Biology subjects

Mathema, B.

Publications and source records attributed to Mathema, B..

6 recordsLinked to original sources

bioRxiv2024

Drug resistant Mycobacterium tuberculosis strains have altered cell envelope hydrophobicity that influences infection outcomes in human macrophages

Mycobacterium tuberculosis (M.tb), the causative agent of tuberculosis (TB), is considered one of the top infectious killers in the world. In recent decades, drug resistant (DR) strains of M.tb have emerged that make TB even more difficult to treat and pose a threat to public health. M.tb has a complex cell envelope that provides protection to the bacterium from chemotherapeutic agents. Although M.tb cell envelope lipids have been studied for decades, very little is known about how their levels change in relation to drug resistance. In this study, we examined changes in the cell envelope lipids [namely, phthiocerol dimycocerosates (PDIMs)], glycolipids [phosphatidyl-myo-inositol mannosides (PIMs)], and the PIM associated lipoglycans [lipomannan (LM); mannose-capped lipoarabinomannan (ManLAM)] of 11 M.tb strains that range from drug susceptible (DS) to multi-drug resistant (MDR) to pre-extensively drug resistant (pre-XDR). We show that there was an increase in the PDIMs:PIMs ratio as drug resistance increases, and provide evidence of PDIM species only present in the DR-M.tb strains studied. Overall, the LM and ManLAM cell envelope levels did not differ between DS- and DR-M.tb strains, but ManLAM surface exposure proportionally increased with drug resistance. Evaluation of host-pathogen interactions revealed that DR-M.tb strains have decreased association with human macrophages compared to DS strains. The pre-XDR M.tb strain with the largest PDIMs:PIMs ratio had decreased uptake, but increased intracellular growth rate at early time points post-infection when compared to the DS-M.tb strain H37Rv. These findings suggest that PDIMs may play an important role in drug resistance and that this observed increase in hydrophobic cell envelope lipids on the DR-M.tb strains studied may influence M.tb-host interactions. AUTHOR SUMMARYTuberculosis (TB) is a leading cause of death due to an infectious organism and is caused by the bacteria Mycobacterium tuberculosis (M.tb). Drug resistant (DR) forms of TB have emerged over the past few decades which make the disease even more difficult to diagnose and treat. Currently, there is very little known about how the bacteria changes as it becomes more drug resistant. Here, we used biochemical techniques to study differences in the M.tb cell envelope across drug resistance categories. We examined 11 M.tb strains that range from drug-susceptible (DS) to multi-drug resistant (MDR) to pre-extensively drug resistant (pre-XDR) and observed that levels of hydrophobic phthiocerol dimycocerosates were increased and levels of hydrophilic higher-order phosphatidyl-myo-inositol mannosides were decreased in DR-M.tb strains compared to DS strains. We also found that DR-M.tb strains had decreased association with human macrophages, and that the pre-XDR-M.tb strain with the highest ratio of hydrophobic to hydrophilic lipids had decreased uptake but increased intracellular growth in macrophages at early timepoints after infection. Our study provides exciting insights into changes in the cell envelope composition of DR-M.tb strains and how these changes may influence infection outcomes in human macrophages.

microbiology↗

bioRxiv2021

What do we gain when tolerating loss? The information bottleneck, lossy compression, and detecting horizontal gene transfer

Most microbes have the capacity to acquire genetic material from their environment. Recombination of foreign DNA yields genomes that are, at least in part, incongruent with the vertical history of their species. Dominant approaches for detecting these transfers are phylogenetic, requiring a painstaking series of analyses including alignment and phylogenetic tree reconstruction. These traditional pan-genomic methods do not scale. Here we propose an unsupervised, alignment-free and tree-free technique based on the sequential information bottleneck (SIB), an optimization procedure designed to extract some portion of relevant information from one random variable conditioned on another. In our case, this joint probability distribution tabulates occurrence counts of k-mers against their genomes of origin with the expectation that recombination will create a strong signal that unifies certain sets of co-occuring k-mers. We conceptualize the technique as a rate-distortion problem, measuring distortion in the relevance information as k-mers are compressed into clusters based on their co-occurrence in the source genomes. The result is fast, model-free, lossy compression of k-mers into groups that learns tracts of shared genome sequence differentiating recombined elements from the vertically inherited core. We show that the technique yields a new recombination measure based purely on information, divorced from any biases and limitations inherent to alignment and phylogeny. SignificanceThe information bottleneck, a lossy compression technique borrowed from the information theoretic and Natural Langauge Processing literature, is well suited to detecting evolutionary patterns in sets of co-occuring k-mers. Here we show that we can detect simulated and real recombination events while highlighting a core set of k-mers that comprise the vertically inherited portion of any set of genomes. Moreover, the compressibility of any given set of genomes offers a new way to compare the pangenomes of clades across the microbial tree of life. In our application, the bottleneck is informed by genome origin, our relevance variable, but the technique is general. The information bottleneck can be used for any biological contingency matrix where the goal is to learn groups from unstructured data.

evolutionary biology↗

bioRxiv2019

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet there is no current consensus on how to measure genomic variation. We investigated the effects of variant identification approaches on transmission inferences for M. tuberculosis by comparing variants identified by five different groups in the same sequence data from a clonal outbreak. We then measured the performance of commonly used variant calling approaches in recovering variation in a simulated tuberculosis outbreak and tested the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant calling approaches used by different groups do not recover consistent sets of variants, often leading to conflicting transmission inferences. Further, performance in recovering true outbreak variation varied widely across approaches. Finally, stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. We conclude that measurements of genetic distance and phylogenetic structure are dependent on variant calling approach. Variant calling algorithms trained upon true sequence data outperform other approaches and enable inclusion of repetitive regions typically excluded from genomic epidemiology studies, maximizing the information gleaned from outbreak genomes.

