ClassTR: Classifying Within-Host Heterogeneity Based on Tandem Repeats with Application to Mycobacterium tuberculosis Infections.

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

Vision screening results in a cohort of bhopal gas disaster survivors

Eye-related symptoms were prominent at the time of and soon after the 1984 Union Carbide gas disaster in Bhopal, India. We conducted a vision screening on the survivors to examine their current ocular status. Fifty-nine patients enrolled. We analysed the results from 48 patients (mean age 51 12 years) who had a documented history of gas exposure. The commonly reported symptoms were vision difficulties (n = 30), watering (n = 21) and headaches (n = 16). Thirty patients needed spectacles, 30 had cataracts and 17 had pinguecula. We found the prevalence of pinguecula to be significantly higher in this cohort. The need for vision care among this underserved population is highlighted

Supply-driven evolution: mutation bias and trait-fitness distributions can drive macro-evolutionary dynamics

Many well-documented macro-evolutionary phenomena still challenge current evolutionary theory. Examples include long-term evolutionary trends, major transitions in evolution, conservation of certain biological features such as hox genes, and the episodic creation of new taxa. Here, we present a framework that may explain these phenomena. We do so by introducing a probabilistic relationship between trait value and reproductive fitness. This integration allows mutation bias to become a robust driver of long-term evolutionary trends against environmental bias, in a way that is consistent with all current evolutionary theories. In cases where mutation bias is strong, such as when detrimental mutations are more common than beneficial mutations, a regime called “supply-driven” evolution can arise. This regime can explain the irreversible persistence of higher structural hierarchies, which happens in the major transitions in evolution. We further generalize this result in the long-term dynamics of phenotype spaces. We show how mutations that open new phenotype spaces can become frozen in time. At the same time, new possibilities may be observed as a burst in the creation of new taxa

Generalizations of the genomic rank distance to indels

MOTIVATION: The rank distance model represents genome rearrangements in multi-chromosomal genomes as matrix operations, which allows the reconstruction of parsimonious histories of evolution by rearrangements. We seek to generalize this model by allowing for genomes with different gene content, to accommodate a broader range of biological contexts. We approach this generalization by using a matrix representation of genomes. This leads to simple distance formulas and sorting algorithms for genomes with different gene contents, but without duplications. RESULTS: We generalize the rank distance to genomes with different gene content in two different ways. The first approach adds insertions, deletions and the substitution of a single extremity to the basic operations. We show how to efficiently compute this distance. To avoid genomes with incomplete markers, our alternative distance, the rank-indel distance, only uses insertions and deletions of entire chromosomes. We construct phylogenetic trees with our distances and the DCJ-Indel distance for simulated data and real prokaryotic genomes, and compare them against reference trees. For simulated data, our distances outperform the DCJ-Indel distance using the Quartet metric as baseline. This suggests that rank distances are more robust for comparing distantly related species. For real prokaryotic genomes, all rearrangement-based distances yield phylogenetic trees that are topologically distant from the reference (65% similarity with Quartet metric), but are able to cluster related species within their respective clades and distinguish the Shigella strains as the farthest relative of the Escherichia coli strains, a feature not seen in the reference tree. AVAILABILITY AND IMPLEMENTATION: Code and instructions are available at https://github.com/meidanis-lab/rank-indel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

PRINCE: Accurate approximation of the copy number of tandem repeats

Variable-Number Tandem Repeats (VNTR) are genomic regions where a short sequence of DNA is repeated with no space in between repeats. While a fixed set of VNTRs is typically identified for a given species, the copy number at each VNTR varies between individuals within a species. Although VNTRs are found in both prokaryotic and eukaryotic genomes, the methodology called multi-locus VNTR analysis (MLVA) is widely used to distinguish different strains of bacteria, as well as cluster strains that might be epidemiologically related and investigate evolutionary rates. We propose PRINCE (Processing Reads to Infer the Number of Copies via Estimation), an algorithm that is able to accurately estimate the copy number of a VNTR given the sequence of a single repeat unit and a set of short reads from a whole-genome sequence (WGS) experiment. This is a challenging problem, especially in the cases when the repeat region is longer than the expected read length. Our proposed method computes a statistical approximation of the local coverage inside the repeat region. This approximation is then mapped to the copy number using a linear function whose parameters are fitted to simulated data. We test PRINCE on the genomes of three datasets of Mycobacterium tuberculosis strains and show that it is more than twice as accurate as a previous method. An implementation of PRINCE in the Python language is freely available at https://github.com/WGS-TB/PythonPRINCE

fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing

SUMMARY: Fastlin is a bioinformatics tool designed for rapid Mycobacterium tuberculosis complex (MTBC) lineage typing. It utilizes an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms associated with specific MTBC lineages. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. AVAILABILITY AND IMPLEMENTATION: fastlin is freely available at https://github.com/rderelle/fastlin and can easily be installed using Conda

Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework

Background Bacterial pathogens exhibit an impressive amount of genomic diversity. This diversity can be informative of evolutionary adaptations, host-pathogen interactions, and disease transmission patterns. However, capturing this diversity directly from biological samples is challenging. Results We introduce a framework for understanding the within-host diversity of a pathogen using multi-locus sequence types (MLST) from whole-genome sequencing (WGS) data. Our approach consists of two stages. First we process each sample individually by assigning it, for each locus in the MLST scheme, a set of alleles and a proportion for each allele. Next, we associate to each sample a set of strain types using the alleles and the strain proportions obtained in the first step. We achieve this by using the smallest possible number of previously unobserved strains across all samples, while using those unobserved strains which are as close to the observed ones as possible, at the same time respecting the allele proportions as closely as possible. We solve both problems using mixed integer linear programming (MILP). Our method performs accurately on simulated data and generates results on a real data set of Borrelia burgdorferi genomes suggesting a high level of diversity for this pathogen. Conclusions Our approach can apply to any bacterial pathogen with an MLST scheme, even though we developed it with Borrelia burgdorferi, the etiological agent of Lyme disease, in mind. Our work paves the way for robust strain typing in the presence of within-host heterogeneity, overcoming an essential challenge currently not addressed by any existing methodology for pathogen genomics

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?

To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we investigate 2 state-of-the-art NPI effectiveness models and propose 6 variants that make different structural assumptions. In particular, we investigate how well NPI effectiveness estimates generalise to unseen countries, and their sensitivity to unobserved factors. Models which account for noise in disease transmission compare favourably. We further evaluate how robust estimates are to different choices of epidemiological parameters and data. Focusing on models that assume transmission noise, we find that previously published results are robust across these choices and across different models. Finally, we mathematically ground the interpretation of NPI effectiveness estimates when certain common assumptions do not hold

Evaluating the potential impact of enhancing HIV treatment and tuberculosis control programmes on the burden of tuberculosis

HIV has fuelled increasing tuberculosis (TB) incidence in sub-Saharan Africa. Better control of TB in this region may be achieved directly through TB programme improvements and indirectly through expanded use of antiretroviral therapy (ART) among those with HIV. We used a mathematical model of TB and HIV in South Africa to examine the potential epidemiological impact in scenarios involving improvements in three dimensions of TB programmes: coverage, diagnosis and treatment effectiveness, as well as expanded ART use through broadened eligibility. We projected the effect of alternative scenarios on TB prevalence, incidence and TB-related mortality over 20 years. Of the three dimensions of TB programme improvement, expanding coverage would produce the greatest reduction in TB burden. Compared with current performance, combined TB programme improvements were projected to decrease TB incidence by 30% over 5 years and 46% over 20 years, and decrease TB-related mortality by 45% over 5 years and 69% over 20 years. Expanded ART eligibility was projected to decrease TB incidence by 22% over 5 years and 45% over 20 years, and TB-related mortality by 22% over 5 years and 50% over 20 years. We found that over a 20-year horizon, TB-specific and HIV-specific programme changes contribute equally to incidence reductions, whereas the TB-specific changes produce a majority of the mortality benefits. An aggressive expansion of ART alongside traditional TB-specific control measures has the potential to greatly reduce TB burden, with the different elements of a combined approach having a synergistic effect in reducing long-term TB incidence and mortality