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Article

Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal

by
Sanjaya K. Shrestha
1,2,*,
Jasmin Shrestha
1,2,*,
Binob Shrestha
1,
Tor A. Strand
2,3,
Susanne Dudman
4,5,
Ashild K. Andreassen
6,
Shree Krishna Shrestha
7,
Anup Bastola
8,9,
Prativa Pandey
10 and
Stefan Fernandez
11 on behalf of the AFRIMS-Department of Virology Group
1
Walter Reed/AFRIMS Research Unit Nepal, Kathmandu 44600, Nepal
2
Center for International Health, University of Bergen, N-5020 Bergen, Norway
3
Department of Research, Innlandet Hospital Trust, N-2609 Lillehammer, Norway
4
Institute of Clinical Medicine, University of Oslo, N-0318 Oslo, Norway
5
Department of Microbiology, Oslo University Hospital, N-0424 Oslo, Norway
6
Department of Virology, Norwegian Institute of Public Health, N-0213 Oslo, Norway
7
Pokhara Academy of Health Sciences, Pokhara 33700, Nepal
8
Sukra Raj Tropical and Infectious Diseases Hospital, Kathmandu 44600, Nepal
9
Curative Service Division, Department of Health Services, MoPH, Kathmandu 44600, Nepal
10
CIWEC Hospital, Kathmandu 44600, Nepal
11
Department of Virology, Armed Force Research Institute of Medical Science, Bangkok 10400, Thailand
 
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*
Authors to whom correspondence should be addressed.
Microbiol. Res. 2024, 15(4), 2247-2260; https://doi.org/10.3390/microbiolres15040150
Submission received: 2 August 2024 / Revised: 20 September 2024 / Accepted: 29 October 2024 / Published: 31 October 2024
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Abstract

:
Acute respiratory infection is one of the leading causes of morbidity and mortality among children in low- and middle-income countries. Due to limited diagnostic capability, many respiratory pathogens causing influenza-like illness go undetected. This study aims to detect enterovirus, respiratory syncytial virus, seasonal coronavirus and respiratory pathogens other than influenza in patients with influenza-like illness. A total of 997 (54.3%) respiratory samples (collected in the years 2016–2018) were randomly selected from 1835 influenza-negative samples. The xTAG Respiratory Viral Panel (RVP) FAST v2 panel was used to detect respiratory pathogens including enterovirus/rhinovirus (EV/RV), respiratory syncytial virus (RSV) and seasonal coronavirus (HKU1, OC43, NL63 and 229E). A total of 78.7% (785/997) were positive for respiratory viruses. Of these viruses, EV/RV was detected in 36.3% (362/997), which is the highest number, followed by RSV in 13.7% (137/997). The seasonal coronaviruses HKU1 and OC43 (1.5%, 15/997), NL63 (1.2%, 12/997) and 229E (1%, 10/997) were also detected. The EV/RV-positive samples were sequenced, of which 16.7% (5/30) were confirmed as EVs and were identified as coxsackievirus (CV) types CVB5, CVB3, CV21 and CVB2. The findings of this study highlight the importance of strengthening influenza-like illness surveillance programs in the region by including other respiratory viruses in their scope besides seasonal human influenza viruses.

