Combining comparative genomic analysis with machine learning reveals some promising diagnostic markers to identify five common pathogenic non-tuberculous mycobacteria

Abstract

Non-tuberculous mycobacteria (NTM) can cause various respiratory diseases and even death in severe cases, and its incidence has increased rapidly worldwide. To date, it's difficult to use routine diagnostic methods and strain identification to precisely diagnose various types of NTM infections. We combined systematic comparative genomics with machine learning to select new diagnostic markers for precisely identifying five common pathogenic NTMs (Mycobacterium kansasii, Mycobacterium avium, Mycobacterium intracellular, Mycobacterium chelonae, Mycobacterium abscessus). A panel including six genes and two SNPs (nikA, benM, codA, pfkA2, mpr, yjcH, rrl C2638T, rrl A1173G) was selected to simultaneously identify the five NTMs with high accuracy (> 90%). Notably, the panel only containing the six genes also showed a good classification effect (accuracy > 90%). Additionally, the two panels could precisely differentiate the five NTMs from M. tuberculosis (accuracy > 99%). We also revealed some new marker genes/SNPs/combinations to accurately discriminate any one of the five NTMs separately, which provided the possibility to diagnose one certain NTM infection precisely. Our research not only reveals novel promising diagnostic markers to promote the development of precision diagnosis in NTM infectious, but also provides an insight into precisely identifying various genetically close pathogens through comparative genomics and machine learning.

Fig. 1

Phylogenetic analysis of 123 NTM and 40 Mtb strains with complete genomes. The five common pathogenic NTMs are shown in different colours. The phylogenetic tree was constructed based on 289 751 core gene SNPs shared by these strains.

Fig. 2

Flower plot showing the core, dispensable, and species‐specific genes of the five NTM species. Mtb were included as controls. The flower plot displays the core gene cluster number (in the centre), the dispensable gene number (in the annulus), and the species‐specific gene number (in the petals) of the five NTM species. The numbers under the species name denote the core gene numbers of related species.

Fig. 3

Importance of the optimized gene/SNP combinations to identify the five common pathogenic NTM species using random forest models. A. Mean Decrease Gini coefficient of the optimized gene combinations. B. Mean Decrease Accuracy of the optimized gene combinations. C. Mean Decrease Gini coefficient of the optimized SNP combinations. D. Mean Decrease Accuracy of the optimized SNP combinations.

Fig. 4

Ensemble classification workflow for data generation and analysis of Gene/SNP panels for simultaneously discriminating the five common pathogenic NTM species. The multiclass classifier was proposed based on the above RF binary classifier (‘non‐Mka’ indicates the Mav, Min, Mch, Mab and Mtb strains; ‘non‐Mav’ indicates the Mka, Min, Mch, Mab and Mtb strains; ‘non‐Min’ indicates the Mka, Mav, Mch, Mab and Mtb strains; ‘non‐Mch’ indicates the Mka, Mav, Min, Mab and Mtb strains; ‘non‐Mab’ indicates the Mka, Mav, Min, Mch and Mtb strains). Confusion matrixes of the gene/SNP panels in the training set and test set were shown on the left and right, respectively.