New Results

Mayaro Virus Infection Elicits an Innate Immune Response and Represses Autophagy in Anopheles stephensi

Cory Henderson, Marco Brustolin, Shivanand Hegde, Grant L. Hughes, Christina Bergey, Jason L. Rasgon
doi: https://doi.org/10.1101/2020.11.15.383596

ABSTRACT

Mayaro virus (MAYV) is an arboviral pathogen in the genus Alphavirus that is circulating in South America with potential to spread to naïve regions. MAYV is also one of the few viruses with the ability to be transmitted by mosquitoes in the genus Anopheles in addition to the typical arbovirus transmitting mosquitoes in the genus Aedes. Few studies have investigated the infection response of Anopheles mosquitoes to arboviruses. In this study we detail the transcriptomic and small RNA responses of An. stephensi to infection with MAYV via infectious bloodmeal at 2, 7, and 14 days post infection (dpi). 487 unique transcripts were significantly regulated and 79 novel miRNAs were identified. Gene ontology analysis of transcripts regulated at each timepoint suggested activation of the Toll pathway at 7 dpi and repression of pathways related to autophagy at 14 dpi. These findings provide a basic understanding of the infection response of An. stephensi to MAYV and help to identify host factors which might be useful to target to inhibit viral replication in Anopheles mosquitoes.

AUTHOR SUMMARY Mayaro virus (MAYV) is a mosquito-borne Alphavirus responsible for outbreaks in South America and the Caribbean. In this study we infected Anopheles stephensi with MAYV and sequenced mRNA and small RNA to understand how MAYV infection impacts gene transcription and the expression of small RNAs in the mosquito vector. Genes involved with innate immunity and autophagy are regulated in response to MAYV infection of An. stephensi, we also discover novel miRNAs and describe their expression patterns following bloodmeal ingestion. These results suggest that MAYV does induce a molecular response to infection in its mosquito vector species and that MAYV may have mechanisms to evade the vector immune response.

INTRODUCTION

Mayaro virus (MAYV) is a mosquito-borne, enveloped positive-sense single-stranded RNA virus in the genus Alphavirus, first isolated from the blood of five febrile workers in Mayaro county, Trinidad in 1954 [1]. Symptoms of MAYV infection are similar to other arboviral infections such as dengue or Chikungunya viruses, and include rash, fever, retro-orbital pain, headache, diarrhea, and arthralgia [2]. While no epidemics or outbreaks with Mayaro virus being the causative agent have been recorded outside of South America, there have been imported cases reported in the Netherlands, Germany, France, and Switzerland [3–6], which demonstrates a need to understand the capacity for the virus to spread into naïve regions, such as the United States.

The principal mosquitoes transmitting Mayaro virus naturally are thought to be the canopy-dwellers in the genus Haemogogus, maintaining the sylvatic cycle between non-human primates as primary hosts and birds as secondary hosts [7]. Human infections are sporadic due to the rare display of anthropophilic biting behaviors by Haemogogus mosquitoes, with transmission due to these species primarily occurring in rural regions with close proximity to forests [8]. Vector competence studies have identified anthropophilic and urban adapted species such as Aedes aegypti and Ae. albopictus, as well as the malaria parasite vectors Anopheles gambiae, An. stephensi, An. freeborni, and An. quadrimaculatus, as being competent vectors for Mayaro virus under laboratory conditions [9–12]. Transmission of an arbovirus with an anopheline mosquito as a primary vector is rare, only having been observed occurring regularly for O’nyong’nyong virus by An. gambiae and An. funestus in Uganda [13], with some limited evidence for Chikungunya and Semliki Forest virus [14].

As arboviral pathogens are transmitted between hosts primarily by arthropod vectors, transmission requires the virus to infect and disseminate from the midgut and salivary glands of the mosquito following an infectious bloodmeal [15]. The molecular underpinnings controlling why MAYV and these closely related viruses can infect Anopheles salivary glands is of epidemiological interest, yet remains poorly understood. A more complete understanding of this phenomenon requires investigation of the molecular pathways involved in viral infection of anopheline mosquitoes. Recent transcriptomic studies have identified a number of genes involved in classical immune pathways, RNA interference (RNAi), metabolism, energy production, and transport as being regulated in response to arboviral infection of mosquitoes [16–19]. In addition, studies focusing on small RNA identification and regulation have identified RNAi activity, such as miRNA and piRNA expression, in response to infection of mosquitoes by arboviruses [20–23].

The available evidence suggests that, should MAYV be introduced into a naïve region, outbreaks and epidemics of the resulting disease could be driven by anopheline vectors [9, 24–25]. Because anopheline, and not aedine, mosquitoes could act as the primary transmitting vectors for MAYV, this study also provides an opportunity to understand how vector competence might emerge in this system and provide insight into why Anopheles are generally poor viral transmitters when compared to Aedes mosquitoes. We used RNA sequencing to study the transcriptomic and small RNA responses of An. stephensi to infection with MAYV via infectious bloodmeal at 2, 7, and 14 days post infection (dpi).

MATERIALS AND METHODS

Anopheles stephensi Rearing

Protocols pertaining to mosquito rearing and presentation of infectious bloodmeal has been described elsewhere [9]. Briefly, An. stephensi (Liston strain) were reared and maintained at the Millennium Sciences Complex insectary (The Pennsylvania State University, University Park, PA, USA) at 27°C ±1°C, 12 hour light 12 hour dark diurnal cycle at 80% relative humidity in 30×30×30-cm cages. Ground fish flakes (TetraMin, Melle, Germany) were used to feed larvae, and upon emergence adult mosquitoes were maintained with a 10% sucrose solution.

