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. 2024 Nov 19;19(11):e0298940. doi: 10.1371/journal.pone.0298940

Complete genome sequencing of SARS-CoV-2 strains that were circulating in Uzbekistan over the course of four pandemic waves

Gulnoza Esonova 1,*, Abrorjon Abdurakhimov 1,2, Shakhnoza Ibragimova 1, Diyora Kurmaeva 1, Jakhongirbek Gulomov 1, Doniyor Mirazimov 3, Khonsuluv Sohibnazarova 1, Alisher Abdullaev 1, Shahlo Turdikulova 1, Dilbar Dalimova 1
Editor: Nihad A M Al-Rashedi4
PMCID: PMC11575833  PMID: 39561193

Abstract

Since the rapid emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a global COVID-19 pandemic affecting millions of people globally, it has become one of the most urgent research topics worldwide to better understand the pathogenesis of COVID-19 and the impact of the harmful variants. In the present study, we conducted whole genome sequencing (WGS) analysis of 110 SARS-CoV-2 genomes, to give more data about the circulation of SARS-CoV-2 variants during the four waves of pandemic in Uzbekistan. The whole genome sequencing of SARS-CoV-2 samples isolated from PCR-positive patients from Tashkent, Uzbekistan, in the period of 2021 and 2022 were generated using next‐generation sequencing approaches and subjected to further genomic analysis. According to our previous studies and the current genome-wide annotations of clinical samples, we have identified four waves of SARS-CoV-2 in Uzbekistan between 2020 and 2022. The dominant variants observed in each wave were Wuhan, Alpha, Delta, and Omicron, respectively. A total of 347 amino acid level variants were identified and of these changes, the most frequent mutations were identified in the ORF1ab region (n = 159), followed by the S gene (n = 115). There were several mutations in all parts of the SAR-CoV-2 genomes but S: D614G, E: T9I, M: A63T, N: G204 R and R203K, NSP12: P323L, and ORF3a(NS3): T223I were the most frequent mutations in these studied viruses. In our previous study, no mutation was found in the envelope (E) protein. In contrast, in our present study, we identified 3 (T9I, T11A and V58F) mutations that made changes to the structure and function of the E protein of SARS-CoV-2. In conclusion, our findings showed that with the emergence of each new variant in our country, the COVID-19 pandemic has also progressed. This may be due to the considerable increase in the number of mutations (Alpha—46, Delta- 146, and Omicron—200 mutations were observed in our samples) in each emerged variant that shows the SARS-CoV-2 evolution.

Introduction

In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was first identified in the city of Wuhan in Hubei Province, China which has been known as the cause of the coronavirus disease 2019 (officially declared as the COVID-19 pandemic by the World Health Organization (WHO) on March 11, 2020) [1–4]. Since its first detection, SARS‐CoV‐2 has spread rapidly to all corners of the world, and by October 31, 2023, 771,549,718 cases of SARS-CoV-2 with 6,974,473 deaths have been reported worldwide (https://covid19.who.int/), while 253,662 confirmed cases with 1,637 deaths (https://www.worldometers.info/coronavirus/country/uzbekistan/) have been reported in Uzbekistan.

The first full-length genome sequences of the novel virus were obtained from five patients at an early stage of the outbreak through metagenomic approaches, supplemented by PCR and Sanger sequencing (January 5, 2020). The genome sequence of Wuhan-Hu-1 (reference genome) was submitted to NCBI/GenBank (GenBank: MN908947) on the same day and released on the open-access virology website (https://virological.org) on January 11, 2020 [1, 2, 5, 6]. As sequencing is essential for epidemiological monitoring, 15,983,874 viral genome sequences of SARS-CoV-2 have been generated and submitted to the online database GISAID (Global Initiative on Sharing All Influenza Data) by 220 countries and territories, as of 31st October 2023 [7].

Over time, SARS-CoV-2 had quickly begun to mutate resulting in sequence diversity and emerging new variants, some of which have been classified by WHO as variants of concern (VOCs): Alpha, first detected in the United Kingdom, in 2020, Beta in South Africa in 2020, Gamma in Brazil in 2020, Delta in India in 2020, and Omicron in South Africa in 2021 [8–10]. Based on the analysis of complete or near-complete viral genomes (sometimes based only on the spike gene of SARS-CoV-2), multiple different nomenclatures have been used to classify SARS-CoV-2. One of the most popular systems for classifying and naming genetically distinct lineages of SARS-CoV-2 used by researchers and public health authorities worldwide is the PANGOLIN (phylogenetic assignment of named global outbreak lineages) nomenclature. The Pango nomenclature uses letters and numbers and is divided into lineages (A, B, B.1, B.1.1, B.1.177, and B.1.1.7.) and sublineages. The following Pango lineages: B.1.1.7, B.1.351, P.1, B.1.617.2, and B.1.1.529 correspond to the Alpha, Beta, Gamma, Delta, and Omicron variants of concern (VOCs), respectively [11]. Based on the GISAID nomenclature, SARS-CoV-2 was also classified into different clades: S, L, V, G, and later G into GH, GR, GV, and more recently GR into GRY and GRA [7]. According to the Nextstrain clade naming strategy SARS-CoV-2 strains were divided into 19A, 19B, 20A, 20B, 20C, 20D, 20E, 20F, 20G, 20H, 20I, 20 J [12, 13].

Since the first confirmed case was reported on March 15, 2020, four waves of the pandemic were recorded in Uzbekistan Fig 1 (https://www.worldometers.info/). The first peak occurred in July—August 2020, when the total number of cases increased rapidly from 12 295 to 43 476, with an increase of 31 181 within two months. As a continuation of the first wave, the second wave occurred in October-November 2020.

Fig 1. COVID-19 cases in Uzbekistan since February 2020 to July 2023 (according to Worldometer, https://www.worldometers.info).

Fig 1

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During these two peaks, the number of deaths was 299 and 367, respectively, representing 40.7% of the total number of deaths since the beginning of the pandemic in Uzbekistan. The third wave occurred in July and August 2021, with a high mortality rate of 59,3% (n = 971) when the number of infected individuals increased from 113 559 to 160 589 (n = 47 030) within two months, Fig 2 (https://www.worldometers.info). Since the beginning of January 2022, the next-fourth outbreak was observed in the country.

Fig 2. Mortality from COVID-19 in Uzbekistan from February 2020 to July 2023 (according to Worldometer, Fig 2 (https://www.worldometers.info).

Fig 2

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Timely identification of new variants is an important requirement for national health policy, as it enables rapid tracking and investigation of infections in hospitals and communities. This is also particularly important for enhancing vaccination strategies through the timely administration of booster doses to mitigate the effects of waning immunity after vaccination, especially in response to the emergence of new variants [14].

As a continuation of previous studies [15, 16], in this study, we focused on the whole genome sequencing analysis of 110 SARS-CoV-2 genomes to provide insights into the spread of infection, evolutionary patterns, and genetic diversity of the virus to enable effective management and preventive measures in Uzbekistan. Here we report on the four waves of SARS-CoV-2 in the country between 2020 and 2022, with the Wuhan, Alpha, Delta, and Omicron variants dominating in each wave, respectively. We identified a total of 347 amino acid variations in the sequenced genomes, with the most common mutations occurring in the ORF1ab region (159) and the S gene (115). While mutations were present throughout the SARS-CoV-2 genomes, certain mutations such as S: D614G, E: T9I, M: A63T, N: G204R and R203K, NSP12: P323L, and ORF3a(NS3): T223I were most prevalent in the studied genomes. In our previous study, no mutation was found in the envelope (E) protein. In contrast, in our present study we identified 3 (T9I, T11A, and V58F) mutations that made changes to the structure and function of the E protein of SARS-CoV-2. Furthermore, we observed that the P45L missense substitution in ORF7a which was found significantly associated with disease severity in our previous study [16], was not detected during the 4th wave of the pandemic when the Omicron variant was dominant in the country. This could be another reason why the mortality rate has decreased with the spread of the Omicron strain.

