Chasing SARS-CoV-2 XBB.1.16 Recombinant Lineage in India and the Clinical Profile of XBB.1.16 Cases in Maharashtra, India

Background SARS-CoV-2 has evolved rapidly, resulting in the emergence of lineages with a competitive advantage over one another. Co-infections with different SARS-CoV-2 lineages can give rise to recombinant lineages. To date, the XBB lineage is the most widespread recombinant lineage worldwide, with the recently named XBB.1.16 lineage causing a surge in the number of COVID-19 cases in India. Methodology The present study involved retrieval of SARS-CoV-2 genome sequences from India (between December 1, 2022 and April 8, 2023) through GISAID; sequences were curated, followed by lineage and phylogenetic analysis. Demographic and clinical data from Maharashtra, India were collected telephonically, recorded in Microsoft® Excel, and analyzed using IBM® SPSS statistics, version 29.0.0.0 (241). Results A total of 2,944 sequences were downloaded from the GISAID database, of which 2,856 were included in the study following data curation. The sequences from India were dominated by the XBB.1.16* lineage (36.17%) followed by XBB.2.3* (12.11%) and XBB.1.5* (10.36%). Of the 2,856 cases, 693 were from Maharashtra; 386 of these were included in the clinical study. The clinical features of COVID-19 cases with XBB.1.16* infection (XBB.1.16* cases, 276 in number) showed that 92% of those had a symptomatic disease, with fever (67%), cough (42%), rhinorrhea (33.7%), body ache (14.5%) and fatigue (14.1%) being the most common symptoms. The presence of comorbidity was found in 17.7% of the XBB.1.16* cases. Among the XBB.1.16* cases, 91.7% were vaccinated with at least one dose of vaccine against COVID-19. While 74.3% of XBB.1.16* cases were home-isolated; 25.7% needed hospitalization/institutional quarantine, of these, 33.8% needed oxygen therapy. Out of 276 XBB.1.16* cases, seven (2.5%) cases succumbed to the disease. The majority of XBB.1.16* cases who died belonged to an elderly age group (60 years and above), had underlying comorbid condition/s, and needed supplemental oxygen therapy. The clinical features of COVID-19 cases infected with other co-circulating Omicron variants were similar to XBB.1.16* cases. Conclusion The study reveals that XBB.1.16* lineage has become the most predominant SARS-CoV-2 lineage in India. The study also shows that the clinical features and outcome of XBB.1.16* cases were similar to those of other co-circulating Omicron lineage infected cases in Maharashtra, India.


