Report on Mycobacterium tuberculosis Genes Modulating Long-Term Survival under Antibiotic Stress

Abstract

In 2023, Mycobacterium tuberculosis (Mtb) caused 10.6 million new tuberculosis (TB) cases and 1.3 million deaths, posing a significant global health challenge aligned with the Sustainable Development Goal (SDG) 3: Good Health and Well-being. Despite WHO-recommended treatments, relapse occurs in up to 15% of patients due to phenotypically tolerant bacterial subpopulations known as persisters. This study employed a genome-wide transposon library of Mtb challenged with rifampicin (RIF) and streptomycin (STM) to identify genes influencing persister frequency, tolerance, and resistance, highlighting antibiotic-specific survival mechanisms.

Introduction

Tuberculosis remains a leading cause of infectious disease mortality worldwide, undermining SDG 3. The prolonged multi-drug regimen required for TB treatment is often compromised by the presence of persister and tolerant bacterial subpopulations, which evade antibiotic killing without genetic resistance. Understanding the genetic basis of these phenotypes is critical to developing more effective therapies and reducing TB burden.

Background on Persistence and Tolerance

• Persisters are phenotypically resistant subpopulations that survive antibiotic treatment and contribute to relapse.
• Tolerance refers to a non-heritable, reduced antibiotic susceptibility of the entire bacterial population.
• Resistance involves heritable genetic mutations leading to increased minimum inhibitory concentrations (MICs).
• Toxin-antitoxin (TA) systems and metabolic pathways have been implicated in modulating persistence and tolerance.

Research Objective

This study aimed to identify Mtb genes modulating persister frequency and antibiotic tolerance/resistance by exposing a saturated transposon mutant library to RIF and STM, two antibiotics with distinct mechanisms of action.

Methods

Experimental Design

1. Generation of a Himar1 transposon library with approximately 500,000 mutants covering 84% of available TA insertion sites in the Mtb genome.
2. Exposure of the library to 3× MIC of RIF (0.15 µg/ml) and 10× MIC of STM (5 µg/ml) to induce biphasic kill curves characteristic of persisters.
3. Enumeration of survivors and resistant mutants over 14 days to monitor selection dynamics.
4. Extraction and sequencing of genomic DNA from input and output samples to analyze transposon insertion frequencies using TRANSIT software.
5. Validation of selected gene knockouts (ΔprpD and ΔfadE5) through time-kill assays and MIC determination.

Results

Selection of Mutants under Antibiotic Pressure

Both RIF and STM treatments resulted in rapid initial killing followed by a plateau phase, indicating enrichment of persister and tolerant populations. Resistant mutants increased to 16% (RIF) and 25% (STM) of survivors by day 14, primarily due to spontaneous SNPs not linked to transposon insertions.

Identification of Genes Affecting Survival

• RIF treatment identified 52 mutants with reduced fitness and 13 with enhanced fitness.
• STM treatment identified 23 mutants with reduced fitness and 3 with enhanced fitness.
• Minimal overlap was observed between RIF and STM-selected mutants, indicating antibiotic-specific survival mechanisms.
• Genes associated with membrane integrity and cell wall assembly (e.g., cpsA/lytR/Psr) were critical for RIF survival.
• Toxin-antitoxin genes were enriched among STM-sensitive mutants, highlighting their role in persistence.
• Enhanced survival under RIF was linked to mutations in the methyl citrate cycle genes (prpC, prpD, prpR) and potassium uptake system genes (ceoB, Rv2690c).
• Enhanced survival under STM involved the resistance-associated gene gidB and anion transport genes (Rv3679c, Rv3680c).

Validation of Key Mutants

• ΔprpD mutants exhibited increased survival under RIF without changes in MIC, indicating tolerance rather than resistance.
• ΔfadE5 mutants showed reduced survival under RIF, confirming the role of lipid metabolism in antibiotic fitness.

Discussion

Implications for Tuberculosis Control and SDGs

This study advances understanding of the genetic determinants of Mtb persistence and tolerance, directly contributing to SDG 3 by informing strategies to improve TB treatment efficacy and reduce relapse rates. Identifying antibiotic-specific mechanisms underscores the need for tailored therapeutic approaches.

