Research Article Upper Airway Epithelial Tissue Transcriptome Analysis Reveals Immune Signatures Associated with COVID-19 Severity in Ghanaians John Demby Sandi ,1,2,3,4 Joshua I. Levy,5 Kesego Tapela,1,2 Mark Zeller,5 Joshua Afari Yeboah,2 Daniel Frimpong Saka,2 Donald S. Grant,3,4 Gordon A. Awandare,1,2 Peter K. Quashie ,1,2 Kristian G. Andersen,5 and Lily Paemka 1,2 1West African Centre for Cell Biology of Infectious Pathogens (WACCBIP), College of Basic and Applied Sciences, University of Ghana, Accra, Ghana 2Department of Biochemistry, Cell and Molecular Biology (BCMB), School of Biological Sciences, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana 3Faculty of Laboratory Medicine, College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Sierra Leone 4Kenema Government Hospital, Kenema, Sierra Leone 5Department of Immunology and Microbiology, The Scripps Research Institute, San Diego, California 92037, USA Correspondence should be addressed to Lily Paemka; lpaemka@ug.edu.gh Received 8 August 2023; Revised 4 November 2023; Accepted 3 January 2024; Published 12 February 2024 Academic Editor: Vladimir Jurisic Copyright © 2024 John Demby Sandi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The immunological signatures driving the severity of coronavirus disease 19 (COVID-19) in Ghanaians remain poorly understood. We performed bulk transcriptome sequencing of nasopharyngeal samples from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-infected Ghanaians with mild and severe COVID-19, as well as healthy controls to characterize immune signatures at the primary SARS-CoV-2 infection site and identify drivers of disease severity. Generally, a heightened antiviral response was observed in SARS-CoV-2-infected Ghanaians compared with uninfected controls. COVID-19 severity was associated with immune suppression, overexpression of proinflammatory cytokines, including CRNN, IL1A, S100A7, and IL23A, and activation of pathways involved in keratinocyte proliferation. SAMD9L was among the differentially regulated interferon-stimulated genes in our mild and severe disease cohorts, suggesting that it may play a critical role in SARS-CoV-2 pathogenesis. By comparing our data with a publicly available dataset from a non-African (Indians) (GSE166530), an elevated expression of antiviral response-related genes was noted in COVID-19-infected Ghanaians. Overall, the study describes immune signatures driving COVID-19 severity in Ghanaians and identifies immune drivers that could serve as potential prognostic markers for future outbreaks or pandemics. It further provides important preliminary evidence suggesting differences in antiviral response at the upper respiratory interface in sub-Saharan Africans (Ghanaians) and non-Africans, which could be contributing to the differences in disease outcomes. Further studies using larger datasets from different populations will expand on these findings. 1. Introduction Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged to be a significant public health concern driving the ongoing coronavirus disease 19 (COVID-19) pandemic [1]. Beyond the conventional health complications, infection with SARS-CoV-2 was also associated with psychological alterations, including heightened levels of anxiety, stress, and depression, even in hospitalized patients, and this was particularly prevalent during the initial wave of the pandemic [2, 3]. SARS-CoV-2 utilizes the angiotensin-converting enzyme 2 as a receptor for host cell tropism, which is mainly enhanced by the transmem- brane protein TMPRSS2 [1, 4]. SARS-CoV-2 infection occurs primarily through the upper respiratory interface, and airway immunity is essential in determining the fate of SARS-CoV-2 infection [5]. COVID-19 is characterized by varying degrees of Hindawi Journal of Immunology Research Volume 2024, Article ID 6668017, 13 pages https://doi.org/10.1155/2024/6668017 https://orcid.org/0000-0002-7474-8156 https://orcid.org/0000-0003-4114-5460 https://orcid.org/0000-0001-7498-520X mailto:lpaemka@ug.edu.gh https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1155/2024/6668017 clinical phenotypes. The majority of SARS-CoV-2 infections remain asymptomatic. Among symptomatic cases, the most common symptoms include fatigue, cough, body pain, weakness, loss of appetite, and fever [6]. About 14%–18% of symptomatic COVID-19 cases progress to a severe clinical phenotype charac- terized by an aberrant inflammatory response associated with cytokine storm-mediatedmultiorgan failure and acute respiratory distress syndrome, ultimately leading to COVID-19-associated death [1, 6, 7]. Though other factors may be involved, the differential host gene expression, particularly in relevant tis- sues, can influence the immune response against infectious pathogens, including SARS-CoV-2. Airway epithelial cells are directly infected by SARS-CoV-2, rendering them essential for identifying immune signatures driving COVID-19 clinical phenotypes. A large body of transcriptomic data describes immune signatures mediating SARS-CoV-2 susceptibility and COVID-19 clinical phenotypes. For instance, using naso- pharyngeal swabs (NS), Jain et al. [8] reported a significant association between overexpression of CCL2, CXCL12, IL10, and COVID-19 severity. In a similar study conducted on 36 COVID-19-positive Indian patients, commonly upregulated genes involved in innate immune response were reported [9]. Additionally, marked expression of Th1 chemokines CXCL9/11 and antiviral genes, including IFIT1 and OAS gene isoforms, was associated with enhanced host antiviral response [10]. Generally, all these studies found an association between a compromised antiviral response and uncontrolled inflamma- tory response mediated by hyperactivation of JAK-STAT, NF-κB, and TGF-β signaling pathways through overexpression of proinflammatory cytokines, including IL6, IL10, IL23A,TNF-α, and IL18, and COVID-19 severity [8–13]. Though some differ- ences exist due to differences in tissue type, studies have also demonstrated that NS and blood samples share common immune response pathways [14, 15]. Although these studies have shed important insights into SARS-CoV-2 pathophysiol- ogy and pathogenic mechanisms, they were primarily con- ducted in non-Africans. Africans are more genetically diverse than non-Africans, and West Africans, in particular, have a high infectious disease burden [16]. Compared with non- Africans and Black African Americans, marked differences in COVID-19 clinical outcomes were observed in