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. 2022 Oct 14;25(11):105369. doi: 10.1016/j.isci.2022.105369

Limited cross-variant immune response from SARS-CoV-2 Omicron BA.2 in naïve but not previously infected outpatients

Hye Kyung Lee 1, Ludwig Knabl 2, Mary Walter 3, Priscilla A Furth 4,5, Lothar Hennighausen 1,5,6,∗
PMCID: PMC9561373  PMID: 36267551

Summary

Omicron is currently the dominant SARS-CoV-2 variant and several sublineages have emerged. Questions remain about the impact of previous SARS-CoV-2 exposure on cross-variant immune responses elicited by the SARS-CoV-2 Omicron sublineage BA.2 compared to BA.1. Here we show that without previous history of COVID-19, BA.2 infection induces a reduced immune response against all variants of concern (VOC) compared to BA.1 infection. The absence of ACE2 binding in sera of previously naïve BA.1 and BA.2 patients indicates a lack of meaningful neutralization. In contrast, anti-spike antibody levels and neutralizing activity greatly increased in the BA.1 and BA.2 patients with a previous history of COVID-19. Transcriptome analyses of peripheral immune cells showed significant differences in immune response and specific antibody generation between BA.1 and BA.2 patients as well as significant differences in the expression of specific immune genes. In summary, prior infection status significantly impacts the innate and adaptive immune response against VOC following BA.2 infection.

Subject areas: Immune response, Virology, Transcriptomics

Graphical abstract

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Highlights

  • •

    Reduced immune response in Omicron BA.2 patients without previous history of COVID-19

  • •

    Elevated immune response in BA.1 and BA.2 patients with a previous history of COVID-19

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    Distinct antibody creation and immune gene expression between BA.1 and BA.2 patients

Immune response; Virology; Transcriptomics.

Introduction

The highly transmissible SARS-CoV-2 Omicron (B.1.1.529) variant has replaced previous variants and is less susceptible to neutralizing antibodies elicited by vaccination or infection (Cele et al., 2022; Edara et al., 2022; Schmidt et al., 2022). Currently, BA.2 is the dominant Omicron sublineage and has replaced BA.1 (Viana et al., 2022). Although BA.1 and BA.2 share 32 mutations, they differ in 28 and demonstrate different antibody neutralization profiles (Arora et al., 2022a). Omicron spike mutations negatively impact neutralizing activity in sera from previously vaccinated and previously infected persons as well as limited efficacy against monoclonal antibody treatment (Dejnirattisai et al., 2022; Hoffmann et al., 2022; Mannar et al., 2022; VanBlargan et al., 2022). Of note, vaccinated individuals with BA.1 infection develop measurable neutralizing antibody titers against BA.1 and BA.2 (Yu et al., 2022). Prior vaccination also broadens the immunological response against variants of concern (VOC) following Omicron infection whereas unvaccinated individuals show an antibody response more limited to the infecting strain (Lee et al., 2022b; Suryawanshi et al., 2022).

The ability of BA.2 infection to induce neutralizing antibodies in unvaccinated individuals, either without or with previous SARS-CoV-2 infection, is pending definition. Here we conducted a comparative investigation of the innate and humoral immune response elicited in 74 Omicron outpatients infected with BA.1 (Lee et al., 2022b) or BA.2 that were either naive prior to Omicron infection or had prior exposure to other SARS-CoV-2 variants. We measured antibody titers and ACE2 binding inhibition as a proxy for neutralization. In addition, we investigated the immune transcriptome and the repertoire of antibody germline alleles in these cohorts.

