amplification curve for the methylated spike-in control. The well labels on the gel represent the identity of the tick with IP being immunoprecipitation and the input DNA from the tick samples. The methylated and unmethylated spike-in DNA were used corresponding with the methylated and unmethylated primers.

https://doi.org/10.1371/journal.pone.0324546.s005

(DOCX)

S5 Fig. Agarose gel electrophoresis of qPCR results A) Synthesized cDNA from each sample underwent qPCR with EHMT2 6 primers, with an amplicon size of 207 bp.

B) Synthesized cDNA from each sample underwent qPCR with EHMT2 8 primers, with an amplicon size of 184 bp. C) Synthesized cDNA from each sample underwent qPCR with rps4 primers, with an amplicon size of 80 bp. D) Synthesized cDNA from each sample underwent qPCR with l13a primers, with an amplicon size of ~280 bp.

https://doi.org/10.1371/journal.pone.0324546.s006

(DOCX)

S6 Fig. Agarose gel electrophoresis of qPCR products from no-RT control samples.

A) No-RT controls from cDNA synthesis for negative samples that underwent qPCR with EHMT2 6 primers, with an amplicon size of 207 bp. One sample, NS038 was found to contain residual genomic DNA (box), whereas the rest contained no genomic DNA. B) No-RT controls from cDNA synthesis for negative samples that underwent qPCR with EHMT2 8 primers, with an amplicon size of 184 bp. NS038 was again found to contain residual genomic DNA (box), whereas the rest contained no genomic DNA. C) No-RT controls from cDNA synthesis for each positive tick sample underwent qPCR with EHMT2 6 primers, with an amplicon size of 207 bp. D) No-RT controls from cDNA synthesis for each positive tick sample underwent qPCR with l13a primers, with an amplicon size of ~280 bp. The ladder goes from 100 bp increments to 1000 bp and the top band is 1,500pb.

https://doi.org/10.1371/journal.pone.0324546.s007

(DOCX)

S7 Fig.

Agarose gel electrophoresis of negative tick samples NS021, 026, 034, 035, 037, 038, 041, 043, 048. A) Synthesized cDNA from each sample underwent qPCR with EHMT2 6 primers, with an amplicon size of 207 bp. B) Synthesized cDNA from each sample underwent qPCR with EHMT2 8 primers, with an amplicon size of 184 bp.

https://doi.org/10.1371/journal.pone.0324546.s008

(DOCX)

Acknowledgments

We would like to extend our gratitude to Glen Parsons for collection of live ticks and the many members of the public and veterinarians for donation of feeding ticks. We thank Samantha Bishop and Ebruvibiyo Daniella Ibru for pathogen testing of the ticks and Julie Lewis for invaluable discussion and feedback on this manuscript.

References

1. 1. Riahi H, Fenckova M, Goruk KJ, Schenck A, Kramer JM. The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species. BMC Biol. 2021;19(1):112. pmid:34030685

* View Article

* PubMed/NCBI

* Google Scholar

2. 2. Taborsky B, English S, Fawcett TW, Kuijper B, Leimar O, McNamara JM, et al. Towards an evolutionary theory of stress responses. Trends Ecol Evol. 2021;36(1):39–48.

* View Article

* Google Scholar

3. 3. Chen B, Feder ME, Kang L. Evolution of heat-shock protein expression underlying adaptive responses to environmental stress. Mol Ecol. 2018;27(15):3040–54. pmid:29920826

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Fitz-James MH, Cavalli G. Molecular mechanisms of transgenerational epigenetic inheritance. Nat Rev Genet. 2022;23(6):325–41.

* View Article

* Google Scholar

5. 5. Ye J, Wu W, Li Y, Li L. Influences of the gut microbiota on DNA methylation and histone modification. Dig Dis Sci. 2017;62(5):1155–64.

