Abstract
Background
The Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2) has been undergoing evolutionary changes to improve its ability to thrive within human hosts, leading to the emergence of specific variants associated with subsequent waves of the coronavirus diseases 2019 (COVID-19) pandemic. Indonesia has grappled with the effects of this pandemic and subsequent waves affecting various regions, including West Sumatra. Although located outside Java island epicenter, West Sumatra experienced significant COVID-19 transmission, especially during the third wave in early 2022.
Objective
This study aimed to investigate the genetic evolution and epidemiological dynamics of SARS-CoV-2 variants in West Sumatra throughout the three pandemic waves.
Methods
We conducted a genotyping study retrospectively using 278 COVID-19 patient samples from 2020 to 2022. The Real-Time Quantitative Reverse Transcription PCR (RT-qPCR) was used for screening, and whole-genome sequence analysis was conducted through the Illumina MiSeq instrument.
Result
The analysis revealed distinct patterns in the prevalence of viral lineages across the waves. The initial wave was predominated by clade 20A (77,4 %) especially lineage B.1.466.2 (50 %). The second wave was marked by a significant emergence of the Delta variant (72,5 %), particularly lineage AY.23 (81,1 %), originating from India, with subsequent local evolution leading to the formation of distinct clusters. We found that about 96,7 % of the third wave variant was dominated by Omicron variants, especially the generation of lineages BA.1 and BA.2, demonstrating widespread global dissemination and local variant development. Phylogenetic analysis indicated a close relatedness of West Sumatra variants to those from Malaysia and other parts of Indonesia, highlighting regional transmission dynamics and potential sources of variant introductions.
Conclusion
This study has identified unique variant clusters within each wave, suggesting distinct evolutionary pathways and local adaptations. These findings provide valuable insights into the genomic landscape of SARS-CoV-2 in West Sumatra and emphasize the crucial role of ongoing genomic surveillance in tracking viral changes and guiding public health measures.
Keywords: Whole genome sequencing, COVID-19, Viral evolution, Transmission dynamics, Genomic surveillance, Phylogenetic analysis
Highlights
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SARS-CoV-2 variants in West Sumatra shift across waves.
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Genetic Analysis of 278 patient samples reveals variant prevalence.
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Close genetic West Sumatra SARS-CoV-2 variants ties to neighboring countries.
1. Introduction
The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Wuhan, China, in late 2019 marked the onset of the worldwide coronavirus disease 2019 (COVID-19) pandemic, which resulted in not just emerging as a health issue but also impacted global economic, geopolitical, social, and cultural landscapes [1,2]. As of June 2023, Indonesia has documented the highest number of confirmed COVID-19 cases in the Southeast Asian region. The majority of COVID-19 cases in Indonesia were documented on Java Island. West Sumatra was located outside Java and conducted the most COVID-19 detections outside Java. There have been three repeated phases of case surges since the initial discovery. In early 2022, Indonesia entered the third wave, including in West Sumatra. The peak of the third wave in West Sumatra occurred in February 2022, with the highest confirmed cases reaching 654 per day [3].
The SARS-CoV-2 mutations have given rise to virus variants that have spread worldwide, contributing to a pandemic characterized by elevated rates of morbidity and mortality [3]. Among these variants, the Alpha, Delta, and Omicron variants have garnered significant attention due to their distinct genetic characteristics and impact on disease transmission and severity. The Alpha variant, also known as B.1.1.7, was first identified in the United Kingdom and quickly spread to other parts of the world. It was characterized by mutations in the spike protein, particularly N501Y. This variant demonstrated increased transmissibility and raised concerns about its potential to evade immunity conferred by previous infection or vaccination [4]. Following the Alpha variant, the Delta variant (B.1.617.2) emerged as a dominant strain, contributing to surges in COVID-19 cases globally. Notable mutations in the Delta variant, including L452R and P681R, were associated with enhanced infectivity and potentially increased severity of illness compared to earlier strains [5]. More recently, the Omicron variant (B.1.1.529) has emerged as a variant of concern, characterized by many mutations in the spike protein. With over 50 sub-lineages identified, the Omicron variant has demonstrated rapid evolution and diversification since its initial detection, posing challenges for vaccine efficacy and diagnostic strategies [6]. Competitive interactions among variants might influence the overall trajectory of the pandemic, affecting factors such as the final size of outbreaks, replacement times of dominant variants, and the emergence of novel strains with enhanced transmissibility [7].