Results

Antibody response after omicron subvariant infection

We measured antibody titers and neutralizing antibody responses in 74 outpatients infected with either Omicron BA.1 or BA.2 sublineages. Demographic and clinical information are provided in Table 1. Although none of the patients had been vaccinated, 24 had been infected by an earlier variant. More than 20% of the BA.1 infected, but none of the BA.2 infected patients, had moderate or severe disease. Humoral immune response was measured between 7 and 21 days (median: days 13–14) post-documented Omicron BA.1 or BA.2 infection. In previously uninfected and unvaccinated individuals, anti-Omicron BA.1 spike IgG titers were approximately 40-fold lower following BA.2 infection compared to BA.1 infection (Figure 1A; Table S1). IgG titers against the ancestral spike were almost 70-fold lower following BA.2 as compared to BA.1 infection. Anti-spike IgG antibody titers in individuals previously infected with earlier SARS-CoV-2 variants were at least 5-fold higher with no difference in titers between those infected with the BA.1 versus BA.2 variants (Figure 1A; Table S1). This highlights that previous infection with variants has a particularly strong impact on the humoral immune response in BA.2 infected patients.

Table 1.

Characteristics of Omicron study population.

N No prior infection
 Prior infection
 Chi-Square (p-value)
BA.1
 BA.2
 BA.1
 BA.2
40 10 21 3
Age (years), median (range) 42 (9–83) 40 (13–65) 33 (10–63) 33 (28–43) 1.3 (0.73)
Gender
Female 31 (78%) 5 (50%) 11 (52%) 2 (67%) 22.22 (0.00006)
Male 9 (22%) 5 (50%) 10 (48%) 1 (33%)
Medical condition 21 (52.5%) 3 (30%) 5 (24%) 1 (33%) 9.47 (0.02)
Auto-immune Disease 1 0 2 0
Chronic Heart Disease 1 0 0 0
Chronic Pulmonary Disease 2 0 0 0
Dementia 1 0 0 0
Diabetes 1 0 0 0
Gout 1 0 0 0
Hypertension 4 0 0 0
Hypothyroid 7 3 3 1
Kidney Disease 1 0 0 0
Multiple Sclerosis 1 0 0 0
S/P Cancer 1 0 0 0
COVID-19 disease severity
Asymptomatic 3 (7.5%) 0 0 0
Mild 15 (37.5%) 6 (60%) 13 (62%) 3 (100%) 46.61 (<0.00001)
Mild with SOBa 13 (32.5%) 4 (40%) 6 (29%) 0
Moderate 7 (17.5%) 0 2 (10%) 0
Severe 2 (5%) 0 0 0
Days from positive PCR test to sampling, median (range)
1st RNA samples 2 (0–4) 3 (0–4) 2 (0–5) 3 (2–3) 0.21 (0.98)
2nd RNA samples 14 (7–15) 14 (12–17) 13 (7–18) 16 (12–19)
Serum samples 14 (7–21) 14 (7–17) 13 (7–18) 14 (7–19)
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a

SOB, shortness of breath.

Figure 1.

Figure 1

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Antibody analysis

(A) Plasma IgG antibody binding to the SARS-CoV-2 RBD (spike) of the ancestral (WA) variant and Omicron BA.1 sublineage.

(B) Plasma IgG antibody binding to the SARS-CoV-2 RBD (spike) of different variants. Plasma from unvaccinated Omicron patients with and without prior infection experience was analyzed. WA, authentic SARS-CoV-2 strain.

(C and D) Neutralization response to virus spike protein of the ancestral strain and Omicron variants (C) as well as the SARS-CoV-2 RBD (spike) of former variants (D). p-value between the two groups is from the one-tailed Mann-Whitney t-test. Percentage of samples with zero inhibition activity for each group is shown above the X axis. Asterisks indicate significance between groups. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001. Line at median, dotted line at 90%.

For reference, titers from individuals infected with the Omicron Beta variant (Knabl et al., 2022) are shown. These approximate those obtained in individuals previously infected with an earlier variant who then experienced BA.1 or BA.2 infection. The differential response in antibody generation following BA.1 and BA.2 infection was consistent across all SARS-CoV-2 variants tested (Figure 1B).