* View Article

* Google Scholar

6. 6. Gasque SN, van Oers MM, Ros VI. Where the baculoviruses lead, the caterpillars follow: baculovirus-induced alterations in caterpillar behaviour. Curr Opin Insect Sci. 2019;33:30–6. pmid:31358192

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. de Bekker C, Ohm RA, Loreto RG, Sebastian A, Albert I, Merrow M, et al. Gene expression during zombie ant biting behavior reflects the complexity underlying fungal parasitic behavioral manipulation. BMC Genomics. 2015;16(1):620. pmid:26285697

* View Article

* PubMed/NCBI

* Google Scholar

8. 8. de Bekker C, Das B. Hijacking time: how Ophiocordyceps fungi could be using ant host clocks to manipulate behavior. Parasite Immunol. 2022;44(3):e12909. pmid:35103986

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Scott AL, Ghedin E, Nutman TB, McReynolds LA, Poole CB, Slatko BE, et al. Filarial and Wolbachia genomics. Parasite Immunol. 2012;34(2–3):121–9. pmid:22098559

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Ye YH, Woolfit M, Huttley GA, Rancès E, Caragata EP, Popovici J, et al. Infection with a virulent strain of wolbachia disrupts genome wide-patterns of cytosine methylation in the mosquito aedes aegypti. Bourtzis K, editor. PLoS One. 2013;8(6):e66482. pmid:23840485

* View Article

* PubMed/NCBI

* Google Scholar

11. 11. Geens B, Goossens S, Li J, Van de Peer Y, Vanden Broeck J. Untangling the gordian knot: the intertwining interactions between developmental hormone signaling and epigenetic mechanisms in insects. Mol Cell Endocrinol. 2024;585:112178. pmid:38342134

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Mukherjee K, Dobrindt U. The emerging role of epigenetic mechanisms in insect defense against pathogens. Curr Opin Insect Sci. 2022;49:8–14. pmid:34710642

* View Article

* PubMed/NCBI

* Google Scholar

13. 13. Merkling SH, Bronkhorst AW, Kramer JM, Overheul GJ, Schenck A, Van Rij RP. The epigenetic regulator G9a mediates tolerance to RNA virus infection in Drosophila. Vale PF, editor PLoS Pathog. 2015;11(4):e1004692. pmid:25880195

* View Article

* PubMed/NCBI

* Google Scholar

14. 14. Riahi H, Brekelmans C, Foriel S, Merkling SH, Lyons TA, Itskov PM, et al. The histone methyltransferase G9a regulates tolerance to oxidative stress-induced energy consumption. Locasale J, editor. PLoS Biol. 2019;17(3):e2006146. pmid:30860988

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Wang Y, Zhang J, Su Y, Shen Y, Jiang D, Hou Y, et al. G9a regulates breast cancer growth by modulating iron homeostasis through the repression of ferroxidase hephaestin. Nat Commun. 2017;8(1):274.

* View Article

* Google Scholar

16. 16. Kramer JM. Regulation of cell differentiation and function by the euchromatin histone methyltranserfases G9a and GLP. Biochem Cell Biol. 2016;94(1):26–32. pmid:26198080

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Fang TC, Schaefer U, Mecklenbrauker I, Stienen A, Dewell S, Chen MS, et al. Histone H3 lysine 9 di-methylation as an epigenetic signature of the interferon response. J Exp Med. 2012;209(4):661–9. pmid:22412156

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Kurokawa C, Lynn GE, Pedra JHF, Pal U, Narasimhan S, Fikrig E. Interactions between Borrelia burgdorferi and ticks. Nat Rev Microbiol. 2020;18(10):587–600. pmid:32651470

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Boulanger N, Boyer P, Talagrand-Reboul E, Hansmann Y. Ticks and tick-borne diseases. Med Mal Infect. 2019;49(2):87–97. pmid:30736991

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Cabezas-Cruz A, Estrada-Peña A, Rego ROM, De La Fuente J. Tick-Pathogen ensembles: do molecular interactions lead ecological innovation? Front Cell Infect Microbiol [Internet]. 2017 Mar 13 [cited 2024 Aug 1];7. Available from: http://journal.frontiersin.org/article/10.3389/fcimb.2017.00074/full

