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. 2020 Aug 3;5:184. [Version 1] doi: 10.12688/wellcomeopenres.16119.1

Integrated genomic view of SARS-CoV-2 in India

Pramod Kumar 1,#, Rajesh Pandey 2,#, Pooja Sharma 2,#, Mahesh S Dhar 1,#, Vivekanand A 2, Bharathram Uppili 2, Himanshu Vashisht 1, Saruchi Wadhwa 2, Nishu Tyagi 2, Saman Fatihi 2, Uma Sharma 1, Priyanka Singh 1, Hemlata Lall 1, Meena Datta 1, Poonam Gupta 1, Nidhi Saini 1, Aarti Tewari 1, Bibhash Nandi 1, Dhirendra Kumar 1, Satyabrata Bag 1, Deepanshi Gahlot 2, Surabhi Rathore 2, Nidhi Jatana 2, Varun Jaiswal 1, Hema Gogia 1, Preeti Madan 1, Simrita Singh 1, Prateek Singh 2, Debasis Dash 2, Manju Bala 1, Sandhya Kabra 1, Sujeet Singh 1, Mitali Mukerji 2, Lipi Thukral 2, Mohammed Faruq 2,#, Anurag Agrawal 2,a,#, Partha Rakshit 1,b,#
PMCID: PMC7506191  PMID: 32995557

Abstract

Background: India first detected SARS-CoV-2, causal agent of COVID-19 in late January 2020, imported from Wuhan, China. From March 2020 onwards, the importation of cases from countries in the rest of the world followed by seeding of local transmission triggered further outbreaks in India.

Methods: We used ARTIC protocol-based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT sequencing from Oxford Nanopore Technology to understand how introduction and local transmission occurred.

Results: The analyses revealed multiple introductions of SARS-CoV-2 genomes, including the A2a cluster from Europe and the USA, A3 cluster from Middle East and A4 cluster (haplotype redefined) from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The local transmission and persistence of genomes A4, A2a and A3 was also observed in the studied locations. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, and are here proposed as A4-clade based on its divergence within the A cluster.

Conclusions: The viral haplotypes may link their persistence to geo-climatic conditions and host response. Multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

Keywords: SARS-CoV-2, COVID-19, COVID, viral genomes, MinION, Whole Genome Sequencing

Introduction

The ongoing pandemic of COVID-19 caused by SARS-CoV-2 following its first appearance in China has pressed the global community to take measures to flatten its transmission ( Chan et al., 2020; Zhu et al., 2020). The severe symptoms of infection can include pneumonia, severe acute respiratory syndrome, kidney failure and even death with a coalescence of factors ( Young et al., 2020; Zhu et al., 2020). Many COVID-19 cases have been reported to be asymptomatic and may serve as carrier of SARS-CoV-2 ( He et al., 2020; Xu et al., 2020). Genome sequences of SARS-CoV-2 suggest its origin and transmission patterns after it enters a new population is proving to be an important step towards formulating strategies for management of this pandemic ( Andersen et al., 2020; Chen & Li, 2020).

The first three cases in India were reported in late January and early February, in individuals with a travel history of Wuhan, China. India took drastic steps to contain the further spread of the virus including imposition of travel restrictions to and from the affected countries. There were no new cases of COVID-19 for almost a month. However, while the global focus was on China and other eastern countries like South Korea and Japan; European countries, the Middle East and the USA reported a surge in cases of COVID-19. March 2020 onwards, India also witnessed a surge of imported cases from countries other than China which has been further assisted with local transmission. In March, imposition of nationwide lockdown checked the epidemic curve. Despite these measurements, India is at the verge of a large outbreak as the transmission is rapidly increasing with more than 450,000 reported cases of COVID-19 by the fourth week of June 2020.

We carried out whole genome sequencing of SARS-CoV-2 (n=104) from Pan-India through Surveillance Program of the National Center for Disease Control (NCDC), Delhi. Here, we combine genetic and epidemiological data to understand the genetic diversity, evolution, and epidemiology of SARS-CoV-2 across India. The spectrum of variations would be an important tool towards contact tracing, effective diagnostics and backbone for drug and vaccine development.

Methods

Subject recruitment

The study was conducted jointly by the NCDC and CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB). Institutional ethical clearance was obtained at both institutes prior to initiation of research; the need for consent from the patients was waived by the committee. A total of 127 laboratory-confirmed cases of COVID-19 from targeted testing and available samples at NCDC which represent different geographic locations or states and travel history from different countries during the early phase of the outbreak ( Table 1 and Extended data, Supplementary figure S1 [ Kumar et al., 2020b]). were included in the study for genomic analyses. Targeted testing involved suspected cases; having symptoms (fever, cough and breathlessness) with recent travel history to high-risk countries (China, South Asia, Middle East, European countries such as Italy, Spain, UK, France and USA) or positive contacts of COVID-19 cases.

Table 1. Frequency and description the variations obtained in the SARS-CoV-2 genome from major identified cluster from 104 sequences.

