key: cord-0992191-1jpbau40
authors: Ahmed, Zeeshan; Renart, Eduard Gibert; Zeeshan, Saman
title: Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19
date: 2022-03-09
journal: Personalized medicine (Online)
DOI: 10.2217/pme-2021-0132
sha: 0c9bfeb9c01cf67ac24e369610b4a6cc417f39c8
doc_id: 992191
cord_uid: 1jpbau40

Aim: A human immunogenetics variation study was conducted in samples collected from diverse COVID-19 populations. Materials & methods: Whole-genome and whole-exome sequencing (WGS/WES), data processing, analysis and visualization pipeline were applied to identify variants associated with genes of interest. Results: A total of 2886 mutations were found across the entire set of 13 genomes. Functional annotation of the gene variants revealed mutation type and protein change. Many variants were found to be biologically implicated in COVID-19. The involvement of these genes was also found in multiple other diseases. Conclusion: The analysis determined that ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI had functional implications and TMPRSS4 was the gene most altered in virally infected patients.

major diseases associated with it, including precursor T-cell acute lymphoblastic leukemia, cholangitis primary sclerosing, CD45 deficiency, celiac disease 1, rheumatoid arthritis and COVID-19 [34, 37, [47] [48] [49] [50] . Interferon gamma (IFNG; ENSG00000111537) is a soluble cytokine that is a member of the type II interferon class [51] and is important for tumor control and immunity against intracellular pathogens [52] . Diseases that can be caused due to IFNG include asthma, mouth disease, autoimmune disease, Behçet syndrome, colorectal cancer, Rasmussen encephalitis, poliomyelitis, dermatitis atopic, cytomegalovirus infection, multiple sclerosis and COVID-19 [51, 52] . CD147 (CD147; ENSG00000172270) is a protein proven to be an important receptor on red blood cells and a key molecule involved in the interaction between hepatocellular carcinoma cells and hematopoietic stem cells. CD147 is involved in a variety of biological processes and diseases including breast cancer, thyroid carcinoma, ameloblastoma, ascending cholangitis, grade III astrocytoma, Newcastle disease, necrotizing ulcerative gingivitis, mucolipidosis-III alpha/beta, accommodative esotropia, tongue squamous cell carcinoma, tumor invasion, metastasis, angiogenesis, energy metabolism, multidrug resistance and COVID-19 [53, 54] . C-X-C motif chemokine receptor type 6 (CXCR6; ENSG00000172215), a coreceptor for simian immunodeficiency virus (SIV) and HIV [55] , can cause disease xanthogranulomatous cholecystitis, sarcoidosis-1, diabetes mellitus and COVID-19 [34, 37, 56] . FYVE and coiled-coil domain autophagy adaptor 1 (FYCO1; ENSG00000163820) encodes a Rab7 adapter protein involved in the microtubule transport of autophagosomes [57] . Mutations in FYCO1 are due to autosomal recessive congenital cataract-2 (CATC2), which can lead to diseases including cataract, cataract-44, cataract-18, early-onset nuclear cataract, inclusion body myositis and COVID-19 [34, 37] . IL-6 (ENSG00000136244) is involved in a variety of clinical and biological features related to the production of acute-phase proteins, a proinflammatory cytokine and an anti-inflammatory myokine [58, 59] . Disorders in the IL-6 gene can cause extramedullary plasmacytoma, plasmacytoma, asthma, hemorrhagic fever, extrinsic cardiomy-opathy, mouth disease, idiopathic neutropenia, glucose metabolism disease, arteries anomalies of arteriovenous malformations of the brain and COVID- 19. LZTFL1 (LZTFL1; ENSG00000163818) is a protein-coding gene and diseases associated with LZTFL1 include Bardet-Biedl syndrome, Bardet-Biedl syndrome-1, situs inversus, Bardet-Biedl syndrome 17 and COVID-19 [34, 37, 60] . Macrophage migration inhibitory factor (MIF; ENSG00000240972) is an important regulator of innate immunity that promotes the proinflammatory functions of immune cells [61] . MIF can lead to disease including macular holes, inflammatory bowel disease, rheumatoid arthritis, photokeratitis, malaria, diffuse cutaneous systemic sclerosis, lepromatous leprosy, sarcoidosis-1, cysticercosis, pulmonary hemosiderosis and COVID-19. Solute carrier family 6 member 20 (SLC6A20; ENSG00000163817) is a transporter for neurotransmitters, proteinogenic amino acids, betaine, taurine and creatine [62] . Mutations in SLC6A20 can lead to hyperglycinuria, Hartnup disorder, iminoglycinuria and COVID-19 [34, 37] . X-C motif chemokine receptor 1 (XCR1; ENSG00000173578) is the receptor for XCL1 and XCL2 and diseases associated with XCR1 include Leber plus disease, Parkinson's and COVID-19 [34, 37, 63] .