sub-Saharan Africans, particularly West Africans [17–19]. There is currently no publicly available bulk host transcriptomic data from sub- Saharan African populations, especially West Africans. that describe the transcriptome profile at the primary site of SARS- CoV-2 infection. It is, therefore, essential to investigate the differential gene expression in the upper airway epithelial tissue of SARS-CoV-2-infected West Africans underpinning the varying clinical phenotypes. Ghana is a sub-Saharan African country that reported considerably higher COVID-19 cases among other African countries. Available epidemiologic data reports about 171,600 SARS-CoV-2 infections in Ghana (https://www.afro.who.int/ health-topics/coronavirus-covid-19), albeit still lower than seroprevalence studies suggest [20, 21]. Though most of the reported COVID-19 cases are asymptomatic or mild, about 0.9% (1,422) of these infections resulted in COVID-19-associated deaths in Ghanaians. The underlying immunological signatures mediating COVID-19 severity in Ghanaians remain elusive. This study investigated the transcriptomic differences in the upper respiratory interface of SARS-CoV-2-infected Gha- naians with mild and severe clinical phenotypes to character- ize immune signatures at the primary SARS-CoV-2 infection site and identify drivers of disease severity. We further com- pared our data with a publicly available dataset from a SARS- CoV-2-infected non-African population to determine if there are differences in antiviral response. 2. Materials and Methods 2.1. Study Population.The study population (n= 75) included 52 unvaccinated SARS-CoV-2 infected and 23 uninfected Ghanaians from whom NS samples were collected following informed consent at the Ridge Hospital Accra, Ghana. COVID-19-related symptoms accompanied by a positive SAR-CoV-2 polymerase chain reaction (PCR) test were the criteria for inclusion into our COVID-19 disease cohort, while a negative SARS-CoV-2 PCR result and no symptoms of respiratory infection were used as criteria for inclusion as healthy controls. Samples from the SARS-CoV-2-infected individuals were collected at an acute stage of the disease. Clinicians classified COVID-19-infected patients as severe or mild cases according to the disease case definitions. Con- firmatory tests for SARS-CoV-2-specific genetic material by real-time reverse transcription-quantitative PCR were per- formed at the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP), University of Ghana. The clinical record was available only for a few study participants (Supplementary 1). 2.2. RNA Extraction. RNA was extracted from 300 µl of NS samples using the Quick-RNA Miniprep Plus kit (Zymo Research) following the manufacturer’s instructions. Briefly, samples were lysed for 30min, and nucleic acid was precipi- tated using absolute ethanol. Sample enrichment for RNA was archived by DNAse treatment followed by column puri- fication. Isolated RNA was eluted in nuclease-free water, and only RNA samples with A260/A280 ratio >1.8 and concen- trations above 1 ng/µl were considered for library prepara- tion, as previously examined [22]. 2.3. Library Preparation and mRNA Sequencing. The NEB- Next® Ultra II Directional RNA Library Prep Kit (#7760 L) for Illumina (New England Biolabs) was used for sequencing library construction according to manufacturers’ instruc- tions. Briefly, oligo dT-bound beads were used to isolate mRNA, followed by fragmentation for 15min at 94°C and complementary DNA (cDNA) synthesis. Sequencing librar- ies were then constructed and amplified using the NEBNext multiple oligos, following manufacturers’ instructions. Qubit and TapeStation were used to determine library concentra- tion and size using the high-sensitivity DNA kits. Libraries were generated and sequenced pair-end (150 cycles× 2) on the Illumina Novaseq 6000 system at the Scripps Research Institute using the Novaseq SP reagent kit. Output read files were adapter trimmed and demultiplexed using bcl2fastq v2.20.0.422 (Illumina) to generate unique FASTQ files per sample, with near zero mismatches. 2 Journal of Immunology Research https://www.afro.who.int/health-topics/coronavirus-covid-19 https://www.afro.who.int/health-topics/coronavirus-covid-19 https://www.afro.who.int/health-topics/coronavirus-covid-19 https://www.afro.who.int/health-topics/coronavirus-covid-19 https://www.afro.who.int/health-topics/coronavirus-covid-19 2.4. Differential Gene Expression Analysis. FASTQ files were pseudo-aligned to an indexed genome generated from the human cDNA fasta sequence (GRCh38) using Kallisto v0.48.0 [23]. Only samples with >5 million pseudo-aligned human reads (Supplementary 1) were used for downstream analysis in RStudio v4.2.1. To control for gender and age in the analy- sis, the median age of participants, 46.5 years (17–94 years), and DDX3Y gene (Y-linked) expression were used to infer participant age and gender, respectively, when absent in the metadata. Transcript IDs were mapped to human genes using an annotated human reference genome (hg38) available in biomaRt v1 [24]. Transcript counts were normalized, and differences in gene expression between groups, while control- ling for gender and age, were examined using the likelihood ratio test (lrt) and Wald test (wt) in Sleuth v0.27.3 [25]. The false discovery rate was corrected using the Benjamin– Hochberg test, and gene expression differences with an adjusted p-value <0.05 were considered statistically signifi- cant. A heatmap of the top differentially expressed genes (DEGs) was generated using the Bioconductor package, ggplot2 version 3.3.6 [26]. Volcano plots were generated using EnhancedVolcano package version 1.14.0 [27], and genes with p-value< 0.05 and log2 fold change (log2fc) >1 were reported as upregulated, while those with log2fc <0 were reported as downregulated. GraphPad Prism v9.4.1 was used to construct the violin plots with log-transformed expression values of selected genes, and the significant level was deter- mined using the unpaired t-test. ClusterProfiler package v 4.8.2 [28] was used in R version 4.3.1 software for gene set enrichment (GSE) analysis of DEGs to identify associated biological pathways. Pathways with adjust-value< 0.05 were reported. 