Introduction
The SARS-CoV-2 has undergone rapid evolution since November 2019, resulting in the emergence of competing lineages, of these 2,779 have been designated by the Pango-designation project [1]. SARS-CoV-2 lineages with D614G mutation, replaced the index SARS-CoV-2 lineage globally by July 2020. Later, the B.1.351 lineage (Beta variant) became dominant to be soon replaced by the B.1.617.2 lineage (Delta variant) by September 2021, which was in turn replaced by a more transmissible and immune evasive lineage, the B.1.1.529 lineage (Omicron variant) by January 2022 [2]. This global trend was also seen in India, where three major COVID-19 waves were caused by the index SARS-CoV-2 lineage, the Delta variant, and the Omicron variant [3]. Thus, lineages with a competitive advantage dominated and influenced the transmission dynamics of the SARS-CoV-2 pandemic [2]. Now, during the current period of Omicron variant domination, with varying ranges of host immunity following earlier infections or vaccinations, an Omicron lineage soup has emerged with 923 non-recombinant Omicron lineages, with periodic surges by different Omicron lineages predominating in distinct geographical locations [4].
Further, co-infection with multiple SARS-CoV-2 lineages has led to the evolution of SARS-CoV-2 recombinant lineages. There are 251 recombinant lineages and sub-lineages designated by the pangodesignation project, of which XBB is the most widespread recombinant lineage to date [5]. The XBB lineage emerged following the recombination of two co-circulating BA.2 Omicron sub-lineages, BJ.1 and BM.1.1.1, with a breakpoint between 22,897 and 22,941 positions in the RBD of the spike protein (corresponding to amino acid positions 445-460). As reported earlier, the XBB variant was first identified in India in August 2022, later spreading to the world [6]. During February 2023, the major XBB sub-lineages circulating in India were XBB.1, XBB.2, and XBB.1.5 [7].
In mid-February 2023, a rise in number of COVID-19 cases was seen in India [8]. In the samples from the state of Maharashtra, India, XBB.1 and XBB.1.5, were the dominant sub-lineages found in our sequencing laboratory. At the same time, there was an increase in SARS-CoV-2 load in sewage samples from Pune, a city in Maharashtra, India, which indicated a rise in COVID-19 cases (Routine official communication, Council of Scientific and Industrial Research-National Chemical Laboratory (CSIR-NCL), Pune, waste-water surveillance, supported by CSIR, New Delhi, Pune Knowledge Cluster Foundation (PKCF), Pune, and The Rockefeller Foundation, USA).
It was perplexing that the pre-existing lineages in circulation were causing a new surge in COVID-19 cases. However, on March 4, 2023, a group of international scientists monitoring the evolution of the SARS-CoV-2 virus discussed the global spread of a saltation lineage, XBB.1 sub-lineage, with the spike (S) mutations E180V, K478R, and S486P on GitHub. They noticed that this sub-lineage had a significant growth advantage over other lineages. The non-Indian sequences submitted on GISAID indicated a travel history mainly from India. The scientists called for rapid monitoring of the lineage's growth rate and suggested an early lineage designation. On March 5, 2023, it was designated as Pango lineage XBB.1.16 [9]. Since the lineage was newly designated, it was identified as XBB.1/XBB.1.5 and clade 22F by the bioinformatic pipelines currently used for lineage analysis, as they were not updated then. Therefore, manual analysis for the presence of XBB.1.16 defining mutations was carried out in the genomic sequences of SARS-CoV-2 from Maharashtra that were sequenced in our sequencing laboratory. This manual analysis showed the presence of the defining mutations of XBB.1.16 in those sequences. The presence of this new, fitter, and immune-evasive XBB.1.16 lineage clarified the reason for the sudden surge in the number of COVID-19 cases in Maharashtra as well as India.
This study describes the distribution of SARS-CoV-2 variants in India based on the sequences downloaded from GISAID during the study period. The study also does a comparative evaluation of the clinical characteristics and outcomes of XBB.1.16 cases and other co-circulating Omicron lineage-infected cases in Maharashtra.
This article was previously posted to the medRxiv preprint server on April 26, 2023.

Materials And Methods
The present study was conducted as a part of the Indian SARS-CoV-2 Genomics Consortium (INSACOG) sequencing activity in Maharashtra to study the evolution of the SARS-CoV-2 virus and its epidemiological trends. The study protocol for SARS-CoV-2 whole genome sequencing was reviewed and approved by the Institutional Ethics Committee at Byramjee Jeejeebhoy Government Medical College (BJGMC), Pune, Maharashtra, India.

SARS-CoV-2 whole genome sequences from India
To study the first appearance and the epidemiological trends of the XBB.1.16 lineage in India, complete genome sequences of the SARS-CoV-2 virus, from December 1, 2022 to April 8, 2023, deposited from different States and union territories of India, were retrieved from the GISAID database [10]. The associated metadata was downloaded and used for curation. Entries with complete geographic locations and sample collection dates were included in the study. The findings of this study are based on metadata associated with 2,944 sequences available on GISAID between December 1, 2022 to April 8, 2023, and accessible at epicov.org/epi3/epi_set/230419bg?main=true (Supplemental Table 1).

Demographic and clinical data collection of SARS-CoV-2 positive cases in Maharashtra
Demographic data, including the patient's age, sex, area of residence, contact number and date of specimen collection and testing, were collected from the metadata files submitted by the RT-PCR laboratories to the sequencing laboratories and the ICMR-COVID-19 Data portal using the unique identification number (ICMR ID). Telephonic interviews with each patient were useful in obtaining clinical details regarding the presence of any symptoms at the time of acute infection, type of isolation required, hospitalization, oxygen requirement, treatment given, and vaccination status. Patients not willing to share their clinical history during the interview were documented and excluded from the study.