* View Article

* Google Scholar

21. 21. de la Fuente J, Villar M, Cabezas-Cruz A, Estrada-Peña A, Ayllón N, Alberdi P. Tick-Host-Pathogen interactions: conflict and cooperation. Miller VL, editor. PLoS Pathog. 2016;12(4):e1005488. pmid:27099928

* View Article

* PubMed/NCBI

* Google Scholar

22. 22. Estrada-Peña A, Ayllón N, De La Fuente J. Impact of climate trends on tick-borne pathogen transmission. Front Physiol [Internet]. 2012 [cited 2024 Aug 1];3. Available from: http://journal.frontiersin.org/article/10.3389/fphys.2012.00064/abstract

* View Article

* Google Scholar

23. 23. Gulia-Nuss M, Nuss AB, Meyer JM, Sonenshine DE, Roe RM, Waterhouse RM, et al. Genomic insights into the Ixodes scapularis tick vector of Lyme disease. Nat Commun. 2016;7:10507. pmid:26856261

* View Article

* PubMed/NCBI

* Google Scholar

24. 24. Bobe JR, Jutras BL, Horn EJ, Embers ME, Bailey A, Moritz RL, et al. Recent progress in Lyme disease and remaining challenges. Front Med. 2021;8:666554.

* View Article

* Google Scholar

25. 25. Borgermans L, Goderis G, Vandevoorde J, Devroey D. Relevance of chronic lyme disease to family medicine as a complex multidimensional chronic disease construct: a systematic review. Int J Fam Med. 2014;2014:1–10.

* View Article

* Google Scholar

26. 26. Day MJ. One health: the importance of companion animal vector-borne diseases. Parasit Vectors. 2011;4:49. pmid:21489237

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Faburay B. The case for a ‘one health’ approach to combating vector-borne diseases. Infect Ecol Epidemiol. 2015;5(1):28132.

* View Article

* Google Scholar

28. 28. Nuttall PA. Climate change impacts on ticks and tick-borne infections. Biologia. 2021;77(6):1503–12.

* View Article

* Google Scholar

29. 29. Rocklöv J, Dubrow R. Climate change: an enduring challenge for vector-borne disease prevention and control. Nat Immunol. 2020;21(5):479–83. pmid:32313242

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Dong Y, Zhou G, Cao W, Xu X, Zhang Y, Ji Z, et al. Global seroprevalence and sociodemographic characteristics of Borrelia burgdorferi sensu lato in human populations: a systematic review and meta-analysis. BMJ Glob Health. 2022;7(6):e007744. pmid:35697507

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Oppler ZJ, O’Keeffe KR, McCoy KD, Brisson D. Evolutionary genetics of Borrelia. Curr Issues Mol Biol. 2022;42:97–112.

* View Article

* Google Scholar

32. 32. Rudenko N, Golovchenko M, Grubhoffer L, Oliver JH Jr. Updates on Borrelia burgdorferi sensu lato complex with respect to public health. Ticks Tick Borne Dis. 2011;2(3):123–8. pmid:21890064

* View Article

* PubMed/NCBI

* Google Scholar

33. 33. Anderson C, Brissette CA. The brilliance of borrelia: mechanisms of host immune evasion by lyme disease-causing spirochetes. Pathogens. 2021;10(3):281. pmid:33801255

* View Article

* PubMed/NCBI

* Google Scholar

34. 34. LaFrance ME, Pierce JV, Antonara S, Coburn J. The Borrelia burgdorferi integrin ligand P66 affects gene expression by human cells in culture. Bliska JB, editor. Infect Immun. 2011;79(8):3249–61. pmid:21576330

* View Article

* PubMed/NCBI

* Google Scholar

35. 35. Cramaro WJ, Revets D, Hunewald OE, Sinner R, Reye AL, Muller CP. Integration of Ixodes ricinus genome sequencing with transcriptome and proteome annotation of the naïve midgut. BMC Genomics. 2015;16:871. pmid:26510422

* View Article

* PubMed/NCBI

* Google Scholar

36. 36. Herrmann C, Gern L. Survival of Ixodes ricinus (Acari: Ixodidae) under challenging conditions of temperature and humidity is influenced by Borrelia burgdorferi sensu lato infection. J Med Entomol. 2010;47(6):1196–204.

