Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Jul 26.
Published in final edited form as: J Asia Pac Entomol. 2018 May 22;21(3):852–863. doi: 10.1016/j.aspen.2018.05.009

The transcriptome of the lone star tick, Amblyomma americanum, reveals molecular changes in response to infection with the pathogen, Ehrlichia chaffeensis

Donghun Kim a,*,1, Deborah C Jaworski b,2, Chuanmin Cheng b, Arathy DS Nair b, Roman R Ganta b, Nic Herndon c,3, Susan Brown c, Yoonseong Park a
PMCID: PMC8312692  NIHMSID: NIHMS1720456  PMID: 34316264

Abstract

The lone star tick, Amblyomma americanum, is an obligatory ectoparasite of many vertebrates and the primary vector of Ehrlichia chaffeensis, the causative agent of human monocytic ehrlichiosis. This study aimed to investigate the comparative transcriptomes of A. americanum underlying the processes of pathogen acquisition and of immunity towards the pathogen. Differential expression of the whole body transcripts in six different treatments were compared: females and males that were E. chaffeensis non-exposed, E. chaffeensis-exposed/uninfected, and E. chaffeensis-exposed/infected. The Trinity assembly pipeline produced 140,574 transcripts from trimmed and filtered total raw sequence reads (approximately 117M reads). The gold transcript set of the transcriptome data was established to minimize noise by retaining only transcripts homologous to official peptide sets of Ixodes scapularis and A. americanum ESTs and transcripts covered with high enough frequency from the raw data. Comparison of the gene ontology term enrichment analyses for the six groups tested here revealed an up-regulation of genes for defense responses against the pathogen and for the supply of intracellular Ca++ for pathogen proliferation in the pathogen-exposed ticks. Analyses of differential expression, focused on functional subcategories including immune, sialome, neuropeptides, and G protein-coupled receptor, revealed that E. chaffeensis-exposed ticks exhibited an upregulation of transcripts involved in the immune deficiency (IMD) pathway, antimicrobial peptides, Kunitz, an insulin-like peptide, and bursicon receptor over unexposed ones, while transcripts for metalloprotease were down-regulated in general. This study found that ticks exhibit enhanced expression of genes responsible for defense against E. chaffeensis.

Keywords: Tick-borne disease, Arthropod vector, Next generation sequence, Differential expression analysis, Ehrlichia chaffeensis

Introduction

The lone star tick, Amblyomma americanum, is the primary vector of the pathogen Ehrlichia chaffeensis, the causative agent of human monocytotropic ehrlichiosis (Anderson et al., 1993; Brian et al., 2010; Childs and Paddock, 2003). This species of tick has a large geographic distribution, spanning nearly the entire eastern half of the United States (Childs and Paddock, 2003). A. americanum is also known to transmit several other human pathogens, including Ehrlichia ewingii, Francisella tularensis, and Borrelia lonestari causing human ewingii ehrlichiosis, tularemia and southern tick-associated rash illness (STARI), respectively (Anderson et al., 1993; Childs and Paddock, 2003; James et al., 2001). The processes of pathogen acquisition and transmission in ticks have been recognized as important areas of study that aim to develop and provide potential tool(s) for the disruption of pathogen transmission, such as a transmission blocking vaccine (Richardson et al., 1993).

Ticks acquire E. chaffeensis, a rickettsial bacterium, during larval and nymphal feedings on infected vertebrate reservoirs, such as white-tailed deer, and later transmit the pathogens to other vertebrate hosts, including humans, during subsequent feedings(Ewing et al., 1995). The infection rate of E. chaffeensis collected from vegetation varied from 1.15–9.8% (Anderson et al., 1993; Irving et al., 2000; Steiert and Gilfoy, 2002; Steiner et al., 1999), although higher infection rate (~up to 78%) was observed under laboratory condition (Nair et al., 2014). Successful invasion of the pathogen into the vector requires the establishment of the pathogen in the midgut before travelling to the salivary glands, where it is then transmissible during subsequent tick blood feedings. Pathogens must cross at least two cellular barriers, including the midgut epithelia and salivary gland cell layers, while simultaneously circumventing the tick’s innate immune system. Thus, the pathogen may manipulate the endogenous physiological and cellular machinery of ticks to facilitate the invasion, while it additionally copes with ticks’ immune systems. In addition, the pathogen takes advantage of the bioactive components present in the tick’s saliva to facilitate its successful transmission and invasion into the vertebrate host upon tick feeding (Francischetti et al., 2009).

Upon infection, the tick’s immune system is activated, presumably to suppress the pathogen population to below harmful levels. For example, Ixodes scapularis has induced expression of 5.3 kDa-antimicrobial peptides via JAK/STAT pathway in the salivary glands, possibly to limit Anaplasma phagocytophilum (Sultana et al., 2010). I. scapularis nymphs and ISE6 tick cells exhibited differential ferritin expression when infected with A. phagocytophilum and A. marginale (Zivkovic et al., 2009). In addition, the Anaplasma species must invade and form intracellular inclusions in tick cells to multiply themselves before being released into extracellular compartments; e.g., travelling from midgut cells to the hemolymph or from salivary gland cells to the saliva (Ayllón et al., 2013; Dedonder et al., 2012; Liu et al., 2011). A. phagocytophilum modulates the transcription of tick genes encoding fodrin and voltage-dependent anion selective channels in the salivary glands and midgut, presumably inhibiting the host’s apoptosis-mediated defense system (Ayllón et al., 2013). These processes may utilize the following host cellular pathways: endosome-mediated cellularization, iron and calcium-dependent proliferation, and cytoskeleton-dependent spreading as previously described in vertebrate hosts (Alves et al., 2014).

Transcriptomic analyses of A. americanum salivary glands had been investigated to understand the roles of salivary proteins, which aid complete tick feeding by inhibiting host hemostasis including vasoconstriction, blood clotting, and platelet aggregation (Aljamali et al., 2009; Bullard et al., 2016; Karim and Ribeiro, 2015). However, it is not fully understood about the transcriptional changes of tick against pathogen infection. In this study, we aimed to elucidate the differences in tick gene expression resulting from exposure to the pathogen to understand the interactions between the vector A. americanum and the pathogen E. chaffeensis. Comparisons of gene expression were made among males and females from groups of adult ticks that have not been exposed to the pathogen (NE) or were exposed to the pathogen and that have either remained uninfected (U) or become infected (I) following nymphal acquisition feeding on an infected host, white tailed deer. Females and males of each group were used for RNA sequencing to compare the differential gene expression. A search was conducted for differences in gene expression between U and I, while NE served as the baseline for comparisons. The study revealed a number of A. americanum genes involved in the immune response against E. chaffeensis as well as several genes whose expression were putatively modulated by E. chaffeensis for subverting the tick defense system. Large numbers of the genes involved in the immune system, in pathogen proliferation, and in the secretion of salivary proteins were captured in the study.