Materials and methods

Ethics committee approval

On February 1, 2021, this study developed as part of the implementation of the practical project No. A-IRV-2021-125 “Study of the genetics of SARS-CoV-2 coronavirus strains spread in the republic and creation of a distribution map in order to create the basis for the development of a COVID-19 vaccine”. Approval for this study was granted by the Ethics Committee of the Center for Advanced Technologies of the Ministry of Higher Education, Science and Innovation on May 5, 2021 (approval number CAT-EC-2021/05-1). The patient information was gathered anonymously, and each patient was assigned a distinct ID number. Due to the anonymity of the collected data, a voluntary participation condition, and non-invasiveness of the sequencing experiment, along with a clear explanation of the purpose of sample collection to each participant, we received their verbal consent.

From April 1, 2022, this project was continued as project No. A-SS-202202113 entitled “Identification and study of the genotypes of SARS-CoV-2 coronavirus strains present in Uzbekistan and determination of the degree of their influence on the course of the disease” and was completed on March 31, 2023.

Sample collection

Samples included in this study were obtained from COVID-19-diagnosed patients undergoing treatment at the State Hospital Zangiota No.1 and the Center for Sanitary and Epidemiological Service of the Republic of Uzbekistan. Nasopharyngeal and oropharyngeal specimens were collected in 3 mL of the viral transport medium (VTM) tube using flocked swabs and sent to the laboratory at a cold temperature (2–8˚C) within 72 hours post-collection. All the swab samples were stored at -80˚C until further analysis.

RNA extraction

SARS-CoV-2 RNAs were extracted from patients’ swabs using a Nucleic Acid Extraction and Purification Kit (Fosun Ultrapure NA) and QIAamp® Viral RNA Mini Handbook kit (QIAGEN) according to the manufacturer’s protocol. After viral extraction, RNAs were quantified through the Qubit ssRNA High Sensitivity Assay kit (Invitrogen by Thermo Fisher Scientific, Eugene, Oregon USA) on Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, California, USA) to validate appropriate concentration.

Real-time PCR for SARS-CoV-2

To detect SARS-CoV-2 viral infection, a one-step real-time PCR assay was performed using Biotest—SARS-CoV-2 RT-qPCR Kit (Biotest Lab LLC, Tashkent, Uzbekistan, patent №FAP02010) developed by the team of Biotechnology Laboratory of Center for Advanced Technologies and Novel Coronavirus (2019-nCoV) RT-PCR detection kit (Shanghai, People’s Republic of China) on the QuantStudio™ 5 real-time PCR System (Applied Biosystems, Foster City, USA) as per the manufacturer’s instructions. The assay includes three viral targets ORF1ab (RdRp), N genes (N1, N2, N3) and E genes. Samples that tested positive for SARS-CoV-2 by RT-PCR with CT (cycle threshold) values ≤28 were selected for this study.

Quality control

To avoid contamination, RNA extraction and RT-qPCR were performed in separate rooms. A reagent negative control was also included during RNA extraction to account for any contamination during extraction. For each PCR and real-time PCR run, triplicate negative controls were included. All negative controls for RNA extraction and RT-qPCR were negative for the targets analyzed.

Library preparation and sequencing

Whole genome amplification of the SARS-CoV-2 was performed using the following two kits: the CleanPlex® SARS-CoV-2 Research and Surveillance Panel (Paragon Genomics Inc., Hayward, CA, USA), and the Illumina COVIDSeq RUO Kit (Illumina, Inc., San Diego, CA, USA).

CleanPlex.SARS-CoV-2 Panel was used according to the manufacturer’s instructions (version UG4001-04, Jan 2021 and UG4004-06 Feb 2022). Briefly, reverse transcription was performed using 5 μl (200 ng) previously extracted RNA followed by RT reaction purification by magnetic beads. Then, 5 μl purified RT reaction product was used for two multiplex PCR (in two non-overlapping SARS-CoV-2 target-specific primer pools, Paragon Genomics design) to amplify the whole SARS-CoV-2 genome. After post-mPCR (multiplex PCR) purification, digestion was performed to remove nonspecific PCR products, followed by post-digestion purification. In the next step, second PCR reaction was performed to amplify and add unique i5 and i7 dual indexes, on Veriti 96 Well Thermal Cycler (Applied Biosystem) with 24 cycles. Finally, the generated libraries were purified using (in all four purification steps) CleanMag® Magnetic Beads (Paragon Genomics Inc., Hayward, CA, USA) as per the manufacturer’s instructions. Libraries were evaluated by gel-electrophoresis and considered for sequencing when a fragment size ~ 275 bp was obtained and were quantified using Qubit 2.0 dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) to confirm that the concentration was above 2.0 ng/μl which is good quality for sequencing.

Illumina COVIDSeq Test. Illumina COVIDSeq RUO Kit was used according to the manufacturer’s instructions (1000000126053 v05 Jun 2021). The first strand synthesis was carried out with 8.5 μL input RNA extracted from nasopharyngeal and oropharyngeal swabs, following the standard protocol. The synthesized cDNA was amplified in two separate PCR reactions (also including primers that target human RNA) within 35 cycles. The amplified PCR products were fragmented and tagged with adapter sequence using IDT for Illumina Nextera UD Indexes Set 1, 2, 3, 4 (384 indexes, 384 samples), followed by 7 cycles amplification of tagged PCR amplicons. Then, pooling was performed by combining libraries from each 96-well plate into one tube. The generated pool was purified using Illumina Tune Beads as per protocols provided by the manufacturer (Illumina, Inc., San Diego, CA, USA). Finally, the purified pool was quantified using the Qubit 2.0 fluorometer dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA).

After confirmation of the library quality, the pooled libraries were further normalized to the final 4nM concentration and were denatured and neutralized with 0.2N NaOH and 400mM Tris-HCL (pH-8), following the Standard Normalization protocol on MiSeq System Denature and Dilute Libraries Guide (Illumina, San Diego, CA, USA) with a final denaturation and dilution to 12 pM. Paired-end sequencing 2×150 bp was performed on a MiSeq instrument (Illumina, San Diego, CA, USA) with Reagent Kit v3 (300 cycles), using 20 pM PhiX control spike-in of 5% for low-diversity libraries.

Data and phylogenetic analysis

Standard bioinformatic tools were used to process the NGS raw data (FASTQ files) that were generated from MiSeq Local Run Manager (Illumina, San Diego, CA, USA). When CleanPlex panel was used, the NGS raw data (FASTQ files) were uploaded on the SOPHiA DDM platform (SOPHiA Genetics, Lausanne, Switzerland) for the external quality check, trimming of adaptors, variant call review, re-alignment of indels, quality measurements, and determination of the consensus genome by mapping to reference sequence MN908947 (NC 045512.2). For this, we have used a proprietary design pipeline to cover the entire genome.

DRAGEN COVIDSeq Test pipeline (Illumina Inc.) on the Illumina DRAGEN v3.6 Bio-IT platform was used for the row data generated from the COVIDSeq workflow as per standard protocol. The analysis involves sample sheet validation, data quality check, FASTQ generation, SARS-CoV-2 detection, and variant calling. For post-assembly quality control, a Phred score of ≥30 was applied, and sequences with a coverage depth of ≥30x were selected for further analysis. The Nextclade Web tool v2.14.1 was used to compare study sequences to SARS-CoV-2 reference sequences, assign them to clades, and determine their position within the SARS-CoV-2 phylogenetic tree [17].