1. Introduction

According to the World Health Organization (WHO), respiratory infections account for 6% of the total global disease burden. Around 6.6 million children under five years of age die each year worldwide; 95 percent of them from low-income countries, with one third of the total deaths being due to acute respiratory infections (ARIs). It is estimated that respiratory tract infection (RTI) is responsible for 40% of mortality in Bangladesh, India, Indonesia and Nepal. It was also found to be responsible for about 30–50% of visits to health facilities and about 20–40% of admissions to hospitals for children aged under five [1]. The etiology of RTI is diverse and accordingly it is difficult to relate it to a pathogen-specific public health threat [2]. Up to 80% of all infections are caused by viruses including influenza, parainfluenza, rhinovirus, human metapneumovirus (hMPV), human bocavirus (hBoV), several human coronaviruses (hCoVs) and human enterovirus (EV) [1,3,4,5,6]. The increase in the circulating viral pathogens among RTI cases intensifies the urgency for epidemiological efforts to characterize the prevalence, transmission, pathogenesis and at-risk populations associated with these diseases in developing countries.
Together with human rhinoviruses (RVs), enteroviruses (EVs) are among the most common causative agents of human disease and a significant burden to the patients as well as to the health care system [7,8,9]. Respiratory EVs can invade the lower respiratory tract and cause complicated diseases, especially in children [10]. Outbreaks of severe respiratory infections and cases of acute flaccid paralysis (AFP) associated with EV-D68 infections were reported in the USA, Canada and Europe [9,11,12,13]. The largest outbreak of EV-D68 occurred in North America in 2014, led to severe respiratory and neurological diseases, mainly in children, and caused considerable hospitalization rates [9,14].
Respiratory syncytial virus (RSV) is leading cause of ARI in both children and adults [15]. This virus is estimated to cause approximately 33.8 million new episodes of acute lower respiratory tract infections annually in children aged <5 years worldwide. This has resulted in 3.2 million hospital admissions and 59,600 hospital deaths in children aged <5 years in 2015 [16]. Although the incidence of RSV infection is lower in adults compared to young children, high hospitalization and mortality rates associated with RSV have been reported in the elderly and high-risk adults [17,18,19].
Similarly, hCoVs have the potential to cause significant RTI, with the most notable example being the COVID-19 pandemic caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which caused more than 7 million deaths globally by the end of 2022 [20]. A number of other hCoVs regularly circulate in human populations causing ARI each year, including hCoV-229E and hCoV-OC43, hCoV-NL63 and hCoV-HKU1, which may have negative impacts on at-risk population such as children [21,22,23,24]. Despite the severity of respiratory disease, these viruses, except for SARS-CoV-2, are not included in the human influenza surveillance program of Nepal or the Global Influenza Surveillance and Response System (GISRS) of the World Health Organization (WHO). Besides influenza and SARS-CoV-2, other respiratory viruses are not routinely tested for influenza-like illness (ILI) or severe acute respiratory illness cases due to the limited availability and affordability of tests.
This study aims to analyze respiratory samples from ILI cases that are influenza-negative by using real-time RT-PCR to identify other viral pathogens including EV/RV, RSV and hCoVs, using a qualitative multiplex molecular diagnostic assay [25,26]. Samples with EV/RV identified by this assay were further sequenced for typing the various EVs circulating in the population in Nepal during the study period.

2. Materials and Methods

2.1. Study Sample and Study Population

The respiratory samples were collected under the “Sentinel Human Surveillance for Influenza in Nepal “program during the years 2016, 2017 and 2018. Patients with ILI features were enrolled from five different hospitals located in four different districts in Nepal: Bharatpur Hospital in Chitwan; Western Regional Hospital in Kaski; Sukra Raj Tropical Infectious Diseases Hospital and CIWEC Hospital in Kathmandu; and Siddhi Memorial Hospital in Bhaktapur. The criteria for enrollment in the influenza surveillance program were being male or female, aged age ≥6 months, presenting at the hospital within 3 days for outpatients and within 5 days for inpatients with a reported fever along with a cough or sore throat. The signs and symptoms reported at the time of enrollment within the influenza survey were included as part of clinical data.
The respiratory nasal and/or throat swabs were collected in Universal Transport Medium (UTM-RT) manufactured by Copan Italia S.p.A., Brescia, Italy and were transported and stored in multiple aliquots at ≤−70 °C.

2.2. Nucleic Acid Extraction

Nucleic acid was extracted from the 997 samples described above using QIAamp MinElute virus kit (Qiagen, Hilden, Germany). The initial volume of the sample for extraction was 200 µL with a final elution volume of 50 µL. Prior to extraction, MS2 bacteriophage provided in the xTAG Respiratory Viral Panel (RVP) FAST v2 kit (Luminex Molecular Diagnostics, Toronto, ON, Canada) was added as an internal control to each sample.

2.3. Influenza Detection

Influenza-like illness samples were routinely tested for influenza A virus (FLUA), its subtypes (H1, H3, H1N1pdm09 and Universal pdmA) and influenza B virus (FLUB) following standard US CDC protocols for using US CDC primers and probe for the detection and characterization of influenza virus [27,28]. All influenza-negative specimens were tested for other respiratory viruses using multiplex realtime PCR.