Material and methods

Preparation of ticks: pathogen non-exposed ticks, uninfected ticks, and infected ticks

To prepare pathogen infected ticks, a white-tailed deer with a confirmed Ehrlichia chaffeensis infection was infested with nymphs. The white-tailed deer used for this study was reared at Oklahoma State University and the Animal Care and Use Protocol was approved by the University IACUC. Lone star nymphs used for these experiments were donated by the tick rearing facility at Oklahoma State University. These nymphal ticks were fed on infected deer (see infections below). After molting into adults, these ticks were then permitted to feed on a naïve white-tailed deer for seven days; at which point the partially engorged females and males were forcibly removed from the deer and collected. Individual ticks were cut along the longitudinal midline, and one half of each tick was analyzed via pathogen specific PCR diagnosis to assess whether the ticks were infected or uninfected by E. chaffeensis (Nair et al., 2014). The remaining half of each tick was used for RNAseq (see below). For preparation of pathogen-free ticks (non-exposed), adult female and male Amblyomma americanum ticks, purchased from the tick-rearing center at Oklahoma State University, fed on a naïve rabbit for seven days, which were then used for RNAseq without pathogen specific PCR diagnosis. Therefore, three categories of samples are compared here: non-exposed females/males (NEf/NEm) that were fed on a naïve rabbit as control groups, infected females/males (If/Im) that were exposed to the pathogen while feeding on an infected host (deer) and became infected, and uninfected females/males (Uf/Um) that were exposed to the pathogen infected host but did not receive infection.

Generating infected nymphal ticks

The infections of E. chaffeensis were completed as previously reported for laboratory-reared white-tailed deer(Nair et al., 2014). Briefly, a naïve white-tailed deer was needle-infected with E. chaffeensis culture. E. chaffeensis organisms were grown to 80% infectivity in canine macrophage cells. Infected cells were washed and recovered in PBS. Approximately 2 × 108E. chaffeensis in 1 ml was injected intravenously. Five days after the deer was inoculated with E. chaffeensis, nymphal ticks were permitted to feed to full engorgement on the infected deer. These ticks were retained in humidified chambers and molted to adults to produce the experimental groups utilized in this study.

Preparation of RNA and libraries

Total RNA was extracted from a pool of three individual ticks from each group (NEf, NEm, If, Im, Uf, and Um) using Direct-zol™ RNA miniprep (Zymo research, Irvine, CA, USA) following the manufacturer’s protocol. The qualities and quantities of total RNAs were analyzed by Bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA, USA) and NanoDrop (Thermo Scientific, Wilmington, DE, USA), respectively. The total RNA from each sample (1 μg) was submitted to Integrated Genomic Facility (IGF) at Kansas State University for generation of six different libraries with group-specific tagging using the TruSeq RNA Library Preparation Kit v2 (Illumina, Inc., San Diego, CA, USA), following the manufacturer’s protocol.

Illumina sequencing and bioinformatics

For each of the six libraries tagged by different adapters, one hundred cycles of single direction sequencing were performed with single biological sample in the Illumina HiSeq 2500 (Illumina, Inc., San Diego, CA, USA) at the genome sequencing facility at the University of Kansas Medical Center (KUMC). The 208,878,905 total raw sequence reads included the following: NEf = 28,403,332 reads, NEm = 39,445,131 reads, If = 35,850,648 reads, Im = 35,128,945 reads, Uf = 33,107,132 reads, and Um = 36,943,717 reads. FastQC (Andrews, 2010) in Galaxy (Blankenberg et al., 2010; Giardine et al., 2005; Goecks et al., 2010) was used for trimming and filtering the sequence reads with the following parameters: sequence trimming- 5′/3′, window size 3, step size 1, and quality score (≥30), sequence filtering-minimum size (> 40 nt), minimum quality (≥30), and maximum number of bases allowed outside of quality range 5. After trimming and filtering, Trinity de novo assemblers (Grabherr et al., 2011) produced 140,574 transcripts (contigs) from the 117,528,041 cleaned sequence reads: NEf = 16,522,481 reads, NEm = 22,133,605 reads, If = 20,231,696 reads, Im = 19,550,397 reads, Uf = 18,616,157 reads, and Um = 20,473,705 reads.

Differential expression and gene ontology (GO) term enrichment analyses

RSEM (RNA-seq by expectation-maximization) (Li and Dewey, 2011) estimated transcript abundances by aligning each library to the Trinity assembly, which in turn provided FPKM (fragments per kilobase transcript length per million fragments mapped) values for transcripts. EdgeR (Empirical analysis of digital gene expression data in R) (Robinson et al., 2010) was used for analysis of differential expression among groups. DE transcripts were filtered for significance in EdgeR to establish the FDR (false discovery rate) < 0.001 & fold change (FC) > 4×. The transcripts with significant levels of DE in pairwise comparisons of libraries were used for the generation of a heatmap and for clustering analysis. Heatmaps were produced by Java Treeview (Saldanha, 2004) after clustering transcripts by Cluster 3.0 (de Hoon et al., 2004). Clustering was performed for the data following normalization of the data to the transcript expression levels in non-exposed ticks (NEf and NEm). GO-term enrichment tests of the DE transcripts against GO-terms of the gold transcript set (GTS) were made by Fisher’s exact test for p-value (< 0.01) in the Blast2GO program (Gotz et al., 2008).

Post-assembly quality control and processing for gold transcript set (GTS)

The quality of the Trinity assembly was evaluated and further processed to build the gold transcript set (GTS). No significant redundancies of the assembled transcripts were identified during selfblast searches of the GTS, although the GTS contained 12,670 isotigs, which are isoforms counted for alternative splicing. As is typical of transcriptomes assembled using short reads, instances of independent contigs covering different regions of the same gene, separated by gaps, were often found. There were no significant contaminations of the data by transposable elements or mitochondrial sequences, while E. chaffeensis contaminations (6 out of 140,574 transcripts, 0.00004% at E < 1E−100 in blastn search) were found and removed from the assembly. To build the GTS, the Trinity assembly was filtered for sequences homologous to A. americanum ESTs (tblastn, E-value < 1e−100) obtained from the NCBI database and to Ixodes scapularis peptides (blastx, E-value < 1e−30) obtained from Vectorbase (Giraldo-Calderón et al., 2015). Lastly, we removed any sequences with FPKM < 1 and lacking homology to any A. americanum ESTs or I. scapularis peptides with the stringency described above.

DE in subsets of genes for immune-related, tick sialome, neuropeptides, and GPCRs

To investigate differential expression of tick transcripts in response to E. chaffeensis infection, subsets of the GTS were separately analyzed: immune-related (Gulia-Nuss et al., 2016), tick sialome (Ribeiro et al., 2006), neuropeptides (Gulia-Nuss et al., 2016), and GPCRs (Gulia-Nuss et al., 2016). The genes were identified by homology searches against subsets of genes previously described for I. scapularis, as cited above. The best hits yielded through tblastn searches (E-value < 1e−10) of the GTS were subsequently back-blasted to I. scapularis genes. Then, only putative orthologues (the best hits in the reciprocal blast) of each gene were selected. In the case of the sialome set, because large numbers of paralogy occur due to recent gene expansions, the results from tblastn searches (e-value < 1e−10) of the GST were directly used. DE transcripts were extracted for FPKM > 1 and > 10-fold difference. To remove artifacts generated by different isoforms, DE was selected only for isoforms with similar expression patterns.