Global Frequency
POS Gene Gene clade Aminoacid
change 		Amino Acid changes effect A T G C N Variant
count
in this
study
11083 NSP6 orf1ab A3 Leu3606Phe QHD43415.1:p.3606L; QHD43415.1:p.3606L>F;
QHD43415.1:p.3606-		 LOW; MODERATE; MODIFIER 0 1137 7544 0 41 68
13730 NSP12/RdRP orf1ab - Ala4489Val QHD43415.1:p.4489A>V MODERATE 0 99 0 8622 1 65
6312 NSP3 orf1ab - Thr2016Lys QHD43415.1:p.2016T>K; QHD43415.1:p.2016T>I;
QHD43415.1:p.2016T>R		 MODERATE 93 3 0 8625 1 62
28311 N-capsid N - Pro13Leu QHD43423.2:p.13P>L; QHD43423.2:p.13P>R;
QHD43423.2:p.13-		 MODERATE; MODIFIER 0 160 1 8560 1 53
23929 S-Protein S - Tyr789Tyr QHD43416.1:p.789Y; QHD43416.1:p.789- LOW; MODIFIER 0 88 0 8627 7 46
14408 NSP12/RdRP orf1ab A2a Pro4715Leu QHD43415.1:p.4715P>L; QHD43415.1:p.4715- MODERATE; MODIFIER 0 5325 0 3381 16 26
23403 S-Protein S A2 Asp614Gly QHD43416.1:p.614-; QHD43416.1:p.614D>G MODIFIER; MODERATE 3346 0 5356 0 20 26
241 5'UTR 5'UTR A2 0 QHD43415.1 MODIFIER,DISTANCE=25 0 5194 0 3149 379 24
3037 NSP3 orf1ab A2 Phe924Phe QHD43415.1:p.924F; QHD43415.1:p.924- LOW; MODIFIER 1 5348 0 3352 21 23
6310 NSP3 orf1ab - Ser2015Arg QHD43415.1:p.2015S>R; QHD43415.1:p.2015-;
QHD43415.1:p.2015S		 MODERATE; MODIFIER; LOW 28 17 0 8672 5 22
25563 ORF3a ORF3a A2a2 Gln57His QHD43417.1:p.57Q>H; QHD43417.1:p.57- MODERATE; MODIFIER 0 2300 6415 1 6 9
1397 NSP2 orf1ab A3 Val378Ile QHD43415.1:p.378V>I MODERATE 137 0 8584 0 1 7
18877 NSP14/Exonuclease orf1ab - Leu6205Leu QHD43415.1:p.6205L LOW 0 208 0 8512 2 7
884 NSP2 orf1ab - Arg207Cys QHD43415.1:p.207R>C MODERATE 0 39 0 8682 1 6
8653 NSP4 orf1ab - Met2796Ile QHD43415.1:p.2796M>I MODERATE 0 37 8680 0 5 6
26735 M-Protein M - Tyr71Tyr QHD43419.1:p.71Y; QHD43419.1:p.71- LOW; MODIFIER 0 41 0 8677 4 6
28688 N-capsid N A3 Leu139Leu QHD43423.2:p.139L; QHD43423.2:p.139- LOW; MODIFIER 0 8586 0 130 6 6
29742 3'UTR 3'UTR A3 0 QHI42199.1 MODIFIER,DISTANCE=68 61 120 8268 0 273 6
12685 NSP8 orf1ab - Gln4140His QHD43415.1:p.4140Q>H; QHD43415.1:p.4140- MODERATE; MODIFIER 0 0 8720 0 2 5
16993 NSP13/Helicase orf1ab - Tyr5577His QHD43415.1:p.5577Y>H MODERATE 0 8715 0 6 1 5
22444 S-Protein S - Asp294Asp QHD43416.1:p.294D LOW 0 13 0 8438 271 5
25461 ORF3a ORF3a - Ala23Ala QHD43417.1:p.23A LOW 0 8714 0 6 2 5
28854 N-capsid N - Ser194Leu QHD43423.2:p.194S>L; QHD43423.2:p.194- MODERATE; MODIFIER 0 64 0 8654 4 5
1706 NSP2 #N/A - Ser481fs #N/A #N/A 0 8721 0 0 1 4
1707 NSP2 orf1ab - Ser481Phe QHD43415.1:p.481S>F; QHD43415.1:p.481- MODERATE; MODIFIER 0 3 0 8716 3 4
658 NSP1 orf1ab - Ala131Ala QHD43415.1:p.131A; QHD43415.1:p.131- LOW; MODIFIER 0 8706 0 14 2 3
1820 NSP2 orf1ab - Gly519Ser QHD43415.1:p.519G>S; QHD43415.1:p.519- MODERATE; MODIFIER 13 0 8708 0 1 3
7621 ORF1a/b orf1ab - Cys2452Cys QHD43415.1:p.2452C LOW 0 8720 0 0 2 3
8782 NSP4 orf1ab - Ser2839Ser QHD43415.1:p.2839S; QHD43415.1:p.2839- LOW; MODIFIER 0 1370 0 7344 8 3
14805 NSP12/RdRP orf1ab A1a1 Tyr4847Tyr QHD43415.1:p.4847Y; QHD43415.1:p.4847- LOW; MODIFIER 0 892 0 7813 17 3
18486 NSP14/Exonuclease orf1ab - Leu6074Leu QHD43415.1:p.6074L LOW 0 3 0 8717 2 3
19524 NSP14/Exonuclease orf1ab - Leu6420Leu QHD43415.1:p.6420L; QHD43415.1:p.6420- LOW; MODIFIER 0 33 0 8501 188 3
21792 S-Protein S - Lys77Met QHD43416.1:p.77K>M MODERATE 8717 0 0 0 5 3
27191 M-Protein M - Ter223Ter QHD43419.1:p.223-; QHD43419.1:p.223* MODIFIER; LOW 8717 0 2 0 3 3
28881 N-capsid N A2a1 Arg203Lys QHD43423.2:p.203R>K; QHD43423.2:p.203R>M;
QHD43423.2:p.203-		 MODERATE; MODIFIER 1357 1 7347 0 17 3
28882 N-capsid N A2a1 Arg203Arg QHD43423.2:p.203R; QHD43423.2:p.203R>S;
QHD43423.2:p.203-		 LOW; MODERATE; MODIFIER 1353 0 7353 0 16 3
28883 N-capsid N A2a1 Gly204Arg QHD43423.2:p.204-; QHD43423.2:p.204G>R MODIFIER; MODERATE 0 0 7353 1353 16 3
1059 NSP2 orf1ab A2a2a - QHD43415.1:p.265T>I; QHD43415.1:p.265- MODERATE; MODIFIER 0 1900 0 6816 6 2
1281 NSP2 orf1ab - Ala339Val QHD43415.1:p.339A>V; QHD43415.1:p.339- MODERATE; MODIFIER 0 3 0 8717 2 2
1947 NSP2 #N/A - - #N/A #N/A 0 8720 0 0 2 2
2480 NSP2 orf1ab - Ile739Val QHD43415.1:p.739-; QHD43415.1:p.739I>V MODIFIER; MODERATE 8419 0 292 0 11 2
2558 NSP2 orf1ab A1a1b Pro765Ser QHD43415.1:p.765P>S; QHD43415.1:p.765- MODERATE; MODIFIER 0 321 0 8390 11 2
7600 ORF1a/b orf1ab - Cys2445Cys QHD43415.1:p.2445C; QHD43415.1:p.2445- LOW; MODIFIER 0 0 0 8721 1 2
13920 NSP12/RdRP #N/A - Lys4552Lys #N/A #N/A 0 0 8721 0 1 2
16355 NSP13/Helicase orf1ab - Lys5364Arg QHD43415.1:p.5364K>R MODERATE 8721 0 0 0 1 2
18395 NSP14/Exonuclease orf1ab - - QHD43415.1:p.6044A>V MODERATE 0 2 0 8719 1 2
18803 NSP14/Exonuclease orf1ab - Ser6180Ile QHD43415.1:p.6180S>I MODERATE 0 0 8721 0 1 2
19684 EndoRNAse orf1ab - Val6474Leu QHD43415.1:p.6474V>L MODERATE 0 43 8663 0 16 2
21137 O-ribose
methyltransferase		 orf1ab - Lys6958Arg QHD43415.1:p.6958-; QHD43415.1:p.6958K>R MODIFIER; MODERATE 8705 0 14 0 3 2
22289 S-Protein S - Ala243Ser QHD43416.1:p.243A>S MODERATE 0 2 8716 0 4 2
26144 ORF3a ORF3a A1a Gly251Val QHD43417.1:p.251G>V; QHD43417.1:p.251- MODERATE; MODIFIER 0 820 7890 0 12 2
28144 ORF8 ORF8 B Leu84Ser QHD43422.1:p.84L>S; QHD43422.1:p.84- MODERATE; MODIFIER 0 7346 0 1353 23 2
28144 ORF8 ORF8 B Leu84Ser QHD43422.1:p.84L>S; QHD43422.1:p.84- MODERATE; MODIFIER 0 7346 0 1353 23 2
29555 orf Intergenic - 0 QHI42199.1 MODIFIER,DISTANCE=3 0 1 0 8710 11 2
29555 orf Intergenic - 0 QHI42199.1 MODIFIER,DISTANCE=3 0 1 0 8710 11 2
29729 3'UTR 3'UTR - - QHI42199.1 MODIFIER,DISTANCE=55 0 8457 1 0 264 2
84 5'UTR 5'UTR - - 0 MODIFIER 1 3 0 8137 581 1
167 5'UTR 5'UTR - - QHD43415.1 MODIFIER,DISTANCE=99 0 1 8337 1 383 1
203 5'UTR 5'UTR - - QHD43415.1 MODIFIER,DISTANCE=63 0 3 1 8355 363 1
337 NSP1 orf1ab - - QHD43415.1:p.24R LOW 0 2 0 8686 34 1
507 NSP1 orf1ab - - QHD43415.1:p.81-86HGHVMV>H MODERATE 8711 0 0 0 11 1
509 NSP1 orf1ab - - QHD43415.1:p.82-85GHVM>V MODERATE 0 0 8710 0 12 1
561 NSP1 #N/A - - #N/A #N/A 0 0 8716 0 6 1
683 NSP1 orf1ab - - QHD43415.1:p.140-143LKSF>L; QHD43415.1:p.140L MODERATE; LOW 0 3 0 8716 3 1
851 NSP2 orf1ab - - QHD43415.1:p.196Y>H MODERATE 0 8721 0 0 1 1
1281 NSP2 orf1ab - Ala339Val QHD43415.1:p.339A>V; QHD43415.1:p.339- MODERATE; MODIFIER 0 3 0 8717 2 1
1601 NSP2 orf1ab - - QHD43415.1:p.446L>I MODERATE 1 0 0 8719 2 1
1601 NSP2 orf1ab - - QHD43415.1:p.446L>I MODERATE 1 0 0 8719 2 1
1887 NSP2 orf1ab - - QHD43415.1:p.541A>V; QHD43415.1:p.541- MODERATE; MODIFIER 0 2 0 8717 3 1
1912 NSP2 orf1ab - - QHD43415.1:p.549S LOW 0 17 0 8703 2 1
1912 NSP2 orf1ab - - QHD43415.1:p.549S LOW 0 17 0 8703 2 1
2040 NSP2 orf1ab - - QHD43415.1:p.592T>I MODERATE 0 1 0 8719 2 1
2485 NSP2 orf1ab - - QHD43415.1:p.740I LOW 0 2 0 8719 1 1