Then the whole dataset of SARS-CoV-2 full genomes from Uzbek deposited in GISAID with accession numbers: EPI_ISL_3189000, EPI_ISL_3189001, EPI_ISL_3188999- EPI_ISL_3188963, EPI_ISL_18378658—EPI_ISL_18378665, EPI_ISL_15941879 -EPI_ISL_15941918, EPI_ISL_15961449, EPI_ISL_15961450, EPI_ISL_18378666- EPI_ISL_18378686.

Results

Clinical samples of nasopharyngeal and oropharyngeal swabs were collected from COVID-19 positive patients treated at Zangiota-1 and -2 State Hospital in Tashkent, Uzbekistan, and from the SES (Center for Sanitary and Epidemiological Services) of the Republic of Uzbekistan, during the four waves of the pandemic. SARS-CoV-2 positive samples (with Ct≤28) were selected for further whole-genome sequencing. The samples covered in this article were sequenced using two amplicon-based approaches: 48 samples (out of 48 samples, 39 high-quality sequences were generated) using the Clean Plex SARS-CoV-2 Panel (Paragon Genomics) and 96 samples (out of 96 samples, 73 high-quality sequences were generated) using the Illumina COVID Seq Test (Illumina Inc). The acquired110 high-quality sequences, 47 of which were from 2021 (third wave) and 63 from 2022 (fourth wave), were analyzed in detail later in this article.

Based on the results of the sequence, a comparative analysis was carried out with the coronavirus variants common in the territory of Uzbekistan, Fig 3. From the previous reports it was determined that the 1st wave of the pandemic corresponds to the wild type of SARS-CoV-2- Wuhan strain [15], from early June 2020. Following the first wave, the Alpha (B.1.1.7) variant started to spread in the country as the 2nd outbreak, October 2020. Further analysis revealed that from the beginning of July 2021, the Delta (B.1.617.2) variant spread quickly and on a large scale as the 3rd wave in Uzbekistan, while the Omicron variant (B.1.1.529) spread as the 4th wave of COVID-19 pandemic, from the beginning of January 2022, Fig 1.

Fig 3. Phylogenetic distribution of Uzbek SARS-CoV-2 genomes.

Fig 3

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The phylogenetic tree was generated by Nextstrain. PANGOLIN lineages of SARS-CoV-2 are represented.

To get an insight into the genetic epidemiology, the genomes were analyzed for their phylogenetic distribution, Figs 3 and 4. When the genome Wuhan/WH01 (EPI_ISL_406798) was used as the reference for constructing the tree, the Nextclade-based phylogenetic analysis of the SARS-CoV-2 genome samples sequenced in our study revealed that the most currently distributed SARS-CoV-2 variants in dataset belong to the following clades: 20A, 20I, 21A, 21J, 21K, 21L, 22B, 22C, 22D, 22E and 22F clades, Fig 4.

Fig 4. Phylogenetic analysis of the SARS-CoV-2 coronavirus, common in the Republic of Uzbekistan clades by Nextstrain.

Fig 4

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110 genomes clustered under 20A, 20I, 21A, 21J, 21K, 21L, 22B, 22C, 22D, 22E and 22F Clades.

By Nextstrain classification out of 110 studied SARS-CoV-2 samples, 61 genomes (55%) fell into 21K, 21L, 22B, 22C, 22D, 22E and 22F clades that represent the Omicron variant. In Fig 5, we summarized the phylogenetic distribution of the clades prorate to the lineages assigned by PANGOLIN.

Fig 5. SARS-CoV-2 common lineages and clades that circulating in Uzbekistan in 2021–2022.

Fig 5

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Among these clades representing Omicron, the predominant occurrence (n = 37, 34%) is clade 22B (BA.5.2), which is a currently circulating sublineage of 21L Omicron with 22C, 22D and 22E, while the 8 genomes (7%) are grouped into clade 22F (XBB.2). The clades 21K (BA.1.1) and 21L (BA.2.9), Omicron sublineages emerged from the South Africa strain 21M (lineage B.1.1.529) [18], were also widespread in the country with the low-frequency rate 1% (GISAID IDs: EPI_ISL_15941881 and EPI_ISL_15941882).

Clades of Delta variant 21A and 21J (lineage B.1.617.2) were the next most common clades to spread in Uzbekistan with a frequency rate of 24% (n = 26), followed by clade Alpha 20I (lineage B.1.1.7). The Alpha 20I clade variants, namely EPI_ISL_3188965, EPI_ISL_3188992, EPI_ISL_3188994 and EPI_ISL_3188999 accounted for 4% of our SARS-CoV-2 sample genome sequences. We also identified the clade 20A (which emerged from 19A and was dominant during the European outbreak in March 2020 [18]) in our dataset, such as EPI_ISL_3188963, EPI_ISL_3188985 and EPI_ISL_3188990.

According to the results of genome-wide sequencing, the most common strains circulating in Uzbekistan belonged to the Delta (B.1.617.2) and Omicron (B.1.1.529) variants that made the third and fourth waves of the pandemic in 2021 and 2022, respectively, Fig 5. If from 2020 to November 2021 the lineages B.1 (20A), B.1.1.7 (Alpha), B.1.617.2 (Delta), AY.122 (Delta) were dominant and subdominant, from February 2022 the strain variants omicron BA.1.1, BA.2.12.1, BA.2.9, BQ.1.1, BN.1.9, BF.5, BA.5., BA.5.2, BA.5.2.20, XBB.1, XBB.2, XBB.8, CK., CL 1 became dominant in the country.

Structural proteins (S-E-M-N)

Detailed analysis of amino acid substitutions in the SARS-CoV-2 genome showed that among the structural proteins (Spike, Envelop, Membrane and Nucleoprotein), the highest number of amino acid substitutions was found in the S gene, 115 changes. In the S protein, the SARS-CoV-2 mutation D614G was identified in all our isolates (n = 110; 100%), followed by T478K (n = 94; 86%), L452R (n = 79; 72%), P681H (n = 64; 58%), H655Y (n = 63; 57%), and G142D (n = 62; 56%). The increase in the number of mutations in the S gene can be explained by the emergence of the Omicron variant because as shown in Fig 6, when comparing the Omicron variant with the Delta variant, we can see that most of the S gene mutations are specific to the Omicron variant.

Fig 6. High-frequency mutations detected in the spike gene of Delta and Omicron variants that were circulated in Uzbekistan.

Fig 6

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As we reported earlier, only one amino acid substitution—I82T was detected in the Membrane (M) protein, and we did not observe any mutation in the Envelope (E) protein [16]. In the present study, apart from the amino acid substitution I82T (n = 39; 35% of the isolates), we observed the following 4 changes: A63T and Q19E were detected in almost half of the isolates (n = 61; 55% and n = 60; 55%, respectively), D3N (n = 43; 39%) and D3G was detected in only one isolate. In contrast to their report, we identified the following 3 amino acid substitutions in Envelop (E) protein: T9I (n = 59; 54% of isolates), T11A (n = 11; 10%) and V58F amino acid substitution in only one sample. This shows that as the pandemic progressed, the amino acid substitutions (in both structural and nonstructural proteins) have also increased. Compared to the E and M proteins, more mutations have occurred in the nucleoprotein, 25 amino acid substitutions. The most frequent of these, found in several isolates, were: G204R and R203K (n = 64; 58% of isolates) and P13L in 63 SARS-CoV-2 isolates, with a frequency of 57%. All amino acid substitutions in structural proteins (S-E-M-N) and the frequency of all identified missense mutations in SARS-CoV-2 isolates from our study are shown in Tables 1 and 2.

Table 1. Amino acid changes detected in spike protein during the four waves of the SARS-CoV-2 pandemic in Uzbekistan.

