Augmenting Vaccine Efficacy against Delta Variant with ‘Mycobacterium-w’-Mediated Modulation of NK-ADCC and TLR-MYD88 Pathways

Mycobacterium-w (Mw) was shown to boost adaptive natural killer (ANK) cells and protect against COVID-19 during the first wave of the pandemic. As a follow-up of the trial, 50 healthcare workers (HCW) who had received Mw in September 2020 and subsequently received at least one dose of ChAdOx1 nCoV-19 vaccine (Mw + ChAdOx1 group) were monitored for symptomatic COVID-19 during a major outbreak with the delta variant of SARS-CoV-2 (April–June 2021), along with 201 HCW receiving both doses of the vaccine without Mw (ChAdOx1 group). Despite 48% having received just a single dose of the vaccine in the Mw + ChAdOx1 group, only two had mild COVID-19, compared to 36 infections in the ChAdOx1 group (HR-0.46, p = 0.009). Transcriptomic studies revealed an enhanced adaptive NK cell-dependent ADCC in the Mw + ChAdOx1 group, along with downregulation of the TLR2-MYD88 pathway and concomitant attenuation of downstream inflammatory pathways. This might have resulted in robust protection during the pandemic with the delta variant.


Introduction
The SARS-CoV-2 pandemic has changed the prevailing concepts related to the timeline for the development of novel vaccines. Collaborative efforts between scientists, industry and regulators resulted in effective vaccines for SARS-CoV-2 being available in less than 12 months. The vaccines used time-tested technologies such as inactivated virus as well as novel technologies such as viral vector or mRNA to provide an antiviral response [1]. While this feat remains a historical milestone, SARS-CoV-2 mutated at a rapid pace and has generated multiple variants of concern (VOC) [2,3]. VOC are known to escape the immune response generated by a previous infection/vaccination, leading to reduced/loss of protection against subsequent VOC as well as a decrease in vaccine efficacy [4,5]. To improve the protection against VOC, booster doses of approved vaccines have been advocated periodically [6,7]. This puts a significant burden on healthcare delivery systems, warranting a more robust solution.
Prior to the availability of a vaccine during the first wave of SARS-CoV-2 infection, our group successfully explored the protection provided by the innate immune pathway, following its boosting using an approved Mycobacterium-w (Mw), an immunomodulator in high-risk health care workers (HCWs) [8]. The protection provided following Mw administration, in the absence of any specific agents for prevention or treatment, was associated with the upregulation of adaptive natural killer (ANK) cells and the antibodydependent cellular cytotoxicity (ADCC) pathway, which was shown to provide protection against symptomatic COVID-19.
Mw is a non-pathogenic saprophytic organism. As a heat-killed suspension of 5 × 10 8 , this is approved in India as an immunomodulator. Studies have shown this to be a TLR agonist, with a stronger induction of TLR2-MYD88 response than BCG [9,10]. Mw has been evaluated as a vaccine adjuvant and found to significantly increase antibody titres in animal as well as human studies. As a single agent, it has been shown to provide protection against infectious diseases such as leprosy, tuberculosis and COVID-19 [11,12]. As a therapeutic agent, when administered with a chemotherapy in patients with leprosy and tuberculosis, Mw improved bacterial clearance. In patients with warts, it was found to be useful as a therapeutic agent when administered into the lesion or away from the lesion via the intradermal route [13]. In none of the studies, either in a prophylactic or therapeutic context, has it been shown to induce any systemic toxicity.
In the second quarter of 2021, India witnessed an unprecedented outbreak with the delta variant (B.1.617 lineage) of SARS-CoV-2, which, despite the introduction of two effective vaccines against SARS-CoV-2 (ChAdOx1 nCoV-19 and BBV152) by February 2021, overwhelmed the system with its rapid transmission and fatality, not quite witnessed during the first wave of the pandemic [14]. By this time (February 2021), we had evaluated our clinical trial data using Mw for protection against COVID-19 and were following up the cohort for long-term outcomes. We took this opportunity to evaluate the impact of Mw administration on the protection mediated by the ChAdOx1 nCoV-19 vaccine in a cohortcontrol study and understand the mechanistic pathways behind this by transcriptome analysis in the two groups.