We next measured neutralization capacity using the angiotensin-converting enzyme 2 (ACE2) binding inhibition assay (Figure 1C). Neutralizing activities against the ancestral strain and Omicron BA.1 were similarly low in antigen-naive individuals infected with either BA.1 or BA.2. Like the overall antibody titers, neutralizing activity was higher in those previously infected. Activity from BA.1 and BA.2 infected individuals with previous SARS-CoV-2 infection approximated that found for individuals infected with the Omicron Beta variant and was similar across all SARS-CoV-2 variants tested against (Figure 1D).

Transcriptome response

Next, we investigated the impact of BA.1 and BA.2 infection on the immune transcriptome in Omicron patients with or without a previous infection history (Figure 2; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection; Table S3: Significantly differential genes between no prior and prior infection in BA.2 infected cohorts). Buffy coats were isolated within the first three days after validated Omicron infection and bulk RNA-seq was conducted with an average sequencing depth of 200 million reads per sample. First, we directly compared the transcriptomes of the BA.1 and BA.2 infected Omicron cohorts without prior infection. In a direct comparison of the two cohorts, expression of 38 and 15 genes was induced significantly in patients infected with BA.1 and BA.2, respectively (Figure 2A; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection). Although genes activated upon BA.1 infection are enriched in neutrophil degranulation and innate immune system, the ones activated upon BA.2 infection are categorized in complement pathway (Figure 2B; Table S2: Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection; Figure S1). Genes induced at significantly higher levels in BA.1 patients, such as C4BPA, initially identified by RNA-seq analyses (Figure 2C) and validated by qRT-PCR (Figure S1A), have also been reported to be regulated in COVID-19 patients (Gutmann et al., 2021; Jabeen et al., 2022). Of note, the complement genes C1QA and C1QB that show elevated expression in nonclassical monocytes from COVID-19 patients (Stephenson et al., 2021) are preferentially expressed in BA.2 patients as initially identified by RNA-seq analyses (Figure 2C) and validated by qRT-PCR (Figure S1B). We then compared the immune transcriptomes from BA.2 patients with and without prior infection and identified 342 and 145 significantly induced genes, respectively (Figure 2D; Table S3: Significantly differential genes between no prior and prior infection in BA.2 infected cohorts). GSEA analyses linked the induced genes in the “no prior infection” cohort to innate immune responses, including interferon and inflammatory pathways (Figure 2E). Genes activated in the “no prior infection” cohort such as CCL2 and IL6, initially identified by RNA-seq (Figure 2F) and validated by qRT-PCR (Figure S1C), are part of the interferon response in severe COVID-19 (Cao, 2021; Munnur et al., 2021). Similar to what has been previously reported for transcriptome analysis of peripheral blood mononuclear cells of SARS-CoV-2 infected patients, in general, there was good agreement between RNA-seq (Figure 2) and qRT-PCR results for genes differentially expressed between different cohorts (Figure S1) (Zhang et al., 2021b).

Figure 2.

Figure 2

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Immune transcriptomes following Omicron infection

(A) Volcano plot of differentially expressed genes (DEGs) in unvaccinated patients within 2–3 days after infection with Omicron sublineages BA.1 or BA.2. The patients had no known prior infection with other SARS-CoV-2 variants.

(B) GSEA categories of genes expressed at significantly higher levels in the BA.1 or BA.2 infection group. X axis denotes statistical significance as measured by minus logarithm of FDR q-values. Y axis ranked the terms by q values.

(C) Bar plots depicting the fold change of specific mRNA levels between the BA.1 and BA.2 groups. Red bars show genes that are higher in the BA.1 group compared to BA.2 and enriched in neutrophil degranulation and innate immune system. Blue bars indicate genes that are higher in the BA.2 group compared to BA.1 and enriched in complement pathways.

