The arrival of the Zika virus (ZIKV) in dengue virus (DENV)-endemic areas has posed challenges for both differential diagnosis and vaccine development. Peptides have shown promise in addressing these issues. The aim of this study was to identify the linear epitope profile recognized by serum samples from dengue and Zika patients in the E and NS1 proteins of DENV and ZIKV. This cross-sectional study included individuals of all ages with laboratory-confirmed DENV and ZIKV infections, who were selected through convenience sampling. The serum samples from dengue and Zika patients detected epitopes evenly distributed across the viral proteins in a peptide microarray platform. However, several epitopes were located within "epitope hotspots", characterized by clusters of peptides recognized in more than 30% of the sub-arrays analyzed using individual or pooled serum samples. The serum samples from dengue and Zika patients showed a high level of cross-reactivity with peptides in the DENV and ZIKV proteins. Analysis using an additional peptide microarray platform, which contained peptides selected based on the results of the initial screening, revealed that two DENV and one ZIKV peptide, highly specific to their related viruses, were located within the epitope hotspots; however, they presented low detection rates (32.5, 35.0, and 28.6%, respectively). In addition, two DENV peptides detected at similarly high rates by both dengue and Zika patients were also found within the epitope hotspots. These hotspots contain several immunodominant epitopes that are recognized by a larger number of individuals when compared to 15-amino acid (aa) sequence peptides. Thus, epitope hotspots may have greater potential to serve as antigens in diagnostic tests and vaccine development than peptides composed of only 15 amino acids.
Dengue and Zika are two mosquito-borne diseases of global concern that mainly affect tropical and subtropical regions [[
Peptides have shown significant potential in advancing the development of highly specific diagnostic tests and vaccines [[
This cross-sectional study was conducted from March 2017 to December 2022. The eligible population, selected through convenience sampling, consisted of individuals of all ages with suspected dengue or Zika disease. Inclusion criteria required participants to have laboratory confirmation of DENV or ZIKV infection. Laboratory tests included reverse transcription-polymerase chain reaction (RT-PCR), IgG/IgM tests, and/or the 50% plaque reduction neutralization test (PRNT50). Exclusion criteria encompassed participants with negative IgG tests. The eligible control population comprised healthy individuals vaccinated against yellow fever, who had negative serological tests for dengue and Zika. Medical records served as the primary source of information regarding the presence of the disease, relying on laboratory test results. This assessment method applied to both the virus-infected groups and the healthy control group.
The Virology Research Center of the Ribeirao Preto Medical School, University of Sao Paulo, and the Virology Laboratory of the School of Pharmaceutical Sciences, University of Sao Paulo, served as the centers for participant recruitment. All participants provided signed written consent for their inclusion in the study. In cases where participants were children under 18 years of age, parent/guardian consent was obtained, and the children themselves also signed the written consent form. In addition, archived serum samples from de-identified collections, dating from April 2011 to February 2020, located at the Virology Research Center of the Ribeirao Preto Medical School, University of Sao Paulo, the Virology Laboratory of the School of Pharmaceutical Sciences, University of Sao Paulo, and the Production Department of the Research Institute for Health Sciences, National University of Asuncion, Paraguay, were also included in the study. For the archived samples, the ethics committees waived the need for consent.
The participants were selected through convenience sampling. Due to budget limitations, the sample size was determined by the number of available microarray slides.
This study was reviewed and approved by the Ethics Committees of the School of Pharmaceutical Sciences of Ribeirao Preto (SPSRP), University of Sao Paulo (USP), Brazil (CEP/FCFRP n° 1845548), and the Research Institute for Health Sciences of the National University of Asuncion, Paraguay (P08/2018).
Representative isolates of DENV-1 (GenBank AKQ00011), DENV-2 (GenBank AGX15379), DENV-3 (GenBank AFK83760), DENV-4 (GenBank AEW50183), and ZIKV (GenBank AMA12085) were selected based on previous studies [[
The peptide microarray slide was incubated for 15 min at room temperature in standard buffer (PBS, pH 7.4, 0.05% Tween 20), then in blocking buffer (PBS, pH 7.4, 0.05% Tween 20, 1% BSA) with shaking (140 rpm) for 60 min at room temperature, and finally in staining buffer (standard buffer plus 10% blocking buffer) with shaking (140 rpm) for 15 min at room for temperature for equilibration. The sub-arrays were incubated with individual or a pool of up to four (equal volumes) serum samples (diluted at 1:80 in staining buffer, pH 7.4) overnight at 4°C. Afterward, the arrays were washed 3x1 min at 140 rpm with the standard buffer, followed by incubation with the secondary antibody (Goat anti-Human IgG Fc Cross-Adsorbed Secondary Antibody—DyLight 650, Invitrogen, USA, diluted at 1:5000 in staining buffer) for 30 min with shaking (140 rpm) at room temperature, in the dark. The arrays were subsequently washed 3x1 min with the standard buffer with shaking (140 rpm). Finally, the slide was dipped two times into the dipping buffer (1 mM Tris, pH 7.4) and dried carefully in an air stream.