2.4. xTAG RVP FAST v2 Test (Multiplex Realtime PCR)

xTAG RVP FAST v2 includes reagents targeting FLUA (H1, H1N1pdm09 and H3), FLUB, RSVA, RSVB, EVs including RVs (EV/RV), PIV1–4, hMPV, human adenovirus (AdV), hCoVs (NL63, HKU1, 229E and OC43) and hBoV. The assay was performed according to the manufacturer’s instructions and it includes a positive control bacteriophage lambda DNA. Briefly, reverse transcription and cDNA amplification were conducted on the Luminex MagPix assay by adding 10 µL of each extracted RNA sample, using an Eppendorf thermocycler (Eppendorf UK Limited, Whittle Way, Stevenage, UK) with a ramp speed of 1.2 °C/s followed by bead hybridization. xTAG RVP FAST v2 uses a target-specific PCR (TS-PCR) and a multiplexed RT-PCR reaction is performed with target-specific upstream primers containing a TAG capture sequence paired with biotinylated downstream primers to amplify the target sequences in a single reaction. The amplified product was added to a combined hybridization/detection reaction containing the corresponding anti-TAG bead sets and streptavidin-R-phycoerythrin (SAPE) reporter. After hybridization, plates were transferred to the Luminex MagPix system for detection (Luminex Molecular Diagnostics, Toronto, ON, Canada). Each well was analyzed for bead hybridization with the TDAS RVP FAST 2.2 software (Luminex Molecular Diagnostics, Toronto, ON, Canada). The cutoff thresholds for each virus were determined based on previously determined thresholds by the manufacturer. Samples were considered positive for a specific virus or its serotype if the threshold was equaled or exceeded.

2.5. Phylogenetic Tree Construction

Maximum likelihood trees were constructed by using IQ-TREE v1.6.9 with model GTR + F+I + G4 and 1000 ultrafast bootstrap replicates. For preliminary analysis, a total of 3661 human EV complete coding sequences available from GenBank on 2 Aug 2022 included EV-A (2016 sequences), EV-B (381 sequences), EV-C (629 sequences) and EV-D (640 sequences) were analyzed with the sequences obtained from this study for construction of an initial tree. To improve tree visualization, 3486 redundant sequences (identical sequences and/or not closely related with the obtained sequences from this study) were excluded from the analysis. The tree was reconstructed by using 175 representative sequences of 4 EV species from GenBank including 38 EV-A, 83 EV-B, 39 EV-C and 15 EV-D sequences and the sequences obtained from this study.

2.6. Statistical Analysis

Statistical analysis was conducted in IBM SPSS Statistics, Version 21. We described categorical variables as numbers and percentages and continuous variables as means and standard deviations. We used the chi-square and Fisher’s exact test when comparing the associations between categorical variables. We compared the proportions of positives between different age categories using logistic regression. In these models, the age category consisting of participants aged 18–59 was the reference category. A p-value < 0.05 was considered statistically significant.

3. Results

3.1. Enterovirus Isolation and Next-Generation Sequencing (NGS)

To obtain and collect viral isolates for further testing, among the samples that showed a positive EV/RV result (N = 362) following the xTAG RVP FAST v2 assay, 30 samples were randomly selected for viral isolation. Briefly, 100 µL of the sample was inoculated into Hep-2 cells (human larynx epithelioma cancer cell line) containing HeLa marker chromosomes and were derived via HeLa contamination (ATCC, Manassas, VA, USA) monolayer and incubated at 35 ± 2 °C for 1 h before adding Minimum Essential Medium supplemented with 10% fetal bovine serum. The inoculated cells were incubated at 35 ± 2 °C and daily observed for cytopathic effect (CPE) formation or unusual cell morphology including giant, round or balloon cells, elongated shape and cell detachment for 7 days. CPE-positive culture supernatant samples were confirmed by a commercial multiplex PCR assay, Fast-Track Respiratory 21(FTD) (Fast-Track Diagnostics, Luxembourg), following the manufacturer’s instruction. Confirmed EV-positive viral isolates were used for NGS by following the methods previously described by Rutvisuttinunt et al., 2017 [29]. DNA library preparation was conducted by using Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus, following the manufacturer’s instructions. Libraries were quantified and normalized before sequencing in the Illumina MiSeq instrument using MiSeq reagent kit V2 (2 × 250 bp, paired ends).
To obtain the whole EV genome sequences and identify the viral species, the short reads were aligned to the viral genome sequences of the accession number KY284011.1, MH144600.1, KR107055.1 and MF683838.1, which were found to be the most similar to the draft sequences of the samples obtained from de novo genome assembly and BlastN. The assembly was performed using Trinity v2.11.0 [30] with quality-trimmed paired reads with a Q score (Q) ≥30 (Trimmomatics v0.39) [31]. There were two steps for the alignment: (1) the raw reads were trimmed for index adapters, low-quality bases at both ends (Q < 30), average quality (Q < 20) and read length (<100 bp) using BBduk v38.86 (18) and (2) the trimmed paired reads were aligned to the most similar sequences using BWA-MEM aligner v0.7.17-r1188 and we generated the consensus sequences using iVar with criteria Q ≥ 30 and depth of coverage (DOC) ≥100 [32,33]. The quality of base calling from the images and sequences was determined by the quality score (Q).