IFNAR is a common receptor consisting of two polypeptide subunits: IFN-α and -β receptor subunit 1 (IF-NAR1; ENSG00000142166) and IFN-α and -β receptor subunit 2 (IFNAR2; ENSG00000159110) [64] . IFNAR1 and IFNAR2 encode type I membrane proteins that form chains of receptors for IFN-α and -β [28, 64, 65] . IFNAR1 disorder can produce multiple sclerosis, viral infectious disease, malaria, hepatitis C, septicemic plague, papilloma type I and COVID-19. IFNAR2 can trigger hepatitis, hepatitis B, measles, primary immunodeficiency with post measles-mumps-rubella vaccine viral infection type I, hepatitis C, coronary aneurysm, mumps, heart aneurysm, rubella and COVID-19 [28] . Interferon regulatory factor 3 (IRF3; ENSG00000126456), interferon regulatory factor 7 (IRF7; ENSG00000185507), and interferon regulatory factor 9 (IRF9; ENSG00000213928) encode members of the IRF family and play an essential part in the innate immune response to DNA and RNA viruses [28, [66] [67] [68] . Disorders in IRF3 can affect mouth disease, pediatric lymphoma, hepatitis, influenza, Kaposi sarcoma, eczema herpeticum, viral infectious disease, HIV type 1, hepatitis C virus and COVID-19 [28] . IRF7 is linked to several diseases including pediatric lymphoma, Kaposi sarcoma, viral infectious disease, yellow fever, hepatitis C virus, vaccinia, immunodeficiency-39, Newcastle disease, hepatitis C, Venezuelan equine encephalitis and COVID-19. IRF9 is associated with skin papilloma and COVID-19 [28] .

Signal transducer and activator of transcription 1 (STAT1; ENSG00000115415) and signal transducer and activator of transcription 2 (STAT2; ENSG00000170581) are vital elements of the cellular antiviral response and adaptive immunity and are important arbitrators of type I and type III IFN signaling [28, 69, 70] . Genetic disorders in STAT1 can cause mouth disease, breast cancer, colorectal cancer, chronic lymphocytic leukemia, Ewing sarcoma, ulceroglandular tularemia, acute promyelocytic leukemia, Fanconi anemia complementation group A, immunodeficiency 31B, fibrosarcoma and COVID-19. STAT2 has been reported to be involved in different disorders, which include but are not limited to avian influenza, dengue hemorrhagic fever, mumps, rabies, microphthalmia with limb anomalies, primary immunodeficiency with post measles-mumps-rubella vaccine viral infection, immunodeficiency 44, dengue virus, skin squamous cell carcinoma and COVID-19 [28] .

TANK binding kinase 1 (TBK1; ENSG00000183735) inhibits IκB proteins and performs crucial functions in the signaling pathway of immunoreceptors (TLRs, RLRs and STING-mediated sensing of cytosolic DNA) [71] . TBK1 disorders can result in diseases such as amyotrophic lateral sclerosis-1, lateral sclerosis, viral infectious disease, glaucoma 1 open-angle p, retinitis pigmentosa-33, frontotemporal dementia, amyotrophic lateral sclerosis 4, low tension glaucoma, dementia, open-angle glaucoma, Bartter syndrome types 3 and 2 and COVID-19 [28] . Toll-like receptor adaptor molecule 1 (TICAM1/TRIF; ENSG00000127666) encodes an adaptor protein including toll/IL-1 receptor (TIR) and reconciles the expression of many genes [72, 73] . Mutations in TICAM1 are associated with acute infection-induced encephalopathy 6, pertussis, encephalitis, herpes simplex encephalitis, herpes simplex and COVID-19 [28] .