Quantitative real-time reverse transcription PCR (qRT-PCR)

Transcript levels of the 11 genes that showed significant DE were confirmed by using qRT-PCR. The qRT-PCR used the RNA prepared for the RNAseq and for the pool of additional three individual ticks of each treatment. The total RNA was extracted using Direct-zol™ RNA miniprep (Zymo research, Irvine, CA, USA). The cDNA of each group was synthesized by ImProm-II™ reverse transcriptase and Oligo(dT)15 (Promega, Madison, WI, USA). Reverse transcription was followed by real-time PCR with three technical replicates using SYBR® Select Master Mix for CFX (Applied Biosystems, Austin, TX, USA). Primers targeting different genes were listed in Supplementary Table E. The A. americanum ribosomal protein S4 (c46694_g1_i1) was used as the reference gene. Transcript levels were quantified by ΔΔCt method, corrected by the amplification efficiency of each target gene, and expressed as log10 fold difference (Pfaffl, 2001).

Results and discussion

The assembly of the Illumina sequences, representing six different groups of ticks (non-exposed female/male, infected female/male, and un-infected female/male), was analyzed for three main purposes: 1) establishing a gold transcript set (GTS) of transcriptome data to minimize noise in the analyses; 2) categorizing genomic level differential expression (DE) by gene ontology (GO) term enrichment analyses for comparisons between males and females and among NE, I, and U groups; and 3) focusing the DE to the subcategories of genes relevant to the immune pathway, sialome, neuropeptides, and GPCRs.

Sequence analyses for a gold transcript set (GTS) and annotation

A total of 208,878,905 raw reads were produced by Illumina 2500 after one hundred cycles of single direction sequencing for six different tagged libraries. The de novo Trinity assembler produced 140,574 transcripts after trimming and filtering the raw data based upon sequence quality (≥30) and length (≥40 nt). The assembled transcripts were then subjected to blast searches against the I. scapularis official peptide set from the annotated genome sequence and against A. americanum ESTs (21,438 sequences), while also being filtered against Ehrlichia and A. americanum mitochondrial sequences, which were present in very low numbers in the data. The GTS was then established by a selection of transcripts showing sufficient frequency (FPKM > 1) or highly similar sequences with I. scapularis peptides or A. americanum ESTs regardless of the frequency (Fig. 1). Manual assessment of the final GTS (61,802 transcripts) for a number of transcripts indicated the presence of a problem characteristic of assembly of short reads: independent pieces of transcripts covering different regions of what has been putatively determined to be the same gene. We found that the redundancy of the transcripts by putative allelic contigs were undetectable or rare, while splicing isoforms occurred at a frequency of approximately 20% in the GTS (12,670 out of 61,802 transcripts). Annotation of 61,802 transcripts in Blast2GO yielded the following statistics (Fig. 2): 36,475 (59%) transcripts were blasted without significant hits at e-value (1e-3), 13,807 (22.3%) transcripts were annotated by Blast2GO, 7320 (11.8%) transcripts were mapped with GO (gene ontology), 4174 (6.8%) transcripts were analyzed with blast hits, and 26 (0.04%) transcripts were analyzed with InterProScan without significant blast hits. GO distribution showed 20% (12,145), 14% (8536), and 16% (9725 transcripts) of GTS were annotated in molecular functions (MF), biological process (BP), and cellular components (CC), respectively. The results of blast hits in the non-redundant (nr) database showed that I. scapularis was the species with the most frequent blast hits (Fig. 2). This GTS, established by the above highly stringent criteria to be of reliable quality, was further used for DE analyses.

Fig. 1.

Fig. 1.

Open in a new tab

Histogram showing FPKM (fragments per kilobase of transcripts per million fragments) of the assemblies and their similarities to the I. scapularis official gene set and A. americanum EST and providing the criteria for forming the gold transcript set (GTS). Among the assembly (140,571 transcripts), the GTS (61,802 transcripts) was selected for Log (FPKM) > 0 and significant blast hits.

Fig. 2.

Fig. 2.

Open in a new tab

Statistics of annotations for gold transcript set (GTS; 61,802 transcripts) in Blast2GO. Pie chart describing proportional distribution for the result of different categories of Blast2GO annotation. Bar chart showing the numbers of blast hits from the top 10 species.

Analysis of differential expression (DE) between males and females

The number of DE transcripts in all combinatorial pairwise comparisons were in the range of 156 to 839 transcripts for FDR (false discovery rate) < 0.001, indicating a 0.1% false positive rate, and fold change (FC) > 4× (Figs. 3 and 4). Because the major categories of the DE transcripts were found to be significantly different from NE in both NEf and NEm, the frequencies of NE were used as the normalization to assess the differences of I and U. For low expression in I and U compared with the NE, three clusters were present: low-low (LL) for low in both I and U, low-normal (LN) for low in I and normal in U, and normal-low (NL) for normal in I and low in U. High expression of I and U compared with the NE were additionally clustered in a similar fashion with clusters representing high-high (HH), high-normal (HN), and normal-high (NH) (Figs. 3 and 4). Approximately 6% (range 4.5 to 10.8% and 1.5 to 9.7% for L and H, respectively) of transcripts from each cluster were found to be common in both males and females, as shown in overlapping regions of the Venn diagrams (Figs. 3 and 4). Relatively low levels of the common transcripts in the DE transcripts are likely caused by largely different feeding biology of male and female ticks.

Fig. 3.

Fig. 3.

Open in a new tab

Clusters with the transcripts that were downregulated in pathogen-exposed females and males. GO-term enrichment analyses for each cluster was performed for p-value (< 0.01) in Fisher’s exact test. Numbers in Venn diagram indicate transcripts for differential expression (FDR < 0.001 & fold differences > 4×). Representative GO-terms are on the top of the Venn diagram.

Fig. 4.

Fig. 4.

Open in a new tab

Clusters with the transcripts that were upregulated in pathogen-exposed females and males. GO-term enrichment analyses for each cluster was performed for p-value (< 0.01) in Fisher’s exact test. Numbers in Venn diagram indicate transcripts for differential expression (FDR < 0.001 & fold changes > 4×). Representative GO-terms are on the top of the Venn diagram.

The GO-term enrichment analyses for each cluster against GTS with p-value < 0.01 in Fisher’s exact test using Blast2GO revealed a number of interesting GO-terms relevant to pathogen-vector interactions, which are on the top of the Venn diagrams in Figs. 3 and 4. It is worth noting that large numbers of GO terms in immune-related, cytoskeletal, and ion transport functions were captured in these searches. Specifically, the enriched GO-terms “negative regulation of defense response” was found in 19% in the LN cluster, and “defense response” was found 24% in the HN male cluster, both commonly supporting upregulation of the immune system in the pathogen-exposed ticks. “Ca++ ion transport”, regulating the intracellular Ca++ concentration that is required in Ehrlichia proliferation (Alves et al., 2014), was additionally found in the HN and NH in female clusters, suggesting that Ehrlichia modulates the set of genes necessary for supplying Ca++ for Ehrlichia proliferation. However, “Cl− transport” was found in both the HH and the LN in female clusters, indicating different subsets of transcripts in this GO-term with Cl− transport being regulated in two different ways during pathogen exposure.