Materials and Methods
The study was reviewed and approved by the institutional ethics committee. All subjects provided written consent for participation in the study. In this single-centre, nonrandomised cohort control study conducted at Dharamshila Narayana Superspeciality Hospital, New Delhi, India, the effect of prior priming with Mw on the protection offered by the ChAdOx1 nCoV-19 vaccine was evaluated from April to June 2021, at the peak of the severe outbreak of delta-variant SARS-CoV2 in New Delhi, India. All enrolled subjects received their first dose of ChAdOx1 nCoV-19 vaccine (Covishield, Serum Institute of India, Pune, India) by 28 February 2021, of which 50 (Mw + ChAdOx1 group) had received 0.1 mL Mw (Sepsivac, Cadila Pharmaceuticals, Ahmedabad, India) intradermally in each arm in September 2020 (≥6 months) as a part of a trial (CTRI/2020/10/028326) [8]. Twenty-four subjects in the Mw group were randomised 1:1 to receive the second dose, 30 days after the first dose. The control arm included 201 HCWs from the same institute who received both doses of the ChAdOx1 nCoV-19 vaccine, but not Mw, during or prior to their enrolment in the study (ChAdOx1 group). All subjects had their body temperature, pulse rate and oxygen saturation evaluated before and after each working day alongside self-reporting of symptoms. Nasopharyngeal swabs were taken on the development of symptoms suggestive of COVID-19 or following contact with a patient or a family member diagnosed with COVID-19 for reverse transcriptase polymerase chain reaction (RT-PCR) for the diagnosis of COVID-19 [15] (Supplementary Materials). The severity of COVID-19 was graded as per established criteria [16]. All subjects were CMV seropositive.

Statistics
Binary variables were compared between the groups using a chi square test. The continuous variables were analysed using an independent sample t-test considering Levene's test for equality of variances and non-parametric tests (Mann-Whitney U test). A p value < 0.05 was considered to be significant.
The efficacy of Mw in reducing the incidence of COVID-19 was calculated in terms of attack rates incidence risk ratio (IRR), absolute risk reduction (ARR) and intervention efficacy (see Table S1). The hazard ratio was calculated using the Cox regression method (SPSSv24, Armonk, NY, USA). GraphPad Prism (version 9.0 for Windows, GraphPad Software, La Jolla, CA, USA) was used for the graphical representation of the data.

RNA Sequencing (RNA-Seq) Analysis
This was carried out on both Mw + ChAdOx1 and ChAdOx1 groups at enrolment on four subjects from each group, as per established protocol [8], and is detailed in the Supplementary Materials. In brief, RNA isolation from peripheral blood mononuclear cells was carried out using the TRIzol method and poly(A) RNA selection. Complementary DNA (cDNA) library preparation and whole-transcriptome sequencing were undertaken using MinION 2.0 Oxford Nanopore Technologies (ONT) Oxford, United Kingdom.
Demultiplexing of the pooled samples and adapter removal was carried out using the inbuilt algorithm of Minknow. The Linux Long Time Support (v.20.04) operating system was used for all of the analyses. A comprehensive report of the sequencing was generated by the NanoComp (https://github.com/NanoComp/h5utils) tool and sequencing quality assessment was carried out using the FastQc tool (https://www.bioinformatics.babraham. ac.uk/projects/fastqc/). The sample reads were subjected to a minimum Phred quality score of 9 and reads of lower quality were filtered out using NanoFilt (https://github.com/ wdecoster/nanofilt). Some initial reads are usually prone to low base-calling quality. Hence, 50 bp of each initial read from every sample were filtered out for quality maintenance using the NanoFilt tool alone. All of the samples were subjected to quality assessment before and after quality filtering.
Differential gene expression (DGE) analysis was conducted using the "pipelinetranscriptome-de" (https://github.com/nanoporetech/pipeline-transcriptome-de) pipeline. This pipeline, from Nanopore Technologies, uses snakemake, minimap2, salmon, edgeR, DEXSeq and stageR to automate DGE workflows on long-read data. The pipeline was set to only make reads aligned to minimum of three samples to be considered for analysis. A separate conda (https://docs.conda.io/en/latest/#) environment was created on Linux OS to host this pipeline. The quantification and DGE analysis were carried out using the R language-based (https://www.r-project.org/) tool edgeR (https://bioconductor.org/ packages/release/bioc/html/edgeR.html), employing gene-wise negative binomial regres-sion model and normalisation factor (Transcript Mean of M-value) for each sequence library. Differentially expressed genes (DEGs) with log2Fold change (log2Fc) ≥ 0.5, ≤−0.5 and associated p-value < 0.05 were selected as significant for further analysis. The annotation of the DEGs was fetched from ENSEMBL database (https://asia.ensembl.org/index.html) using the R-based biomaRt package (https://bioconductor.org/packages/release/bioc/ html/biomaRt.html). The file compilation was ultimately achieved using Microsoft Excel and Linux Libre Office calc.
Hierarchical clustering analysis was performed among all four samples of each group to generate a heatmap from the normalised log2 counts per million (log2CPM) expression values using the R/Shiny-based rnaseqDRaMA (https://hssgenomics.shinyapps. io/RNAseq_DRaMA/) package. Comparative gene expression boxplots on the basis of log2CPM expression values between Mw + ChAdOx1 and ChAdOx1 group were generated from START (https://kcvi.shinyapps.io/START/). Volcano plots involving all DEGs were created using the R-based ggplot2 (https://ggplot2.tidyverse.org/) package.

