Next Article in Journal
Routine Diagnostics Confirm Novel Neurodevelopmental Disorders
Next Article in Special Issue
Some New Aspects of Genetic Variability in Patients with Cutaneous T-Cell Lymphoma
Previous Article in Journal
Unaffected Li-Fraumeni Syndrome Carrier Parent Demonstrates Allele-Specific mRNA Stabilization of Wild-Type TP53 Compared to Affected Offspring
Previous Article in Special Issue
Genome-Wide DNA Methylation Profiling Solves Uncertainty in Classifying NSD1 Variants
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal

The Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Finance and Administration, Medical College of Wisconsin, Milwaukee, WI 53226, USA
The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
Author to whom correspondence should be addressed.
Genes 2022, 13(12), 2304;
Received: 9 November 2022 / Revised: 2 December 2022 / Accepted: 4 December 2022 / Published: 7 December 2022
(This article belongs to the Special Issue Feature Papers in Human Genomics and Genetic Diseases)


The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD’s COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

1. Introduction

Coronavirus Disease 2019 (COVID-19) was declared a global pandemic in March 2020 and now, two years later, the disease still plagues the globe due to rapid mutations within the causal virus, SARS-CoV-2, and inadequate preventive measures. Individuals infected with the virus exhibit a wide spectrum of symptoms, from asymptomatic infection to acute respiratory distress requiring hospitalization [1]. The major manifestation of COVID-19 is in the respiratory system, where the virus infects nasal mucosa and spreads into the host body [2,3]; however, non-respiratory systems such as liver, heart, kidney and brain, are also involved in certain patients and, in severe cases, result in multiple organ failure and death [4]. ACE2, the receptor for SARS-CoV-2, has a broad distribution in tissues such as blood vessels [5], small intestine, heart, kidney, thyroid, adipose and testis [6]. These ACE 2 expressing organs become targets of SARS-CoV-2 infection. Once entering the cell, the binding of viral spike protein to ACE2 initiates signaling pathways promoting inflammatory mediator production [7]. Other non-ACE2 mediated infection might involve viral entry through nerve endings like olfactory nerves to the central nervous system causing neurological symptoms [8]. In addition to direct viral damage to the target organs, dysregulation of cytokine release, referred as cytokine storm, plays a role in the severity of the disease. For example, the pre-existing cytokine dysregulation in diabetic patients amplified the cardiovascular damage resulting from COVID-19 [9] and poor disease prognosis [10]. Dysregulation of cytokine secretion causes a severe inflammatory response in organs leading to multiple organ failure [4] or contributes to long COVID even after patients recovered from COVID-19 [11,12].
The vaccine development for COVID-19 has been a successful example of moving from knowledge to saving lives. So far, 11 vaccines have been approved by the World Health Organization (WHO) that are able to protect people from infection or severe disease outcome ( (accessed on 1 June 2022)) [13]. However, the frequently changing viral genome demands development of therapeutics as another solution to control the pandemic. Remdesivir, approved for treatment of hepatis C originally, was the first antiviral treatment of COVID-19 approved 10 months after identification of SARS-Co2 virus [14]. To meet the urgent need, several repurposed drugs were on clinical trials and recommended for COVID-19 under specified conditions [15].
Both basic and clinical research is progressing at an unprecedented rate to tackle the COVID-19 pandemic and the long-term health effects of SARS-CoV-2 infection, resulting in 265,064 COVID-19-related publications in PubMed and 23,621 unreviewed COVID-19 preprints (June 2022). Since COVID-19 is an emerging disease, the surge in original publications is complemented by numerous reviews and large-scale meta-analyses to present organized knowledge of COVID-19 to the public. In just over two and a half years, there are more than 3,000 review articles focusing on COVID-19 in PubMed. Leveraging this vast knowledge and applying adequate standards to integrate COVID-19 data from different fields is challenging yet at the same time critical to accelerate the ongoing development of effective preventive measurements and new therapies.
The Rat Genome Database (RGD; is a cross-species database targeting specific disease areas for its ongoing manual disease curation [16]. During early 2020, the RGD curation team launched an infectious disease curation project with an initial focus on the coronavirus infectious diseases. The COVID-19 Portal and the Infectious Disease Portal were released sequentially to present integrated data resources to the research community. Here, we introduce the COVID-19 Disease Portal, that was released within six months of the pandemic, and present results analysis on COVID-19 associated genes using a series of RGD resources and tools [17] in order to visualize organ distribution of COVID-19 genes and their relationship with other diseases. The Gene Annotator [18] tool, which provides counts of query genes that are associated with other diseases across organ systems, based on their annotations, was used to serve this purpose. Disease ontology enrichment analyses examined which disease terms are over-represented in the annotations. Two enrichment tools: Multi Ontology Enrichment Tool, MOET [19] from RGD, and Set Analyzer [20] from CTD (Comparative Toxicogenomics Database) were used to find enriched disease terms and to compare the results between the tools.
We first examined the disease gene distribution among organ system diseases using the Gene Annotator tool and found that over sixty percent of COVID-19 genes were also associated with “nervous system disease.” Both MOET and Set Analyzer identified liver diseases as the significantly enriched disease terms on the COVID-19 associated gene list. To further understand the molecular mechanism involved in COVID-19 pathogenesis, we examined Gene Ontology (GO) annotations enrichment of these genes. Both biological process and molecular function annotations are highly enriched in the immune-associated branches which confirm the involvement of immune dysregulation among COVID-19 patients.