Genomic surveillance efforts have played an essential role in tracking the evolution of SARS-CoV-2 variants over time. Researchers have identified key mutations associated with changes in viral phenotype and transmission dynamics by analyzing viral genomes sampled from different geographical regions and time points. For instance, studies have highlighted the role of mutations in the spike protein, such as the D614G mutation, in enhancing viral infectivity and transmissibility [8]. Additionally, the emergence of variants carrying mutations in the receptor-binding domain (RBD), such as the E484K mutation found in the Beta variant, has raised concerns regarding immune evasion and vaccine escape [9]. Epidemiological assessments based on genomic data have elucidated the evolutionary connections of the virus, utilizing whole genome sequencing (WGS) and phylogenetic analysis, providing significant insights into the origins of the COVID-19 pandemic in various nations [10]. However, the whole-genome sequencing data analysis for SARS-CoV-2 variants in West Sumatra was unavailable. Therefore, conducting a molecular epidemiological analysis of the sequences of SARS-CoV-2 isolated from West Sumatra was essential. This study aimed to identify and characterize the SARS-CoV-2 variants isolated from specimens taken from COVID-19 individuals across various locations within West Sumatra. Additionally, the study explored the correlation between the genetic evolution of SARS-CoV-2 and the spread of COVID-19 within the population.
2. Materials and methods
Ethical Statement
Our study was conducted after obtaining ethical approval from the Ethical Committee of the Faculty of Medicine, Universitas Andalas with approval number: 664/UN.16.2/KEP-FK/2022, March 25, 2024. We confirm that our study followed the relevant guidelines and regulations outlined in the granted approval.
2.1. Clinical samples
The samples in this study were collected from nasopharyngeal swabs of confirmed COVID-19 patients that have been detected and stored at the Center for Infectious Disease Diagnostic and Research (PDRPI), Faculty of Medicine, Universitas Andalas. The SARS-CoV-2 diagnosis was performed using the real-time Reverse Transcriptase Polymerase Chain reaction (qRT-PCR). This involved utilizing kits from various manufacturers targeting specific genes: the Sansure Biotech Inc kit focusing on the ORF1ab and N gene, the Kaira kit targeting the E and RdRp genes, followed by the Zybio kit targeting the N and ORF1ab genes, and finally, the UNI MEDICA-freeze-dried kit which targeted the ORF1ab and N genes.
Particularly, these samples were collected from various districts in the West Sumatra province, with a minimum volume of 500 μL and Cycle threshold (CT) value of initial quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) when first detected <30. The PDRPI is the first laboratory in West Sumatra that conducted RT-qPCR examinations for COVID-19 patients since the first COVID-19 case appeared in the area, and samples from all districts in West Sumatra were sent to PDRPI. A retrospective study of epidemiology data was conducted in West Sumatra during the three pandemic waves.
2.2. Quantitative reverse transcription polymerase chain reaction (RT-qPCR)
The samples were screened to ensure their quality before sequencing. We utilized a QIAamp Viral Mini kit (Qiagen, Tokyo, Japan) to extract total viral RNA from the stored samples according to the manufacturer's instructions. Subsequently, we conducted repeated RT-qPCR tests on the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) to identify distinct genes of the SARS-CoV-2. The RT-qPCR process followed the mBioCoV-19 RT-PCR kit protocol (Biofarma, Bandung, Indonesia), and the equipment settings for each stage, predenaturation, denaturation, annealing, and extension, were adjusted according to the optimization results. Negative and positive controls were also added to the same PCR plate during the process to enhance the validity of the procedure. The RT-qPCR results can be seen in Supplementary Table S1. Samples with RNA concentration >20 ng/ul and RT-qPCR CT value < 30 were proceeded to sequencing. About 250 samples from the initial wave and 101 samples from the second wave were subjected to screening, and 145 samples from the third wave underwent screening. The sample selection process can be seen in Fig. 1.