3.2. Demographic Distribution of Sample Population

During the years 2016–2018, 4593 ILI samples had been collected from the Sentinel Human Surveillance for Influenza in Nepal program and were tested for influenza virus by real-time RT-PCR. Among the 4593 specimens collected, 2758 (60.1%) were positive for influenza and 1835/4593 (39.5%) were negative for FLUA and FLUB. Of these influenza-negative samples, our study focused on the randomly selected 997/1835 (54.3%) ILI samples. Among this population, 803 (80%) were children (≤15 years old) and 194 (19.5%) were adults aged 16 to 73 years. The male/female ratio was 601/396. The median age of children and adults was 3.0 years (interquartile range (IQR: 1.7–5.1) and 28 years (IQR: 22.3–40.9), respectively. The most common occupation within the study population was student. An overview of the samples selected for this study is shown in Table 1.

3.3. Distribution of Respiratory Pathogens

The overall prevalence of ARI was detected in 78.7% (758/997) of the influenza-negative samples. The most frequently detected viruses were EV/RV in 36.3% (362/997), RSV 13.7% (137/997), AdV 12.1% (121/997) and hCoV 5.2% (51/997). Among these positives, hCoV-229E, -HKU1, -NL63 and -OC43 were detected in 19.6% (10/51), 29.4% (15/51), 23.5% (12/51) and 29.4% (15/51) of samples, respectively. All the viral pathogens detected, including a few influenza-positive samples, both for FLU A and FLU B, are shown in Table 2.

3.4. Clinical Characteristic of Study Population

Being ARI positive (N = 785) was associated with cough, runny nose and sore throat among the recorded signs and symptoms. Partial correlation, controlling for ARI positive samples, of both EV/RV (N = 362) and hCoV (N = 51) was explored for all recorded clinical signs and symptoms. EV/RV had a significant positive correlation with malaise (0.108, p-value < 0.05) and injected pharynx (0.088, p-value < 0.05). The general clinical characteristics collected in the study population are shown in Table 3.

3.5. Age Distribution of ARI, EV/RV, RSV and hCoV

We demonstrate the association between age and ARI or virus detected, as shown in Table 4. We found that the incidence of ARIs varies by age. The odds of having ARIs in the age group below 5 years was four times higher than in the reference group. Similarly, for hCoV, the frequency of infection in the age group below 5 years was lower than the reference group. In addition, RSV was found to be 12 times higher in the age groups below 5 years and above 65 years than in the reference group. The association of RSV detected in those age groups was statistically significant (p < 0.05). However, RSV was detected in all age groups except between 50 and 64 years.

3.6. Coinfection of Enterovirus, RSV and Coronavirus with Other Respiratory Viruses

A total of 785 (78.7%) samples were positive for at least one pathogen and 125 (12.5%) were positive for two or more pathogens. EV/RV was found to be highly coinfected with AdV (35/362, 9.7%), RSV (20/362, 5.5%) and HMV (19/362, 5.2%). RSV and hCoV were found to be highly coinfected with EV/RV (20/137, 14.6%) and (21/51, 41.2%), respectively. The coinfection of EV/EV, RSV and hCoVs with various other respiratory viruses detected is shown in Table 5.

3.7. Seasonality of Enterovirus, RSV and Coronavirus

ARI positives detected among the influenza-negative ILI cases showed two peaks, February–March and August–September. Similarly, EV/RV and hCoV followed the same trend, except hCoV had reverse trend compared to EV/RV during May–June. In our study data, RSV is detected mainly in the months of August and September in all three years of the study. The month-wise detection of EV/RV, RSV and hCoV in the three years of the study period is shown in Figure 1.

3.8. NGS Data Analysis

Among the 30 EV/RV PCR positive samples selected for fresh viral isolation, subsequent multiplex system analysis found detectable levels of EVs in only 5 (16.7%) samples. Following NGS of these five samples by the Illumina MiSeq platform, the produced NGS data were composed of a total of 3.4 Gbases from both Read1 (Forward read) and Read2 (Reverse read), which were generated from a 996 ± 283 K/mm2 cluster density. Approximately 64% of the clusters passed QC filters and 79% of the Read1 and 63% of the Read2 sequences were ≥ Q30 (99.9% accuracy of base calling at a particular sequence position). The obtained consensus sequences were identified as Coxsackie viruses (CVs) (one CVA21, one CVB2, one CVB3 and two CVB5). Each of these sequences was subsequently deposited in the GenBank database (accession no. MZ396299-303). The raw reads were deposited in the NCBI Sequence Read Archive (SRA accession no. SRR16996986-90). The Bio Project accession no. is PRJNA782407 and the Bio Sample accession no. is SAMN23377878-82.