2. Materials and Methods

Targeted curation of literature related to coronavirus infection was performed at RGD using the in-house curation tool [18] integrated with the OntoMate [21] literature searching tool. A standalone OntoMate tool is also accessible at The prioritized disease gene list was constructed as previously described [22] with added COVID-19-related genes from other sources such as the Gene Ontology Consortium (GOC) ( (accessed on 1 June 2020)) and LitCovid ( (accessed on 1 May 2020 )). In brief, three data bases, MalaCards ( (accessed on 1 May 2020)), DisGeNET ( (accessed on 1 May 2020)) and PhenoPedia ( (accessed on 1 May 2020)) were queried for coronavirus disease, related viral diseases, and other infectious diseases. The purpose of the queries was to find human genes associated with those diseases in the biomedical literature. A prioritized list of genes was made based on appearance of the genes in multiple databases and the combined number of publications connected to each gene-disease association across those databases. A small subset of unique coronavirus disease-related genes not found in multiple databases was curated first, before curation of the main infectious gene list began. The gene symbol and the disease term “coronavirus infectious disease” was used to find publications associated with coronavirus disease. Using ontological approaches, OntoMate retrieves publications tagged with the coronavirus infectious disease, and any of its child terms such as COVID-19, Middle East respiratory syndrome, severe acute respiratory syndrome…, etc. The resulting publication list was ranked by relevance or sorted by publication dates. In the curation process the relationship between a gene and a disease is indicated by evidence codes [23]. The evidence code IDA (inferred from direct assay) is used to indicate direct involvement of a gene product in causing or treating a disease. IMP (inferred from phenotype manipulation) is used in cases where gene expression/function is artificially altered and a genetic or mechanistic connection between a disease is implied. IAGP (Inferred by Association of Genotype from Phenotype) is used in an association of a disease with genetic mutations or polymorphisms of a gene. IEP (Inferred from Expression Pattern), or HEP (expression changes measured by high throughput assays) is used when a gene changes its expression pattern during the disease course. In addition to in-house manual annotations, RGD regularly imports annotations from other data resources, including the Gene Ontology Consortium, Clinvar, Mouse Genome Informatics, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals and the Comparative Toxicogenomic Database (CTD) [22]. The evidence codes of imported annotations are assigned with the same criteria except for EXP evidence codes used in the annotations imported from CTD. EXP indicates a gene may be a biomarker of a disease or play a role in the etiology of a disease. RGD also propagate annotations from other organisms to human orthologous genes by using the ISO (Inferred from Sequence Orthology) evidence code. These imported annotations are organized into categories such as ‘Disease’, ‘Human Phenotype’, ‘Mammalian Phenotypes’ for non-human organisms, and others as shown in the COVID-19 Disease Portal (Figure 1A).
The genes derived from the COVID-19 Disease Portal were further evaluated using tools available at RGD. The Gene Annotator tool retrieves all functional annotations for a gene list or a chromosomal region and visualizes the gene count distribution across disease terms in a Comparison Heat Map ( (accessed on 1 June 2022)). Two publicly available enrichment tools, MOET (the Multi Ontology Enrichment Tool ( (accessed on 1 June 2022)) at RGD, and Set Analyzer ( (accessed on 1 June 2022)) at the CTD were used to perform enrichment analysis of COVID-19 disease genes. Both tools are web-based analysis tools that generate a list of ontology terms statistically over-represented with the input gene symbol list. The Set Analyzer finds enriched disease, Gene Ontology, pathways, and gene-gene interaction terms for human genes while MOET is capable of performing enrichment analyses in multiple species (including rat, mouse, human, bonobo, squirrel, dog, pig, chinchilla, naked mole-rat and vervet) and multiple ontologies (including Disease, GO, Pathway, Phenotype, and Chemical entities (ChEBI)). The Ancestor chart from QuickGO ( (accessed on 1 June 2022)) [24] was used to visualize the relationship among enriched GO terms.

3. Results

3.1. The COVID-19 Disease Portal

The landing page of the COVID-19 Disease Portal ( (accessed on 1 June 2022)) (Figure 1) provides accesses to all the integrated COVID-19 data. The disease browser (Figure 1B) links to the Annotations page where annotations can be downloaded for analysis. The annotations associated with COVID-19 and its child terms were downloaded from the Annotations page and the associated genes were sent to the Gene Annotators and MOET tool [19] for further analysis. The disease gene list can also be obtained from the OLGA tool ( using the target disease term as a key word. The “Gene Set Enrichment” section (Figure 1C) at the bottom of the page sends genes curated with the highlighted ontology term (COVID-19) to the enrichment tool MOET for analysis. Seven ontologies are available in MOET for enrichment analysis.

3.2. Human COVID-19-Associated Gene Analysis

COVID-19 is a member of the coronavirus infectious disease family. This family includes ‘Middle East respiratory syndrome’ (DOID:0080642) (MERS) and ‘severe acute respiratory syndrome’ (DOID:2945) (SARS) (Figure 1B). There are 1257 human genes associated with COVID-19, 19 genes associated with MERS and 90 genes associated with SARS, totaling 1338 coronavirus infectious disease genes at RGD (accessed on 1 June 2022). Their overlapping coverage is visualized in the Venn diagram in Figure 2. The intensity of COVID-19 disease research is reflected by more than one thousand disease related genes identified in just over two years of the pandemic. They comprise more than 90% of the disease genes associated with coronaviruses.

3.3. Gene Disease Association

Most of the COVID-19 annotations were identified by IEP and HEP evidence codes. They account for more than 95% of the total 1407 COVID-19 annotations. The COVID-19 annotations are associated with 1257 unique genes (Table 1A). Among these genes, only ACE2 is associated with four types of evidence codes: IAGP, IDA, IMP and EXP, most of them (1221 genes) are with one type of evidence code (Table 1B).
These COVID-19 associated genes are also involved in other diseases as viewed from the annotation distribution heatmap in the Gene Annotator tool (Figure 3A). More than half (703) of the COVID genes are associated with ‘developmental disease.’ In the ‘developmental disease’ branch, 650 genes are associated with ‘congenital, hereditary and neonatal disease’ and 334 with ‘neurodevelopmental disorders’ (Figure 3B). There are 911 COVID genes associated with ‘disease of anatomical entity.’ The breakdowns of the anatomical entities associated COVID-19 disease genes are shown in Figure 3C and discussed in the ‘COVID-19 affected organ systems’ section later.

3.4. Disease Term Enrichment Analysis

COVID-19 is a disease with a broad spectrum of symptoms, including some atypical symptoms of respiratory diseases like loss of smell, and neurological symptoms [25]. Use of the existing knowledge of how these COVID-19 genes are involved in other human diseases could shed light on the pathogenesis of COVID-19 and facilitate development of therapeutic strategies. To find these relationships, we next looked at the disease enrichment patterns of the COVID-19 disease genes using MOET [19] developed at RGD. Several high-level disease terms such as ‘coronavirus infectious disease’, ‘RNA virus infection’ and ‘viral infectious disease’ are highly enriched since they are parent terms for COVID-19. The enriched disease table was downloaded from MOET, and the top 40 enriched diseases were selected, from ‘respiratory tract infections’ to ‘lung injury’ and were listed in Table 2A. As expected, the term ‘respiratory tract infections’ was on top of the list. Surprisingly, there were several enriched terms in the liver disease branch, including liver neoplasms, hepatobiliary system cancer and others. Additional enriched terms included rheumatic disease, autoimmune disease of musculoskeletal system, allergic disease, pneumonia, and immune/inflammatory diseases of non-respiratory system diseases. The same COVID-19 disease genes were sent to the Set Analyzer, another enrichment tool at CTD, and the top 40-enriched diseases are listed in Table 2B. Most of the enriched diseases in MOET were also enriched in the Set Analyzer, however, some of the ranking orders were shifted. The ‘respiratory tract infections’ was on top of the MOET list while it was ranked 17th on the list from the Set Analyzer. These differences could be attributed to different ways of data integration and two different disease vocabularies used by RGD [22] and CTD [20]. Overall, liver diseases, immune system diseases, autoimmune diseases and respiratory tract diseases were highly enriched in both tools. The enriched list of the Set Analyzer includes more organ system disease terms while there are more granular terms on the MOET list. Using ‘Nervous System Diseases’ (MESH: D009422) as an example, on the MOET enrichment list, ‘autoimmune disease of central nervous system (DOID:0060004)’ is ranked 38th, however, the high level term of its parents ‘Nervous System Diseases’ (MESH: D009422) is ranked 15th on the enrichment list from Set Analyzer. On the MOET list several granular kidney disease terms such as nephritis, glomerulonephritis, and glomerular diseases are ranked 14th, 17th, and 21st, respectively, while on the Set Analyzer list, ‘Urologic Diseases’ (MESH: D014570) (parent of kidney diseases) is ranked 26th and ‘Nephritis’ (MESH: D009393) 32nd.

