Long-Read Sequencing with Hierarchical Clustering for Antiretroviral Resistance Profiling of Mixed Human Immunodeficiency Virus Quasispecies.

BACKGROUND: HIV infections often develop drug resistance mutations (DRMs), which can increase the risk of virological failure. However, it has been difficult to determine if minor mutations occur in the same genome or in different virions using Sanger sequencing and short-read sequencing methods. Oxford Nanopore Technologies (ONT) sequencing may improve antiretroviral resistance profiling by allowing for long-read clustering. METHODS: A new ONT sequencing-based method for profiling DRMs in HIV quasispecies was developed and validated. The method used hierarchical clustering of long amplicons that cover regions associated with different types of antiretroviral drugs. A gradient series of an HIV plasmid and 2 plasma samples was prepared to validate the clustering performance. The ONT results were compared to those obtained with Sanger sequencing and Illumina sequencing in 77 HIV-positive plasma samples to evaluate the diagnostic performance. RESULTS: In the validation study, the abundance of detected quasispecies was concordant with the predicted result with the R2 of > 0.99. During the diagnostic evaluation, 59/77 samples were successfully sequenced for DRMs. Among 18 failed samples, 17 were below the limit of detection of 303.9 copies/µL. Based on the receiver operating characteristic analysis, the ONT workflow achieved an F1 score of 0.96 with a cutoff of 0.4 variant allele frequency. Four cases were found to have quasispecies with DRMs, in which 2 harbored quasispecies with more than one class of DRMs. Treatment modifications were recommended for these cases. CONCLUSIONS: Long-read sequencing coupled with hierarchical clustering could differentiate the quasispecies resistance profiles in HIV-infected samples, providing a clearer picture for medical care.

A multi-omics investigation into the mechanisms of hyper-virulence in Mycobacterium tuberculosis.

Clinical manifestations of tuberculosis range from asymptomatic infection to a life-threatening disease such as tuberculous meningitis (TBM). Recent studies showed that the spectrum of disease severity could be related to genetic diversity among clinical strains of Mycobacterium tuberculosis (Mtb). Certain strains are reported to preferentially invade the central nervous system, thus earning the label "hypervirulent strains".However, specific genetic mutations that accounted for enhanced mycobacterial virulence are still unknown. We previously identified a set of 17 mutations in a hypervirulent Mtb strain that was from TBM patient and exhibited significantly better intracellular survivability. These mutations were also commonly shared by a cluster of globally circulating hyper-virulent strains. Here, we aimed to validate the impact of these hypervirulent-specific mutations on the dysregulation of gene networks associated with virulence in Mtb via multi-omic analysis. We surveyed transcriptomic and proteomic differences between the hyper-virulent and low-virulent strains using RNA-sequencing and label-free quantitative LC-MS/MS approach, respectively. We identified 25 genes consistently differentially expressed between the strains at both transcript and protein level, regardless the strains were growing in a nutrient-rich or a physiologically relevant multi-stress condition (acidic pH, limited nutrients, nitrosative stress, and hypoxia). Based on integrated genomic-transcriptomic and proteomic comparisons, the hypervirulent-specific mutations in FadE5 (g. 295,746 C >T), Rv0178 (p. asp150glu), higB (p. asp30glu), and pip (IS6110-insertion) were linked to deregulated expression of the respective genes and their functionally downstream regulons. The result validated the connections between mutations, gene expression, and mycobacterial pathogenicity, and identified new possible virulence-associated pathways in Mtb.

MIRU-profiler: a rapid tool for determination of 24-loci MIRU-VNTR profiles from assembled genomes of Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis (TB) resulted in an estimated 1.7 million deaths in the year 2016. The disease is caused by the members of Mycobacterium tuberculosis complex, which includes Mycobacterium tuberculosis, Mycobacterium bovis and other closely related TB causing organisms. In order to understand the epidemiological dynamics of TB, national TB control programs often conduct standardized genotyping at 24 Mycobacterial-Interspersed-Repetitive-Units (MIRU)-Variable-Number-of-Tandem-Repeats (VNTR) loci. With the advent of next generation sequencing technology, whole-genome sequencing (WGS) has been widely used for studying TB transmission. However, an open-source software that can connect WGS and MIRU-VNTR typing is currently unavailable, which hinders interlaboratory communication. In this manuscript, we introduce the MIRU-profiler program which could be used for prediction of MIRU-VNTR profile from WGS of M. tuberculosis. IMPLEMENTATION: The MIRU-profiler is implemented in shell scripting language and depends on EMBOSS software. The in-silico workflow of MIRU-profiler is similar to those described in the laboratory manuals for genotyping M. tuberculosis. Given an input genome sequence, the MIRU-profiler computes alleles at the standard 24-loci based on in-silico PCR amplicon lengths. The final output is a tab-delimited text file detailing the 24-loci MIRU-VNTR pattern of the input sequence. VALIDATION: The MIRU-profiler was validated on four datasets: complete genomes from NCBI-GenBank (n = 11), complete genomes for locally isolated strains sequenced using PacBio (n = 4), complete genomes for BCG vaccine strains (n = 2) and draft genomes based on 250 bp paired-end Illumina reads (n = 106). RESULTS: The digital MIRU-VNTR results were identical to the experimental genotyping results for complete genomes of locally isolated strains, BCG vaccine strains and five out of 11 genomes from the NCBI-GenBank. For draft genomes based on short Illumina reads, 21 out of 24 loci were inferred with a high accuracy, while a number of inaccuracies were recorded for three specific loci (ETRA, QUB11b and QUB26). One of the unique features of the MIRU-profiler was its ability to process multiple genomes in a batch. This feature was tested on all complete M. tuberculosis genome (n = 157), for which results were successfully obtained in approximately 14 min. CONCLUSION: The MIRU-profiler is a rapid tool for inference of digital MIRU-VNTR profile from the assembled genome sequences. The tool can accurately infer repeat numbers at the standard 24 or 21/24 MIRU-VNTR loci from the complete or draft genomes respectively. Thus, the tool is expected to bridge the communication gap between the laboratories using WGS and those using the conventional MIRU-VNTR typing.