3. Results After quality control steps, 64 samples (n= 64) were ana- lyzed to characterize SARS-CoV-2-induced immune signa- tures in Ghanaians. Age and gender were self-reported by study participants or, in some cases, by a close relative. The median age of participants, 46.5 years (17–94 years), and DDX3Y gene (Y-linked) expression were used to infer par- ticipant age and gender, respectively, when absent in the metadata. Females were slightly more represented in the study population at ∼53.1%. Thirty-six individuals (18 males and 18 females) with a median age of 46 years had mild COVID-19. The median age for severe cases in the study population was 79.5 years, and severity was higher in females (4 (66.7%)) compared with men in our study cohort (Table 1). Hypotension was reported in one of the severe cases, while HIV infection, stroke, and hyperglycemia were reported for some individuals with mild COVID-19 for whom clinical records were available (Supplementary 1). 3.1. Heightened Antiviral Response in the Upper Respiratory Interface of SARS-CoV-2-Infected Ghanaians. To define immune pathways activated during COVID-19 infection in Gha- naians, differences in gene expression in upper respiratory airway epithelial tissue from unvaccinated, uninfected con- trols and COVID-19-infected Ghanaians (Table 1) were investigated via bulk RNA sequencing of NS. On average, 79% of sequence reads were successfully mapped to the human transcriptome (hg38) (Supplementary 1). The likeli- hood ratio test (lrt) was implemented in the Sleuth package v0.27.3 to identify DEGs [25]. As expected, there was a marked difference in the expression of some immune response genes in the upper respiratory interfaces of COVID-19-infected individuals compared with uninfected controls. We found 1,922 host genes to be differentially expressed in the infected cohort compared with uninfected controls, q-value< 0.05, of which 508 and 1,414 were upregulated (log2fc> 1) and downregulated (log2fc<−1), respectively (Figure 1(b), Sup- plementary 2 and 3). Most upregulated genes in the SARS- CoV-2-infected Ghanaian cohort were interferon-stimulated genes (ISGs) such as BST2, ISG15, OAS1, IRF7, IF16, IFIT1, IFTIM, SAMD9L, CCL8, RSAD2, CCL2, CXCL10, and IFI44L (Supplementary 2), known to interfere with viral replication [29–31]. Pathways and processes involved in antiviral immune response, including cytokine-mediated signaling pathway, regu- lation of adaptive immune response, and immune response process, were significantly activated in the COVID-19-infected cohort (Figure 2(a)), suggestive of a heightened antiviral immune response [29, 32]. There was also evidence of adaptive immune system activation marked byHLA-A andHLA-DR upregulation (Supplementary 2, Figure 2(a)) [32]. In addition to protein- coding genes, the noncoding gene LGALS17A was among the top five upregulated genes in SARS-CoV-2-infected Ghanaians. Downregulated genes in our SARS-CoV-2- infected cohort, including TAF9B, TUBA1A, and NPBWR1, are known to be involved in biosynthesis and cellular pro- cesses (Supplementary 3) [33, 34]. These genes enriched for cellular component biogenesis, which was a significantly downregulated pathway in the COVID-19-infected Cohort (Figure 2(a)), suggesting host cellular function suppression. The expression of certain ISGs, such as ISG15, IFIT1, and CXCL8, have been reported to be different in males versus females infected with SARS-CoV-2 (p-value< 0.05) [35]. By comparing the expression of these genes in our dataset, the difference in their expression in Ghanaian males vs females in our COVID-19 cohort was not statistically significant (Figure 2(d)–2(f)), contrary to a previous report [35]. 3.2. Impaired Upper-Airway Antiviral Response and Dysregulated Inflammatory Response Mediated by CRNN and IL1A Overexpression Drive COVID-19 Severity in Ghanaians. To identify immune signatures mediating COVID-19 severity in Ghanaians, we compared gene expression differences in the upper respiratory airway of Ghanaians with severe (n= 6) and mild (n= 36) COVID-19. The median age for severe and mild COVID-19 was 79.5 and 46 years, respectively. Females were more likely to have severe COVID-19 in our study cohort (Table 1). We found 4750 genes to be downregulated (log2fc<−1), while 87 genes were upregulated (log2fc> 1) in individuals with Ghanaians with severe COVID-19 (Figure 3(a), Supplementary 4 and 5). Most downregulated genes in the severe COVID-19 cohort, including ISG15, OAS1, SAMD9L, and IFIT1, are associated with antiviral response pathways, and immune response-related pathways and processes were Journal of Immunology Research 3 TABLE 1: Disease characteristics of COVID-19-infected Ghanaians used in this study. Participants characteristics All participants (N= 64) COVID-19 cases (N= 42) Uninfected control (N= 22) Mild (N= 36) Severe (N= 6) Female 34 (53.1%) 18 (50%) 4 (66.7%) 12 (54.5%) Male 30 (46.9%) 18 (50%) 2 (33.3%) 10 (45.5%) Age (median) 46.5 (years) 46 (years) 79.5 (years) 49.7 (years) Symptoms Fever — 31 (86.1%) 6 (100%) 1 (4.5%) Cough — 27 (75%) 6 (100%) 0 Shortness of breath — 0 6 (100%) 0 Headache — 36 (100%) 4 (66.7%) 2 (9.1%) Running nose — 32 (88.9%) 5 (83.3%) 0 Sore throat — 19 (52.8%) 5 (83.3%) 0 Fatigue — 9 (25%) 6 (100%) 0 Muscle and joint pain — 13 (36.1%) 6 (100%) 0 Chill — 0 6 (100%) 0 Required mechanical ventilation — 0 2 (33.3%) 0 Shortness of breath, chills, and mechanical ventilation were associated with severity. Group 10 8 6 4 2 0 Age Gender CAT XCL2 HERC6 AXL OAS1 MOV10 XAF1 CFB BST2 CTSO IFIT1 ATXN2L IRF7 MDK SCO2 ZDHHC14 CCL8 CXCL11 USP18 PLVAP OASL CFH TARS1 PSME2 IFITM3 PSME2 ISG15 RTP4 IFI44 MGAT3 IFI44L RUFY4 IFI27 APOC2 IGFBP4 SIGLEC1 IFI6 TF NUDT3 PSAT1 Group SARS-CoV-2 infected Uninfected control Age 90 20 Gender Female Male L- 05 9 L- 07 1 L- 09 6 L- 03 7 L- 06 6 L- 01 2 L- 01 3 L- 08 9 L- 03 3 L- 07 4 L- 01 0 L- 05 6 L- 00 3 L- 06 1 L- 06 2 L- 06 8 L- 07 2 L- 05 7 L- 06 3 L- 07 0 L- 09 7 L- 06 9 L- 09 1 L- 01 4 L- 02 2 L- 09 4 L- 07 3 L- 08 0 L- 03 6 L- 00 8 L- 05 5 L- 08 6 L- 00 1 L- 04 6 L- 06 0 L- 02 8 L- 01 5 L- 03 8 L- 08 2 L- 04 1 L- 09 5 L- 02 3 L- 04 3 L- 09 3 L- 02 0 L- 02 5 L- 08 8 L- 02 1 L- 04 8 L- 04 4 L- 04 0 L- 04 9 L- 07 5 L- 03 5 L- 11 8 L- 01 7 L- 02 4 L- 05 3 L- 03 4 L- 20 1 L- 03 9 L- 04 2 L- 01 9 L- 11 9 ðaÞ HERC6 MOV10 LY6E BST2 IFI44L OAS1 ISG15OAS2 UBE2L6 IFI6SERPING1 RTP4 IFIT1IFI44 APOL3 CXCL11MDK OAS3 SIGLEC1LGALS3BP LAG3S1PR2 GBP1P1 XCL2PSMB9 SAMD9L PARP14 CCL8IFIT2CFBLAP3 RSAD2 GZMBAPOL2 CALHM6 SLC38A5SHFLGBP1SOCS1BCL2L14GNMT ACOD1HSH2D C1RTFF1IL4I1NMI GNLYTMEM92LRIG1 FASLGNUPR1 CCL5MRLN TRBC1FBXO39HSD11B2 CCL2ABCD1HLTF FGL2BBOX1 CD300EACTN4SLC23A1 LILRB2IRF4EEF1A1P13 C4BSTAB1 LILRA3 SULT1E1 PKD1P1 0 10 20 30 40 −2.5 0.0 2.5 5.0 Log2 fold change −L og 10 p NS Log2 FC p-Value p-Value and log2 FC ðbÞ FIGURE 1: (a) Heatmap of transcript abundance for the top 40 differentially expressed genes in each sample. (b) Volcano plot of upregulated and downregulated genes in SARS-CoV-2 infected Ghanaians compared with uninfected controls. Log2 fold change (FC) cutoff= 1, −Log10 p-value. 