Abstract
Genomic tools have revealed genetically diverse pathogens within some hosts. Within-host pathogen diversity, which we refer to as “complex infection”, is increasingly recognized as a determinant of treatment outcome for infections like tuberculosis. Complex infection arises through two mechanisms: within-host mutation (which results in clonal heterogeneity) and reinfection (which results in mixed infections). Estimates of the frequency of within-host mutation and reinfection in populations are critical for understanding the natural history of disease. These estimates influence projections of disease trends and effects of interventions. The genotyping technique MLVA (multiple loci variable-number tandem repeats analysis) can identify complex infections, but the current method to distinguish clonal heterogeneity from mixed infections is based on a rather simple rule. Here we describe ClassTR, a method which leverages MLVA information from isolates collected in a population to distinguish mixed infections from clonal heterogeneity. We formulate the resolution of complex infections into their constituent strains as an optimization problem, and show its NP-completeness. We solve it efficiently by using mixed integer linear programming and graph decomposition. Once the complex infections are resolved into their constituent strains, ClassTR probabilistically classifies isolates as clonally heterogeneous or mixed by using a model of tandem repeat evolution. We first compare ClassTR with the standard rule-based classification on 100 simulated datasets. ClassTR outperforms the standard method, improving classification accuracy from 48% to 80%. We then apply ClassTR to a sample of 436 strains collected from tuberculosis patients in a South African community, of which 92 had complex infections. We find that ClassTR assigns an alternate classification to 18 of the 92 complex infections, suggesting important differences in practice. By explicitly modeling tandem repeat evolution, ClassTR helps to improve our understanding of the mechanisms driving within-host diversity of pathogens like Mycobacterium tuberculosis.
Highlights
The genotyping technique known as MLVA, which identifies the number of copies of tandem repeat regions at specific preselected loci, has benefited the study of many bacteria
Within-host heterogeneity of an infection can arise through two distinct mechanisms: within-host mutation and reinfection
While current genotyping techniques based on MLVA can identify within-host diversity, standard methods for classifying the mechanism driving this diversity have limitations
Summary
The genotyping technique known as MLVA (multiple loci variable-number tandem repeats analysis), which identifies the number of copies of tandem repeat regions at specific preselected loci, has benefited the study of many bacteria. Genetic and genomic approaches for interrogating the composition of Mycobacterium tuberculosis infections occurring within individuals has in some settings revealed an impressive degree of complexity, reflecting both within-host mutation and reinfection as distinct routes to complexity [2]. These complex infections, especially those comprising both drug-susceptible and drug-resistant isolates (i.e. heteroresistance), can undermine the effective treatment of individual patients [3,4,5], complicate laboratory testing and evaluation of treatment programs [2], and affect the transmission dynamics of disease in communities [6, 7]. Accurate estimates of the prevalence of complex infections among tuberculosis patients and new methods for distinguishing the relative contributions of within-host mutation and reinfection to withinhost diversity would be valuable
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