Differential Proteome Analysis of Chikungunya Virus and Dengue

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Differential proteome analysis of chikungunya virus and dengue virus co-infection in Aedes mosquitoes Jatin Shrinet, Priyanshu Srivastava, Ankit Kumar, Sunil Kumar Dubey, Pahala Dickwellage Nadeera Nilupamali Sirisena, Pratibha Srivastava, and Sujatha Sunil J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.8b00211 • Publication Date (Web): 07 Sep 2018 Downloaded from http://pubs.acs.org on September 10, 2018

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Journal of Proteome Research

Differential proteome analysis of chikungunya virus and dengue virus co-infection in Aedes mosquitoes

Jatin Shrinet#, Priyanshu Srivastava#, Ankit Kumar$, Sunil Kumar Dubey$, Pahala Dickwellage Nadeera Nilupamali Sirisena$, Pratibha Srivastava, Sujatha Sunil* Affiliations: Vector Borne Diseases Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi

#,$: Authors have contributed equally. *: Corresponding author Email: [email protected] Contact no: 011-26741358

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Abstract Chikungunya virus (CHIKV) and dengue virus (DENV) are important arboviruses transmitted by Aedes mosquitoes. These viruses are known to co-exist within the same vector and co-infect the same host. Although information is available on the mechanism of replication of CHIKV and DENV when present independently in a vector, reports are lacking on the dynamics of virus– vector interactions when these viruses co-exist in a mosquito. The current study attempts to understand the perturbations in the proteome of Aedes mosquitoes when infected with CHIKV and DENV either independently or together. Global proteome profiling of chikungunya and dengue mono- and co-infection revealed 28 proteins to be significantly regulated. Validation of the transcripts of these proteins using qRT-PCR indicated differences in the expression patterns between transcript profiling and quantitative proteome analyses. Pathway analysis of the 28 differentially regulated proteins revealed 11significant pathways, which include oxidative phosphorylation, carbon metabolism, and glycolysis/gluconeogenesis.

Keywords: chikungunya virus, dengue virus, co-infection, proteomics, Aedes aegypti, mass spectrometry, qPCR


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Introduction Sanguinivorous insects such as mosquitoes and ticks transmit arboviruses, or arthropod-borne viruses, to their vertebrate hosts during their physiological requirement of blood feeding, which is exploited by the viruses to their advantage. Arboviruses replicate both in insect’s tissues in high titers so as to achieve a high transfer rate and inside the vertebrate hosts for survival and to continue their life cycle. When another vector or mosquito bites the viremic host, the arbovirus infects the midgut before finally reaching the salivary glands for the next round of transfer to its new vertebrate host1, 2. For an efficient survival in this multiple host cycle, arboviruses overcome several physical as well as immunological barriers3. Chikungunya virus (CHIKV) and dengue virus (DENV) are among those arboviruses that share their vector and host. Although these two viruses belong to distinct virus families and have different genome structures and replication mechanisms, they exhibit similar pathogenic manifestation in humans, due to which infected individuals present with overlapping clinical symptoms4. In endemic regions where both these viruses are known to exist, co-infections in human have been reported to be either by the bite of a single mosquito harboring both the viruses or by sequential bites by different infected mosquitoes. Although the presence of both viruses within the same mosquito is rare, studies performed on field mosquitoes have reported the cohabitation of CHIKV and DENV in mosquitoes collected from the same localities5, 6 as well in the same mosquito7. Additionally, several studies have established that both these viruses can coexist in mosquitoes under experimental conditions8, 9. The vector of both these viruses, namely, the Aedes sp., is known to be the most intrusive species, being present in more than 175 countries. Amongst these countries, CHIKV and DENV have been reported in 98 countries5. However, within these 98 countries, only 13 countries have