Statistical analysis
All demographic and clinical data were recorded using Microsoft® Excel, and analysis was performed using Microsoft® Excel and IBM® SPSS statistics, version 29.0.0.0 (241). The continuous variables were presented as the median and interquartile range (IQR). The Kruskal-Wallis test was used to compare the median values between the lineages. The categorical variables were presented as numbers and percentages. The chi-square test was used to compare categorical variables between the lineages. Fisher's exact test compared the categorical values with limited data. A p-value ≤ 0.05 was considered statistically significant.

Distribution of SARS-CoV-2 lineages in India
From December 1, 2022 to April 8, 2023, a total of 2,944 sequences were retrieved from the GISAID database, and 2,856 sequences were included in the study following data curation. A total of 225 different lineages were identified in our dataset following Nextclade Pangolin nomenclature, of which XBB* was the most common lineage identified (79.87%) followed by BQ.1* (6.37%) and BA.2.75* (5.50%) (*indicates sublineage/s of that lineage) ( Table 1).     The recent increase in COVID-19 cases in India appeared to be linked to the emergence of the XBB.1.16* lineage, as its prevalence has risen concurrently with the upsurge in cases. Figure 4 shows the distribution of XBB.1.16* in Indian states, with the highest number of sequences from Maharashtra (42.40%) and Gujarat (37.27%) deposited on the GISAID database.

Distribution of SARS-CoV-2 lineages in Maharashtra
The lineage distribution of SARS-CoV-2 in Maharashtra showed a pattern consistent with that observed throughout India (Figures 5, 6) with XBB.1.16* increasing from 3.85% in the second week of 2023 to 79.17% in the 13th week of 2023. In Maharashtra, XBB.1.16* was first found on January 11, 2023 in a sample collected in Mumbai, Maharashtra. Figure 7 shows the distribution of XBB.1.16* in various districts of Maharashtra, with most sequences from the Pune district (60.27%) followed by Mumbai suburban (9.13%) and Aurangabad and Amravati (6.16%, each).

16* and other Omicron lineages in Maharashtra
Covishield (ChAdOx1nCoV-19 coronavirus vaccine) (81.9%) was the most common vaccine administered, followed by Covaxin (BBV152A-a whole inactivated virus-based COVID-19 vaccine) (5.7%). Among the 386 cases, 350 (90.7%) were vaccinated with at least one dose of the COVID-19 vaccine, and 36 (9.3%) were unvaccinated. Most unvaccinated individuals were in the age group of 0 to 10 years (44.4%) and were not offered vaccination as a part of the vaccination policy in the country.
It is important to note that the study did not find any significant difference in the clinical presentations of XBB.

