SARS-CoV-2 Lineage Tracking, and Evolving Trends Seen during Three Consecutive Peaks of Infection in Delhi, India: a Clinico-Genomic Study

Abstract

Since its advent, the pandemic has caused havoc in multiple waves due partly to amplified transmissibility and immune escape to vaccines. Delhi, India also witnessed brutal multiple peaks causing exponential rise in cases. Here we had retrospectively investigated clade variation, emergence of new lineages and varied clinical characteristics during those three peaks in order to understand the trajectory of the ongoing pandemic. In this study, a total of 123,378 samples were collected for a time span of 14 months (1 June 2020 to 3 August 2021) encompassing three different peaks in Delhi. A subset of 747 samples was processed for sequencing. Complete clinical and demographic details of all the enrolled cases were also collected. We detected 26 lineages across three peaks nonuniformly from 612 quality passed samples. The first peak was driven by diverse early variants, while the second one by B.1.36 and B.1.617.2, unlike third peak caused entirely by B.1.617.2. A total of 18,316 mutations with median of 34 were reported. Majority of mutations were present in less than 1% of samples. Differences in clinical characteristics across three peaks was also reported. To be ahead of the frequently changing course of the ongoing pandemic, it is of utmost importance that novel lineages be tracked continuously. Prioritized sequencing of sudden local outburst and community hot spots must be done to swiftly detect a novel mutation/lineage of potential clinical importance. IMPORTANCE Genome surveillance of the Delhi data provides a more detailed picture of diverse circulating lineages. The added value that the current study provides by clinical details of the patients is of importance. We looked at the shifting patterns of lineages, clinical characteristics and mutation types and mutation load during each successive infection surge in Delhi. The importance of widespread genomic surveillance cannot be stressed enough to timely detect new variants so that appropriate policies can be immediately implemented upon to help control the infection spread. The entire idea of genomic surveillance is to arm us with the clues as to how the novel mutations and/or variants can prove to be more transmissible and/or fatal. In India, the densely populated cities have an added concern of the huge burden that even the milder variants of the virus combined with co-morbidity can have on the community/primary health care centers.

Keywords: COVID-19; COVID-19 in Delhi; COVID-19 tracking; Delhi; India; SARS-CoV-2 lineages; SARS-CoV-2 variants; genome surveillance; lineage tracking; pandemic.

FIG 1

Trend of SARS-CoV-2 lineages in Delhi. A: Daily new cases, recovered cases and deaths reported in Delhi (https://github.com/covid19india/api.git). B: Trend of the 10 most detected lineages in the present study.

FIG 2

Phylogenetic tree of detected lineages. The tree is rooted along Wuhan-Hu-1 (NCBI Reference Sequence: NC_045512.2) as reference (denoted by black node). The four horizontal color panels represent the corresponding clade membership, peaks period, SARI and overall symptomatic relationship respectively.

FIG 3

Mutational landscape of 612 samples. x axis shows the genomic coordinates of the SARS-Cov-2 virus. y axis depicts the frequency of detected mutation, color coded by their types. The horizontal dotted red line denotes the 10% frequency cutoff.

FIG 4

The mutations detected and their distribution with respect to their type, genomic location, and frequency. A: The plot shows the most frequent single base substitutions present in our data. B: The total mutation events in different genic and nongenic regions of SARS-CoV-2. C: Type of mutations observed. D: Most frequent nonsynonymous amino acid changes observed.

FIG 5

The phylogenetic relationship and mutation burden across different peaks. A: Lineages and their relationship with respect to each peak. The nodes are colored according to different peaks. The radial axis shows the total number of mutations in a lineage. B: Mutational burden per sample in different peaks. Strong correlation between mutational load and each consecutive peak is demonstrated (r² = 0.82).

FIG 6

Drift in the number and types of mutations detected daily during the three peaks in Delhi. The stacked area plot shows the types of mutations detected in sequenced samples per day and line plot (yellow) depicts the overall case burden in Delhi. x axis depicts the time duration (in days). y axis (left-hand) depicts fraction of mutation types. y axis (right-hand) depicts number of daily infected cases in Delhi (https://github.com/covid19india/api.git).

FIG 7

Comparison of mutations between B.1.36 and B.1.617.2 lineages. A: Nonsynonymous SNPs present in both lineages. B: The comparison of indel events present in both lineages.

FIG 8

Exclusive S gene mutations detected in both lineages across different peaks. The mutations in B.1.36 and B.1.617.2 are coded in red and black, respectively.

FIG 9

Common mutations across different peaks. A: The shared mutations between all three peaks. All types of mutations present in at least 10% of samples in a peak are considered. B: The topmost common mutations during each peak. The third peak was found to contain 17 very frequent mutations compared with 4 in first and second peak. C: Change in average mutation load/sample in lineages across peaks. Similar lineages in different peaks were found to have increased mutation load with time. D: The mutational burden on a per sample basis as observed during three different peaks. The linear trend line is given as dotted blue line. Mutation load/sample on each day is a blue solid blue circle (total days of collection = 133) and plot in black shows the daily infection cases in Delhi during the same time period (https://github.com/covid19india/api.git).