3.9. Phylogenetic Analysis

The maximum likelihood tree of the 180 EV complete coding sequences included 175 sequences from GenBank and 5 sequences obtained from this study and is shown in Figure 2. The MZ396299/CVA21 (2017) sequence obtained from this study was grouped with sequences from China (2016), USA (2015) and Malaysia (2014). The MZ396300/CV B2 (2018) and MZ396301/CVB3 (2017) sequences obtained from this study were most closely related to CVB2 (2013) and CVB3 (2009) sequences from India. Two CVB5 sequences, MZ396302-3 (2016 and 2018) obtained from this study were most closely related with CVB5 sequences from USA (2015) and Australia (2008).

4. Discussion

This study includes respiratory samples collected from the year 2016–2018 from patients presenting with ILI features, but who tested negative for influenza virus. The Luminex MAGPIX multiplex PCR, xTAG RVP FAST v2, detected different respiratory pathogens including FLU A and B in 78.8% of the tested ILI samples that were negative for influenza when tested by CDC real-time PCR. Influenza was detected in 21 influenza-negative ILI samples, which could be due to the different formats of interpretation of the two different assays. Both assays were qualitative and there is no standard cutoff for the comparison of the results. The comparison result was not concurrent with other similar studies carried out comparing the xTAG RVP FAST v2 panel with the real-time reverse transcriptase PCR [25].
Focusing on hCoV, RSV and EV/RV, we detected 5%, 13.7% and 36%, respectively, in our FLU-negative ILI cases. All three viruses were found throughout the year, with the main peak seen in the months of August and September.
Unlike SARS-CoV, MERS-CoV and SARS-CoV-2, which are associated with severe respiratory disease, the four common hCoVs (229E, OC43, NL63 and HKU1) generally cause mild to moderate upper respiratory tract illness, presumably contributing 15% to 30% of cases of common colds in humans [34]. Different epidemiological studies have shown a substantial variation in the percentage of hCoV infections associated with acute respiratory tract infection [35]. Various other studies have shown prevalences of seasonal coronavirus ranging from 5 to 18% and among the detected seasonal coronavirus, 4% were children below the age of 5 years [36,37]. A community-based surveillance study in rural, southern Nepal detected hCoV in approximately 8% of infants with ARI [38]. Heimdal et al. reported in a nine-year study that hCoV was involved in 9.1% of the episodes among Norwegian children hospitalized with respiratory infection [39].
Among seasonal coronaviruses, hCoV-OC43 was the most common type while hCoV-229E was the rarest, and the infections occurred during winter months [39]. Other studies reported hCoV-229E and hCoV-OC43 as the most common hCoVs associated with upper respiratory tract infections [40]. In our population, we found OC43 and HKU1 to be the most dominating seasonal coronaviruses. They circulated all year round with increased rates of detection in February–March and August–September. In temperate countries, seasonal variations of hCoVs are distinct, with most cases occurring in winter [40,41,42]. In our study, hCoV-OC43 was observed in March and hCoV-NL63 in August and September. Similarly in China, the detection of hCoV-OC43 is reported to increase in summer and hCoV-229E and hCoV-NL63 occur mainly in autumn [43,44]. Hongkong, however, has reported peak detections of hCoV-OC43 in winter and hCoV-NL63 in the autumn [45]. Our study showed that among the hCoV strains, HKU1 was predominant in the winter, whereas hCoV-229E had no particular seasonality, which is similar to the finding of Al-Khannaq et al. in Israel and Friedman et al. in Kuala Lumpur [46,47].
The overall burden of RSV among health care-seeking populations has mostly been detected in young children, adults above 65 years of age, pregnant women or immunocompromised patients, in whom an equivalent disease burden to influenza has been reported [48]. A well-established influenza surveillance system has been recognized as a valuable tool to monitor the circulation and impact of RSV [49]; therefore, based on the current case definition of ILI, we used our surveillance sample for detection of RSV in the influenza-negative ILI samples. The results obtained from the analysis of 997 samples from ILI cases demonstrates that RSV significantly contributes to ILI, accounting for 13.7% of cases, infecting mostly children under 5 years of age, children aged 5–17 years and adults over 65 years. Similar results were shown in a study from Portugal (2010–2018), which stated higher detection of RSV in children aged ≤14 years and the association of RSV infection was significant in the age groups 0–5 and above 65 years [49]. Our study reported August and September as high detection months for RSV. Previous studies in temperate countries in the Northern Hemisphere showed that the RSV season was between September and January, whereas in the Southern Hemisphere, it was from March to June [50,51]. In a study conducted in rural Nepal from 2011 to 2014, a high level of RSV was reported in the month of November among infants [52].