The array images were obtained with the Axon GenePix 4000B scanner (Molecular Devices, USA) or InnoScan 710 (Innopsys, France), using a 635-nm laser and 10-μm resolution. Median fluorescence intensity for each spot, with local background subtraction, was computed using the GenePix Pro 7 (Molecular Devices, USA) or Mapix (Innopsys, France) software. Fluorescence intensity values <1 were converted to 0. The fluorescence intensity values were then normalized against the negative controls (HA peptides). This normalization involved dividing the fluorescence intensity value of each spot by the mean fluorescence intensity of the negative control spots. Normalized fluorescence intensity values <1 were converted to 1 and log2-transformed to reduce variability. The final fluorescence intensity value for each peptide consisted of the mean of the fluorescence intensity values of the duplicated peptide spots.
The linear epitopes were mapped using serum samples from dengue (n = 19) and Zika (n = 28) patients in 16 and 11 arrays, respectively. In addition, serum samples from healthy individuals (n = 4), who had negative serological tests for dengue and Zika but were vaccinated against yellow fever, were analyzed in 3 arrays and served as controls. The fluorescence intensity threshold for each peptide was determined as the mean fluorescence intensity value plus two times the standard deviation of the mean intensity obtained with the control serum samples [[
After analyzing the results from the initial microarray platform, we selected several 15-aa peptides to design an additional peptide microarray slide. This microarray slide consisted of five identical sub-arrays, each containing duplicated spots of viral peptides, the influenza virus YPYDVPDYAG peptide in 38 spots, and the poliovirus KEVPALTAVETGAT peptide in 36 spots (PEPperPRINT, Heidelberg, Germany). The influenza virus and poliovirus peptides served as negative and positive controls in the immunoassays, respectively. Each sub-array was evaluated using individual serum samples obtained from dengue (n = 40) and Zika (n = 21) patients, as well as uninfected controls (n = 3). In order to compare the peptide detection rates obtained with the serum samples from dengue and Zika patients, we constructed a contingency (2 × 2) table and analyzed the data using Fisher's exact test [[
The BLASTP 2.14.0+ program [[
This study included serum samples from 48 dengue patients, 28 Zika patients, and five healthy individuals. The control group consisted of healthy individuals with negative serological tests for dengue and Zika and who had been vaccinated against yellow fever. The mean age was 33 and 34.4 years for the dengue and Zika patients, respectively, and 24 years for the healthy individuals. Among the dengue patients, 70.8% were female, while 75% were male in the Zika patient group. All healthy individuals were female. The demographic characteristics of the participants and the laboratory data used for diagnosis are shown in Table 1.
Graph
Table 1 Demographic and laboratory characteristics of the study participants.
Characteristics Dengue patients (n = 48) Zika patients (n = 28) Healthy individuals (n = 5) Mean age ± SD (years) 33 ± 18.1 34.4 ± 7.6 24 ± 10.9 Sex Male 14 (29.2%) 21 (75%) 0 Female 34 (70.8%) 7 (25%) 5 (100%) DENV-RT-PCR Positive 48 (100%) - Negative 0 - Not determined - 28 (100%) DENV Type DENV-1 2 (4.2) - DENV-2 8 (16.7) - DENV-3 6 (12.5%) - DENV-4 16 (33.3) - DENV 16 (33.3) - ZIKV-RT-PCR Positive - 2 (7.1%) Negative - 0 Not determined 48 (100%) 26 (92.9%) DENV-IgG Positive 28(58.3%) 0 0 Negative 2 (4.2%) 20 (71.4%) 5 (100%) Not determined 18 (37.5%) 8 (28.5%) - DENV-IgM Positive 18 (37.5%) 0 0 Negative 12 (25%) 20 (71.4%) 5 (100%) Not determined 18 (37.5%) 8 (28.5%) - ZIKV-IgG Positive - 20 (71.4%) 0 Negative - 0 5 (100%) Not determined 48 (100%) 8 (28.5%) - ZIKV-IgM Positive - - 0 Negative - - 5 (100%) Not determined 48 (100%) 28 (100%) - DENV-PRNT50 Titer: <40 - 28 (100%) - Titer: >160 - 0 - Not determined 48 (100%) - - ZIKV-PRNT50 Titer: <40 - 0 - Titer: >160 - 28 (100%) - Not determined 48 (100%) - -
A peptide microarray platform, specifically designed for epitope mapping in the E and NS1 proteins of DENV-1, DENV-2, DENV-3, DENV-4, and ZIKV, was subjected to analysis using individual or pooled serum samples obtained from dengue (19 serum samples in 15 sub-arrays) and Zika (28 serum samples in 11 sub-arrays) patients. According to Fig 1, the serum samples detected peptides distributed evenly throughout the viral proteins, indicating no specific preference for particular regions. Moreover, there was a high degree of cross-reactivity, as the serum samples exhibited similar detection rates for peptides derived from unrelated viruses when compared to those from their corresponding related viruses.