4 Journal of Immunology Research Activated Suppressed 0.60 0.65 0.70 0.75 0.80 0.60 0.65 0.70 0.75 0.80 Cellular response to cytokine stimulus Response to chemical Response to organic substance Heparin binding Biological process involved in interspecies interaction between organisms Response to cytokine Calcium ion transport Response to other organism Response to external biotic stimulus Response to biotic stimulus Immune response Cytokine-mediated signaling pathway Cellular component biogenesis Cellular component assembly Cytokine receptor binding Cytokine activity Regulation of adaptive immune response Gene ratio Count 10 20 30 40 50 0.02 0.03 0.04 p.Adjusted ðaÞ Cellular response to cytokine Stimulus Calcium ion transport Regulation of adaptive immune response Response to biotic Stimulus Response to external biotic Stimulus CCL8 CXCL11 CXCL10 ISG15 IRF7 OASL LILRB1IFIT1 ZBP1 XCL2 SOCS1 IFITM1 OAS1 RUFY4 IFITM3 OAS3 GBP4 PLVAP USP18 IL4I1 MICB RSAD2 IFIT2 IFI6 IFI44L IFIT3 LAG3 IDO1 SLAMF7 CFB SERPING1 CMPK2 BST2 TF IFI44 MX2 ISG20 BATF2 HERC6 MX1 PTGIR IFI27 OAS2 RTP4 IFI35 SAMD9 IFIH1 Size 10 20 30 40 50 Category Calcium ion transport Cellular response to cytokine Stimulus Regulation of adaptive immune response Response to biotic Stimulus Response to external biotic Stimulus ðbÞ FIGURE 2: Continued. Journal of Immunology Research 5 Activated Suppressed 0.4 0.6 0.8 1.0 0.4 0.6 0.8 1.0 Defense response Defense response to other organism Innate immune response Response to other organism Response to external biotic stimulus Response to biotic stimulus Intermediate filament Response to bacterium Intermediate filament cytoskeleton organization Intermediate filament-based process Cytosolic ribosome Peptide cross-linking Chemokine-mediated signaling pathway Intermediate filament organization Keratinocyte proliferation Cornified envelope Keratin filament G protein-coupled peptide receptor activity Peptide receptor activity Regulatory ncRNA processing miRNA processing Keratinization Gene ratio Count 50 100 0.010 0.011 0.012 0.013 0.014 p.Adjust ðcÞ Males Females 0 5 15 10 20 SARS-CoV-2-infected Ghanaians lo g2 tp m ns ISG15 ðdÞ Males Females –5 0 10 5 15 SARS-CoV-2-infected Ghanaians lo g2 tp m ns IFIT1 ðeÞ Males Females –5 0 5 15 10 20 SARS-CoV-2-infected Ghanaians lo g2 tp m ns CXCL8 ðfÞ FIGURE 2: Gene ontology (GO) pathway analysis of top differentially expressed genes in the study cohort. (a) Dotplot showing top activated and suppressed pathways in SARS-CoV-2-infected Ghanaians. Immune response pathways were activated, while cellular biogenesis-related processes were suppressed. (b) Cnetplot showing protein–protein interaction network analysis for the top DEG genes in the COVID-19-infected cohort. (c) Dotplot showing activated and suppressed pathways in severe compared to mild COVID-19 cohorts. Immune response-related pathways or processes were suppressed in individuals with severe COVID-19. Top enriched pathways are shown p.adjusted<0.05. (d–f ) Violin plots compare the expression of selected antiviral gene expressions in male and female SARS-Cov-2-infected Ghanaians. 6 Journal of Immunology Research 3 –L og 10 p 0 –6 –3 0 Log2 fold change 3 6 9 NS Log2 FC p-Value p-Value and log2 FC IVL APOD SARS2 TMPRSS11EZNF473 GGNBP2 CNFN SLC27A3 CRNN GCOM1NKAPP1 BBXUSP18 SCAMP3 CHCHD10 A2ML1 TMPRSS11BRPL3P4 PRSS27CLN8 PSMB8 HSPB8 HERC2P2PSMB9 SLC26A9IFI6 BIRC5HRAS ACSS3 PIR ARHGAP11AUBD DLX6CIZ1 SPRR2F RNF39 ðaÞ Severe Mild –5 0 5 10 COVID-19-infected Ghanaians lo g2 tp m ∗∗ S100A7 ðbÞ Severe Mild –10 0 10 –5 5 15 COVID-19-infected Ghanaians lo g2 tp m ∗∗∗∗ IL1A ðcÞ Severe Mild –5 0 5 10 COVID-19-infected Ghanaians lo g2 tp m ∗ IL23A ðdÞ Severe Mild 0 5 15 10 20 COVID-19-infected Ghanaians lo g2 tp m ∗ ISG15 ðeÞ Severe Mild –20 40 80 20 0 60 100 COVID-19-infected Ghanaians lo g2 tp m ∗∗ SAMD9L ðfÞ Severe Mild –5 0 5 10 15 COVID-19-infected Ghanaians lo g2 tp m ∗ IFIT1 ðgÞ Severe Mild –10 0 10 –5 5 15 COVID-19-infected Ghanaians lo g2 tp m ∗ CXC11 ðhÞ FIGURE 3: Differentially expressed genes in SARS-CoV-2-infected Ghanaians with severe clinical phenotype compared with mild. (a) Volcano plot showing up- and downregulated genes. CRNN was the top overexpressed gene in the severe COVID-19 cohort. (b–d) Violin plots of Journal of Immunology Research 7 suppressed in individuals with severe COVID-19, suggesting an impaired upper-respiratory airway immune response (Figures 2(c) and 3(e)–3(h), Supplementary 5). These antiviral- related genes were, however, upregulated in individuals with mild COVID-19, which could explain the immune features underlying the disease’s mildness. There was a hyperactivation of keratinization pathways associated with CRNN overexpres- sion [36] and overexpression of proinflammatory cytokines, including IL23A, S100A7, and IL1A (log2fc> 1, p-value< 0.05) in Ghanaians with severe COVID-19 compared with mild (Figures 2(c) and 3(a)–3(d). CRNN overexpression has been associated with inflammatory disease [36]. The MAL gene, an essential component in NF-κB pathway activation [37], and serine protease TMPRSS11B were among the top overexpressed genes in our severeCOVID-19 cohort (Figure 3(a), Supplementary 4). Taken together, we found that COVID-19 severity in the Ghanaian cohort was associated with dysregulated inflamma- tory response mediated by MAL, IL1A, IL23A, CRNN, and S100A7 overexpression and suppression of antiviral immune response-related pathways. A similar association has been reported in other populations [8, 11–13, 38]. 