In our study, EV/RV was the most frequently found pathogen in patients with ILI. From a clinical standpoint, co-detection with EV/RV has been associated with higher admission to the ICU and longer hospitalization [53]. This association could not be evaluated in our study as all the cases enrolled in the analysis of this study were outpatients. We reported EVs together with RVs since the assay used was not able to differentiate between EVs and RVs, which is a limiting factor for the interpretation. However, additional sequencing confirmed EV cases. EVs are associated with a wide variety of clinical syndromes, including benign aseptic meningitis and acute flaccid paralysis/myelitis, the latter caused by polio or non-polio EVs. Certain types of EVs have been linked to outbreaks of CNS infections worldwide, including in the neighboring countries of India and China [54]. Studies have suggested that the seasonal outbreaks of suspected viral acute encephalitis syndrome (AES) in Nepal are due to EV infection [55]. We performed sequencing of subsets of selected positive EV/RV samples and found EV group B and EV group C. These were identified as CVB2, CVB3, CVB5 and CVA21. This shows the variety of EV serotypes circulating in the Nepalese population, highlighting the importance of EV surveillance. Historically, polioviruses were the only clinically relevant EVs, due to the paralysis of hundreds of thousands of children every year until effective vaccines were implemented [56]. Similarly, EV-A71 has been implicated in millions of cases of hand, foot and mouth disease, with numerous encephalitis and polio-like paralytic diseases, and EV-D68 is responsible for severe respiratory diseases [9,57]. Limited studies on other non-polio EVs have been conducted; however, some studies that reported that EV B species like CVB1, CVB5 and CVB3 together with EV-A71 and EV-D68 have been shown to cause diverse neurological complications such as encephalitis, meningitis and AFP and are also responsible for sporadic outbreaks in different part of the world [11,58,59,60,61]. The EV types identified by NGS in our study were all CVs, which may be an incidental finding among cases without any association to ILI. However, CVs are identified as being among the main EV types linked to ARTI infections among non-rhinovirus enteroviruses, specifically CVA21, also detected in our study. Indeed, with a high detection rate in patients with respiratory diseases, CVA21 has assumed increasing significance as the causal agent of outbreaks of respiratory infection in humans [62,63,64]. Therefore, regular monitoring of CVA21 is warranted in the future to better understand the real contribution of this virus in human airway infections.
Since not all EV/RV samples could be successfully sequenced, we cannot rule out that there were other respiratory EVs like EV-D68 among the cases. The lower detection rate of EVs could indeed be influenced by issues such as sample quality, viral concentration, cell culture conditions and temperature settings; considering adjustments to those factors could help improve the yield and detection rates of enteroviruses. Moreover, the lack of a particular test for EV-D68, a significant EV type linked to neurological issues and respiratory illnesses, should be considered as a constraint.
It is important to note that the surveillance and genomic data presented have limitations, such as the low number of positive samples, restricting the ability to demonstrate the seasonality of certain types of RSV, hCoV and EV. However, by the detection of additional respiratory pathogens in the influenza-negative samples, this study has highlighted the need for the expansion of respiratory virus surveillance for the timely reporting of different emerging or remerging and novel viruses. These findings provide important information on viral etiologies and the circulating types of EV, RSV and hCoV, which may contribute to developing strategies to reduce respiratory tract infection and decrease the public health burden. This would facilitate planning for mitigation to control the spread of disease and develop effective outbreak preparedness for public health management.

Author Contributions

Conceptualization, S.K.S. (Sanjaya K. Shrestha); Writing—original draft preparation, S.K.S. (Sanjaya K. Shrestha) and J.S.; Investigation, S.K.S. (Sanjaya K. Shrestha); Formal analysis, J.S., B.S. and T.A.S.; Methodology, J.S. and S.F.; Data curation, B.S.; Supervision, T.A.S., S.D. and A.K.A.; Review & editing, T.A.S., S.D., A.K.A., S.K.S. (Shree Krishna Shrestha), A.B., P.P. and S.F.; Funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for the laboratory work was provided by the Armed Forces Health Surveillance Division, Global Emerging Infections Surveillance Branch (GEIS) grant number P0081_18_AF.

