
==== Front
eBioMedicine
EBioMedicine
eBioMedicine
2352-3964
Elsevier

S2352-3964(24)00309-8
10.1016/j.ebiom.2024.105273
105273
Articles
The significance of recurrent de novo amino acid substitutions that emerged during chronic SARS-CoV-2 infection: an observational study
Ip Jonathan Daniel a
Chu Wing-Ming b
Chan Wan-Mui a
Chu Allen Wing-Ho ac
Leung Rhoda Cheuk-Ying ac
Peng Qi d
Tam Anthony Raymond b
Chan Brian Pui-Chun a
Cai Jian-Piao a
Yuen Kwok-Yung acef
Kok Kin-Hang ac
Shi Yi d
Hung Ivan Fan-Ngai bfg
To Kelvin Kai-Wang kelvinto@hku.hk
acef∗
a State Key Laboratory for Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
b Division of Infectious Diseases, Department of Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region, China
c Centre for Virology, Vaccinology and Therapeutics, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
d CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
e Department of Microbiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
f Department of Infectious Disease and Microbiology, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
g Infectious Diseases Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China
∗ Corresponding author. Department of Microbiology, 19th Floor, Block T, Queen Mary Hospital, Pokfulam, Hong Kong Special Administrative Region, China. kelvinto@hku.hk
14 8 2024
9 2024
14 8 2024
107 10527325 1 2024
24 7 2024
27 7 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Summary

Background

De novo amino acid substitutions (DNS) frequently emerge among immunocompromised patients with chronic SARS-CoV-2 infection. While previous studies have reported these DNS, their significance has not been systematically studied.

Methods

We performed a review of DNS that emerged during chronic SARS-CoV-2 infection. We searched PubMed until June 2023 using the keywords “(SARS-CoV-2 or COVID-19) and (mutation or sequencing) and ((prolonged infection) or (chronic infection) or (long term))”. We included patients with chronic SARS-CoV-2 infection who had SARS-CoV-2 sequencing performed for at least 3 time points over at least 60 days. We also included 4 additional SARS-CoV-2 patients with chronic infection of our hospital not reported previously. We determined recurrent DNS that has appeared in multiple patients and determined the significance of these mutations among epidemiologically-significant variants.

Findings

A total of 34 cases were analyzed, including 30 that were published previously and 4 from our hospital. Twenty two DNS appeared in ≥3 patients, with 14 (64%) belonging to lineage-defining mutations (LDMs) of epidemiologically-significant variants and 10 (45%) emerging among chronically-infected patients before the appearance of the corresponding variant. Notably, nsp9-T35I substitution (Orf1a T4175I) emerged in all three patients with BA.2.2 infection in 2022 before the appearance of Variants of Interest that carry nsp9-T35I as LDM (EG.5 and BA.2.86/JN.1). Structural analysis suggests that nsp9-T35I substitution may affect nsp9-nsp12 interaction, which could be critical for the function of the replication and transcription complex.

Interpretation

DNS that emerges recurrently in different chronically-infected patients may be used as a marker for potential epidemiologically-significant variants.

Funding

Theme-Based Research Scheme [T11/709/21-N ] of the 10.13039/501100002920 Research Grants Council (See acknowledgements for full list).

Keywords

COVID-19
SARS-CoV-2
Chronic infection
EG.5
BA.2.86
JN.1
==== Body
pmc Research in context

Evidence before this study

Chronic SARS-CoV-2 infection has been hypothesized to be one of the potential sources of epidemiologically-significant variants, such as Variant of Concern (VOC). We searched PubMed on 3rd June 2023 for articles using the search terms “(SARS-CoV-2 or COVID-19) and (mutation or sequencing) and ((prolonged infection) or (chronic infection) or (long term))”. Three studies have reviewed previously published cases. However, these studies either focus on the spike mutations, reviewed studies within a certain country, or mainly summarized the clinical information. Furthermore, none of them includes cases infected with the Omicron variant.

Added value of this study

This study systematically analyzed 34 chronically infected patients reported worldwide including 30 cases reported in published articles and 4 new cases reported in this study. We showed that 64% (14/22) of DNS that recurrently occurred in multiple chronically-infected patients were later found to be the lineage-defining mutations (LDMs) of subsequent epidemiologically-significant variants. Importantly, many of these DNS emerged among chronically-infected patients before the declaration of the epidemiologically-significant variants by the World Health Organization (WHO). Moreover, we identified a DNS from nsp9 (T35I) which emerged in 3 of 5 patients with Omicron infection and was later found to be the LDM of EG.5, BA.2.86 and JN.1, which are the VOIs that dominate the world since mid 2023. Structural analysis showed that nsp9 T35I substitution may affect the structure of the replication and transcription complex (RTC), which might in turn affect the RNA synthesis of the virus.

Implications of all the available evidence

DNS recurrently arising from multiple chronically-infected patients may predict mutations that characterize novel variants of epidemiological significance. Our results support continual efforts to study DNS among patients with chronic infection.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is typically cleared from the respiratory tract of coronavirus virus diseases 2019 (COVID-19) patients within a few weeks after the onset of symptoms.1 However, prolonged virus shedding can occur for months or years in some patients, especially those with immunocompromised conditions.1, 2, 3, 4, 5 De novo amino acid substitutions (DNS) can arise during chronic SARS-CoV-2 infection.6,7 These DNS frequently emerge in the spike (S) protein because the S protein is subjected to the immune pressure induced by prior natural infection, vaccination or monoclonal antibody treatment.8, 9, 10 In addition, DNS can occur among non-structural proteins, especially the antiviral targets. DNS has been reported in non-structural protein 12 (nsp12) (or RdRp) and nsp5 (or 3CLpro) among patients who have received remdesivir and nirmatrelvir-ritonavir treatment, respectively.11,12 Furthermore, the introduction of molnupiravir, an antiviral that inhibits SARS-CoV-2 by lethal metagenesis, has led to the emergence of specific lineages with high proportion of G-to-A and C-to-T mutations.13

DNS emerging during chronic infection may be associated with improved in vivo fitness or resistance to treatment of SARS-CoV-2. For example, the S protein E484K substitution has emerged in multiple immunocompromised patients with chronic infection,3 and this substitution can destabilize the native conformation of the RBD tip and affect antibody binding.14,15 It has been postulated that the emergence of epidemiologically-significant variants, designated as variants of concern (VOCs), variants of interest (VOIs) or variants under monitoring (VUMs) by the World Health Organization (WHO), may be originated from viruses that evolve during chronic infection.16 Hence, a better understanding of DNS would be important for assessing the risk of novel variants.