Analysis of DE among non-exposed female (NEf), infected female (If), and uninfected female (Uf)

In addition to the analyses of the entire transcriptome, we performed in-depth analyses of the focused sets of transcripts. The categories of transcripts selected for further analyses were the genes involved in the following: immune pathways, tick sialome, neuropeptides, and GPCRs. Differential expression of transcripts among NEf, If, and Uf were categorized based on the gene sets previously published for I. scapularis (Gulia-Nuss et al., 2016; Ribeiro et al., 2006) by performing blast searches (e-value < 1e−10) of GTS against the I. scapularis database and retaining the best hits. We obtained a number of interesting transcripts showing DE depending on pathogen exposure.

Comparison of immune-related genes (Fig. 5)

Fig. 5.

Fig. 5.

Open in a new tab

Differential expression of the transcripts in immune-related pathway. Heatmap indicating the level of differential expression of transcripts, which are shown as either magenta (up-regulation) or cyan (down-regulation). The values of the color key indicate fold changes as log10. More details of the methods are in the text.

The vectorial capacity of ticks may be associated with an optimal level of immune response that allows for pathogen transmission while concurrently suppressing the pathogen population below levels that would be detrimental to the fitness of the tick itself. A previous study in I. scapularis has identified a set of immune-related 88 genes in 14 groups (or pathways) (Gulia-Nuss et al., 2016). Based on the immune-related gene set, our initial homology search identified 703 transcripts (1.1%; Supplementary Table A & Supplementary file B) from the GTS showing immune-related transcripts. Among these, 19 DE transcripts, having FPKM > 1 and fold change (FC) > 10×, were captured in NEf, If, and Uf comparisons (Fig. 5). DE transcripts were identified as putative orthologs based on the best hits in reciprocal blast. Among the immune-related genes showing DE, most transcripts were captured with high expression in If and Uf compared with NEf. Seven transcripts involved in the IMD pathway were all moderately up-regulated in both If and Uf (Fig. 5), while two transcripts, including the toll receptor, in the toll pathway were additionally found to be upregulated. The most significantly up-regulated transcript was the hebraein-like antimicrobial protein, showing 43 and 27-fold higher expression in If and Uf, respectively, compared with NEf. Hebraein, a histidine-rich antimicrobial peptide (AMP), was originally characterized from the hemolymph of A. hebraeum and showed antimicrobial activity against gram-negative bacteria (Lai et al., 2004). In addition, transcripts in the Jak/Stat pathway and fibrinogen-related protein were up-regulated, while some transcripts in the Jak/Stat pathway were down-regulated. Interestingly, lysozyme, a gene known for serving as a defensive mechanism in insects (Shelby et al., 1998), was down regulated in If but not in Uf. Down regulation of the lysozyme transcript may be a consequence of tick cells being manipulated by the intracellular Ehrlichia pathogen. Additionally, DUOX, a gene known for the production of reactive oxygen species as a defensive mechanism against infectious microbes (Ha et al., 2009), was up regulated in Uf but not in If. Up regulation of DUOX in Uf may be indicative of a defense mechanism that has successfully suppressed pathogen invasion. Overall, a large number of the transcripts in the IMD pathway were upregulated in response to the Gram-negative bacteria, E. chaffeensis, while other transcripts in the toll and JAK/STAT pathways also exhibited changes in expression levels. An important limitation of transcriptome analysis of gene function is that it does not capture post-translational modifications which likely occurred for the proteins related to immune pathway.

Comparison of sialome-related genes (Figs. 6 and 7)

Fig. 6.

Fig. 6.

Open in a new tab

Up-regulated transcripts in the sialome. Heatmap indicating the level of differential expression of transcripts, which are shown as either magenta (upregulation) or cyan (down-regulation). The values of the color key indicate fold changes as log10. More details of the methods are in the text.

Fig. 7.

Fig. 7.

Open in a new tab

Down-regulated transcripts in the sialome. Heatmap indicating the level of differential expression of transcripts, which are shown as either magenta (upregulation) or cyan (down-regulation). The values of the color key indicate fold changes as log10. More details of the methods are in the text.

Tick saliva containing bioactive molecules facilitates tick feeding by inhibiting mammalian host defensive mechanisms, such as hemostatic, inflammation, and immune systems(Francischetti et al., 2009), and also includes the components facilitating pathogen transmission (Ramamoorthi et al., 2005). A previous study in I. scapularis has described the gene set for the sialome, 459 genes in 27 groups (Ribeiro et al., 2006). Based on that sialome gene set, our initial homology search identified 507 transcripts (0.8%; Supplementary Table B & Supplementary file B) from the GTS showing sialome related genes. Among these, 55 DE transcripts were captured in the same fashion with immune-related DE transcripts in pairwise comparisons among NEf, If and Uf (Figs. 6 and 7). The major groups of DE were found in Kunitz domain-containing proteins (referred to as “Kunitz” hereafter), 9&7-kDa families of peptides (referred to as “9&7 families” hereafter), other putative antiprotease polypeptide (referred to as “other antiproteases” hereafter), metalloprotease, other enzymes, and other unknown function (referred to as “unknown” hereafter) and lipocalin. Because different subsets in each group of sialome-related genes were up- or down-regulated, it is difficult to predict the functional implications.

A number of transcripts in Kunitz, 9&7 families, other antiproteases, and other enzymes were significantly up regulated in pathogen-exposed females (Fig. 6). In the Kunitz group, all DE transcripts were expressed higher in Uf than in If. Kunitz domain-containing proteins are normally known to provide anticoagulant activity in saliva to facilitate blood feeding. In addition, bacteriostatic properties of Kunitz-type proteins in the tick midgut have been reported in Dermacentor variabilis (Ceraul et al., 2008). Upregulation within the Kunitz group in Uf could be indicative of similar defensive bacteriostatic properties against an E. chaffeensis invasion. Most transcripts in 9&7 families, other antiproteases, and other enzymes were expressed either at higher levels in If than Uf or similarly in both. We speculate that E. chaffeensis may induce upregulation of these transcripts and consequently facilitate its transmission into the host. Upregulation of transcripts in other antiproteases, such as serpin, may increase activities inhibiting blood clotting and activation of the complement system in the host, which then promotes pathogen transmission, while upregulation of other enzymes, including serine carboxypeptidase, may facilitate blood digestion in the tick midgut (Motobu et al., 2007).

Large numbers of transcripts grouped in metalloprotease, lipocalin, and unknown functions were significantly down regulated from pathogen exposed female ticks (Fig. 7). Lipocalin functions in anti-inflammatory activities by scavenging histamine and serotonin in mammalian hosts (Mans and Ribeiro, 2008; Mans et al., 2008). Interestingly, five out of the total seven lipocalin DE transcripts were down-regulated. These transcripts are clustered in the phylogenetic tree constructed using their amino acid sequences (Fig. 8). Upregulated transcripts were also clustered, with the exception of one. The fact that the subgroup of genes within the lipocalin group exhibit similar patterns of expression suggests that they may share cis- or trans- gene regulatory factors and therefore are regulated in a similar manner (either up or down) in response to pathogen invasion (Supplementary file A).

Fig. 8.