2558 NSP2 orf1ab A1a1b Pro765Ser QHD43415.1:p.765P>S; QHD43415.1:p.765- MODERATE; MODIFIER 0 321 0 8390 11 1
3145 NSP3 orf1ab - - QHD43415.1:p.960L>F MODERATE 0 9 8710 0 3 1
3176 NSP3 orf1ab - - QHD43415.1:p.971P>S MODERATE 0 4 0 8712 6 1
3429 NSP3 orf1ab - - QHD43415.1:p.1055T>I MODERATE 0 1 0 8720 1 1
3604 NSP3 orf1ab - - QHD43415.1:p.1113H LOW 0 0 0 8721 1 1
4144 NSP3 #N/A - - #N/A #N/A 0 0 8718 0 4 1
4680 NSP3 #N/A - - #N/A #N/A 0 0 0 8721 1 1
4859 NSP3 #N/A - - #N/A #N/A 0 0 8720 0 2 1
5008 NSP3 orf1ab - - QHD43415.1:p.1581T LOW 0 0 8720 0 2 1
5151 NSP3 orf1ab - - QHD43415.1:p.1629V>A MODERATE 0 8720 0 0 2 1
5657 NSP3 orf1ab - - QHD43415.1:p.1798V>I; QHD43415.1:p.1798V>L MODERATE 1 0 8720 0 1 1
5730 NSP3 orf1ab - - QHD43415.1:p.1822T>I MODERATE 0 10 0 8711 1 1
6395 NSP3 orf1ab - - QHD43415.1:p.2044L LOW 0 3 0 8709 10 1
7071 NSP3 #N/A - - #N/A #N/A 1 0 8718 0 3 1
7350 ORF1a/b #N/A - - #N/A #N/A 0 0 0 8720 2 1
7734 ORF1a/b #N/A - - #N/A #N/A 0 8717 0 0 5 1
8595 NSP4 orf1ab - - QHD43415.1:p.2777T>I MODERATE 0 0 0 8721 1 1
9477 NSP4 orf1ab - - QHD43415.1:p.3071F>Y MODERATE 150 8567 0 0 5 1
9634 NSP4 orf1ab - - QHD43415.1:p.3123L>F MODERATE 8710 4 0 0 8 1
10039 NSP4 orf1ab - - QHD43415.1:p.3258T; QHD43415.1:p.3258- LOW; MODIFIER 0 1 0 8717 4 1
10449 Mpro orf1ab - - QHD43415.1:p.3395P>L; QHD43415.1:p.3395- MODERATE; MODIFIER 0 3 0 8717 2 1
11074 NSP6 orf1ab - - QHD43415.1:p.3604F>X; QHD43415.1:p.3603F;
QHD43415.1:p.3603F>FX;		 HIGH; LOW; MODERATE;
MODIFIER		 0 22 0 86 3 1
11109 NSP6 orf1ab - - QHD43415.1:p.3615A>V MODERATE 0 20 0 8699 3 1
12929 NSP9 orf1ab - - QHD43415.1:p.4222- MODIFIER 0 0 8720 0 2 1
13038 NSP10 #N/A - - #N/A #N/A 0 0 0 8720 2 1
13862 NSP12/RdRP orf1ab - - QHD43415.1:p.4533T>I; QHD43415.1:p.4533- MODERATE; MODIFIER 0 13 0 8707 2 1
14220 NSP12/RdRP orf1ab - - QHD43415.1:p.4652D LOW 0 1 0 8720 1 1
15290 NSP12/RdRP #N/A - - #N/A #N/A 0 0 8721 0 1 1
15344 NSP12/RdRP #N/A - - #N/A #N/A 0 0 0 8721 1 1
15435 NSP12/RdRP orf1ab - - QHD43415.1:p.5057- MODIFIER 8720 0 0 0 2 1
15669 NSP12/RdRP #N/A - - #N/A #N/A 0 8720 0 1 1 1
16221 NSP12/RdRP orf1ab - - QHD43415.1:p.5319P LOW 0 0 8721 0 1 1
16377 NSP13/Helicase orf1ab - - QHD43415.1:p.5371P LOW 0 5 8716 0 1 1
17122 NSP13/Helicase orf1ab - - QHD43415.1:p.5620A>T; QHD43415.1:p.5620A>S MODERATE 1 0 8719 0 2 1
17122 NSP13/Helicase orf1ab - - QHD43415.1:p.5620A>T; QHD43415.1:p.5620A>S MODERATE 1 0 8719 0 2 1
17403 NSP13/Helicase orf1ab - - QHD43415.1:p.5713A LOW 0 2 0 8718 2 1
17415 NSP13/Helicase orf1ab - - QHD43415.1:p.5717A LOW 0 8721 0 0 1 1
17415 NSP13/Helicase orf1ab - - QHD43415.1:p.5717A LOW 0 8721 0 0 1 1
17656 NSP13/Helicase orf1ab - - QHD43415.1:p.5798M>V MODERATE 8700 0 3 0 19 1
17747 NSP13/Helicase orf1ab B1a1 - QHD43415.1:p.5828P>L; QHD43415.1:p.5828- MODERATE; MODIFIER 0 856 0 7842 24 1
17858 NSP13/Helicase orf1ab B1a - QHD43415.1:p.5865Y>C MODERATE 7845 0 876 0 1 1
18060 NSP14/Exonuclease orf1ab B1 - QHD43415.1:p.5932L; QHD43415.1:p.5932- LOW; MODIFIER 0 886 0 7832 4 1
18078 NSP14/Exonuclease orf1ab - - QHD43415.1:p.5938K LOW 1 0 8720 0 1 1
18312 NSP14/Exonuclease orf1ab - - QHD43415.1:p.6016V LOW 1 2 0 8718 1 1
18312 NSP14/Exonuclease orf1ab - - QHD43415.1:p.6016V LOW 1 2 0 8718 1 1
18573 NSP14/Exonuclease orf1ab - - QHD43415.1:p.6103S LOW 0 8720 0 1 1 1
19644 EndoRNAse #N/A - - #N/A #N/A 0 8697 0 0 25 1
19861 EndoRNAse orf1ab - - QHD43415.1:p.6533A>T MODERATE 1 0 8681 0 40 1
19862 EndoRNAse orf1ab - - QHD43415.1:p.6533A>V; QHD43415.1:p.6533- MODERATE; MODIFIER 0 5 0 8676 41 1
19875 EndoRNAse #N/A - - #N/A #N/A 0 0 0 8682 40 1
20255 EndoRNAse orf1ab - - QHD43415.1:p.6664-; QHD43415.1:p.6664D>G MODIFIER; MODERATE 8720 0 0 0 2 1
20429 EndoRNAse orf1ab - - QHD43415.1:p.6722P>L MODERATE 0 1 0 8720 1 1
20580 EndoRNAse orf1ab - - QHD43415.1:p.6772V; QHD43415.1:p.6772- LOW; MODIFIER 1 3 8715 0 3 1
20749 O-ribose
methyltransferase		 #N/A - - #N/A #N/A 0 0 8721 0 1 1
21707 S-Protein S - - QHD43416.1:p.49H>Y MODERATE 0 29 0 8687 6 1
21989 S-Protein S - - QHD43416.1:p.143-144VY>D; QHD43416.1:p.143V>F MODERATE 0 1 8714 0 7 1
21990 S-Protein S - - QHD43416.1:p.143-144VY>V MODERATE 0 8712 0 0 10 1
22430 S-Protein #N/A - - #N/A #N/A 0 0 8518 0 204 1
22458 S-Protein S - - QHD43416.1:p.299T>I MODERATE 0 1 0 8469 252 1
23042 S-Protein S - - QHD43416.1:p.494- MODIFIER 0 8686 0 1 35 1
23108 S-Protein #N/A - - #N/A #N/A 0 0 8678 1 43 1
23660 S-Protein #N/A - - #N/A #N/A 0 0 8721 0 1 1
23677 S-Protein S - - QHD43416.1:p.705V LOW 0 8721 0 0 1 1
24166 S-Protein #N/A - - #N/A #N/A 8719 0 0 0 3 1
24622 S-Protein S - - QHD43416.1:p.1020- MODIFIER 0 8719 0 0 3 1
24694 S-Protein S B1a1a - QHD43416.1:p.1044G LOW 8634 87 0 0 1 1
24904 S-Protein S - - QHD43416.1:p.1114-; QHD43416.1:p.1114I MODIFIER; LOW 0 5 0 8715 2 1
25318 S-Protein S - - QHD43416.1:p.1252S LOW 0 0 0 8719 3 1
25318 S-Protein S - - QHD43416.1:p.1252S LOW 0 0 0 8719 3 1
25350 S-Protein S A2a10 - QHD43416.1:p.1263P>L; QHD43416.1:p.1263- MODERATE; MODIFIER 0 51 0 8661 10 1
25642 ORF3a ORF3a - - QHD43417.1:p.84L LOW 0 1 0 8720 1 1
25793 ORF3a ORF3a - - QHD43417.1:p.134R>H; QHD43417.1:p.134R>L MODERATE 0 3 8706 0 13 1
25826 ORF3a #N/A - - #N/A #N/A 8706 1 0 0 15 1
25919 ORF3a ORF3a - - QHD43417.1:p.176T>I MODERATE 0 1 0 8711 10 1
25979 ORF3a ORF3a - - QHD43417.1:p.196G>V; QHD43417.1:p.196- MODERATE; MODIFIER 0 149 8570 0 3 1
27191 M-Protein M - Ter223Ter QHD43419.1:p.223-; QHD43419.1:p.223* MODIFIER; LOW 8717 0 2 0 3 1
27195 orf #N/A - - #N/A #N/A 8720 0 0 0 2 1
27208 ORF6 ORF6 - - QHD43420.1:p.3H>Y; QHD43420.1:p.3- MODERATE; MODIFIER 0 3 0 8716 3 1
27364 ORF6 ORF6 - - QHD43420.1:p.55E>*; QHD43420.1:p.55E>Q HIGH; MODERATE 0 1 8720 0 1 1
27384 ORF6 ORF6 - - QHD43420.1:p.61D; QHD43420.1:p.61- LOW; MODIFIER 0 8682 0 38 2 1
27874 ORF7b Intergenic - - QHD43422.1 MODIFIER,DISTANCE=20 0 0 0 8705 17 1
28115 ORF8 ORF8 - - QHD43422.1:p.74I LOW 0 1 0 8703 18 1
28115 ORF8 ORF8 - - QHD43422.1:p.74I LOW 0 1 0 8703 18 1
28253 ORF8 ORF8 - - QHD43422.1:p.120F>L; QHD43422.1:p.120F;
QHD43422.1:p.120-; QHD43422.1:p.121I>X		 MODERATE; LOW; MODIFIER; HIGH 3 5 2 8703 9 1
28657 N-capsid N - - QHD43423.2:p.128D; QHD43423.2:p.128- LOW; MODIFIER 0 151 0 8569 2 1
28863 N-capsid N - - QHD43423.2:p.197S>L; QHD43423.2:p.197- MODERATE; MODIFIER 0 149 0 8570 3 1
29574 ORF10 #N/A - - #N/A #N/A 0 8711 0 0 11 1
29614 ORF10 ORF10 - - QHI42199.1:p.19C LOW 0 8 0 8703 11 1
29614 ORF10 ORF10 - - QHI42199.1:p.19C LOW 0 8 0 8703 11 1
29807 3'UTR #N/A - - #N/A #N/A 1 80 0 1 68 1
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Sample collection and molecular investigations