Amino acid changes in Spike Total number of sequenced samples—110 Frequency 20A Alpha Delta Omicron
D614G 110 100% 3 4 42 61
T478K 94 86% 2 1 41 49
L452R 79 72% 2 1 40 36
P681H 64 58% 0 3 0 61
H655Y 63 57% 0 0 2 61
G142D 62 56% 0 0 5 57
N679K, Q954H 61 56% 0 0 0 61
D796Y, N764K, N969K 60 55% 0 0 0 60
A27S, L24del, P25del, P26del, T19I 59 54% 0 0 0 59
N501Y 58 53% 2 4 1 51
D405N, S373P, S375F 56 51% 0 0 0 56
R408S, S371F, T376A 55 50% 0 0 0 55
K417N 54 49% 0 0 0 54
H69del, V70del 52 47% 0 4 1 47
E484A 51 46% 0 0 0 51
Q498R, S477N, Y505H 50 46% 0 0 0 50
G339D 49 45% 0 0 0 49
V213G 46 42% 0 0 0 46
E156G, F157del, R158del 44 40% 2 2 40 0
D950N 43 39% 1 0 42 0
P681R 42 38% 1 0 41 0
T19R 42 38% 2 0 40 0
F486V 37 34% 0 0 0 37
Y144del 36 33% 3 4 12 17
N440K, N460K 18 16% 0 0 0 18
G446S, R346T 13 12% 0 0 0 13
F490S, G339H, Q183E, V213E 12 11% 0 0 0 12
F486S, H146Q, V445P, V83A 11 10% 0 0 0 11
T95I 11 10% 0 0 10 1
I850L 8 7% 0 0 8 0
G252V, L368I 8 7% 0 0 0 8
K444N 5 5% 0 0 1 4
A570D, D1118H 4 4% 0 4 0 0
T716I 3 3% 0 3 0 0
S982A 3 3% 0 2 1 0
A243del, A475V, E484V, L244del, N501T, S494P, T604N, T859N, 2 2% 0 0 2 0
N450D, Q493R, V143del, Y145del 2 2% 0 0 0 2
I1114T, S254F 1 1% 0 1 0 0
D253A, E484Q, G1223S, I418V, L1063F, S680P, V1230L 1 1% 0 0 1 0
A1020S, A67V, D253G, F157L, G496S, I210V, ins214EPE, K147E, K150D, K150R, K356T, K444T, L212I, L212S, L452Q, L455S, L752F, L981F, N211del, N856K, P631S, Q677H, R1014T, R21T, R237M, R346K, S151I, S255F, S371L, S704L, S940F, T547K, W152R 1 1% 0 0 0 1
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Table 2. Amino acid changes detected in structural proteins (E-M-N) during the four waves of the SARS-CoV-2 pandemic in Uzbekistan.

Amino acid changes in Structural Proteins (E-M-N) Total number of samples 110 Frequency 20A Alpha Delta Omicron
E T9I 59 54% 0 0 0 59
T11A 11 10% 0 0 0 11
V58F 1 1% 0 0 0 1
M A63T 61 55% 0 0 0 61
Q19E 60 55% 0 0 0 60
D3N 43 39% 0 0 0 43
I82T 39 35% 0 0 39 0
D3G 1 1% 0 0 0 1
N G204R, R203K 64 58% 0 3 0 61
P13L 63 57% 0 0 2 61
E31del, R32del, S33del 61 55% 0 0 0 61
S413R 56 51% 0 0 0 56
D63G 43 39% 1 0 42 0
D377Y 39 35% 0 0 39 0
R203M 38 35% 0 1 37 0
G215C 20 18% 0 0 20 0
R385K 10 9% 0 0 10 0
T362I 5 5% 0 0 5 0
D3L, S235F 4 4% 0 4 0 0
P365S 2 2% 0 0 0 2
A156S, A182S, A182T, D144H, M234I, 1 1% 0 0 1 0
A218S, E136D, R40H, S37A 0 0 0 1
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Non—structural proteins (ORF1ab)

The other high-frequency mutations were identified in the ORF1ab region (113 and 46 mutations in ORF1a and ORF1b, respectively), which encodes 16 non-structural proteins (NSP1-NSP16). Of these 16 NSPs, the highest number of mutations was observed in NSP3, NSP2 and NSP13 with 41, 19 and 17 amino acid substitutions, respectively. The P323L substitution in NSP12 protein, which plays an important role in SARS-CoV-2 genome replication and transcription [19], was the most frequently detected substitution (identified in 98% of samples, n = 108), followed by the T492I substitution (n = 80; 73% of isolates) in NSP4. There were no mutations identified in the NSP11 gene. The all-amino acid substitutions of nonstructural proteins (ORF1ab) are illustrated in the S1 Table.

Accessory proteins

During the four waves of the COVID-19 pandemic, mutations were observed in the genes for NS3, NS6, NS7a, NS7b and NS8. The most frequent amino acid substitutions were detected in NS3, and they were: T223I, S26L and K16T with high-frequency rates of 55% (n = 60), 38% (n = 42) and 6% (n = 7), respectively. A72T, G172R, Q185H, Q57R, E181K, F43C, G224C, H78Y, L41R, Q38R, T64I, T89I, V202L, Y107H, Y113H were identified in different samples with the low-frequency rate. Only three: I14V, T21I, and V24I substitutions in three different samples were detected in NS6. When NS7a had H47Y, L116F, P45L, R89I, T120I, V29L, V82A, NS7a had only I27T and T40I. NS8 had also several mutations, including stop codons at G8 (G8stop), K68 (K68stop) and Q27 (Q27stop) and F41C, I71F, R52I, T26I, T87I, Y73C, S1 Table.

Discussion

The current study shows that in Uzbekistan four clear waves were identified during the entire period of the COVID-19 pandemic, Fig 1. Based on previous efforts to sequence SARS-CoV-2 samples from Uzbekistan, the first wave was thought to have been triggered by the spread of the original Wuhan variant in mid-2020 (June to September) [15]. The present research showed that the emerged Alpha variant resulted in the 2nd wave of pandemics, followed by the 3rd and 4th waves of Delta and Omicron variants, respectively. In each new wave, the infection rate steadily increased, Fig 1. The highest number of COVID-19 cases detected in Uzbekistan (n = 1478) was observed in January 2022 during the fourth outbreak by Omicron. On the contrary to increased transmission of SARS-CoV-2, the mortality rate has decreased with the spread of the Omicron strain, Fig 2.

The whole genome sequencing of samples selected during the four pandemic waves in Uzbekistan showed important mutations in the different parts of the genome. Some of the mutations affect the structure and properties of SARS-CoV-2 like transmissibility, virulence, and susceptibility to vaccines.

Amino acid substitution D614G became the most prevalent of all known SARS-CoV-2 mutations of the S protein (found in all 100% of our sequenced isolates), since its first occurrence in March 2020 [20]. As a missense mutation, D614G facilitates proteolysis at the furin cleavage site and alters the conformation of the RBD, leading to an increase in SARS-CoV-2 infectivity, transmissibility, and density of virion spikes [21–23]. In the Spike protein, the next high-frequency mutations were T478K and G142D (covering the 94–62% of samples sequenced) which can be found in both Omicron and Delta variants. Previous studies reported that the T478K mutation led to a higher binding affinity for human ACE2 with the following mutations: L452R, N501Y, S375F, S477N, S373P, Q498R, G339D, N440K, Q493R, and S371L that were identified in our samples [24, 25]. Two significant mutations: L452R and E484Q, also known as the spike double mutation, were first identified in samples from India. These unique mutations led to the emergence of a new Delta variant (B.1.617.2), which is classified as a VOC (Variants of Concern) by the WHO. Mutations specific to the Delta variant during the third pandemic wave in Uzbekistan L452R (72%), P681R (58%), and D950N (39%) were also identified in the sequence of Spike protein of studied samples [26].