Fig. 8.

Open in a new tab

Phylogenetic relationship of transcripts for lipocalins of A. americanum and I. scapularis. The phylogenetic tree was built by Maximum likelihood method with 500 bootstrap replicates. Four clusters are highlighted by different color boxes: Cluster 1 (C.1, green), Cluster 2 (C.2, yellow), Cluster 3 (C.3, dark red), and Cluster 4 (C.4, purple). Lipocalin genes of I. scapularis are shown as Genebank numbers, and the homologous transcripts of A. americanum are shown by the GTSid (c#####). Transcripts for differential expression (FPKM > 1 and fold change > 10×) are highlighted by red (upregulation) and blue (downregulation), respectively.

Comparison of neuropeptide-related genes (Fig. 9)

Fig. 9.

Fig. 9.

Open in a new tab

Differential expression of the transcripts in neuropeptides or GPCRs. Heatmap indicating the level of differential expression of transcripts, which are shown as either magenta (up-regulation) or cyan (down-regulation). The values of the color key indicate fold changes as log10. More details of the methods are in the text.

Ticks contain large numbers of neuropeptides that are crucial in behavioral events and physiology homeostasis (Egekwu et al., 2014; Egekwu et al., 2016; Gulia-Nuss et al., 2016; Šimo et al., 2009a; Šimo et al., 2014a, 2014b). In addition, at least two neuropeptides, myoinhibitory peptide (MIP) and SIFamide, and their receptors were identified in innervation of the salivary glands (Šimo et al., 2013; Šimo et al., 2009b). Changes in many neuropeptide transcripts were associated with blood feeding (Egekwu et al., 2016), while expression levels in response to interactions with a pathogen have not been studied. In insects, neuropeptides, such as bursicon and sex peptide (paralogy of allatostatin B or myoinhibitory peptide), induce the expression of genes encoding antimicrobial peptides as a part of an immune defense mechanism (An et al., 2012; Domanitskaya et al., 2007; Peng et al., 2005). A set of 71 tick neuropeptide genes has previously been identified in the I. scapularis genome annotation (Gulia-Nuss et al., 2016) and in other hard tick species EST databases (Park et al., unpublished data). Based on the neuropeptide gene set, our initial homology search identified 28 transcripts (0.04%; Supplementary Table C & Supplementary file B) from our GTS as being putative neuropeptide transcripts. Only one of these transcripts, encoding an insulin-like peptide, was captured for DE in the same fashion as immune-related DE transcripts in pairwise comparisons among NEf, If, and Uf (Fig. 9). In mosquitoes, a hematophagous insect, it was demonstrated that an insulin-like peptide in Aedes aegypti functions in the proliferation of hemocytes as a defense mechanism (Castillo et al., 2011), while insulin-like peptides (bombyxin) in other insects are well known for playing multiple roles in the regulation of digestion, growth, and egg formation in Bombyx mori (Iwami, 2000; Mizoguchi and Okamoto, 2013) and Drosophila melanogaster (Colombani et al., 2005). When comparing these two groups of blood feeding arthropods, the role of an insulin-like peptide serving as a factor responsible for activating hemocyte proliferation in mosquitoes may additionally be applicable to ticks that have been exposed to pathogens.

Comparison of G-protein coupled receptor (GPCR)-related genes (Fig. 9)

Like the cases of neuropeptide genes, changes in GPCR gene expression are known to be associated with blood feeding, while their involvement in interactions with pathogens remains to be studied (Egekwu et al., 2016). Intracellular signaling via GPCR for calcium mobilization, leading to the activation of DUOX, has been hypothesized (i.e., review in 46). GPCRs are involved in the expression of antimicrobial peptides (AMPs) as observed in the innate immune responses of insects (Buchon et al., 2014) and have been known to influence immunity by regulating the expression of noncanonical UPR (unfolded protein response) genes in nematodes (Sun et al., 2011).

A previous study in I. scapularis has identified a set of 186 genes in four classes of GPCR (Ribeiro et al., 2006). Based on the GPCR gene set, our initial homology search identified 282 transcripts (0.5%; Supplementary Table D & Supplementary file B) from our GTS as potentially being GPCR related genes. Of these, the method described in Section 0 identified nine transcripts as being DE in the pairwise comparisons among NEf, If, and Uf (Fig. 9). A transcript (c107526_g1_i1) for the bursicon receptor was upregulated in If and Uf (Fig. 9). Upregulation of a transcript for the bursicon receptor may indicate its involvement in the expression of AMPs as part of a defense mechanism against E. chaffeensis, as bursicon was reported to induce expression of AMP genes in insects (An et al., 2012). Additionally, transcriptome analysis of synganglion from hard ticks (I. scapularis and Dermacentor variabilis) confirmed the presence of transcripts for bursicon (Bissinger et al., 2011; Egekwu et al., 2014). Interestingly, the metabotropic glutamate receptor (mGluR) was upregulated only in If. We speculate that E. chaffeensis may be modulating the regulation of mGluR expression to facilitate the proliferation of the pathogen inside the tick. A similar data were observed in Kaposi’s sarcoma-associated herpesvirus (KSHV) infected primary human microvascular dermal endothelial cells (HMVEC-d), where an increased secretion of glutamate and expression of mGluR were documented in conjunction with virus infected cell proliferation (Valiya Veettil et al., 2014). In contrast, a transcript (c16075_g1_i1) for an ACP (AKH/corazonin-related peptide) receptor in class A GPCR (Fig. 9) and class D receptors were up- or down-regulated in pathogen-exposed ticks. It is possible that these genes regulate defensive functions against pathogen invasion or that E. chaffeensis modulates the expression of these GPCRs to facilitate its invasion of tick cells. As was discussed in Section 0, modulation of tick salivary secretion could be the target of E. chaffeensis to increase transmission rate. Dopamine, produced in salivary glands (Kaufman and Wong, 1983; Koči et al., 2014), is known to be the most potent signal for tick salivary secretion (Kaufman and Phillips, 1973a, 1973b; Sauer et al., 2000). Two distinct dopamine receptors in salivary glands play critical roles in salivary secretion: influx of water/solute into lumen of salivary gland acini and expulsion of water/solute for emptying the lumen (Kim et al., 2014; Šimo et al., 2014a, 2014b; Šimo et al., 2011). However, transcription level of dopamine receptors had no difference in all group (Supplementary Table D). We find no evidence of transcriptional changes of genes involved in tick salivation in E. chaffeensis infection of the ticks.

Validation of transcript levels by quantitative reverse transcription PCR (qRT-PCR)

In order to validate the differential expression observed through RNAseq, we quantified the transcript levels of 11 representative genes from the same RNA utilized in RNAseq and from the RNA of additional three individuals obtained from the same deer feeding. In general, strong corelationship between two independent experiments was found with the regressed lines in the ranges of 1.02–1.13 for the slopes and 0.67–0.92 for the R-squares. Exceptions were found in two genes (7 and 10 in Fig. 10D) c76073_g1_i1 (lipocalin) and c60137_g1_i1 (Other polypeptides of unknown function). Upregulations of these two genes in Uf compared to the NEf found in earlier sample were not repeatable in the qRT-PCR using the RNA of additional individuals. Further study with true biological replication is required to draw a conclusion for this inconsistent data.