Fig. 1.
Study Flowchart on the sample selection process.
2.3. SARS-CoV-2 genome sequencing
Whole genome next-generation sequencing was conducted at the PDRPI Universitas Andalas and the Health Policy Agency of the Ministry of Health of the Republic of Indonesia. A total of 137 samples from the first wave, 51 from the second wave, and 90 from the third wave were sequenced. Whole-genome sequencing (WGS) was conducted via next-generation sequencing on the Illumina MiSeq instrument. The RNA library was prepared using the Illumina RNA prep with enrichment (L) tagmentation Kit (Illumina, California, USA) and included the following steps: RNA denaturation; first strand cDNA synthesis; second strand cDNA synthesis; cDNA tagmentation; clean up library; normalization of libraries; probe hybridization; capture hybridized probe; enrichment library amplification; clean up; and finally after analyzing the quality of the RNA libraries processing and their respective concentrations, library sequencing was performed on MiSeq system (Illumina Inc.) [11]. The MiSeq system generated a FASTQ file wherein the primary sequence and adapter sequences were automatically trimmed during the demultiplexing process. Subsequently, all files underwent analysis using CLC Genomics Workbench® 21 version 21.0.3 [12]. The total reads underwent filtration to eliminate reads of poor quality (quality score < Q30, length <26 bp, or containing more than two consecutive ambiguous bases). The final genome consensus sequences were generated using the CLC Genomic Workbench app by aligning them to the SARS-CoV-2 reference genome with the GenBank accession number MN908947 (Wuhan-Hu-1) isolate, yielding a consensus sequence for the SARS-CoV-2 in West Sumatra. The lineage categorization of the genome sequences was accomplished by utilizing Phylogenetic Assignment of Named Global Outbreak LINeages (Pangolin) and Nextclade. We also categorized the sequencing results according to the WHO labels using Greek letters for variants of concern (VOC) and variants of interest (VOI) [13].
2.4. Phylogenetic analysis
Utilizing phylogenetic data enabled the comparison of the genetic profiles of West Sumatra with those of nearby nations across Asia. The analysis involved examining a comprehensive dataset comprising complete genomes of SARS-CoV-2 virus variants, ensuring high coverage and absence of repetitive unknown nucleotides (NNNN). This dataset incorporated genome sequences from all identified lineages within the study and 65 additional SARS-CoV-2 genomes sourced from GISAID, originating from Indonesia and other Asian countries. Multiple nucleotide sequences were aligned, and the phylogenetic tree construction was done using the neighbor-joining method with 1000 bootstrap replications in the CLC Genomic Workbench app and visualization by MEGA7 [14].
3. Results
3.1. Retrospective analysis of available data on epidemiology of SARS-CoV-2 in West Sumatra
Diagnostic testing for COVID-19 at PDRPI commenced on 26 March 2020. After the declaration of the initial COVID-19 case on March 2, 2020, in Indonesia, West Sumatra Province underwent three consecutive SARS-CoV-2 pandemic waves from March 2020 to May 2021, From June 2021 to December 2021, and the third commenced in January 2022 and concluded in June 2022. The cumulative count of registered COVID-19 cases in West Sumatra from the onset of the pandemic until May 11, 2022 reached 103,799 cases, with 2,348 deaths [15].
During the first wave of the pandemic in West Sumatra, 44,247 cases of COVID-19 were identified. The peak occurred in October 2020, registering 8260 cases (Fig. 2A). In the Second wave, from June to December 2021, 45,608 individuals tested positive for SARS-CoV-2. July and August 2021 had the highest case numbers, with 19,843 and 15,843 cases, respectively (Fig. 2A). The corresponding death toll for the first two waves stood at 945 and 1.208, reaching peaks in October 2020 (151 deaths) and August 2021 (489 deaths) (Fig. 2B). As the third wave commenced in September 2021, West Sumatra recorded 13,950 new COVID-19 cases and 197 deaths [15].