recorded co-infection cases, clearly showing that the mere presence of a vector and the viruses is not sufficient for the occurrence of co-infection and that there are several other factors that may contribute to the phenomenon of co-infection in the vector as well as the host. Recent studies have established that there is a fitness cost to the vector due to viral replication 10. Infection of the vector with a virus results in a cascade of events that decide the course of virus establishment 11, 12. The initial time-points of viral replication in the vector are crucial in deciding the fate of the virus in the vector and becomes even more complicated in the presence of multiple viruses within the vector owing to the phenomenon of competitive suppression/enhancement. Evaluating the underlying molecular events during co-infection is important to understand this phenomenon better. The current study was undertaken to evaluate the regulation of the Aedes proteome during mono and co-infections of CHIKV and DENV in Aedes aegypti mosquitoes. To study the changes in the global proteome of Aedes aegypti, a common vector of both CHIKV and DENV, upon mono-and co-infection of CHIKV and DENV, we used isotope labeling by Tandem Mass Tag (TMT) and performed mass spectrometry analysis at 24 hours post infection (h.p.i). Validation of the regulated proteins was confirmed by evaluating transcript expression under similar test conditions. Functional relevance of these molecules was further assessed using pathway analysis and tissue-specific profiling.

Experimental Procedures Mosquito rearing Aedes aegypti mosquitoes were reared at 28 ± 1°C, maintaining the humidity at 75–80% (v/v). Sterile 8% (w/v) sucrose solution was prepared and used to feed the adults. All experiments were performed on 4–6-day-old female mosquitoes.


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Virus propagation Standard viral cultures of DENV2 and CHIKV were inoculated into Vero cells (MOI 1) in T-175 flasks and incubated at 37°C in 5% CO2 till the cells started to exhibit a cytopathic effect (CPE). RT-PCR was performed to test for the presence of DENV and CHIKV in the cultures. After confirmation of CPE, the flasks were first frozen and then thawed at -80°C and 37°C, respectively, for 10 minutes. The freeze–thaw cycles were carried out twice to rupture the cells and harvest the virus from the cultures. After the centrifugation of the culture at 2500rpm for 15 minutes, the supernatant was harvested and mixed with 4X PEG and kept for 72 hrs at4oCwithout disturbance. After 72 hrs, the contents were centrifuged at 4000 rpm for 60 minutes. Finally, the pellet was harvested and dissolved in 200 µl TEN buffer. Aliquots were prepared, and plaque assay was carried out to quantify the virus.

Establishment of co-infection using blood feeding Female mosquitoes (4–6 days old) were collected and allowed to feed on mice blood mixed with 106pfu/ml of CHIKV and DENV separately and mixed in the ratio of 1:1.The mosquitoes were collected 24, 48 and 72 hours after blood feeding and separated into two groups. One group was analyzed for the presence of the respective virus in single mosquitoes, and in the other group, the mosquitoes were pooled and analyzed for the presence of virus using qRT-PCR.

Establishment of co-infection using nano-injection Groups of 10 mosquitoes were anesthetized at 4°C and viruses/PBS were injected in the thoracic region using a Drummond nano-injector. One group of mosquitoes was used as control, in which only PBS was injected, and in the other three groups, combinations of viruses were injected.



Solutions of CHIKV and DENV (each containing 106pfu/ml) were injected separately into 10 mosquitoes each in groups 1 and 2, while in the third group, equal quantity of both the viruses were mixed and 106 pfu/ml of this mixture was injected. Mosquitoes were allowed to recover and were used for the experiment 24 hours post injection. All mosquito experiments were performed in triplicates.

Sample preparation for proteomics Mosquitoes from each batch were homogenized in a buffer solution composed of 50 mM Tris– Cl, 150 mM EDTA, 1% NP-40 (w/v), 1% SDC (w/v), and 0.1% SDS (w/v). Samples were briefly sonicated and kept on ice for 30 minutes for extraction of proteins. The samples were then centrifuged at 8000 g for 10 min to remove the insoluble debris. The supernatant was collected, and protein estimation was performed by the Bradford method13.