Toll-like receptor 3 (TLR3; ENSG00000164342) is a receptor for dsRNA, which includes an extracellular leucine-rich repeat (LRR) motif, a transmembrane (TM) domain and intracellular toll and IL-1R (TIR) domains. It is generated during most viral infections [74] . Alterations in TLR3 can cause herpes simplex encephalitis, agerelated macular degeneration 1, measles, HIV type 1, retinal vasculitis, hepatitis C, encephalopathy acute infection induced-2, Vogt-Koyanagi-Harada disease, rabies, allergic conjunctivitis and COVID-19 [28] . TNF receptor associated factor 3 (TRAF3; ENSG00000131323) is an enigmatic part of the TRAF family that is involved in substantial physiological and cellular functions in multiple organs [75, 76] . Diseases caused by TRAF3 mutations include herpes simplex, splenic marginal zone lymphoma, herpes simplex encephalitis, encephalopathy acute infection-induced 5, hereditary fructose intolerance and COVID-19 [28] . Unc-93 homolog B1 (UNC93B1; ENSG00000110057) encodes a transmembrane protein that regulates the movement of TLRs from the endoplasmic reticulum [77, 78] . It is associated with several disorders, including encephalopathy acute infection-induced 1, encephalitis, Melkersson-Rosenthal syndrome, herpes simplex, herpes simplex encephalitis, lymphoid interstitial pneumonia, mite infestation and COVID-19 [28] .

Recombination activating 1 (RAG1; ENSG00000166349) and recombination activating 2 (RAG2; ENSG00000175097) begin V(D)J recombination [79, 80] . RAG2 mutations can cause common variable immunodeficiency, baylisascariasis, combined immunodeficiency x-linked, immune deficiency disease, combined cellular and humoral immune defects with granulomas, salivary gland disease, sialadenitis, malignant histiocytosis, immunodeficiency with hyper-IgM type 3, severe combined immunodeficiency and COVID-19. Whereas, RAG2 disorder can lead to severe combined immunodeficiency (autosomal recessive T-cell negative B-cell positive NK-cell negative), Omenn syndrome, Lig4 syndrome, severe combined immunodeficiency, malignant histiocytosis, recombinase activating gene-1 deficiency, combined cellular and humoral immune defects with granulomas, immune deficiency disease, combined immunodeficiency x-linked, gastroduodenitis and COVID-19 [3] .

Whole-genome sequencing (WGS) is widely applied to sequence the entirety of the genome and whole-exome sequencing (WES) sequences mainly the protein-coding structures. To realize the clinical impact of this study and perform gene-variant analysis at the listed known immunity genes, a prospective dataset was created that includes total of 752 WGS samples. The sample population selection criteria included short read sequencing (paired), diversity, open-access availability through authenticated resources, reproducibility and success rate. The genomics pipeline was applied to the complete dataset and most of the samples were with very low sequencing quality and were unable to produce expected results. Therefore, only those results which were reproduced from good-quality WGS samples (n = 13; SRR12474733, SRR12486921, SRR12328890, ERR4387385, ERR4387386, ERR4387388, SRR12336742, SRR12336753, SRR12336755, SRR12336756, SRR12336761, SRR12336765 and SRR12336766) were selected and analyzed. All samples were collected from public resources (COVID-19 WGS data-related information is shown in Supplementary Table 1 ). Collected, sequenced WGS samples were from variable COVID-19 populations and belonging to different regions and sizes and were produced using Illumina sequencing technology. All the samples were downloaded from public repositories and analyzed in accordance with relevant guidelines and regulations and protocols were approved by the Institutional Review Board (IRB), Rutgers University.