Fig. 2.
Incidence of COVID-19 cases and fatalities in West Sumatra from March 2020 to September 2022. (A) Monthly count of COVID-19 cases; (B) Monthly tally of COVID-19-related deaths.
3.2. Distribution of SARS-CoV-2 variants during the three waves
The dominant sequences from the initial wave belong to clade 20A/GISAID clade GH (106/137; 77.4 %), with 53 (50 %) attributed to lineage B.1.466.2, and the rest distributed among lineages B.1 (6.6 %), B.1.36.19 (22.6 %), B.1.456 (0.9 %), B.1.459 (3.8 %), B.1.468 (15.1 %), and B.1.470 (0.9 %). Other circulation of viruses belonging to clade 19A/GISAID O (3/137; 2.2 %) and 20B/GISAID GR (28/137; 20.4 %) (Table 1). All the genomes sequenced during the first wave carried the D614G spike mutation, except for the three genomes of clade 19A. Clade 19A, 20A, and 20B phylogenetically belong to the early SARS-CoV-2 virus (ancestral) lineage found in the early stages of the global spread of the SARS-CoV-2. The majority of the genomes detected in the second wave (37/51) (72.5 %) corresponded to the Delta variant, comprising lineage AY.23/Nextstrain clade 21J/GISAID clade GK (81.1 %) and AY.24/Nextstrain clade 21I/GISAID clade GK (18.9 %), while the rest still belonged to lineage B.1.466.2 (14/51) (27.5 %). Meanwhile, in the third wave, almost all SARS-CoV-2 variants circulating were Omicron variants (97 %), dominated by BA.1.13.1/Nextrain clade 21K (23/87; 26.4 %) and BA.2.64/Nextstrain clade 21L (16/87; 18.4 %) lineages.
Table 1.
The distribution of SARS-CoV-2 variants/lineages in isolates from West Sumatra.
| Wave | Number of isolates | Variant Classification |
|||
|---|---|---|---|---|---|
| WHO (n) | Nextstrain clade (n) | Pangolin (n) | GISAID (n) | ||
| 1 | 137 | Unassigned (137) | 19A (3) | B.6 (3) | O (3) |
| 20A (106) | B.1 (7) | GH (106) | |||
| B.1.36.19 (24) | |||||
| B.1.456 (1) | |||||
| B.1.459 (4) | |||||
| B.1.466.2 (53) | |||||
| B.1.468 (16) | |||||
| B.1.470 (1) | |||||
| 20B (28) | B.1.1 (3) | GR (28) | |||
| B.1.1.216 (2) | |||||
| B.1.1.398 (23) | |||||
| 2 | 51 | Delta (37) | 21J (30) | AY.23 (30) | GK (37) |
| 21I (7) | AY.24 (7) | ||||
| Unassigned (14) | 20A (14) | B.1.466.2 (14) | GH (14) | ||
| 3 | 90 | Omicron (87) | 21K (46) | BA.1 (13) | GRA (87) |
| BA.1.1 (2) | |||||
| BA.1.13.1 (23) | |||||
| BA.1.15 (7) | |||||
| BA.1.17.2 (1) | |||||
| 21L (36) | BA.2 (7) | ||||
| BA.2.3 (12) | |||||
| BA.2.32 (1) | |||||
| BA.2.64 (16) | |||||
| 22A (1) | BA.4.1 (1) | ||||
| 22B (4) | BA.5.2 (3) | ||||
| BA.5.2.5 (1) | |||||
| Unassigned (3) | 20A (3) | B.1.36.19 (2) | GH (3) | ||
| B.1.466.2 (1) | |||||
The average of amino acid mutations of SARS-CoV-2 isolates from West Sumatra also demonstrated a tendency of increasing amino acid mutations with the emergence of new variants during the course of the pandemic, when contrasted with the original MN908947 SARS-CoV-2 Wuhan-Hu-1 variant. The average of SARS-CoV-2 amino acid mutations per gene is shown in Fig. 3.