Viral Production and Infection via Bloodmeal

Mayaro virus strain BeAn 343102 (BEI Resources, Manassas, VA, USA) was utilized in this study, a genotype D strain originally isolated from a monkey in Para, Brazil, in May 1978. Virus-infected supernatant was aliquoted and stored at −80°C until used for mosquito infections. Viral stock titers were obtained by focus forming assay (FFA) technique. Adult female mosquitoes at 6 days post emergence that had not previously blood-fed were used for experimentation. Mosquitoes were allowed to feed on either human blood spiked with Mayaro virus at 1*107 FFU/mL or a control bloodmeal with no virus via a glass feeder jacketed with 37°C distilled water for 1 h.

At 2, 7, and 14 days post infection, mosquitoes were anesthetized with triethylamine (Sigma, St. Louis, MO, USA) and RNA was extracted from each individual mosquito using mirVana RNA extraction kit (Life Technologies) applying the protocol for extraction of total RNA. Infection was confirmed via qPCR using primers published by Wiggins et. al. 2018 (Forward: 5□-TGGACCTTTGGCTCTTCTTATC-3□, Reverse: 5□-GACGCTCACTGCGACTAAA-3□) [10], a CT value of 20 or less was used to confirm infection (Supplementary Table 1). 3 pools of total RNA were created for each time point and infection status to be used for library preparation, each consisting of 750 ng of RNA from 4 mosquitoes for a total of 3 mg per pool as confirmed via nanodrop. The protocol for mosquito rearing, viral production, and infection via bloodmeal is described in more detail in Brustolin et al. 2018 [9].

Transcriptomic Library Preparation and Sequencing

All pools were sent to University of Texas Medical Branch for library preparation where total RNA was quantified using a Qubit fluorescent assay (Thermo Scientific) and RNA quality was assessed using an RNA 6000 chip on an Agilent 2100 Bioanalyzer (Agilent Technologies). See Etebari et all. 2017 for more detail on library preparation and sequencing [17]. 1 mg of total RNA per pool was poly-A selected and fragmented using divalent cations and heat (940 C, 8 min). The NEBNext Ultra II RNA library kit (New England Biolabs) was used for RNA-Seq library construction. Fragmented poly-A RNA samples were converted to cDNA by random primed synthesis using ProtoScript II reverse transcriptase (New England Biolabs). After second strand synthesis, the double-stranded DNAs were treated with T4 DNA polymerase, 5’phosphorylated and then an adenine residue was added to the 3’ends of the DNA. Adapters were then ligated to the ends of these target template DNAs. After ligation, the template DNAs were amplified (5-9 cycles) using primers specific to each of the non-complimentary sequences in the adapters. This created a library of DNA templates that have non-homologous 5’and 3’ends. A qPCR analysis was performed to determine the template concentration of each library. Reference standards cloned from a HeLa S3 RNA-Seq library were used in the qPCR analysis. Cluster formation was performed using 15.5-17 billion templates per lane using the Illumina cBot v3 system. Sequencing by synthesis, paired end 75 base reads, was performed on an Illumina NextSeq 5500 using a protocol recommended by the manufacturer.

Small RNA Library Preparation and Sequencing

Small RNA libraries were created using the New England Biolabs small RNA library protocol. See Saldaña et. al. 2017 for more information on small RNA sequencing [21]. Library construction used a two-step ligation process to create templates compatible with Illumina based next generation sequence (NGS) analysis. Where appropriate, RNA samples were quantified using a Qubit fluorometric assay. RNA quality was assessed using a pico-RNA chip on an Agilent 2100 Bioanalyzer. Library creation uses a sequential addition of first a 3’adapter sequence followed by a 5’adapter sequence. A cDNA copy was then synthesized using ProtoScript reverse transcriptase and a primer complimentary to a segment of the 3’adapter. Amplification of the template population was performed in 15 cycles (94°C for 30 sec; 62°C for 30 sec; 70°C for 30 sec) and the amplified templates were PAGE (polyacrylamide gel electrophoresis) purified (147 bp DNA) prior to sequencing. All NGS libraries were indexed. The final concentration of all NGS libraries was determined using a Qubit fluorometric assay and the DNA fragment size of each library was assessed using a DNA 1000 high sensitivity chip and an Agilent 2100 Bioanalyzer. Single end 75 base sequencing by synthesis on an Illumina NextSeq 5500.

Transcriptomic RNA Sequencing Data Analysis

Raw sequencing data was uploaded to the ICS-ACI high performance computing cluster at Pennsylvania State University to perform all computational analyses. Transcriptomic libraries had adapters were trimmed and low-quality bases removed using Trimmomatic read trimming software with base settings [26]. Quality trimmed reads were aligned to the current build of the Anopheles stephensi Indian strain genome in Vectorbase (AsteI2) using the STAR RNA sequencing aligner [27]. Reads less than 75 bp in length and with a mapping quality of less than 20 were dropped from the analysis, and read counts were calculated in R using the rSubread package [28], following which a principal components analysis was performed and differential expression conducted using a negative binomial GLM with the EdgeR package [29]. Contrasts considered in the GLM were infected against control at 2, 7, and 14 dpi, and differences between 2 - 7 dpi and 7–14 dpi for infected treatments corrected for the response from the control treatments between the same time points. Gene IDs that were differentially expressed with a log2FC value of +/-1 and P value < 0.05 were uploaded to g:Profiler to run GO term overrepresentation analysis [30].