Several studies that summarized cases of chronic infection diagnosed during the pre-Omicron era noted that some DNS of the S protein were similar to the lineage-defining mutations (LDMs) of the VOCs Alpha, Beta, Delta or Gamma variants.2,3,17 In this study, we systematically analyzed 34 cases of chronic infection, including 4 cases which were not reported previously. We showed that many DNS arising from multiple chronically-infected patients were found to be the LDMs of VOCs, VOIs or VUMs. Moreover, the emergence of many DNS among chronically-infected patients preceded the appearance of these epidemiologically significant variants. From our 3 patients infected with Omicron BA.2.2, the DNS nsp9 T35I emerged in 2022, before the appearance of VUMs XBB.1.9.1 and XBB.1.9.2 and the VOIs EG.5 (a descendant of XBB.1.9.2), BA.2.86 and JN.1 (a descendent of BA.2.86).

Methods

Search strategy, selection criteria, and analysis of published cases of chronic infection

We conducted a review according to the 2020 PRISMA guideline.18 We searched PubMed using the search query “(SARS-CoV-2 or COVID-19) and (mutation or sequencing) and ((prolonged infection) or (chronic infection) or (long term))”. We performed the search for studies published on or before 3rd June 2023.

We screened the titles and abstracts of all studies, and screened the reference lists of the identified publications for additional publications that may be relevant. We only included English publications and excluded preprints. Two independent reviewers (JDI and KKWT) screened the identified publications and included patients that fulfilled the selection criteria. Patients were included if i) the time interval of the collection dates between the first and last sequenced specimens were 60 days or above; and ii) at least 3 specimens were sequenced. Patients were excluded from analysis if i) whole genome DNS information was not complete (eg. whole genome sequencing was not performed, whole viral genome data were not available in publicly available database, or the sequence accession numbers were not stated in the manuscript); ii) DNS information was not reliable (eg. discrepancy of the collection dates within the manuscript or between the manuscript and in the public database); iii) exact time interval between sequenced specimens could not be determined based on the information in the manuscript.

Data were extracted from the selected publications using a standardized spreadsheet. For each publication, we extracted the information on authors, year of publication, and country/region. For each patient, we extracted information on age, sex, underlying immunosuppressive conditions, date of symptom onset, dates of specimen collection, and amino acid (aa) substitutions at each time point.

We only considered aa substitutions that were present at consensus level or as a major population as defined in the respective publications. Insertions or deletions were not assessed. An aa substitution was considered to be a LDM if it is present in at least 75% of that lineage according to the data in outbreak.info (https://outbreak.info/).19

Patients

Patients P31, P32, P33 and P34 were recruited from Queen Mary Hospital in Hong Kong. Patients were included if their first and last available specimens were collected at least 60 days apart. This study has been approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority of Hong Kong West Cluster (HKU/HA HKW IRB) (Reference number: UW 13–265). Written informed consent was obtained from all 4 study participants.

Viral genome sequencing and bioinformatics analysis

Whole genome sequencing was performed using Oxford Nanopore MinION device (Oxford Nanopore Technologies, Oxford, United Kingdom) as we described previously.20, 21, 22 Nanopore sequencing was performed following the Nanopore protocol—PCR tiling of COVID-19 (Version: PTC_9096_v109_revH_06Feb2020) according to the manufacturer's instructions with minor modifications (Oxford Nanopore Technologies, Oxford, United Kingdom). Briefly, extracted RNA was first reverse transcribed to cDNA using SuperScript™ IV reverse transcriptase (Thermo Fisher Scientific, Waltham, Massachusetts, USA). PCR amplification was performed using the hCoV-2019/nCoV-2019 Version 3 Amplicon Set (Integrated DNA Technologies, Coralville, Iowa, USA) with the Q5® Hot Start High-Fidelity 2× Master Mix kit (New England Biolabs, Ipswich, Massachusetts, USA) according to the Nanopore protocol. PCR products were purified using 1 × AMPure XP beads (Beckman Coulter, Brea, California, USA) and quantified using Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA). The purified DNA was then normalized for end-prep and native barcode ligation reactions according to the PCR tiling of COVID-19 virus protocol with Native Barcoding Expansion 96 (EXP-NBD196, Oxford Nanopore Technologies, Oxford, United Kingdom). Barcoded libraries were then pooled, purified with 0.4 × AMPure XP beads and quantified using Qubit dsDNA HS Assay Kit. Purified pooled libraries were ligated to sequencing adapters and sequenced with the Oxford Nanopore MinION device using R9.4.1 flow cells for 24–48 h.

For bioinformatics analysis, ARTIC bioinformatics workflow (version 1.2.1) was used with minor modifications applied as described previously.21,22 The modifications include reducing the minimum length at the guppyplex step to 350 to allow potential small deletions to be detected and increasing the “–normalise” value to 999,999 to incorporate all the sequenced reads and the super accuracy mode was used for basecalling with an increased QC passing score from 7 to 10. The sequence NC_045512.2 obtained from NCBI was used as the reference and the alignment files produced by Medaka (https://github.com/nanoporetech/medaka) were inspected using Integrative Genomics Viewer (IGV) (2.8.0)23 to verify the mutations called by the ARTIC pipeline. We considered the consensus nucleotide as reported by Medaka. SARS-CoV-2 lineage was assigned using the online Nextclade tool (v2.14.1; https://clades.nextstrain.org; accessed on October 4, 2023).24 All sequences were deposited onto the Global Initiative on Sharing All Influenza Data (GISAID) database (Supplementary Table S5). The DNS emerging during chronic infection were extracted using an in-house Python Script.

Phylogenetic tree

A maximum likelihood phylogenetic tree was created using the IQTree2 program v2.2.0 with the number of ultrafast bootstraps set to 1000, and the collapse near zero branches set to True.25 The outgroup (EPI_ISL_8641944) is an early BA.2 sequence from Australia collected in late 2021. The tree was visualized and being exported using FigTree (https://github.com/rambaut/figtree).