Fig. 3.
The average of SARS-CoV-2 amino acid mutations per gene. The increase in amino acid mutations is commonly found in the structural protein-forming genes (S, N, E, and M genes) in the Omicron variant (21K, 21L, 22A, 22B), especially in the S gene, which has the highest average amino acid mutation rate (2.59).
3.3. Phylogenetic analysis
The genetic data enabled a comparative analysis of West Sumatra genomes with those from neighboring countries and other Asian nations. Phylogenetic investigations involved constructing three distinct phylogenetic trees, each dedicated to one of the three waves of the pandemic.
The first wave's West Sumatra sequences (SB code) variant cluster was generally clustered with variants from Malaysia and other parts of Indonesia (Fig. 4). This indicated close relatedness to the ancestral variant MN908947. Different variants from 2020 in this study lineage B.1.1.398, such as SB-68-FKUA/2020 and SB-57-FKUA/2020 clustered with the Indonesian variant JK-GSILab-22034/2020, indicate transmission and development of SB variants in Indonesia. Some variants like SB-626500/2021 (lineage B.1.468), SB-626503/2021 (lineage B.1.466.2), and SB-638606/2021 (lineage B.1.466.2) showed distant relationships with the central SB cluster. Variant SB-102-FKUA/2021 and SB-50-FKUA/2020 (B.1.1.216) closely cluster with Beijing and Indonesian 2020 variants, indicated further evolution of West Sumatra variants in Indonesia. Variant SB_19/2021 did not cluster with other SB variants but with Indian 2020 variants.
Fig. 4.
The phylogenetic analysis employed the Maximum Likelihood method and utilized 137 sequences from strains present during the First wave in West Sumatra. Additionally, 25 SARS-CoV-2 sequences sourced from GISAID were included in the analysis. Red nodes indicate the samples originating from West Sumatra.
In the second wave, the early local Delta variant SB-638474/2021_(20A) emerged from the same node as the sample from India hCoV-19_India_un-ILS15_2020 (Fig. 5). This indicated that the first entry of the Delta variant into Indonesia originated from India. Subsequently, close relatives of SB-638474, such as SB-626517/2021_(20A) and SB-626508/2021_(20A), emerged, indicating the accumulation of local mutations in Delta. Following this, a large sub-cluster of local Delta variants consisting of SB-638490/2021_(20A), SB-626559/2021_(20A), SB-626519/2021_(20A), and so forth emerged. Another unique variant is SB-638476/2021_(20A), which is separated from the main sub-cluster, indicated the formation of its own Delta variant. At the end of the Delta cluster, there were also SB-638598/2021_(20A) and SB-638526/2021_(21J), transitional variants from Delta to Omicron.
Fig. 5.
The phylogenetic analysis employed the Maximum Likelihood method and utilized 51 sequences from strains present during the Second wave in West Sumatra. Additionally, 35 SARS-CoV-2 sequences sourced from GISAID were included in the analysis. Red nodes indicate the samples originating from West Sumatra.
Variant BA.1 and BA.2 demonstrated widespread global dissemination based on samples collected from various countries within their respective lineages in the third wave (Fig. 6). The Omicron variant BA.2 (referred to as 21L, 22B) and its offshoots form a unique cluster distinct from the BA.1 variant (referred to as 21K), highlighted notable genetic disparities between BA.1 and BA.2. Variant SB-PDRPI-05/2022_(20A) was situated within the foundational cluster of BA.1, representing its early emergence. Similarly, variants SB-PDRPI-38/2022_(20A) and SB-PDRPI-35/2022_(20A) were categorized as early BA.1 variants. Variant SB.22.03452/2022_(21K) and SB_22_02563/2022_(21K) are part of the Omicron BA.1 cluster, clustered with samples from Malaysia and Indonesia, signified local BA.1 variants. Variant SB-PDRPI-32/2022_(21L) exhibited the closest relation to a sample from India hCoV-19/India/DL-726886963/2022. Other SB variants such as SB-PDRPI-27/2022_(21L) and SB-PDRPI-06/2022_(21L) showed close affiliations with samples from China and Malaysia like hCoV-19/Shanghai/SJTU-236137/2022 and hCoV19/Malaysia/MKAK_CL_22_67688/2022. Within the BA.2 cluster, SB-PDRPI-32/2022_(21L), SB-PDRPI-26/2022_(21L), SB-PDRPI-34/2022_(21L), SB-PDRPI-29/2022_(21L), SB-PDRPI-27/2022_(21L), SB-PDRPI-07/2022_(21L), and others display close associations, indicated local BA.2 variants.