Gene Ontology (GO) Pathway Analysis
Pathway enrichment analysis was carried out for significant DEGs using the R-based clusterProfiler (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html, v.4.2.1) tool, employing the GO database. Pathways related to ADCC and innate immune inflammatory pathways were considered for focused analysis. An adjusted p-value threshold of ≤0.05 was considered for this study.

Results
The current study reports the outcome of the Mw + ChAdOx1 group vs. the ChAdOx1 group in terms of COVID-19, along with the gene expression profile, with regard to inflammatory as well as ANK-ADCC pathways.

Effect of Mw Priming on Incidence and Outcome of COVID-19
Both groups were identical in relation to age and gender (Table 1). However, infection during the first wave of COVID-19, prior to receiving the vaccine, was significantly higher in the ChAdOx1 group. Similarly, all subjects in the ChAdOx1 group had received both doses of the vaccine, but only 24/50 (48%) subjects in the Mw + ChAdOx1 group had received both doses (p = 0.0001). In the Mw + ChAdOx1 group, two subjects had mild COVID-19. Both were from the sub-group receiving both doses of the ChAdOx1 vaccines, with none of the 26 participants receiving only one dose of ChAdOx1 developing COVID-19 (p = 0.22, Table 2).   . The x-axis shows the time in weeks and the y-axis shows the cumulative incidence (CI) in %. Red and black shaded circles represent Mw + ChAdOx1 and ChAdOx1 groups, respectively.
Severe COVID-19 requiring hospitalisation was not seen in the Mw + ChAdOx1 group, but was seen in nine participants (25%) in the ChAdOx1 group. The reinfection rate was identical in both groups: 13 subjects in the ChAdOx1 group and 1 in the Mw + ChAdOx1 group (p = 0.3). No deaths were recorded in either group.

NK and T Cell Subset Analysis
In the Mw + ChAdOx1 group, the longitudinal analysis of the NK cell subsets until day 100 was detailed in a previous publication. Further analysis of both ANK and iNK cells were carried out on 18 subjects at 180 days, i.e., at enrolment for the current study. These 18 subjects were randomly selected from the 38 subjects who had received only one dose of Mw, for the sake of uniformity. There was no significant change in the subsets (NKG2C + ANK cells p = 0.43, NKG2A + iNK cells p = 0.33, Figure 2A). A similar analysis of NK cell subsets was carried out on 18 randomly selected subjects from the ChAdOx1 group. There was no difference in the NK subsets between the groups at enrolment (NKG2C + Vaccines 2023, 11, 328 6 of 12 ANK cells p = 0.61, NKG2A + iNK cells p = 0.15, Figure 2B). There was no difference noted in the T cell subsets on longitudinal analysis within the Mw + ChAdOx1 group, or between the two groups at enrolment. day 100 was detailed in a previous publication. Further analysis of both ANK and iNK cells were carried out on 18 subjects at 180 days, i.e., at enrolment for the current study. These 18 subjects were randomly selected from the 38 subjects who had received only one dose of Mw, for the sake of uniformity. There was no significant change in the subsets (NKG2C + ANK cells p = 0.43, NKG2A + iNK cells p = 0.33, Figure 2A). A similar analysis of NK cell subsets was carried out on 18 randomly selected subjects from the ChAdOx1 group. There was no difference in the NK subsets between the groups at enrolment (NKG2C + ANK cells p = 0.61, NKG2A + iNK cells p = 0.15, Figure 2B). There was no difference noted in the T cell subsets on longitudinal analysis within the Mw + ChAdOx1 group, or between the two groups at enrolment. Red shaded triangles and dots represent NKG2C + ANK and NKG2A + iNK, respectively, for Mw + ChAdOx1 group. Black shaded triangles and dots represent NKG2C + ANK and NKG2A + iNK, respectively, for ChAdOx1 group. ** p < 0.01, *** p < 0.001, **** p < 0.0001 and ns = p-value not significant.