An in-house developed gene-variant analysis pipeline (JWES) was applied for the whole genome and exome data preprocessing, modeling and downstream analysis ( Figure 1 ). JWES is mainly based on processing the raw sequence data, converting raw signals into base calling, identifying regions of interest in the genome, aligning, and assembling contigs and scaffolds and variant detection [81] . Its overall operations are divided into three modules: data preprocessing, storage and management, and visualization. JWES is a cross-platform and user-friendly Javabased application that integrates multiple open-source command-line tools for sequence data processing and analysis, consisting of FASTQC (quality assessment) [82] , Burrows-Wheeler Aligner software (BWA for short read alignment to reference human genome) [83, 84] , MarkDuplicates (removes redundant reads) [85] , SAMtools (sorting and indexing) [86] and Genome Analysis Tool Kit (GATK for finding SNPs and indels) [87] [88] [89] . JWES is freely available for download and use by the community [81] .

Gene-variant analysis was performed at the sequenced WGS and WES samples on COVID-19 populations from diverse backgrounds and regions across the world. Using published sequence data from SARS-CoV-2-infected individuals, the likelihood that genetics may have a role in the risk of severity of COVID-19 through the components of the immune system was examined. The gene variants were annotated for mutation type and protein change using known algorithms and tools for interpreting mutations. To better understand the clinical impact of these variants, an ontology of gene-disease organizations, progressions and networks was created.

The gene-variant analysis was performed using the JWES pipeline to identify abundantly mutated core immune genes in COVID-19 patients with WGS samples. Inborn errors in the immune genes can tip the delicate balance Figure 2B shows the Circos plot of COVID-19 core genes with known and experimentally deleterious variants and their parent genes in the chromosome. The outer circle is composed of the chromosome ideogram linking the location of the parent gene in the chromosomes. The inner circle shows a histogram of the number of mutations reported in the genes. The most prevalent gene was TMPRSS4, which occurred in all patients, while the least prevalent genes included RAG2, IFNG, CCR9 and IL6 ( Figure 2C ). Further investigation revealed all earlier reported human transcripts for each gene among the list (Table 1 & Figure 2C ). Except for ACE2, IFNAR1, IRF3, IRF7, STAT1, STAT2 and TBK1, all reported transcripts (ENST) were found for TMPRSS4, TMPRSS2,  CCR9, IFNG, CD147/BSG, CXCR6, FYCO1, IL6, LZTFL1, MIF, SLC6A20, XCR1, IFNAR2, IFNAR2, IRF9,  TICAM1/TRIF, TLR3, TRAF3 , UNC93B1, RAG1 and RAG2 (Table 2) . Gene-variant analysis details are shown in Supplementary Table 2 .

The mutations in genes implicated in COVID-19 were analyzed as coding proteins with labeled recurrent hotspots. Lollipop plots were produced representing the number of mutations per gene. The variant data was annotated for biological and functional implications with three computational algorithms: Scale-Invariant Feature Transform (SIFT) [90] [91] [92] , Polymorphism Phenotyping v2 (PolyPhen-2) [93] , and MutationAssessor [94] . SIFT was used to predict the effect of coding variants on protein function [91] , as the challenge is to identify causative variants for the phenotypes linked to COVID-19. Originally, SIFT was proposed to predict the impact of amino acid substitutions on protein function [95] . PolyPhen-2 is an automatic web tool used for the extraction of sequences and structurebased features of the substitution site. Single-nucleotide polymorphisms (SNPs) were analyzed in a batch mode, predicted for the functional impact and searched in a database of precomputed predictions for WES data.

MutationAssessor was applied to differentiate between conserved patterns using conservation and specificity scores to account for functional shifts between subfamilies of proteins [94] . MutationMapper [96] was used to generate lollipop plots for all genes [97] . It displays the highly recurrent mutations (amino acid alterations) but does not annotate mutations with low frequency (Table 3) . ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI were found to have mutations with functional implications (Figure 3 ). Three functional missense mutations were found in ACE2 (V404A, G405W and S409P) that were computationally annotated to have adverse effects. Three functional missense mutations were also found in SLC6A20 (A9G, V591G and V591M) but none were convincingly deleterious. TMPRSS4 had four missense mutations (V208G, K257M, N357Y and G402V) reported. Only G402V was predicted to be deleterious. TMPRSS2 had three functional missense mutations (V160M, W267R and Q431H) with the first two displaying evidence of adverse effects. FYCO1 was found to have the greatest number of functional missense mutations (12) but all were predicted to be benign. The remaining genes had no functional impact reported. This may be because the functional databases used, SIFT, PolyPhen-2 and MutationAssessor, are not updated (Figure 3 ). Additional gene-variant-protein analysis details are shown in Supplementary Table 2 .