Role of the Mycobacterial Cell Wall

• Genes involved in arabinogalactan assembly and peptidoglycan synthesis are crucial for maintaining cell wall integrity and RIF tolerance.
• Disruption of these genes increases cell wall permeability, enhancing RIF susceptibility but not affecting STM sensitivity.

Toxin-Antitoxin Systems in Persistence

• TA modules significantly contribute to STM tolerance by inducing reversible bacteriostasis.
• Multiple independent TA systems in Mtb regulate stress responses and persistence, representing potential drug targets.

Metabolic Pathways Influencing Antibiotic Tolerance

• Disruption of the methyl citrate cycle promotes RIF tolerance by altering propionate metabolism and inducing growth arrest.
• Potassium uptake system disruption (Trk system) also enhances RIF survival, possibly by inducing non-replicating states.

Summary and Conclusions

The genome-wide transposon screen identified a diverse set of Mtb genes modulating long-term survival under RIF and STM treatment, including genes linked to persistence, tolerance, and resistance. The antibiotic-specific nature of these genes highlights the complexity of Mtb survival strategies. Validation of ΔprpD and ΔfadE5 mutants confirmed their roles in tolerance phenotypes without altering resistance profiles. Several identified genes have clinical relevance, offering promising targets for novel TB therapies. These findings support global efforts under the Sustainable Development Goals to combat tuberculosis by improving treatment outcomes and reducing disease burden.

References

Sequence data supporting this study are available at the NCBI Sequence Read Archive under accession number PRJNA1102339.

1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted in the Article

1. SDG 3: Good Health and Well-being
   • The article discusses tuberculosis (TB), a major infectious disease causing 10.6 million new cases and 1.3 million deaths in 2023, directly relating to the goal of ensuring healthy lives and promoting well-being for all ages.
   • Focus on antibiotic resistance, persistence, and tolerance in Mycobacterium tuberculosis (Mtb) addresses challenges in combating infectious diseases.
   • Research on genetic factors modulating survival of Mtb under antibiotic treatment supports efforts to improve treatment outcomes and reduce mortality.
2. SDG 9: Industry, Innovation and Infrastructure
   • The use of genome-wide transposon library screening and next-generation sequencing represents innovation in scientific research and infrastructure development.
   • Identification of novel gene targets for drug development aligns with fostering innovation in health technologies.
3. SDG 17: Partnerships for the Goals
   • The article references collaboration among multiple researchers and institutions, and data sharing (e.g., NCBI Sequence Read Archive), supporting global partnerships in research and development.

2. Specific Targets Under Those SDGs Identified Based on the Article’s Content

1. Under SDG 3: Good Health and Well-being
   • Target 3.3: End the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases.
   • Target 3.8: Achieve universal health coverage, including access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.
   • Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries.
2. Under SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including encouraging innovation and substantially increasing the number of research and development workers.
3. Under SDG 17: Partnerships for the Goals
   • Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation.
   • Target 17.8: Fully operationalize the technology bank and science, technology and innovation capacity-building mechanism for least developed countries.

3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets

1. For SDG 3 Targets:
   • Indicator 3.3.1: Number of new tuberculosis cases per 100,000 population.
   • Indicator 3.3.2: Tuberculosis mortality rate per 100,000 population.
   • Indicator 3.b.1: Proportion of the population with access to affordable essential medicines on a sustainable basis.
   • The article provides data on TB incidence (10.6 million cases) and mortality (1.3 million deaths) in 2023, which correspond to these indicators.
2. For SDG 9 Targets:
   • Indicator 9.5.1: Research and development expenditure as a proportion of GDP.
   • Indicator 9.5.2: Researchers (in full-time equivalent) per million inhabitants.
   • The article implies progress in scientific research through the use of advanced genomic techniques and identification of novel drug targets.
3. For SDG 17 Targets:
   • Indicator 17.6.1: Fixed Internet broadband subscriptions per 100 inhabitants, by speed.
   • Indicator 17.8.1: Proportion of individuals using the Internet.
   • The article mentions data sharing through NCBI Sequence Read Archive and international collaboration, implying progress in technology and knowledge exchange.

4. Table of SDGs, Targets, and Indicators Relevant to the Article

Source: nature.com