HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis

Wang,Min; Hu,Yuling; Cai,Feng; Qiu,Jiayong; Mao,Yimin; Zhang,Yingmin

doi:10.3892/etm.2024.12375

HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis

Authors:
- Min Wang
- Yuling Hu
- Feng Cai
- Jiayong Qiu
- Yimin Mao
- Yingmin Zhang
View Affiliations

Affiliations: Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan 471003, P.R. China, Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226000, P.R. China
Published online on: January 8, 2024 https://doi.org/10.3892/etm.2024.12375
Article Number: 86
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Invasive aspergillosis (IA) is a severe disease, the pathogenesis of which remains unclear. The present study aimed to determine the molecular mechanism of IA and to identify potential biomarkers using bioinformatics analysis. The GSE78000 dataset, which includes data from patients with IA and healthy individuals, was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the IA and control groups were identified with the ‘affy’ package in R software. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases were then used to analyse the function and pathway enrichment of DEGs. The protein‑protein interaction network was analysed with the Search Tool for the Retrieval of Interacting Genes (STRING) website. In addition, DEGs were confirmed using reverse transcription‑quantitative PCR and western blotting in samples with IA (n=6) and control samples (n=6) collected from the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Henan University of Science and Technology (Luoyang, China). The present study identified 735 DEGs, including 312 upregulated and 423 downregulated genes. Through GO and KEGG analyses of the DEGs, macrophage activation and hypoxia‑inducible factor 1 (HIF‑1) signalling pathways were revealed to be significantly upregulated and downregulated, respectively, in patients with IA compared with that of the healthy individuals. Subsequently, correlation analysis of macrophage activation and HIF‑1 signalling pathways was revealed using correlation as a distance metric for hierarchical clustering correlation analysis. However, there was no protein‑protein interaction between the macrophage activity regulation and HIF‑1 signalling pathways based on STRING analysis. In summary, the present study identified candidate genes and associated molecules that may be associated to IA and revealed potential biomarkers and therapeutic targets for IA.

