ABSTRACT
Reading the viral genome through whole genome sequencing enables the detection of changes in the viral genome. The rapid changes in the SARS-CoV-2 viral genome may cause immune escape leading to an increase in the pathogenicity or infectivity. Monitoring mutations through genomic surveillance helps understand the amino acid changes resulting from the mutation. These amino acid changes, especially in the spike glycoprotein, may have implications on the pathogenicity of the virus by rendering it immune-escape. The region of Vidarbha in Maharashtra represents 31.6% of the total area and 21.3% of the total population of the state. In total, 7457 SARS-CoV-2 positive samples belonging to 16 Indian States were included in the study, out of which 3002 samples passed the sequencing quality control criteria. The metadata of 7457 SARS-CoV-2 positive samples included in the study was sourced from the Integrated Health Information Platform. The metadata of 3002 sequenced samples, including the FASTA sequence, was submitted to the Global initiative on sharing Avian Influenza Data and the Indian biological data centre. This study identified 104 different SARS-CoV-2 pango-lineages classified into 19 clades. We have also analysed the mutation profiles of the variants found in the study, which showed eight mutations of interest, including L18F, K417N, K417T, L452R, S477N, N501Y, P681H, P681R, and mutation of concern E484K in the spike glycoprotein region. The study was from November 2020 to December 2022, making this study the most comprehensive genomic surveillance of SARS-CoV-2 conducted for the region.
Subject(s)
COVID-19ABSTRACT
The SARS-CoV-2 exhibits similar aetiology, mode of transmission and clinical presentation as the H1N1pdm09 (a subtype of Influenza A) and Influenza A (other subtypes), and can exist as a coinfection in the same patient. It is essential to understand the coinfection dynamics of these viruses for effective management of the disease. This study examined 959 SARS-CoV-2 positive samples collected from the six states and three union territories in India from May to December 2022. The clinical data was accessed from the Integrated Health Information Platform (IHIP) and the Indian council of medical research (ICMR) COVID-19 data portal. The samples were tested for SARS-CoV-2, H1N1pdm09 and Influenza A using Reverse Transcriptase Real-Time Polymerase Chain Reaction q(RT-PCR). All 959 samples were subjected to SARS-CoV-2 whole genome sequencing (WGS) using Oxford Nanopore Next Generation Sequencing (NGS). From the 959 SARS-CoV-2 positive samples, 17.5% were co-infected with H1N1pdm09, 8.2% were co-infected with Influenza A, and 74.2% were only positive for SARS-CoV-2. The comparative analysis of viral load among the coinfected cases revealed that Influenza A and H1N1pdm09 had higher viral loads than SARS-CoV-2 in the studied samples. Out of 959 samples subjected to WGS, 815 and 144 were considered quality control (QC) passed, and QC failed, respectively, for SARS-CoV-2 variant calling. SARS-CoV-2 WGS identified 46 different variants belonging to the Omicron lineage. The SARS-CoV-2 and Influenza A coinfection group; and the SARS-CoV-2 and H1N1pdm09 coinfection group showed a higher proportion of symptomatic cases. This work demonstrates the need for coinfection analysis for the H1N1pdm09 virus, Influenza A virus and SARS-CoV-2 while studying the etiological agent in individuals with ILI/SARI symptoms. It is recommended that, in addition to determining the aetiology of ILI/SARI, an examination for H1N1pdm09 and Influenza A be conducted concurrently utilising molecular tools such as WGS and RT-PCR to understand the variant dynamics and the viral load for taking an informed decision during the patient management and treatment discourse.
Subject(s)
COVID-19 , Coinfection , Genomic Instability , Severe Acute Respiratory SyndromeABSTRACT
Voluntary participation of the public in disease surveillance can be encouraged by deploying user-friendly sample collection processes that can minimise the discomfort to the participants. To increase sample collection throughput and reduce patient discomfort, the Council of Scientific and Industrial Research-National Environmental Engineering Research Institute (CSIR-NEERI) developed a non-invasive, patient-friendly saline gargle sample collection method for detecting the SARS-CoV-2 virus. This method can also be deployed for other respiratory viruses. This study evaluated the suitability of saline gargle-based sample collection for genomic surveillance of SARS-CoV-2. This study included 589 SARS-CoV-2 positive samples collected using the Gargle-based sample collection method from Nagpur city in central India from March to December 2021. The viral RNA was isolated from saline gargle samples using an RNA release buffer followed by SARS-CoV-2 RTPCR. The SARS-CoV-2 positive samples were subjected to SARS-CoV-2 whole genome sequencing using the oxford nanopore technologies (ONT) next-generation sequencing platform. Out of 589 samples, 500 samples qualified for the SARS-CoV-2 WGS, and the SARS-CoV-2 WGS results revealed 8 different clades of SARS-CoV-2 encompassing 37 different Pango-lineage types. Our findings indicate that non-invasive gargle-based genomic surveillance is scalable and does not need significant changes to the existing workflow post-sample collection. This makes it advantageous for underdeveloped or remote areas as a reliable and high-throughput sample collection; and a technique of choice for surveillance in post-pandemic scenarios, which can find more user acceptance than the invasive swab-VTM sample collection method.