Graph: Fig 1 Peptide profile detected by the serum samples from dengue (A) and Zika (B) patients in the E and NS1 proteins of DENV-1, DENV-2, DENV-3, DENV-4, and ZIKV. The y-axis quantifies the intensity of detected peptides (depicted on the x-axis) by the patient samples, represented by vertical bars.
The peptides detected by the serum samples, with the exception of those containing part of the linker sequence (GSGSGSG), provided the epitope identification (Tables 2, S1 and S2). In each array, the serum samples detected a range of 60 to 303 epitopes, with sizes ranging from 3 to 15 aa, with the latter being more frequently observed. The amino acid sequences of the detected epitopes exhibited a high degree of variability (S1 and S2 Tables).
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Table 2 The number of peptides and epitopes detected by the serum samples from dengue and Zika patients in the E and NS1 proteins of DENV-1, DENV-2, DENV-3, DENV-4, and ZIKV.
Protein E Protein NS1 Serum samples Patient code DENV-1 (n = 197) DENV-2 (n = 197) DENV-3 (n = 197) DENV-4 (n = 197) ZIKV (n = 201) DENV-1 (n = 176) DENV-2 (n = 176) DENV-3 (n = 176) DENV-4 (n = 176) ZIKV (n = 176) TOTAL Dengue patients D1 P 32,0 43 27 45 22 17 17 20 24 23 270 E 22,0 23 16 24 18 14 14 14 20 19 184 D2 P 48,0 47 46 61 43 44 41 47 38 40 455 E 20,0 24 26 24 25 21 16 19 21 23 219 D3-D4 P 28,0 36 37 41 31 14 21 12 17 27 264 E 11,0 13 14 16 20 5 13 8 11 15 126 D5-D6 P 64,0 72 60 72 62 62 56 50 52 63 613 E 25,0 29 31 27 26 31 21 25 23 25 263 D7-D8 P 29,0 24 28 34 32 24 26 16 21 26 260 E 13,0 11 15 15 17 13 13 13 16 18 144 D9 P 21,0 27 20 19 14 18 15 13 19 7 173 E 11,0 14 8 9 9 13 13 10 12 6 105 D10-D11 P 46,0 38 42 37 28 11 20 20 19 19 280 E 19 18 26 17 20 9 15 16 14 12 166 D12 P 38 32 17 34 25 25 35 26 28 23 283 E 23 17 13 24 24 16 21 16 21 20 195 D13 P 27 39 30 44 33 19 16 16 13 21 258 E 10 16 14 22 17 11 16 11 13 11 141 D14 P 41 59 50 58 52 36 46 45 33 42 462 E 22 19 19 25 17 19 23 20 19 22 205 D15 P 30 50 45 44 54 35 43 36 41 46 424 E 17 24 26 21 37 25 22 21 22 31 246 D16 P 23 38 16 23 22 15 27 22 21 28 235 E 15 20 14 13 16 15 15 10 15 16 149 D17 P 10 30 14 15 18 15 19 17 15 14 167 E 9 12 8 10 10 14 14 11 13 9 110 D18 P 39 54 34 51 40 23 26 24 20 26 337 E 22 23 21 20 24 13 14 13 17 13 180 D-19 P 27 29 30 30 33 19 17 17 16 19 237 E 19 27 22 17 21 13 13 13 13 13 171 Zika patients Z6-Z7 P 5 12 9 5 9 7 9 8 4 7 75 E 5 8 7 4 7 5 7 7 3 7 60 Z1-Z2 P 43 57 43 49 37 38 35 45 40 61 448 E 22 25 21 21 20 21 18 20 19 23 210 Z4-Z5 P 36 55 36 41 34 35 31 37 39 39 383 E 18 21 20 20 17 23 18 19 14 17 187 Z3-Z8 P 24 34 30 41 30 26 31 23 26 41 306 E 16 18 22 25 16 14 17 16 15 17 176 Z27 P 26 40 40 41 45 29 21 25 29 25 321 E 14 19 19 26 30 23 13 20 19 21 204 Z16-Z12-Z17 P 32 40 30 50 34 32 32 34 29 32 345 E 22 21 18 26 25 23 20 24 22 22 223 Z13-Z18-Z9 P 22 27 23 31 21 24 18 20 18 20 224 E 15 15 11 14 14 17 8 14 6 14 128 Z15-Z14-Z24 P 26 36 27 29 20 27 18 27 13 20 243 E 14 20 15 15 17 15 10 16 10 12 144 Z11-Z26-Z19 P 16 31 22 37 19 16 16 15 17 17 206 E 13 17 13 17 14 12 14 10 14 11 135 Z10-Z20-Z21 P 48 46 29 48 52 33 26 35 36 41 394 E 24 25 16 19 30 17 15 20 20 20 206 Z22-Z23-Z25-Z28 P 72 79 51 81 59 54 49 64 55 53 617 E 30 33 28 36 33 31 27 30 29 26 303
1 *Peptides
2
The detection rate for each peptide in the arrays varied from 0 to 86.7% and 0 to 90.9% for the serum samples from dengue and Zika patients, respectively. When analyzing the peptide detection rates, we identified epitope hotspots constituted by clusters of peptides that consistently exceeded a 30% detection rate (Fig 2 and S3 Table).