3.5. COVID-19 Affected Organ Systems

We now look at the target organ distribution of these COVID-19 associated genes (Figure 3). COVID-19 associated disease genes are associated with nervous system disease (772), gastrointestinal system disease (513), endocrine system disease (498), musculoskeletal system disease (498), skin & connective tissue disease (479) and immune & inflammatory disease (431) (Figure 3C). Among these six organ systems affected by COVID genes, we drill down into each disease branch and list 10 granular disease terms selected by gene counts from the Comparison Heat Map in the Gene Annotator tool (Table 3). Among COVID-19 genes, over sixty percent (772/1257) are also involved in nervous system diseases. Out of 772 COVID/nervous system disease genes, over 650 are involved in the central nervous system followed by sensory system, neurologic manifestation, and neurodegenerative disease. Among gastrointestinal system diseases and endocrine system diseases, liver diseases, including liver neoplasms and cancers, show high prevalence. This correlates with the results of enrichment analysis where liver diseases were highly enriched by both tools. There are only 431 genes involved in the immune & inflammatory disease branch; however, diseases related to immune and inflammatory are present in all the six organ systems listed in Table 3. They are autoimmune disease of the nervous system, autoimmune disease of gastrointestinal tract, autoimmune disease of endocrine system, autoimmune disease of musculoskeletal system and dermatitis.

3.6. Gene Ontology Enrichment of COVID Genes

The Gene Ontology enrichment patterns of COVID genes were examined using the MOET tool. The Biological Process (BP) enrichment list is heavily concentrated in the ‘immune system process’ branch which includes immune response, immune effector process, leukocyte activation, and their child terms (Table 4 and Figure S1). Another highly represented branch is ‘response to stimulus’ where the fourth enriched term ‘defense response’ resides. The cellular component annotations of COVID genes are highly enriched in the branches of ‘immunoglobulin complex,’ ‘extracellular region’, ‘membrane’ and ‘cell periphery’ (Table 4 and Figure S2). Most of the top 40 Molecular Function (MF) terms are either ‘binding’ or its child terms such as antigen binding, protein binding or carbohydrate binding in the binding branch. The rest of the terms are ‘molecular function regulator,’ and its child terms under the branch (Table 4 and Figure S3).

4. Discussion

COVID-19 was declared a pandemic in March 2020, less than three months after its first identification. The whole world has allocated resources to study COVID-19 with the hope to find preventive and therapeutic measures to control the pandemic. The immense efforts in studying COVID-19 have produced over 280,000 publications and related datasets. From these available resources, the RGD team was able to curate and integrate COVID-19 associated data and to release the COVID-19 Disease Portal just four months later in July 2020 with regular updates. In this manuscript, COVID-19 genes were taken from the portal and analyzed with tools developed in-house and other publicly available tools.
Most of the curated disease genes were curated with evidence codes HEP or IEP, which identify changes in the gene expression pattern during COVID-19 disease (Table 1). These genes can serve as biomarkers to monitor the disease course and devise treatment plans. Currently, immune/inflammatory cytokine patterns are known to be useful in predicting disease progression [10], and treatments based on blocking excessive cytokine release have been proposed as a treatment regime [26]. The COVID-19 associated genes were examined by their distribution among anatomical entities and the over-represented diseases. Over sixty percent (772/1257) of the COVID-19 disease genes are also involved in ‘nervous system diseases’ and ‘autoimmune disease of central nervous system’ was among the top 40 enriched diseases. According to the breakdowns of nervous system disease gene counts in Table 3, the central nervous system, peripheral nervous system and sensory system are affected by the COVID genes and activation of autoimmunity in these systems is the major disease mechanism. More than likely, the central nervous system is the most significant target since its autoimmune disease is among one of the top 40 most enriched diseases. These results suggest that immune/inflammatory attacks on the nervous system play roles in the neurological manifestations such as loss of smell, headache, nausea, and impaired consciousness experienced by some COVID-19 patients [8,25,27]. The immune system, which is important to fight off infection, when dysregulated, becomes destructive and causes severe disease complications such as ‘cytokine storm’ in severe COVID cases [10,28]. Our enrichment analysis shows that several immune/inflammation diseases were on the top 40 list, and immune dysregulation was implicated in all the six organ systems examined in Table 3. The involvement of COVID-19 genes in the immune system was further confirmed by Gene Ontology enrichment profiles as shown in Table 4. All three aspects pointed to terms associated with the immune system. It has been shown previously that different disease gene sets exhibit unique GO enrichment profiles which reflect the unique pathophysiology of the disease [29]. Our analysis of COVID-19 disease genes suggests that dysregulation of immune function is a common mechanism affecting pathogenesis of COVID-19 disease, especially in the severe cases where multiple organ systems are affected.
COVID-19 was first identified as a respiratory disease and in severe cases, caused serious lung injury, particularly the high ACE 2 expression type II alveoli, and resulting respiratory distress [2,30]. In addition to lungs, ACE2 has a broad distribution in tissues and vasculature and its expression provides viral entry to the organ system. Viral damage to liver, brain, and kidney has been documented in biopsies from patients [2,5,6]. However, as a newly evolving disease, whether direct SARS-CoV2 entry through ACE2 leads to multiple organ damages is not clear. The unexpected finding of liver diseases as being highly enriched in genes associated with COVID-19 by two enrichment tools could point to an important direction to understand the pathogenesis of COVID-19. It has been shown that liver organoids express viral receptor ACE2 and can be infected by SARS-Co-V2 and become a replication reservoir for the virus [2]. The percentage of liver injury associated with COVID-19 patients varied among patient groups [31]; however, liver damages seemed to link to severe cases and poor disease outcome [32,33]. The liver damage caused by SARS-CoV2 infectionis attributed to several mechanisms such as augmented expression of the viral receptor ACE2 during diseases, uncontrolled immune cell infiltration, and cytokine storms after infection in the hepatobiliary system [32,33,34]. These mechanisms might also be involved in multiple organ injuries observed COVID-19 patients. What is unique in liver injury and COVID is its association with unbalanced coagulation control. It has been shown that COVID-19 patients exhibited a hypercoagulable state and this condition is related to impaired liver function resulted from liver injury [34]. Since the liver is the major organ producing coagulation factors [35], damage to liver would aggravate coagulation control thus exacerbating liver failure or even multiple organ failure in the severe cases. However, alteration in hemostasis control could be secondary to cytokine dysregulation since our enrichment analyses did not show overrepresentation of blood coagulation diseases. Here, we performed a detailed analysis of COVID-19 genes by their association with organ systems, disease enrichment, and GO enrichment using the resources available at RGD. Some of the results confirm with the clinical features of COVID-19 such as the involvement of nervous system and immune system in the disease. Additionally, the enrichment results point out the link between COVID-19 and liver diseases. As a globally accessible disease bioinformatic resource, RGD strives to provide researchers with utilities to resolve the complexity of disease research. During the challenging time of the COVID-19 pandemic, producing, organizing, and integrating data sets related to the disease is a timely contribution to the disease research community.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1: Ancestor chart of enriched biological process terms; Figure S2: Ancestor chart of enriched cellular component terms; Figure S3: Ancestor chart of enriched molecular function terms.

Author Contributions

Conceptualization, S.-J.W.; software, J.L.D.P., A.C.G., L.L., H.S.N., J.T., K.T. and M.A.T.; data curation, S.-J.W., W.M.D., G.T.H., M.L.H., M.L.K., S.J.F.L., M.T., M.V. and J.R.S.; writing—original draft, S.-J.W.; writing—review & editing, all authors; supervision J.L.D.P., M.R.D. and A.E.K.; project administration, K.C.B., S.Z. and J.R.S.; funding acquisition, M.R.D. and A.E.K. All authors have read and agreed to the published version of the manuscript.