The presence of the K417N mutation (49%) with the combination of R346K may reduce the neutralizing activity of the antibodies. Moreover, the R346K mutation which was only observed in one isolate hCoV-19/Uzbekistan/3_1465-CAT/2022|EPI_ISL_15941881, was reported as the only mutation that is distinctive to the sub-lineage BA.1.1 of Omicron [20, 27].

The most frequent mutations in E, M, and N proteins were T9I (54%), A63T (55%), and G204R, R203K (58%). The single point mutation T9I (T9I, threonine to isoleucine) in the envelope protein can influence the configuration of the E protein and ensure stronger anchoring to the viral membrane. Although the V58F mutation in the E-protein is not very common, it reduces the immune response of the B cells in the antigenic region [28–30]. Bingqing Xia et al, reported in their previous studies that T11A expression which was found in 11 of our isolates significantly alleviated cell death but did not alter the transfection efficiency and expression levels of SARS-CoV-2-E (envelop) protein [31].

The mutations Q19E and A63T in the membrane protein were not reported in our previous study [16] suggesting that they were only observed in all major Omicron subvariants (100%, in 61 Omicron variants) which were circulated during the 4th wave in Uzbekistan. And there is evidence about the impact of A63T which is the most frequent mutation on the stabilization of the M protein dimer in the studies by Anamica Hossain et al [32]. The N-terminal D3G mutation was present only in BA.1. 1 sub-lineage of Omicron variant (hCoV-19/Uzbekistan/3_1465-CAT/2022|EPI_ISL_15941881), and this mutation may affect the interactions with host cells [20, 32, 33].

Among the nucleocapsid mutations, the R203K and G204R mutations were the most common and these mutations made changes in the structure of the proteins [34]. Together, these mutations increase the viral load and the expression of sub-genomic RNA, additionally to SARS-CoV-2 replication, virulence, and pathogenicity [20, 22, 32, 35]. E31del, R32del, and S33del deletions in the N—terminal domain of nucleoprotein, may affect the assembly with M protein [36]. Some studies have indicated that the R203M mutation alone can enhance SARS-CoV-2 replication [37], while the P13L is associated with lower levels of transmissibility and death rates [38]. The impact of other nucleoprotein mutations on protein function has not been further studied well yet.

Non-structural proteins (NSP1-NSP16) encoded by the ORF1ab region of the SARS-CoV-2 genome are proteins that are not components of the virion but are transcribed and translated during host cell infection and play an important role in viral replication, translation, post-translational modification, assembly, evasion of the host immune system and other important functions [32]. Among these NSPs, NSP3, the largest protein of the virus, which plays an important role in virus replication, showed the highest number of mutations (n = 41), S1 Table. In NSP3, G489S, a mutation specific to Omicron, was observed with high frequency in all subvariants of Omicron detected in Uzbekistan, except in BA.1.1 (hCoV-19/Uzbekistan/3_1465-CAT/2022|EPI_ISL_15941881), as well as the S135R mutation in NSP1. However, in the Omicron BA.1.1 sub-variant, A1892T, I388T, K38R, L1266I mutations, and S1265 deletion were observed.

The mutation T492I in the NSP4 protein was observed in all sub-variants of Omicron (except BA.1), while the mutation L438F was only observed specifically in BA.2 and its descendant lineage BA.2.12.1. One study showed that these two mutations (L264F and T328I) might have functional significance in viral RNA replication [32]. NSP5 is the main protease that is important for SARS-CoV-2 as an antagonist of type I interferon (IFN) [12]. In this study, the mutation P132H in NSP5 which might affect enzyme activity, was observed in all subvariants of Omicron.

The next novel mutations which may play an important role in understanding of the COVID-19 disease, found in the NSP12 region of SARS-CoV-2 genome. NSP12 is necessary for the replication and transcription of the SARS-CoV-2 genome, as it encodes RNA-dependent RNA polymerase (RdRp) [16]. Previous studies reported that the P323L substitution of NSP12 that has been found in a large number of our samples (98%) could induce structural changes and adverse effect on proofreading during the replication of the SARS-CoV-2 genome [12]. The unique mutation R392C in NSP13 found in samples belonging to the Omicron variant and subvariants (except BA.1 subvariant) has been identified in this study.

NSP13 is a highly conserved RNA triphosphatase (RTPase), a vital coronavirus enzyme that unwinds double-stranded RNA in a 5′–3′ direction [32, 39, 40]. Anamica Hossain et al. suggested in their studies that the R392C which is located in the Rec1A domain may impact the NTPase activity (RNA Nucleoside Triphosphatase) of NSP13 helicase. Another study reported that this mutation may lead to a slight change in protein folding and alter the secondary structure of the protein [20].

The accessory proteins are important virulence factors involved in various pathogenesis mechanisms during SARS-CoV-2 infection. In addition, accessory proteins may play an important role in immune evasion mechanisms, that enhance viral survival in the host system [32, 41]. The T223I mutation in NS3 is the largest accessory protein. Except in the BA.1 subvariant, this mutation (T223I) was present in all Omicron subvariants studied in this research. However, no significant effects have been noted for this mutation. In our previous study, we reported that the amino acid substitution ORF7a: P45L in the Delta isolates (n = 8, 7% of our current isolates) had a significant association with disease severity [16]. Our present study showed that P45L was not detected during the 4th wave of the pandemic when the Omicron variant was dominant in the country. This could be another reason why the mortality rate has decreased with the spread of the Omicron strain. Moreover, one study [42] suggested that the infection with the Omicron variant results in fewer long-COVID symptoms compared to previous variants (Alpha, Delta, and the historical variant-20A.EU2). As they indicated, after infection with SARS-CoV-2, post-COVID symptoms were observed in the individuals over the different periods, however, individuals infected with the historical variant (20A.EU2) were more likely to develop long-COVID symptomatology.

Conclusion

Whole genome sequencing samples from patients with COVID-19 infection over the period of 2020–2022 using the above mentioned NGS methods allowed us to detect different variants (lineages and clades) of SARS-CoV-2 contributing to all four waves of COVID-19 pandemic. Our investigation showed that all viruses circulating during the 3rd wave and 4th waves belonged to the Delta and Omicron variants, respectively. Analysis of the mutations identified in our genome-wide study, showed that with the emergence of each new variant in our country, the COVID-19 pandemic has also progressed. This may be due to the considerable increase in the number of mutations (Alpha—46, Delta- 146, and Omicron—200 mutations were observed in studied samples) in each emerged variant that shows the SARS-CoV-2 evolution. Here, it is reasonable to mention that we have encountered several challenges during our research on SARS-CoV-2. When SARS-CoV-2 was first introduced to Uzbekistan, there was a shortage of test systems for widespread population testing. Consequently, during the initial pandemic wave, there was a global demand for reagents of genome sequencing, leading to high costs. As a consequence, our laboratory was unable to sequence a large number of samples compared to other research facilities. In addition, we struggled to maintain a consistent sample size across different waves. We believe that we would have achieved even better results if we had been able to sequence a larger number of samples and the same number of samples from each wave. Furthermore, during the pandemic, researchers have gained expertise in genome sequencing, and the laboratory has been well-equipped, leading to an increased level of preparedness for similar situations. We hoped that the detection of new variants that were circulated in Uzbekistan would have important implications for national health policy by enabling rapid tracking and investigation of infections in hospitals and communities and developing on-time alternative measures against them.

Supporting information

S1 Table. The substitutions of nonstructural proteins and accessory proteins during the four waves of SARS-CoV2 pandemic in Uzbekistan.

(DOCX)

pone.0298940.s001.docx (32.6KB, docx)
S1 File. Genome sequences of 110 SARS-CoV-2 samples from Uzbekistan collected during four pandemic waves.

(TXT)

pone.0298940.s002.txt (3.2MB, txt)
S2 File. SARS-CoV-2 genome annotation metadata of 110 samples from Uzbekistan collected during four pandemic waves.