genomics↗

bioRxiv2019

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

The transmission patterns of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases in 2017. Modeling TB transmission networks can provide insight into the nature and drivers of transmission, but incomplete and non-random sampling of TB cases can pose challenges to making inferences from epidemiologic and molecular data. We conducted a quantitative bias analysis to assess the effect of missing cases on a transmission network inferred from Mtb sequencing data on extensively drug-resistant (XDR) TB cases in South Africa. We tested scenarios in which cases were missing at random, differentially by clinical characteristics, or differentially by transmission (i.e., cases with many links were under or over-sampled). Under the assumption cases were missing at random, cases in the complete, modeled network would have had a mean of 20 or more transmission links, which is far higher than expected, in order to reproduce the observed, partial network. Instead, we found that the most likely scenario involved undersampling of high-transmitting cases, and further models provided evidence for superspreading behavior. This is, to our knowledge, the first study to define and assess the support for different mechanisms of missingness in a study of TB transmission. Our findings should caution interpretation of results of future studies of TB transmission in high-incidence settings, given the potential for biased sampling, and should motivate further research aimed at identifying the specific host, pathogen, or environmental factors contributing to superspreading.

epidemiology↗

bioRxiv2018

Genotypic clustering does not imply recent tuberculosis transmission in a high prevalence setting: A genomic epidemiology study in Lima, Peru

BackgroundWhole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based transmissibility study of 4,000 TB patients in Lima, Peru, we identified a large MIRU-VNTR Mtb cluster with a range of resistance phenotypes and studied host and bacterial factors contributing to its spread.\n\nMethodsWGS was performed on 61 of 148 isolates in the cluster. We compared transmission link inference using epidemiological or genomic data with and without the inclusion of controversial variants, and estimated the dates of emergence of the cluster and antimicrobial drug resistance acquisition events by generating a time-calibrated phylogeny. We validated our findings in genomic data from an outbreak of 325 TB cases in London. Using a larger set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages.\n\nFindingsFour isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95% HPD: 1945-1985). Isoniazid resistance arose once, whereas rifampicin resistance emerged subsequently at least three times. Amplification of other drug resistance occurred as recently as within the last year of sampling. High quality PE/PPE variants and indels added information for transmission inference. We identified five cluster-defining SNPs, including esxV S23L to be potentially contributing to transmissibility.\n\nInterpretationClusters defined by MIRU-VNTR typing, could be circulating for decades in a high-burden setting. WGS allows for an improved understanding of transmission, as well as bacterial resistance and fitness factors.\n\nFundingThe study was funded by the National Institutes of Health (Peru Epi study U19-AI076217 and K01-ES026835 to MRF). The funding sources had no role in any aspect of the study, manuscript or decision to submit it for publication.\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSUse of whole genome sequencing (WGS) to study tuberculosis (TB) transmission has proven to have higher resolution that traditional typing methods in low-burden settings. The implications of its use in high-burden settings are not well understood.\n\nAdded value of this studyUsing WGS, we found that TB clusters defined by traditional typing methods may be circulating for several decades. Genomic regions typically excluded from WGS analysis contain large amount of genetic variation that may affect interpretation of transmission events. We also identified five bacterial mutations that may contribute to transmission fitness.\n\nImplications of all the available evidenceAdded value of WGS for understanding TB transmission may be even higher in high-burden vs. low-burden settings. Methods integrating variants found in polymorphic sites and insertions and deletions are likely to have higher resolution. Several host and bacterial factors may be responsible for higher transmissibility that can be targets of intervention to interrupt TB transmission in communities.

microbiology↗

bioRxiv2018

Beyond the SNP threshold: identifying outbreak clusters using inferred transmissions

Whole genome sequencing (WGS) is increasingly used to aid in understanding pathogen transmission [1]. Very often the number of single nucleotide polymorphisms (SNPs) separating isolates collected during an epidemiological study are used to identify sets of cases that are potentially linked by direct transmission. However, there is little agreement in the literature as to what an appropriate SNP cut-off threshold should be, or indeed whether a simple SNP threshold is appropriate for identifying sets of isolates to be treated as \"transmission clusters\". The SNP thresholds that have been adopted for inferring transmission vary widely even for one pathogen. As an alternative to reliance on a strict SNP threshold, we suggest that the key inferential target when studying the spread of an infectious disease is the number of transmission events separating cases. Here we describe a new framework for deciding whether two pathogen genomes should be considered as part of the same transmission cluster, based jointly on the number of SNP differences and the length of time over which those differences have accumulated. Our approach allows us to probabilistically characterize the number of inferred transmission events that separate cases. We show how this framework can be modified to consider variable mutation rates across the genome (e.g. SNPs associated with drug resistance) and we indicate how the methodology can be extended to incorporate epidemiological data such as spatial proximity. We use recent data collected from tuberculosis studies from British Columbia, Canada and the Republic of Moldova to apply and compare our clustering method to the SNP threshold approach. In the British Columbia data, different cases break off from the main clusters as cut-off thresholds are lowered; the transmission-based method obtains slightly different clusters than the SNP cut-offs. For the Moldova data, straightforward application of the methods shows no appreciable difference, but when we take into account the fact that resistance conferring sites likely do not follow the same mutation clock as most sites due to selection, the transmission-based approach differs from the SNP cut-off method. Outbreak simulations confirm that our transmission based method is at least as good at identifying direct transmissions as a SNP cut-off. We conclude that the new method is a promising step towards establishing a more robust identification of outbreaks.

genomics↗