Fig. 6.
The phylogenetic analysis employed the Maximum Likelihood method and utilized 90 sequences obtained from strains present during the third wave in West Sumatra. Additionally, 65 SARS-CoV-2 sequences sourced from GISAID were included in the analysis. Red nodes indicate the samples originating from West Sumatra.
Subsequently, the majority of the next cluster comprises SB 22B variants, such as SB-PDRPI-22/2022_(22B), which closely resembles a sample from Japan hCoV-19_Japan_PG-415103_2022, and SB-PDRPI-42/2022_(22B), which exhibited close ties with samples from various countries including Indonesia, India, Qatar, and others, suggesting BA.2.75 or BA.2 derivatives. Lastly, the 21K variant cluster included numerous SB members with numeric designations, indicating BA.2.10 or other derivatives of BA.2.
4. Discussion
Our retrospective analysis of available data on the epidemiology of SARS-CoV-2 in West Sumatra provided valuable insights into the pattern and impact of the COVID-19 pandemic in the region. The findings highlighted the occurrence of three distinct waves of the pandemic, each characterized by varying case counts and mortality rates. The peak of the first wave occurred in October 2020. The substantial increase in cases underscored the rapid transmission dynamics of the virus and the challenges in containing its spread, particularly in the early stages of the pandemic. The second wave, in July and August 2021, recorded the highest case numbers, indicating a peak in transmission during this period. The corresponding death toll for the first two waves reflected the severity of the pandemic, with peaks in mortality observed in October 2020 and August 2021. These findings underscored the critical need for effective public health measures and healthcare infrastructure to mitigate the impact of the pandemic on morbidity and mortality rates. As the third wave commenced in January 2022, West Sumatra faced additional challenges in controlling the spread of the virus. The region experienced continued pressure on its healthcare system and resources. The persistence of the pandemic beyond the initial waves highlighted the importance of ongoing surveillance and response efforts to adapt to evolving epidemiological trends and mitigate the risk of resurgence.
The complete sequencing of SARS-CoV-2 isolates for genomic surveillance has played a crucial role in comprehending the virus's evolution and transmission patterns while also keeping track of the appearance of mutations [16,17]. The high mutation rate increased the ability of this viral pathogen to adapt to efficient human-to-human transmission and potentially enhanced its virulence [18]. Throughout the initial phase of the pandemic in West Sumatra, the prevalent SARS-CoV-2 lineage was characterized by the presence of the B.1.466.2 variant. This particular variant was identified as a predominant indigenous strain in Indonesia, contributing to over 50 % of the daily infections nationwide between March and May 2021 [19,20]. Originating from Java Island in mid-June 2020, the B.1.466.2 variant has undergone further evolution, leading to the emergence of two distinct sub-lineages. The B.1.466.2 mutation exhibited a notable presence of non-synonymous mutations, particularly in the spike and NSP3 proteins, where co-mutations such as S-D614G/N439K/P681R were prevalent within its broader sub-lineage [21]. The proliferation of this variant in West Sumatra raised concerns due to its potential for facilitating the transmission of COVID-19. The variant has rapidly expanded and circulated within a consistent transmission cycle between Java and Sumatra, establishing numerous long-distance transmission connections across seas [22]. As time progresses, new variants could arise as viruses undergo mutations in their genetic makeup, potentially enhancing their adaptability and ability to spread [23].