The nasopharyngeal and oropharyngeal swabs (in viral transport medium) were received at NCDC, Delhi through the Integrated Disease Surveillance Programme were subjected to viral inactivation followed by RNA extraction using QIAamp Viral RNA Mini Kit (Cat. No. 52906, Qiagen). Total RNA content in the elute was quantified using NanoDrop (Thermo Fisher Scientific). The 260/280 ratio ranged between 1.6–2.2 for the majority of the samples. To ensure that sub-optimal RNA samples are also included in the study, we made use of SuperScript IV (Cat. No. 18091050, Thermo Fisher Scientific, Waltham, MA, USA), for superior first strand cDNA synthesis and included them for sequencing.

Molecular diagnosis of COVID-19

A quantitative reverse transcription (RT)-PCR assay was used on purified RNA for detection of SARS-CoV-2 in the samples. Quantitative RT-PCR was carried out using TaqMan assay chemistry on ABI7500 platform. The primer/probe concentrations and reaction conditions for diagnostics were as per the WHO protocols ( Corman et al., 2020). Two target genes were used for diagnosis of SARS-CoV-2, envelope (E) gene for screening and RNA dependent RNA polymerase (RdRp gene) for confirmation. The positive samples were analyzed based on the country of origin (traveller), contact with positive case, geographical location (community), gender and age. Samples from each group were selected and further processed for WGS of the SARS-CoV-2.

Whole genome sequencing of SARS-CoV-2

cDNA synthesis: Total RNA from SARS-CoV-2 positive samples were quantified using Nanodrop and 50 ng of the RNA was taken for double-stranded cDNA synthesis. Briefly, first strand cDNA was made using 1.0 μl of random hexamer (50 ng/μl), 1.0 μl of dNTPs (10 nM) and 13.0 μl of total RNA with volume adjusted with nuclease-free water (NFW), followed by incubation at 65°C for 5 mins and cooling on ice. To this, 4.0 μl of 5X SSRT IV Buffer, 1.0 μl of 100 mM DTT, 1.0 μl of ribonuclease inhibitor and 1.0 μl of SSRT IV enzyme (200 U/μl) was added (Cat. No. 18091050, Thermo Fisher Scientific, Waltham, MA, USA) with incubation at 23°C for 10 minutes, 50°C for 10 minutes and 80°C for 10 minutes. 1.0 μl of RNase H was added to this and incubated at 37°C for 20 mins. Next, 20.0 μl of first strand cDNA was heated at 95°C for 3 minutes after addition of 10 pmol of random primers, 10 μM dNTPs and 1X Klenow Buffer, followed by immediate cooling on ice. Soon after, 1.0 μl of Klenow Fragment (Cat. No. M0210S, New England Biolabs) was added with incubation at 37°C for 60 mins, 75°C for 10 mins and 4°C for 10 mins. This was followed by Ampure beads purification (Cat. No. A63881, Beckman Coulter) and quantification using Qubit dsDNA HS assay kit (Cat. No. Q32854, Invitrogen).