This research was funded by National Heart Lung and Blood Institute: R01HL064541.

Data Availability Statement

The datasets and computer tools used to generate and/or analyze the results during the current study are either publicly available or available from the corresponding authors on request.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Guan, W.J.; Ni, Z.Y.; Hu, Y.; Liang, W.H.; Ou, C.Q.; He, J.X.; Liu, L.; Shan, H.; Lei, C.L.; Hui, D.S.C.; et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  2. da Silva, C.G.L.; Santana, J.R.P.; Pinheiro, L.F.G.; de Sousa, G.O.; Galvao, L.M.A.; Gomes, K.G.S.; Medeiros, K.A.; Diniz, L.A.; de Oliveira, I.G.P.; Felix, E.B.G.; et al. Anatomopathological Aspects and Clinical Correlation of COVID-19: A Systematic Review. Adv. Exp. Med. Biol. 2021, 1353, 217–224. [Google Scholar] [CrossRef] [PubMed]
  3. Osuchowski, M.F.; Winkler, M.S.; Skirecki, T.; Cajander, S.; Shankar-Hari, M.; Lachmann, G.; Monneret, G.; Venet, F.; Bauer, M.; Brunkhorst, F.M.; et al. The COVID-19 puzzle: Deciphering pathophysiology and phenotypes of a new disease entity. Lancet Respir. Med. 2021, 9, 622–642. [Google Scholar] [CrossRef] [PubMed]
  4. Chen, P.; Tang, Y.; He, W.; Yang, R.; Lan, Z.; Chen, R.; Zhang, P. Potential Pathophysiological Mechanisms Underlying Multiple Organ Dysfunction in Cytokine Release Syndrome. Mediat. Inflamm. 2022, 2022, 7137900. [Google Scholar] [CrossRef]
  5. Xu, B.; Li, G.; Guo, J.; Ikezoe, T.; Kasirajan, K.; Zhao, S.; Dalman, R.L. Angiotensin-converting enzyme 2, coronavirus disease 2019, and abdominal aortic aneurysms. J. Vasc. Surg. 2021, 74, 1740–1751. [Google Scholar] [CrossRef]
  6. Li, M.Y.; Li, L.; Zhang, Y.; Wang, X.S. Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues. Infect. Dis. Poverty 2020, 9, 45. [Google Scholar] [CrossRef]
  7. Chen, I.Y.; Chang, S.C.; Wu, H.Y.; Yu, T.C.; Wei, W.C.; Lin, S.; Chien, C.L.; Chang, M.F. Upregulation of the chemokine (C-C motif) ligand 2 via a severe acute respiratory syndrome coronavirus spike-ACE2 signaling pathway. J. Virol. 2010, 84, 7703–7712. [Google Scholar] [CrossRef][Green Version]
  8. Haidar, M.A.; Shakkour, Z.; Reslan, M.A.; Al-Haj, N.; Chamoun, P.; Habashy, K.; Kaafarani, H.; Shahjouei, S.; Farran, S.H.; Shaito, A.; et al. SARS-CoV-2 involvement in central nervous system tissue damage. Neural Regen. Res. 2022, 17, 1228–1239. [Google Scholar] [CrossRef]
  9. Srivastava, A.; Rockman-Greenberg, C.; Sareen, N.; Lionetti, V.; Dhingra, S. An insight into the mechanisms of COVID-19, SARS-CoV2 infection severity concerning beta-cell survival and cardiovascular conditions in diabetic patients. Mol. Cell Biochem. 2022, 477, 1681–1695. [Google Scholar] [CrossRef]
  10. Del Valle, D.M.; Kim-Schulze, S.; Huang, H.H.; Beckmann, N.D.; Nirenberg, S.; Wang, B.; Lavin, Y.; Swartz, T.H.; Madduri, D.; Stock, A.; et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 2020, 26, 1636–1643. [Google Scholar] [CrossRef]
  11. Deer, R.R.; Rock, M.A.; Vasilevsky, N.; Carmody, L.; Rando, H.; Anzalone, A.J.; Basson, M.D.; Bennett, T.D.; Bergquist, T.; Boudreau, E.A.; et al. Characterizing Long COVID: Deep Phenotype of a Complex Condition. EBioMedicine 2021, 74, 103722. [Google Scholar] [CrossRef] [PubMed]
  12. Phetsouphanh, C.; Darley, D.R.; Wilson, D.B.; Howe, A.; Munier, C.M.L.; Patel, S.K.; Juno, J.A.; Burrell, L.M.; Kent, S.J.; Dore, G.J.; et al. Immunological dysfunction persists for 8 months following initial mild-to-moderate SARS-CoV-2 infection. Nat. Immunol. 2022, 23, 210–216. [Google Scholar] [CrossRef] [PubMed]
  13. Zinatizadeh, M.R.; Zarandi, P.K.; Zinatizadeh, M.; Yousefi, M.H.; Amani, J.; Rezaei, N. Efficacy of mRNA, adenoviral vector, and perfusion protein COVID-19 vaccines. Biomed. Pharmacother. 2022, 146, 112527. [Google Scholar] [CrossRef] [PubMed]
  14. Lamb, Y.N. Remdesivir: First Approval. Drugs 2020, 80, 1355–1363. [Google Scholar] [CrossRef] [PubMed]
  15. Moreno, S.; Alcazar, B.; Duenas, C.; Del Castillo, J.G.; Olalla, J.; Antela, A. Use of Antivirals in SARS-CoV-2 Infection. Critical Review of the Role of Remdesivir. Drug Des. Dev. Ther. 2022, 16, 827–841. [Google Scholar] [CrossRef] [PubMed]
  16. Kaldunski, M.L.; Smith, J.R.; Hayman, G.T.; Brodie, K.; De Pons, J.L.; Demos, W.M.; Gibson, A.C.; Hill, M.L.; Hoffman, M.J.; Lamers, L.; et al. The Rat Genome Database (RGD) facilitates genomic and phenotypic data integration across multiple species for biomedical research. Mamm. Genome 2022, 33, 66–80. [Google Scholar] [CrossRef] [PubMed]
  17. Smith, J.R.; Hayman, G.T.; Wang, S.J.; Laulederkind, S.J.F.; Hoffman, M.J.; Kaldunski, M.L.; Tutaj, M.; Thota, J.; Nalabolu, H.S.; Ellanki, S.L.R.; et al. The Year of the Rat: The Rat Genome Database at 20: A multi-species knowledgebase and analysis platform. Nucleic Acids Res. 2020, 48, D731–D742. [Google Scholar] [CrossRef]
  18. Laulederkind, S.J.F.; Hayman, G.T.; Wang, S.J.; Hoffman, M.J.; Smith, J.R.; Bolton, E.R.; De Pons, J.; Tutaj, M.A.; Tutaj, M.; Thota, J.; et al. Rat Genome Databases, Repositories, and Tools. Methods Mol. Biol. 2019, 2018, 71–96. [Google Scholar] [CrossRef]
  19. Vedi, M.; Nalabolu, H.S.; Lin, C.W.; Hoffman, M.J.; Smith, J.R.; Brodie, K.; De Pons, J.L.; Demos, W.M.; Gibson, A.C.; Hayman, G.T.; et al. MOET: A web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses. Genetics 2022, 220, iyac005. [Google Scholar] [CrossRef]
  20. Davis, A.P.; Grondin, C.J.; Johnson, R.J.; Sciaky, D.; Wiegers, J.; Wiegers, T.C.; Mattingly, C.J. Comparative Toxicogenomics Database (CTD): Update 2021. Nucleic Acids Res. 2021, 49, D1138–D1143. [Google Scholar] [CrossRef]
  21. Liu, W.; Laulederkind, S.J.; Hayman, G.T.; Wang, S.J.; Nigam, R.; Smith, J.R.; De Pons, J.; Dwinell, M.R.; Shimoyama, M. OntoMate: A text-mining tool aiding curation at the Rat Genome Database. Database 2015, 2015, bau129. [Google Scholar] [CrossRef] [PubMed][Green Version]
