Dataset Information

Direct transmission of within-host Mycobacterium tuberculosis diversity to secondary cases can lead to variable between-host heterogeneity without de novo mutation: A genomic investigation.

ABSTRACT:

Background

Whole genome sequencing (WGS) has enabled the development of new approaches to track Mycobacterium tuberculosis (Mtb) transmission between tuberculosis (TB) cases but its utility may be challenged by the discovery that Mtb diversifies within hosts. Nevertheless, there is limited data on the presence and degree of within-host evolution.

Methods

We profiled a well-documented Mtb transmission cluster with three pulmonary TB cases to investigate within-host evolution and describe its impact on recent transmission estimates. We used deep sequencing to track minority allele frequencies (<50·0% abundance) during transmission and standard treatment.

Findings

Pre-treatment (n = 3) and serial samples collected over 2 months of antibiotic treatment (n = 16) from all three cases were analysed. Consistent with the epidemiological data, zero fixed SNP separated all genomes. However, we identified six subclones between the three cases with an allele frequency ranging from 35·0% to 100·0% across sampling intervals. Five subclones were identified within the index case pre-treatment and shared with one secondary case, while only the dominant clone was observed in the other secondary case. By tracking the frequency of these heterogeneous alleles over the two-month therapy, we observed distinct signatures of drift and negative selection, but limited evidence for de novo mutations, even under drug pressure.

Interpretation

We document within-host Mtb diversity in an index case, which led to transmission of minority alleles to a secondary case. Incorporating data on heterogeneous alleles may refine our understanding of Mtb transmission dynamics. However, more evidence is needed on the role of transmission bottleneck on observed heterogeneity between cases.

SUBMITTER: Seraphin MN

PROVIDER: S-EPMC6796532 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.MethodsWe developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.ResultsTo demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.ConclusionsWe developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.