(D) Volcano plot of DEGs in unvaccinated patients, with and without prior SARS-Cov-2 exposure, 2–3 days after infection with the Omicron BA.2 sublineage.

(E) GSEA categories of genes expressed at significantly higher levels in the BA.2 groups with and without prior SARS-CoV-2 infection.

(F) Bar plots showing the fold change of specific mRNA levels between BA.2 groups with and without prior SARS-CoV-2 with earlier variants. Blue bars present genes that are higher in the no prior infection group compared to the prior infection group and are enriched in innate immune responses.

Antibody germline repertoire

The higher level of anti-spike antibodies and neutralization activity in BA.1 patients of the “no prior infection” group compared to BA.2 infection, led us to dig deeper and interrogate the germline antibody gene usage. Specifically, we determined the range of immunoglobulin heavy chain (IGHV) gene usage elicited by BA.1 and BA.2 sublineage infection in the “no previous infection” cohort (Figure 3). RNA-seq was conducted 13–16 days after the positive PCR test. The number of total immunoglobulin genes (heavy chain variable (IGHV), light chain variable (IGKV and IGLV) and T cell receptor alpha and beta variable (TRAV and TRBV)) revealed the use of a broad range of germlines in all cohorts without statistically significance (Figure 3A). However, we observed an increased frequency of transcription of eight VH genes, including IGHV3-11, IGHV3-66, IGHV1-2 and IGHV4-61, in the BA.1 infection group compared to the BA.2 cohort (Figure 3B). These particular heavy chain variants have been identified in SARS-CoV-2 infected patients and COVID-19-vaccinated individuals that developed strongly neutralizing antibodies (Andreano et al., 2021; Andreano and Rappuoli, 2021; Collier et al., 2021; He et al., 2021; Lee et al., 2022a; Zhang et al., 2021a). Expression of some of these genes was also activated in octogenarians who had received a single dose of the BNT162b mRNA vaccine 15 months after recovery from COVID-19 (Lee et al., 2022a).

Figure 3.

Figure 3

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SARS-CoV-2-RBD-specific B-cell memory in Omicron patients

(A) The number of total immunoglobulin alleles in each group at days 13–14.

(B) Box plots show the frequency (%) of IGHV usage in the BA.1 and BA.2 groups with no prior infection. Alleles previously identified in neutralizing antibodies to COVID-19 infection are marked in red. p-value between the two groups is from the one-tailed Mann-Whitney t-test. ∗p < 0.05. Median, middle bar inside the box; IQR, 50% of the data; whiskers, 1.5 times the IQR.

Discussion

While the Omicron BA.1 and BA.2 sublineages share mutations they also carry unique mutations, 28 in BA.2, which can explain their differential response to vaccine-induced and therapeutic antibodies (Ai et al., 2022; Arora et al., 2022a, 2022b, 2022b). Previous studies have also demonstrated that BA.1 infection of naive persons yields a very limited antibody response to SARS-CoV-2 VOCs other than Omicron itself (Lee et al., 2022b; Rössler et al., 2022). However, prior vaccination or prior infection with older SARS-CoV-2 variants augment the immune response upon BA.1 breakthrough infection (Lee et al., 2022b; Rössler et al., 2022). Our study now reveals that in antigen-naive individuals the immunologic response following BA.2 infection is even lower than that elicited by BA.1 infection as indicated by significantly lower antibody titers against all VOCs.

While other studies have focused on the humoral response including neutralizing activity induced by Omicron infection (Suryawanshi et al., 2022), we have extended the investigations and explored the presence of germline immunoglobulin variants induced by BA.1 and BA.2 infection. RBD-targeting antibodies can block SARS-CoV-2 binding to ACE2 and neutralizing immunoglobulin G heavy-chain variable (IGHV) gene usage has been reported (Yuan et al., 2020). Our study identified several IGHV germline alleles in Omicron patients that have been identified in neutralizing antibodies in COVID-19 patients (Yan et al., 2021; Yuan et al., 2020).