Nanopore library preparation and sequencing: A total of 100 ng of double stranded cDNA was taken for next generation sequencing (NGS) using a highly multiplexed PCR amplicon approach for sequencing on the Oxford Nanopore Technologies (ONT) (Oxford, United Kingdom) MinION using V3 primer pools (ARTIC Protocol, see https://artic.network/resources/ncov/ncov-amplicon-v3.pdf). Amplification was done using Takara LA Taq® DNA Polymerase Hot-Start Version (Cat. No. RR042B, Takara) along with 2.5 mM dNTPs, 10X Buffer II LA Takara, 10 μM primer pool, with the final volume adjusted to 25 μl using NFW. Primers were made into two pools, pool A and pool B, with 5 μl of each primer from the 100 μM primer stock. The stock (100 μM) was diluted with NFW in order to obtain a working stock of each pool at 10 μM. PCR was performed with initial denaturation at 98°C for 30 secs followed by denaturation at 98°C for 15 secs, 65°C for 5 mins, for a total of 35 cycles, with hold at 4°C. Post PCR pooling (pool A and pool B) of PCR amplicons was followed by purification using Ampure beads. Following purification, 1.0 μl of the library was run on a DNA1000 Agilent bioanalyzer (Cat. No. 5067-1504, Agilent) to check for a size of ~400 bp. Next, 125 ng of each sample was taken forward for End prep with NEBNext Ultra II End Repair/dA tailing module (Cat. No. E7546, New England Biolabs). The reaction mix was incubated on a thermal cycler at 20°C for 5 mins followed by 65°C for 5 mins. Following this, 1.5 μl of End prep DNA was taken forward for native barcode ligation using native barcodes (EXP-NBD104 and EXP-NBD114, ONT) and Blunt/TA Ligase master mix (Cat. No. M0367, New England Biolabs). The mix was incubated at room temperature for 15 mins followed by purification using Ampure beads. The purified product was used for adaptor ligation using Adapter Mix II and Quick T4 DNA ligase (Cat. No. M2200L, New England Biolabs). After adaptor ligation, it was purified using a combination of short fragment buffer and Ampure beads resulting in a sequencing ready library. Library quantification was conducted using the Qubit dsDNA HS assay kit (Cat. No. Q32854, Invitrogen) and 70 ng of the library was used for sequencing. Barcoding, adaptor ligation, and sequencing were performed on samples with Ct values between 16–31. A ‘no template control’ was created at the cDNA synthesis step and amplicon generation step to detect cross-contamination between samples. Controls were barcoded and sequenced with both the high- and low-titer sample groups. The sequencing flowcell was primed and used for sequencing using MinION Mk1B.

Illumina library preparation and sequencing

A common pool of cDNA was used for making both Illumina and Nanopore sequencing libraries and subsequent sequencing. cDNA (100 ng) was used to construct the Illumina library using theNextera XT protocol, as per manufacturer’s instructions (15031942 v05, Illumina Inc). Briefly, tagmentation of cDNA was done which tagged and fragmented the cDNA by addition of amplicon tagment mix (ATM) and tagment DNA buffer, as per manufacturer’s protocol, Illumina Inc with incubation at 55°C for 5 mins with heated lid option. Tagmentation was stopped by addition of neutralization tagment buffer. This was followed by the addition of unique index adapters (i7 and i5 adapters) to the samples. Index adapters are then used for PCR amplification at 72°C for 3 mins, 95°C for 30 secs and 12-cycles of 95°C for 10 secs, 55°C for 30 secs, 72°C for 30 secs; and 72°C for 5 mins. The PCR product was purified using AgencourtAMPure XP beads. The quantity of the sequencing ready library was measured using Qubit dsDNA HS assay kit (Cat. No. Q32854, Invitrogen) and quality by Agilent DNA HS kit (Cat. No. 5067-4626, Agilent). Illumina’s MiSeq platform was used for sequencing.

Analysis pipeline for nanopore sequencing data

The raw fast5 files were base-called and demultiplexed using the Guppy basecaller (version 3.5.2). The fastq files were normalized by read length, thereby eliminating possible chimeric reads. Pre-alignment quality control was carried out to assess the read quality using Nanopack tools (version 1.29.0) ( De Coster et al., 2018). Minimap2 (version 2.17) has been used to align the raw reads with the reference ( MN908947.3) ( Li, 2018). Nanopolish were used for accurate variant calling from the aligned output ( Loman, 2015) with options, minimum flanking sequence - 10, ploidy - 1 and minimum candidate frequency - 0.15. The possible heterozygous variants are filtered out as a separate group after the variants have been called. Post-alignment QC was then performed with Nanopack tools as well as the seaborn (version 0.10.1) package in python to create the distribution of amplicon quality and CT-value vs coverage and depth. Finally, a consensus fasta was created, wherein genomic regions with low coverage and low quality were masked using BCFtools (version 1.9).

Miseq data analysis

The raw reads from the miseq were quality-checked by FASTQC (version 0.11.8). Trimgalore was used to trim the reads containing bad quality and the minimum length of 40 base pairs was kept as a threshold for the reads. HISAT2 is used to map the reads to the human genome (GRCh37) to remove potential the human rRNA reads for the contamination with default parameters [ Kim et al., 2019]. The unmapped reads from the human are converted from bam to fastq using bam2fastq (version 2.29) to align to the SARS-CoV-2. HISAT2 was used to align unmapped reads with the SARS-CoV-2 reference genome (MN908947.3 build). Using both samtools (version 1.9) and BCFtools from the SARS-CoV-2 aligned bam files the consensus fasta was generated. The variants in the samples were called using BCFtools and VarScan.

Phylogeny and network analysis

The fasta sequences were aligned using MAFFT (version 7.455) considering the MN90894.3 version as the reference sequence. Phylogenetic trees were constructed using the Neighbour joining algorithm as statistical method and maximum composite likelihood as model in MEGA X software. FIGTREE (version 1.4.4) was used for the graphical visualisation of phylogenetic analysis. Pheatmap (version 1.0.12) and ComplexHeatmap (version 1.10.2) packages from R 3.6.2 were used to plot the heatmaps. Haplotype network analysis was conducted using PopART (version 1.7) [ Leigh & Bryant, 2015].

Protein based annotation

In order to categorize the specific amino acid change and the proteins containing the variants, they were annotated with SnpEff (version 4.5) [ Cingolani et al., 2012]. The annotation was performed according to the known reference genome of SARS-CoV-2 (i.e. NC_045512) in the NCBI database [ Wang et al., 2020]. SARS-CoV-2 polypeptide ORF1ab encodes 16 non-structural proteins (nsp) as a result of proteolytic processing. Hence, for better mapping of the variants present in ORF1ab, we annotated the variants according to the respective nsp residue number.

Further, conservation analysis of the full-length sequences of proteins harbouring these mutations was done on the basis of the six other coronaviruses. The multiple sequence alignment of seven protein sequences was performed with Clustal Omega [ Madeira et al., 2019]. The conservation score of ORF3a and ORF8 were calculated with low confidence due to introduced gaps at these positions during alignment. The amino acid type was defined as hydrophobic (G, A, V, L, I, M, P, F, W), polar (S, T, N, Q) or charged (H, K, R, D, E). With this definition, the type of change of residues was calculated.