Variants, lineages, clades, and all amino acid substitutions are listed.

(XLSX)

pone.0298940.s003.xlsx (25.3KB, xlsx)

Acknowledgments

The authors would like to express their great gratitude to the Republican Special Hospital No. 1 and No. 2 Zangiota and the Center for Sanitary and Epidemiological Services of the Republic of Uzbekistan for collecting the biological specimens and generating the metadata for GISAID. We also acknowledge all those who contributed to this research, i.e., the authors and the researchers.

Data Availability

The data generated and analysed in this study were submitted to GISAID and available for registered users at https://gisaid.org, with accession IDs from EPI_ISL_18378686 to EPI_ISL_3189001. The whole genome sequencing reads of 110 SARS-CoV-2 samples and genome metadata used and analysed in this study are available in the S1 and S2 Files.

Funding Statement

This study has been supported by the research grant from the Ministry of Innovative Development, Republic of Uzbekistan (Research Grant number: А-IRV-2021-125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study and no authors received award from this foundation.

References

Decision Letter 0

Nihad AM Al-Rashedi

20 Mar 2024

PONE-D-24-01894Complete genome sequencing of SARS-COV-2 strains that were circulating in Uzbekistan over the course of four pandemic wavesPLOS ONE

Dear Dr. Esonova,

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Reviewer #2: Partly

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Reviewer #1: The study conducted whole genome sequence analysis of 110 SARS-CoV-2 genomes in Uzbekistan to elucidate the circulation of variants during the pandemic waves. Results revealed that the Omicron variant (B.1.1.529) predominated at 55%, followed by the Delta variant (B.1.617.2) at 38%. A total of 347 amino acid level variations were identified, with mutations primarily clustered in the ORF1ab and S gene regions, including the notable D614G mutation in the S gene. These findings indicate an adaptive evolution of SARS-CoV-2, with an increasing number of mutations over time potentially affecting transmissibility. While the study provides intriguing insights, I recommend further revisions to refine the manuscript:

1. In the introduction, it's essential to underscore that the emergence of new variants has facilitated the virus's evasion of detection using molecular assays (DOI: 10.1002/jmv.28241) and immune escape, leading to breakthrough infections post-vaccination (DOI: 10.1002/jmv.27688).

2. Kindly emphasize the novelty of the present study amidst the existing genomic characterization data for SARS-CoV-2 and its variants.

3. In the discussion section, highlight that efforts to detect new variants extended beyond clinical surveillance to include wastewater-based genome monitoring. This method rapidly detects viruses up to the strain level, enabling tracking of variant transmission routes and evolutionary dynamics (DOI: 10.1016/j.ijheh.2023.114224).

4. Discuss the necessity of characterizing emerging variants and their potential impact on long COVID development, now recognized as the most dreaded sequelae of acute SARS-CoV-2 infection. Reference studies such as DOI: 10.1016/j.jinf.2023.12.004 and DOI: 10.3390/v14122629.

5. Kindly address the limitations of the present study and provide recommendations to mitigate these limitations.

Reviewer #2: My specific comments are the followings:

Table 1: it is not known the type, total number, and the frequency of mutations detected in a specific VOC. The authors need to show the frequency of mutations within a specific VOC. As a reference, please see figure 3 of this publication: https://pubmed.ncbi.nlm.nih.gov/38244104/ Those should be the main table/ figure, and for the original table 1, you can move it as supplementary table because the information is not that informative (it combines all sequence data from the four waves).

Table 2: it is also similar to Table 1. The current presentation is not informative. It is not known which mutation that is highly frequent (>10%) and which mutation that is rarely found. You can move it as a supplementary table. It is not known which mutation that belonged to a specific VOV (for example, mutation that is exclusively found in Delta but not in Omicron variant).

Line 44: change “4” to “four”.

Line 96-98: The statement is not correct. The emergence of new strains is not associated with increased transmission and virulence of the new strains compared to previous strains.

Line 277-280: Is that because the majority of samples sequenced were from the fourth wave? So, it is due to sampling bias. The authors should provide the details how many samples out of 110 samples belonged to a specific wave.

Line 232-233: the correct one is “phylogenetic tree was generated by Nextstrain”.

Line 238, 241: the correct one is “wave”.

Line 352: the correct one is “affect”.

Line 356: D614G is not a silent mutation, please revise.

Line 360: : the correct one is “found”.

Line 378-380: in what protein?

Line 425: change to “variant and subvariants”.

Line 56-59 and 451-452: which data supports this statement?

Be consistent with “Covid-19” in the whole manuscript since different writings were identified, such as COVID-19 or covid-19.

Table 1, for the frequency, change into percentage (for example: 1,000 to 100%)

In addition, the article still needs an English editing by professionals.

**********

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Reviewer #1: No

Reviewer #2: Yes: Mohamad S. Hakim, PhD.

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PLoS One. 2024 Nov 19;19(11):e0298940. doi: 10.1371/journal.pone.0298940.r002

Author response to Decision Letter 0

30 Apr 2024

Respond to the Journal Requirements:

1. When we wrote and revised the manuscript, we tried to follow the requirements of the PLOS ONE journal.

2. This study has been supported by a research grant from the Ministry of Innovative Development, Republic of Uzbekistan (Research Grant number: А-IRV-2021-125). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No authors received an award from this foundation.

We included the Funding Statement within our cover letter and reuploaded it.

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3. We changed the manuscript submission data to include authors.

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Response to Reviewers:

Response to Reviewer #1

Dear reviewer,

We appreciate your thoughtful feedback on our article. Your comments were insightful, and we tried to cover them as much as we can. Below are our responses to the points you raised.

1. In the introduction, it's essential to underscore that the emergence of new variants has facilitated the virus's evasion of detection using molecular assays (DOI: 10.1002/jmv.28241) and immune escape, leading to breakthrough infections post-vaccination (DOI: 10.1002/jmv.27688).

Our response:

We appreciate your suggestion regarding the introduction section. After reviewing the articles you recommended, we have tried to incorporate additional information on lines 116-118 in the Manuscript file (139-141 in the Revised Manuscript with Track Changes file), taking into account the focus of our research.

2. Kindly emphasize the novelty of the present study amidst the existing genomic characterization data for SARS-CoV-2 and its variants.

Our response:

Based on our obtained results, we tried to highlight the novelty of our present study on lines 52-54 in the Manuscript file (54-56 in the Revised Manuscript with Track Changes file).

3. In the discussion section, highlight that efforts to detect new variants extended beyond clinical surveillance to include wastewater-based genome monitoring. This method rapidly detects viruses up to the strain level, enabling tracking of variant transmission routes and evolutionary dynamics (DOI: 10.1016/j.ijheh.2023.114224).

Our response:

We appreciate the reviewer for emphasizing the significance of wastewater-based genomic surveillance. After reviewing the suggested article, we considered that wastewater-based genome monitoring would be one possible method that helps to increase the pandemic monitoring in Uzbekistan and preferred to highlight it in our manuscript on lines 463-467 in the Manuscript file (612-616 in the Revised Manuscript with Track Changes file).

4. Discuss the necessity of characterizing emerging variants and their potential impact on long COVID development, now recognized as the most dreaded sequelae of acute SARS-CoV-2 infection. Reference studies such as DOI: 10.1016/j.jinf.2023.12.004 and DOI: 10.3390/v14122629.

Our response:

After reviewing the articles you suggested, we have tried to give a brief information about long-COVID symptoms on 457-462 lines in the Manuscript file (606-611 in the Revised Manuscript with Track Changes file).

5. Kindly address the limitations of the present study and provide recommendations to mitigate these limitations.

Our response:

Thank you so much for your feedback, as we found it very reasonable. In the conclusion section (478-588 lines in the Manuscript file, 627-637 in the Revised Manuscript with Track Changes file), we have made an effort to address the limitations of our research and provide our insights on them.