Determination of global prevalence of nsp9 T35I substitution

We determined the global prevalence of nsp9 T35I substitution using data from the GISAID database. We downloaded the metadata of SARS-CoV-2 sequences from GISAID on 23 November 2023.26 A SARS-CoV-2 sequence was included if it had been marked as complete in the metadata file and was directly obtained from a human clinical specimen without in vitro passage. A sequence was excluded if the information on collection date was not complete. All processing was performed as described previously with Python v3.9.1223 (pandas v1.4.2, json v2.0.9) running on Anaconda Software Distribution v4.11.0.27

Structural analysis of nsp9 T35I substitution

The structural analysis and figure preparation were performed using ChimeraX version 1.7 (https://www.cgl.ucsf.edu/chimerax/)28 based on the structure of SARS-CoV-2 replication and transcription complex (RTC) with RNA-nsp9 (PDB ID:8GWB). The structure of nsp9 T35I mutation was modeled using Pymol (https://pymol.org/).

Statistical analysis

All statistical analysis was performed using GraphPad Prism v10.0.2. The frequency of DNS of each protein was compared with the overall frequency of DNS across the whole genome using the z test. The difference between the frequency of DNS within a protein and the whole genome was considered statistically significant if the P value is less than 0.05 after Bonferroni multiple-test correction.

Role of funders

All funding sources had no role in the study design, data collection, analysis, interpretation, or writing of the manuscript.

Results

Characteristics of patients analyzed in this study

In this study, we first conducted a literature search and included previously reported cases who had chronic infection with at least 3 specimens available over a timespan of at least 60 days. The detailed inclusion and exclusion criteria are listed under the Methods section. A total of 30 patients from 25 studies fulfilled the selection criteria (P1–P30) (Fig. 1 and Table 1).2,3,8,11,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41Fig. 1 PRISMA reporting for systematic review. Abbreviations: DNS, de novo amino acid substitutions.

Table 1 Characteristics of 34 patients included in this analysis.

Patient number	Sex	Age (years)	Underlying immunosuppressive condition	Month/Year of initial infectionb	Pango lineage	Reference	
P1	M	45	Antiphospholipid syndrome	April 2020	B.1	8	
P2	N/A	N/A	HIV	November 2020	B.1.1.241	29	
P3	M	70–79a	Marginal B cell lymphoma	May 2020	B.1.1.1	30	
P4	M	N/A	N/A	April 2022	BA.1.23	31	
P5	F	Late 30s	HIV	August–September 2020	B.1.1.273	32	
P6	F	38	DLBCL	December 2020	B.1.2	33	
P7	M	60–69a	DLBCL	November 2020	B.1.517	2	
P8	M	58	Kidney transplant recipient	March 2020	B.1.1	34	
P9	F	22	HIV	Jane 2021	B.1.351	35	
P10	F	70	Non-Hodgkin's lymphoma	May 2020	B.1	11	
P11	M	30–39a	Hodgkin's lymphoma	February 2021	B.1.362	3	
P12	M	0–9a	Acute lymphoblastic leukemia	October 2020	B.1.1.50	3	
P13	N/A	50–59a	CVID, Evans syndrome
Extranodal MALT lymphoma	July–August 2020	B.1.2	36	
P14	M	52	Follicular lymphoma	March 2020	B	37	
P15	M	47	Follicular lymphoma	March 2020	B.1	37	
P16	M	35	Rheumatoid arthritis	April 2020	B.1	38	
P17	N/A	1	Severe combined immunodeficiency	August 2021	P.1	39	
P18	F	53	Follicular lymphoma	Between April 2020 and January 2021	B.1.8	40	
P19	M	61	Diabetes mellitus	Between April 2020 and January 2021	B.1.36.1	40	
P20	F	66	Goodpasture syndrome	Between April 2020 and January 2021	A.9	40	
P21	M	61	Follicular lymphoma	December 2021	AY.122	41	
P22	F	70–79	Follicular lymphoma	May 2020	B.1.1.515	42	
P23	M	25	X-linked agammaglobulinemia	August 2020	B.1.1.284	43	
P24	M	64	Mantle cell lymphoma	January 2021	B.1.1.432	44	
P25	N/A	40–49	Diffuse large B cell lymphoma, HIV	2021	B.1.1.7	45	
P26	N/A	30–39	HIV	2021	B.1.1.7	45	
P27	F	52	Follicular lymphoma	April 2020	B.1.1	46	
P28	F	47	Non-Hodgkin's lymphoma	April 2020	B.1.1	47	
P29	M	57	Eosinophilic granulomatosis with polyangiitis	September 2020	B.1	48	
P30	F	63	Malignant lymphoma	September 2020	B.1.1.284	49	
P31	F	85	Follicular lymphoma	July 2022	BA.2.2	Current study	
P32	M	68	B cell lymphoma	March 2022	BA.2.2	Current study	
P33	M	59	Follicular lymphoma	February 2022	BA.2.2	Current study	
P34	M	40	Acute myeloid leukemia	November 2022	CM.12	Current study	
Abbreviations: CVID, common variable immunodeficiency; DLBCL, diffuse large B-cell lymphoma; F, female; HIV, human immunodeficiency virus; M, male; MALT, mucosa-associated lymphoid tissue; N/A, not applicable.

a The exact age was not stated in the original publication.

b The month and year were estimated from the original publication.

Additionally, we recruited 4 patients from Queen Mary Hospital in Hong Kong Special Administrative Region (P31–P34). All 4 patients had haematological malignancies. P31, P32 and P33 received anti-CD20 monoclonal antibody treatment, while P34 received the JAK-inhibitor ruxolitinib. All 4 patients were first confirmed to have SARS-CoV-2 infection by RT-PCR between February and November 2022 (Table 1) and were hospitalized during acute SARS-CoV-2 infection. P31, P32, and P33 were infected with the Omicron variant BA.2.2 sublineage, while P34 was infected with CM.12, which is a descendant of the Omicron lineage BA.2.3.20 (Fig. 2a).Fig. 2 SARS-CoV-2 strains of patients P31, P32, P33 and P34. a) Phylogenetic tree analysis. The phylogenetic tree was constructed using the IQTree2 tool with sequences from cases P31, P32, P33, and P34. The outgroup (EPI_ISL_8641944) is an early BA.2 sequence from Australia collected in late 2021. Scale bar indicates expected number of nucleotide mutations. The PANGO lineage is indicated in the bracket. b) Emergence of DNS. Shading indicates the frequency of mutation. The DNS are listed below the corresponding substitutions in the heatmap. The cross in the boxes represent positions with insufficient coverage to accurately determine the frequency of a mutant at a given position.