Graph: Fig 2 Example showing several epitope hotspots (marked in red) found in the viral E protein of DENV-1, DENV-2, DENV-3, and DENV-4.
The peptides' specificity was further analyzed using an additional peptide microarray containing several peptides (n = 248) selected from the initial screening (S4 Table). These peptides included those found within the hotspots, outside the hotspots, and undetected peptides. Each sub-array analyzed with individual serum samples from dengue (n = 40) and Zika (n = 21) patients provided information on the specificity of the serum samples. When comparing the peptide detection rates, three DENV peptides were significantly more frequently detected by patients with dengue than by those with Zika, and one ZIKV peptide was detected significantly more frequently by patients with Zika than by those with dengue (Tables 3 and S5).
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Table 3 Identification of virus-specific peptides.
Virus Protein Peptide sequence Sera detection rate (%) Dengue Zika DENV-2 E VHRQWFLDLPLPWLP 22.0 0.0 0.0215 DENV-2 E TQGEPSLNEEQDKRF 32.5 4.76 0.0224 DENV-4 NS1 TQTVGPWHLGKLEID 35.0 9.52 0.0365 ZIKV E LELDPPFGDSYIVIG 5.0 28.57 0.0160
The peptides TQGEPSLNEEQDKRF, TQTVGPWHLGKLEID, and LELDPPFGDSYIVIG were located within epitope hotspots (Table 4), whereas the peptide VHRQWFLDLPLPWLP was not.
Graph
Table 4 Virus-specific peptides within the epitope hotspots.
Virus Protein Sequence Detection rate (%) DENV-2 E TTTESRCPTQGEPSL 13.3 DENV-2 E TESRCPTQGEPSLNE 53.3 DENV-2 E SRCPTQGEPSLNEEQ 40.0 DENV-2 E CPTQGEPSLNEEQDK 53.3 DENV-2 E TQGEPSLNEEQDKRF 73.3 DENV-2 E GEPSLNEEQDKRFIC 46.7 DENV-2 E PSLNEEQDKRFICKH 13.3 DENV-4 NS1 QGYATQTVGPWHLGK 0.0 DENV-4 NS1 YATQTVGPWHLGKLE 6.7 DENV-4 NS1 TQTVGPWHLGKLEID 53.3 DENV-4 NS1 TVGPWHLGKLEIDFG 53.3 DENV-4 NS1 GPWHLGKLEIDFGEC 13.3 DENV-4 NS1 WHLGKLEIDFGECPG 6.7 ZIKV E STENSKMMLELDPPF 20.0 ZIKV E ENSKMMLELDPPFGD 53.3 ZIKV E SKMMLELDPPFGDSY 53.3 ZIKV E MMLELDPPFGDSYIV 53.3 ZIKV E LELDPPFGDSYIVIG 40.0 ZIKV E LDPPFGDSYIVIGVG 33.3 ZIKV E PPFGDSYIVIGVGEK 26.7
3 The detection rate value of the peptides within the epitope hotspots is marked in bold letters.
In contrast, our findings revealed comparable detection rates between serum samples from dengue and Zika patients for most of the DENV and ZIKV peptides examined. Among these, we identified five peptides with high detection rates (exceeding a minimum arbitrary threshold of 40%) by both dengue and Zika patients (Table 5), two of which (WEVEDYGFGVFTTNI and LELDFDLCEGTTVVV) were located within the epitope hotspots (Table 6).
Graph
Table 5 Peptides detected by the serum samples from dengue and Zika patients at similar high detection rates.
Sera detection rate (%) Virus Protein Sequence Dengue Zika DENV-3 NS1 WEVEDYGFGVFTTNI 52.50 76.19 0.0997 DENV-1 NS1 LELDFDLCEGTTVVV 42.50 57.14 0.2963 DENV-4 E DCEPRSGIDFNEMIL 42.50 52.38 0.5901 DENV-1 E PIVTDKEKPVNIETE 40.00 52.38 0.4215 DENV-2 NS1 TAGPWHLGKLEMDFD 42.50 47.62 0.7889
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Table 6 Peptides with high detection rates within the epitope hotspots.