Three-dimensional protein models

To map the high frequency mutations on proteins, we took protein structure models of SARS-CoV-2 from the Swiss Model repository ( https://swissmodel.expasy.org/repository/species/2697049) [ Waterhouse et al., 2018] and models were generated through comparative modeling and by using Robetta prediction server [ Kim et al., 2004]. In total three structural models were obtained from swiss model repository and the nsp12 structure was obtained from RCSB [PDB ID 6M71] [ Gao et al., 2020]. The details of missing residues or structural domains of each protein is described below. The Spike protein exists as a homotrimer consisting of 1273 residues in each chain with a total of 3819 amino acids. While electron microscopy structures are available for different conformations of Spike protein, in particular S1 region, many residues within the S1 and S2 stalks are missing [ Walls et al., 2020; Wrapp et al., 2020]. Therefore, to map the mutations onto the structure we obtained the S1 stalk model of the spike protein (residues 15-1137). Similarly, nsp3 also known as PL-PRO (papain-like proteinase) is a large multi-domain transmembrane protein. For mapping the nsp3 mutations, we considered the model for the nucleic acid binding domain (residue 1089-1203), which is conserved in betacoronaviruses [ Angelini et al., 2013]. The Nucleocapsid protein comprise of N-terminal and C-terminal domains connected by linker region. However, structural information for the linker region is unknown.

Results

Demographic details and travel history of the subjects

The majority of the SAS-CoV-2-positive samples were obtained from New Delhi, covering the national capital region of Delhi, India and a few clusters identified by the surveillance team (covering the states of Delhi, Tamil Nadu, Maharashtra, Uttar Pradesh, Andhra Pradesh, West Bengal, Bihar, Orissa, Rajasthan, Haryana, Punjab, Assam and Union territory of Ladakh). The mean (standard deviation) age of the total 127 subjects was 41.4±17.5 years with age range 0.5–76 years and median of 39 years. The male-to-female gender ratio of in the age group <39 years was 35:28, while the remaining 46 subjects >40 years had the ratio of 58:6. Exposure to COVID-19 was suggestive of travel history of subjects to Europe, West Asia and East Asia. A minority of subjects were from foreign countries: Indonesia (n=14), Thailand (n=2) and Kyrgyzstan (n=2). The identified localities of the subjects will further help in molecular surveillance of SARS-CoV-2 in respective geographical regions.

Profile of SARS-CoV-2 genome sequences

The average amplicon coverage for the V3 ARTIC primers used in the study was more than 1000X coverage across the majority of the samples ( Extended data, Supplementary figure S2 [ Kumar et al., 2020b]). We also looked into whether lower Ct values are a good indicator of genome coverage using a minimal set of virus mapping reads. We plotted genome coverage and average sequencing depth across Ct value of both the genes (E and RdRp). It was observed that higher Ct values (27 onwards) have increased possibility of lower genome coverage ( Extended data, Supplementary figure S2 [ Kumar et al., 2020b]), although some lower Ct value samples also had incomplete genome coverage. We sequenced a subset of samples on orthogonal platforms and sequencing methods (shotgun and amplicon) using ONT and Illumina platform. Significantly, we observed that the genetic variants were common between both the platforms.

NGS analysis and construction of phylogeny network for SARS-CoV-2 sequences

A total of 104 samples passed the quality threshold for mapping full genome coverage threshold for SARS-CoV-2 genome <0.05 N content with median coverage ~1500× (see Underlying data for each accession number [ Kumar et al., 2020a]. A total of 23 samples that did not qualify the threshold criteria were excluded from strain identification. The phylogenetic analysis of 104 high quality sequences reveal all the strains to be grouped into two major clades, a sub-clade and other clades ( Figure 1 and Extended data, Supplementary figure S3 [ Kumar et al., 2020b]). From variants perspectives, we observed 163 variants representing singletons (107 variants), rare: 2-5% (45 variants), and common variants: >5% (11 variants). The common variants observed were 241 (Leader sequence), 3037 (NSP3), 6310 (NSP3), 6312 (NSP3), 11083 (NSP6), 13730 (NSP12/RdRp), 14408 (NSP12/RdRp), 23403 (S-Protein), 23929 (S-Protein), 25563 (ORF3a), 28311 (N-capsid). The following cluster-based segregation of SARS-CoV2 sequences was observed.

Figure 1. Haplotype network and phylogenetic analysis of SARS-CoV-2 sequences.

Figure 1.

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( A) The network analysis (integer neighbourhood joining network at reticulation tolerance value of 0.5, popART) of SARS-CoV-2 sequences from this study showing distinct clades with their geographical locations. A4 clade described in this study for the first time has widespread geographical affiliations. A3 being more confined to Ladakh and mostly these clusters represent introduction of the infection through travel history. Mutations are marked with hatch lines connecting the nodes. ( B) Phylogenetic tree (generated using MEGA) of SARS-CoV-2 genomes from sequences submitted from across India depicting major clade-based distribution of SARS-CoV-2 in India. ( C) Network analysis (Median spanning network) of A4 clade sequences submitted from Indian and neighbouring East Asians countries.

Cluster 1: The sequences (n=26) in this cluster belonged to the G-clade [variant 23403 (Spike protein D614G) as per GISAID nomeclature (Global Inititative on Sharing All Influenza Data)] Based on Nextstrain classification these 26 sequence belonged to A2a clade [(denoted by positions C241T; C3037T, A23403G (S: D614G); C14408T (ORF1b/RdRp: P314L)] ( Figure 1A). The additional frequent variants in this cluster observed were 25563 (ORF3a, n=9),18877 (NSP14/Exonuclease, n=7), 26735 (M-protein, n=6), 22444 (S-protein, n=5), and 28854 (N-capsid, n=5). One novel variant 1947 T>C (NSP2) was observed in two strains in this cluster.

Cluster 2: In our study large numbers of strains (n=65) belong to this unclassified cluster (as per GISAID and Nextstrain). The strains in this cluster had the predominant variants, G11083T variant (NSP6) (n=65), C13730T in RdRp (n=65), C28311T (N-capsid); n=65, C6312A (NSP3 variant); n=64 (one sequence being called N), C23929T (S-protein), n=50 (other being low depth/N bases). The variant C6310A (NSP3); n=22 being observed as another frequent alteration ( Figure 1B). We also observed few novel variants, G12685T ( NSP8); n=5 and TC1706T ( NSP2); n=4, T7621C ( ORF1a/b); n=3, A21792T (S protein); n=3, G13920A (NSP12/RdRp); n=2, A16355G (NSP13/Helicase); n=2 and G18803T ( NSP14/Exonuclease); n=2 in this cluster. The majority of the key cluster variants 11083, 13730, 28311, 6312, 23929 are also shared in sequences submitted from Singapore and Brunei; additionally, similar clade sequences were observed in India submitted by National Institute of Mental Health and Neuro-Sciences (NIMHANS) and Gujarat Biotechnology Research Centre (GBRC) cohort (see Underlying data for details of all accession numbers [ Kumar et al., 2020a]. Based on the geographical location of the subjects of this cluster, a considerable number of Indonesians (n=7) and two each from Thailand and Kyrgyzstan were part of this cluster from our study site. This probably suggests introduction of this particularly from East Asian countries into India.

Cluster 3: This subclass of strains (n=7) harbouring a common variant G11083T (NSP6), G1397A (NSP2) and T28668C (N-capsid) are described for the A3 clade (Nextstrain) in additions to G29742T ( Figure 1A). Other mutated positions, i.e. C884T (NSP2), G8653T (NSP4) were observed in 5 samples, whereas T16993C (NSP13), n=4; T25461C (ORF3a), n=4 and A27191G (M-protein), n=2 are putative novel sites.

