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.
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Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME
Abstract The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME ( P henotype of R egulatory influences I ntegrated with M etabolism and E nvironment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs.
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- Award ID(s):
- 2042948
- NSF-PAR ID:
- 10343467
- Date Published:
- Journal Name:
- npj Systems Biology and Applications
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2056-7189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41540-021-00205-6