* View Article

* Google Scholar

37. 37. Herrmann C, Voordouw M, Gern L. Ixodes ricinus ticks infected with the causative agent of Lyme disease, Borrelia burgdorferi sensu lato, have higher energy reserves. Int J Parasitol. 2013;43(6):477–83.

* View Article

* Google Scholar

38. 38. Lefcort H, Durden LA. The effect of infection with Lyme disease spirochetes (Borrelia burgdorferi) on the phototaxis, activity, and questing height of the tick vector Ixodes scapularis. Parasitology. 1996;113 (Pt 2):97–103. pmid:8760310

* View Article

* PubMed/NCBI

* Google Scholar

39. 39. Alekseev AN, Dubinina HV. Abiotic parameters and diel and seasonal activity of Borrelia-infected and uninfected Ixodes persulcatus (Acarina: Ixodidae). J Med Entomol. 2000;37(1):9–15.

* View Article

* Google Scholar

40. 40. Lemoine M, Cornetti L, Reeh K, Tschirren B. Tick range expansion to higher elevations: does Borrelia burgdorferi sensu lato facilitate the colonisation of marginal habitats?. BMC Ecol Evol. 2022;22(1):104. pmid:36028800

* View Article

* PubMed/NCBI

* Google Scholar

41. 41. Ogden NH, Lindsay LR, Leighton PA. Predicting the rate of invasion of the agent of Lyme disease Borrelia burgdorferi. Sheppard A, editor. J Appl Ecol. 2013;50(2):510–8.

* View Article

* Google Scholar

42. 42. Pal U, Kitsou C, Drecktrah D, Yaş ÖB, Fikrig E. Interactions between ticks and lyme disease spirochetes. Curr Issues Mol Biol. 2021;42:113–44. pmid:33289683

* View Article

* PubMed/NCBI

* Google Scholar

43. 43. Pal U, Li X, Wang T, Montgomery RR, Ramamoorthi N, Desilva AM, et al. TROSPA, an Ixodes scapularis receptor for Borrelia burgdorferi. Cell. 2004;119(4):457–68. pmid:15537536

* View Article

* PubMed/NCBI

* Google Scholar

44. 44. Dai J, Narasimhan S, Zhang L, Liu L, Wang P, Fikrig E. Tick histamine release factor is critical for Ixodes scapularis engorgement and transmission of the lyme disease agent. Coburn J, editor. PLoS Pathog. 2010;6(11):e1001205. pmid:21124826

* View Article

* PubMed/NCBI

* Google Scholar

45. 45. Hovius JWR, van Dam AP, Fikrig E. Tick-host-pathogen interactions in Lyme borreliosis. Trends Parasitol. 2007;23(9):434–8. pmid:17656156

* View Article

* PubMed/NCBI

* Google Scholar

46. 46. Pham M, Underwood J, OlivaChávez A. Changing the recipe: pathogen directed changes in tick saliva components. Int J Environ Res Public Health. 2021;18(4):1806.

* View Article

* Google Scholar

47. 47. Adamson SW, Browning RE, Budachetri K, Ribeiro JMC, Karim S. Knockdown of selenocysteine-specific elongation factor in Amblyomma maculatum alters the pathogen burden of Rickettsia parkeri with epigenetic control by the Sin3 histone deacetylase corepressor complex. Munderloh UG, editor. PLoS One. 2013;8(11):e82012. pmid:24282621

* View Article

* PubMed/NCBI

* Google Scholar

48. 48. Martins LA, Palmisano G, Cortez M, Kawahara R, de Freitas Balanco JM, Fujita A, et al. The intracellular bacterium Rickettsia rickettsii exerts an inhibitory effect on the apoptosis of tick cells. Parasit Vectors. 2020;13(1):603. pmid:33261663

* View Article

* PubMed/NCBI

* Google Scholar

49. 49. Cabezas-Cruz A, Alberdi P, Ayllón N, Valdés J, Pierce R, Villar M. Anaplasma phagocytophilum increases the levels of histone modifying enzymes to inhibit cell apoptosis and facilitate pathogen infection in the tick vector Ixodes scapularis. Epigenetics. 2016;11(4):303–19.