Small RNA Sequencing Data Analysis

Small RNA libraries had adapters trimmed using Trimmomatic and were subsequently passed into the miRDeep2 pipeline to identify novel and known miRNAs in all samples and determine expression of all known and novel miRNAs at each time point and treatment status [31, 26]. Novel miRNAs with a miRDeep score of less than 3, a minimum free energy value of less than – 20, or a non-significant Randfold p-value were considered false IDs and excluded from further analysis. miRNA targets were identified in the AsteI2 genome using miRanda software package [32]. Differential expression of miRNAs in response to infection status and time point was conducted using a negative binomial GLM with the EdgeR package and contrasts as described for the transcriptomic analysis [29]. miRNAs which were differentially expressed with log2FC +/-1 and P value < 0.05 had their miRanda genomic targets uploaded to g:Profiler to determine if any GO terms were overrepresented by transcripts potentially regulated by differentially expressed miRNAs [33]. piRNAs were isolated from the small RNA libraries by selecting all 24 – 30 nt reads from the trimmed datasets and filtering out all identified mature miRNAs, and those mapping to the AsteI2 genome were considered potential piRNAs. piRNA alignment to the AsteI2 genome was performed using STAR RNA sequencing aligner allowing for 3 mismatches across the length of the read [27].

RESULTS/DISCUSSION

Transcriptome

RNA Sequencing

We assayed genome-wide gene expression in pools of An. stephensi (Liston strain) experimentally infected with MAYV at 2, 7, and 14 dpi, along with blood fed uninfected negative controls. RNAseq libraries were sequenced on the Illumina NextSeq 5500 platform, yielding 20.6 - 28.4 million paired end reads per library. (Supplementary Table 1). Principal components analysis (PCA) performed on read counts of each annotated gene in the An. stephensi (Indian strain) reference transcriptome (AsteI2) at each time point distributed infected and control samples into distinct groups (Figure 1).

Figure 1:
Figure 1:

Principal Components Analysis (PCA) on filtered read counts mapping to annotated genes from the AsteI2 build of the Anopheles stephensi genome in Vectorbase. A., B., and C. are read counts from samples in the 2, 7, and 14 dpi groupings respectively. In all PCAs, blue is Mayaro infected, and red are control.

Differential Expression

To determine which genes exhibit differential expression by infection status and between time points a general-linearized model (GLM) was performed on filtered and normalized read counts mapping to the AsteI2 genome (Table 1, details in Supplementary Table 2; Figure 2). Contrasts considered in the GLM were infected compared to control at 2, 7, and 14 dpi, and differences between 2-7 dpi and 7–14 dpi for infected treatments correcting for results from control treatments. Genes were considered significantly regulated if they had a log fold-change (log2FC) value of +/-1 and P value < 0.05.

Figure 2:
Figure 2:

Volcano plots visualizing differential expression of Anopheles stephensi transcripts in response to Mayaro infection. The Y-axis shows −log10 transformed P-values, and the X-axis shows log2 transformed fold change values. Red points represent transcripts downregulated by more than −1 log2FC in response to infection with a FDR < 0.05, while blue points are transcripts upregulated by more than 1 log2FC in response to infection with a P value < 0.05. A. - C. are transcripts regulated in the 2 dpi, 7 dpi, and 14 dpi groupings respectively, while D. and E. are transcripts regulated in the infected treatment between 2 - 7 dpi and 7-14 dpi respectively.

View this table:
Table 1:

Differentially expressed Anopheles stephensi transcripts in response to Mayaro virus infection. A. – C. demonstrate differentially expressed transcripts between control and infected treatments at 2, 7, and 14 dpi respectively. D. and E. represent differentially expressed transcripts between 2 – 7 dpi and 7–14 dpi for infected treatments correcting results from control treatments. Only transcripts differentially expressed with logFC of +/- 2 are shown here, more detailed results are provided in Supplementary Table 2.

There were 161 (64 enriched, 97 depleted), 45 (29 enriched, 16 depleted), and 204 (149 enriched, 55 depleted) of 10,313 annotated genes regulated between control and infected at 2, 7, and 14 dpi respectively. 3 genes were regulated in the same direction at each time point, 2 enriched (ASTEI09037, ASTEI03083) and 1 depleted (ASTEI04716). The gene with the strongest response to infection at any time point was ASTEI04601 at 2 dpi with a log2FC of −9.8 and the most enriched gene was ASTEI04639 at 14 dpi with log2FC pf 5.7. When considering changes between time points for the infected treatment when controlling for the response from the uninfected treatments, there were 96 positively and 44 negatively regulated genes between 2 - 7 dpi, and 129 upregulated and 32 downregulated genes between 7-14 dpi. Regulated transcripts for 2-7 dpi ranged from −6.2 (ASTEI08168) to 7.5 (ASTEI09252) log2FC in terms of magnitude of expression, and −3.4 (ASTEI10804) to 7.4 (ASTEI04639) log2FC for 7-14 dpi. When considering a FDR threshold as a multiple testing correction very few transcripts in any contrast can be considered significantly regulated; 3 transcripts at 2 dpi (ASTEI04601, ASTEIO5497, ASTEI05732) and 2 transcripts at 14 dpi (ASTEI00644, ASTEI08604) fall below a FDR < 0.1 threshold for significance.

Gene Ontology

A gene ontology (GO) over-representation analysis was performed using g:Profiler on gene IDs which were significantly enriched or depleted in any considered contrast in the GLM described above when using a P value cutoff of 0.05 and any overrepresented GO terms with an FDR < 0.5 were considered significant (Supplementary Table 3) [33]. At 2 dpi depleted terms were overrepresented by proteins categorized as integral membrane components, but no terms were considered overrepresented by upregulated transcripts. At 7 dpi depleted transcripts were biased for odorant binding proteins, and enriched transcripts by endopeptidases. At 14 dpi the upregulated transcripts were not biased for any GO terms MAPK/JNK signaling cascades were overrepresented by downregulated genes at 14 dpi with activity being localized to peroxisomes and chromatin. Between 2 and 7 dpi upregulated transcripts were biased for serine type endopeptidases, specifically serine hydrolases. 7 to 14 dpi saw terms associated with G protein-coupled receptor signaling overrepresented by upregulated transcripts.

