{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:07:28Z","timestamp":1785161248500,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GSU Institutional Funds"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>Since the onset of the COVID-19 pandemic, various severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants have emerged. Although the primary site of SARS-CoV-2 infection is the lungs, it can also affect the brain and induce neurological symptoms. However, the specific effects of different variants on the brain remain unclear. In this study, a whole-transcriptome analysis was conducted using the brain tissues of K18-hACE2 mice infected with the ancestral B.1 (Wuhan) variant and with major SARS-CoV-2 variants of concern, including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta) and B.1.529 (Omicron). After sequencing, differential gene expression, gene ontology (GO) and genome pathway enrichment analyses were performed. An Immune Cell Abundance Identifier (ImmuCellAI) was used to identify the abundance of different cell populations. Additionally, RT-qPCR was used to validate the RNA-seq data. The viral load and hierarchical clustering analyses divided the samples into two different clusters with notable differences in gene expression at day 6 post-infection for all variants compared to the control group. GO and the Kyoto Encyclopedia of genes and genomes enrichment analyses revealed similar patterns of pathway enrichment for different variants. ImmuCellAI revealed the changes in immune cell populations, including the decrease in CD4+ T and B cell proportions and the increase in CD8+ T and dendritic cell proportions. A co-expression network analysis revealed that some genes, such as STAT1, interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-\u03b1), were dysregulated in all variants. A RT-qPCR analysis for IL-6, CXCL10 and IRF7 further validated the RNA-seq analysis. In conclusion, this study provides, for the first time, an extensive transcriptome analysis of a K18-hACE2 mouse brain after infection with major SARS-CoV-2 variants.<\/jats:p>","DOI":"10.3390\/v17030329","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T03:37:57Z","timestamp":1740627477000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Neurobiological Alterations Induced by SARS-CoV-2: Insights from Variant-Specific Host Gene Expression Patterns in hACE2-Expressing Mice"],"prefix":"10.3390","volume":"17","author":[{"given":"Hamid Reza","family":"Jahantigh","sequence":"first","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3645-5605","authenticated-orcid":false,"given":"Amany","family":"Elsharkawy","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"},{"name":"Center of Diagnostics and Therapeutics, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anchala","family":"Guglani","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Komal","family":"Arora","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2502-5793","authenticated-orcid":false,"given":"Lila D.","family":"Patterson","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0970-4875","authenticated-orcid":false,"given":"Mukesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Biology, College of Arts and Sciences, Georgia State University, Atlanta, GA 30303, USA"},{"name":"Center of Diagnostics and Therapeutics, Georgia State University, Atlanta, GA 30303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109968","DOI":"10.1016\/j.intimp.2023.109968","article-title":"Update on the effectiveness of COVID-19 vaccines on different variants of SARS-CoV-2","volume":"117","author":"Firouzabadi","year":"2023","journal-title":"Int. Immunopharmacol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e86014","DOI":"10.7554\/eLife.86014","article-title":"Immune mechanisms underlying COVID-19 pathology and post-acute sequelae of SARS-CoV-2 infection (PASC)","volume":"12","author":"Mohandas","year":"2023","journal-title":"Elife"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Natekar, J.P., Pathak, H., Stone, S., Kumari, P., Sharma, S., Auroni, T.T., Arora, K., Rothan, H.A., and Kumar, M. (2022). Differential pathogenesis of SARS-CoV-2 variants of concern in human ACE2-expressing mice. Viruses, 14.","DOI":"10.1101\/2022.04.04.486975"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1080\/22221751.2022.2122582","article-title":"Effectiveness of COVID-19 vaccines against SARS-CoV-2 variants of concern in real-world: A literature review and meta-analysis","volume":"11","author":"Shao","year":"2022","journal-title":"Emerg. Microbes Infect."