* View Article

* Google Scholar

50. 50. Liu S, Aageaard A, Bechsgaard J, Bilde T. DNA Methylation Patterns in the Social Spider, Stegodyphus dumicola. Genes (Basel). 2019;10(2):137. pmid:30759892

* View Article

* PubMed/NCBI

* Google Scholar

51. 51. Hunt BG, Glastad KM, Yi SV, Goodisman MAD. The function of intragenic DNA methylation: insights from insect epigenomes. Integr Comp Biol. 2013;53(2):319–28.

* View Article

* Google Scholar

52. 52. Head JA. Patterns of DNA methylation in animals: an ecotoxicological perspective. Integr Comp Biol. 2014;54(1):77–86.

* View Article

* Google Scholar

53. 53. Sarda S, Zeng J, Hunt B, Yi S. The evolution of invertebrate gene body methylation. Mol Biol Evol. 2012;29(8):1907–16.

* View Article

* Google Scholar

54. 54. Kotsarenko K, Vechtova P, Hammerova Z, Langova N, Malinovska L, Wimmerova M, et al. Newly identified DNA methyltransferases of Ixodes ricinus ticks. Ticks Tick Borne Dis. 2020;11(2):101348. pmid:31812591

* View Article

* PubMed/NCBI

* Google Scholar

55. 55. Meyer JM, Kurtti TJ, Van Zee JP, Hill CA. Genome organization of major tandem repeats in the hard tick, Ixodes scapularis. Chromosome Res. 2010;18(3):357–70. pmid:20306126

* View Article

* PubMed/NCBI

* Google Scholar

56. 56. Nwanade CF, Wang Z, Bai R, Wang R, Zhang T, Liu J, et al. DNA methylation variation is a possible mechanism in the response of haemaphysalis longicornis to low-temperature stress. Int J Mol Sci. 2022;23(23):15207. pmid:36499526

* View Article

* PubMed/NCBI

* Google Scholar

57. 57. Bensaoud C, Hackenberg M, Kotsyfakis M. Noncoding RNAs in parasite-vector-host interactions. Trends Parasitol. 2019;35(9):715–24. pmid:31320293

* View Article

* PubMed/NCBI

* Google Scholar

58. 58. De S, Kitsou C, Sonenshine DE, Pedra JHF, Fikrig E, Kassis JA, et al. Epigenetic regulation of tick biology and vectorial capacity. Trends Genet. 2021;37(1):8–11.

* View Article

* Google Scholar

59. 59. Gobl J, Kumar Sinha D, Sima R, Perner J, Kopáček P, Valdés JJ, et al. Histone methyltransferase DOT1L is involved in larval molting and second stage nymphal feeding in ornithodoros moubata. Vaccines (Basel). 2020;8(2):157. pmid:32244625

* View Article

* PubMed/NCBI

* Google Scholar

60. 60. Artigas-Jerónimo S, Alberdi P, Villar Rayo M, Cabezas-Cruz A, Prados P, Mateos-Hernández L, et al. Anaplasma phagocytophilum modifies tick cell microRNA expression and upregulates isc-mir-79 to facilitate infection by targeting the Roundabout protein 2 pathway. Sci Rep. 2019;9(1):9073.

* View Article

* Google Scholar

61. 61. Wills MKB, Kirby AM, Lloyd VK. Detecting the Lyme disease spirochete, Borrelia burgdorferi, in ticks using nested PCR. J Vis Exp. 2018;132:56471.

* View Article

* Google Scholar

62. 62. Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res. 2007;35(Web Server):W43–6.

* View Article

* Google Scholar

63. 63. Koči J, Šimo L, Park Y. Validation of internal reference genes for real-time quantitative polymerase chain reaction studies in the tick, Ixodes scapularis (Acari: Ixodidae). J Med Entomol. 2013;50(1):79–84.