A gene-variant-disease network analysis was also performed to identify which immune genes are associated with other disease phenotypes (Supplementary Figure 1 & Table 4 ). To elaborate the clinical associations between the genes utilized, the authors' previously designed the gene-SNP-disease-drug smart database [97] which was used to draw the network of diseases linking to the genes ( Figure 4 ). Furthermore, a comparative analysis of gene-associated diseases investigated in this study and reported in the recently published literature to be included among the symptomatology of COVID-19 was performed ( Figure 5 ). To annotate the genes with diseases, the authors used an in-house developed gene-annotation database [95, 98, 99] . Many diseases were found shared between the genes. They included deafness autosomal recessive-24-2, leukemia, sarcoidosis-1, Hartnup disorder, papilloma, viral infectious disease, SARS, malaria, influenza, asthma, fever, mouth disease, herpes simplex, acute infection-induced encephalopathy, encephalitis, mumps, rabies, hepatitis, combined immunodeficiency x-linked, immune deficiency disease, combined cellular and humoral immune defects with granulomas, severe combined immunodeficiency, malignant histiocytosis, measles, Kaposi sarcoma, pediatric lymphoma, HIV type-1 and COVID-19. Five diseases had more than two associated genes. Additional gene-variant-disease analysis details are shown in Supplementary  Table 3 .

SARS-CoV-2 infection is a complex multisystem disorder with a wide spectrum of clinical manifestations [100] .

The clinical presentation of COVID-19 varies from patient to patient [101] . To precisely diagnose and treat patients infected with COVID-19, it is important to understand the functional impact of variations in the patient's DNA [102, 103] . Furthermore, to establish a deeper understanding of novel changes that may increase susceptivity to COVID-19, it is essential to combine phenotypically similar probands to distinguish connected clusters of rare genes [104, 105] , analyze the rare mutations and phenotypes and identify substantial gene-disease associations [106] . The genomic and transcriptomic basis of the complications associated with COVID- 19 has not yet been fully determined [107] . We speculate that COVID-19 patients with inborn errors of IFNAR1, IFNAR2, TLR3, IRF7 and IRF3 may also be indicative of viral hepatitis. However, genes with identified pathogenic mutations have also been reported to show hepatic expression of ACE2, TMPRSS4 andTMPRSS2 and have been implicated in chronic liver damage. Various studies have reported varying degrees of liver damage with high levels of alanine aminotransferase (ALT) or aspartate aminotransferase (AST) in patients infected with SARS-CoV-2 [19, 108] . Genetic testing can be helpful for accurate clinical decision-making for the diagnosis and prediction of symptomatic individuals with COVID-19 [109] . It can support the definition of pharmacogenetic profiles, guiding treatment and reproductive genetic counseling [110] . Substantial cosegregation of gene-variants with COVID-19 disease can provide solid genetic relationships to support pathogenicity [111] . Most of the symptomologies of COVID-19 are based on common signs and symptom-driven diagnosis and treatment [112] . Therefore, it is important that physicians and patients understand and correctly interpret the relevant findings of clinical-genomics research to COVID-19 disease. The clinical significance of a known immunity gene in association with COVID-19 can be guided by functional change (gain or loss) and clinical conditions [113] . Variant interpretation guidelines are also standardized by the American College of Medical Genetics and Genomics (ACMG) [114] [115] [116] .