  22. Hayman, G.T.; Laulederkind, S.J.; Smith, J.R.; Wang, S.J.; Petri, V.; Nigam, R.; Tutaj, M.; De Pons, J.; Dwinell, M.R.; Shimoyama, M. The Disease Portals, disease-gene annotation and the RGD disease ontology at the Rat Genome Database. Database 2016, 2016, baw034. [Google Scholar] [CrossRef] [PubMed][Green Version]
  23. Nadendla, S.; Jackson, R.; Munro, J.; Quaglia, F.; Meszaros, B.; Olley, D.; Hobbs, E.T.; Goralski, S.M.; Chibucos, M.; Mungall, C.J.; et al. ECO: The Evidence and Conclusion Ontology, an update for 2022. Nucleic Acids Res. 2022, 50, D1515–D1521. [Google Scholar] [CrossRef] [PubMed]
  24. Binns, D.; Dimmer, E.; Huntley, R.; Barrell, D.; O’Donovan, C.; Apweiler, R. QuickGO: A web-based tool for Gene Ontology searching. Bioinformatics 2009, 25, 3045–3046. [Google Scholar] [CrossRef] [PubMed][Green Version]
  25. Wu, Y.; Xu, X.; Chen, Z.; Duan, J.; Hashimoto, K.; Yang, L.; Liu, C.; Yang, C. Nervous system involvement after infection with COVID-19 and other coronaviruses. Brain Behav. Immun. 2020, 87, 18–22. [Google Scholar] [CrossRef]
  26. Moftah, M.B.; Eswayah, A. Intricate relationship between SARS-CoV-2-induced shedding and cytokine storm generation: A signaling inflammatory pathway augmenting COVID-19. Health Sci. Rev. 2022, 2, 100011. [Google Scholar] [CrossRef]
  27. Montalvan, V.; Lee, J.; Bueso, T.; De Toledo, J.; Rivas, K. Neurological manifestations of COVID-19 and other coronavirus infections: A systematic review. Clin. Neurol. Neurosurg. 2020, 194, 105921. [Google Scholar] [CrossRef]
  28. Merad, M.; Martin, J.C. Pathological inflammation in patients with COVID-19: A key role for monocytes and macrophages. Nat. Rev. Immunol. 2020, 20, 355–362. [Google Scholar] [CrossRef]
  29. Wang, S.J.; Laulederkind, S.J.; Hayman, G.T.; Smith, J.R.; Petri, V.; Lowry, T.F.; Nigam, R.; Dwinell, M.R.; Worthey, E.A.; Munzenmaier, D.H.; et al. Analysis of disease-associated objects at the Rat Genome Database. Database 2013, 2013, bat046. [Google Scholar] [CrossRef][Green Version]
  30. Stolp, B.; Stern, M.; Ambiel, I.; Hofmann, K.; Morath, K.; Gallucci, L.; Cortese, M.; Bartenschlager, R.; Ruggieri, A.; Graw, F.; et al. SARS-CoV-2 variants of concern display enhanced intrinsic pathogenic properties and expanded organ tropism in mouse models. Cell Rep. 2022, 38, 110387. [Google Scholar] [CrossRef]
  31. Deng, H.; Lin, H.; Mai, Y.; Liu, H.; Chen, W. Clinical features and predictive factors related to liver injury in SARS-CoV-2 Delta and Omicron variant-infected patients. Eur. J. Gastroenterol. Hepatol. 2022, 34, 933–939. [Google Scholar] [CrossRef] [PubMed]
  32. Nardo, A.D.; Schneeweiss-Gleixner, M.; Bakail, M.; Dixon, E.D.; Lax, S.F.; Trauner, M. Pathophysiological mechanisms of liver injury in COVID-19. Liver Int. 2021, 41, 20–32. [Google Scholar] [CrossRef] [PubMed]
  33. Wang, X.; Lei, J.; Li, Z.; Yan, L. Potential Effects of Coronaviruses on the Liver: An Update. Front. Med. 2021, 8, 651658. [Google Scholar] [CrossRef] [PubMed]
  34. D’Ardes, D.; Boccatonda, A.; Cocco, G.; Fabiani, S.; Rossi, I.; Bucci, M.; Guagnano, M.T.; Schiavone, C.; Cipollone, F. Impaired coagulation, liver dysfunction and COVID-19: Discovering an intriguing relationship. World J. Gastroenterol. 2022, 28, 1102–1112. [Google Scholar] [CrossRef]
  35. Heinz, S.; Braspenning, J. Measurement of Blood Coagulation Factor Synthesis in Cultures of Human Hepatocytes. Methods Mol. Biol. 2015, 1250, 309–316. [Google Scholar] [CrossRef]
Figure 1. The COVID-19 Disease Portal landing page. (A). Annotations made to COVID-19 genes are organized into nine categories displayed in the COVID-19 Disease Portal. (B). The customized disease ontology browser showing terms associated with COVID-19 disease genes is shown at the middle. (C). Seven ontology enrichment analyses allow users to send COVID-19 disease genes to the MOET tool for gene set enrichment analysis.
Figure 1. The COVID-19 Disease Portal landing page. (A). Annotations made to COVID-19 genes are organized into nine categories displayed in the COVID-19 Disease Portal. (B). The customized disease ontology browser showing terms associated with COVID-19 disease genes is shown at the middle. (C). Seven ontology enrichment analyses allow users to send COVID-19 disease genes to the MOET tool for gene set enrichment analysis.
Genes 13 02304 g001
Figure 2. The coronavirus disease gene distribution among the parent term (Coronavirus infectious disease) and its three child terms: COVID-19, Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). The numbers in each area represent the gene count of that section and the percentage to all the coronavirus infectious disease genes. There are 1257 genes associated with COVID-19, 19 genes associated with MERS and 90 genes with SARS, totaling 1338 coronavirus infectious disease genes on display.
Figure 2. The coronavirus disease gene distribution among the parent term (Coronavirus infectious disease) and its three child terms: COVID-19, Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS). The numbers in each area represent the gene count of that section and the percentage to all the coronavirus infectious disease genes. There are 1257 genes associated with COVID-19, 19 genes associated with MERS and 90 genes with SARS, totaling 1338 coronavirus infectious disease genes on display.
Genes 13 02304 g002
Figure 3. The disease term Comparison Heat Map visualized in the Gene Annotator tool. (A). COVID disease genes were visualized by their association with high level disease terms. (B). COVID genes associated with developmental disease were expanded to show their association with more granular terms under the branch. (C). COVID genes associated with disease of anatomical entity were expanded to show their association with more granular terms under the branch.
Figure 3. The disease term Comparison Heat Map visualized in the Gene Annotator tool. (A). COVID disease genes were visualized by their association with high level disease terms. (B). COVID genes associated with developmental disease were expanded to show their association with more granular terms under the branch. (C). COVID genes associated with disease of anatomical entity were expanded to show their association with more granular terms under the branch.
