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pmid:38155943
Statistical and network analyses reveal mechanisms for the enhancement of macrophage immunity by manganese in Mycobacterium tuberculosis infection.
Lidong Shan · Zihai Wang · Lingshan Wu · Kaiqiang Qian · Guisen Peng · MeiLi Wei · Bikui Tang · Xi Jun
Biochemistry and biophysics reports · 2024 doi:10.1016/j.bbrep.2023.101602
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Experiment
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Data
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Analysis
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Complete file
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Our previous research has indicated that manganese ions reduce the bacterial load of Mycobacterium tuberculosis in macrophages, but the exact immune defense mechanism remains unknown. Several critical proteins and pathways involved in the host's immune response during this process are still unidentified. Our research aims to identify these proteins and pathways and provide a rationale for the use of manganese ions in the adjuvant treatment of tuberculosis. We downloaded GSE211666 data from the GEO database and selected the RM (Post-infection manganese ion treatment group) and Ra (single-infection group) groups for comparison and analysis to identify differential genes. These differential genes were then enriched and analyzed using STRING, Cytoscape, and NDEx tools to identify the two most relevant pathways of the \"Host Response Signature Network.\" After conducting an in-depth analysis of these two pathways, we found that manganese ions mainly mediate (1) the interferon -gamma (IFN-γ) and its receptor IFNGR and the downstream JAK-STAT pathway and (2) the NFκB pathway to enhance macrophage response to interferon, autophagy, polarization, and cytokine release. Using qPCR experiments, we verified the increased expression of CXCL10, MHCII, IFNγ, CSF2, and IL12, all of which are cytokines that play a key role in resistance to Mycobacterium tuberculosis infection, suggesting that macrophages enter a state of pro-inflammatory and activation after the addition of manganese ions, which enhances their immunosuppressive effect against Mycobacterium tuberculosis. We conclude that our study provides evidence of manganese ion's ability to treat tuberculosis adjuvantly.", "claims": [{"id": "claim-method", "statement": "DESeq2 processes each gene using a negative binomial distribution to simulate the counts and fit the normalized count data. A Wald test was used to determine whether there was a significant difference between the two sample groups regarding dispersion and fold change using DESeq2.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38155943", "locator": "sections[12] \"RNA data acquisition and data processing\" paragraph 2", "quote": "DESeq2 processes each gene using a negative binomial distribution to simulate the counts and fit the normalized count data. 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