3.3. Antiviral Genes Are Differentially Expressed in COVID- 19-Infected Ghanaians Compared with Non-Africans. We further sought to determine whether the expression of anti- viral response genes in the upper respiratory interface of SARS-CoV-2-infected Ghanaians differs in other popula- tions. Toward this, we compared our data with a publicly available dataset (GSE166530) from Singh et al. [9] studying COVID-19 immune response signatures in a small cohort of SARS-CoV-2-infected Indians (n= 36) within South Telan- gana, a population characterized by higher COVID-19 sever- ity and mortality [9]. The selection of this data was based on the availability of publicly accessible raw FASTQ data files. Additionally, the data were generated from a similar tissue type, specifically upper airway epithelial tissue, which facilitated a direct comparison. We grouped all cases reported by Singh et al. [9] as a SARS-CoV-2-infected Indian cohort and grouped all the cases from our study to form a SARS-CoV-2-infected Ghanaian cohort. Compared with SARS-CoV-2-infected Indians, an overexpression of antiviral responses-related genes, including TMEM265, IFI6, ISG15, IFITM3, IFIT1, BST2, CCL2, LCN2, and OAS1, was observed in Ghanaians infected with SARS-CoV-2 (Figures 4(a) and 4(b(1))–4(b(3)), Supplementary 6 and 7). Though preliminary, these observed differences in anti- viral gene expression at the primary site of SARS-CoV-2 infection may suggest a more robust innate antiviral immune response in SARS-CoV-2-infected Ghanaians compared to their Indian counterparts. This may have contributed to the reduced COVID-19 severity in Ghanaians and likely other sub-Saharan Africans. Most of these upregulated antiviral genes in SARS-CoV-2-infected Ghanaians were also found to be upregulated in Ghanaians with mild COVID-19 com- pared to those with severe COVID-19 and uninfected con- trols (Supplementary 2). 4. Discussion The immunological signatures driving COVID-19 severity in Ghanaians remain elusive and need to be better understood. This study investigated the transcriptome differences at the upper respiratory interface of SARS-CoV-2-infected Gha- naians with mild and severe clinical phenotypes to character- ize immune signatures at the primary SARS-CoV-2 infection site and identify drivers of disease severity. Consistent with earlier studies [8, 9, 11, 12], we report an upregulation of immune response-related genes accompanied by activation of antiviral pathways and suppression of cellular biogenesis path- ways in the upper airway epithelial tissue from COVID-19- infected Ghanaians compared with uninfected controls. HLA- A and HLA-DR genes were upregulated in the upper airway of SARS-CoV-2-infected Ghanaians (Supplementary 2) and are known mediators of the adaptive immune response by antigen processing and presentation [39, 40], suggesting that HLA-A and HLA-DR overexpression may be activating the adaptive immune response vital to virus-infected cell elimination [32]. Cytokines are known regulators of immune response via cell- to-cell communication. Regulation of adaptive immune response was the top enriched activated pathway in our COVID-19-infected cohort compared to controls (Figure 2(a)), suggesting the involvement of cytokineswith immune regulatory potential, including IL-2 [41–43]. In addition to protein-coding genes, non-protein-coding LGALS17Awas found among the top upregulated genes. Considering the role of noncoding genes in regulating the activities of their target protein-coding genes, LGALS17A upregulation may suggest a critical role in SARS- CoV-2 pathophysiology by regulating the activities of a relevant gene(s) involved in SARS-CoV-2 replication.Neuropeptide B/W receptor-1 (NPBWR1) is the receptor for Neuropeptides B (NPB) and is required for the activation of NPB/NPBWR1 sig- naling, which plays a vital role in physiological processes, includ- ing energy homeostasis and metabolism [44]. Earlier work has shown that NPBWR1 knockout mice had defective cellular metabolic processes compared to the wild-type [33, 34]. In this study, NPBWR1 was among the top downregulated protein-coding genes in our SARS-Cov-2-infected cohort, with cellular component biogenesis being one of the suppressed pro- cesses (Figure 2(a)). Noting the critical role of NPBWR1 in metabolic processes to provide the energy and building blocks required for cellular component biogenesis, NPBWR1 downre- gulation may be driving the suppression of cellular component biogenesis pathways. This could present a previously unde- scribed SARS-CoV-2 pathogenic mechanism. Comparing Gha- naians with mild vs severe COVID-19 reveals a diminished antiviral response in Ghanaians with severe COVID-19 marked by downregulation of antiviral genes OAS1, CCL8, SAMD9L, selected overexpressed proinflammatory cytokines (IL1A, IL23A, and S100A7) in Ghanaians with severe COVID-19. (e–h). Violin plots of selected antiviral-related genes (ISG15, SAMD9L, IFIT1, and CXC11) that were downregulated in severe cases. Log2fc cutoff= 1, −Log10 p-value, ∗p-value< 0.05; ∗∗p-value< 0.01; ∗∗∗∗p-value< 0.001. 8 Journal of Immunology Research HLA-A, CXCL11, ISG15, IL32, and IFIT2, and suppression of antiviral immune response pathways. A similar trend was also observed in previous studies in other populations [8, 11, 13]. Severe COVID-19 has been chiefly associated with inflamma- tory cytokines such as interleukin 6 (IL-6), IL-8, and IL-10 over- expression [8, 11, 12, 45, 46]. Though Tapela et al. [47] reported some association between IL-6 and IL-8 cytokine concentration in plasma samples and COVID-19 severity, the expression of these cytokines was not found to be significantly upregulated in our severe COVID-19 cohort. However, in this study, an upre- gulation of other pro-inflammatory cytokines, includingCRNN, IL1A, IL23A, IVL, and S100A7, was associated with severe COVID-19. CRNN was the most upregulated gene, and kerati- nization was the top-activated process in individuals with severe COVID-19 cohort. Keratinocytes represent the first line of the host defense system, and their hyperproliferation contributes to the pathogenesis by infiltration of inflammatory cells [48, 49]. CRNN overexpression was previously shown to aberrantly reg- ulate keratinization by activating the Phosphoinositide 3- Kinase/Akt Pathway, leading to inflammatory diseases, such as psoriasis [36]. Epithelial cells are directly infected during SARS-CoV-2; thus, CRNN overexpression in our severe COVID-19 cohort may represent a potential pathogenic mech- anism employed by SARS-CoV-2 to induce dysregulated 20 –L og 10 p 0 –10 –5 0 Log2 fold change NS 5 40 60 Log2 FC p-Value p-Value and log2 FC ðaÞ Ghanaians Asians (Indians) 0 5 10 15 SARS-CoV-2 infected (b1) (b2) (b3) lo g2 tp m OAS1 ∗∗∗∗ ∗∗∗∗ ∗∗∗ Ghanaians Asians (Indians) 0 2 6 4 8 10 SARS-CoV-2 infected lo g2 tp m Ghanaians Asians (Indians) 0 2 6 4 8 10 SARS-CoV-2 infected lo g2 tp m IFIT1 APOE ðbÞ FIGURE 4: Differentially expressed genes in SARS-CoV-2-infected Ghanaians compared to SARS-CoV-2-infected Indians. (a) Volcano plot showing up and downregulated genes. (b(b1–b3)) Boxplots showing relative expression of selected antiviral genes. Log2 fc cutoff= 1, -Log10 p-value, ∗∗∗p-value< 0.01; ∗∗∗∗p-value< 0.001. Journal of Immunology Research 