In conclusion, our study demonstrates that Omicron BA.2 infection of unvaccinated individuals without prior COVID-19 does not elicit effective antibodies against BA.1 and non-Omicron variants. In previously infected individuals, however, BA.2 infection successfully induces immunity against other variants.

Limitations of the study

There are several limitations to the current study. First, the study was conducted on volunteers from a specific geographical area, Tyrol (Austria). Secondly, the limitation is the confinement of the study to a timeframe of three weeks following Omicron infection. Third, the number of BA.2 patients with prior infection was very limited. Additional individuals for this cohort could not be recruited due to the replacement of BA.2 infection by BA.4 and BA.5 in Tyrol. Similar pandemic-related cohort size limitations have been noted for other transcriptome-based COVID studies (Sureshchandra et al., 2021; Zhang et al., 2021b). Lastly, some of the data collected on breakthrough infections were reliant on retrospective chart review and not collected as part of a prospective study.

STAR★Methods

Key resources table

REAGENT or RESOURCE SOURCE IDENTIFIER
Biological samples
Human serum Tyrol, Austria N/A
Human immune cells Tyrol, Austria N/A
Critical commercial assays
Maxwell RSC simply RNA Blood Kit Promega Cat# ASB1380
V-PLEX SARS-CoV-2 Panel 23 (IgG) Kit Meso Scale Discovery Cat# K15567U
V-PLEX SARS-CoV-2 Panel 23 (ACE2) Kit Meso Scale Discovery Cat# K15570U
TruSeq Stranded mRNA Library Prep Kit Illumina Cat# RS-20020595
SuperScript III First-Strand Synthesis SuperMix Invitrogen Cat#18080-400
SsoAdvanced Universal SYBR Green Supermix Bio-Rad Cat#1725271
Human C4BPA primer set Realtimeprimers.com Cat#VHPS-1289
Human ITLN1 primer set Realtimeprimers.com Cat#VHPS-4672
Human MMP8 primer set Realtimeprimers.com Cat#VHPS-5768
Human OLFM4 (GW112) primer set Realtimeprimers.com Cat#VHPS-3935
Human OLR1 primer set Realtimeprimers.com Cat#VHPS-6460
Human C1QA primer set Realtimeprimers.com Cat#VHPS-1148
Human C1QB primer set Realtimeprimers.com Cat#VHPS-1149
Human CCL2 primer set Realtimeprimers.com Cat#VHPS-1614
Human CD83 primer set Realtimeprimers.com Cat#VHPS-1701
Human IL6 primer set Realtimeprimers.com Cat#VHPS-4559
Human FOSB primer set Realtimeprimers.com Cat#VHPS-3373
Human AXL primer set Realtimeprimers.com Cat#VHPS-698
Human Housekeeping Gene Set Realtimeprimers.com Cat# HHK-1
Deposited data
Raw and analyzed data This paper GEO: GSE205244
Omicron patient (Days 2–3) Lee et al. (2022b) GEO: GSE201530
Human reference genome UCSC, hg19 UCSC Genome Browser http://hgdownload.soe.ucsc.edu/downloads.html#mouse
Software and algorithms
MSD DISCOVERY WORKBENCH analysis software https://www.mesoscale.com/en/products_and_services/software
Trimmomatic (version 0.36) Bolger et al. (2014) http://www.usadellab.org/cms/?page=trimmomatic
STAR (2.5.4a) Dobin et al. (2013) https://anaconda.org/bioconda/star/files?version=2.5.4a
HTSeq Anders et al. (2015) https://htseq.readthedocs.io/en/master/
R (3.6.3) https://www.R-project.org/
Bioconductor Huber et al., 2015 https://www.bioconductor.org/
DESeq2 Love et al. (2014) https://bioconductor.org/packages/release/bioc/html/DESeq2.html
RUVSeq Risso et al. (2014) https://bioconductor.org/packages/release/bioc/html/RUVSeq.html
Dplyr https://CRAN.R-project.org/package=dplyr
ggplot2 Wickham (2009) https://ggplot2.tidyverse.org/
GSEA https://www.gsea-msigdb.org/gsea/msigdb
MiXCR Bolotin et al. (2015) https://mixcr.readthedocs.io/en/master/
PRISM GraphPad (9.0.0) https://www.graphpad.com/scientific-software/prism/
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Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact Lothar Hennighausen (lotharh@nih.gov).