Genes 13 02304 g003
Table 1. Evidence code analysis of COVID-19 disease annotations and associated genes.
Table 1. Evidence code analysis of COVID-19 disease annotations and associated genes.
A. Evidence Code (ECO) DistributionB. Unique ECO among Genes
ECO TypeAnnotation CountGene CountGene/Unique ECOGene Count
A. The breakdown of 1407 COVID-19 disease annotations and 1257 associated disease genes according to evidence code types. B. The breakdown of gene counts by their association with unique evidence codes.
Table 2. A. Top 40 enriched disease terms identified by MOET and Set Analyzer. * The Bonferroni corrected p-values are shown; #, the number of the COVID-19 related disease gene annotated with the disease term and its child term; &, the number of genes in the reference genome annotated with the disease term and its child terms. MOET, the Multi Ontology Enrichment Tool, (accessed on 1 June 2022), developed by RGD. Set Analyzer, the enrichment tool developed at CTD ( (accessed on 1 June 2022)). B. * The Bonferroni corrected p-values are shown; #, the number of the COVID-19 related disease gene annotated with the disease term and its child term; &, the number of genes in the reference genome annotated with the disease term and its child terms. Set Analyzer, the enrichment tool developed at CTD ( (accessed on 1 June 2022)).
Table 2. A. Top 40 enriched disease terms identified by MOET and Set Analyzer. * The Bonferroni corrected p-values are shown; #, the number of the COVID-19 related disease gene annotated with the disease term and its child term; &, the number of genes in the reference genome annotated with the disease term and its child terms. MOET, the Multi Ontology Enrichment Tool, (accessed on 1 June 2022), developed by RGD. Set Analyzer, the enrichment tool developed at CTD ( (accessed on 1 June 2022)). B. * The Bonferroni corrected p-values are shown; #, the number of the COVID-19 related disease gene annotated with the disease term and its child term; &, the number of genes in the reference genome annotated with the disease term and its child terms. Set Analyzer, the enrichment tool developed at CTD ( (accessed on 1 June 2022)).
Top 40 Enriched Disease Ontology Terms from MOET and Set Analyzer
RankDisease Term (ID)p *Count #Ref Count &
1Respiratory Tract Infections (DOID:9008680)1.92 × 10−40135553
2liver disease (DOID:409)2.16 × 10−353352638
3hepatocellular carcinoma (DOID:684)9.75 × 10−34161853
4liver carcinoma (DOID:686)1.13 × 10−33161854
5hepatobiliary disease (DOID:3118)1.49 × 10−323372745
6liver cancer (DOID:3571)5.51 × 10−32164908
7hepatobiliary system cancer (DOID:0080355)7.52 × 10−32171975
8Adenocarcinoma (DOID:299)1.31 × 10−282281617
9Liver Neoplasms (DOID:9007188)1.05 × 10−271761100
10rheumatic disease (DOID:1575)1.18 × 10−27133698
11autoimmune disease of musculoskeletal system (DOID:0060032)7.15 × 10−27156922
12allergic disease (DOID:1205)3.50 × 10−26122624
13pneumonia (DOID:552)1.18 × 10−2579292
14nephritis (DOID:10952)7.85 × 10−2591388
15bacterial infectious disease (DOID:104)4.20 × 10−24130728
16respiratory allergy (DOID:0060496)2.25 × 10−2390397
17Glomerulonephritis (DOID:2921)2.83 × 10−2382337
18Wounds and Injuries (DOID:9001600)3.76 × 10−23147906
19Immediate Hypersensitivity (DOID:9002850)8.83 × 10−2399477
20Bacterial Infections and Mycoses (DOID:9004384)1.24 × 10−22149936
21Glomerular Diseases (DOID:9000104)1.26 × 10−2282344
22Inflammation (DOID:9005372)3.57 × 10−222972605
23parasitic protozoa infectious disease (DOID:2789)3.64 × 10−2256170
24rheumatoid arthritis (DOID:7148)3.65 × 10−2294444
25obstructive lung disease (DOID:2320)6.72 × 10−2296464
26carcinoma (DOID:305)1.02 × 10−213463235
27arthritis (DOID:848)2.80 × 10−21140875
28bone inflammation disease (DOID:3342)5.18 × 10−21143910
29lung disease (DOID:850)9.56 × 10−212482066
30parasitic infectious disease (DOID:1398)9.89 × 10−2177327
31Orthomyxoviridae Infections (DOID:9001499)8.07 × 10−2049144
32asthma (DOID:2841)8.18 × 10−2080361
33hepatitis (DOID:2237)1.05 × 10−1971293
34Thoracic Injuries(DOID:9001954)1.35 × 10−1968272
35bronchial disease (DOID:1176)1.57 × 10−19145962
36lower respiratory tract disease (DOID:0050161)1.92 × 10−192482110
37influenza (DOID:8469)2.23 × 10−1948141
38autoimmune disease of central nervous system (DOID:0060004)4.09 × 10−1972307
39respiratory system disease (DOID:1579)4.16 × 10−193433306
40Lung Injury (DOID:9000310)1.17 × 10−1866267
B. Set Analyzer
RankMESH Term (ID)p*Count #Ref Count &
1Neoplasms (MESH:D009369)5.22 × 10−1263873777
2Digestive System Diseases (MESH:D004066)1.52 × 10−1173252810
3Liver Diseases (MESH:D008107)1.22 × 10−1142721974
4Neoplasms by Site (MESH:D009371)4.75 × 10−993102978
5Immune System Diseases (MESH:D007154)2.63 × 10−911951219
6Carcinoma (MESH:D002277)1.28 × 10−841961341
7Digestive System Neoplasms (MESH:D004067)8.85 × 10−791971461
8Respiratory Tract Diseases (MESH:D012140)2.39 × 10−771711099
9Carcinoma, Hepatocellular (MESH:D006528)7.71 × 10−76122515
10Adenocarcinoma (MESH:D000230)1.00 × 10−741631028
11Liver Neoplasms(MESH:D008113)1.47 × 10−72136707
12Skin and Connective Tissue Diseases (MESH:D017437)7.70 × 10−722001650
13Lung Diseases (MESH:D008171)1.13 × 10−71151910
14Urogenital Diseases (MESH:D000091642)1.08 × 10−702262135
15Nervous System Diseases(MESH:D009422)3.17 × 10−692532695
16Female Urogenital Diseases and Pregnancy Complications (MESH:D005261)1.42 × 10−651851535
17Respiratory Tract Infections (MESH:D012141)2.29 × 10−6574177
18Infections (MESH:D007239)1.62 × 10−64114543
19Skin Diseases (MESH:D012871)2.51 × 10−631701336
20Pneumonia (MESH:D011014)1.51 × 10−625793
21Female Urogenital Diseases (MESH:D052776)3.36 × 10−621721392
22Pneumonia, Viral(MESH:D011024)1.02 × 10−573838
23Fibrosis (MESH:D005355)4.37 × 10−571421020
24Male Urogenital Diseases (MESH:D052801)3.56 × 10−561721528