* View Article

* Google Scholar

64. 64. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001 Dec;25(4):402–8. pmid:11846609

* View Article

* PubMed/NCBI

* Google Scholar

65. 65. Ruijter J, Barnewall R, Marsh I, Szentirmay A, Quinn J, van Houdt R. Efficiency correction is required for accurate quantitative PCR analysis and reporting. Clin Chem. 2021;67(6):829–42.

* View Article

* Google Scholar

66. 66. Cutler SJ, Vayssier-Taussat M, Estrada-Peña A, Potkonjak A, Mihalca AD, Zeller H. Tick-borne diseases and co-infection: current considerations. Ticks Tick Borne Dis. 2021;12(1):101607. pmid:33220628

* View Article

* PubMed/NCBI

* Google Scholar

67. 67. Pfäffle M, Littwin N, Muders SV, Petney T. The ecology of tick-borne diseases. Int J Parasitol. 2013;43(12–13):1059–77.

* View Article

* Google Scholar

68. 68. Villares M, Berthelet J, Weitzman JB. The clever strategies used by intracellular parasites to hijack host gene expression. Semin Immunopathol. 2020;42(2):215–26. pmid:32002610

* View Article

* PubMed/NCBI

* Google Scholar

69. 69. Narasimhan S, Schuijt TJ, Abraham NM, Rajeevan N, Coumou J, Graham M, et al. Modulation of the tick gut milieu by a secreted tick protein favors Borrelia burgdorferi colonization. Nat Commun. 2017;8(1):184.

* View Article

* Google Scholar

70. 70. Kovalchuk I. Genome Stability. In: Genome Stability [Internet]. Elsevier; 2016 [cited 2024 Aug 27]. p. 1–18. Available from: https://linkinghub.elsevier.com/retrieve/pii/B978012803309800001X

71. 71. Lewis SH, Ross L, Bain SA, Pahita E, Smith SA, Cordaux R, et al. Widespread conservation and lineage-specific diversification of genome-wide DNA methylation patterns across arthropods. Reik W, editor. PLOS Genet. 2020;16(6):e1008864.

* View Article

* Google Scholar

72. 72. Thomas GWC, Dohmen E, Hughes DST, Murali SC, Poelchau M, Glastad K, et al. Gene content evolution in the arthropods. Genome Biol. 2020;21(1):15. pmid:31969194

* View Article

* PubMed/NCBI

* Google Scholar

73. 73. Wurm Y. Arthropod genomics beyond fruit flies: bridging the gap between proximate and ultimate causation. Brief Funct Genomics. 2015;14(6):381–3. pmid:26586720

* View Article

* PubMed/NCBI

* Google Scholar

74. 74. Baradaran E, Moharramipour S, Asgari S, Mehrabadi M. Induction of DNA methyltransferase genes in Helicoverpa armigera following injection of pathogenic bacteria modulates expression of antimicrobial peptides and affects bacterial proliferation. J Insect Physiol. 2019;118:103939. pmid:31493391

* View Article

* PubMed/NCBI

* Google Scholar

75. 75. Bhattacharya T, Newton ILG, Hardy RW. Wolbachia elevates host methyltransferase expression to block an RNA virus early during infection. PLoS Pathog. 2017;13(6):e1006427. pmid:28617844

* View Article

* PubMed/NCBI

* Google Scholar

76. 76. Kausar S, Abbas MN, Cui H. A review on the DNA methyltransferase family of insects: aspect and prospects. Int J Biol Macromol. 2021;186:289–302. pmid:34237376

* View Article

* PubMed/NCBI

* Google Scholar

77. 77. Kunert N, Marhold J, Stanke J, Stach D, Lyko F. A Dnmt2-like protein mediates DNA methylation in Drosophila. Development. 2003;130(21):5083–90. pmid:12944428

* View Article

* PubMed/NCBI

* Google Scholar

78. 78. Özbek R, Mukherjee K, Uçkan F, Vilcinskas A. Reprograming of epigenetic mechanisms controlling host insect immunity and development in response to egg-laying by a parasitoid wasp. Proc R Soc B. 2020;287(1928):20200704.

