Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) kills 1.6 million people worldwide every year, and there is an urgent need for targeting host-pathogen interactions as a strategy to reduce mycobacterial resistance to current antimicrobials. Non-coding RNAs are emerging as important regulators of numerous biological processes and avenues for exploitation in host-directed therapeutics. Although long non-coding RNAs (lncRNAs) are abundantly expressed in immune cells, their functional role in gene regulation and bacterial infections remains under-studied. Here, we identify an immunoregulatory, lincRNA-MIR99AHG, which is upregulated in macrophages upon IL-4/IL-13 stimulation and downregulated after Mtb infection and in active TB patients. To evaluate the functional role of lincRNA-MIR99AHG, we employed antisense GapmeR-mediated lncRNA knockdown experiments. Knockdown of lincRNA-MIR99AHG with LNA-GapmeRs significantly reduced intracellular Mtb growth in mouse and human macrophages and reduced proinflammatory cytokine production. In addition, in vivo treatment with MIR99AHG LNA-GapmeRs reduced the mycobacterial burden in the lung and spleen. In vivo LNA-GapmeR treatment experiments demonstrated a role of lincRNA-MIR99AHG as a regulator of macrophage polarization and a host-mediated response post Mtb infection. Further, lincRNA-MIR99AHG translocated to the nucleus and interacts with a high affinity to hnRNPA2/B1 following IL-4/IL-13 stimulation and Mtb infection. Together, these findings identify lincRNA-MIR99AHG as a positive regulator of inflammation to promote Mtb growth and a possible for host-directed targeting or for adjunctive therapeutics against TB.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uct/oai:localhost:11427/32630 |
| Date | 21 January 2021 |
| Creators | Gcanga, Lona |
| Contributors | Guler, Reto, Tamgue, Ousman, Brombacher, Frank |
| Publisher | Faculty of Health Sciences, Department of Clinical Laboratory Sciences |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, Doctoral, PhD |
| Format | application/pdf |
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