Please, check the revision and suggest your opinion.

Response to Reviewer #2

Dear Mohamad S. Hakim,

We want to express our gratitude for the insightful feedback you provided on our article. Your comments were not only reasonable but also so valuable that we have made an effort to integrate them into our work. Please find below our responses to the comments you raised.

Table 1: it is not known the type, total number, and the frequency of mutations detected in a specific VOC. The authors need to show the frequency of mutations within a specific VOC. As a reference, please see figure 3 of this publication: https://pubmed.ncbi.nlm.nih.gov/38244104/ Those should be the main table/ figure, and for the original table 1, you can move it as supplementary table because the information is not that informative (it combines all sequence data from the four waves).

Our response:

Thank you very much for your feedback on Table 1. After reviewing the suggested article, we found it helpful in creating a clear visualization to display the frequency of mutations found in our sequenced Delta and Omicron variants. Following your advice, we attempted to detail the mutations identified in a particular VOC in Table 1. As a result, Table 1 was reorganized, however, we would like to keep it in the manuscript 302-308 and 325-327 in (373-379 and 413-420 in the Revised Manuscript with Track Changes file).

Please, check this version and suggest your opinion.

Table 2: it is also similar to Table 1. The current presentation is not informative. It is not known which mutation that is highly frequent (>10%) and which mutation that is rarely found. You can move it as a supplementary table. It is not known which mutation that belonged to a specific VOV (for example, mutation that is exclusively found in Delta but not in Omicron variant).

Our response:

As you suggested we have rearranged Table 2 and move it to supplementary material.

Line 96-98: The statement is not correct. The emergence of new strains is not associated with increased transmission and virulence of the new strains compared to previous strains.

Our response:

After carefully read the statement, we realized that it is not applicable to all variants of SARS-CoV-2 and therefore inaccurate. Thus, we prefer to remove it.

Line 277-280: Is that because the majority of samples sequenced were from the fourth wave? So, it is due to sampling bias. The authors should provide the details how many samples out of 110 samples belonged to a specific wave.

Our response:

Thank you so much for your comment as we found it very reasonable. We tried to get the same number of samples from 2021 and 2022, however, out of 144 samples, we obtained 110 high-quality sequencing results (20A-3, Alpha-4, Delta-42, and Omicron-61) for analysis. As we could not take, the same number of samples from each wave, we acknowledge that we cannot assert that "the most common strains circulating in Uzbekistan belong to the Omicron variant...". Therefore, we have included information about the distribution of samples from each period (wave) on lines 242-244 in the Manuscript file (299-301 in the Revised Manuscript with Track Changes file).

We have reviewed and addressed the comments on the lines you have mentioned: Line 44, Line 232-233, Line 238, 241, Line 352, Line 356, Line 360, Line 378-380, Line 425, Line 56-59, 451-452, and the final ones. We have made efforts to correct them and incorporate the necessary revisions.

Once again, we express our sincere gratitude to the reviewers.

Kind regards,

Esonova Gulnoza

PhD student and junior researcher

Biotechnology Laboratory of Center for Advanced Technologies.

esonovagulnoza96@gmail.com

Attachment

Submitted filename: Response to Reviewers.docx

pone.0298940.s004.docx (20.8KB, docx)

Decision Letter 1

Nihad AM Al-Rashedi

1 Jul 2024

PONE-D-24-01894R1Complete genome sequencing of SARS-COV-2 strains that were circulating in Uzbekistan over the course of four pandemic wavesPLOS ONE

Dear Dr. Esonova,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Aug 15 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: (No Response)

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Partly

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: N/A

Reviewer #4: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #3: Yes

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #3: No

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: Major concern:

L221, “Standard bioinformatic tools” is not quite specific enough. Obviously DRAGEN can analyze the samples prepared with the COVIDSeq workflow, however, the DRAGEN pipeline would not be appropriate to analyze the results from the CleanPlex (Paragon) workflow. Specifically, the Paragon primer sequences should be removed/masked from the reads prior to read mapping or assembly to prevent false base calls. Regardless of bioinformatic pipeline, it would be nice to know a little more about the pipeline – quality trimming, host removal, read mapping, consensus calling – even if DRAGEN was used. The authors state elsewhere that only “high quality” sequences were used (e.g., L240, L241), and so they should specifically state the post-assembly quality control implemented. E.g., was it % coverage? At what depth? With/out ambiguous base calls? Was it determined in the bioinformatic pipeline or in the analysis pipeline (e.g., nextclade quality score?) This is quite important, as some of the sequences that were deposited in GISAID show a relatively high level of unlabeled private mutations and reversions, resulting in some long branch lengths relative to reference strains. This could obviously be true, but it does not exclude the possibility that these are simply the result of inappropriate bioinformatics (e.g., unmasked primer sequences, index hopping, or contamination). The authors have provided info about which samples were processed with the COVIDSeq workflow in GISAID – and I assume consensus sequences were generated with DRAGEN – but no information was provided about the CleanPlex workflow. Some of the sequences were stated in GISAID to be assembled with UGENE v.39.0 – were these reads generated with the CleanPlex workflow? And, if so, this should be described in the manuscript. The main issues with sequencing data have been known/understood since very early in the pandemic (e.g., https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473), and have been demonstrated with WGS ring tests/EQAs/proficiency testing (there are a few publications now). However, the quality of the sequencing is not mentioned in this manuscript as a limitation of the analysis, and might be mentioned in this manuscript.

Similarly, the second major concern is the discussion of limitations.

The authors state their aim (L120) was “to provide insights into the spread of infection, evolutionary patterns, and genetic diversity of the virus to enable effective management and preventive measures in Uzbekistan.” They report on genetic diversity and specific mutations detected in a subset of circulating strains sequenced from in-patients (?). Their approach is whole genome sequencing – which is appropriate, however, there are (practical, sampling, and methodological) limitations with this method to capture genetic diversity and detect specific mutations (e.g., see ref 1, ref 2, ref 3, etc). The authors should probably discuss these as they relate to the main findings of their study (i.e., frequency of mutations in specific lineages), and how they relate to effective management/preventive measures in [a country].

The “Conclusion” section could be re-labeled “Limitations,” but the authors should discuss how these practical challenges specifically affected the data presented in this manuscript. In this section, they discuss the challenges to onboarding NGS in routine virus diagnostics, so they could mention how this affected their confidence in calculating the frequencies of specific mutations and/or lineages (e.g., how/why did they only select "high quality sequences" for comparison). I think the “retrospective” narrative of best practices and experiences is really nice, and very appropriate for this manuscript, given that the COVID-19 pandemic expanding capacity for virus sequencing around the globe. So please keep this, but it doesn’t really address the limitations of the study as presented.

L451, here might be a good time to mention the limitation/strength of this study in relation to previous studies. E.g., identifying ORF7a:P45L as a virulence determinant – was the sample size better in this study, or was it simply because another variant was prevalent? What was the evidence that virulence was decreased with Omicron (presented here or cited elsewhere)?

Another example of a missed opportunity to discuss the limitations with respect to the findings in this study is the new paragraph on line 463. This paragraph was requested by Reviewer 1, but the point is not presented with respect to the findings of this study. There are problems with wastewater-based surveillance in accurately identifying “strain” composition (major variants, at best), and – as far as I know – this technique has never been capable of tracking transmission routes with molecular epidemiology (unless I am misunderstanding what the authors are intending to convey – fecal-oral route?). The study cited [ref 44] does not address these limitations in their analysis, and I would not cite it as an example of how wastewater is useful in surveillance of viruses. Consider the complicated analysis presented by Amman et al. (2002, Nat Biotech) to deconvolute sequencing data and semi-accurately assign lineages, and consider that ref 44 did not take any of that into account. I think the point is valid, that if the authors wished to survey the genetic diversity of circulating strains, an alternative to testing symptomatic patients is environmental sampling; however, patient sampling provides more accurate genetic detail at the individual level.