Fig. 10.

Fig. 10.

Open in a new tab

Comparison of transcript levels measured by the fold differences in FPKM results of RNAseq and qRT-PCR. X and Y-axes are the log10 fold difference in FPKM and in qRT-PCR, respectively, for 11 genes. The results for infected female (If) and uninfected female (Uf) compared to the non-exposed female (NEf) were shade by red and blue color, respectively. Panel A and B described the results of NGS sample, while panel C and D described the results of new biological replication. Each panel included R square and slope of the linear regression. The numbers on each data points indicate the genes 1. c11558_g1_i1 (Lipocalin); 2. c107785_g1_i1 (9/7-kDa families of peptides); 3. c93769_g1_i1 (Other Enzyme); 4. c38307_g1_i1 (Kunitz); 5. c47273_g1_i1 (Metalloprotease); 6. c107526_g1_i1 (Bursicon receptor); 7. c60137_g1_i1 (Other Polypeptides of unknown function); 8. c39223_g1_i1 (AMP); 9. c116545_g1_i1 (9/7-kDa families of peptides); 10. c33149_g2_i1 (Kunitz); 11. c76073_g1_i1 (Lipocalin).

Concluding remarks

This study, which focused on the DE of the lone star tick to examine the effects of the pathogen, E. chaffeensis, is summarized below:

  • A gold transcript set (GTS) of the transcriptome was established to minimize the noise in the analyses by using FPKM (> 1) and high sequence similarity with screen databases of peptides and ESTs from close/same species.

  • Comparisons of GO term enrichment among NE, I, and U groups revealed upregulation of genes for tick defenses against pathogen invasion but also for the genes supplying Ca++ for pathogen proliferation in the pathogen-exposed ticks.

  • Analyses of DE of genes from several subcategories, including theimmune pathway, sialome, neuropeptides, and GPCRs, revealed that the invasion of E. chaffeensis induced the expression of genes for the IMD pathway, AMPs, bacteriostatic properties of Kunitz, insulin-like peptide, and a GPCR, bursicon receptor, involved in the expression of AMPs. DE of lipocalins revealed that clusters of similar lipocalins are co-regulated for up- or downregulation.

The current study uses comparative transcriptomics to provide insights into the molecular interactions between the vector, the lone star tick, and the pathogen, E. chaffeensis. Major categories of the DE genes indicated that the tick suppresses the pathogen population by changing transcriptional levels of immune-related genes, while the pathogen may also modulate expression of tick genes that facilitate its invasion into tick cells and its eventual transmission into the mammalian host.

Supplementary Material

supplementary file A
Supplementary Tables
Supplementary file B

Acknowledgements

This paper is contribution no. 16-xxx-J from the Kansas Agricultural Experiment Station. Funding: This work was supported by the National Institutes of Health [grant numbers: R01AI090062 and R01AI070908]. Ms. Lisa Coburn, tick rearing facility at Oklahoma State University, provided ticks for this study.

Footnotes

Declarations of interest

None.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aspen.2018.05.009.