Materials availability

This study did not generate new unique reagents.

Experimental model and subject details

Study population, study design and recruitment

Data from a total of 74 patients that were unvaccinated against COVID19 and infected with either Omicron BA.1 or BA.2 were analyzed. 13 BA.2 patients and 10 new BA.1 patients were recruited for the study under informed consent 50 with no history of prior vaccination and prior infection with other SARS-CoV-2 variants and 24 unvaccinated patients with prior infections (Table 1). Recruitment and blood sample collection took place between December 2021 and March 2022. All samples identified from individuals with BA.1 and BA.2 infection within the community within this timeframe who provided consent were included. A resource constraint was that the specific sub-sample sizes were determined after recruitment. The smallest sub-sample was BA.2 infected individuals with history of prior infection (n=3) as compared to BA.2 infected individuals without prior infection (n=10), BA.1 infected individuals with history of prior infection (n=21) and BA.1 infected individuals without history of prior infection (n=40). Day numbers for samples refers to the number of days following initial positive SARS-CoV-2 RT-PCR test. An additional 53 BA.1 patients from our previous study (Lee et al., 2022b) were included. Patient recruitment was performed by a medic who assessed clinical status including performance of an oxygen saturation (SpO2) test. The clinical spectrum of the patients’ SARS-CoV-2 symptoms were classified based on the National Institutes of Health (NIH) treatment guidelines (https://files.covid19treatmentguidelines.nih.gov/guidelines/section/section_43.pdf). This study was approved (EK Nr: 1064/2021) by the Institutional Review Board (IRB) of the Office of Research Oversight/Regulatory Affairs, Medical University of Innsbruck, Austria, which is responsible for all human research studies conducted in the State of Tyrol (Austria). The investigators do not need to have an affiliation with the University of Innsbruck. Written informed consent was obtained from all subjects. Participant information was coded and anonymized. This study was determined to impose minimal risk on participants. All methods were carried out in accordance with relevant guidelines and regulations. All research has been have been performed in accordance with the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/). In addition, we followed the ‘Sex and Gender Equity in Research – SAGER – guidelines’ and included sex and gender considerations where relevant.

Method details

Antibody assay

Whole blood was collected by medical personnel after subjects had tested positive for SARS-CoV-2. Antibody containing sera were obtained by centrifuging EDTA blood samples for 10 min at 4,000 g. End-point binding IgG antibody titers to various SARS-CoV-2–derived antigens were measured using the Meso Scale Discovery (MSD) platform. SARS-CoV-2 spike, nucleocapsid, Alpha, Beta, Gamma, Delta, and Omicron spike subdomains were assayed using the V-plex multispot COVID-19 serology kits (Panel 23 (IgG) Kit, K15567U). Plates were coated with the specific antigen on spots in the 96 well plate and the bound antibodies in the samples (1:50,000 dilution) were then detected by anti-human IgG antibodies conjugated with the MSD SULPHO-TAG which is then read on the MSD instrument which measures the light emitted from the tag.

ACE2 binding inhibition (neutralization) ELISA

The V-PLEX COVID-19 ACE2 Neutralization kit (Meso Scale Discovery, Panel 23 (ACE2) Kit, K15570U) was used to quantitatively measure antibodies that block the binding of ACE2 to its cognate ligands (SARS-CoV-2 and variant spike subdomains). Plates were coated with the specific antigen on spots in the 96 well plate and the bound antibodies in the samples (1:10 dilution) were then detected by Human ACE2 protein conjugated with the MSD SULPHO-TAG which is then read on the MSD instrument which measures the light emitted from the tag.