Trypsin digestion Forty micrograms of each protein sample was transferred into a new 1.5 ml tube, and the volume was adjusted to 100 µl with 100 mM tetraethylammonium bromide (TEAB). Proteins in the samples were reduced by adding 5 µl of 200 mMTris (2-carboxyethyl) phosphine (TCEP) and incubating the resultant solution at 55 °C for 1 hr. The protein samples were then brought to room temperature, and the cysteine residues were alkylated by the addition of 5 µl of 375 mM indole-3-acetic acid (IAA) and incubated for 30 min at room temperature in the dark. Chilled acetone (600 µl) was added, and the tubes were kept at -20 °C overnight for protein precipitation. The following day, the protein precipitates were pelleted by centrifuging at 8000g for 10 min at 4


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°C. The pellet was then suspended in 100 µl of 50 mM TEAB. The protein samples were digested by adding 2 µl of 1 mg/ml trypsin and were incubated overnight at 37 °C.

TMT labeling of peptides Tandem mass tags (TMT) label reagents were brought to room temperature before they were opened. Anhydrous acetonitrile (85 µl) was added to each of the four vials of TMT labels that were used in the experiment. After brief vortexing, 41 µl of TMT label was added to each sample and the resultant mixture was incubated at room temperature for 1 hr. The reaction was stopped by the addition of 8 µl of 5% (w/v) hydroxyl amine and the solution was incubated for 15 min. After labeling, peptides from the same experiment group were pooled and stored at -20 °C until further processing (Table 1). Table 1. TMT label used for proteins samples. S. no. 1 2 3 4 5 6 7 8

Experiment

Experiment-1

Sample ID CHIKV-1 (C) DENV-1 (D) MOCK-1 (M) Co-infection-1 (CD)

TMT label TMT10 – 126 TMT10– 127N TMT10– 127C TMT10– 128N

Experiment-2

CHIKV-2 (C) DENV-2 (D) MOCK-2 (M) Co-infection-2 (CD)

TMT10 – 126 TMT10– 127N TMT10– 127C TMT10– 128N

Purification and fractionation of TMT labeled peptides TMT-labeled peptides were fractionated using a polysulfoethyl strong cation exchange column (200 mm × 2.1 mm) having 5 µm-diameter particles and pore size of 200 Ǻ. The peptides were diluted in Buffer A containing 5 mM K2HPO4, 25 % (v/v) acetonitrile, pH 3.0 and separated by a linear gradient elution with Buffer B(5 mM K2HPO4, 25 % (v/v) acetonitrile, 350 mMKCl, pH



3.0). The peptides were loaded onto the column with 2.5 % Buffer B. For elution of peptides, Buffer B was increased linearly from 10 % to 65% over a period of 5 min and was maintained at 65% over the next 15 minutes followed by equilibration with 10% Buffer B for another 12 minutes. A total of five fractions were collected, concentrated, and desalted by zip-tipping using the C18 matrix. The fractionated peptides were loaded on to the LC-MS column as per the standard protocol.

Mass spectrometry analysis of proteins The Agilent 1260 Infinity HPLC-Chip/MS system is a micro-fluidic chip-based technology that incorporates peptide enrichment and separation and provides high-sensitivity nano-spray. Charged peptides from the HPLC-Chip system were directly infused into the massspectrometer for detection.

qRT-PCR analysis of significant genes Expression profiling of 26 significant genes in the whole body of mosquitoes and profiling of 10 transcripts related to co-infection in the midguts of the mosquitoes were carried out by qRTPCR. Real-time reactions were set up using Qiagen and Toyobo real-time kit according to the manufacturer’s instructions. Mosquito infection experiments were conducted at least two times, and all qPCR assays were set up in triplicates for the time points. Rpl8 was used as an endogenous control for gene expression profiling. 2-ddctwas used to calculate the expression of the target genes.

Statistical analysis of qRT-PCR


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Analysis of variance (ANOVA) and Student t-test were performed to study the significant regulation of the genes. Tukey’s multiple comparisons test was also performed on the data. Pvalue ≤ 0.0001 were considered significant. GraphPad Prism (Version 5) was used to perform statistical analysis and to generate graphs of qRT-PCR data.