Characteristics of death cases during the study
A total of eight cases (2.1%) died during the study. Six out of eight (75%) deaths occurred in the age group of 60 years and above (75%) ( Table 5, Figure 8). Presence of comorbidity was seen in five (62.5%) cases ( Figure  9). Vaccination with at least one dose of vaccine was present in 87.5% of cases. However, there was no significant difference in vaccination status of survived and death cases. Seven out of eight cases (87.5%) were hospitalized of which six cases (85.7%) needed oxygen therapy. Out of the total number of cases, five cases belonged to the elderly age group (60 years and above) and had underlying comorbidities. In one case, the individual belonged to the age group of zero to nine years and the cause of death was drowning, while COVID-19 was an incidental finding. For the remaining two cases, the cause of death could not be determined.    [16]. Since its identification on March 4, 2023, the XBB.1.16* lineage has already spread to 31 countries, with India reporting the highest percentage of sequences (32.47%), followed by Brunei (4.50%) and Singapore (3.21%) [17]. Worldwide, the cumulative prevalence of XBB.1.16 is less than 0.5%, while its cumulative prevalence in India is 2% (on April 17, 2023) [18]. With the current relative growth advantage of 62% (95% Confidence Interval -56% to 68%) in India, the lineage has already been found in sequences from 15 Indian states and union territories (till April 10, 2023) [19] and accounts for 49% (95% Confidence Interval -46% to 51%) of SARS-CoV-2 sequences over the past 60 days [18].  [23].
In addition to the spike proteins, the non-spike proteins of the virus have significant roles in regulating the immune response, controlling viral transcription and in viral pathogenesis [24]. For example, the XBB variant has retained the mutation T9I (99.70% in BA.1) in its envelope (E) protein, found earlier in Omicron sub-variants. It has also gained a new negative mutation T11A at a high frequency (90.52%). These changes in the envelope protein are believed to reduce the virulence and pathogenicity of the variant [25,26]. Therefore, it is possible that individuals infected with XBB.1.16 lineage, like those infected with other Omicron lineages, experienced a mild disease, despite exhibiting symptoms. Similarly, accessory proteins like ORF9b play a significant role in viral pathogenesis by reducing the host antiviral response [24]. The ORF9b gene of the XBB.1.16 lineage has two distinct substitutions, I5T (Isoleucine → Threonine) and N55S (Asparagine → Serine) [9]. ORF9b suppresses and antagonizes the type I interferon response (IFN-I) by interacting with TOM70 and by targeting multiple signaling pathways like the RIG-I-MAVS-dependent IFN signaling pathway, leading to innate immune suppression [24,25,27]. The mutation N55S lies in the ORF9b region (43-78 residues) that interacts with TOM70, while the I5T mutation is located in the N terminal site, which helps to stabilize the TOM70-ORF9b structure. However, the effect of these mutations remains uncertain [24]. it is essential to conduct further studies to determine the effects of these unique mutations, as they could potentially impact the virus's interaction with the host immune system and contribute to disease pathogenesis. In this regard, it is important to note that the present study found XBB.

Conclusions
The study reveals that XBB.1.16* lineage has become the most predominant SARS-CoV-2 lineage in India.
The study also shows that the clinical features and outcome of XBB.1.16* cases were similar to those of other co-circulating Omicron lineage infected cases in Maharashtra, India. The XBB.1.16 variant did not cause severe infections just like other Omicron sub-lineages. However, its increased transmissibility and immune evasive properties are alarming. Moreover, due to the increased growth efficiency, XBB.1.16 variant is progressively replacing all other co-circulating lineages in India. This study underlines the importance of a prompt assessment of clinical characteristics and outcomes following the rapid identification of a new SARS-CoV-2 lineage (XBB.1.16 variant in this study) as well as the immediate dissemination of genomic data to public databases, such as GISAID. The first action mentioned above is important for clinical genomic surveillance and the second is essential in the detection and naming of new lineages. The results of both measures mentioned previously provide prompt actionable evidence to aid policymakers in making informed public health decisions and interventions.

Appendices
Supplemental Table 1 Data Availability GISAID Identifier: EPI_SET_230419bg doi: 10.55876/gis8.230419bg All genome sequences and associated metadata in this dataset are published in GISAID's EpiCoV database.
To view the contributors of each individual sequence with details such as accession number, Virus name, Collection date, Originating Lab and Submitting Lab and the list of Authors, visit 10.55876/gis8.230419bg Data Snapshot EPI_SET_230419bg is composed of 2,944 individual genome sequences. The collection dates range from 2022-12-01 to 2023-04-03; Data were collected in one country and territories. All sequences in this dataset are compared relative to hCoV-19/Wuhan/WIV04/2019 (WIV04), the official reference sequence employed by GISAID (EPI_ISL_402124). Learn more at https://gisaid.org/WIV04.

Additional Information Disclosures
Human subjects: Consent was obtained or waived by all participants in this study. Institutional Ethics Committee, Byramjee Jeejeebhoy Government Medical College, Pune issued approval BJGMC/IEC/Pharmac/0721233-233. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.