For P33, we noticed 2 distinct genotypes that were found with overlapping time period (Fig. 2a and Supplementary Figure S1). Genotype 1 was found in specimens collected between 9th September 2022 and 31st January 2023, while genotype 2 was found between 16th October 2022 and 21st December 2022. Genotype 1 was characterized by nsp9 T35I, while genotype 2 was characterized by the S N417I.

Patients P31 and P32 had relatively mild disease without the need for oxygen supplementation, while P33 required oxygen supplementation. P31, P32, and P33 have received nirmatrelvir-ritonavir, molnupiravir, tixagevimab/cilgavimab (Evusheld) and dexamethasone during acute hospitalization or upon follow-up at out-patient clinic, while P34 only received molnupiravir. The time interval between the first and last sequenced specimens ranged from 72 to 287 days (Fig. 2b and Supplementary Table S1).

Details of all 34 patients are summarized in Table 1. The most common immunocompromised condition was haematological malignancy (n = 20), followed by HIV (n = 5) and autoimmune disease (n = 3). The time interval between the initial diagnosis and the first sequenced specimen ranged from 0 to 212 days (Supplementary Table S1). The time interval between the first and last collected specimen ranged from 65 to 392 days. The number of time points of specimen collection ranged from 3 to 30, with a median of 9. Of the 30 previously reported cases, 24 were infected with ancestral-like strains in 2020, 2 with Alpha variant, and 1 each with Beta variant, Delta variant, Gamma variant and Omicron variant (BA.1.23) (Table 1).

DNS emerging during chronic infection

Next, we assessed the number of aa residues where DNS emerged during chronic infections. DNS emerged in at least one patient for 409 aa residues (Supplementary Table S2). The number of aa residues with DNS vary greatly between patients (Supplementary Table S3 and Supplementary Figure S2). For example, P7 had 6 aa residues with DNS in the first 56 days and over 24 aa residues with DNS at 315 days, while P19, P20 and P29 did not have any aa residues with DNS over 65, 117 and 172 days, respectively (Supplementary Figure S2).

While the total number of DNS generally increases during the course of chronic infection for most patients, the number of DNS fluctuates for some patients. For patient P34, 11 DNS emerged on day 62, but these DNS were not found at all other time points (Supplementary Figure S2). Further analysis showed that the patient received molnupiravir from day 13 to 18, and from day 68 to 77 (Supplementary Figure S3). While the specimens on day 22 and 34 did not contain any DNS at the consensus level, the specimen on day 62 contained 11 DNS, including 10 DNS that may be driven by molnupiravir (6 G-to-A, 1 C-to-T, 2 A-to-G and 1 T-to-C substitution). These DNS persisted on day 67, although as a minor population. Subsequently, all these DNS disappeared on day 72 while the patient was taking molnupiravir. Our results suggest that the DNS that transiently appeared on day 62 is likely driven by molnupiravir, but these DNS did not confer fitness advantage.

Next, we compared the frequency of aa residues with DNS between different viral proteins. Since the number of aa varies between different viral proteins, we determined the frequency adjusting for the number of aa residues of each protein. The frequency of DNS was highest for the envelope (E) protein (0.2133 DNS per aa), followed by S (0.1406 DNS per aa), orf7a (0.1322 DNS per aa), orf7b (0.1163 DNS per aa), nsp9 (0.1062 DNS per aa), and orf8 (0.0909 DNS per aa) (Fig. 3a). No DNS were found in nsp7 or nsp10. Within the S protein, the DNS frequency was highest for the receptor binding motif (RBM) (0.8310 DNS per aa), followed by receptor binding domain (RBD) (0.3408 DNS per aa) and N-terminal domain (NTD) (0.2069 DNS per aa) (Fig. 3b). The DNS frequency of S, E, and orf7a proteins were statistically significantly higher than the overall DNS frequency of all proteins, while the DNS frequency of RBM, RBD and NTD were statistically significantly higher than the overall DNS frequency of the S protein.Fig. 3 Frequency of DNS that emerged during chronic infection. a) All viral proteins; b) Different domains of the S protein. The frequency is calculated by the sum of DNS at an amino acid residue of a protein, and divided by the length of the protein. All DNS at a particular amino acid residue for a single patient is counted as one DNS. The dotted line represents the overall frequency of DNS for all proteins (a) or the spike protein (b). # represents proteins that have statistically significantly higher frequency of DNS than the overall frequency of DNS after Bonferroni correction.

DNS hotspots

To identify DNS hotspots, we determined aa residues that have DNS in multiple patients. Out of 409 aa residues with DNS, only 22 aa residues (5.4%) had DNS emerging in 3 or more patients, including 10 aa residues located in the S protein, 3 in the M protein, 2 in nsp3 protein, and 1 each in nsp5, nsp6, nsp9, nsp12, nsp13, E and orf8 proteins (Table 2). S aa residues 484 was the most frequently substituted aa residue (29% [10 of 34 patients]), followed by E aa residue 30 (24% [8 of 34 patients]), and S aa residue 490 (21% [7 of 34 patients]). 64% (14/22) of these aa residues with DNS were also the aa residues of LDM for VOCs or VOIs as designated by the WHO (Supplementary Table S4). 45% (10/22) emerged before the designation of the corresponding variant as VOCs/VOIs (Fig. 4 and Supplementary Table S4), including nsp9 aa residue 35; S aa residue 13, 417, 440, 450, 484, 490, 493, and 655; and M aa residue 82.Table 2 Amino acid residues with de novo substitutions in at least 3 patients in this study.