Virus Protein Sequence Detection rate (%) DENV-3 NS1 AWNVWEVEDYGFGVF 6.7 DENV-3 NS1 NVWEVEDYGFGVFTT 60.0 DENV-3 NS1 WEVEDYGFGVFTTNI 40.0 DENV-3 NS1 VEDYGFGVFTTNIWL 26.7 DENV-3 NS1 HLGKLELDFDLCEGT 20.0 DENV-3 NS1 GKLELDFDLCEGTTV 40.0 DENV-3 NS1 LELDFDLCEGTTVVV 53.3 DENV-3 NS1 LDFDLCEGTTVVVDE 0.0
The BLAST analysis showed that the DENV-specific peptides from Table 3 exhibited high similarity to all four DENV serotypes, while displaying comparatively lower similarity to the ZIKV strains (Table 7). Similarly, the ZIKV-specific peptide demonstrated substantial sequence identity with several ZIKV strains, while exhibiting minor similarity with DENV serotypes, except for DENV-4. The high-detection-rate peptides (Table 4) were found exclusively in DENV, and their high similarity to other strains from all four DENV serotypes supports this observation. These peptides also displayed substantial similarity to ZIKV strains, except for LELDFDLCEGTTVVV and PIVTDKEKPVNIETE, which showed median identity rates of up to 50.60% with them. However, it is noteworthy that these peptides contain conserved epitopes shared between DENV and ZIKV, such as the LEIRFE, GTKVHV, PVITE, and LELD epitopes from ZIKV, a fact that may explain the high detection rates observed in Zika patients for both peptides.
Graph
Table 7 BLAST comparison test for peptide similarity analysis against other DENV-1, DENV-2, DENV-3, DENV-4, and ZIKV strains.
Query Virus—Protein Query Cover (%)—Interval Query Cover—Mean (%) Query Cover—Median (%) Per. Ident (%)—Interval Per. Ident—Mean (%) Per. Ident—Median (%) VHRQWFLDLPLPWLP DENV-1—E 100.00–86.00 86.00 86.00 100.00–92.31 92.52 92.31 DENV-2—E 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-3—E 100.00–86.00 93.80 100.00 100.00–92.31 92.43 92.31 DENV-4—E 86.00–86.00 86.01 86.00 100.00–92.31 92.33 92.31 ZIKV—E 100.00–86.00 86.00 86.00 76.92–69.23 69.27 69.23 TQGEPSLNEEQDKRF DENV-1—E 100.00–80.00 80.73 80.00 100.00–75.00 75.86 75.00 DENV-2—E 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-3—E 93.00–80.00 84.81 80.00 83.33–75.01 75.01 75.00 DENV-4—E 100.00–80.00 94.02 100.00 83.33–73.33 83.32 83.33 ZIKV—E 100.00–46.00 96.98 100.00 100.00–42.11 43.93 42.11 TQTVGPWHLGKLEID DENV-1—NS1 100.00–100.00 100.00 100.00 93.33–86.67 86.73 86.67 DENV-2—NS2 100.00–100.00 100.00 100.00 100.00–86.67 86.75 86.67 DENV-3—NS3 100.00–100.00 100.00 100.00 86.67–86.67 86.67 86.67 DENV-4—NS4 100.00–40.00 83.76 100.00 100.00–66.67 87.94 93.33 ZIKV—NS5 100.00–26.00 93.44 100.00 100–57.14 65.29 64.29 LELDPPFGDSYIVIG DENV-1—E 100.00–93.00 99.63 100.00 92.86–78.57 78.88 78.57 DENV-2—E 100.00–93.00 94.37 93.00 92.86–78.57 79.61 78.57 DENV-3—E 100.00–93.00 99.91 100.00 78.57–71.43 71.45 71.43 DENV-4—E 100.00–93.00 93.11 93.00 92.86–92.86 92.86 92.86 ZIKV—E 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 WEVEDYGFGVFTTNI DENV-1—NS1 100.00–100.00 100.00 100.00 100.00–93.33% 97.19 100.00 DENV-2—NS2 100.00–100.00 100.00 100.00 93.33–83.33 83.33 83.33 DENV-3—NS3 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-4—NS4 100.00–13.00% 95.40 100.00 100.00–75.00 78.01 77.78 ZIKV—NS5 100.00–13.00% 79.79 86.00 100.00–69.23 70.91 69.23 LELDFDLCEGTTVVV DENV-1—NS1 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-2—NS2 100.00–100.00 100.00 100.00 100.00–86.67 87.94 86.67 DENV-3—NS3 100.00–100.00 100.00 100.00 100.00–80.00 80.03 80.00 DENV-4—NS4 100.00–33.00 90.43 100.00 100.00–66.67 76.58 69.23 ZIKV—NS5 100.00–53.00 92.47 100.00 100.00–50.00 58.09 50.00 DCEPRSGIDFNEMIL DENV-1—E 100.00–100.00 100.00 100.00 100.00–73.33 74.06 73.33 DENV-2—E 100.00–100.00 100.00 100.00 100.00–66.67 66.80 66.67 DENV-3—E 100.00–100.00 100.00 100.00 80.00–73.33 73.34 73.33 DENV-4—E 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 ZIKV—E 100.00–46.00 96.38 100.00 100.00–50.00 61.55 61.54 PIVTDKEKPVNIETE DENV-1—E 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-2—E 100.00–100.00 100.00 100.00 93.33–73.33 73.45 73.33 DENV-3—E 100.00–100.00 100.00 100.00 93.33–73.33 73.60 73.33 DENV-4—E 60.00–13.00 23.63 26.00 100.00–57.14 98.70 100.00 ZIKV—E 80.00–73.00 76.50 76.50 58.33–42.86 50.60 50.60 TAGPWHLGKLEMDFD DENV-1—NS1 100.00–100.00 100.00 100.00 100.00–93.33 93.41 93.33 DENV-2—NS2 100.00–100.00 100.00 100.00 100.00–100.00 100.00 100.00 DENV-3—NS3 100.00–100.00 100.00 100.00 93.33–86.70 86.68 86.67 DENV-4—NS4 100.00–40.00 98.63 100.00 100.00–50.00 87.55 85.71 ZIKV—NS5 93.00–26.00 48.33 26.00 100.00–66.67 88.89 100.00