Endopeptidases, specifically serine proteases were upregulated at 7 dpi and from 2-7 dpi, suggesting an activation of the Toll pathway as part of the innate humoral response to infection once the virus has had time to establish an infection in the mosquito [33]. Activation of serine proteases is not uncommon in pathogenic infection of insects, and has been identified specifically as upregulated in Ae. aegypti in response to dengue and Zika virus infection, and in An. gambiae and An. coluzzii in response to O’nyong’nyong virus infection [16–18, 34]. At late stages of infection there was depletion of the autophagic inducing JNK and MAPK cascades in addition to repression of JAK/STAT signaling pathways through repression MAPK signaling, both of which have positive impacts on alphaviral replication [33, 35].

Small RNA

miRNA Identification

We next identified novel miRNAs in the small RNA transcriptomes of the MAYV infected samples and controls using miRDeep [31]. We searched for matches in our sequencing reads to all miRNAs in the miRBase database for the species An. gambiae, Aedes aegypti, Culex quinquefasciatus, Drosophila melanogaster, Bombyx mori, Apis mellifera, and Acyrthosiphon pisum. We found matches to 74 known miRNAs, all from An. gambiae, and 79 novel miRNAs identified across all samples, with between 2.2 – 4.0 million reads mapping to identified miRNAs per-sample (Supplementary Table 4). We found no explicit relationship between diversity of miRNA population and either dpi or infection status. Of the 153 total miRNAs identified across all samples, 83 were present in at least one replicate per treatment (Figure 3). PCA of read counts for all identified miRNAs in each sample showed no obvious grouping patterns at 2 and 7 dpi, however there was a correlation between infection status and PC1/PC2 placement at 14 dpi (Figure 4).

Figure 3:
Figure 3:

The top histogram represents the number of miRNAs shared between treatments (intersection size), and each row below the histogram represents a treatment. The lines connecting treatments below the top histogram represent treatments which share that number of miRNAs, and the histogram to the side of the treatments represents the number of miRNAs contained within each treatment.

Figure 4:
Figure 4:

Principal Components Analysis (PCA) on read counts mapping to miRNAs identified in the AsteI2 build of the Anopheles stephensi genome in Vectorbase. A. - C. are the 2 dpi, 7 dpi, and 14 dpi groupings respectively. In all PCAs, blue is Mayaro infected, and red is control.

miRNA Differential Expression

We next identified known and novel miRNAs that were differentially expressed by infection status (Figure 5; Table 2, Supplementary Table 5). Contrasts considered in the GLM were infected against control at 2, 7, and 14 dpi, and differences between 2-7 dpi and 7–14 dpi for infected treatments with results from control treatments filtered out. miRNAs were considered differentially expressed by having a log fold-change (log2FC) value of+/-1 and P value < 0.05.

Figure 5:
Figure 5:

Volcano plots visualizing differential expression of identified Anopheles stephensi miRNAs in response to Mayaro infection. The Y-axis shows −log10 transformed P-values, and the X-axis shows log2 transformed fold change values. Red points represent transcripts downregulated by more than −1 log2FC in response to infection with a FDR < 0.05, while blue points are transcripts upregulated by more than 1 log2FC in response to infection with a FDR < 0.05. A. - C. are the 2 dpi, 7 dpi, and 14 dpi groupings respectively, while D. and E. are miRNAs regulated in the infected treatment between 2 - 7 dpi and 7-14 dpi respectively.

View this table:
Table 2:

Differentially expressed Anopheles stephensi miRNAs in response to Mayaro virus infection. A. and B. demonstrate differentially expressed miRNAs between control and infected treatments at 2, and 7 dpi respectively. C. and D. represent differentially expressed miRNAs between 2 – 7 dpi and 7–14 dpi for infected and control treatments.

There were a total of 7 miRNAs differentially regulated in any considered contrast, novel miRNAs as-mir10, as-mir16, and as-mir17 as well as known miRNAs aga-miR-286b-5p, aga-miR-2944a-5p, aga-miR-2944b-5p, and aga-miR-309. as-mir10 was upregulated at both 2 and 7 dpi, as-mir16 was upregulated at 7 dpi, and as-mir 17 was downregulated at 14 dpi and between 7 – 14 dpi. The known miRNAs were downregulated as a group at 7 dpi and in the 2 – 7 dpi contrast, but upregulated in the 7–14 dpi contrast. The only miRNA found to be significantly regulated with an FDR cutoff of 0.1 was aga-miR-2944a-5p downregulated at 7 dpi.

The miR-309/286/2944 has been found to be upregulated in An. gambiae in response to bloodfeeding [36–37], and to be associated with Argonaute proteins post-bloodmeal [36]. When experimentally repressed aga-miR-309 was found to retard oocyte development [36], so it’s downregulation in response to MAYV infection may suggest that viral replication sequesters resources normally requires for host oocyte development, and as a result associated miRNAs are also downregulated.

miRNA Target Prediction

We next identified putative targets in the An. stephensi genome for all known and novel miRNAs identified in all samples [32]. For each of the identified miRNAs we found an average 537 potential annotated targets within the AsteI2 genome (Supplementary Table 6). Targets for significantly regulated miRNAs were loaded into g:Profiler and any overrepresented GO terms with an FDR < 0.5 were considered significant (Table 3) [33].