9 inflammatory response via upregulating keratinization at the primary site of infection. In addition, theMAL gene, an impor- tant component in NF-κB signaling pathway activation [37], and TMPRSS11B were among the top 10 upregulated genes in Ghanaians with severe COVID-19. TMPRSS11B is impli- cated as a driver of lung carcinoma [50], and severe COVID-19 is associated with lung abnormalities [51, 52]. Since SARS-CoV-2 is known to induce pathology in the lung, TMPRSS11B upregulation in individuals with severe COVID-19 may also represent another SARS-CoV-2 patho- genic mechanism. TMPRSS11B also interacts with CRNN (Supplementary 1). Our result on immune signatures mediat- ing COVID-19 severity in Ghanaians agrees substantially with findings from other studies [8, 11, 12, 46, 53]. The SAMD9L pathway was previously shown to be a critical host barrier that poxviruses subvert most to establish an infection [54] and was among the ISGs found to be significantly downregulated in Ghanaians with severe COVID-19 compared with mild cases. The suppression of SAMD9L in individuals with severe COVID-19 suggests that it may also be a critical host restric- tion factor that SARS-CoV-2 must antagonize to establish disease. Additionally, MUC21, a gene previously associated with lung adenocarcinoma, was also upregulated in Ghanaians with severe COVID-19 [55]. We found an insignificant differ- ence in the expression of previously reported antiviral genes, ISG15, IFIT1, and CXCL8, in males and females Ghanaians infected with SARS-CoV-2, contrary to a previous report [35]. However, this observation might be influenced by the sample size used in this study (Table 1). COVID-19 severity is considerably lower in sub-Saharan Africans, particularly West Africans, compared with non- Africans and BlackAfricanAmericans [16, 21, 56].We observe an upregulation of genes involved in antiviral response path- ways, includingOAS1 thatmediates RNase L pathway [57, 58], IFIT1, and APOE at the upper respiratory airway of COVID- 19-infected Ghanaians compared with a relevant publicly available dataset (GSE166530) from an Indian COVID-19 cohort [9] (Figure 4). The upregulation of these antiviral genes in COVID-19-infected Ghanaians may suggest a more robust antiviral response at this critical interface. Though prelimi- nary, this observed difference in antiviral gene expression at primary infection sites may have contributed to the reduced COVID-19 severity in sub-Saharan Africans, particularly Gha- naians. To our knowledge, this is the first direct comparison of immune response-related gene expression in the upper respi- ratory interface between SARS-CoV-2-infected West Africans and a non-African population and the first COVID-19 bulk host transcriptome dataset from West Africans. 5. Conclusions In conclusion, this study describes immune signatures at the primary site of SARS-CoV-2 infection and identifies immune signatures driving COVID-19 severity in SARS-CoV-2-infected Ghanaians. It further provides important preliminary evidence suggesting that antiviral genes are more highly expressed at the primary site of SARS-CoV-2 infection in sub-Saharan Africans (Ghanaians) compared with non-Africans (Indians), which may be driving the differences in antiviral response and clinical out- comes. Our overall report on DEGs in COVID-19-infected Ghanaians corroborates previous reports from similar studies. Data Availability Processed data are available in the Gene Expression Omni- bus (GEO) database with accession number GSE215906. Additional Points Limitations. First, the proportion of individuals with severe COVID-19 was small (n= 6) compared with the mild (n= 36). Additionally, records on comorbid conditions for most of the participants were unavailable. This was partly due to the challenge of obtaining clinical records during pandemics. Nevertheless, since our findings are generally consistent with previous studies in other populations, we can confidently assume that there were no major comorbid- ities in our cohort that could have significantly impacted our results. Future bulk transcriptome profiling studies using air- way epithelial tissue from a much larger COVID-19-infected Ghanaian cohort with established clinical records would strengthen and extend this work. Ethical Approval Ethics approval was obtained from the Ghana Health Service Ethics Review Committee (GHS-ERC:005/06/20) and the IRB committee at Scripps Research, USA (IRB-20-7549). Consent Informed consent was obtained from all subjects involved in this study. Disclosure The views expressed in this publication are those of the authors and not necessarily those of the funders. Conflicts of Interest All the authors declared that they have no conflicts of interest. Authors’ Contributions JDS performed the main experiments, data analysis, and interpretation and wrote the main manuscript. JIL and MZ helped with data analysis and reviewed and edited the man- uscript. KT collected patients’ samples and metadata and reviewed the manuscript. JAY and DFS helped in sample processing. DSG and GAA made the funding acquisition, reviewed and edited the manuscript. KGA made the funding acquisition, supervised the work at the Scripps Research Institute, USA, reviewed and edited the manuscript. PKQ and LP made the funding acquisition, supervised the work at WACCBIP, Ghana, study design and data interpretation, reviewed and edited the manuscript. 10 Journal of Immunology Research Acknowledgments We are grateful to the entire Andersen Lab team at Scripps Research, USA, the Paemka and Quashie Lab members, WACCBIP, and the Lassa fever research program in Sierra Leone for providing support and expertise that greatly enhanced this work. This work was partly supported by an award from the West African Network of Infectious Diseases ACEs (WANIDA) partial doctoral scholarship (grant number: WAN100635P). This scholarship package is funded by the AFD, IRD, and World Bank under the African Centre of Excellence (ACE) Partner Project. The West African Research Network for Infectious Diseases (WARN-ID; NIH NIAID grant no.: U01AI151812) and the Coalition for Epidemic Preparedness Innovation (CEPI) (Project Number: ESEP1904) also sup- ported this work. It was also supported partly by a grant from the Rockefeller Foundation (2021 HTH 006), a Crick African Network Fellowship (CAN/A00004/1 to PKQ), which receives its funding from the UK’s Global Chal- lenges Research Fund (MR/P028071/1), and by the Fran- cis Crick Institute which receives its core funding from Cancer Research UK (FC1001647), and by for open access, the author has applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. Supplementary Materials Supplementary 1. Processed data. Supplementary 2. Up sars-CoV-2-infected vs uninfected con- trol Ghanaians. Supplementary 3. Down sars-CoV-2-infected vs uninfected control Ghanaians. Supplementary 4. Up COVID-19-severe vs mild Ghanaians. Supplementary 5. DownCOVID-19-severe vsmildGhanaians. Supplementary 6. Up infected-Ghanaians vs Indians. Supplementary 7. Down infected-Ghanaians vs Indians. Supplementary 8. Figure 1: Protein-Protein interaction of top differentially expressed genes. References [1] P. Zhou, X.