Protein	Amino acid residue	Number of patients (%) n = 34	Lineage-defining mutationb	
S	484	10 (29.4)	Beta, Gamma, Omicron, Eta, Theta, Kappa, Mu, Zeta, XBB.1.5, XBB.1.16, EG.5, BA.2.86, JN.1	
E	30	8 (23.5)	N/A	
S	490	7 (20.6)	Lambda, XBB.1.5, XBB.1.16, EG.5	
Nsp6	37	6 (17.6)	N/A	
S	95	6 (17.6)	Iota, Mu, Omicron	
Nsp3	504	5 (14.7)	N/A	
S	494	5 (14.7)	N/A	
M	125	5 (14.7)	N/A	
Nsp9	35	4 (11.8)a	EG.5, BA.2.86, JN.1	
S	493	4 (11.8)	Omicron	
M	82	4 (11.8)	Delta, Eta, Kappa	
Nsp3	977	3 (8.8)	Gamma	
Nsp5	90	3 (8.8)	Beta	
Nsp12	671	3 (8.8)	Delta, XBB.1.5, XBB.1.16	
Nsp13	238	3 (8.8)	N/A	
S	13	3 (8.8)	Epsilon	
S	417	3 (8.8)	Beta, Gamma, Omicron, XBB.1.5, XBB.1.16, EG.5, BA.2.86, JN.1	
S	440	3 (8.8)	Omicronc	
S	450	3 (8.8)	BA.2.86, JN.1	
S	655	3 (8.8)	Omicron, XBB	
M	2	3 (8.8)	N/A	
Orf8	121	3 (8.8)	N/A	
a 3 of 4 patients were infected with Omicron variant BA.2.2.

b According to outbreak.info (VOC/VOI only).19

c Only for BA.2, BA.4, and BA.5.

Fig. 4 The time of emergence of LDM-related DNS among chronically infected patients relative to the time of designation of the corresponding VOCs/VOIs by the World Health Organization. ∗The month of the collection date is not known.

Nsp9 T35I substitution

Nsp9 threonine-to-isoleucine substitution at aa residue 35 (T35I) (Orf1a T4175I) substitution, a LDM of the VOI EG.5, BA.2.86 and JN.1, is the only DNS located in a non-structural protein that emerged in multiple patients and subsequently found to be the LDM of epidemiologically-significant variants. According to the prediction by Bloom et al., nsp9 T35I substitution improves the viral fitness with a fitness score of 1.4.50 Notably, nsp9 T35I appeared in 11.8% (4/34) patients, including 3 patients with BA.2.2 infection (P31, P32, P33) (Fig. 2b). In contrast, nsp9 T35I was present in only 0.22% (124/55,255) of all BA.2.2 strains deposited to GISAID. Whole viral genome sequencing showed that these 3 patients were infected with phylogenetically distinct strains (Fig. 2a).

Since nsp9 T35I substitution appeared in multiple BA.2.2 patients and is a LDM for epidemiologically-significant variants, we further assessed the potential functional consequences of this substitution. Structural analysis showed that nsp9 T35I may affect the binding between nsp9 and nsp12 (Fig. 5). In the previously determined structure of SARS-CoV-2 replication and RTC with RNA-nsp9, the residue T35 of nsp9 was closed to the palm subdomain of nsp12 and might form weak interaction.51 The T35I substitution of nsp9 would create a hydrophobic environment that might affect the local conformation of nsp9 and then change the interaction between nsp9 and nsp12.Fig. 5 Molecular analysis of the effect of nsp9 T35I substitution on SARS-CoV-2 RTC complex structure. a and b) Surface and cartoon representation of SARS-CoV-2 RTC complex, and the residue T35 of nsp9 was marked in red colour; c) The measurement of distances between the residue T35 of nsp9 and the residues N734 and D736 from palm subdomain of nsp12 subunit; d) The detailed interaction between nsp9 and nsp12 close to the position of residue T35, and the residue K36 was inserted into the pocket of nsp12; e) The modeled structure revealed the potential interaction between the I35 and the residues from palm subdomain of nsp12.

Discussion

In this study, we have systematically reviewed and analyzed DNS that emerged during chronic infection in 30 previously reported cases and 4 additional cases from our hospital. Of the 409 aa residues where DNS was found in at least one patient, 5.4% (22/409) were detected in 3 or more patients. Among these 22 DNS that are found in multiple chronically-infected patients, 64% (14/22) were also found to be the LDM of subsequent epidemiologically-significant variants, suggesting that these DNS may confer an adaptive evolution advantage. Remarkably, 45% (10/22) of these LDM emerged in chronically-infected patients before the designation of the corresponding variants as VOCs or VOIs by the WHO, including 8 DNS in the S protein, 1 in the M protein, and 1 in the non-structural protein nsp9. DNS that appeared in multiple chronically-infected patients may improve the fitness of the virus, which is kept in balance by the defective immune response without killing the host. Since these structural and non-structural protein LDM could be found in multiple chronically-infected patients before the rapid expansion of the epidemiologically-significant variants, genomic analysis of viral sequences from chronically-infected patients can be an important strategy to predict these variants.

Unlike previous studies which focused on DNS in the S protein or among antiviral targets such as nsp5 (3CL protease) or nsp12 (RdRp), our study revealed the recurrence of DNS in nsp9 among multiple patients. Nsp9 T35I is the LDM of the VOIs EG.5, BA.2.86, and JN.1 which are the predominant circulating strains in December 2023.52 Nsp9 T35I emerged in 4 patients before November 2022, including all 3 BA.2.2 patients from our hospital, which preceded the appearance of EG.5 (February 2023), BA.2.86 (July 2023) and JN.1 (August 2023).53 Notably, a study by Ghafari et al., which was published after the first submission of our manuscript, identified two patients with BA.1 infection having the emergence of nsp9 T35I substitution.54 Nsp9 is a short non-structural protein consisting of 113 aa. Nsp9 is well conserved among coronaviruses, with 97% aa identity between SARS-CoV-2 and SARS-CoV-1.55 Being a member of the RTC,51 nsp9 interacts with nsp12 and is crucial in the regulation of viral RNA biogenesis, including RNA capping which stabilizes the RNA and allows efficient translation,56 and in the priming of viral RNA synthesis through the interaction with the host protein staphylococcal nuclease domain-containing protein 1 (SND1) and the 5’ ends of both positive and negative sense viral RNAs.57 Our structural analysis suggests that the nsp9 T35I substitution may affect the interaction of nsp9-nsp12 interaction, which may affect the function of nsp9. Since nsp9 is not under the immune pressure of antibodies induced by natural infection or vaccination, nor is under the pressure from currently available antivirals, the nsp9 T35I substitution may be responsible for enhancing the viral fitness in the human respiratory tract.