Institutional Review Board Statement

The study was approved by the Government of Nepal National IRB the “Nepal Health Research Council” and the Human Subject Protection Branch, Walter Reed Army Institute of Research, Silver Springs, MD.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors would like to acknowledge the study participants, the staff of Walter Reed/AFRIMS Research Unit Nepal (WARUN) for subject enrollment, sample management and molecular testing at WARUN and the Department of Virology at Armed Forces Research Institute of Medical Sciences (AFRIMS) for performing viral culture and sequencing work for this project. The authors would also like to thank Amod Bahadur Thapa from Bharatpur hospital for supporting enrollment of ILI cases and Rose Vikse from Norwegian Institute of Public Health for reviewing the manuscript.

Conflicts of Interest

The authors have no conflicts of interest.

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Microbiolres 15 00150 g001
Figure 1. Detection of enterovirus/rhinovirus, RSV and seasonal coronavirus in different months of the year (2016–2018).
Figure 1. Detection of enterovirus/rhinovirus, RSV and seasonal coronavirus in different months of the year (2016–2018).
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Microbiolres 15 00150 g002
Figure 2. Maximum likelihood tree of 180 EV complete coding sequences (6464–6657 nt) including 175 sequences from GenBank (black) and 5 sequences obtained from this study (red). Only bootstrap values above 70 are shown. CV A21, B2, B3 and B5 sequences are located in green, blue, yellow and purple areas, respectively.
Figure 2. Maximum likelihood tree of 180 EV complete coding sequences (6464–6657 nt) including 175 sequences from GenBank (black) and 5 sequences obtained from this study (red). Only bootstrap values above 70 are shown. CV A21, B2, B3 and B5 sequences are located in green, blue, yellow and purple areas, respectively.
Microbiolres 15 00150 g002
Table 1. Summary of sample selection for this study.
Table 1. Summary of sample selection for this study.
GroupTotal SampleMale (%)Mean Age (Median, SD)
ILI: 2016–2018 *4593 2690 (58.6)12.6 (5.6,14.8)
Influenza RT PCR-Pos **2758 1590 (57.6)13.9 (6.8, 15.1)
Influenza RT PCR-Neg ***1835 1100 (59.6)11.4 (4.1, 15.0)
Luminex xTAG RVP FAST v2 assay ****997 601 (60.3)9.8 (3.9, 13.4)
* ILI: Total influenza-like illness cases enrolled in the surveillance during 2016–2018; ** Total number of ILI cases that tested positive for seasonal human influenza by RT-PCR; *** Total number of ILI cases that tested negative for seasonal human influenza by RT-PCR; **** Total number of randomly selected influenza RT-PCR negative samples and tested by Luminex xTAG RVP FAST v2 assay for detection of other respiratory pathogens.
Table 2. Summary of all pathogens detected in influenza-negative samples by xTAG RVP FAST v2.
Table 2. Summary of all pathogens detected in influenza-negative samples by xTAG RVP FAST v2.
Targets (N = 997)Total Positive (%)Positive Among Children Aged <= 15 Yrs, N = 803 (%)
Influenza A non-typed *5 (0.5)5 (0.6)
Influenza A (H1N1pdm09)6 (0.6)4 (0.5)
Influenza A H10 (0.0)0 (0.0)
Influenza A H31 (0.1)1 (0.1)
Influenza B9 (0.9)8 (1.0)
Respiratory syncytial virus137 (13.7)132 (16.4)
Corona 229E10 (1.0)6 (0.8)
Corona HKU115 (1.5)9 (1.1)
Corona NL6312 (1.2)8 (1.0)
Corona OC4315 (1.5)11 (1.4)
Parainfluenza 126 (2.6)23 (2.9)
Parainfluenza 28 (0.8)6 (0.8)
Parainfluenza 362 (6.2)54 (6.7)
Parainfluenza 420 (2.0)16 (2.0)
Enterovirus/Rhinovirus362 (36.3)291 (36.2)
Human metapneumovirus98 (9.8)87 (10.8)
Adenovirus121 (12.1)117 (14.6%)
Human bocavirus20 (2.0)20 (2.5)
* Influenza A non-typed: Influenza A other than H1N1pdm09, H1 and H3.
Table 3. Summary of clinical data in this study among ARI-, EV/RV-, RSV- and hCoV-positive cases.
Table 3. Summary of clinical data in this study among ARI-, EV/RV-, RSV- and hCoV-positive cases.
Sign and SymptomARIs (N = 785) *EV/RV (N = 362) **RSV (N = 137) ***hCoV (N = 51) ****
YesNoUnknownYesNoUnknownYesNoUnknownYesNoUnknown
Cough7787035840136105010
Runny nose77410135660137005100
Diarrhea387425143480513025460
Injected pharynx129649772289116118310401
Chill139543103722513961082315279
Sore throat57110910525664429012353786
Breathing difficulty51560174302577551092333711
Malaise32821624116490108474289271113
Generalized body pain277230278141101120284762231315
Headache28418032113884140234173221118
* ARIs: Total number of samples (N-785) that tested positive for any of the 19 viral species included in the multiplex PCR system among the selected (N = 997) influenza RT-PCR negative ILI samples; ** EV/RV: Total number of samples (N = 362) that were positive for EV/RV among the ARIs; *** RSV: Total number of samples (N = 137) that were positive for RSV among the ARIs; **** hCoV: Total number of samples (N = 51) that were positive for human coronavirus among the ARIs.
Table 4. The association between age and detection of ARI, EV/RV, RSV and hCoV.
Table 4. The association between age and detection of ARI, EV/RV, RSV and hCoV.
Age GroupARIsEV/RVRSVhCoV
+veOR *95% CIp-Value+veOR *95% CIp-Value+veOR *95% CIp-Value+veOR *95% CIp-Value
0–04
(N = 588)		5114.172.78–6.24<0.00012181.080.75–1.570.684011612.293.85–39.23<0.0001250.480.24–0.960.0376
05–17
(N = 226)		1581.460.99–2.250.0870770.950.62–1.460.8059174.071.17–14.130.027290.450.19–1.070.0714
18–49
(N = 153)		94 54 3 13
50–64
(N = 25)		181.610.64–4.100.3140101.220.51–2.910.649800.840.04–16.810.911042.050.61–6.890.2449
65 and above (N = 5)42.510.27–23.010.415432.750.45–16.970.2759112.51.06–147.990.045200.950.05–18.050.9706
* OR: odds ratio, calculated in reference to age group 18–49 years.
Table 5. Coinfection of various respiratory viruses detected in influenza-negative respiratory samples.
Table 5. Coinfection of various respiratory viruses detected in influenza-negative respiratory samples.
TargetsEnterovirus/RhinovirusRSVCoronavirus
Adenovirus3531
Human bocavirus93-
Coronaviruses 1541
Enterovirus/Rhinovirus-2015
Influenza A non-typed *1--
Human metapneumovirus1933
Parainfluenza 121-
Parainfluenza 220-
Parainfluenza 3121-
Parainfluenza 4312
Respiratory syncytial virus 20-4
* Influenza A non-typed: Influenza A other than 2009 H1N1, H1 and H3.
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Shrestha, S.K.; Shrestha, J.; Shrestha, B.; Strand, T.A.; Dudman, S.; Andreassen, A.K.; Shrestha, S.K.; Bastola, A.; Pandey, P.; Fernandez, S., on behalf of the AFRIMS-Department of Virology Group. Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal. Microbiol. Res. 2024, 15, 2247-2260. https://doi.org/10.3390/microbiolres15040150