Understanding the epitope profiles recognized by the humoral immune response for specific antigens, such as DENV and ZIKV proteins, is vital for developing candidate vaccines and diagnostic tests. Epitopes can be either linear or conformational [[
Most of the detected peptides showed similar detection rates when analyzed with sera from dengue and Zika patients, i.e., the antibodies derived from these patients presented high levels of cross-reactivity against the viral proteins. This observation aligns with previous findings reported in the literature [[
During our analysis, we identified several epitopes located within epitope hotspots. These hotspots correspond to clusters of peptides detected in more than 30% of the sub-arrays during the initial screening. This observation emphasizes the crucial role of specific sites within the viral proteins in eliciting an immune response against these viruses. It also suggests the potential value of further investigating these regions in the context of vaccine development and diagnostic applications. We also analyzed the immunogenicity of several peptides from the epitope hotspots using individual serum samples in each sub-array. However, we could not include all peptides within the clusters for this analysis due to budget limitations. Our findings revealed the presence of four virus-specific peptides, three of which (DENV: TQGEPSLNEEQDKRF and TQTVGPWHLGKLEID; and ZIKV: LELDPPFGDSYIVIG) were located within the epitope hotspots. Additionally, two peptides (WEVEDYGFGVFTTNI and LELDFDLCEGTTVVV) within the epitope hotspots exhibited high detection rates by the serum samples of both dengue and Zika patients. Epitope hotspots contain immunodominant epitopes recognized by a higher number of individuals when compared to 15-aa sequence peptides. Consequently, peptides with sequences containing all the epitope hotspots, spanning up to ~30 aa, have more potential than peptides of only 15 aa for developing diagnostic tests and vaccines. Mishra and colleagues found that a concatemer of an immunoreactive 20-aa ZIKV NS2B peptide (49 aa) demonstrated better performance than shorter peptides in immunoassays with Zika patients' sera [[
A limitation of our study was the restricted number of individuals in the control group. In dengue-endemic regions, such as Brazil and Paraguay, locating individuals unexposed to DENV is challenging. Despite this limitation, epitope hotspot identification within viral proteins has provided valuable insights and instilled confidence in the results obtained in this study. Kam and colleagues [[
In summary, our study has yielded valuable insights into the linear epitope profiles recognized by serum samples from patients with dengue and Zika infections, particularly within the E and NS1 proteins of DENV and ZIKV. These findings contribute to our understanding of the immune response to these viruses and may have potential implications for diagnostic tests and vaccine development. We identified several epitopes within epitope hotspots, indicative of specific regions within the viral proteins that elicit strong immune responses. Among these epitope hotspots, we discovered two DENV-specific peptides (TQGEPSLNEEQDKRF and TQTVGPWHLGKLEID) and one ZIKV-specific peptide (LELDPPFGDSYIVIG). However, they exhibited low detection rates in our assays. On the other hand, we also identified two peptides (WEVEDYGFGVFTTNI and LELDFDLCEGTTVVV) within the epitope hotspots that showed high detection rates. Based on these findings, we propose that peptides encompassing all epitope hotspots, spanning approximately 30 amino acids, hold promising potential for use in diagnostic tests and vaccine development.
S1 Checklist. STROBE statement—Checklist of items that should be included in reports of observational studies.
(DOCX)
S1 Table. Epitope mapping with sera of DENV-infected individuals.
(XLSX)
S2 Table. Epitope mapping with sera of ZIKV-infected individuals.
(XLSX)
S3 Table. Identification of epitope hotspots.