DGE Analysis
DGE analysis was carried out on four subjects from each group. The selection process for subjects undergoing the RNA-Seq study was computer-based randomisation from the subjects, who had undergone NK and T subset analysis in each group.
The sequencing and mapping metrics are explained in supplementary materials, Table S6. Based on the criteria of a p-value ≤ 0.05 and a log2 fold change (log2FC) > 0.5 and ≤−0.5, a total of 584 out of 14,148 genes were found to be differentially expressed between the two groups, with 223 and 361 genes significantly up-and downregulated, respectively, in the Mw + ChAdOx1 group. Forty-six DEGs related to ANK-ADCC and innate immune inflammatory pathways were selected to analyse the differential expression pattern between the two groups ( Figure 3A,B). The detailed statistical data on the DGE analysis are detailed in the supplementary materials, Tables S7 and S8. Red shaded triangles and dots represent NKG2C + ANK and NKG2A + iNK, respectively, for Mw + ChAdOx1 group. Black shaded triangles and dots represent NKG2C + ANK and NKG2A + iNK, respectively, for ChAdOx1 group. ** p < 0.01, *** p < 0.001, **** p < 0.0001 and ns = p-value not significant.

DGE Analysis
DGE analysis was carried out on four subjects from each group. The selection process for subjects undergoing the RNA-Seq study was computer-based randomisation from the subjects, who had undergone NK and T subset analysis in each group.
The sequencing and mapping metrics are explained in supplementary materials, Table  S6. Based on the criteria of a p-value ≤ 0.05 and a log2 fold change (log2FC) > 0.5 and ≤−0.5, a total of 584 out of 14,148 genes were found to be differentially expressed between the two groups, with 223 and 361 genes significantly up-and downregulated, respectively, in the Mw + ChAdOx1 group. Forty-six DEGs related to ANK-ADCC and innate immune inflammatory pathways were selected to analyse the differential expression pattern between the two groups ( Figure 3A,B). The detailed statistical data on the DGE analysis are detailed in the supplementary materials, Tables S7 and S8.

GO Pathway Analysis
The pathway enrichment of the selected DEGs was carried out focusing on the ANK-ADCC and innate immune inflammatory pathways using the ClusterProfiler package employing the GO database (Supplementary materials, Figure S1). Among the significant pathways of the ANK-ADCC pathway, antibody-dependent cellular cytotoxicity (GO:0001788), the positive regulation of natural killer cell-mediated cytotoxicity (GO:0045954) and the innate immune response-activating cell surface receptor signalling pathway (GO:0002220) were enriched for upregulation. Thirteen major innate inflammatory-associated pathways were enriched for downregulation in the Mw + ChAdOx1 group. These included MyD88-  (Figure 4). The statistical data on the pathway analysis is detailed in the supplementary materials, Figure S2, Tables S9 and S10.

Discussion
During the study period of April-June 2021, India witnessed a raging second wave of COVID-19, caused by the delta variant of SARS-CoV-2. Nineteen million infections were recorded in this period, with nearly one million cases in New Delhi, the national capital, where the study was undertaken [18,19]. The efficacy of two doses of the ChA-dOx1 nCoV-19 vaccine against the delta variant was subsequently reported to be 63.1%