The phylogeny analysis of these clusters segregated with the other Indian SARS-CoV-2 genome sequences as recently reported (GISAID) ( Figure 1B).

Other SARS-CoV-2 genomes: Two SARS-CoV-2 belonging to the A1a clade had a SNP profile of 11083(NSP6)/14805 (NsP12/RdRp)/2480 (NSP2)/2558 (NSP2)/26144 (ORF3a). In addition, we observed three B clade sequences having position 8782 C>T (NSP4) and 28144 T>C (ORF8; S clade GISAID) mutated and with one sequence with an additional C18060T B1 variant. One genome from Maharashtra had no variants and probably represented the first genome sequenced from Wuhan, China.

Redefining cluster 2 with neighbourhood re-joining

With over represented variants in cluster 2 for variants 11083/13730/28311/6312/23929, we defined this cluster with A4 clade. This has similarity with sequences submitted from Singapore, Brunei and other Indian sequences submitted. The haplotype network analysis suggests that these sequences are having a common origin from East Asia/South-East Asia ( Figure 1C and Figure 2). This A4 clade has multiple variants in important region of viral genome, RdRp (A97V), N-capsid (P12L), NSP3 (T2016K), NSP6 (L37F) and NSP3 (S1197R) variants. In our cohort of samples, the majority of subjects were from Tamil Nadu, Delhi and Indonesia and others were from various other states ( Figure 2).

Figure 2. Distribution of A4 clade within India and globally.

Figure 2.

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( A) Distribution of A4 clade variants across different geographical regions from the cohort. ( B) Distribution of A4 clade variants across different geographical regions across the globe. ( C) Comparison of A4 cluster sequences across the South-East Asian region showing sharing of variants and haplotype across South-East Asian region.

Protein-wise analysis of SARS-CoV-2 variants

To provide quantitative insights into the mutant proteins, we characterized amino acid substitutions across the 104 viral genomes. Of the 53 point mutations identified, 29 were missense that resulted in amino acid substitutions. Extended data, Supplementary figure S4 [ Kumar et al., 2020b] plots the occurrence of these mutations as a function of each viral protein. The frequency of amino acid variations was highest in nsp6 (L37F), present in 68 genomes, followed by nsp12 (A97V) in 65, nsp3 (T1198K) in 62 and nucleocapsid (P13L) in 53 genomes. Interestingly, the D614G mutation in spike protein, which is considered as a prevalent global mutation [ Bhattacharyya et al., 2020; Korber et al., 2020], was present in only 26 of the 104 sequenced genomes.

The analysis of occurrence of each mutation with the type of amino acid change have shown that ~45% of these are synonymous changes ( Extended data, Supplementary figure S5 [ Kumar et al., 2020b]). Within frequently occurring mutations, P13L, L37F, A97V also showed no major residue alterations. However, T1198K in nsp3 involve acquisition of a charged group along with the key S protein mutation (D614G) also involves loss of the charged group. These mutations that lead to positively charged groups may cause more severe structural and functional effects.

We also compared SARS-CoV-2 mutation sites with other six coronavirus sequences ( Extended data, Supplementary figure S5b [ Kumar et al., 2020b]). Most of the mutations were present in variable locations. Out of 29 mutations, 10 are present on highly conserved residue locations. Interestingly, a higher frequency of mutations are at positions that evolve faster/are variable across the coronaviruses, except for A97L and L37F, which are present on conserved locations.

The structural analysis of different viral proteins, nucleocapsid, nsp3, nsp12, and spike protein was conducted and analysis of nucleocapsid protein [ Kang et al. 2020] showed its variants were present in the linker region ( Figure 3A). The observed mutations in nsp12 (a highly conserved protein) are overlaying onto the interface (P323L) and NiRAN (A97L) region. The latter is critical as it contains a Zn+ binding site; however, little is known about the exact functional output. In contrast, the P323L mutation is present on protein interaction junctions where a hydrophobic cleft is known to bind to inhibitors ( Figure 3B).

Figure 3. Mapping of high frequency mutations on proteins displaying top-most mutations in SARS-CoV2.

Figure 3.

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( A) Nucleocapsid, the N- and C-terminal domains are coloured in red and purple, respectively, where the missing linker region is shown as dotted line. ( B) NSP12/RdRp, showing the five of its domains, NIRAN (olive), Palm (red), Thumb (green), Fingers(blue) and Interface(orange). ( C) Nucleic acid-binding domain (NAB) of Nsp3 protein shown in pink color. ( D) The trimeric spike protein showing 3 chain like structure. The right panel shows a single chain where the NTD, RBD and HR1 domains are coloured in orange, purple and green, respectively, whereas the rest of the structure is coloured grey. The mutations in all the proteins are marked within black boxes shown in yellow spheres.

The amino acid change from proline to leucine may result in significant backbone changes, due to the absence of unique proline-induced distortions in the protein backbone. Next, we mapped mutations within Nsp3 protein ( Figure 3C). In particular, the mutations were present on the NAB domain of nsp3, which is a nucleic acid binding domain and also interacts with nsp12 [ Jian et al., 2018]. This mutation may impact RNA synthesis machinery; however, little is known about its exact mechanism of action. Lastly, the D614G mutation in spike protein is an interesting substitution and has been reported with increased tally ( Figure 3D) [ Bhattacharyya et al., 2020; Korber et al., 2020]. Structurally, this mutation is located in the S1 subunit that also contains the RBD domain. Although present outside the functional region, the proximity of D614G around S1 cleavage site implicates an important change in the local environment.

Discussion

This is the first comprehensive genomic picture of the SARS-CoV-2 prevalent in the Indian population during the early phase of outbreaks. The understanding is important keeping in view the vast geographical expanse and population density of India. There were three major waves of viral entry in India associated with multiple outbreaks ( Extended data, Supplementary figure S6 [ Kumar et al., 2020b]). The first wave includes importation of SARS-CoV-2 (A2a cluster) through travelers from Europe (Italy, UK, France, etc.) and the USA. Second wave of SARS-CoV-2 (A3 cluster) was linked with the Middle East (Iran and Iraq). The third wave comprises combined viral (haplotype redefined as A4) entries from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The study, taken together with those of other reported genomes ( Potdar et al., 2020), revealed that the A4 cluster (previously unclassified) is the most prevalent in the available genome sequences from India. The observed distinct A4 genome lineage of SARS-CoV2 in the Indian Subcontinent, which is present in East Asian Countries like Singapore and Indonesia, may allow further research and investigation to understand the evolution of SARS-CoV-2 genomes in Southeast Asian countries. Many novel mutations identified may be specific to Indian conditions, but more genomic data is needed to strengthen the assumption to rule out sampling bias and other factors ( Lu et al., 2020). However, a more detailed analysis of these genomes might provide information whether these variations need to be considered during design of diagnostic primers as the need for testing shoots up. It may allow for creation of cost-effective panels to trace the movement of lineage specific strains across geographical regions more rapidly and effectively. Lots of efforts are ongoing to identify suitable vaccine candidates through docking studies. These observations are important to consider the variants that map to the Indian genomes during such prioritization studies, since these strains would now form a major fraction of the genomes that are likely to become more prevalent in India after lockdown. Mapping of these variant genomes in conjunction with the clinical history in terms of recovery, hospitalization and co-morbidity might allow identification of variants that should be actionable and would also have relevance for prognosis. It is imperative that robust genomic data based on large sample size, including rural populations with even distribution can bring out the real scenario once correlated with epidemiological data eventually helping in drafting of further management policies.