With current technological advancements and reduced cost in high-throughput sequencing, high-quality nextgeneration sequencing (NGS) data can be quickly produced, processed and analyzed [14] . However, the field of genomics is struggling with the successful implementation of gold standard machine learning (ML) algorithms for clinically proven reproducible computational predictions [38] . It is necessary to automate the process of genevariant data annotation, expression and simulation to produce timely presentable results [117] . Current limitations in this context imply gaps among clinics and fundamental basic and applied research; difficulties in getting exigent approvals and timeliness of data availability; levels of granularity in clinical information; and application of appropriate modeling strategies that allow learning in the data continuum [118] . Robust scientific solutions are needed in everyday clinical and public health practices for intelligent and integrative analysis of clinical and multiomics data with the application of artificial intelligence (AI) [112] . Layering ML techniques to quantify and annotate the leading indicators in population data will enhance the public health response to bring resources and prepare regions for what is to come [112] . Gene expression analysis is planned as a follow-up to this work to identify enrichment of these immune genes in COVID-19 patients and validate that genetics may determine the clinical course of the infection. This will improve the identification of genetic alterations facilitating COVID-19 disease mechanisms, leading to new predictive models, pinpointing major causes of morbidity and mortality, building personalized therapies and reducing medical costs.

A generation of genomic surveillance data with deep immune genotyping is the need of the hour, as most COVID-19 diagnostics are stranded on clinical assays [112] . Hyper-inflammatory factors and their response to drugs that can target them and block inflammation require investigation [119, 120] . It is conceivable that even after clearing the virus, the residual viral genetic material and particles persistently elicit an immune response with variable degrees of symptomology that lead to fatigue, myalgia and neurological issues. The potential of monoclonal antibodies, gamma-globulins and convalescent plasma usage is significant in bringing mortality down, but its administration is late in treatment. A sophisticated analysis is needed based on deep phenotyping of inflammatory markers along with gene expression at different time points. Furthermore, it is important to anticipate the evolution of more COVID-19 mutants with variations in lineages. Comprehensive genome and transcriptome sequencing of large cohorts is needed to determine the prevalence of these factors [121] .

To implement effective precision medicine with enhanced ability to positively impact COVID-19 patient outcomes and provide real-time decision support, it is important to examine disease-causing variants and genotype and phenotype associations. The need to investigate disease-causing variants and genotype and phenotype associations among the COVID-19 population data to find the root cause of uncertainties in patient care, such as genetic variants possibly associated with phenotypic manifestations, has become critical. In this study, the authors investigated underlying immunity genes, including their implications in complex diseases and their sequel relationships with COVID-19. Sequence alignment data analysis was performed for known immunity genes revealing inborn mutation errors that may be responsible for complications in COVID-19. The analysis shows the separation of subsets of COVID-19 patients with significantly variable expression for a cluster of genes. The clinical significance of these known immunity genes includes their implications in other complex diseases and their possible sequel relationships with COVID-19. To better understand the gene-disease organization, progression and network, an ontology was also created. Gene-variants were annotated for mutation type and protein change using algorithms and tools for interpreting mutations.

• Investigating COVID-19 disease-causing variants among highly expressed genes enables the determination of root causes of uncertainties in patient care. • Informative exposure signatures can help in assessing the associations of the COVID-19 transcriptome, offering new insights into the biological and pathological underpinnings of health disparities. • The current analysis shows subsets of COVID-19 patients with significantly variable expression for a cluster of genes.

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/ suppl/10.2217/pme-2021-0132

Author contributions Z Ahmed proposed, designed and lead the study. Z Ahmed and S Zeeshan designed the WGS/WES data processing and analysis pipeline and Z Ahmed and EG Renart implemented the pipeline. Z Ahmed, S Zeeshan and EG Renart performed data analysis and visualization. Z Ahmed drafted the manuscript and S Zeeshan and EG Renart participated in writing and review. All authors approved the paper.

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The authors appreciate the great support from the Rutgers Institute for Health, Health Care Policy and Aging Research (IFH); 

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties.No writing assistance was utilized in the production of this manuscript.

All project data were completely anonymous and collected from public resources. The study was conducted in accordance with relevant guidelines and regulations, and protocols approved by Institutional Review Board (IRB), Rutgers University.

This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/