View Figures

View References

1	Park HS, Jun SC, Han KH, Hong SB and Yu JH: Diversity, application, and synthetic biology of industrially important aspergillus fungi. Adv Appl Microbiol. 100:161–202. 2017.PubMed/NCBI View Article : Google Scholar
2	Becker KL, Ifrim DC, Quintin J, Netea MG and van de Veerdonk FL: Antifungal innate immunity: Recognition and inflammatory networks. Semin Immunopathol. 37:107–116. 2015.PubMed/NCBI View Article : Google Scholar
3	Takakura S: Zygomycosis. Nihon Rinsho. 66:2356–2361. 2008.PubMed/NCBI(In Japanese).
4	Cadena J, Thompson GR III and Patterson TF: Invasive aspergillosis: Current strategies for diagnosis and management. Infect Dis Clin North Am. 30:125–142. 2016.PubMed/NCBI View Article : Google Scholar
5	Cadena J, Thompson GR III and Patterson TF: Aspergillosis: Epidemiology, diagnosis, and treatment. Infect Dis Clin North Am. 35:415–434. 2021.PubMed/NCBI View Article : Google Scholar
6	Latge JP and Chamilos G: Aspergillus fumigatus and aspergillosis in 2019. Clin Microbiol Rev. 33:e00140–18. 2019.PubMed/NCBI View Article : Google Scholar
7	Oren I and Goldstein N: Invasive pulmonary aspergillosis. Curr Opin Pulm Med. 8:195–200. 2002.PubMed/NCBI View Article : Google Scholar
8	Darling BA and Milder EA: Invasive aspergillosis. Pediatr Rev. 39:476–478. 2018.PubMed/NCBI View Article : Google Scholar
9	Verma N, Singh S, Syal A, Pradhan P and Singh M and Singh M: Invasive aspergillosis is a critical determinant of mortality in cirrhosis: A systematic review with meta-analysis. Med Mycol. 59:1092–1100. 2021.PubMed/NCBI View Article : Google Scholar
10	Arvanitis M and Mylonakis E: Diagnosis of invasive aspergillosis: Recent developments and ongoing challenges. Eur J Clin Invest. 45:646–652. 2015.PubMed/NCBI View Article : Google Scholar
11	Dhesi Z, Herbst S and Armstrong-James D: Transcript profiling of the murine immune response to invasive aspergillosis. Methods Mol Biol. 845:435–444. 2012.PubMed/NCBI View Article : Google Scholar
12	Wang M, Zhang M, Qiu J, Liu C, Lou Y, Wang T, Zhang Y and Mao Y: PU.1-CD23 signaling mediates pulmonary innate immunity against Aspergillus fumigatus infection by driving inflammatory response. BMC Immunol. 24(4)2023.PubMed/NCBI View Article : Google Scholar
13	Fliesser M, Morton CO, Bonin M, Ebel F, Hünniger K, Kurzai O, Einsele H and Löffler J: Hypoxia-inducible factor 1α modulates metabolic activity and cytokine release in anti-Aspergillus fumigatus immune responses initiated by human dendritic cells. Int J Med Microbiol. 305:865–873. 2015.PubMed/NCBI View Article : Google Scholar
14	Morris NL, Michael DN, Crotty KM, Chang SS and Yeligar SM: Alcohol-Induced glycolytic shift in alveolar macrophages is mediated by hypoxia-inducible factor-1 Alpha. Front Immunol. 13(865492)2022.PubMed/NCBI View Article : Google Scholar
15	Dix A, Czakai K, Springer J, Fliesser M, Bonin M, Guthke R, Schmitt AL, Einsele H, Linde J and Löffler J: Genome-Wide expression profiling reveals S100B as biomarker for invasive aspergillosis. Front Microbiol. 7(320)2016.PubMed/NCBI View Article : Google Scholar
16	Wang Y, Sun FZ, Lin W and Zhang SQ: AC-PCoA: Adjustment for confounding factors using principal coordinate analysis. PLoS Comput Biol. 18(e1010184)2022.PubMed/NCBI View Article : Google Scholar
17	Delbeke H, Casteels I and Joossens M: DNA extraction protocol impacts ocular surface microbiome profile. Front Microbiol. 14(1128917)2023.PubMed/NCBI View Article : Google Scholar
18	Zhan Z, Chen Y, Duan Y, Li L, Mew K, Hu P, Ren H and Peng M: Identification of key genes, pathways and potential therapeutic agents for liver fibrosis using an integrated bioinformatics analysis. PeerJ. 7(e6645)2019.PubMed/NCBI View Article : Google Scholar
19	Haldermans P, Shkedy Z, Van Sanden S, Burzykowski T and Aerts M: Using linear mixed models for normalization of cDNA microarrays. Stat Appl Genet Mol Biol. 6(Article 19)2007.PubMed/NCBI View Article : Google Scholar
20	Ding J and Zhang Y: Analysis of key GO terms and KEGG pathways associated with carcinogenic chemicals. Comb Chem High Throughput Screen: Dec 18, 2017 (Epub ahead of print).
21	Chen L, Zhang YH, Lu G, Huang T and Cai YD: Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways. Artif Intell Med. 76:27–36. 2017.PubMed/NCBI View Article : Google Scholar
22	Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4(P3)2003.PubMed/NCBI
23	Goutzelas Y, Kontou P, Mamuris Z, Bagos P and Sarafidou T: Meta-analysis of gene expression data in adipose tissue reveals new obesity associated genes. Gene. 818(146223)2022.PubMed/NCBI View Article : Google Scholar
24	Wallach T, Schellenberg K, Maier B, Kalathur RK, Porras P, Wanker EE, Futschik ME and Kramer A: Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions. PLoS Genet. 9(e1003398)2013.PubMed/NCBI View Article : Google Scholar
25	Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, et al: The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49(D1):D605–D612. 2021.PubMed/NCBI View Article : Google Scholar
26	Liu J, Zhou S, Li S, Jiang Y, Wan Y, Ma X and Cheng W: Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis. Cancer Cell Int. 19(136)2019.PubMed/NCBI View Article : Google Scholar
27	Chen Y, Li H, Lai L, Feng Q and Shen J: Identification of common differentially expressed genes and potential therapeutic targets in ulcerative colitis and rheumatoid arthritis. Front Genet. 11(572194)2020.PubMed/NCBI View Article : Google Scholar
28	Livak KJ and Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 25:402–408. 2001.PubMed/NCBI View Article : Google Scholar
29	Johnson G, Ferrini A, Dolan SK, Nolan T, Agrawal S, Doyle S and Bustin SA: Biomarkers for invasive aspergillosis: The challenges continue. Biomark Med. 8:429–451. 2014.PubMed/NCBI View Article : Google Scholar
30	Margalit A and Kavanagh K: The innate immune response to aspergillus fumigatus at the alveolar surface. FEMS Microbiol Rev. 39:670–687. 2015.PubMed/NCBI View Article : Google Scholar
31	Seifert M, Nairz M, Schroll A, Schrettl M, Haas H and Weiss G: Effects of the aspergillus fumigatus siderophore systems on the regulation of macrophage immune effector pathways and iron homeostasis. Immunobiology. 213:767–778. 2008.PubMed/NCBI View Article : Google Scholar
32	Heinekamp T, Schmidt H, Lapp K, Pahtz V, Shopova I, Koster-Eiserfunke N, Kruger T, Kniemeyer O and Brakhage AA: Interference of aspergillus fumigatus with the immune response. Semin Immunopathol. 37:141–152. 2015.PubMed/NCBI View Article : Google Scholar
33	Tan HY, Wang N, Zhang C, Chan YT, Yuen MF and Feng Y: Lysyl Oxidase-Like 4 Fosters an immunosuppressive microenvironment during hepatocarcinogenesis. Hepatology. 73:2326–2341. 2021.PubMed/NCBI View Article : Google Scholar
34	Fecher RA, Horwath MC, Friedrich D, Rupp J and Deepe GS Jr: Inverse correlation between IL-10 and HIF-1alpha in macrophages infected with histoplasma capsulatum. J Immunol. 197:565–579. 2016.PubMed/NCBI View Article : Google Scholar
35	Goncalves SM, Duarte-Oliveira C, Campos CF, Aimanianda V, Ter Horst R, Leite L, Mercier T, Pereira P, Fernandez-Garcia M, Antunes D, et al: Phagosomal removal of fungal melanin reprograms macrophage metabolism to promote antifungal immunity. Nat Commun. 11(2282)2020.PubMed/NCBI View Article : Google Scholar
36	Cheng Y, Feng Y, Xia Z, Li X and Rong J: ω-Alkynyl arachidonic acid promotes anti-inflammatory macrophage M2 polarization against acute myocardial infarction via regulating the cross-talk between PKM2, HIF-1α and iNOS. Biochim Biophys Acta Mol Cell Biol Lipids. 1862:1595–1605. 2017.PubMed/NCBI View Article : Google Scholar