Abstract
Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis (TB), a disease that claims ~1.6 million lives annually. The current treatment regime is long and expensive, and missed doses contribute to drug resistance. Therefore, development of new anti-TB drugs remains one of the highest public health priorities. Mtb has evolved a complex cell envelope that represents a formidable barrier to antibiotics. The Mtb cell envelop consists of four distinct layers enriched for Mtb specific lipids and glycans. Although the outer membrane, comprised of mycolic acid esters, has been extensively studied, less is known about the plasma membrane, which also plays a critical role in impacting antibiotic efficacy. The Mtb plasma membrane has a unique lipid composition, with mannosylated phosphatidylinositol lipids (phosphatidyl-myoinositol mannosides, PIMs) comprising more than 50% of the lipids. However, the role of PIMs in the structure and function of the membrane remains elusive. Here, we used multiscale molecular dynamics (MD) simulations to understand the structure-function relationship of the PIM lipid family and decipher how they self-organize to shape the biophysical properties of mycobacterial plasma membranes. We assess both symmetric and asymmetric assemblies of the Mtb plasma membrane and compare this with residue distributions of Mtb integral membrane protein structures. To further validate the model, we tested known anti-TB drugs and demonstrated that our models agree with experimental results. Thus, our work sheds new light on the organization of the mycobacterial plasma membrane. This paves the way for future studies on antibiotic development and understanding Mtb membrane protein function.
| Original language | English |
|---|---|
| Article number | e2212755120 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 120 |
| Issue number | 5 |
| Early online date | 24 Jan 2023 |
| DOIs | |
| Publication status | Published - 31 Jan 2023 |
Bibliographical note
Funding Information:ACKNOWLEDGMENTS. C.M.B. is supported by an MRC studentship (MR/
Funding Information:
N014294/1). R.A.C. is funded by Wellcome (208361/Z/17/Z). Research in P.J.S.’s lab is funded by Wellcome (208361/Z/17/Z), the MRC (MR/S009213/1), and BBSRC (BB/P01948X/1, BB/R002517/1 and BB/S003339/1). M.C. is supported by the CNRS-MITI grant “Modélisation du vivant” 2020. W.I. is funded by NSF (MCB-2111728). E.F is a Sir Henry Dale Fellow jointly funded by the Wellcome Trust and Royal Society (104193/Z/14/Z and 104193/Z/14/B). M.G. would like to acknowledge the European Union’s Horizon 2020 research and innovation program under grant agreement H2020-PHC-08-2014-643381, TBVAC2020. This work was granted access to the HPC resources of CALMIP supercomputing center (under the allocation 2021-17036) and TGCC Joliot-Curie supercomputer (under the GENCI allocation A0110712941). This project made use of time on ARCHER2 and JADE2 granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim (http://hecbiosim.ac.uk), supported by EPSRC (grant no.EP/R029407/1).This project also used Athena and Sulis at HPC Midlands+, which were funded by the EPSRC on grants EP/P020232/1 and EP/T022108/1. This work has benefited from the facilities and expertise of the Biophysical and Structural Chemistry platform (BPCS) at IECB, CNRS UAR3033, INSERM US001, and Bordeaux University. We thank the University of Warwick Scientific Computing Research Technology Platform for computational access.We acknowledge Life Science Editors for proofreading the manuscript. We thank C. Cooper for fruitful discussions and M. Costic for comments on the manuscript.