View this table:
Table 3:

Overrepresented GO terms represented by targets of significantly regulated miRNAs.

as-mir-16 was significantly upregulated in response to infection at 7 dpi and the only GO terms overrepresented by the predicted genomic targets of this miRNA are associated with protein binding, as-mir17 was downregulated at 14 dpi and between 7–14 dpi and has GO terms related to transmembrane ion channels overrepresented by its genomic targets, aga-mir-2944a-5p and aga-mir-2944b-5p were both downregulated at 7 dpi and between 2 – 7 dpi but upregulated between 7–14 dpi and both have GO terms primarily associated with intracellular signaling and various binding functions, and aga-mir-2944b-5p also appears to be involved with lipid localization and transport.

The novel miRNA as-mir-17 has 498 predicted genomic targets, and those targets overlap with 8 upregulated and 3 downregulated genes at 14 dpi and 6 upregulated and 2 downregulated genes between 7 and 14 dpi, when as-mir-17 was significantly repressed in response to MAYV infection. The known miRNAs also showed a bias for upregulated targets between 2-7 dpi and 7-14 dpi where they are repressed and activated in each contrast respectively. These patterns are consistent with the miRNAs acting as effector molecules for RNAi, except for the novel miRNAs between 7-14 dpi where their expression is enhanced but they still have a bias for upregulated genomic targets [38]. Recent studies have demonstrated that through targeting of promotor elements miRNAs can have a positive impact on gene transcription, so this could explain the phenomenon happening between 7–14 dpi where miRNA targets are upregulated when the miRNAs themselves are also upregulated [39].

piRNA Identification

Virus-derived piRNA-like small RNAs (25–30 nt), have been identified in insects and insect cells infected with Flaviviruses, Bunyaviruses and Alphaviruses. Knockdown of the piRNA pathway proteins leads to enhanced replication of arboviruses in mosquito cells, suggesting their potential antiviral properties in mosquitoes [40–45]. For example, knockdown of Piwi-4 in Ae. aegypti Aag2 cell line increased replication of Semliki-Forest virus, and silencing of Ago3 and Piwi-5 led to significantly reduced production of piRNAs against Sindbis virus [41, 44].

We identified putative piRNAs in the trimmed small RNA datasets for each sample by isolating all 24-30 nt reads, removing those that were identified positively as miRNAs, and mapping the remaining reads to the Mayaro Virus BeAr 20290 genome using the STAR sequence aligner [26–27, 46]. There was no particular bias for viral piRNA abundance in infected samples over control, and the proportion of potential piRNAs mapping to the viral genome remained consistent across time points (Table 4). There was a bias for piRNAs mapping to the negative strand over the positive strand, and a hotspot in the reads mapping to the negative strand at position 6990 overlapping with nonstructural protein 4 (nsP4) and at 7915 overlapping with structural C polyprotein (C) (Figure 6, Supplementary Figure 1) [47]. There does appear to be a bias in potential piRNAs mapped to the viral genome for U in position 1, but no bias for A at position 10 was observed in this study (Figure 7, Supplementary Figure 2) [48].

Figure 6:
Figure 6:

Histograms demonstrating read depth across the Mayaro virus genome for reads with a piRNA size profile (24 - 30 nt). Y-axis is read depth, and X-axis is position in viral genome. Blue demonstrates reads for that sample mapping to the positive strand, while red demonstrates those mapping to the negative strand. A. - C. are control for 2, 7, and 14 dpi respectively, and D. - F. are infected for 2, 7, and 14 dpi. One replicate per treatment is chosen here to demonstrate, but all replicates are in Supplementary Figure 1.

Figure 7:
Figure 7:

Nucleotide bias at first 15 bp of potential piRNA reads mapping to the Mayaro virus genome. X-axis is position in read, and Y-axis is nucleotide bias. Size of nucleotide demonstrates relative bias at that position in the read. A. - C. are control for 2, 7, and 14 dpi respectively, and D. - F. are infected for 2, 7, and 14 dpi. One replicate per treatment is chosen here to demonstrate, but all replicates are in Supplementary Figure 2.

View this table:
Table 4:

Number and proportion of reads from each small RNA sample sequenced mapping to the host genome, mapping to miRNAs, mapping to the viral genome, and of those mapping to the viral genome that are potential piRNAs.

Mosquito cells infected with Alphaviruses and Bunyaviruses show clear U1 and A10 ping-pong piRNA signature [41, 43, 49]. In the current study reads in the 25-30 nt range mapping to the MAYV genome have a clear U1 bias but not A10, suggesting perhaps primary piRNA production without secondary piRNA biogenesis as a result as the A10 bias is suggested to be a product of cleavage activity (Figure 7) [48]. It is also observed that there are reads in the control groupings also matching the MAYV genome with the same hotspots being produced.

What may be observed is instead a general antiviral response as the viral ORFs nsP4 and C are hotspots [50], suggesting these reads are possibly constituently circulating, or expressed following bloodmeal ingestion (Figure 6).

Conclusion

The transcriptomic profiles suggest that MAYV activates the Toll pathway at mid-stages of infection as an innate humoral response from the host to fight infection. At later stages of infection MAYV appears to repress autophagic processes to promote replication. The small RNA profiles produced suggest a potential reliance on piRNA biogenesis as a generalized antiviral immune response, but that it was not active against MAYV in this study in the sense that a ping-pong profile of potential piRNA reads mapping to the viral genome was not observed. miRNAs were also elicited in response to infection and some overlap was observed with transcripts identified as regulated in response to infection, but not to the extent that they appear to be strongly regulating transcriptional profiles in response to infection.

Supplementary Figure 1: Histograms demonstrating read depth across the Mayaro virus genome for reads with a piRNA size profile (24 - 30 nt). Represents all samples, chosen subset demonstrated by Figure 6.

Supplementary Figure 2: Nucleotide bias at first 15 bp of potential piRNA reads mapping to the Mayaro virus genome. Represents all samples, chosen subset demonstrated by Figure 7.