In addition, DNS at E aa residue 30 and orf8 aa residue 121 also emerged in multiple patients (24% [8 of 34 patients] and 8.8% [3 of 34 patients]), respectively. The E protein forms a homopentameric cation channel and is a multifunctional protein that affects the viral life cycle and induces necroptosis.58,59 Orf8 is a known virulence factor. Patients infected with orf8 deletion had milder illness.60 Furthermore, orf8 affects tissue fibrosis by dysregulating the TGF-beta pathway.61 While these are not LDMs in VOCs/VOIs, we should continue to monitor for these DNS hotspots as they may be potential LDMs.

We found that several proteins are well conserved. No aa mutations were found in nsp7 and nsp10 proteins. The nsp7 protein inhibits type I and type III interferon production.62 The nsp10 protein, in complex with nsp14, plays a role in the proofreading of RNA synthesis during viral replication.63 These proteins were also found to be most conserved among strains deposited into GISAID.64 Our results suggest that the aa in these proteins are likely optimized and aa substitutions may not be well tolerated.

Several case reports of chronic infection found a high number of DNS emerging among immunocompromised patients.2,4,31 Recently, Li et al. showed that intrahost single nucleotide variants were more common among chronically-infected SARS-CoV-2 patients with underlying hematological malignancies than those with transplant recipients.5 In our analysis, the number of DNS can vary widely between patients. Three patients, including P19 (with diabetes mellitus) and P20 (with Goodpasture syndrome), and P29 (with eosinophilic granulomatosis with polyangiitis) did not have any DNS over 65, 117, and 172 days, respectively. However, we were not able to compare the frequency of DNS emergence between patients with different immunocompromised conditions due to non-uniform specimen collection in terms of number and timing.

We found that one of our patients (P33) had two distinct genotypes that emerged about 4–5 months after the first sequenced specimen. Interestingly, both genotypes co-existed for 2 months (between October and December 2022). This observation is similar to the patient described by Chaguza et al. (P7).2 Hence, intrahost evolution can indeed lead to the emergence of multiple genotypes with similar fitness.

There are several limitations in this study. First, we only analyzed DNS that are present at the consensus level or found to be a major population as defined in the respective publications. Second, since we wanted to assess serial changes of patients with true chronic infection instead of those with slow viral decline, we specifically selected patients with at least 3 time points over at least 60 days. Therefore, our analysis did not include all patients classified as having chronic infection in the literature. Third, the first specimens available for sequencing were often collected long after the date of the first RT-PCR positive specimens. Therefore, some DNS that emerged during chronic infection may not be classified as DNS because the sequence of the initial specimen was not available. Fourth, we cannot completely rule out the possibility that our patients had a new reinfection that was misclassified as chronic infection. However, we believe that this is unlikely because all sequences of individual patients belonged to the same lineage throughout the course of infection. If a patient had reinfection, we expect that the specimens at later time points would have lineages that predominate at that later time points.65 Fifth, different mutations within the same residue can exert varied fitness effect.50 Further studies are required to establish the functional impact of individual mutations at a particular amino acid residue. Sixth, publication bias may affect our conclusion. It is more likely that researchers publish mutations that arise after treatment that may be clinically relevant (eg. resistance). Finally, this is mainly a descriptive study as the number of patients included in this study is relatively small and the timing of specimen collection is not uniformed.

Predicting the epidemiological significance of a novel variant is crucial for risk assessment. Currently, a variant is considered to have the potential for emergence if it carries unusually large number of aa substitutions in the S protein which may confer immune escape. Our findings suggest that regular monitoring of all immunosuppressed patients with persistent virus shedding may predict epidemiologically-significant variants and may also allow us to understand the contribution to pathogenesis by different nsp mutants.

Contributors

J.D.I. and K.K.W.T. had roles in study design, data collection, data analysis, data interpretation, literature search and writing of the manuscript. J.D.I., W.M.C., A.W.H.C., Q.P., A.R.T., R.C.Y.L., B.C.P.C., J.P.C., K.Y.Y., K.H.K., Y.S., and I.F.N.H. had roles in performing the experiments, data collection, data analysis, and/or data interpretation. J.D.I. and K.K.W.T. have accessed and verified the data. All authors interpreted the data, revised the manuscript critically for important intellectual content and approved the final version of the manuscript.

Data sharing statement

The genome sequences have been deposited into GISAID database (Supplementary Table S5). Anonymised participant data can be made available upon reasonable request from the corresponding author.

Declaration of interests

IFNH declared that he has received speaker honoraria or travel support from Pfizer, MSD, Gilead, and AstraZeneca, and he participated on the DSMB or Advisory Board for Moderna, Fosun, Sinovac, Sinopharm and AstraZeneca. All other authors declare no competing interests.

Appendix A Supplementary data

Supplementary Tables and Figures

Supplemental Table

Acknowledgements

We gratefully acknowledge the authors originating and submitting laboratories of the SARS-CoV-2 genetic sequences and metadata made available through GISAID on which this research is based (Supplementary Table S6). This work was supported by the Theme-Based Research Scheme [T11/709/21-N ] of the 10.13039/501100002920 Research Grants Council , Hong Kong Special Administrative Region, China; 10.13039/501100012166 National Key R&D Program of China (projects 2021YFC0866100 and 2023YFC3041600); and Emerging Collaborative Project of Guangzhou Laboratory [EKPG22-01 ]. We acknowledge funding received from private donors, including Richard Yu and Carol Yu, Shaw Foundation Hong Kong, May Tam Mak Mei Yin, Michael Seak-Kan Tong, Respiratory Viral Research Foundation Limited, Lee Wan Keung Charity Foundation Limited, Providence Foundation Limited (in memory of the late Lui Hac-Minh), Hui Ming, Hui Hoy and Chow Sin Lan Charity Fund Limited, Chan Yin Chuen Memorial Charitable Foundation, and Marina Man-Wai Lee.

Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2024.105273.
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References

1 To K.K. Tsang O.T. Leung W.S. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study Lancet Infect Dis 20 2020 565 574 32213337
2 Chaguza C. Hahn A.M. Petrone M.E. Accelerated SARS-CoV-2 intrahost evolution leading to distinct genotypes during chronic infection Cell Rep Med 4 2023 100943
3 Harari S. Tahor M. Rutsinsky N. Drivers of adaptive evolution during chronic SARS-CoV-2 infections Nat Med 28 2022 1501 1508 35725921
4 Corey L. Beyrer C. Cohen M.S. Michael N.L. Bedford T. Rolland M. SARS-CoV-2 variants in patients with immunosuppression N Engl J Med 385 2021 562 566 34347959
5 Li Y. Choudhary M.C. Regan J. SARS-CoV-2 viral clearance and evolution varies by type and severity of immunodeficiency Sci Transl Med 16 2024 eadk1599
6 Ip J.D. Kok K.H. Chan W.M. Intra-host non-synonymous diversity at a neutralizing antibody epitope of SARS-CoV-2 spike protein N-terminal domain Clin Microbiol Infect 27 2021 1350.e1 1350.e5
7 Li J. Du P. Yang L. Two-step fitness selection for intra-host variations in SARS-CoV-2 Cell Rep 38 2022 110205
8 Choi B. Choudhary M.C. Regan J. Persistence and evolution of SARS-CoV-2 in an immunocompromised host N Engl J Med 383 2020 2291 2293 33176080
9 Gliga S. Lubke N. Killer A. Rapid selection of sotrovimab escape variants in severe acute respiratory syndrome coronavirus 2 omicron-infected immunocompromised patients Clin Infect Dis 76 2023 408 415 36189631
10 Duerr R. Dimartino D. Marier C. Selective adaptation of SARS-CoV-2 Omicron under booster vaccine pressure: a multicentre observational study eBioMedicine 97 2023 104843
11 Gandhi S. Klein J. Robertson A.J. De novo emergence of a remdesivir resistance mutation during treatment of persistent SARS-CoV-2 infection in an immunocompromised patient: a case report Nat Commun 13 2022 1547 35301314
12 Hirotsu Y. Kobayashi H. Kakizaki Y. Multidrug-resistant mutations to antiviral and antibody therapy in an immunocompromised patient infected with SARS-CoV-2 Med 4 2023 813 824.e4 37683636
13 Sanderson T. Hisner R. Donovan-Banfield I. A molnupiravir-associated mutational signature in global SARS-CoV-2 genomes Nature 623 2023 594 600 37748513
14 Chen L.L. Lu L. Choi C.Y. Impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant-associated receptor binding domain (RBD) mutations on the susceptibility to serum antibodies elicited by coronavirus disease 2019 (COVID-19) infection or vaccination Clin Infect Dis 74 2022 1623 1630 34309648
15 Gobeil S.M. Janowska K. McDowell S. Effect of natural mutations of SARS-CoV-2 on spike structure, conformation, and antigenicity Science 373 2021 eabi6226
16 Markov P.V. Ghafari M. Beer M. The evolution of SARS-CoV-2 Nat Rev Microbiol 21 2023 361 379 37020110
17 Wilkinson S.A.J. Richter A. Casey A. Recurrent SARS-CoV-2 mutations in immunodeficient patients Virus Evol 8 2022 veac050
18 Page M.J. Moher D. Bossuyt P.M. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews BMJ 372 2021 n160 33781993
19 Gangavarapu K. Latif A.A. Mullen J.L. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations Nat Methods 20 2023 512 522 36823332
20 Yap D.Y. Fong C.H. Zhang X. Humoral and cellular immunity against different SARS-CoV-2 variants in patients with chronic kidney disease Sci Rep 13 2023 19932
21 Chen L.L. Abdullah S.M.U. Chan W.M. Contribution of low population immunity to the severe Omicron BA.2 outbreak in Hong Kong Nat Commun 13 2022 3618 35750868
22 Cheng V.C.C. Ip J.D. Chu A.W.H. Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant BA.2 in a single-source community outbreak Clin Infect Dis 75 2022 e44 e49 35271728
23 Robinson J.T. Thorvaldsdottir H. Winckler W. Integrative genomics viewer Nat Biotechnol 29 2011 24 26 21221095
24 Aksamentov I. Roemer C. Hodcroft E.B. Neher R.A. Nextclade: clade assignment, mutation calling and quality control for viral genomes J Open Source Softw 6 2021 3773
25 Minh B.Q. Schmidt H.A. Chernomor O. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era Mol Biol Evol 37 2020 1530 1534 32011700
26 Shu Y. McCauley J. GISAID: global initiative on sharing all influenza data - from vision to reality Euro Surveill 22 2017 30494 28382917
27 Ip J.D. Chu A.W. Chan W.M. Global prevalence of SARS-CoV-2 3CL protease mutations associated with nirmatrelvir or ensitrelvir resistance eBioMedicine 91 2023 104559
28 Goddard T.D. Huang C.C. Meng E.C. UCSF ChimeraX: meeting modern challenges in visualization and analysis Protein Sci 27 2018 14 25 28710774
29 Quaranta E.G. Fusaro A. Giussani E. SARS-CoV-2 intra-host evolution during prolonged infection in an immunocompromised patient Int J Infect Dis 122 2022 444 448 35724829
30 Kemp S.A. Collier D.A. Datir R.P. SARS-CoV-2 evolution during treatment of chronic infection Nature 592 2021 277 282 33545711
31 Gonzalez-Reiche A.S. Alshammary H. Schaefer S. Sequential intrahost evolution and onward transmission of SARS-CoV-2 variants Nat Commun 14 2023 3235 37270625
32 Cele S. Karim F. Lustig G. SARS-CoV-2 prolonged infection during advanced HIV disease evolves extensive immune escape Cell Host Microbe 30 2022 154 162.e5 35120605
33 Scherer E.M. Babiker A. Adelman M.W. SARS-CoV-2 evolution and immune escape in immunocompromised patients N Engl J Med 386 2022 2436 2438 35675197
34 Weigang S. Fuchs J. Zimmer G. Within-host evolution of SARS-CoV-2 in an immunosuppressed COVID-19 patient as a source of immune escape variants Nat Commun 12 2021 6405 34737266
