Drug-resistant tuberculosis viewed from bacterial and host genomes

Ben-Kahla, Imen; Al‐Hajoj, Sahal

doi:10.1016/j.ijantimicag.2016.07.010

Cited by 10 publications

(13 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In recent years, pathogen-host PPI networks reconstruction and pathogen-host signaling cross-talk modeling have attracted much attention from computational biologists [ 11 – 18 ], most of which focus on virus-host protein interactions. Comparatively, t the experimental studies on bacteria-host protein interactions are much less than that on virus-host protein interactions, partly because of the complex bacterial cell wall, which forms a strong permeability barrier to the mutual access of the bacterial genome and the host genome [ 19 ]. The two genomes could come across to physically interact only if bacterial proteins are located at the surface or membrane of bacterial cell, or bacterial proteins could transport or secret into the host cell.…”

Section: Introductionmentioning

confidence: 99%

“…Knowledge transfer between two co-evolving species is more credible than that between two evolutionarily distant species. The bacterial pathogen protein interaction networks of 1678 bacteria in STRING [ 7 ] provide rich information for us to study bacteria-host protein interactions, because many evidences have demonstrated that bacterial genome is co-evolving with its host genome [ 19 , 26 , 27 ]. In [ 27 ], it has been concluded that Mycobacterium tuberculosis complex (MTBC) has been anatomically co-evolving with modern humans for tens of thousands of years on the basis of the evidences of its origin in Africa, the congruence in phylogeography and the dating of major branching events.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the drug resistance of Mycobacterium tuberculosis (MTB) strains is also evolving with the host genome. In [ 19 ], it has been claimed that human genetic factors may play important roles in MTB drug resistance and different MTB lineages (e.g. lineage 1: Indo-Oceanic; lineage 2: East Asian; lineage 3: Central Asian; lineage 4: Euro American; lineage 7: Ethiopia) acquire different levels of drug resistance.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the interaction between bacteria and host also somewhat contributes to bacterial drug resistance. As suggested in [ 19 ], the interaction of MTB with its macrophage microenvironment may play an important role in the risk of progression to drug-resistant TB. Meanwhile from the host side, polymorphisms within genes involved in macrophage activity ( SLC11A1 , VDR and HLA genes) have been reported to be associated with susceptibility to MTB drug resistance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

2018

View full text Add to dashboard Cite

BackgroundBacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date.MethodsIn this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l2-regularized logistic regression model.ResultsThe computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance.ConclusionsThe proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4873-9) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

2018

View full text Add to dashboard Cite

show abstract

“…Однако устойчивость к противотуберкулезным препаратам формируется в первую очередь в результате селекции микобактерий с мутациями, обеспечивающими выживание возбудителя в организме больного при воздействии химиотерапии. Во вторую очередь на возникновение резистентности влияют особенности внутренней среды макроорганизма, отвечающего комплексом разнообразных реакций (от молекулярного до организменного уровня) на туберкулезную инфекцию, но это вопрос, заслуживающий отдельного рассмотрения [3].…”

Section: Introductionunclassified

The Growth Rate Phenotypic Property of Mycobacterium Tuberculosis Clinical Strains: Dependence on Tuberculosis Localization, Treatment, Drug Susceptibility

Manicheva

Dogonadze

Melnikova

et al. 2018

Russian Journal of Infection and Immunity

View full text Add to dashboard Cite

The phenotypic properties of the M. tuberculosis strains obtained from patients with pulmonary or extra-pulmonary tuberculosis are determined by a complex set of factors: the genetic characteristics of the pathogen, its ability to adapt in vivo and in vitro, the influence of the host’s immune system and chemotherapy. The growth rate as the phenotypic property is the most accessible for the study of the host-pathogen relationships at the level of host/strain population interactions. The aim of the study is to assess in vitro of the growth rate of M. tuberculosis strains isolated from patients with pulmonary and extra-pulmonary tuberculosis: untreated and treated (with surgical and non-surgical treatment) and also sensitive and resistant isolates in comparison with the reference strain H37Rv. To estimate the growth rate of 116 clinical isolates we have used the modified method originally developed by von Groll and co-authors: to get the bacteria growth curve the fluorescence intensity of growing strains (with indicator resazurin) has been measured daily for 8 days in 96- well plate. The growth rate is determined as the slope of the growth curve. The mean values of the growth rate have been calculated in the following groups of patients: 1 — untreated patients with pulmonary tuberculosis (PT), respiratory material; 2 — non-surgical treated PT patients, respiratory material; 3 — surgical treated PT patients (mainly with chronic and hyperchronic process), respiratory material; 4 — patients like in 3rd group, surgical material; 5 — bone and joint tuberculosis (BJT), surgical material. In addition, groups of sensitive and resistant strains have been examined, but there are no significant differences in growth rates. It has been obtained that the growth rate of strains isolated from the PT patients is higher than in BJT patients: it can be explained less favorable conditions for the pathogen vegetation in the BJT. In the case of a closed tuberculous lesion where the pathogen transmission to another host is impossible, then the selection of strains with the property to survive in the tissues of the osteoarticular system is impossible too, therefor it should be observed only an adaptation of the pathogen strain population to the individual host. The growth rate of isolates from untreated PT patients is higher than that of the treated ones. Comparison of the growth parameters of only MDR strains 1–5 groups to eliminate the influence of the sensitivity/resistance has resulted in the same conclusions. We suggest that the decrease in the growth rate of strains from the treated PT patients is in not only result of the treatment, but also is conditioned by adaptation of the pathogen to its external environment, which is the internal environment of the macroorganism. To confirm this assumption, the bacterial load of 1,083 diagnostic specimens grouped in a similar manner has been estimated, taking into account only MDR/XDR strains. In the group of treated patients the frequency of high bacterial load (CFU ≥ 100) reached 52.5–63.8% that shows the conserved fitness of bacteria in such patients. The mean values of the growth rate of the strain H37Rv non-adapted to the macroorganism (due to numerous passages on artificial media) are higher than in all groups of clinical strains. Thus, heterogeneity of phenotypic properties of M. tuberculosis clinical strains on the basis of growth rate has been obtained. The growth rate of M. tuberculosis clinical strains is depended on the tuberculosis localization (PT, BJT) and on the joint effect of patient treatment and pathogen adaptation to the host.

show abstract

Mycobacterial quorum quenching and biofilm inhibition potential of medicinal plants

Seaman

Oosthuizen

Gibango

et al. 2022

Medicinal Plants as Anti-Infectives

View full text Add to dashboard Cite

Drug-resistant tuberculosis viewed from bacterial and host genomes

Cited by 10 publications

References 117 publications

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis

The Growth Rate Phenotypic Property of Mycobacterium Tuberculosis Clinical Strains: Dependence on Tuberculosis Localization, Treatment, Drug Susceptibility

Mycobacterial quorum quenching and biofilm inhibition potential of medicinal plants

Contact Info

Product

Resources

About