Supplementary Table 1: Information related to infection of Anopheles stephensi with Mayaro virus. Includes number of mosquitoes in each treatment and time point and associated mortality, nanodrop readings for all RNA extractions collected, pooling scheme for sequencing of mRNA and small RNA, and qPCR data from each sample using primers specific for Mayaro virus strain BeAn to confirm infection status.

Supplementary Table 2: Differentially expressed transcripts from the Anopheles stephensi AsteI2 genome.

Supplementary Table 3: GO term overrepresentation for differentially regulated transcripts.

Supplementary Table 4: Read counts mapping to the identified Anopheles stephensi miRNAs in each small RNA sample sequenced.

Supplementary Table 5: Differential expression of Anopheles stephensi miRNAs.

Supplementary Table 6: Genomic targets from the Anopheles stephensi AsteI2 genome for all identified miRNAs.

Acknowledgments

We thank the UTMB sequencing core for assistance with next generation sequencing. This work was supported by NIH grants R01AI150251, R01AI128201, R01AI116636, USDA Hatch funds (Accession #1010032; Project #PEN04608), and a grant with the Pennsylvania Department of Health using Tobacco Settlement Funds to JLR, BBSRC awards BB/T001240/1 and V011278/1, Royal Society Wolfson Fellowship RSWF\R1\180013, NIH grants R21AI138074 and R21AI129507, URKI grant 20197, and NIHR grant NIHR2000907 to GLH. CAH was supported by an NSF graduate research fellowship program award (ID 2018258101). SH was supported the Liverpool School of Tropical Medicine Director’s Catalyst Fund award. GLH is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford. GLH is based at LSTM. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England. We would also like to thank Dr. Martin Donnelly at Liverpool School of Tropical Medicine for comments on an early version of this manuscript.

Footnotes

  • Manuscript has been updated to reflect minor corrections in data analysis and an additional author