-L. Yang, X.-G. Wang et al., “A pneumonia outbreak associated with a new coronavirus of probable bat origin,” Nature, vol. 579, no. 7798, pp. 270–273, 2020. [2] A. Makevic, A. Ilic, M. Pantovic-Stefanovic, N. Muric, N. Djordjevic, and V. Jurisic, “Anxiety in patients treated in a temporary hospital in Belgrade, Serbia, during the first epidemic wave of COVID-19,” International Journal of Disaster Risk Reduction, vol. 77, Article ID 103086, 2022. [3] N. Salari, A. Hosseinian-Far, R. Jalali et al., “Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis,” Globalization and Health, vol. 16, no. 1, Article ID 57, 2020. [4] M. Hoffmann, H. Kleine-Weber, S. Schroeder et al., “SARS- CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor,” Cell, vol. 181, no. 2, pp. 271–280, 2020. [5] S. L. Johnston, D. L. Goldblatt, S. E. Evans, M. J. Tuvim, and B. F. Dickey, “Airway epithelial innate immunity,” Frontiers in Physiology, vol. 12, Article ID 749077, 2021. [6] W.-J. Guan, Z.-y. Ni, Y. Hu et al., “Clinical characteristics of coronavirus disease 2019 in China,” The New England Journal of Medicine, vol. 382, pp. 1708–1720, 2020. [7] R. Verity, L. C. Okell, I. Dorigatti et al., “Estimates of the severity of coronavirus disease 2019: a model-based analysis,” The Lancet Infectious Diseases, vol. 20, no. 6, pp. 669–677, 2020. [8] R. Jain, S. Ramaswamy, D. Harilal et al., “Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes,” Computational and Structural Biotechnology Journal, vol. 19, pp. 153–160, 2021. [9] N. K. Singh, S. Srivastava, L. Zaveri et al., “Host transcrip- tional response to SARS-CoV-2 infection in COVID-19 patients,” Clinical and Translational Medicine, vol. 11, no. 9, Article ID e534, 2021. [10] N. A. P. Lieberman, V. Peddu, H. Xie et al., “In vivo antiviral host transcriptional response to SARS-CoV-2 by viral load, sex, and age,” PLoS Biology, vol. 18, no. 9, Article ID e3000849, 2020. [11] D. Blanco-Melo, B. E. Nilsson-Payant, W.-C. Liu et al., “Imbalanced host response to SARS-CoV-2 drives development of COVID-19,” Cell, vol. 181, no. 5, pp. 1036–1045, 2020. [12] A. Islam, M. A. Khan, R. Ahmed et al., “Transcriptome of nasopharyngeal samples from COVID-19 patients and a comparative analysis with other SARS-CoV-2 infection models reveal disparate host responses against SARS-CoV-2,” Journal of Translational Medicine, vol. 19, Article ID 32, 2021. [13] C. G. K. Ziegler, V. N. Miao, A. H. Owings et al., “Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19,” Cell, vol. 184, no. 18, pp. 4713–4733, 2021. [14] D. L. Nig, A. C. Granados, Y. A. Santos, V. Servellita, G. M. Goldgof, and C. Y. Chiu, A Diagnostic Host Response Biosignature for COVID-19 from RNA Profiling of Nasal Swabs and Blood, Science Advances, 2021. [15] J. T. Sims, J. Poorbaugh, C.-Y. Chang et al., “Relationship between gene expression patterns from nasopharyngeal swabs and serum biomarkers in patients hospitalized with COVID- 19, following treatment with the neutralizing monoclonal antibody bamlanivimab,” Journal of Translational Medicine, vol. 20, no. 1, Article ID 134, 2022. [16] G. A. Roth, D. Abate, K. H. ABATE et al., “Global, regional, and national age-sex-specificmortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017,” The Lancet, vol. 392, pp. 1736–1788, 2018. [17] F. Al Zahmi, T. Habuza, R. Awawdeh et al., “Ethnicity-specific features of COVID-19 among Arabs, Africans, South Asians, East Asians, and Caucasians in the United Arab emirates,” Frontiers in Cellular and Infection Microbiology, vol. 11, Article ID 773141, 2021. [18] J. F. Shelton, A. J. Shastri, C. Ye et al., “Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity,” Nature Genetics, vol. 53, no. 6, pp. 801–808, 2021. [19] U. Singh, K. M. Hernandez, B. J. Aronow, and E. S. Wurtele, “African Americans and European Americans exhibit distinct Journal of Immunology Research 11 https://downloads.hindawi.com/journals/jir/2024/6668017.f1.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f2.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f3.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f4.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f5.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f6.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f7.txt https://downloads.hindawi.com/journals/jir/2024/6668017.f8.pdf gene expression patterns across tissues and tumors associated with immunologic functions and environmental exposures,” Scientific Reports, vol. 11, no. 1, Article ID 9905, 2021. [20] P. K. Quashie, J. K. Mutungi, F. Dzabeng et al., “Trends of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody prevalence in selected regions across Ghana,” Wellcome Open Research, vol. 6, 2021. [21] B. A. Mensah, J. L. Myers-Hansen, E. O. Amoako, M. Opoku, B. K. Abuaku, and A. Ghansah, “Prevalence and risk factors associated with asymptomatic malaria among school children: repeated cross-sectional surveys of school children in two ecological zones in Ghana,” BMC Public Health, vol. 21, no. 1, Article ID 1697, 2021. [22] F. Cornejo-Granados, G. Lopez-Leal, D. A. Mata-Espinosa et al., “Targeted RNA-Seq reveals the M. tuberculosis transcriptome from an in vivo infection model,” Biology, vol. 10, no. 9, Article ID 848, 2021. [23] N. L. Bray, H. Pimentel, Páll Melsted, and L. Pachter, “Near- optimal probabilistic RNA-seq quantification,” Nature Bio- technology, vol. 34, no. 5, pp. 525–527, 2016. [24] S. Durinck, P. T. Spellman, E. Birney, andW.Huber, “Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomart,” Nature Protocols, vol. 4, pp. 1184–1191, 2009. [25] H. Pimentel, N. L. Bray, S. Puente, Páll Melsted, and L. Pachter, “Differential analysis of RNA-seq incorporating quantification uncertainty,” Nature Methods, vol. 14, no. 7, pp. 687–690, 2017. [26] H. Wickham, W. Chang, L. Henry et al., Elegant Graphics for Data