Data availability

Underlying data

All sequence data have been deposited with GISAID [ Shu & McCauley, 2017].

Figshare: Acknowledgement table GISAID and Accession. https://doi.org/10.6084/m9.figshare.12624815.v4 [ Kumar et al., 2020a].

This project contains all GISAID accession numbers generated and analysed in this study.

Extended data

Figshare: Supplementary figures S1-S6. https://doi.org/10.6084/m9.figshare.12631880.v1 [ Kumar et al., 2020b].

This file contains the following extended data:

  • Supplementary Figure S1: A schematic diagram showing numbers of samples with their geographical affiliations with respect to states of India.

  • Supplementary Figure S2: Sequencing data quality parameters and orthogonal platform validation.

  • Supplementary figure S3: A heat map representation of SARS-CoV-2 variants per sample and their respective segregation in respective clusters.

  • Supplementary Figure S4: Protein annotation of the amino acid substitutions.

  • Supplementary Figure S5: Amino acid properties marked as a function of mutations.

  • Supplementary Figure S6: Scheme showing importation of prevalent SARS-CoV-2 genomes (3 major waves) in India.

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

NCDC greatly acknowledge the support of Prof. Dr. Christian Drosten, Charite – Universitatsmedizin, Berlin for promptly providing the positive controls for qPCR. The authors do acknowledge GISAID for sharing the genomic sequences in public domain and other contributors SARS-CoV-2 genomic data (detailed acknowledgement mentioned in the supplementary material). We would like to gratefully acknowledge the efforts of officials involved in IDSP network and associated hospitals in sample collection and timely data sharing. We would like to thank the financial aid provided by Ministry of Health and Family Welfare, Government of India. CSIR-IGIB would like to acknowledge Genotypic Technologies Pvt. Ltd., Bangalore, India for its role in facilitating ONT sequencing. Authors also acknowledge the role of all the technical and support staff of NCDC involved in COVID-19 testing and Subhash Gurjar (CSIR-IGIB) for facilitating reagents procurement amidst lockdown and for providing other assistance in the laboratory work. ICMR fellowship - 2019-6259 (ISRM/11(34)/2019) for Vivekanand A is acknowledged.

Funding Statement

This work was funded by the Wellcome Trust/DBT India Alliance [Senior Fellowship Grant IA/CPHS/14/1/501489 to AA] and the Council of Scientific and Industrial Research [Grant MLP2005].

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

[version 1; peer review: 3 approved]

Author information

Pramod Kumar, Rajesh Pandey, Pooja Sharma and Mahesh S. Dhar are joint first authors. Mohammed Faruq, Anurag Agrawal and Partha Rakshit are joint senior authors.

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Wellcome Open Res. 2020 Sep 28. doi: 10.21956/wellcomeopenres.17692.r40421

Reviewer response for version 1

Sushil Kumar Sharma 1

This is an extensive study revealing the comprehensive genomic picture of the SARS-CoV-2 prevalence in the Indian population during the early phase of outbreaks explaining the three major waves of viral entry in India associated with multiple outbreaks i.e. first wave (A2a cluster from Europe and USA), second wave (A3 cluster from Middle East); and the third wave (A4 cluster from Southeast Asia) of importation of SARS-CoV-2 from various countries. The study has been designed and executed meticulously with logical conclusions and publication-worthy merit.

 

  1. Results: Demographic details and travel history of the objects: “The majority of the SAS-CoV-2…” may please be replaced with “ The majority of the SARS-CoV-2…”

  2. The Supplementary Figure S6: “Scheme showing importation of prevalent SARS-CoV-2 genomes (3 major waves) in India” may be included in the main text.

  3. D614G mutation has been reported in several studies. For example in one of the studies it has been claimed by Mr Babu V Bassa that the frequency of G614 variant in India is at least 90% as of June, 2020 (Bassa and Uppu, 2020 1) as may be seen at the biorxiv.org site ( https://www.biorxiv.org/content/10.1101/2020.06.04.128751v1). This data from different regions of India needs to be discussed for better comprehension.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecukar biology,

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

References

  • 1. : The Frequency of G614 SARS-CoV-2 Variant in India.2020; 10.20944/preprints202008.0148.v1 10.20944/preprints202008.0148.v1 [DOI] [Google Scholar]
Wellcome Open Res. 2020 Sep 21. doi: 10.21956/wellcomeopenres.17692.r40420

Reviewer response for version 1

Suresh Singh Yadav 1

The study entitled ‘Integrated genomic view of SARS-CoV-2 in India’ explores the comprehensive genomic analysis of the SARS-CoV-2 early outbreaks in India. The study is highly relevant to the current epidemic and may add important information for further research and strain evolution of SARS-CoV-2, especially in India. Along with other clades previously known (named A2a, A2 and A3….), the authors reported that A4 (previously unclassified) is the most prevalent in the available genome sequences from India. The authors discussed that several novel mutations identified may be exclusive to the Indian population/conditions. It can also be helpful to trace the movement of lineage-specific strains across geographical regions more efficiently. The study is well designed, executed and concluded, and has significant merit to be published. The authors may address the following minor comments before indexing:

  1. The authors should mention what was the positive and negative control for the qPCR.

  2. Abstract - Methods: Sentence may be refreshed into two small sentences.

  3. Methods: subheading ‘Sample collection and molecular investigations’ line seven - may be as ‘Absorbance ratio (260/280 nm) was ranged between 1.6–2.2 for most of the samples indicating its good quality

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular biology, cell signalling, epigenetics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Wellcome Open Res. 2020 Aug 24. doi: 10.21956/wellcomeopenres.17692.r39810

Reviewer response for version 1

Vijay Nema 1

This manuscript is well designed and executed to explore importation of SARS-CoV-2 genome with respect to major clade depicting A2a, A3 and A4 wave to India however, SARS-COV-2 Cluster B as well as A1a though minor is not mentioned for its entry in India and the Indian subcontinent. 

The following amino acid substitutions are reported predominantly in Orf1ab region of the SARS-CoV-2 genome and its occurrence is in the following order L37F > A97V > T1198K > D614G > P323L > S1197R, however only D614G from spike region seems to be showing more infectivity and transmission efficiency in Europe while rest of the amino acid substitutions need to be explored for their infectivity and transmission efficiency. This will provide significant association between different mutation patterns and transmission profile with respect to India and the Indian subcontinent.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular Biology, Metagenomics, Tuberculosis drug discovery

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Associated Data

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

    Data Availability Statement

    Underlying data

    All sequence data have been deposited with GISAID [ Shu & McCauley, 2017].

    Figshare: Acknowledgement table GISAID and Accession. https://doi.org/10.6084/m9.figshare.12624815.v4 [ Kumar et al., 2020a].

    This project contains all GISAID accession numbers generated and analysed in this study.

    Extended data

    Figshare: Supplementary figures S1-S6. https://doi.org/10.6084/m9.figshare.12631880.v1 [ Kumar et al., 2020b].

    This file contains the following extended data:

    • Supplementary Figure S1: A schematic diagram showing numbers of samples with their geographical affiliations with respect to states of India.

    • Supplementary Figure S2: Sequencing data quality parameters and orthogonal platform validation.

    • Supplementary figure S3: A heat map representation of SARS-CoV-2 variants per sample and their respective segregation in respective clusters.

    • Supplementary Figure S4: Protein annotation of the amino acid substitutions.

    • Supplementary Figure S5: Amino acid properties marked as a function of mutations.

    • Supplementary Figure S6: Scheme showing importation of prevalent SARS-CoV-2 genomes (3 major waves) in India.

    Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

    Articles from Wellcome Open Research are provided here courtesy of The Wellcome Trust

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