References

  1. 1.↵
    Anderson CR, Wattley GH, Ahin NW, Downs WG, Reese AA. Mayaro Virus: A New Human Disease Agent. Am J Trop Med Hyg. 1957;6(6):1012–1016. doi:10.4269/ajtmh.1957.6.1012
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    Esposito DLA, Fonseca BAL da. Will Mayaro virus be responsible for the next outbreak of an arthropod-borne virus in Brazil? Brazilian J Infect Dis. 2017;21(5):54O–544. doi:10.1016/j.bjid.2017.06.002
    OpenUrlCrossRef
  3. 3.↵
    Hassing R-J, Leparc-Goffart I, Blank SN, et al. Imported Mayaro virus infection in the Netherlands. J Infect. 2010;61(4):343–345. doi:10.1016/j.jinf.2010.06.009
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.
    Llagonne-Barets M, Icard V, Leparc-Goffart I, et al. A case of Mayaro virus infection imported from French Guiana. J Clin Virol. 2016;77:66–68. doi:10.1016/j.jcv.2016.02.013
    OpenUrlCrossRef
  5. 5.
    Receveur MC, Grandadam M, Pistone T, Malvy D. Infection with Mayaro virus in a French traveller returning from the Amazon region, Brazil, January, 2010. Euro Surveill. 2010;15(18). http://www.ncbi.nlm.nih.gov/pubmed/20460093. Accessed February 24, 2019.
  6. 6.↵
    Neumayr A, Gabriel M, Fritz J, et al. Mayaro virus infection in traveler returning from Amazon Basin, northern Peru. Emerg Infect Dis. 2012;18(4):695–696. doi:10.3201/eid1804.111717
    OpenUrlCrossRefPubMed
  7. 7.↵
    Mota MT de O, Ribeiro MR, Vedovello D, Nogueira ML. Mayaro virus: a neglected arbovirus of the Americas. Future Virol. 2015;10(9):1109–1122. doi:10.2217/fvl.15.76
    OpenUrlCrossRef
  8. 8.↵
    1. Weaver SC
    Abad-Franch F, Grimmer GH, de Paula VS, Figueiredo LTM, Braga WSM, Luz SLB. Mayaro Virus Infection in Amazonia: A Multimodel Inference Approach to Risk Factor Assessment. Weaver SC, ed. PLoS Negl Trop Dis. 2012;6(10):e1846. doi:10.1371/journal.pntd.0001846
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Christofferson RC
    Brustolin M, Pujhari S, Henderson CA, Rasgon JL. Anopheles mosquitoes may drive invasion and transmission of Mayaro virus across geographically diverse regions. Christofferson RC, ed. PLoS Negl Trop Dis. 2018;12(11):e0006895. doi:10.1371/journal.pntd.0006895
    OpenUrlCrossRef
  10. 10.↵
    Wiggins K, Eastmond B, Alto BW. Transmission potential of Mayaro virus in Florida Aedes aegypti and Aedes albopictus mosquitoes. Med Vet Entomol. 2018;32(4):436–442. doi:10.1111/mve.12322
    OpenUrlCrossRef
  11. 11.
    Smith GC, Francy DB. Laboratory studies of a Brazilian strain of Aedes albopictus as a potential vector of Mayaro and Oropouche viruses. J Am Mosq Control Assoc. 1991;7(1):89–93. http://www.ncbi.nlm.nih.gov/pubmed/1646286. Accessed February 24, 2019.
    OpenUrlPubMedWeb of Science
  12. 12.↵
    Tesh RB, Higgs S, Hausser NL, et al. Experimental Transmission of Mayaro Virus by Aedes aegypti. Am J Trop Med Hyg. 2011;85(4):750–757. doi:10.4269/ajtmh.2011.11-0359
    OpenUrlAbstract/FREE Full Text
  13. 13.↵
    Rezza G, Chen R, Weaver SC. O’nyong-nyong fever: a neglected mosquito-borne viral disease. Pathog Glob Health. 2017;111(6):271–275. doi:10.1080/20477724.2017.1355431
    OpenUrlCrossRef
  14. 14.↵
    Yadav P, Barde P, Singh D, Mishra A, Mourya D. EXPERIMENTAL TRANSMISSION OF CHIKUNGUNYA VIRUS BY ANOPHELES STEPHENSI MOSQUITOES. Acta Virol. 2003;47:45–47. http://www.elis.sk/download_file.php?product_id=8&session_id=jc2533t2t4ld65j13rlddb4r55. Accessed February 24, 2019.
    OpenUrlPubMed
  15. 15.↵
    Franz A, Kantor A, Passarelli A, Clem R. Tissue Barriers to Arbovirus Infection in Mosquitoes. Viruses. 2015;7(7):3741–3767. doi:10.3390/v7072795
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Moreira LA
    Bonizzoni M, Dunn WA, Campbell CL, Olson KE, Marinotti O, James AA. Complex Modulation of the Aedes aegypti Transcriptome in Response to Dengue Virus Infection. Moreira LA, ed. PLoS One. 2012;7(11):e50512. doi:10.1371/journal.pone.0050512
    OpenUrlCrossRefPubMed
  17. 17.↵
    Etebari K, Hegde S, Saldana MA, et al. Global transcriptome analysis of Aedes aegypti mosquitoes in response to Zika virus infection. bioRxiv. August 2017:179416. doi:10.1101/179416
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Traub-Csekö YM
    Waldock J, Olson KE, Christophides GK. Anopheles gambiae Antiviral Immune Response to Systemic O’nyong-nyong Infection.Traub-Csekö YM, ed. PLoS Negl Trop Dis. 2012;6(3):e1565. doi:10.1371/journal.pntd.0001565
    OpenUrlCrossRefPubMed
  19. 19.↵
    Sim C, Hong YS, Vanlandingham DL, et al. Modulation of Anopheles gambiae gene expression in response to o’nyong-nyong virus infection. Insect Mol Biol. 2005;14(5):475–481. doi:10.1111/j.1365-2583.2005.00578.x
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.↵
    Palatini U, Miesen P, Carballar-Lejarazu R, et al. Comparative genomics shows that viral integrations are abundant and express piRNAs in the arboviral vectors Aedes aegypti and Aedes albopictus. BMC Genomics. 2017. doi:10.1186/s12864-017-3903-3
    OpenUrlCrossRef
  21. 21.↵
    1. Armstrong PM
    Saldaña MA, Etebari K, Hart CE, et al. Zika virus alters the microRNA expression profile and elicits an RNAi response in Aedes aegypti mosquitoes. Armstrong PM, ed. PLoS Negl Trop Dis. 2017;11(7):e0005760. doi:10.1371/journal.pntd.0005760
    OpenUrlCrossRef
  22. 22.
    1. Olson KE
    Varjak M, Donald CL, Mottram TJ, et al. Characterization of the Zika virus induced small RNA response in Aedes aegypti cells. Olson KE, ed. PLoS Negl Trop Dis. 2017;11(10):e0006010. doi:10.1371/journal.pntd.0006010
    OpenUrlCrossRefPubMed
  23. 23.↵
    Carissimo G, Pondeville E, McFarlane M, et al. Antiviral immunity of Anopheles gambiae is highly compartmentalized, with distinct roles for RNA interference and gut microbiota. Proc Natl Acad Sci USA. 2015;112(2):E176–85. doi:10.1073/pnas.1412984112
    OpenUrlAbstract/FREE Full Text
  24. 24.↵
    Sinka ME, Rubio-Palis Y, Manguin S, et al. The dominant Anopheles vectors of human malaria in the Americas: occurrence data, distribution maps and bionomic précis. Parasit Vectors. 2010;3(1):72. doi:10.1186/1756-3305-3-72
