title: Inferred spoligoforest topology unravels spatially bimodal distribution of mutations in the DR region.
abstract: Biomarkers of Mycobacterium tuberculosis complex (MTBC) mutate over time. Among the biomarkers of MTBC, spacer oligonucleotide type (spoligotype) and mycobacterium interspersed repetitive unit (MIRU) patterns are commonly used to genotype clinical MTBC strains. In this study, we present an evolution model of spoligotype rearrangements using MIRU patterns to disambiguate the ancestors of spoligotypes. We use a large patient dataset from the United States Centers for Disease Control and Prevention (CDC) to generate this model. Based on the contiguous deletion assumption and rare observation of convergent evolution, we first generate the most parsimonious forest of spoligotypes, called a spoligoforest, using three genetic distance measures. An analysis of topological attributes of the spoligoforest and number of variations at the direct repeat (DR) locus of each strain reveals interesting properties of deletions in the DR region. First, we compare our mutation model to existing mutation models of spoligotypes and find that our mutation model produces as many within-lineage mutation events as other models, with slightly higher segregation accuracy. Second, based on our mutation model, the number of descendant spoligotypes follows a power law distribution. Third, contrary to prior studies, the power law distribution does not plausibly fit to the mutation length frequency. Moreover, we find that the total number of mutation events at consecutive spacers follows a spatially bimodal distribution. The two modes are spacers 13 and 40, which are hotspots for chromosomal rearrangements, and the change point is spacer 34, which is absent in most MTBC strains. Based on this observation, we built two alternative models for mutation length frequency: the Starting Point Model (SPM) and the Longest Block Model (LBM). Both models are plausibly good fits to the mutation length frequency distribution, as verified by the goodness-of-fit test. We also apply SPM and LBM to a dataset from Institut Pasteur de Guadeloupe and verify that these models hold for different strain datasets.
authors.affiliation: Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. ozcagc2@cs.rpi.edu
authors.forename: Cagri
authors.initials: C
authors.lastname: Ozcaglar
authors.forename: Amina
authors.initials: A
authors.lastname: Shabbeer
authors.forename: Natalia
authors.initials: N
authors.lastname: Kurepina
authors.forename: Nalin
authors.initials: N
authors.lastname: Rastogi
authors.forename: Bülent
authors.initials: B
authors.lastname: Yener
authors.forename: Kristin P
authors.initials: KP
authors.lastname: Bennett
mesh_terms: D000465:Algorithms; D015373:Bacterial Typing Techniques; D004269:DNA, Bacterial / Q000032:analysis / Q000737:chemistry; D030541:Databases, Genetic; D019143:Evolution, Molecular; D005798:Genes, Bacterial; D005819:Genetic Markers; D020071:Interspersed Repetitive Sequences / Q000235:genetics*; D008957:Models, Genetic; D009154:Mutation; D009169:Mycobacterium tuberculosis / Q000145:classification / Q000235:genetics*
chemical_list: D004269:DNA, Bacterial; D005819:Genetic Markers