Discussion
During the study period of April-June 2021, India witnessed a raging second wave of COVID-19, caused by the delta variant of SARS-CoV-2. Nineteen million infections were recorded in this period, with nearly one million cases in New Delhi, the national capital, where the study was undertaken [18,19]. The efficacy of two doses of the ChAdOx1 nCoV-19 vaccine against the delta variant was subsequently reported to be 63.1% [14]. Despite double-dose vaccination, 17.9% of the HCWs in the control group were infected in our study, with a quarter of them requiring oxygen support and hospitalisation. This was similar to a nationwide survey that reported a prevalence of breakthrough infections after two doses of vaccine in healthcare workers in India of 23.4% [20]. In contrast, only 4% in the Mw + ChAdOx1 group became infected, with all experiencing mild infection.
Enhanced protection in the Mw + ChAdOx1 group during the delta variant outbreak was observed, despite only 48% receiving two doses of the vaccine in the Mw + ChAdOx1 group, compared to all receiving both doses in the ChAdOx1 group. This suggests that factors other than neutralising antibodies might be at play. ADCC is another known mechanism that operates through the engagement of cellular effectors via Fc receptors [21]. Amongst these, NK cell-mediated ADCC is the most potent and consistent. While VOC might escape neutralisation by mutational changes in the receptor-binding domains, NK cell-mediated ADCC effector functions are more resilient and remain preserved across the variants [22][23][24][25] for longer periods [26].
We earlier demonstrated that ANK cells might provide protection against SARS-CoV-2 and Mw could provide protection via the augmentation of ANK cells-an effect that seems to persist beyond 6 months [8,27]. The ANK as well as iNK subsets were sustained in the Mw + ChAdOx1 group at 180 days. Both natural COVID-19 infection as well as the vaccines have been shown to produce a potent NK cell-mediated ADCC response [23]. Several studies have shown that ADCC induced following vaccination is more resilient against mutants (VOC) in contrast to variable neutralising antibody response against various VOC [22,25]. Based on the DEGs related to ANK-ADCC gene pathways in this study, ADCC generated following the Mw priming of vaccination seems to be more potent compared to the ChAdOx1 vaccine alone. The increased expression of CD247, along with the downregulation of FCεR1G observed in the Mw + ChAdOx1 group, is a typical signature of ANK-ADCC [28]. Both CD247 and FCεR1G are adapter molecules for FCγRIIIA (CD16), with CD247 possessing three immunoreceptor Tyrosine-based activation motifs (ITAM) as against one ITAM of FCεR1G, increasing ADCC by several folds [29]. Thus, the persistent upregulation of ANK cells and the augmented ADCC pathway via ANK cells following exposure to Mw might have boosted the protective efficacy of even a single dose of the ChAdOx1 vaccine.
Disease severity in SARS-CoV-2 infection is a result of an inappropriate hyperinflammatory cytokine response, resulting from the increased release of IFNγ, TNFα, IL1β, IL6 and IL18, amongst others. In COVID-19, TLR2 has been shown to be the primary innate sensor of the envelope protein of SARS-CoV-2, generating a cytokine response mediated via the MYD88 pathway with the subsequent activation of NF-κB and MAPK signalling [30,31]. There was further downregulation of the NLRP3-IL1β pathways, which have also been implicated in the pathogenesis of COVID-19 [32]. Based on the RNA-Seq findings, we hypothesise that the Mw-mediated downregulation of the key TLR2-MYD88 pathway and its subsequent downstream signalling pathways might have contributed substantially to its efficacy terms of the absolute reduction in the development of symptomatic COVID-19 as well as the moderation of the severity of COVID-19.
Continued agonist-induced downregulation might be the most likely explanation for this phenomenon, but would require longitudinal studies to confirm the same. Further limitations of our study were that it was carried out on a small cohort without randomisation and lacked further exploration of other humoral, T cell and monocyte/macrophagemediated pathways at a cellular level. However, the distinct gene expression signature indicates a salutary effect of Mw priming on the NK-ADCC pathway and the downregu-lation of the critical TLR2-MYD88 pathway at the same time, which might overcome the limitations of vaccines against current and evolving VOC.

Conclusions
Prior exposure to Mw seemed to provide robust protection against the delta variant of SARS-CoV-2 during the massive outbreak with this VOC, even with limited ChAdOx1 nCoV-19 vaccination. Transcriptomic studies suggested that this might have been contributed to by an enhanced adaptive NK cell-dependent ADCC in the Mw + ChAdOx1 group, along with the downregulation of the TLR2-MYD88 pathway and concomitant attenuation of downstream inflammatory pathways.
Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/vaccines11020328/s1, Figure S1: Correlation matrix of eight RNA-Seq libraries; Figure S2: Tree plot depicting clustering of enriched GO terms based on the 'average' hierarchical clustering method; Table S1: Statistical method for calculation of efficacy of Mw; Table S2: SARS-CoV-2 infections in Mw + ChAdOx1 and ChAdOx1 group; Table S3: Incidence rate of SARS-CoV-2 infections; Table S4: Incidence rate ratio of SARS-CoV-2 infections and Mw efficacy; Table S5: Multivariate analysis of outcomes; Table S6: Sequencing and mapping metrics; Table S7:Statistics of ANK-ADCC-related pathway DEGs; Table S8: Statistics of innate immune inflammatory-related pathway DEGs; Table S9: Enriched pathways related to ANK-ADCC pathways; Table S10: Enriched pathways related to innate immune inflammatory pathways.

Institutional Review Board Statement:
The study was conducted according to the guidelines of the Declaration of Helsinki, approved by the Ethics Committee of Dharamshila Narayana Superspeciality Hospital and also registered with the Clinical Trials Registry of India (CTRI/2020/10/028326).

Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.

Data Availability Statement:
The data presented in this study are openly available in the NCBI Gene Expression Omnibus (GEO) database at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc= GSE216811, GSE216811.