Analysis, Eggplot2, 2016. [27] K. Blighe, S. Rana, andM. Lewis, “Enhancedvolcano: publication- ready volcano plots with enhanced colouring and labeling,” 2018. [28] T. Wu, E. Hu, S. Xu et al., “Clusterprofiler 4.0: a universal enrichment tool for interpreting omics data,” Innovation, vol. 2, no. 3, Article ID 100141, 2021. [29] K. M. Crosse, E. A. Monson, M. R. Beard, and K. J. Helbig, “Interferon-stimulated genes as enhancers of antiviral innate immune signaling,” Journal of Innate Immunity, vol. 10, no. 2, pp. 85–93, 2018. [30] P. Luthra, D. Sun, R. H. Silverman, and B. He, “Activation of IFN-β expression by a viral mRNA through RNase L and MDA5,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 5, pp. 2118–2123, 2011. [31] E. Yang and M. M. H. Li, “All about the RNA: interferon- stimulated genes that interfere with viral RNA processes,” Frontiers in Immunology, vol. 11, Article ID 605024, 2020. [32] A. Bouayad, “Features of HLA class I expression and its clinical relevance in SARS-CoV-2: what do we know so far?” Reviews in Medical Virology, vol. 31, Article ID e2236, 2021. [33] M. Ishii, H. Fei, and J. M. Friedman, “Targeted disruption of GPR7, the endogenous receptor for neuropeptides B and W, leads to metabolic defects and adult-onset obesity,” Proceed- ings of the National Academy of Sciences of the United States of America, vol. 100, pp. 10540–10545, 2003. [34] T. Wojciechowicz, M. Billert, M. Jasaszwili, M. Z. Strowski, K. W. Nowak, and M. Skrzypski, “The role of neuropeptide B and its receptors in controlling appetite, metabolism, and energy homeostasis.” International Journal of Molecular Sciences, vol. 22, no. 12, 2021. [35] T. Liu, L. Balzano-Nogueira, A. Lleo, and A. Conesa, “Transcriptional differences for COVID-19 disease map genes between males and females indicate a different basal immunophe- notype relevant to the disease,” Genes, vol. 11, no. 12, 2020. [36] C. Li, L. Xiao, J. Jia et al., “Cornulin is induced in psoriasis lesions and promotes keratinocyte proliferation via phosphoi- nositide 3-Kinase/Akt pathways,” The Journal of investigative Dermatology, vol. 139, no. 1, pp. 71–80, 2019. [37] Iène Belhaouane, E. Hoffmann,M. Chamaillard, P. Brodin, and A. Machelart, “Paradoxical roles of the MAL/Tirap adaptor in pathologies,” Frontiers in Immunology, vol. 11, Article ID 569127, 2020. [38] J. Hadjadj, A. Corneau, J. Boussier et al., “Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients,” Science, vol. 369, pp. 718–724, 2020. [39] N. Murray and A. McMichael, “Antigen presentation in virus infection,” Current Opinion in Immunology, vol. 4, no. 4, pp. 401–407, 1992. [40] M. Allard, R. Oger, H. Benlalam et al., “Soluble HLA-I/peptide monomers mediate antigen-specific CD8 T cell activation through passive peptide exchange with cell-bound HLA-I molecules,” Journal of Immunology, vol. 192, pp. 5090–5097, 2014. [41] C. Beadling and M. K. Slifka, “Regulation of innate and adaptive immune responses by the related cytokines IL-12, IL-23, and IL-27,” Archivum Immunologiae Et Therapiae Experimentalis, vol. 54, no. 1, pp. 15–24, 2006. [42] F. Belardelli and M. Ferrantini, “Cytokines as a link between innate and adaptive antitumor immunity,” Trends in Immunol- ogy, vol. 23, no. 4, pp. 201–208, 2002. [43] V. Jurisic, “Multiomic analysis of cytokines in immuno- oncology,” Expert Review of Proteomics, vol. 17, no. 9, pp. 663– 674, 2020. [44] R. Fujii, H. Yoshida, S. Fukusumi et al., “Identification of a neuropeptide modified with bromine as an endogenous ligand for GPR7,” The Journal of Biological Chemistry, vol. 277, no. 37, pp. 34010–34016, 2002. [45] S. Hojyo, M. Uchida, K. Tanaka et al., “How COVID-19 induces cytokine storm with high mortality,” Inflammation and Regeneration, vol. 40, Article ID 37, 2020. [46] A. Gómez-Carballa, I. Rivero-Calle, J. Pardo-Seco et al., “A multi-tissue study of immune gene expression profiling highlights the key role of the nasal epithelium in COVID-19 severity,” Environmental Research, vol. 210, Article ID 112890, 2022. [47] K. Tapela, F. O. Oyawoye, C. O.’ Olwal et al., “Probing SARS- CoV-2-positive plasma to identify potential factors correlating with mild COVID-19 in Ghana, West Africa,” BMC medicine, vol. 20, no. 1, Article ID 370, 2022. [48] M. Coates, S. Blanchard, and A. S. MacLeod, “Innate antimicrobial immunity in the skin: a protective barrier against bacteria, viruses, and fungi,” Plos Pathogens, vol. 14, no. 12, Article ID e1007353, 2018. [49] P. Chieosilapatham, C. Kiatsurayanon, Y. Umehara et al., “Keratinocytes: innate immune cells in atopic dermatitis,”Clinical and Experimental Immunology, vol. 204, no. 3, pp. 296–309, 2021. [50] B. L. Updegraff, X. Zhou, Y. Guo et al., “Transmembrane protease TMPRSS11B promotes lung cancer growth by enhancing lactate export and glycolytic metabolism,” Cell Reports, vol. 25, no. 8, pp. 2223–2233, 2018. [51] S. Tian, Y. Xiong, H. Liu et al., “Pathological study of the 2019 novel coronavirus disease (COVID-19) through postmortem core biopsies,” Modern Pathology, vol. 33, no. 6, pp. 1007– 1014, 2020. [52] H. Esakandari, M. Nabi-Afjadi, J. Fakkari-Afjadi, N. Farahmandian, S.-M. Miresmaeili, and E. Bahreini, “A 12 Journal of Immunology Research comprehensive review of COVID-19 characteristics,” Biologi- cal Procedures Online, vol. 22, Article ID 19, 2020. [53] B. A. Khalil, N. M. Elemam, and A. A. Maghazachi, “Chemo- kines and chemokine receptors during COVID-19 infection,” Computational and Structural Biotechnology Journal, vol. 19, pp. 976–988, 2021. [54] S. A. Osei, R. P. Biney, A. S. Anning, L. N. Nortey, and G. Ghartey-Kwansah, “A paralogous pair of mammalian host restriction factors form a critical host barrier against poxvirus infection,” Plos Pathogens, vol. 14, Article ID e1006884, 2018. [55] T. Yoshimoto, D. Matsubara, M. Soda et al., “Mucin 21 is a key molecule involved in the incohesive growth pattern in lung adenocarcinoma,” Cancer Science, vol. 110, no. 9, pp. 3006– 3011, 2019. [56] S. A. Osei, R. P. Biney, A. S. Anning, L. N. Nortey, and G. Ghartey-Kwansah, “Low incidence of COVID-19 case severity and mortality in Africa; could malaria co-infection provide the missing link?” BMC Infectious Diseases, vol. 22, Article ID 78, 2022. [57] M. M. Lamers and B. L. Haagmans, “SARS-CoV-2 pathogen- esis,” Nature Reviews Microbiology, vol. 20, no. 5, pp. 270– 284, 2022. [58] A. J. Sadler and B. R. G. Williams, “Interferon-inducible antiviral effectors,” Nature Reviews Immunology, vol. 8, no. 7, pp. 559–568, 2008. Journal of Immunology Research 13