(XLSX)
S4 Table. List of peptides included in the microarray platform for specificity analysis.
(XLSX)
S5 Table. Comparison of peptide detection rates with sera from dengue and Zika patients.
(XLSX)
Sambri Vittorio Academic Editor
17 Aug 2023
PONE-D-23-18775Linear epitope mapping in the E and NS1 proteins of dengue and Zika viruses: prospection of peptides for vaccines and diagnostics.PLOS ONE
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Reviewer #2: Partly
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Reviewer #2: Yes
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Reviewer #1: In the manuscript by Aquino et al., the authors rigorously map dengue and Zika virus protein epitopes that are found in serum of infected patients. The identify epitope hot spots and delineate peptides that are found in these regions that may be used for improved diagnostic tests and vaccine development. The manuscript is well-written and the results are explained well. My minor comments focus on presentation of the data, which needs to be improved for clarity to the reader.
It would be helpful if the authors better described to the reader how to interpret Fig 1 and Table 2 together. Since the table and figure contain similar data, what is the purpose of including each? With a better explanation, it would make more sense to use both. Also, in Figure 1, it should be more clearly stated in the caption that the vertical bars represent patient samples. In Table 2, is the formatting is incorrect, or do some patients in the Zika population not have P and E data? There are blank rows.
For Table 7, it would be helpful if the authors highlighted rows with significant results or peptides for the reader to pay attention to.
Table 4 and 6 – What is the purpose of including a column called "code?" This is not defined in the text.
Figure 2 is nearly illegible. Can the authors reduce the redundancy of text by combing cells that contain the same text? Also, what is the purpose of the "code" column? The figure needs to be simplified or split into multiple panels.
Table 6 – why are the p-values such high numbers, greater than 1?
Line 68 – "practically the entire country" is vague and colloquial. This should be more precise.
Reviewer #2: The manuscript PONE-D-23-18775 "Linear epitope mapping in the E and NS1 proteins of dengue and Zika viruses: prospection of peptides for vaccines and diagnostics" describes the mapping of four novel peptides which are suitable candidates for developing peptide-derived vaccines and diagnostic tests specific to DENV and ZIKV. They propose that larger sequences, instead of the peptides alone, containing the entire epitope hot spots would enhance the performance and reliability of such vaccines and diagnostic tests. My major concern is the low number of negative control serum samples used in this work. I would like to recommend a couple of additional experiments (comments 1&2), and a few minor corrections/inquiries (comments 3-9), which I believe will substantially improve the quality of the article.
1) Only 5 serum samples from YFV vaccinated and DENV/ZIKV seronegative healthy women were used as controls. From these, 4 or 3 serum samples were used for the epitope mapping or peptide microarray assays, respectively. To arrive to the conclusions the authors state in their work, a bigger and more representative (age-gender) population should be included as control group.
- 2) It is not clear to me how many DENV serum samples, from each serotype, have been used in the work. Authors should characterize and inform the number of samples collected and analyzed for each DENV serotype, in order to demonstrate similar sample sizes for each of the four DENV serotypes.
- 3) How do you establish the 30% threshold detection rate value for the determination of hot spots?
- 4) Line 36-37: Rephrase "Dengue and Zika patients detected..."
- 5) Line 148: "Dyligt" misspelt
- 6) Line 215-216: Correct "The average age was 33 and 34.4 years for dengue Zika..."
- 7) Table 6: "p-value" column shoud be "Detection rate (%)" instead
- 8) Line 348: "ZKIKV" misspelt
- 9) The Discussion section is too long. It should be shortened.
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Reviewer #2: No
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19 Sep 2023
Answer to Editor and Reviewers
Editor
1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.
Answer
The manuscript style was reviewed to meet PLOS ONE's style requirements.
2. We note that the grant information you provided in the 'Funding Information' and 'Financial Disclosure' sections do not match.
Answer
We have included the correct grant information: This study was financially supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP, Brazil (grant numbers 2017/09194-3 and 2019/26119-0), and Consejo Nacional de Ciencia y Tecnología, CONACYT, Paraguay (grant number PIRT19-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
3. Please upload a new copy of Figure 2 as the detail is not clear. Please follow the link for more information:
Answer
We uploaded an improved version of Figures 1 and 2.
4. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.
Answer
The manuscript underwent professional copyediting by a scientific editing service, enhancing the overall linguistic quality.
Reviewer #1:
Answer
We have enhanced the clarity of our manuscript by revising the text that elaborates on the findings presented in Fig 1 and Table 2 (lines 235-240 and 254-258). Additionally, we have provided in the caption a comprehensive explanation to enhance the interpretation of Fig 1 (lines 242-245).
The occurrence of empty rows within Table 2 resulted from initial formatting issues, which have been resolved.
2- For Table 7, it would be helpful if the authors highlighted rows with significant results or peptides for the reader to pay attention to.