Protein purification and antibody generation The transcripts were cloned in pet28a having His-tag at N-terminal and transformed into BL21 cells. The cultures were induced for overnight at 18oC at optical density of 600 nm by adding 1 mM IPTG to it. The cells were collected and lysed in Tris lysis buffer (5% glycerol) followed by sonication. The lysate was centrifuged at 13,000 rpm at 4°C. 2 ml of Ni-NTA beads (Invitrogen, USA) were used separately to bind the His-tagged proteins. The proteins were eluted using Tris buffer (300mM, 500mM imidazole and 5% glycerol) and dialysed before confirming through Western analysis. The purified proteins were mixed separately with Freund’s adjuvant and injected to Balb/C mice for the production of antibodies.

Western blot analysis to evaluate the regulated proteins Western blot were performed for randomly selected proteins in mono- and co-infected mosquitoes at 24 h.p.i, 48 h.p.i. and 72 h.p.i. Briefly, the 4-6 days old female mosquitoes (n=100 per group) were blood-fed with CHIKV and DENV spiked blood. The fully fed mosquitoes were collected and kept separately in RIPA buffer at respective days. The whole mosquitoes and tissues were lysed separately using homogenizer. The concentration of proteins was quantified by BCA (Bicinchoninic Acid) Protein Assay. Equal amount of proteins were loaded into each well for different conditions on SDS-PAGE. The proteins were transferred from SDS-PAGE to



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nitrocellulose membrane. The blots were incubated with primary antibodies against respective proteins (raised in mice) and secondary anti-mouse IgG, HRP-linked antibody (7076, Cell Signaling technology) using electro-chemiluminescence method. Beta Actin (sc-47778, Santa Cruz Biotechnology) served as positive control. Further quantification of western blot images was done by ImajeJ.

Bioinformatics analysis of data Spectral analysis and quantitative analysis of proteins The mass spectrometry proteomics data have been deposited to online digital repository Figshare (https://figshare.com)

with

the

data

set

identifier

https://doi.org/10.6084/m9.figshare.5746134.v1. The TMT-enabled quantitative analysis was performed using MaxQuant software. A custom database was created using the Aedes aegypti protein sequences downloaded from VectorBase (www.vectorbase.org) through the BioMart tool available on the website14. The custom database with 17,158 sequences was used to identify the proteins. A decoy library was also constructed by either shuffling or reversing the sequences, and a search was also performed so as to control false discovery rates (FDRs). Proteins identified with 2 unique peptides and < 1% FDR were identified. One hundred forty-five proteins were common to the experiments and were considered for further analysis. Out of these proteins, 141 proteins were commonly present in each of the condition; two proteins (AAEL017455-RA and AAEL002023RA) were absent in dengue-infected samples. Two more proteins, AAEL014452-RA and


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AAEL006836-RB, were found in all three infected samples but absent in the control sample. The corrected TMT intensity ratio was calculated by using the “control” intensity among each group. The average values of the identified proteins in the infected conditions were divided by the value of the mock/control, and log2 (fold change; hereafter log2 (FC)) was calculated for each of the identified proteins. The modified Z-score was also calculated using these log2-transformed values. Heat map showing the expression pattern of the identified proteins is shown in figure 3.

Figure 3: Heat map showing the expression pattern of all identified proteins. A color key is also provided. White color represents the absence of the protein. M refers to mock/control, C refers to chikungunya virus, D refers to the dengue virus, and CD represents co-infection samples.

a)

Differential regulation of proteins upon mono- and co-infection

Majority of the proteins identified in each condition were not substantially differentially regulated after 24 h.p.i. Differentially expressed proteins were identified by log2 transformation of the data and by computing the modified Z-score. The regulated proteins for each condition are shown in figure 4.



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Figure 4: Venn diagram showing the distribution of significantly regulated proteins upon monoand co-infection of CHIKV and DENV.