Extraction of the buffy coat and purification of RNA

Whole blood was collected, and total RNA was extracted from the buffy coat and purified using the Maxwell RSC simply RNA Blood Kit (Promega) according to the manufacturer’s instructions. The concentration and quality of RNA were assessed by an Agilent Bioanalyzer 2100 (Agilent Technologies, CA).

mRNA sequencing (mRNA-seq) and data analysis

The Poly-A containing mRNA was purified by poly-T oligo hybridization from 1 mg of total RNA and cDNA was synthesized using SuperScript III (Invitrogen, MA). Libraries for sequencing were prepared according to the manufacturer’s instructions with TruSeq Stranded mRNA Library Prep Kit (Illumina, CA, RS-20020595) and paired-end sequencing was done with a NovaSeq 6000 instrument (Illumina) yielding 200–350 million reads per sample.

The raw data were subjected to QC analyses using the FastQC tool (version 0.11.9) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). mRNA-seq read quality control was done using Trimmomatic (Bolger et al., 2014) (version 0.36) and STAR RNA-seq (Dobin et al., 2013) (version STAR 2.5.4a) using 150 bp paired-end mode was used to align the reads (hg19). HTSeq (Anders et al., 2015) (version 0.9.1) was to retrieve the raw counts and subsequently, Bioconductor (Huber et al., 2015) package DESeq2 (Love et al., 2014) in R (https://www.R-project.org/) was used to normalize the counts across samples (Zhao et al., 2021) and perform differential expression gene analysis. Additionally, the RUVSeq (Risso et al., 2014) package was applied to remove confounding factors. The data were pre-filtered keeping only genes with at least ten reads in total. The visualization was done using dplyr (https://CRAN.R-project.org/package=dplyr) and ggplot2 (Wickham, 2009). The genes with log2 fold change >1 or < -1 and adjusted p-value (pAdj) < 0.05 corrected for multiple testing using the Benjamini-Hochberg method were considered significant and then conducted gene enrichment analysis (GSEA, https://www.gsea-msigdb.org/gsea/msigdb). DESeq2 is an established recommended program for analysis of RNA-seq data for ≥3 replicates per condition (Schurch et al., 2016).

For T- or B-cell receptor repertoire sequencing analysis, trimmed fastq files from bulk RNA-seq were aligned against human V, D and J gene sequences using the default settings with MiXCR (Bolotin et al., 2015, 2017).

Quantitative real-time PCR (qRT–PCR)

Total RNA (1 μg) was reverse transcribed for 50 min at 50°C using 50 μM oligo dT and 2 μL of SuperScript III First-Strand Synthesis SuperMix (Invitrogen) in a 20 μL reaction. Quantitative real-time PCR (qRT-PCR) was performed using primer sets (C4BPA, VHPS-1289; ITLN1, VHPS-4672; MMP8, VHPS-5768; OLFM4 (GW112), VHPS-3935; OLR1, VHPS-6460; C1QA, VHPS-1148; C1QB, VHPS-1149; CCL2, VHPS-1614; CD83, VHPS-1701; IL6, VHPS-4559; FOSB, VHPS-3373; AXL, VHPS-698; Human Housekeeping Gene Set, HHK-1, Realtimeprimers.com) and SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) on the CFX384 Real-Time PCR Detection System (Bio-Rad) according to the manufacturer’s instructions. PCR conditions were 95°C for 10 s and 58°C for 45 s for 50 cycles. All reactions were done in triplicate and normalized to the housekeeping gene GAPDH. Relative differences in PCR results were calculated using the comparative cycle threshold (CT) method.