Answer
We have highlighted the Percent Identity - Median (%) column values within Table 7, as they encompass the most pertinent results.
3- Table 4 and 6 – What is the purpose of including a column called "code?" This is not defined in the text.
Answer
Each peptide has a specific code within the microarray slide. However, we have removed the Code containing column from Tables 4 and 6 to avoid confusion.
4- Figure 2 is nearly illegible. Can the authors reduce the redundancy of text by combing cells that contain the same text? Also, what is the purpose of the "code" column? The figure needs to be simplified or split into multiple panels.
Answer
We uploaded an improved version of Figures 1 and 2.
5- Table 6 – why are the p-values such high numbers, greater than 1?
Answer
Apologies for the error. That specific column corresponds to the Detection rate (%) value, which has now been rectified.
6- Line 68 – "practically the entire country" is vague and colloquial. This should be more precise.
Answer
The manuscript underwent professional copyediting by a scientific editing service, enhancing the overall linguistic quality.
Reviewer #2:
Answer
We agree with the reviewer's suggestion regarding the potential benefits of increasing the number of control samples to enhance the reliability of our study. As acknowledged in our manuscript, the limitation of our study lies in the constrained availability of resources, preventing us from conducting additional microarray assays. Nonetheless, we maintain confidence in the presented results. We have revised the discussion section to incorporate relevant findings from the literature that further support our conclusions (lines 428-441).
2) It is not clear to me how many DENV serum samples, from each serotype, have been used in the work. Authors should characterize and inform the number of samples collected and analyzed for each DENV serotype, in order to demonstrate similar sample sizes for each of the four DENV serotypes.
Answer
The information regarding the DENV serotype at the time of recruitment has been incorporated into Table 1. However, it's important to note that we did not include an analysis of this variable in our study. This decision stems from the fact that in countries with DENV transmission, such as Brazil and Paraguay, individuals often encounter multiple DENV serotype infections throughout their lives. The identification of these infections can be a formidable challenge. The widespread prevalence of multi-serotype infections introduces a significant layer of complexity when attempting to explore potential associations between DENV epitope detection and the specific DENV serotype individuals had at the time of recruitment.
3) How do you establish the 30% threshold detection rate value for the determination of hot spots?
Answer
We rephrase the text for clarification (lines 262-264).
"When analyzing peptide detection rates, we identified epitope hotspots constituted by clusters of peptides that consistently exceeded a 30% detection rate".
4) Line 36-37: Rephrase "Dengue and Zika patients detected..."
Answer
The manuscript underwent professional copyediting by a scientific editing service, enhancing the overall linguistic quality.
5) Line 148: "Dyligt" misspelt
Answer
The spelling of "Dyligt" was corrected.
6) Line 215-216: Correct "The average age was 33 and 34.4 years for dengue Zika..."
Answer
The statement was corrected (lines 219-220).
7) Table 6: "p-value" column shoud be "Detection rate (%)" instead
Answer
We apologize for the error that occurred. We have rectified the issue.
8) Line 348: "ZKIKV" misspelt
Answer
The spelling of "ZKIKV" was corrected.
9) The Discussion section is too long. It should be shortened.
Since the PLOS ONE journal does not impose restrictions on manuscript length, we respectfully seek the reviewer's approval to maintain the expanded Discussion section. We have revised this section to improve its overall clarity. Furthermore, we have included an additional statement to strengthen our findings and acknowledge the study's limitation arising from the limited number of control samples as mentioned above.
Attachment.
Submitted filename: Response to Reviewers.docx
Sambri Vittorio Academic Editor
20 Sep 2023
Linear epitope mapping in the E and NS1 proteins of dengue and Zika viruses: prospection of peptides for vaccines and diagnostics.
PONE-D-23-18775R1
Dear Dr. Aquino,
We're pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.
Within one week, you'll receive an e-mail detailing the required amendments. When these have been addressed, you'll receive a formal acceptance letter and your manuscript will be scheduled for publication.
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Kind regards,
Vittorio Sambri, M.D., Ph.D.
Academic Editor
PLOS ONE
Additional Editor Comments (optional):
Reviewers' comments:
Sambri Vittorio Academic Editor
25 Sep 2023
PONE-D-23-18775R1
Linear epitope mapping in the E and NS1 proteins of dengue and Zika viruses: prospection of peptides for vaccines and diagnostics.
Dear Dr. Aquino:
I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.
If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.
If we can help with anything else, please email us at plosone@plos.org.
Thank you for submitting your work to PLOS ONE and supporting open access.
Kind regards,
PLOS ONE Editorial Office Staff
on behalf of
Professor Vittorio Sambri
Academic Editor
PLOS ONE
By Victor Hugo Aquino; Marcilio J. Fumagalli; Angélica Silva; Bento Vidal de Moura Negrini; Alejandra Rojas; Yvalena Guillen; Cynthia Bernal and Luiz Tadeu Moraes Figueiredo
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