References

  1. Aljamali MN, Hern L, Kupfer D, Downard S, So S, Roe BA, Sauer JR, Essenberg RC, 2009. Transcriptome analysis of the salivary glands of the female tick Amblyomma americanum (Acari: Ixodidae). Insect Mol. Biol 18, 129–154. [DOI] [PubMed] [Google Scholar]
  2. Alves RN, Levenhagen MA, Levenhagen MMMD, Rieck SE, Labruna MB, Beletti ME, 2014. The spreading process of Ehrlichia canis in macrophages is dependent on actin cytoskeleton, calcium and iron influx and lysosomal evasion. Vet. Microbiol 168, 442–446. [DOI] [PubMed] [Google Scholar]
  3. An S, Dong S, Wang Q, Li S, Gilbert LI, Stanley D, Song Q, 2012. Insect neuropeptide bursicon homodimers induce innate immune and stress genes during molting by activating the NF-?B transcription factor relish. PLoS One 7, e34510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Anderson BE, Sims KG, Olson JG, Childs JE, Piesman JF, Happ CM, Maupin GO, Johnson BJB, 1993. Amblyomma americanum: a potential vector of human ehrlichiosis. Am. J. Trop. Med. Hyg 49, 239–244. [DOI] [PubMed] [Google Scholar]
  5. Andrews S, 2010. FastQC A Quality Control tool for High Throughput Sequence Data
  6. Ayllón N, Villar M, Busby AT, Kocan KM, Blouin EF, Bonzón-Kulichenko E,Galindo RC, Mangold AJ, Alberdi P, Pérez de la Lastra JM, Vázquez J, de la Fuente J, 2013. Anaplasma phagocytophilum inhibits apoptosis and promotes cytoskeleton rearrangement for infection of tick cells. Infect. Immun 81, 2415–2425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bissinger BW, Donohue KV, Khalil SMS, Grozinger CM, Sonenshine DE, Zhu J, Roe RM, 2011. Synganglion transcriptome and developmental global gene expression in adult females of the American dog tick, Dermacentor variabilis (Acari: Ixodidae). Insect Mol. Biol 20, 465–491. [DOI] [PubMed] [Google Scholar]
  8. Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, Mangan M, Nekrutenko A, Taylor J, 2010. Galaxy: a web-based genome analysis tool for experimentalists. Curr. Protoc. Mol. Biol 10, 11–21 (Chapter 19, Unit 19). [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brian FA, Lisa SG, Gregory AS, Robert ET, 2010. Blood meal analysis to identify reservoir hosts for Amblyomma americanum ticks. Emerg. Infect. Dis. J 16, 433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Buchon N, Silverman N, Cherry S, 2014. Immunity in Drosophila melanogaster — from microbial recognition to whole-organism physiology. Nat. Rev. Immunol 14, 796–810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Bullard RL, Williams J, Karim S, 2016. Temporal gene expression analysis and RNA silencing of single and multiple members of gene family in the lone star tick Amblyomma americanum. PLoS One 11, e0147966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Castillo J, Brown MR, Strand MR, 2011. Blood feeding and insulin-like peptide 3 stimulate proliferation of hemocytes in the mosquito Aedes aegypti. PLoS Pathog 7, e1002274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Ceraul SM, Dreher-Lesnick SM, Mulenga A, Rahman MS, Azad AF, 2008. Functional characterization and novel Rickettsiostatic effects of a Kunitz-type serine protease inhibitor from the tick Dermacentor variabilis. Infect. Immun 76, 5429–5435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Childs JE, Paddock CD, 2003. The ascendancy of Amblyomma americanum as a vector of pathogens affecting humans in the United States. Annu. Rev. Entomol 48, 307–337. [DOI] [PubMed] [Google Scholar]
  15. Colombani J, Bianchini L, Layalle S, Pondeville E, Dauphin-Villemant C, Antoniewski C, Carre C, Noselli S, Leopold P, 2005. Antagonistic actions of ecdysone and insulins determine final size in Drosophila. Science 310, 667–670. [DOI] [PubMed] [Google Scholar]
  16. Dedonder SE, Cheng C, Willard LH, Boyle DL, Ganta RR, 2012. Transmission electron microscopy reveals distinct macrophage-and tick cell-specific morphological stages of Ehrlichia chaffeensis. PLoS One 7, e36749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Domanitskaya EV, Liu H, Chen S, Kubli E, 2007. The hydroxyproline motif of male sex peptide elicits the innate immune response in Drosophila females. FEBS J 274, 5659–5668. [DOI] [PubMed] [Google Scholar]
  18. Egekwu N, Sonenshine DE, Bissinger BW, Roe RM, 2014. Transcriptome of the female synganglion of the black-legged tick Ixodes scapularis (Acari: Ixodidae) with comparison between illumina and 454 systems. PLoS One 9, e102667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Egekwu N, Sonenshine DE, Garman H, Barshis DJ, Cox N, Bissinger BW, Zhu J, Roe RM, 2016. Comparison of synganglion neuropeptides, neuropeptide receptors and neurotransmitter receptors and their gene expression in response to feeding in Ixodes scapularis (Ixodidae) vs. Ornithodoros turicata (Argasidae). Insect Mol. Biol 25, 72–92. [DOI] [PubMed] [Google Scholar]
  20. Ewing SA, Dawson JE, Kocan AA, Barker RW, Warner CK, Panciera RJ, Fox JC, Kocan KM, Blouin EF, 1995. Experimental transmission of Ehrlichia chaffeensis (Rickettsiales: Ehrlichieae) among white-tailed deer by Amblyomma americanum (Acari: Ixodidae). J. Med. Entomol 32, 368–374. [DOI] [PubMed] [Google Scholar]
  21. Francischetti IMB, Sá-Nunes A, Mans BJ, Santos IM, Ribeiro JMC, 2009. The role of saliva in tick feeding. Front. Biosci 14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ, Nekrutenko A, 2005. Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15, 1451–1455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Giraldo-Calderón GI, Emrich SJ, MacCallum RM, Maslen G, Dialynas E, Topalis P, Ho N, Gesing S, Consortium TV, Madey G, Collins FH, Lawson D, 2015. VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic Acids Res 43, D707–D713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Goecks J, Nekrutenko A, Taylor J, Galaxy T, 2010. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 11, R86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gotz S, Garcia-Gomez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talon M, Dopazo J, Conesa A, 2008. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36, 3420–3435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A, 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol 29, 644–652. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gulia-Nuss M, Nuss AB, Meyer JM, Sonenshine DE, Roe RM, Waterhouse RM, Sattelle DB, de la Fuente J, Ribeiro JM, Megy K, Thimmapuram J, Miller JR, Walenz BP, Koren S, Hostetler JB, Thiagarajan M, Joardar VS, Hannick LI, Bidwell S, Hammond MP, Young S, Zeng Q, Abrudan JL, Almeida FC, Ayllon N, Bhide K, Bissinger BW, Bonzon-Kulichenko E, Buckingham SD, Caffrey DR, Caimano MJ, Croset V, Driscoll T, Gilbert D, Gillespie JJ, Giraldo-Calderon GI, Grabowski JM, Jiang D, Khalil SMS, Kim D, Kocan KM, Koci J, Kuhn RJ, Kurtti TJ, Lees K, Lang EG, Kennedy RC, Kwon H, Perera R, Qi Y, Radolf JD, Sakamoto JM, Sanchez-Gracia A, Severo MS, Silverman N, Simo L, Tojo M, Tornador C, Van Zee JP, Vazquez J, Vieira FG, Villar M, Wespiser AR, Yang Y, Zhu J, Arensburger P, Pietrantonio PV, Barker SC, Shao R, Zdobnov EM, Hauser F, Grimmelikhuijzen CJP, Park Y, Rozas J, Benton R, Pedra JHF, Nelson DR, Unger MF, Tubio JMC, Tu Z, Robertson HM, Shumway M, Sutton G, Wortman JR, Lawson D, Wikel SK, Nene VM, Fraser CM, Collins FH, Birren B, Nelson KE, Caler E, Hill CA, 2016. Genomic insights into the Ixodes scapularis tick vector of Lyme disease. Nat. Commun 7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Ha E-M, Lee K-A, Seo YY, Kim S-H, Lim J-H, Oh B-H, Kim J, Lee W-J, 2009. Coordination of multiple dual oxidase-regulatory pathways in responses to commensal and infectious microbes in drosophila gut. Nat. Immunol 10, 949–957. [DOI] [PubMed] [Google Scholar]
  29. de Hoon MJL, Imoto S, Nolan J, Miyano S, 2004. Open source clustering software. Bioinformatics 20, 1453–1454. [DOI] [PubMed] [Google Scholar]
  30. Irving RP, Pinger RR, Vann CN, Olesen JB, Steiner FE, 2000. Distribution of Ehrlichia chaffeensis (Rickettsiales: Rickettsiaeceae) in Amblyomma americanum in southern Indiana and prevalence of E. chaffeensis—reactive antibodies in white-tailed deer in Indiana and Ohio in 1998. J. Med. Entomol 37, 595–600. [DOI] [PubMed] [Google Scholar]
  31. Iwami M, 2000. Bombyxin: an insect brain peptide that belongs to the insulin family. Zool. Sci 17, 1035–1044. [DOI] [PubMed] [Google Scholar]