There are a few minor concerns, which should be considered strong suggestions for clarification to reduce ambiguity or correct inaccuracies:

1. L106, why is May 2021 not considered a “peak”?

2. L124, this is a stylistic suggestion, but I believe the introduction could end here (with “…preventive measures in Uzbekistan.”) The rest of the paragraph lists specific findings that are included in the results; and, in particular, the last sentence is discussion/conclusion without direct evidence presented in this study.

3. L132, P45L is ambiguous – which gene/orf? (ORF7a?) Check to make sure that all substitutions/indels are labeled as "gene:A##B", or that their reference gene is otherwise unambiguous.

4. L176-177, I hope the authors realize that the “RT” in “RT-PCR” stands for “reverse transcription” and the “q” in “RT-qPCR” indicates it is a “real-time” assay, even if the result is semi-quantitative. Therefore, in this case, “RT-qPCR” is “real-time reverse transcription PCR”. See the MIQE guidelines. It seems clear in L188-189, but here it is a bit conflated.

5. L183 & L186, what is the difference between “CleanMag®” and “CleanPlex”?

6. L302 (“The increase in the number…”), L317 (“This shows that…”) and elsewhere: these are discussion points.

7. Table 1 is now two tables, somehow - the authors should fix the formatting. Note that the some rows of the “Spike” portion of Table 1 contain duplicated sequence features.

8. L332 and elsewhere – is this nsp12: P314L? nsp12:K323 has been largely unchanged. I think the authors should re-evaluate their conclusions about this mutation. The website they cite (ref. 14, not a peer-reviewed scientific source) does not mention this mutation. However, there have been several peer-reviewed scientific publications that discuss this specific mutation.

9. L358, where is the increased “rate” of infection shown?

10. L437, I am confused by this sentence and the following paragraph. nsp13:R392C was “reported for the first time in this study”, but was previously studied [ref 41]? Actually, ref 41 (Kuman et al., published since March 2023 in Vacunas, please update the reference) does discuss nsp13, but does not identify this mutation. However, the authors seem to be referring to Hossain et al. 2022 (Microb Pathog) [ref 34], which discusses this mutation in nsp13 in their review of “all” nonstructural mutations associated with Omicron (although they are not clear about how they identified the mutations),

Please double-check all references, that (1) they are correctly cited in the text, (2) they are up-to-date, and (3) they are appropriate. Specifically:

Is [ref 8] necessary? They have already cited the source [ref 7]

Ref [13] is not an appropriate citation of the GISAID clade nomenclature

Ref [14] is not a peer-reviewed scientific manuscript

It is not required, but the manuscript would benefit from a lot of editing. Below, I list just a few suggestions from the introduction, but the entire manuscript would be much better with English language editing.

L41 “…conducted whole genome sequencing (WGS) analysis…”

L42, already defined WGS

“respectively” (correct on L357), and not “appropriately” (L47), “correspondingly” (L332), nor “properly” (L342)

L48, “variants” not “variations”

L49, “…followed by the S gene...”

L53, “…envelope (E) protein. In contrast, in our present study, we…” (same mistake is repeated in line 129)

L54, did you test the “structure and function”?

L56 & L475, “progressed” is ambiguous.

only capitalize proper nouns: L60 severe acute respiratory syndrome coronavirus 2; L86 phylogenetic assignment of named global outbreak lineages (move ref 11 here); L167 and L179 “real-time”

L70, “metagenomic” is probably more appropriate then “metatranscriptomics”

L74, “As sequencing is essential…”

L78, “…had quickly begun…”

L81, “newest” is obviously relative if this paper is published in 2024; and the recombinant genotypes are being ignored here.

L173, “Samples that tested positive for SARS-CoV-2…"

L174, probably should clarify that Ct = cycle threshold

L176-177, maybe re-word this sentence for clarity: “To avoid contamination, RNA extraction and RT-PCR amplification were performed in separate rooms.”

L272, “predominant”

L376, I suggest “samples from India”

L391, “…in 11 of our isolates…” (also, were these isolates?)

L392, seems to be missing a word (e.g., “…significantly alleviated cell death but did not alter…”)?

L394, beginning here, why point out amino acid abbreviations here and nowhere previously? I think it is unnecessary.

L397, is a sentence fragment with strange comma splicing

L423, NSP or Nsp? L471 COVID or Covid?

L449, reword “…that can be met…”

Reviewer #4: 1. The authors must be commended for addressing the comments raised by the previous reviewers.

2. This is an important article showing the genomic mutations of SARS-COV-2 strains circulating in Uzbekistan. However, the title does not (really) convey the content and findings. Should the title not read, "Complete genome sequencing of SARS-COV-2 strains that were circulating in Uzbekistan during the third and fourth pandemic waves in 2021 and 2022"? The authors state, "We tried to get the same number of samples from 2021 and 2022", and later refer to waves 3 and 4. This supports my recommendation to change the title.

3. The previous reviewers commented: "Line 96-98: The statement is not correct. The emergence of new strains is not associated with increased transmission and virulence of the new strains compared to previous strains." The authors responded, "After carefully read the statement, we realized that it is not applicable to all variants of

SARS-CoV-2 and therefore inaccurate [sic]. Thus, we prefer to remove it." However, I believe that this is an important concept and I advise the authors not to remove the statement, but rather to amend it, eg "The emergence of new strains is more often associated with increased transmission, but not necessarily incfreased virulence of the new strains compared to previous strains" see:https://pubmed.ncbi.nlm.nih.gov/36591708/ and https://www.nature.com/articles/s41579-023-00878-2

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Reviewer #3: No

Reviewer #4: Yes: Burtram Clinton Fielding

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PLoS One. 2024 Nov 19;19(11):e0298940. doi: 10.1371/journal.pone.0298940.r004

Author response to Decision Letter 1

11 Aug 2024

We have addressed the reviewers' comments in our "Response to Reviewers" file. Regarding the editor's comment about the reference list, we carefully reviewed and rearranged it. We have noted the changes in the rebuttal letter.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0298940.s005.docx (22.7KB, docx)

Decision Letter 2

Nihad AM Al-Rashedi

15 Aug 2024

Complete genome sequencing of SARS-CoV-2 strains that were circulating in Uzbekistan over the course of four pandemic waves

PONE-D-24-01894R2

Dear Dr. Esonova,

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.

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Nihad A.M Al-Rashedi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Nihad AM Al-Rashedi

22 Aug 2024

PONE-D-24-01894R2

PLOS ONE

Dear Dr. Esonova,

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. The substitutions of nonstructural proteins and accessory proteins during the four waves of SARS-CoV2 pandemic in Uzbekistan.

    (DOCX)

    pone.0298940.s001.docx (32.6KB, docx)
    S1 File. Genome sequences of 110 SARS-CoV-2 samples from Uzbekistan collected during four pandemic waves.

    (TXT)

    pone.0298940.s002.txt (3.2MB, txt)
    S2 File. SARS-CoV-2 genome annotation metadata of 110 samples from Uzbekistan collected during four pandemic waves.

    Variants, lineages, clades, and all amino acid substitutions are listed.

    (XLSX)

    pone.0298940.s003.xlsx (25.3KB, xlsx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0298940.s004.docx (20.8KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0298940.s005.docx (22.7KB, docx)

    Data Availability Statement

    The data generated and analysed in this study were submitted to GISAID and available for registered users at https://gisaid.org, with accession IDs from EPI_ISL_18378686 to EPI_ISL_3189001. The whole genome sequencing reads of 110 SARS-CoV-2 samples and genome metadata used and analysed in this study are available in the S1 and S2 Files.

    Articles from PLOS ONE are provided here courtesy of PLOS

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