Quantification and statistical analysis

Differential expression gene (DEG) identification used Bioconductor package DESeq2 in R. P-values were calculated using a paired, two-side Wilcoxon test and adjusted p-value (pAdj) corrected using the Benjamini–Hochberg method. The cut-off value for the false discovery rate was pAdj >0.05. Genes with log2 fold change >1 or < -1, pAdj <0.05 and without 0 value from all sample were considered significant. For significance of each GSEA category, significantly regulated gene sets were evaluated with the Kolmogorov-Smirnov statistic. p-values of antibody and qRT-PCR between two groups were calculated using one-tailed Mann-Whitney t-test on GraphPad Prism software (version 9.0.0). A value of ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001 was considered statistically significant.

Acknowledgments

This work was supported by the Intramural Research Program (IRP) of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), USA.

Our gratitude goes to the participants who contributed to this study to advance our understanding of SARS-CoV-2 Omicron infection. We thank Yuhai Dai from the NIDDK clinical core for helping antibody assay. This work was utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). RNA-sequencing and single-cell RNA-sequencing were conducted at the NIH Intramural Sequencing Center, NISC (https://www.nisc.nih.gov/contact.htm).

Author contributions

H.K.L., L.K., and L.H. designed the study. L.K recruited patients and collected material. H.K.L. analyzed RNA-seq data. M.W. conducted antibody assays. H.K.L., L.K., P.A.F., and L.H. analyzed data. L.H. supervised the project. H.K.L., L.K., P.A.F., and L.H. wrote the article. All authors read and approved the article.

Declaration of interests

The authors declare no competing interests.

Inclusion and diversity

We worked to ensure gender balance in the recruitment of human subjects. We worked to ensure ethnic or other types of diversity in the recruitment of human subjects. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as an underrepresented ethnic minority in their field of research or within their geographical location. One or more of the authors of this paper self-identifies as a gender minority in their field of research. One or more of the authors of this paper self-identifies as living with a disability. While citing references scientifically relevant for this work, we also actively worked to promote gender balance in our reference list. We avoided “helicopter science” practices by including the participating local contributors from the region where we conducted the research as authors on the paper.

Published: November 18, 2022

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2022.105369.

Supplemental information

Document S1. Figures S1 and Table S1
mmc1.pdf (111.9KB, pdf)
Table S2. Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection, related to Figure 2

Days 2–3 after infection, log2 (fold change) and adjusted p-value.

mmc2.xlsx (15.7KB, xlsx)
Table S3. Significantly differential genes between no prior and prior infection in BA.2 infected cohorts, related to Figure 2

Days 2–3 after infection, log2 (fold change) and adjusted p-value.

mmc3.xlsx (54.2KB, xlsx)

Data and code availability

  • •

    RNA-seq data generated from this study were deposited under the accession GEO: GSE205244 in the Gene Expression Omnibus (GEO).

  • •

    RNA-seq data of Omicron patients were obtained under GEO: GSE201530.

  • •

    This paper does not report original code.

  • •

    Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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Associated Data

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

Supplementary Materials

Document S1. Figures S1 and Table S1
mmc1.pdf (111.9KB, pdf)
Table S2. Significantly differential genes between BA.1 and BA.2 infected cohorts with no prior infection, related to Figure 2

Days 2–3 after infection, log2 (fold change) and adjusted p-value.

mmc2.xlsx (15.7KB, xlsx)
Table S3. Significantly differential genes between no prior and prior infection in BA.2 infected cohorts, related to Figure 2

Days 2–3 after infection, log2 (fold change) and adjusted p-value.

mmc3.xlsx (54.2KB, xlsx)

Data Availability Statement

  • •

    RNA-seq data generated from this study were deposited under the accession GEO: GSE205244 in the Gene Expression Omnibus (GEO).

  • •

    RNA-seq data of Omicron patients were obtained under GEO: GSE201530.

  • •

    This paper does not report original code.

  • •

    Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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