After the log2 transformation of the data (log2 (FC)) in the chikungunya samples, seven proteins (AAEL001158-RA, AAEL010235-RA, AAEL017320-RA, AAEL003193-RA, AAEL003011RA, AAEL012359-RA, and AAEL003835-RC) were found to be upregulated and two proteins (AAEL001134-R and AAEL013613-RA) were downregulated. We considered the proteins to be upregulated if log2(FC) ≥ 1.5 and downregulated if log2(FC) ≤ -1.5. Four proteins (AAEL001158-RA, AAEL010235-RA, AAEL014889-RA, and AAEL017134-RA) were upregulated

in

dengue-infected

mosquitoes

whereas

16

proteins

(AAEL002309-RA,

AAEL012311-RA, AAEL013515-RA, AAEL010180-RA, AAEL010697-RB, AAEL007868RA,

AAEL010884-RA,

AAEL012035-RA,

AAEL013613-RA,

AAEL008289-RA,

AAEL001134-RA, AAEL009872-RA, AAEL009955-RA, AAEL015450-RA, AAEL017349RA, and AAEL015524-RA) were downregulated. In the case of proteins identified in co-infected samples, three proteins (AAEL001158-RA, AAEL010235-RA, and AAEL003193-RA) were upregulated and six proteins (AAEL015524-RA, AAEL013613-RA, AAEL006834-RA, AAEL012035-RA, AAEL002023-RA, and AAEL004059-RB) were downregulated. To reduce


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the false positive results in the analysis, we increased the stringency by adding another step and also calculated the modified Z-score using the log2 (FC) values. Proteins with a Z-score ≥2.5 were considered upregulated and ≤ -2.5 were marked as downregulated. Using the modified Zscore criterion, five proteins (AAEL001158-RA, AAEL010235-RA, AAEL017320-RA, AAEL003193-RA, and AAEL003011-RA) were upregulated, and surprisingly three proteins (AAEL013515-RA, AAEL001134-RA, and AAEL013613-RA) were downregulated in the chikungunya sample. Four proteins (AAEL001158-RA, AAEL010235-RA, AAEL014889-RA, and AAEL017134-RA) were upregulated and six proteins (AAEL001134-RA, AAEL009872RA, AAEL009955-RA, AAEL015450-RA, AAEL017349-RA, and AAEL015524-RA) were downregulated in the case of dengue. In the co-infected samples, four proteins (AAEL001158RA, AAEL010235-RA, AAEL003193-RA, and AAEL003835-RC) were upregulated and six proteins

(AAEL015524-RA,

AAEL013613-RA,

AAEL006834-RA,

AAEL012035-RA,

AAEL002023-RA, and AAEL004059-RB) were downregulated. The proteins identified using their log2 (FC) values and the modified Z-score are represented in Supplementary Table 1.

b)

Pathway analysis of the regulated proteins in mono- and co-infections

The differentially regulated proteins were analyzed, and their respective significant pathways were identified using the KOBAS 2.0 web server tool16. The pathways with a p-value ≤ 0.05 were considered as significantly differentially regulated. The regulated proteins identified through the two methods, namely, log2 (FC) and the modified Z-score (hereafter the log2 (FC) category and the modified-score category, respectively), were categorized individually according to their upregulation and downregulation profiles and on the basis of infection type and were further analyzed for the identification of significantly dysregulated pathways. The analysis



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revealed a total of eleven pathways to be significantly perturbed. A separate analysis using proteins from both the categories showed almost similar significant pathways with an exception of one extra significant pathway each for CHIKV proteins (upregulated), namely, fructose and mannose metabolism, and DENV proteins (upregulated), namely, glycolysis/gluconeogenesis, in the modified Z-score category in comparison to the log2(FC) category. Also, for the log2 (FC) category proteins found to be substantially downregulated during the DENV infection, in addition to the common pathways between the two categories, two pathways, namely, fatty acid elongation and fatty acid degradation, were predicted to be significantly dysregulated. It is interesting to note that the pathways related to carbohydrate metabolism were significantly upregulated in the case of all three infections and that carbon metabolism pathway and other pathways related to amino acid metabolism were found to be significantly downregulated in all three infection types. The oxidative phosphorylation pathway, which plays an important role in oxidative stress, was significantly upregulated in the case of chikungunya infection. Data related to the pathway analysis are listed in Table 2.

Table 2. Table showing the pathway details of differentially expressed proteins. P-values of the significant pathways are also shown. Here, “NS” means not significant.