* View Article

* Google Scholar

79. 79. Landmann F, Orsi GA, Loppin B, Sullivan W. Wolbachia-mediated cytoplasmic incompatibility is associated with impaired histone deposition in the male pronucleus. PLoS Pathog. 2009;5(3):e1000343. pmid:19300496

* View Article

* PubMed/NCBI

* Google Scholar

80. 80. Vilcinskas A. The role of epigenetics in host-parasite coevolution: lessons from the model host insects Galleria mellonella and Tribolium castaneum. Zoology (Jena). 2016;119(4):273–80. pmid:27341739

* View Article

* PubMed/NCBI

* Google Scholar

81. 81. Wu P, Jie W, Shang Q, Annan E, Jiang X, Hou C, et al. DNA methylation in silkworm genome may provide insights into epigenetic regulation of response to Bombyx mori cypovirus infection. Sci Rep. 2017;7(1):16013.

* View Article

* Google Scholar

82. 82. Abudukadier A, Huang X, Peng Y, Zhang F, Liu H, Chen J, et al. The potential association between Wolbachia infection and DNA methylation in Hylyphantes graminicola (Araneae: Linyphiidae). Symbiosis. 2021;83(2):183–91.

* View Article

* Google Scholar

83. 83. Pavet V, Quintero C, Cecchini NM, Rosa AL, Alvarez ME. Arabidopsis displays centromeric DNA hypomethylation and cytological alterations of heterochromatin upon attack by Pseudomonas syringae. Mol Plant Microbe Interact. 2006;19(6):577–87. pmid:16776291

* View Article

* PubMed/NCBI

* Google Scholar

84. 84. Schmitz RJ, Ecker JR. Epigenetic and epigenomic variation in Arabidopsis thaliana. Trends Plant Sci. 2012;17(3):149–54. pmid:22342533

* View Article

* PubMed/NCBI

* Google Scholar

85. 85. Allshire RC, Madhani HD. Ten principles of heterochromatin formation and function. Nat Rev Mol Cell Biol. 2018;19(4):229–44. pmid:29235574

* View Article

* PubMed/NCBI

* Google Scholar

86. 86. Benelli G. Pathogens manipulating tick behavior—through a glass, darkly. Pathogens. 2020;9(8):664.

* View Article

* Google Scholar

87. 87. Romashchenko AV, Ratushnyak AS, Zapara TA, Tkachev SE, Moshkin MP. The correlation between tick (Ixodes persulcatus Sch.) questing behaviour and synganglion neuronal responses to odours. J Insect Physiol. 2012;58(7):903–10. pmid:22497860

* View Article

* PubMed/NCBI

* Google Scholar

88. 88. Hu X, Li J, Fu M, Zhao X, Wang W. The JAK/STAT signaling pathway: from bench to clinic. Signal Transduct Target Ther. 2021;6(1):402. pmid:34824210

* View Article

* PubMed/NCBI

* Google Scholar

89. 89. O’Shea JJ, Holland SM, Staudt LM. JAKS and STATS in immunity, immunodeficiency, and cancer. N Engl J Med. 2013;368(2):161–70.

* View Article

* Google Scholar

90. 90. Rana VS, Kitsou C, Dutta S, Ronzetti MH, Zhang M, Bernard Q, et al. Dome1-JAK-STAT signaling between parasite and host integrates vector immunity and development. Science. 2023;379(6628):eabl3837. pmid:36634189

* View Article

* PubMed/NCBI

* Google Scholar

91. 91. Cabezas-Cruz A, Espinosa P, Alberdi P, de la Fuente J. Tick-Pathogen interactions: the metabolic perspective. Trends Parasitol. 2019;35(4):316–28. pmid:30711437

* View Article

* PubMed/NCBI

* Google Scholar

Citation: MacIntosh GH, Nuyens AC, Vickery JL, Berthold A, Lloyd VK (2025) Epigenetic responses in Borrelia-infected Ixodes scapularis ticks: Over-expression of euchromatic histone lysine methyltransferase 2 and no change in DNA methylation. PLoS One 20(6): e0324546. https://doi.org/10.1371/journal.pone.0324546

About the Authors:

Grace Hadley MacIntosh

Roles: Investigation, Writing – original draft