Current Microbiology

, Volume 67, Issue 3, pp 362–371 | Cite as

Comparison of Gene Expression Profiles Between Pansensitive and Multidrug-Resistant Strains of Mycobacterium tuberculosis

  • K. Peñuelas-Urquides
  • L. González-Escalante
  • L. Villarreal-Treviño
  • B. Silva-Ramírez
  • D. J. Gutiérrez-Fuentes
  • R. Mojica-Espinosa
  • C. Rangel-Escareño
  • L. Uribe-Figueroa
  • G. M. Molina-Salinas
  • J. Dávila-Velderrain
  • F. Castorena-Torres
  • M. Bermúdez de León
  • S. Said-Fernández


Mycobacterium tuberculosis has developed resistance to anti-tuberculosis first-line drugs. Multidrug-resistant strains complicate the control of tuberculosis and have converted it into a worldwide public health problem. Mutational studies of target genes have tried to envisage the resistance in clinical isolates; however, detection of these mutations in some cases is not sufficient to identify drug resistance, suggesting that other mechanisms are involved. Therefore, the identification of new markers of susceptibility or resistance to first-line drugs could contribute (1) to specifically diagnose the type of M. tuberculosis strain and prescribe an appropriate therapy, and (2) to elucidate the mechanisms of resistance in multidrug-resistant strains. In order to identify specific genes related to resistance in M. tuberculosis, we compared the gene expression profiles between the pansensitive H37Rv strain and a clinical CIBIN:UMF:15:99 multidrug-resistant isolate using microarray analysis. Quantitative real-time PCR confirmed that in the clinical multidrug-resistant isolate, the esxG, esxH, rpsA, esxI, and rpmI genes were upregulated, while the lipF, groES, and narG genes were downregulated. The modified genes could be involved in the mechanisms of resistance to first-line drugs in M. tuberculosis and could contribute to increased efficiency in molecular diagnosis approaches of infections with drug-resistant strains.


Tuberculosis Intergenic Region H37Rv Strain BACTEC MGIT Pyrazinoic Acid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by the National Council for Science and Technology (CONACyT), Mexico, Grant No. 99792.

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • K. Peñuelas-Urquides
    • 1
    • 2
  • L. González-Escalante
    • 1
    • 2
  • L. Villarreal-Treviño
    • 2
  • B. Silva-Ramírez
    • 3
  • D. J. Gutiérrez-Fuentes
    • 4
  • R. Mojica-Espinosa
    • 4
  • C. Rangel-Escareño
    • 4
  • L. Uribe-Figueroa
    • 4
  • G. M. Molina-Salinas
    • 5
  • J. Dávila-Velderrain
    • 6
    • 7
  • F. Castorena-Torres
    • 8
    • 9
  • M. Bermúdez de León
    • 1
  • S. Said-Fernández
    • 10
  1. 1.Departamento de Biología MolecularCentro de Investigación Biomédica del Noreste, Instituto Mexicano del Seguro SocialMonterreyMexico
  2. 2.Fac. de Biología, Universidad Autónoma de Nuevo León, UANLAv. Universidad s/n Ciudad UniversitariaSan Nicolás de los GarzaMexico
  3. 3.Departamento de Inmunogenética, Centro de Investigación Biomédica del NoresteInstituto Mexicano del Seguro SocialMonterreyMexico
  4. 4.Instituto Nacional de Medicina GenómicaMexicoMexico
  5. 5.Departamento de Farmacognosia, Centro de Investigación Biomédica del NoresteInstituto Mexicano del Seguro SocialMonterreyMexico
  6. 6.Centro de Ciencias de la Complejidad C3Universidad Nacional Autónoma de MéxicoMexicoMexico
  7. 7.Instituto de EcologíaUniversidad Nacional Autónoma de MéxicoMexicoMexico
  8. 8.Escuela de Biotecnología y Alimentos, Tecnológico de MonterreyMonterreyMexico
  9. 9.Cátedra de Cardiología y Medicina VascularEscuela de Medicina, Tecnológico de MonterreySan Pedro Garza GarcíaMexico
  10. 10.Departamento de Bioquímica y Medicina MolecularFacultad de Medicina, Universidad Autónoma de Nuevo LeónMonterreyMexico

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