25COVID-19 (MESH:D000086382)3.99 × 10−563737
26Urologic Diseases (MESH:D014570)6.12 × 10−55128852
27Autoimmune Diseases (MESH:D001327)1.01 × 10−53104552
28Virus Diseases (MESH:D014777)5.58 × 10−4878324
29Kidney Diseases (MESH:D007674)2.34 × 10−47108694
30Musculoskeletal Diseases (MESH:D009140)5.70 × 10−471591529
31Hypersensitivity (MESH:D006967)4.50 × 10−4576331
32Nephritis (MESH:D009393)6.61 × 10−4552126
33Gastrointestinal Diseases (MESH:D005767)2.78 × 10−441291072
34Liver Cirrhosis(MESH:D008103)3.25 × 10−44118897
35Vascular Diseases (MESH:D014652)5.72 × 10−44122965
36Cardiovascular Diseases (MESH:D002318)4.13 × 10−421511515
37Connective Tissue Diseases (MESH:D003240)5.25 × 10−4188521
38Liver Cirrhosis, Experimental (MESH:D008106)6.80 × 10−41106777
39Central Nervous System Diseases (MESH:D002493)3.99 × 10−401381330
40Glomerulonephritis (MESH:D005921)4.90 × 10−4046109
Table 3. COVID disease gene distribution within organ systems.
Table 3. COVID disease gene distribution within organ systems.
Nervous System Disease (772)Gastrointestinal System Disease (513)Endocrine System Disease (498)Musculoskeletal System Disease (498)Skin and Connective Tissue Disease (479)Immune & Inflammatory Disease (431)
central nervous system disease (676)gastrointestinal disease (401)liver disease (335)connective tissue disease (376)bone disease (270)primary innunodeficiency disease (348)
sensory system disease (477)digestive system neoplasm (339)endocrine gland neoplasm (301)bone disease (270)connective tissue neoplasm (161)autoimmune disease (246)
neurologic Manifestation (356)liver disease (335)diabetes Mellitus (134)neuromusclular disease (174)breast disase (161)lymphatic system disease (176)
neurodegenerative disease (278)intestinal disease (248)pancreas disease (119)musculoskeletal annormalities (157)rheumatic disease (123)rheumatic disease (133)
peripheral nervous system disease (232)mouth disease (120)gonaldal disease (109)autoimmune disease of musculoskeletal system (156)genetic skin disease (119)allergic disease (121)
nervous system neoplasm (148)stomach disease (99)parathyoid gland disease (88)joint disease (156)interstitial lung disease (85)immunoproliferative disorders (127)
nervous system malformation (122)gastroenteritis (97)autoimmune disease of endcrine system (61)muscular disease (113)dermatitis (78)immune ststem cancer (99)
nervous system trauma (98)biliary tract disease (66)thyroid gland disease (53)musculoskeletal system cancer (93)collagen disease (65)gastroenteritis (97)
autoimmune disease of the nervous system (83)esophageal disease (56)dwarfism (29)jaw disease (37)eczematous skin disease (57)dermatitis (78)
congenital nervous system abnormality (75)autoimmune disease of gastrointestinal tract (55)adrenal gland disease (24)musculoskeketal system benign neoplasm (10)infectious skin disease (62)pneumonia (79)
The six organ system diseases associated with highest number of COVID-19 disease genes were drilled down to more granular diseases within the branch. The number in parentheses next to the disease term shows the number of COVID-19 associated genes that are associated the disease term. These numbers were taken from the Comparison Heat Map in the Gene Annotator tool. The 6 organ system diseases with highest disease gene counts were selected from Figure 3C.
Table 4. Top 40 enriched gene ontology terms identified by MOET.
Table 4. Top 40 enriched gene ontology terms identified by MOET.
A. Biological Process
RankTerm (ID)p *Count #Ref Count &
1immune system process (GO:0002376)5.93 × 10−764223246
2immune response (GO:0006955)3.97 × 10−743312154
3adaptive immune response (GO:0002250)1.28 × 10−65186805
4defense response (GO:0006952)6.89 × 10−522862067
5response to external stimulus (GO:0009605)6.18 × 10−453823525
6biological process involved in interspecies interaction between organisms (GO:0044419)8.29 × 10−422692093
7response to stimulus (GO:0050896)3.90 × 10−3975210120
8response to other organism (GO:0051707)1.58 × 10−382491925
9response to biotic stimulus (GO:0009607)1.93 × 10−382531977
10response to external biotic stimulus (GO:0043207)2.09 × 10−382491928
11response to stress (GO:0006950)2.16 × 10−384434670
12defense response to other organism (GO:0098542)3.13 × 10−381981327
13complement activation classical pathway (GO:0006958)3.51 × 10−3859123
14leukocyte activation (GO:0045321)1.03 × 10−371831171
15humoral immune response (GO:0006959)1.11 × 10−37101394
16humoral immune response mediated by circulating immunoglobulin (GO:0002455)6.29 × 10−3761138
17adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains (GO:0002460)8.14 × 10−37103418
18complement activation (GO:0006956)5.99 × 10−3663153
19cell activation (GO:0001775)1.53 × 10−351941337
20innate immune response (GO:0045087)3.71 × 10−351661035
21leukocyte mediated immunity (GO:0002443)4.46 × 10−35113520
22lymphocyte mediated immunity (GO:0002449)6.42 × 10−3599405
23positive regulation of immune system process (GO:0002684)1.03 × 10−341721108
24immune effector process (GO:0002252)3.67 × 10−34140784
25immunoglobulin mediated immune response (GO:0016064)5.01 × 10−3474230
26B cell mediated immunity (GO:0019724)2.63 × 10−3374235
27regulation of immune system process (GO:0002682)1.57 × 10−322211729
28phagocytosis recognition (GO:0006910)4.59 × 10−3154128
29positive regulation of B cell activation (GO:0050871)5.68 × 10−3163180
30regulation of B cell activation (GO:0050864)1.24 × 10−3072241
31regulation of leukocyte activation (GO:0002694)5.52 × 10−30132771
32positive regulation of immune response (GO:0050778)1.3 × 10−29123687
33phagocytosis engulfment (GO:0006911)6.23 × 10−2956150
34cell surface receptor signaling pathway (GO:0007166)1.92 × 10−283193186
35plasma membrane invagination (GO:0099024)2.15 × 10−2857159
36response to bacterium (GO:0009617)2.31 × 10−281611114
37inflammatory response (GO:0006954)3.21 × 10−28149983
38lymphocyte activation (GO:0046649)3.59 × 10−28148973
39membrane invagination (GO:0010324)6.48 × 10−2858168
40positive regulation of response to stimulus (GO:0048584)1.09 × 10−272722545