  32. James AM, Liveris D, Wormser GP, Schwartz I, Montecalvo MA, Johnson BJB, 2001. Borrelia lonestari infection after a bite by an Amblyomma americanum tick. J. Infect. Dis 183, 1810–1814. [DOI] [PubMed] [Google Scholar]
  33. Karim S, Ribeiro JMC, 2015. An insight into the sialome of the lone star tick, Amblyomma americanum, with a glimpse on its time dependent gene expression. PLoS One 10, e0131292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kaufman WR, Phillips JE, 1973a. Ion and water-balance in ixodid tick dermacentor-andersoni .1. Routes of ion and water excretion. J. Exp. Biol 58, 523–536. [Google Scholar]
  35. Kaufman WR, Phillips JE, 1973b. Ion and water-balance in ixodid tick dermacentor-andersoni .2. Mechanism and control of salivary secretion. J. Exp. Biol 58, 537–547. [Google Scholar]
  36. Kaufman WR, Wong DL, 1983. Evidence for multiple receptors mediating fluid secretion in salivary glands of ticks. Eur. J. Pharmacol 87, 43–52. [DOI] [PubMed] [Google Scholar]
  37. Kim D, Šimo L, Park Y, 2014. Orchestration of salivary secretion mediated by two different dopamine receptors in the blacklegged tick Ixodes scapularis. J. Exp. Biol 217, 3656–3663. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Koči J, Šimo L, Park Y, 2014. Autocrine/paracrine dopamine in the salivary glands of the blacklegged tick Ixodes scapularis. J. Insect Physiol 62, 39–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lai R, Takeuchi H, Lomas LO, Jonczy J, Rigden DJ, Rees HH, Turner PC, 2004. A new type of antimicrobial protein with multiple histidines from the hard tick, Amblyomma hebraeum. FASEB J 18, 1447–1449. [DOI] [PubMed] [Google Scholar]
  40. Li B, Dewey CN, 2011. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Liu L, Narasimhan S, Dai J, Zhang L, Cheng G, Fikrig E, 2011. Ixodes scapularis salivary gland protein P11 facilitates migration of Anaplasma phagocytophilum from the tick gut to salivary glands. EMBO Rep 12, 1196–1203. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Mans BJ, Ribeiro JMC, 2008. A novel clade of cysteinyl leukotriene scavengers in soft ticks. Insect Biochem. Mol. Biol 38, 862–870. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Mans BJ, Ribeiro JMC, Andersen JF, 2008. Structure, function, and evolution of biogenic amine-binding proteins in soft ticks. J. Biol. Chem 283, 18721–18733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mizoguchi A, Okamoto N, 2013. Insulin-like and IGF-like peptides in the silkmoth Bombyx mori: discovery, structure, secretion and function. Front. Physiol 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Motobu M, Tsuji N, Miyoshi T, Huang X, Islam MK, Alim MA, Fujisaki K, 2007. Molecular characterization of a blood-induced serine carboxypeptidase from the ixodid tick Haemaphysalis longicornis. FEBS J 274, 3299–3312. [DOI] [PubMed] [Google Scholar]
  46. Nair ADS, Cheng C, Jaworski DC, Willard LH, Sanderson MW, Ganta RR, 2014. Ehrlichia chaffeensis infection in the reservoir host (white-tailed deer) and in an incidental host (dog) is impacted by its prior growth in macrophage and tick cell environments. PLoS One 9, e109056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Peng J, Zipperlen P, Kubli E, 2005. Drosophila sex-peptide stimulates female innate immune system after mating via the toll and Imd pathways. Curr. Biol 15, 1690–1694. [DOI] [PubMed] [Google Scholar]
  48. Pfaffl MW, 2001. A new mathematical model for relative quantification in real-time RT–PCR. Nucleic Acids Res 29, e45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Ramamoorthi N, Narasimhan S, Pal U, Bao F, Yang XF, Fish D, Anguita J, Norgard MV, Kantor FS, Anderson JF, Koski RA, Fikrig E, 2005. The Lyme disease agent exploits a tick protein to infect the mammalian host. Nature 436, 573–577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ribeiro JMC, Alarcon-Chaidez F, Francischetti IM, Mans BJ, Mather TN, Valenzuela JG, Wikel SK, 2006. An annotated catalog of salivary gland transcripts from Ixodes scapularis ticks. Insect Biochem. Mol. Biol 36, 111–129. [DOI] [PubMed] [Google Scholar]
  51. Richardson MA, Smith DRJ, Kemp DH, Tellam RL, 1993. Native and baculovirus-expressed forms of the immunoprotective protein BM86 from Boophilus microplus are anchored to the cell membrane by a glycosylphosphatidyl inositol linkage. Insect Mol. Biol 1, 139–147. [DOI] [PubMed] [Google Scholar]
  52. Robinson MD, McCarthy DJ, Smyth GK, 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Saldanha AJ, 2004. Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248. [DOI] [PubMed] [Google Scholar]
  54. Sauer JR, Essenberg RC, Bowman AS, 2000. Salivary glands in ixodid ticks: control and mechanism of secretion. J. Insect Physiol 46, 1069–1078. [DOI] [PubMed] [Google Scholar]
  55. Shelby KS, Cui L, Webb BA, 1998. Polydnavirus-mediated inhibition of lysozyme gene expression and the antibacterial response. Insect Mol. Biol 7, 265–272. [DOI] [PubMed] [Google Scholar]
  56. Šimo L, Slovák M, Park Y, Žitňan D, 2009a. Identification of a complex peptidergic neuroendocrine network in the hard tick, Rhipicephalus appendiculatus. Cell Tissue Res 335, 639–655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Šimo L, Žitňan D, Park Y, 2009b. Two novel neuropeptides in innervation of the salivary glands of the black-legged tick, Ixodes scapularis: Myoinhibitory peptide and SIFamide. J. Comp. Neurol 517, 551–563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Šimo L, Koci J, Zitnan D, Park Y, 2011. Evidence for D1 dopamine receptor activation by a paracrine signal of dopamine in tick salivary glands. PLoS One 6, e16158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Šimo L, Koči J, Park Y, 2013. Receptors for the neuropeptides, myoinhibitory peptide and SIFamide, in control of the salivary glands of the blacklegged tick Ixodes scapularis. Insect Biochem. Mol. Biol 43, 376–387. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Šimo L, Koči J, Kim D, Park Y, 2014a. Invertebrate specific D1-like dopamine receptor in control of salivary glands in the black-legged tick Ixodes scapularis. J. Comp. Neurol 522, 2038–2052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Šimo L, Sonenshine D, Park Y, Žitňan D, 2014b. Nervous and sensory systems: structure, function, genomics, and proteomics. In: Daniel E, Sonenshine RMR (Eds.), Biology of Ticks, 2nd ed. Oxford University Press, pp. 309–367. [Google Scholar]
  62. Steiert JG, Gilfoy F, 2002. Infection rates of Amblyomma americanum and Dermacentor variabilis by Ehrlichia chaffeensis and Ehrlichia ewingii in Southwest Missouri. Vector-Borne Zoonotic Dis 2, 53–60. [DOI] [PubMed] [Google Scholar]
  63. Steiner FE, Pinger RR, Vann CN, 1999. Infection rates of Amblyomma americanum (Acari: Ixodidae) by Ehrlichia chaffeensis (Rickettsiales: Ehrlichieae) and prevalence of E. chaffeensis-reactive antibodies in white-tailed deer in southern Indiana, 1997. J. Med. Entomol 36, 715–719. [DOI] [PubMed] [Google Scholar]
  64. Sultana H, Neelakanta G, Kantor FS, Malawista SE, Fish D, Montgomery RR, Fikrig E, 2010. Anaplasma phagocytophilum induces actin phosphorylation to selectively regulate gene transcription in Ixodes scapularis ticks. J. Exp. Med 207, 1727–1743. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sun J, Singh V, Kajino-Sakamoto R, Aballay A, 2011. Neuronal GPCR controls innate immunity by regulating noncanonical unfolded protein response genes. Science 332, 729–732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Valiya Veettil M, Dutta D, Bottero V, Bandyopadhyay C, Gjyshi O, Sharma-Walia N, Dutta S, Chandran B, 2014. Glutamate secretion and metabotropic glutamate receptor 1 expression during Kaposi’s sarcoma-associated herpesvirus infection promotes cell proliferation. PLoS Pathog 10, e1004389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zivkovic Z, Blouin EF, Manzano-Roman R, Almazán C, Naranjo V, Massung RF, Jongejan F, Kocan KM, de la Fuente J, 2009. Anaplasma phagocytophilum and Anaplasma marginale elicit different gene expression responses in cultured tick cells. Comp. Funct. Genom 2009, 9. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplementary file A
NIHMS1720456-supplement-supplementary_file_A.txt (39.9KB, txt)
Supplementary Tables
NIHMS1720456-supplement-Supplementary_Tables.xlsx (88.3KB, xlsx)
Supplementary file B
NIHMS1720456-supplement-Supplementary_file_B.txt (2.9MB, txt)

RESOURCES