Our research revealed a change in the variant landscape during different phases of the pandemic in West Sumatra, accompanied by alterations in critical amino acids of prevalent SARS-CoV-2 strains. The first wave phylogenetic analysis of the 137 complete genomes indicated that 77.4 % (106/137) of the SARS-CoV-2 strains belonged to clade 20A. West Sumatra variants were closest to Malaysian, Indonesian, and ancestral variants from 2020. Some variants showed more distant relationships. The newer the west Sumatra variant, the further its genetic changes from the ancestral, which was expected due to mutation accumulation over time. Overall, the development direction of West Sumatra variants started from Malaysia and moved towards Indonesia, then underwent further changes in both countries over time, leading to newer variants. In the second wave, the Delta cluster was dominated by closely related local West Sumatra variants belonging to AY.23 (37/51) (72.5 %), depicting the widespread spread of the Delta variant in Indonesia for a considerable period until Omicron eventually replaced it in the third wave. They represented the rapid evolution of the Delta virus at the local level. Furthermore, the West Sumatra Delta variant evolved locally and became isolated after the initial introduction from India [24,25]. This was demonstrated by forming a large SB cluster separate from most variants of other countries on the phylogenetic tree. This pattern illustrated the endemic spread of Delta in Indonesia and the limited introduction of new variants from abroad [24]. In contrast to the identification of the Delta variant in different regions of Indonesia (Makassar), the prevailing variant detected was lineage B.1.617.2 [26]. Another research indicates that SARS-CoV-2 strains from Indonesia exhibited close genetic ties to those from other nations. These findings suggested that mutations may arise throughout transmission, potentially influenced by travel history and enhanced patient immunity [27,28].
This study brought the emergence and spread of diverse variants during three pandemic cycles. Nevertheless, there is a need for ongoing and long-term genomic surveillance to keep an eye on the evolutionary trends of SARS-CoV-2 and the appearance of new variants. This would help in predicting future outbreaks and developing public health strategies.
However, we could not confidently confirm the evolutionary dynamics due to the limitations of our study. These limitations included the inability of the survey samples to proportionally represent each pandemic wave and each region in West Sumatra. Furthermore, serial data covered each epidemic wave period but did not capture both virus evolution and transmission over time, especially additional changes after the study period.
5. Conclusions
In conclusion, we observed a distinct pattern of SARS-CoV-2 specificity across the initial three pandemic waves in West Sumatra. Notably, the first wave was characterized by the prevalence of the B.1.466.2 (clade 20B) variant on a global scale, while the Delta variant, particularly lineages AY.23, dominated during the second wave. The emergence of the Omicron variants (BA.1 and BA.2 sub-lineages) marked the third wave. Although the phylogenetic analysis did not provide definitive insights into the origins of the West Sumatra genomes, we found similarities with sequences from other Asian countries.
Funding
This study was supported by The Health Department of West Sumatra Province, Indonesia and was funded by the Center for Infectious Disease Diagnostic and Research (PDRPI) Faculty of Medicine, Universitas Andalas.
Data availability statement
The data from this study's sequences has been stored in the https://gisaid.org/EpiCoV™ database. Accession numbers can be provided upon request.
CRediT authorship contribution statement
Linosefa Linosefa: Writing – review & editing, Writing – original draft, Methodology, Investigation, Data curation. Hasmiwati Hasmiwati: Writing – review & editing, Validation. Jamsari Jamsari: Writing – review & editing, Software, Formal analysis. Andani Eka Putra: Writing – review & editing, Methodology, Formal analysis, Conceptualization.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Linosefa Linosefa reports financial support was provided by Health Department of West Sumatera Province. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors thank Mutia Lailani, Dede Rahman Agustian, Siskalil Fahma, SM Rezvi, and Alponsin for their technical assistance in this study.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e34365.
Appendix A. Supplementary data
The following is/are the supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data from this study's sequences has been stored in the https://gisaid.org/EpiCoV™ database. Accession numbers can be provided upon request.