    OpenUrlCrossRefPubMed
  25. 25.↵
    Hay SI, Sinka ME, Okara RM, et al. Developing Global Maps of the Dominant Anopheles Vectors of Human Malaria. PLoS Med. 2010;7(2):e1000209. doi:10.1371/journal.pmed.1000209
    OpenUrlCrossRefPubMed
  26. 26.↵
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi:10.1093/bioinformatics/btu170
    OpenUrlCrossRefPubMedWeb of Science
  27. 27.↵
    Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi:10.1093/bioinformatics/bts635
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    Liao Y, Smyth GK, Shi W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 2019;47(8):e47–e47. doi:10.1093/nar/gkz114
    OpenUrlCrossRefPubMed
  29. 29.↵
    Chen Y, Mccarthy D, Ritchie M, Robinson M, Smyth GK. EdgeR User’s Guide.; 2008. https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf. Accessed September 24, 2018.
  30. 30.↵
    Uri Reimand J, Arak T, Adler P, et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016;(1). doi:10.1093/nar/gkw199
    OpenUrlCrossRefPubMed
  31. 31.↵
    GitHub - rajewsky-lab/mirdeep2: Discovering known and novel miRNAs from small RNA sequencing data. https://github.com/rajewsky-lab/mirdeep2. Accessed October 8, 2019.
  32. 32.↵
    Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008;36(Database issue):D149–53. doi:10.1093/nar/gkm995
    OpenUrlCrossRefPubMedWeb of Science
  33. 33.↵
    Brackney DE. Implications of autophagy on arbovirus infection of mosquitoes. Curr Opin Insect Sci. 2017;22:1–6. doi:10.1016/j.cois.2017.05.001
    OpenUrlCrossRef
  34. 34.↵
    Carissimo G, Pain A, Belda E, Vernick KD. Highly focused transcriptional response of Anopheles coluzzii to O’nyong nyong arbovirus during the primary midgut infection. BMC Genomics. 2018;19(1):526. doi:10.1186/s12864-018-4918-0
    OpenUrlCrossRef
  35. 35.↵
    Samuel GH, Adelman ZN, Myles KM. Antiviral Immunity and Virus-Mediated Antagonism in Disease Vector Mosquitoes. 2017. doi:10.1016/j.tim.2017.12.005
    OpenUrlCrossRef
  36. 36.↵
    Fu X, Dimopoulos G, Zhu J. Association of microRNAs with Argonaute proteins in the malaria mosquito Anopheles gambiae after blood ingestion. Sci Rep. 2017;7(1). doi:10.1038/s41598-017-07013-1
    OpenUrlCrossRef
  37. 37.↵
    Biryukova I, Ye T, Levashina E. Transcriptome-wide analysis of microRNA expression in the malaria mosquito Anopheles gambiae. BMC Genomics. 2014;15(1):557. doi:10.1186/1471-2164-15-557
    OpenUrlCrossRef
  38. 38.↵
    Blair CD, Olson KE. The role of RNA interference (RNAi) in arbovirus-vector interactions. Viruses. 2015;7(2):820–843. doi:10.3390/v7020820
    OpenUrlCrossRefPubMed
  39. 39.↵
    Xiao M, Li J, Li W, et al. MicroRNAs activate gene transcription epigenetically as an enhancer trigger. RNA Biol. 2017;14(10):1326–1334. 777 doi:10.1080/15476286.2015.1112487
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. O’Neill SL
    Scott JC, Brackney DE, Campbell CL, et al. Comparison of Dengue Virus Type 2-Specific Small RNAs from RNA Interference-Competent and -Incompetent Mosquito Cells. O’Neill SL, ed. PLoS Negl Trop Dis. 2010;4(10):e848. doi:10.1371/journal.pntd.0000848
    OpenUrlCrossRefPubMed
  41. 41.↵
    Schnettler E, Donald CL, Human S, et al. Knockdown of piRNA pathway proteins results in enhanced semliki forest virus production in mosquito cells. J Gen Virol. 2013;94(PART7):1680–1689. doi:10.1099/vir.0.053850-0
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.
    1. Ding S-W
    Morazzani EM, Wiley MR, Murreddu MG, Adelman ZN, Myles KM. Production of Virus-Derived Ping-Pong-Dependent piRNA-like Small RNAs in the Mosquito Soma. Ding S-W, ed. PLoS Pathog. 2012;8(1):e1002470. doi:10.1371/journal.ppat.1002470
    OpenUrlCrossRefPubMed
  43. 43.↵
    1. Pfeffer S
    Vodovar N, Bronkhorst AW, van Cleef KWR, et al. Arbovirus-Derived piRNAs Exhibit a Ping-Pong Signature in Mosquito Cells. Pfeffer S, ed. PLoS One. 2012;7(1):e30861. doi:10.1371/journal.pone.0030861
    OpenUrlCrossRefPubMed
  44. 44.↵
    Miesen P, Girardi E, Van Rij RP. Distinct sets of PIWI proteins produce arbovirus and transposon-derived piRNAs in Aedes aegypti mosquito cells. Nucleic Acids Res. 2015;43(13):6545–6556. doi:10.1093/nar/gkv590
    OpenUrlCrossRefPubMed
  45. 45.↵
    Schnettler E, Ratinier M, Watson M, et al. RNA interference targets arbovirus replication in Culicoides cells. J Virol. 2013;87(5):2441–2454. doi:10.1128/JVI.02848-12\
    OpenUrlAbstract/FREE Full Text
  46. 46.↵
    Espósito DLA, da Fonseca BAL. Complete genome sequence of Mayaro virus (Togaviridae, Alphavirus) strain BeAr 20290 from Brazil. Genome Announc. 2015;3(6). doi:10.1128/genomeA.01372-15
    OpenUrlAbstract/FREE Full Text
  47. 47.↵
    Lavergne A, Thoisy B de, Lacoste V, et al. Mayaro virus: Complete nucleotide sequence and phylogenetic relationships with other alphaviruses. Virus Res. 2006;117(2):283–290. doi:10.1016/j.virusres.2005.11.006
    OpenUrlCrossRefPubMed
  48. 48.↵
    Brennecke J, Aravin AA, Stark A, et al. Discrete Small RNA-Generating Loci as Master Regulators of Transposon Activity in Drosophila. Cell. 2007;128(6):1089–1103. doi:10.1016/j.cell.2007.01.043
    OpenUrlCrossRefPubMedWeb of Science
  49. 49.↵
    Dietrich I, Jansen S, Fall G, et al. RNA Interference Restricts Rift Valley Fever Virus in Multiple Insect Systems. mSphere. 2017;2(3). doi:10.1128/msphere.00090-17
    OpenUrlCrossRef
  50. 50.↵
    Auguste AJ, Liria J, Forrester NL, et al. Evolutionary and Ecological Characterization of Mayaro Virus Strains Isolated during an Outbreak, Venezuela, 2010. Emerg Infect Dis. 2015;21(10):1742–1750. doi:10.3201/eid2110.141660
    OpenUrlCrossRefPubMed
Back to top
Posted January 21, 2021.
Download PDF
Email
Mayaro Virus Infection Elicits an Innate Immune Response and Represses Autophagy in Anopheles stephensi
Cory Henderson, Marco Brustolin, Shivanand Hegde, Grant L. Hughes, Christina Bergey, Jason L. Rasgon
bioRxiv 2020.11.15.383596; doi: https://doi.org/10.1101/2020.11.15.383596
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools

Subject Area