The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance, and determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding, and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling the genetic epidemiology of SARS-CoV-2.

The rapid emergence of coronavirus disease 2019 (COVID-19) as a global pandemic affecting millions of individuals globally has necessitated sensitive and high-throughput approaches for the diagnosis, surveillance and for determining the genetic epidemiology of SARS-CoV-2. In the present study, we used the COVIDSeq protocol, which involves multiplex-PCR, barcoding and sequencing of samples for high-throughput detection and deciphering the genetic epidemiology of SARS-CoV-2. We used the approach on 752 clinical samples in duplicates, amounting to a total of 1536 samples which could be sequenced on a single S4 sequencing flow cell on NovaSeq 6000. Our analysis suggests a high concordance between technical duplicates and a high concordance of detection of SARS-CoV-2 between the COVIDSeq as well as RT-PCR approaches. An in-depth analysis revealed a total of six samples in which COVIDSeq detected SARS-CoV-2 in high confidence which were negative in RT-PCR. Additionally, the assay could detect SARS-CoV-2 in 21 samples and 16 samples which were classified inconclusive and pan-sarbeco positive respectively suggesting that COVIDSeq could be used as a confirmatory test. The sequencing approach also enabled insights into the evolution and genetic epidemiology of the SARS-CoV-2 samples. The samples were classified into a total of 3 clades. This study reports two lineages B.1.112 and B.1.99 for the first time in India. This study also revealed 1,143 unique single nucleotide variants and added a total of 73 novel variants identified for the first time. To the best of our knowledge, this is the first report of the COVIDSeq approach for detection and genetic epidemiology of SARS-CoV-2. Our analysis suggests that COVIDSeq could be a potential high sensitivity assay for the detection of SARS-CoV-2, with an additional advantage of enabling genetic epidemiology of SARS-CoV-2.

Zika virus (ZIKV) is a mosquito borne pathogen, belongs to Flaviviridae family having a positive-sense single-stranded RNA genome, currently known for causing large epidemics in Brazil. Its infection can cause microcephaly, a serious birth defect during pregnancy. The recent outbreak of ZIKV in February 2016 in Brazil realized it as a major health risk, demands an enhanced surveillance and a need to develop novel drugs against ZIKV. Amodiaquine, prochlorperazine, quinacrine, and berberine are few promising drugs approved by Food and Drug Administration against dengue virus which also belong to Flaviviridae family. In this study, we performed molecular docking analysis of these drugs against nonstructural 3 (NS3) protein of ZIKV. The protease activity of NS3 is necessary for viral replication and its prohibition could be considered as a strategy for treatment of ZIKV infection. Amongst these four drugs, berberine has shown highest binding affinity of –5.8 kcal/mol and it is binding around the active site region of the receptor. Based on the properties of berberine, more similar compounds were retrieved from ZINC database and a structure-based virtual screening was carried out by AutoDock Vina in PyRx 0.8. Best 10 novel drug-like compounds were identified and amongst them ZINC53047591 (2-(benzylsulfanyl)-3-cyclohexyl-3H-spiro[benzo[h]quinazoline-5,1'-cyclopentan]-4(6H)-one) was found to interact with NS3 protein with binding energy of –7.1 kcal/mol and formed H-bonds with Ser135 and Asn152 amino acid residues. Observations made in this study may extend an assuring platform for developing anti-viral competitive inhibitors against ZIKV infection.

The influenza A (H1N1) virus, also known as swine flu is a leading cause of morbidity and mortality since 2009. There is a need to explore novel anti-viral drugs for overcoming the epidemics. Traditionally, different plant extracts of garlic, ginger, kalmegh, ajwain, green tea, turmeric, menthe, tulsi, etc. have been used as hopeful source of prevention and treatment of human influenza. The H1N1 virus contains an important glycoprotein, known as neuraminidase (NA) that is mainly responsible for initiation of viral infection and is essential for the life cycle of H1N1. It is responsible for sialic acid cleavage from glycans of the infected cell. We employed amino acid sequence of H1N1 NA to predict the tertiary structure using Phyre2 server and validated using ProCheck, ProSA, ProQ, and ERRAT server. Further, the modelled structure was docked with thirteen natural compounds of plant origin using AutoDock4.2. Most of the natural compounds showed effective inhibitory activity against H1N1 NA in binding condition. This study also highlights interaction of these natural inhibitors with amino residues of NA protein. Furthermore, among 13 natural compounds, theaflavin, found in green tea, was observed to inhibit H1N1 NA proteins strongly supported by lowest docking energy. Hence, it may be of interest to consider theaflavin for further in vitro and in vivo evaluation.

The leading cause of cancer mortality globally amongst the women is due to human papillomavirus (HPV) infection. There is need to explore anti-cancerous drugs against this life-threatening infection. Traditionally, different natural compounds such as withaferin A, artemisinin, ursolic acid, ferulic acid, (-)-epigallocatechin-3-gallate, berberin, resveratrol, jaceosidin, curcumin, gingerol, indol-3-carbinol, and silymarin have been used as hopeful source of cancer treatment. These natural inhibitors have been shown to block HPV infection by different researchers. In the present study, we explored these natural compounds against E6 oncoprotein of high risk HPV18, which is known to inactivate tumor suppressor p53 protein. E6, a high throughput protein model of HPV18, was predicted to anticipate the interaction mechanism of E6 oncoprotein with these natural inhibitors using structure-based drug designing approach. Docking analysis showed the interaction of these natural inhibitors with p53 binding site of E6 protein residues 108-117 (CQKPLNPAEK) and help reinstatement of normal p53 functioning. Further, docking analysis besides helping in silico validations of natural compounds also helped elucidating the molecular mechanism of inhibition of HPV oncoproteins.