35 Maponga T.G. Jeffries M. Tegally H. Persistent severe acute respiratory syndrome coronavirus 2 infection with accumulation of mutations in a patient with poorly controlled human immunodeficiency virus infection Clin Infect Dis 76 2023 e522 e525 35793242
36 Halfmann P.J. Minor N.R. Haddock Iii LA. Evolution of a globally unique SARS-CoV-2 Spike E484T monoclonal antibody escape mutation in a persistently infected, immunocompromised individual Virus Evol 9 2023 veac104
37 Perez-Lago L. Aldamiz-Echevarria T. Garcia-Martinez R. Different within-host viral evolution dynamics in severely immunosuppressed cases with persistent SARS-CoV-2 Biomedicines 9 2021 808 34356872
38 Tarhini H. Recoing A. Bridier-Nahmias A. Long-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectiousness among three immunocompromised patients: from prolonged viral shedding to SARS-CoV-2 superinfection J Infect Dis 223 2021 1522 1527 33556961
39 Filippi M. Ribeiro Amorim M. Soares da Silva M. Prolonged SARS-CoV-2 infection and intra-patient viral evolution in an immunodeficient child Pediatr Infect Dis J 42 2023 212 217 36728777
40 Heyer A. Gunther T. Robitaille A. Remdesivir-induced emergence of SARS-CoV2 variants in patients with prolonged infection Cell Rep Med 3 2022 100735
41 Brandolini M. Zannoli S. Gatti G. Viral population heterogeneity and fluctuating mutational pattern during a persistent SARS-CoV-2 infection in an immunocompromised patient Viruses 15 2023 291 36851504
42 Khatamzas E. Antwerpen M.H. Rehn A. Accumulation of mutations in antibody and CD8 T cell epitopes in a B cell depleted lymphoma patient with chronic SARS-CoV-2 infection Nat Commun 13 2022 5586 36151076
43 Morita R. Kubota-Koketsu R. Lu X. COVID-19 relapse associated with SARS-CoV-2 evasion from CD4(+) T-cell recognition in an agammaglobulinemia patient iScience 26 2023 106685
44 Simons L.M. Ozer E.A. Gambut S. De novo emergence of SARS-CoV-2 spike mutations in immunosuppressed patients Transpl Infect Dis 24 2022 e13914
45 Riddell A.C. Kele B. Harris K. Generation of novel severe acute respiratory syndrome coronavirus 2 variants on the B.1.1.7 lineage in 3 patients with advanced human immunodeficiency virus-1 disease Clin Infect Dis 75 2022 2016 2018 35616095
46 Lynch M. Macori G. Fanning S. Genomic evolution of SARS-CoV-2 virus in immunocompromised patient, Ireland Emerg Infect Dis 27 2021 2499 2501 34161223
47 Stanevich O.V. Alekseeva E.I. Sergeeva M. SARS-CoV-2 escape from cytotoxic T cells during long-term COVID-19 Nat Commun 14 2023 149 36627290
48 Spinicci M. Mazzoni A. Coppi M. Long-term SARS-CoV-2 asymptomatic carriage in an immunocompromised host: clinical, immunological, and virological implications J Clin Immunol 42 2022 1371 1378 35779200
49 Nagasaki Y. Kadowaki M. Nakamura A. A case of a malignant lymphoma patient persistently infected with SARS-CoV-2 for more than 6 months Medicina (Kaunas) 59 2023 108 36676732
50 Bloom J.D. Neher R.A. Fitness effects of mutations to SARS-CoV-2 proteins Virus Evol 9 2023 vead055
51 Yan L. Ge J. Zheng L. Cryo-EM structure of an extended SARS-CoV-2 replication and transcription complex reveals an intermediate state in cap synthesis Cell 184 2021 184 193.e10 33232691
52 World Health Organization JN.1 initial risk evalution 18 December 2023 Available at: https://www.who.int/docs/default-source/coronaviruse/18122023_jn.1_ire_clean.pdf?sfvrsn=6103754a_3 2023
53 World Health Organization Tracking SARS-CoV-2 variants Available at: https://www.who.int/activities/tracking-SARS-CoV-2-variants 2023
54 Ghafari M. Hall M. Golubchik T. Prevalence of persistent SARS-CoV-2 in a large community surveillance study Nature 626 2024 1094 1101 38383783
55 Chan J.F. Kok K.H. Zhu Z. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan Emerg Microbes Infect 9 2020 221 236 31987001
56 Park G.J. Osinski A. Hernandez G. The mechanism of RNA capping by SARS-CoV-2 Nature 609 2022 793 800 35944563
57 Schmidt N. Ganskih S. Wei Y. SND1 binds SARS-CoV-2 negative-sense RNA and promotes viral RNA synthesis through NSP9 Cell 186 22 2023 4834 4850.e23 10.1016/j.cell.2023.09.002 37794589
58 Mandala V.S. McKay M.J. Shcherbakov A.A. Dregni A.J. Kolocouris A. Hong M. Structure and drug binding of the SARS-CoV-2 envelope protein transmembrane domain in lipid bilayers Nat Struct Mol Biol 27 2020 1202 1208 33177698
59 Baral B. Saini V. Tandon A. SARS-CoV-2 envelope protein induces necroptosis and mediates inflammatory response in lung and colon cells through receptor interacting protein kinase 1 Apoptosis 28 2023 1596 1617 37658919
60 Young B.E. Fong S.W. Chan Y.H. Effects of a major deletion in the SARS-CoV-2 genome on the severity of infection and the inflammatory response: an observational cohort study Lancet 396 2020 603 611 32822564
61 Stukalov A. Girault V. Grass V. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV Nature 594 2021 246 252 33845483
62 Deng J. Zheng Y. Zheng S.N. SARS-CoV-2 NSP7 inhibits type I and III IFN production by targeting the RIG-I/MDA5, TRIF, and STING signaling pathways J Med Virol 95 2023 e28561
63 Liu C. Shi W. Becker S.T. Schatz D.G. Liu B. Yang Y. Structural basis of mismatch recognition by a SARS-CoV-2 proofreading enzyme Science 373 2021 1142 1146 34315827
64 Abbasian M.H. Mahmanzar M. Rahimian K. Global landscape of SARS-CoV-2 mutations and conserved regions J Transl Med 21 2023 152 36841805
65 Choudhary M.C. Crain C.R. Qiu X. Hanage W. Li J.Z. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequence characteristics of coronavirus disease 2019 (COVID-19) persistence and reinfection Clin Infect Dis 74 2022 237 245 33906227