AMA Style

Shrestha SK, Shrestha J, Shrestha B, Strand TA, Dudman S, Andreassen AK, Shrestha SK, Bastola A, Pandey P, Fernandez S on behalf of the AFRIMS-Department of Virology Group. Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal. Microbiology Research. 2024; 15(4):2247-2260. https://doi.org/10.3390/microbiolres15040150

Chicago/Turabian Style

Shrestha, Sanjaya K., Jasmin Shrestha, Binob Shrestha, Tor A. Strand, Susanne Dudman, Ashild K. Andreassen, Shree Krishna Shrestha, Anup Bastola, Prativa Pandey, and Stefan Fernandez on behalf of the AFRIMS-Department of Virology Group. 2024. "Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal" Microbiology Research 15, no. 4: 2247-2260. https://doi.org/10.3390/microbiolres15040150

APA Style

Shrestha, S. K., Shrestha, J., Shrestha, B., Strand, T. A., Dudman, S., Andreassen, A. K., Shrestha, S. K., Bastola, A., Pandey, P., & Fernandez, S., on behalf of the AFRIMS-Department of Virology Group. (2024). Enteroviruses, Respiratory Syncytial Virus and Seasonal Coronaviruses in Influenza-like Illness Cases in Nepal. Microbiology Research, 15(4), 2247-2260. https://doi.org/10.3390/microbiolres15040150

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