Samples CHIKV (upregulated)

CHIKV (downregulated)

Pathway

p-value (log2(FC) analysis)

p-value (modified Zscore analysis)

Oxidative phosphorylation Pentose phosphate pathway Fructose and mannose metabolism

0.02849712

0.015240553

0.041916308

0.030671796

NS

0.037374852

Carbon metabolism

0.018665079

0.018665079


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DENV (upregulated)

DENV (downregulated)

co-infection (upregulated)

co-infection (downregulated)

c)

beta-alanine metabolism

0.046473672

0.046473672

Propanoate metabolism Pentose phosphate pathway Fructose and mannose metabolism Glycolysis / gluconeogenesis Valine, leucine, and isoleucine degradation

0.048744956

0.048744956

0.026892739

0.019287892

0.032783966

0.023533393

NS

0.038277011

0.00430586

0.016021422

Carbon metabolism

0.007805651

0.011222633

Fatty acid elongation

0.011171353

NS

beta-alanine metabolism

0.013571985

0.005048258

Propanoate metabolism

0.014848932

0.005535645

Fatty acid degradation Pentose phosphate pathway Fructose and mannose metabolism Glycolysis / gluconeogenesis Biosynthesis of amino acids

0.030215372

NS

0.023098127

0.023098127

0.028170187

0.028170187

0.045749929

0.045749929

0.044005705

0.033176902

Validation of significant genes of proteome analysis in Aedes aegypti whole body

using qRT-PCR analysis qRT-PCR profiling of significant genes were performed at 24 hours post nano-injection. Rpl8 was taken as internal control and uninfected samples were used as normalization control in the analysis. The results revealed the downregulation of majority of genes upon DENV and coinfections(Figure 5). Nineteen out of 26 genes were upregulated upon CHIKV infection; three genes, namely, AAEL002023, AAEL009955, and AAEL004059, were upregulated upon DENV infection, and two genes (AAEL002023 and AAEL009955) showed upregulation upon co-



infection of CHIKV and DENV. All other genes were found to be downregulated at 24 h.p.i. Surprisingly, a comparison between the proteomics data and qRT-PCR of the whole body of mosquitoes showed that in the case of CHIKV-infected samples, of the seven genes found to be upregulated in proteome analysis, only four genes, namely, AAEL010235, AAEL017320, AAEL003193, and AAEL003011, showed upregulation in qRT-PCR analysis; on the other hand, of the two genes found to be downregulated in proteomics analysis, one gene, AAEL013613, showed a similar pattern, whereas the other, AAEL001134, was found to be upregulated as opposed to the expression pattern shown in proteomics analysis. In the case of DENV infection and co-infection, no genes that were found to be upregulated during proteome data analysis showed a similar pattern in qRT-PCR analysis. All other genes were found to be downregulated upon mono- and co-infections of CHIKV and DENV.

Figure 5: Graph showing qRT-PCR analysis of whole body of adult female mosquitoes upon mono- and co-infection of CHIKV and DENV at 24 hours post nano-injection. ANOVA analysis


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of the data was performed to predict significantly regulated transcripts. P-Value < 0.0001 were considered as significant. Tukey’s multiple comparisons test were also performed for the data.

d)

Network analysis of the regulated genes upon co-infection

To establish the relationship among the ten genes found to be significant upon co-infection, we further carried out an interaction study. Network analyses of these 10 genes were performed using the STRING database (Figure 6).The analysis revealed that out of the ten significant genes, AAEL015524 plays an important role during co-infection and was found to be downregulated upon DENV mono-infection and co-infection both in the proteomics study and in the qRT-PCR analysis and upregulated in CHIKV mono-infection, as confirmed by qRT-PCR analysis at 24 h.p.i. The other important genes are AAEL006834, AAEL004059, and AAEL013613. Five out of ten genes showed differences in the expression profiles determined using the proteomics analysis and qRT-PCR data. AAEL003835 was found to be downregulated upon CHIKV and co-infections in qRT-PCR analysis but was significantly upregulated in proteomics analysis. Similarly, AAEL002023 showed downregulation in the proteomics analysis but was found to be upregulated in qRT-PCR analysis during co-infection. AAEL003193 showed upregulation upon CHIKV infection in both the analyses but was predicted to be upregulated in proteomics study and showed downregulation in PCR analysis. AAEL012035 showed downregulation upon co-infection in both the analyses but was predicted to be upregulated in the proteomics study and showed downregulation in PCR analysis. AAE010235 showed upregulation upon CHIKV mono-infection in both the analyses but showed downregulation upon DENV and co-infections in qRT-PCR analysis and opposite expression pattern in proteomics analysis.