B. Cellular Component
RankTerm (ID)p *Count #Ref Count &
1immunoglobulin complex (GO:0019814)2.22 × 10−95114186
2extracellular space (GO:0005615)2.41 × 10−514093812
3external side of plasma membrane (GO:0009897)6.87 × 10−42124558
4extracellular region (GO:0005576)8.80 × 10−424534820
5immunoglobulin complex circulating (GO:0042571)7.25 × 10−395190
6cell surface (GO:0009986)7.98 × 10−351721147
7side of membrane (GO:0098552)9.48 × 10−34140822
8cell periphery (GO:0071944)7.43 × 10−265186662
9blood microparticle (GO:0072562)6.74 × 10−2248153
10plasma membrane (GO:0005886)6.93 × 10−224766160
11vesicle (GO:0031982)8.59 × 10−203624363
12extracellular exosome (GO:0070062)4.55 × 10−162142254
13membrane (GO:0016020)1.96 × 10−1467410362
14extracellular vesicle (GO:1903561)3.25 × 10−142152354
15extracellular organelle (GO:0043230)4.13 × 10−142152359
16extracellular membran × 10−bounded organelle (GO:0065010)4.13 × 10−142152359
17intracellular vesicle (GO:0097708)5.91 × 10−122292684
18cytoplasmic vesicle (GO:0031410)9.94 × 10−122282681
19secretory granule lumen (GO:0034774)3.68 × 10−1052324
20cytoplasmic vesicle lumen (GO:0060205)5.33 × 10−1052327
21vesicle lumen (GO:0031983)6.80 × 10−1052329
22lytic vacuole (GO:0000323)9.38 × 10−989801
23lysosome (GO:0005764)9.38 × 10−989801
24protein-containing complex (GO:0032991)9.66 × 10−94506667
25vacuole (GO:0005773)3.28 × 10−895900
26secretory vesicle (GO:0099503)6.92 × 10−81151190
27secretory granule (GO:0030141)8.02 × 10−897942
28IgG immunoglobulin complex (GO:0071735)7.01 × 10−6811
29endomembrane system (GO:0012505)9.94 × 10−63405003
30vacuolar lumen (GO:0005775)1.18 × 10−428176
31endocytic vesicle (GO:0030139)1.23 × 10−445372
32collagen-containing extracellular matrix (GO:0062023)1.60 × 10−452464
33membrane raft (GO:0045121)1.90 × 10−449428
34cellular anatomical entity (GO:0110165)1.95 × 10−4107220217
35membrane microdomain (GO:0098857)2.04 × 10−449429
36tertiary granule lumen (GO:1904724)5.41 × 10−41455
37extracellular matrix (GO:0031012)1.06 × 10−360602
38external encapsulating structure (GO:0030312)1.18 × 10−360604
39cytoplasm (GO:0005737)1.26 × 10−372912592
40specific granule lumen (GO:0035580)2.57 × 10−31462
C. Molecular Function
RankTerm (ID)p *Count #Ref Count &
1antigen binding (GO:0003823)9.64 × 10−6596201
2immunoglobulin receptor binding (GO:0034987)1.43 × 10−365195
3signaling receptor binding (GO:0005102)9.52 × 10−211941748
4cytokine activity (GO:0005125)1.75 × 10−1451240
5cytokine receptor binding (GO:0005126)3.61 × 10−1355294
6immune receptor activity (GO:0140375)3.27 × 10−1137160
7chemokine activity (GO:0008009)4.59 × 10−102050
8signaling receptor activator activity (GO:0030546)1.85 × 10−970525
9receptor ligand activity (GO:0048018)2.34 × 10−969516
10chemokine receptor binding (GO:0042379)5.32 × 10−92375
11signaling receptor regulator activity (GO:0030545)7.91 × 10−973577
12protein binding (GO:0005515)2.21 × 10−889515279
13cytokine binding (GO:0019955)2.29 × 10−832151
14identical protein binding (GO:0042802)3.85 × 10−82002394
15cytokine receptor activity (GO:0004896)7.13 × 10−82599
16chemokine binding (GO:0019956)8.63 × 10−61333
17binding (GO:0005488)8.63 × 10−598517676
18growth factor activity (GO:0008083)1.00 × 10−428167
19C-C chemokine binding (GO:0019957)2.76 × 10−41024
20heparin binding (GO:0008201)2.97 × 10−429186
21growth factor binding (GO:0019838)3.31 × 10−426156
22enzyme binding (GO:0019899)1.75 × 10−31772362
23C-C chemokine receptor activity (GO:0016493)2.19 × 10−3923
24CXCR chemokine receptor binding (GO:0045236)2.70 × 10−3818
25CCR chemokine receptor binding (GO:0048020)2.94 × 10−31351
26G protein-coupled chemoattractant receptor activity (GO:0001637)7.23 × 10−3926
27chemokine receptor activity (GO:0004950)7.23 × 10−3926
28glycosaminoglycan binding (GO:0005539)7.48 × 10−333264
29protein homodimerization activity (GO:0042803)1.00 × 10−269749
30growth factor receptor binding (GO:0070851)1.13 × 10−223154
31G protein-coupled receptor binding (GO:0001664)1.31 × 10−238333
32molecular function regulator (GO:0098772)3.08 × 10−21532082
33exogenous protein binding (GO:0140272)3.91 × 10−21582
34kinase binding (GO:0019900)4.78 × 10−277904
35cell adhesion molecule binding (GO:0050839)4.97 × 10−255585
36protease binding (GO:0002020)6.58 × 10−222160
37sulfur compound binding (GO:1901681)7.12 × 10−234307
38carbohydrate derivative binding (GO:0097367)1.10 × 10−11682383
39virus receptor activity (GO:0001618)1.38 × 10−11481
40protein-containing complex binding (GO:0044877)1.49 × 10−11221642
* The Bonferroni corrected p-values are shown; #, the number of the COVID-19 related disease gene annotated with the GO term and its child term; &, the number of genes in the reference genome annotated with the GO term and its child terms.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wang, S.-J.; Brodie, K.C.; De Pons, J.L.; Demos, W.M.; Gibson, A.C.; Hayman, G.T.; Hill, M.L.; Kaldunski, M.L.; Lamers, L.; Laulederkind, S.J.F.; Nalabolu, H.S.; Thota, J.; Thorat, K.; Tutaj, M.A.; Tutaj, M.; Vedi, M.; Zacher, S.; Smith, J.R.; Dwinell, M.R.; Kwitek, A.E. Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal. Genes 2022, 13, 2304.

AMA Style

Wang S-J, Brodie KC, De Pons JL, Demos WM, Gibson AC, Hayman GT, Hill ML, Kaldunski ML, Lamers L, Laulederkind SJF, Nalabolu HS, Thota J, Thorat K, Tutaj MA, Tutaj M, Vedi M, Zacher S, Smith JR, Dwinell MR, Kwitek AE. Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal. Genes. 2022; 13(12):2304.

Chicago/Turabian Style

Wang, Shur-Jen, Kent C. Brodie, Jeffrey L. De Pons, Wendy M. Demos, Adam C. Gibson, G. Thomas Hayman, Morgan L. Hill, Mary L. Kaldunski, Logan Lamers, Stanley J. F. Laulederkind, Harika S. Nalabolu, Jyothi Thota, Ketaki Thorat, Marek A. Tutaj, Monika Tutaj, Mahima Vedi, Stacy Zacher, Jennifer R. Smith, Melinda R. Dwinell, and Anne E. Kwitek. 2022. "Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal" Genes 13, no. 12: 2304.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop