{"id":60301,"date":"2016-12-22T03:00:48","date_gmt":"2016-12-22T03:00:48","guid":{"rendered":"http:\/\/healthmedicinet.com\/news\/the-variability-and-reproducibility-of-whole-genome-sequencing-technology-for-detecting-resistance-to-anti-tuberculous-drugs\/"},"modified":"2016-12-22T03:00:48","modified_gmt":"2016-12-22T03:00:48","slug":"the-variability-and-reproducibility-of-whole-genome-sequencing-technology-for-detecting-resistance-to-anti-tuberculous-drugs","status":"publish","type":"post","link":"http:\/\/healthmedicinet.com\/news\/the-variability-and-reproducibility-of-whole-genome-sequencing-technology-for-detecting-resistance-to-anti-tuberculous-drugs\/","title":{"rendered":"The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs"},"content":{"rendered":"

Advances in next-generation sequencing technology have expanded opportunities for genome analysis in the clinical laboratory. Determining resistance to anti-TB drugs by whole genome sequencing has been demonstrated as feasible and is being implemented in some specialist centres [6<\/span>]. For acceptance as a diagnostic tool to guide treatment of drug-resistant TB the sequencing platforms and analytical tools employed must be robust and reliable. Here we have investigated the performance of two commercial \u2018bench-top\u2019 next generation sequencing platforms and attempted to assess the robustness of a bioinformatics analysis pipeline with respect to variant calling, across sequencing replicates.<\/p>\n

The MiSeq and Ion PGM both proved satisfactory for determining drug-resistance profiles. Compared to Ion PGM, MiSeq sequence coverage was more uniform and was better represented in regions with high GC content. However, we did not investigate the impact of the different library preparation methods used (mechanical (MiSeq) and enzymatic (Ion PGM) processing). Sample quality and the mode or preparation have been shown to influence the depth of coverage in high GC regions [27<\/span>], and further work is required to investigate this. The Ion PGM platform has previously been used to characterise mutations in XDR-TB strains [6<\/span>], but the minimum read depth used to call alleles (fourfold) were less stringent than the tenfold coverage threshold adopted here.<\/p>\n

\n Samtools<\/em> and GATK<\/em> when used to process the raw sequence data produced diverse outputs but filtering based on coverage and allelic frequency led to almost complete agreement on resistance causing SNPs. There was, however, lower concordance between the final sets of indels. As previously reported, the false discovery rate for Samtools<\/em> is higher than for GATK<\/em> and rises as coverage increases [28<\/span>]. A common strategy is to undertake dual analysis and consider the intersection of the Samtools<\/em> and GATK<\/em> derived SNPs but select only the GATK<\/em> indels [16<\/span>]. The high reproducibility of sequence data from replicate samples is reassuring as it affirms the validity of next-generation sequencing as a tool for investigating transmission events.<\/p>\n

Of the two rapid tools examined, the TBProfiler<\/em> gave 100% concordance with phenotypic DST results for INH, RIF, STR, ETB, ETH and the fluoroquinolones. Of the nine PZA-resistant isolates, known resistance SNPs were reported for seven isolates with an insertion and deletion observed for the remaining two. Possible novel resistance mutations were also observed for both the PAS-resistant isolates. The Mykrobe predictor<\/em> detected resistance for nine drugs, of which eight had DST results. Concordance was 100% for RIF, OFX and MOX, but resistance was missed for one or more isolates for the remaining five drugs. Misclassification of resistance of amikacin and capreomycin as susceptible has significant clinical implications as patients may be assigned treatment that is not effective for XDR-TB.<\/p>\n

The identification of a PAS resistance-related dfrA-thyA<\/em> double deletion in an XDR-TB sample highlights the need to look at non-SNP variants. Significantly, the laboratory platform being used may impact the detection of putative drug resistance. This is critical in XDR-TB and resistance beyond XDR-TB where use of drugs like PAS may make the difference in providing a life-saving effective regimen of at least five drugs [29<\/span>]. Large deletions and other structural variants may be detected by applying a combination of complementary approaches (pair-end, split-read and depth of coverage) followed by a validation process involving de novo assembly of bordering reads and re-alignment to the reference genome [10<\/span>, 16<\/span>, 24<\/span>]. However, high genome-wide sequence coverage is necessary to perform such analyses.<\/p>\n

As expected the genotypic profiling was concordant with the phenotypic determination of drug-resistance levels confirming the reliability and robustness of the selected genes and mutations as predictors of resistance for almost all drugs tested; with discrepancies still being noticed for PZA and PAS due to lack of enough information on their mechanism of action [12<\/span>, 30<\/span>]. Surprisingly, no discrepancies were found for EMB, a drug known to have low correlation between the emb<\/em> genes and phenotypic resistance [12<\/span>].<\/p>\n","protected":false},"excerpt":{"rendered":"

Advances in next-generation sequencing technology have expanded opportunities for genome analysis in the clinical laboratory. Determining resistance to anti-TB drugs by whole genome sequencing has been demonstrated as feasible and is being implemented in some specialist centres [6]. For acceptance as a diagnostic tool to guide treatment of drug-resistant TB the sequencing platforms and analytical […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-60301","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts\/60301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/comments?post=60301"}],"version-history":[{"count":0,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/posts\/60301\/revisions"}],"wp:attachment":[{"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/media?parent=60301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/categories?post=60301"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/healthmedicinet.com\/news\/wp-json\/wp\/v2\/tags?post=60301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}