Figure 6: Interaction network of significant genes upon co-infection. Up and down arrows denote upregulation and downregulation of the genes in proteomics profiling, respectively. Red represents CHIKV infection, blue represents DENV infection, and orange represents coinfection. The solid lines represent high-confidence interactions, and the dashed lines represent low-confidence interactions. Genes showing different profiles between the proteomics study and qRT-PCR are marked with a star.

Functional characterization of the regulated proteins of co-infections in the midgut Proteins found to be differentially regulated in the proteome analysis upon co-infection were further characterized to understand their role in the midgut of mosquito upon CHIKV and DENV co-infection. For this, we collected blood-fed adult female mosquitoes at three different time points, namely, 24, 48, and 72 h.p.i., and isolated their midguts. Expression profiling of the nine genes selected out of the total ten genes was performed using qRT-PCR. We were unable to get the expression profile for one gene, AAEL001158. Further, three proteins, namely,


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AAEL001158, AAEL003835 and AAEL012035 were assayed for their regulation using Western blot analysis.

qRT PCR analysis of significant genes of co-infection Tissue-specific qRT-PCR analysis of nine genes (AAEL010235, AAEL003193, AAEL003835, AAEL015524, AAEL013613, AAEL006834, AAEL012035, AAEL002023, and AAEL004059) that were found to be differentially regulated upon co-infection was performed. Data from genes under different conditions, namely, blood fed, CHIKV-infected-blood fed, DENV-infected-blood fed, and co-infected-blood fed, were normalized to the data obtained from the analysis of the midguts of unfed mosquitoes (Figure 7).

Genes unique to co-infection Three genes (AAEL004059, AAEL006834, and AAEL002023) were found to be significantly regulated only in the case of co-infection. Two genes (AAEL006834 and AAEL002023) were found to be significantly regulated during each condition at all three time points. Both these genes were downregulated in the infected samples when compared to blood fed, at all time points except upon DENV infection at 72 h.p.i., where they were found to be significantly upregulated. AAEL004059 was significantly upregulated upon CHIKV infection at 24, 48, and 72 h.p.i. and also upon DENV infection after 48 and 72 h.p.i. but was found to be significantly downregulated upon co-infection at 48 h.p.i. Genes common between CHIKV and co-infection AAEL003193 showed significant downregulation at 48 h.p.i. upon CHIKV and DENV monoinfection. It was found to be significantly upregulated upon co-infection at 24, 48, and 72 h.p.i.



and also upon DENV infection at 72 h.p.i. AAEL003835 was found to be significantly regulated at 48 and 72 h.p.i. and was upregulated upon DENV infection at both the time points and upon CHIKV infection and co-infection at 72 h.p.i.

Genes common between DENV infection and co-infection AAEL012035 was found to be significantly upregulated upon co-infection at 48 and 72 h.p.i. but was not significantly regulated in the other stages. AAEL015524 was significantly regulated under all conditions except upon co-infection at 72 h.p.i. It was found to be upregulated upon CHIKV and DENV mono-infection at 72 h.p.i. Genes common between mono- and co-infection of CHIKV and DENV The expression pattern of AAEL010235 was not significant at 48 h.p.i. upon CHIKV infection but was found to be significantly regulated at other conditions. AAEL013613 was found to be significantly upregulated upon co-infection at 24 and 48 h.p.i but has not shown any significant regulation under other conditions. The analysis revealed that all genes play a significant role in the midgut upon co-infection.


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Figure 7:qRT-PCR analysis of the midgut of adult female mosquitoes upon blood feeding. CD denotes co-infection, CHIKV denotes chikungunya virus, and DENV denotes dengue virus. ANOVA and Tukey's multiple comparison test were performed and data with p-value

Differential Proteome Analysis of Chikungunya Virus and Dengue

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