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jin, Y., Yang, H., Ji, W., Wu, W., Chen, S., Zhang, W., and Duan, G. (2020). Virology, epidemiology, pathogenesis, and control of COVID-19. Viruses, 12.","DOI":"10.3390\/v12040372"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rothan, H.A., Acharya, A., Reid, S.P., Kumar, M., and Byrareddy, S.N. (2020). Molecular aspects of COVID-19 differential pathogenesis. Pathogens, 9.","DOI":"10.3390\/pathogens9070538"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s12974-023-02861-3","article-title":"Differential effects of SARS-CoV-2 variants on central nervous system cells and blood\u2013brain barrier functions","volume":"20","author":"Proust","year":"2023","journal-title":"J. Neuroinflamm."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Torabi, S.H., Riahi, S.M., Ebrahimzadeh, A., and Salmani, F. (2023). Changes in symptoms and characteristics of COVID-19 patients across different variants: Two years study using neural network analysis. BMC Infect. Dis., 23.","DOI":"10.1186\/s12879-023-08813-9"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Munblit, D., Nicholson, T.R., Needham, D.M., Seylanova, N., Parr, C., Chen, J., Kokorina, A., Sigfrid, L., Buonsenso, D., and Bhatnagar, S. (2022). Studying the post-COVID-19 condition: Research challenges, strategies, and importance of Core Outcome Set development. BMC Med., 20.","DOI":"10.1186\/s12916-021-02222-y"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wenting, A., Gruters, A., van Os, Y., Verstraeten, S., Valentijn, S., Ponds, R., and de Vugt, M. (2020). COVID-19 neurological manifestations and underlying mechanisms: A scoping review. Front. Psychiatry, 11.","DOI":"10.3389\/fpsyt.2020.00860"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Almutairi, M.M., Sivandzade, F., Albekairi, T.H., Alqahtani, F., and Cucullo, L. (2021). Neuroinflammation and Its Impact on the Pathogenesis of COVID-19. Front. Med., 8.","DOI":"10.3389\/fmed.2021.745789"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Livieratos, A., Gogos, C., and Akinosoglou, K. (2024). Impact of Prior COVID-19 Immunization and\/or Prior Infection on Immune Responses and Clinical Outcomes. Viruses, 16.","DOI":"10.3390\/v16050685"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wise, J. (2022). Covid-19: Symptomatic infection with omicron variant is milder and shorter than with delta, study reports. BMJ Br. Med. J., 377.","DOI":"10.1136\/bmj.o922"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1111\/jcmm.17747","article-title":"Severity of the Omicron SARS-CoV-2 variant compared with the previous lineages: A systematic review","volume":"27","author":"Arabi","year":"2023","journal-title":"J. Cell Mol. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1038\/s41401-020-0485-4","article-title":"Structural and functional properties of SARS-CoV-2 spike protein: Potential antivirus drug development for COVID-19","volume":"41","author":"Huang","year":"2020","journal-title":"Acta Pharmacol. Sin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"dmm049632","DOI":"10.1242\/dmm.049632","article-title":"Comparison of the pathogenesis of SARS-CoV-2 infection in K18-hACE2 mouse and Syrian golden hamster models","volume":"15","author":"Jeong","year":"2022","journal-title":"Dis. Models Mech."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tarr\u00e9s-Freixas, F., Trinit\u00e9, B., Pons-Gr\u00edfols, A., Romero-Durana, M., Riveira-Mu\u00f1oz, E., \u00c1vila-Nieto, C., P\u00e9rez, M., Garcia-Vidal, E., Perez-Zsolt, D., and Mu\u00f1oz-Basagoiti, J. (2022). Heterogeneous infectivity and pathogenesis of SARS-CoV-2 variants beta, delta and omicron in transgenic K18-hACE2 and wildtype mice. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.840757"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/s41586-020-2943-z","article-title":"COVID-19 treatments and pathogenesis including anosmia in K18-hACE2 mice","volume":"589","author":"Zheng","year":"2021","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aw, Z.Q., Mok, C.K., Wong, Y.H., Chen, H., Mak, T.M., Lin, R.T., Lye, D.C., Tan, K.S., and Chu, J.J.H. (2022). Early pathogenesis profiles across SARS-CoV-2 variants in K18-hACE2 mice revealed differential triggers of lung damages. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.950666"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rothan, H.A., Kumari, P., Stone, S., Natekar, J.P., Arora, K., Auroni, T.T., and Kumar, M. (2022). SARS-CoV-2 infects primary neurons from human ACE2 expressing mice and upregulates genes involved in the inflammatory and necroptotic pathways. Pathogens, 11.","DOI":"10.3390\/pathogens11020257"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kumar, M., Belcaid, M., and Nerurkar, V.R. (2016). Identification of host genes leading to West Nile virus encephalitis in mice brain using RNA-seq analysis. Sci. Rep., 6.","DOI":"10.1038\/srep26350"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Iqbal, N., and Kumar, P. (2022). Integrated COVID-19 Predictor: Differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data. Comput. Biol. Med., 147.","DOI":"10.1016\/j.compbiomed.2022.105684"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","article-title":"Trimmomatic: A flexible trimmer for Illumina sequence data","volume":"30","author":"Bolger","year":"2014","journal-title":"Bioinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1038\/nbt.3122","article-title":"StringTie enables improved reconstruction of a transcriptome from RNA-seq reads","volume":"33","author":"Pertea","year":"2015","journal-title":"Nat. Biotechnol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Batut, B., van den Beek, M., Doyle, M.A., and Soranzo, N. (2021). RNA-seq data analysis in galaxy. RNA Bioinform., 367\u2013392.","DOI":"10.1007\/978-1-0716-1307-8_20"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, R.C., Huang, K., Zhang, H.P., Li, L., Tan, C., Chen, H.C., Jin, M.L., and Wang, X.R. (2022). Transcriptional landscape of human neuroblastoma cells in response to SARS-CoV-2. BMC Neurosci., 23.","DOI":"10.1186\/s12868-022-00728-6"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5960","DOI":"10.1002\/cam4.3240","article-title":"Development and validation of an immune-related prognostic signature in lung adenocarcinoma","volume":"9","author":"Sun","year":"2020","journal-title":"Cancer Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1093\/bioinformatics\/btab711","article-title":"ImmuCellAI-mouse: A tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction","volume":"38","author":"Miao","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_29","first-page":"1","article-title":"Package \u2018ggplot2\u2019. Create elegant data visualisations using the grammar of graphics","volume":"2","author":"Wickham","year":"2016","journal-title":"Version"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kohl, M., Wiese, S., and Warscheid, B. (2011). Cytoscape: Software for visualization and analysis of biological networks. Data Mining in Proteomics: From Standards to Applications, Springer.","DOI":"10.1007\/978-1-60761-987-1_18"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e5180","DOI":"10.7717\/peerj.5180","article-title":"Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis","volume":"6","author":"Cao","year":"2018","journal-title":"PeerJ"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ma, H., He, Z., Chen, J., Zhang, X., and Song, P. (2021). Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-79235-9"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rabaan, A.A., Al-Ahmed, S.H., Muhammad, J., Khan, A., Sule, A.A., Tirupathi, R., Mutair, A.A., Alhumaid, S., Al-Omari, A., and Dhawan, M. (2021). Role of Inflammatory Cytokines in COVID-19 Patients: A Review on Molecular Mechanisms, Immune Functions, Immunopathology and Immunomodulatory Drugs to Counter Cytokine Storm. Vaccines, 9.","DOI":"10.3390\/vaccines9050436"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1016\/j.csbj.2021.01.034","article-title":"Chemokines and chemokine receptors during COVID-19 infection","volume":"19","author":"Khalil","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Suvarna, K., Biswas, D., Pai, M.G.J., Acharjee, A., Bankar, R., Palanivel, V., Salkar, A., Verma, A., Mukherjee, A., and Choudhury, M. (2021). Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential. Front. Physiol., 12.","DOI":"10.3389\/fphys.2021.652799"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Navhaya, L.T., Blessing, D.M., Yamkela, M., Godlo, S., and Makhoba, X.H. (2024). A comprehensive review of the interaction between COVID-19 spike proteins with mammalian small and major heat shock proteins. Biomol. Concepts, 15.","DOI":"10.1515\/bmc-2022-0027"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, B., Tang, N., Peluso, M.J., Iyer, N.S., Torres, L., Donatelli, J.L., Munter, S.E., Nixon, C.C., Rutishauser, R.L., and Rodriguez-Barraquer, I. (2021). Characterization and Biomarker Analyses of Post-COVID-19 Complications and Neurological Manifestations. Cells, 10.","DOI":"10.3390\/cells10020386"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kanwal, A., and Zhang, Z. (2024). Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1435170"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rice, J.M., Zweifach, A., and Lynes, M.A. (2016). Metallothionein regulates intracellular zinc signaling during CD4+ T cell activation. BMC Immunol., 17.","DOI":"10.1186\/s12865-016-0151-2"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dai, H., Wang, L., Li, L., Huang, Z., and Ye, L. (2021). Metallothionein 1: A New Spotlight on Inflammatory Diseases. Front. Immunol., 12.","DOI":"10.3389\/fimmu.2021.739918"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6559","DOI":"10.1038\/s41467-021-26803-w","article-title":"SARS-CoV-2 B. 1.1. 7 (alpha) and B. 1.351 (beta) variants induce pathogenic patterns in K18-hACE2 transgenic mice distinct from early strains","volume":"12","author":"Radvak","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Nagano, T., Nakano, M., Nakashima, A., Onishi, K., Yamao, S., Enari, M., Kikkawa, U., and Kamada, S. (2016). Identification of cellular senescence-specific genes by comparative transcriptomics. Sci. Rep., 6.","DOI":"10.1038\/srep31758"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4827","DOI":"10.1038\/s41467-022-32552-1","article-title":"A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues","volume":"13","author":"Saul","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kumari, R., and Jat, P. (2021). Mechanisms of cellular senescence: Cell cycle arrest and senescence associated secretory phenotype. Front. Cell Dev. Biol., 9.","DOI":"10.3389\/fcell.2021.645593"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13024-021-00507-7","article-title":"The landscape of human tissue and cell type specific expression and co-regulation of senescence genes","volume":"17","author":"Xu","year":"2022","journal-title":"Mol. Neurodegener."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1186\/s12974-022-02514-x","article-title":"SARS-CoV-2 productively infects human brain microvascular endothelial cells","volume":"19","author":"Yang","year":"2022","journal-title":"J. Neuroinflamm."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wu, D., Wu, X., Huang, J., Rao, Q., Zhang, Q., and Zhang, W. (2021). Lymphocyte subset alterations with disease severity, imaging manifestation, and delayed hospitalization in COVID-19 patients. BMC Infect. Dis., 21.","DOI":"10.1186\/s12879-021-06354-7"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gil-Manso, S., Herrero-Quevedo, D., Carbonell, D., Mart\u00ednez-Bonet, M., Bernaldo-de-Quir\u00f3s, E., Kennedy-Batalla, R., Gallego-Valle, J., L\u00f3pez-Esteban, R., Bl\u00e1zquez-L\u00f3pez, E., and Miguens-Blanco, I. (2023). Multidimensional analysis of immune cells from COVID-19 patients identified cell subsets associated with the severity at hospital admission. PLoS Pathog., 19.","DOI":"10.1371\/journal.ppat.1011432"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Diao, B., Wang, C., Tan, Y., Chen, X., Liu, Y., Ning, L., Chen, L., Li, M., Liu, Y., and Wang, G. (2020). Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Front. Immunol., 11.","DOI":"10.3389\/fimmu.2020.00827"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Peng, X., Ouyang, J., Isnard, S., Lin, J., Fombuena, B., Zhu, B., and Routy, J.-P. (2020). Sharing CD4+ T cell loss: When COVID-19 and HIV collide on immune system. Front. Immunol., 11.","DOI":"10.3389\/fimmu.2020.596631"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Laine, L., Sk\u00f6n, M., V\u00e4is\u00e4nen, E., Julkunen, I., and \u00d6sterlund, P. (2022). SARS-CoV-2 variants Alpha, Beta, Delta and Omicron show a slower host cell interferon response compared to an early pandemic variant. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1016108"}],"container-title":["Viruses"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4915\/17\/3\/329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:43:37Z","timestamp":1760028217000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4915\/17\/3\/329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,27]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["v17030329"],"URL":"https:\/\/doi.org\/10.3390\/v17030329","relation":{},"ISSN":["1999-4915"],"issn-type":[{"value":"1999-4915","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,27]]}}}