Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact

Usage Information

Development and preclinical evaluation of next-generation ΔsigH-based live candidate vaccines
Garima Arora, Caden W. Munson, Mushtaq Ahmed, Vinay Shivanna, Annu Devi, Venkata S.R. Devireddy, Basil Antony, Shannan Hall-Ursone, Olga D. Gonzalez, Edward J. Dick Jr., Chinnaswamy Jagannath, Xavier Alvarez, Smriti Mehra, Shabaana A. Khader, Dhiraj K. Singh, Deepak Kaushal
Garima Arora, Caden W. Munson, Mushtaq Ahmed, Vinay Shivanna, Annu Devi, Venkata S.R. Devireddy, Basil Antony, Shannan Hall-Ursone, Olga D. Gonzalez, Edward J. Dick Jr., Chinnaswamy Jagannath, Xavier Alvarez, Smriti Mehra, Shabaana A. Khader, Dhiraj K. Singh, Deepak Kaushal
View: Text | PDF
Research Article Infectious disease Microbiology

Development and preclinical evaluation of next-generation ΔsigH-based live candidate vaccines

  • Text
  • PDF
Abstract

To radically diminish tuberculosis (TB) incidence and mortality by 2035, as set out by the WHO End TB Strategy, there is a desperate need for improved TB therapies and a more effective vaccine against the deadly pathogen Mycobacterium tuberculosis. Aerosol vaccination with the MtbΔsigH mutant protects 2 species of nonhuman primates against lethal TB challenge by invoking vastly superior T and B cell responses in the lungs through superior antigen presentation and interferon conditioning. Since the Geneva Consensus on essential steps toward the development of live mycobacterial vaccines recommends that live TB vaccines incorporate at least 2 independent gene knockouts, we have now generated several rationally designed, double-knockout (DKO) and triple-knockout (TKO) mutants in Mtb, each containing the ΔsigH deletion. Here, we report preclinical studies in the rhesus macaque model of aerosol infection and SIV/HIV coinfection, aimed at assessing the safety of these MtbΔsigH-based DKOs and TKOs. We found that most of these mutant strains were attenuated in both immunocompetent and SIV-coinfected macaques, and combinatorial infection with these generated strong cellular immune responses in the lung, akin to MtbΔsigH. Aerosol infection with these KO strains elicited inducible bronchus-associated lymphoid tissue, which is a correlate of protection from TB.

Authors

Garima Arora, Caden W. Munson, Mushtaq Ahmed, Vinay Shivanna, Annu Devi, Venkata S.R. Devireddy, Basil Antony, Shannan Hall-Ursone, Olga D. Gonzalez, Edward J. Dick Jr., Chinnaswamy Jagannath, Xavier Alvarez, Smriti Mehra, Shabaana A. Khader, Dhiraj K. Singh, Deepak Kaushal

×

Usage data is cumulative from August 2025 through June 2026.

Usage JCI PMC
Text version 2,916 140
PDF 714 27
Figure 720 0
Table 92 0
Supplemental data 500 0
Citation downloads 322 0
Totals 5,264 167
Total Views 5,431

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2026 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts