Composition and method for detection of multiple drug resistance in tuberculosis by multiplex short-amplicon based targeted NGS (TNGS) using ribonucleotide bases in the primers to increase target specificity

Abstract

This invention relates to a composition and method for the detection of Tuberculosis drug resistance by multiplex small amplicon based, targeted Next-Gen sequencing (tNGS) using ribonucleotide bases in the primers. It also provides a bioinformatic data analysis tool, for the prediction of drug resistance / susceptibility towards anti- tuberculosis therapy drugs. The invention provides an efficient, cost-effective solution for identifying drug resistance / susceptibility right after tuberculosis diagnosis. In addition, a short read NGS platform with massive parallel sequencing, along with data analysis of this invention, offers high sensitivity and specificity for detection of drug resistance.

Description

[0001] TITLE:

[0002] Composition and method for detection of multiple drug resistance in tuberculosis by multiplex short-amplicon based targeted NGS (tNGS) using ribonucleotide bases in the primers to increase target specificity.

[0003] FIELD OF INVENTION

[0004] The invention relates to the detection of drug resistance in Mycobacterium tuberculosis complex using targeted amplicon based Next Generation sequencing assay.

[0005] OBJECT OF THE INVENTION

[0006] The invention relates to a method of detecting drug resistant Mycobacterium tuberculosis complex using an amplicon based Next Gen sequencing assay. More particularly, the invention refers to the identification and detection of mutations in Mycobacterium tuberculosis complex that relate to drug resistance.

[0007] BACKGROUND OF THE INVENTION

[0008] Worldwide, tuberculosis (TB) is the 13th leading cause of death and the second leading infectious killer after COVID-19 according to World Health Organization (WHO). In 2021, an estimated 10.6 million people fell ill with tuberculosis (TB) worldwide. But TB is curable and preventable. Multi drug-resistant TB (MDR-TB) remains a public health crisis and a health security threat. Ending the TB epidemic by 2030 is among the health targets of the United Nations Sustainable Development Goals.

[0009] One key to effectively control tuberculosis and the spread of multi-drug resistant strains is accurate information pertaining to drug resistance and susceptibility. Next-generation sequencing (NGS) has the potential to effectively change global health and the management of TB. NGS can rapidly impact health care in the area of infectious disease diagnostics in low- and middle-income countries. The rapid evolution of knowledge about the genetic foundations of tuberculosis drug resistance makes sequencing a versatile technology platform for providing rapid, accurate, and actionable results for treating this disease.

[0010] Multi drug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis are increasing worldwide. M. tuberculosis does not naturally contain plasmids, and almost all cases of clinical drug-resistance are caused by singlenucleotide polymorphisms (SNPs) or small insertions / deletions in relevant genes. Heteroresistance, the coexistence of susceptible and resistant organisms in the same patient has created uncertainty in the treatment and diagnosis of tuberculosis. It is thought to be an important driver of multi-drug resistance in Mycobacterium tuberculosis. Tuberculosis heteroresistance is not a rare phenomenon, occurring in 9-30% of Mycobacterium tuberculosis populations studied, and has been identified in Mycobacterium tuberculosis populations with phenotypic resistance to first line-drugs (Isoniazid INH, Rifampicin RIF, Ethionamide ETO, and Streptomycin STM) and second-line fluoroquinolones (ofloxacin-OFX) and injectables (Amikacin, AMK). It is highly likely that drugresistant organisms are present in most tuberculosis lesions, even as very minor population components, given the high bacillary loads that are typically found in patients.

[0011] There is a need for methods, primers, and kits useful for rapid molecular assays to identify and / or quantify M. tuberculosis susceptible or resistant to any given antituberculosis drug. In particular, there is a need for compositions and methods useful for detecting and / or quantifying minor resistance variant subpopulations in clinical samples early in therapy to allow for effective treatment of tuberculosis. The background information herein below relates to the present disclosure but is not necessarily prior art.

[0012] Advances in Next Gen-sequencing (NGS) technology have enabled the routine use of NGS for both targeted NGS and Whole genome sequencing (WGS) of Mycobacterium tuberculosis complex (MTBC) samples, especially in high resource settings. Currently, WGS is generally performed only on strains grown in culture due to the need for a relatively high quantity of good quality DNA to generate full WGS data for a given sample. There have also been attempts to perform WGS directly from sputum, results have been variable as the assay could not predict drug resistance due to a high level of human DNA contamination in the samples. WGS has been recommended for global use by the WHO since 2018. Due to concerns of cost and availability of infrastructure it has not been mandated in the guidelines. There are several technologies that exist in this field of invention. However, those are technically cumbersome, time consuming, challenging or expensive for low- and medium-income countries. Some of those are Mycobacterial DNA Extraction for Whole-Genome Sequencing from Early Positive Liquid (MGIT) Cultures (1), SPIT SEQ by Medgenome (2), QTB® by Haystack Analytics (3). These whole genome sequencing (WGS) based technology are technically cumbersome and also expensive. Targeted Next Gen sequencing (tNGS) based on short read sequencing such as Deeplex® Myc-TB (4) is technically challenging and expensive for low- and / or medium-income countries. A composition for detecting Mycobacterium tuberculosis complex and a drug-resistant genotype based on NGS, comprising amplification of multiple primer sets (5) that requires DNA fragmentation and repair for library preparation; Mycobacterium tuberculosis drug resistance detection kit and method based on NGS technology (6) where the sample has been collected from sputum and has ample possibility for non-specific amplification; early detection of drug-resistant mycobacterium tuberculosis (7) that covers regions of interest from 8 genes across the genome; single moleculeoverlapping read analysis for minor variant mutation detection in pathogen samples (8) in which assay covers regions of interest from 6 genes for SNP and indel detection. Most of the existing technologies have limitations such as target specificity, assay coverage with respect to number of genes and drugs, technically challenging, cost etc. The small amplicon, short read targeted Next-Gen sequencing based detection of this invention provides a target specific cost effective solution.

[0013] SUMMARY:

[0014] Next-generation sequencing (NGS) has modernized and provided insights into the genetic diversity of Mycobacterium tuberculosis (MTB), that is crucial for the identification of drug-resistant strains, enabling rapid and accurate tailoring of treatment. However, the high cost and the technical complexities of NGS currently limit its widespread use. This invention provides a cost-effective amplicon based targeted NGS assay for identifying drug resistance / susceptibility right after tuberculosis diagnosis. It relates to composition, method and kit for the detection of resistance conferring genotype of Mycobacterium tuberculosis complex. The method of this invention offers the flexibility of adding new assays to incorporate new mutations of relevance from future WHO catalogue / Indian catalogue of mutations, for the same or new drugs in future designs. In addition, a computer based bioinformatics tool was developed to analyse the resulted sequencing data of this invention.

[0015] BRIEF DESCRIPTION OF THE DRAWINGS:

[0016] Figure 1 : Schematic representation of rhAmpSeq technology used for NGS based sequencing of TB drug resistance targets

[0017] Figure 2: Data analysis flow chart for variant calling

[0018] DETAILED DESCRIPTION:

[0019] The experimental details are illustrated by the following examples:

[0020] Example 1:

[0021] Selection of targets for drug resistance tuberculosis (DRTB) panel

[0022] World health organisation (WHO) released the first catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance in 2021 (9) of 17396 variants. These are categorized based on final confidence grading a. Associated with resistance (Assoc w R): 196 variants b. Associated with resistance-interim (Assoc w R-Interim): 1004 variants c. Not associated with resistance (Not assoc w R): 213 d. Not associated with resistance-interim (Not assoc w R- Interim): 33 e. Uncertain significance: 15910 f. Combo: 40

[0023] Including all mutations from categories a. and b. above, would have been an intuitive approach, but would add to the cost. In order to have a cost effective solution using a robust technology such as tNGS for tuberculosis drug resistance prediction, a smaller targeted panel has been designed by including: i) all variants “Assoc w R”, ii) selecting a subset of the variants from category “Assoc w R- Interim”, iii) picking a few from “Uncertain significance” based on literature (10, 11) evidence for Bedaquiline and / or Clofazamine resistance and iv) Adding 3 variants from the Indian TB mutation catalogue (12) specifically for Bedaquiline that are associated with resistance.

[0024] For the selection of a subset from “Assoc w R-Interim”, we used an additional filter of:

[0025] 1. “Assoc w R” in Initial Confidence Grading for all drugs except Pyrazinamide (PZA)

[0026] 2. “Assoc w R” or “Assoc w R-Interim” in Initial Confidence Grading for drug PZA encompassing gene pncA

[0027] The 321 variants thus chosen (table 1), encompassed 17 gene targets of Mycobacterium tuberculosis for 15 drugs (Table 2).

[0028] Table 1: List of 321 variants with coordinates within our assay for the prediction of drug resistance to various drugs (LFX: Levofloxacin, MFX: Moxifloxacin, RIF: Rifampicin, BDQ: Bedaquiline, CFZ: Clofazamine, STM: Streptomycin, LZD: Linezolid, AMK: Amikacin, KAN: Kanamycin, CAP: Capreomycin, ETO: Ethionamide, INH: Isoniazid, PZA: Pyrazinamide, DLM: Delaminid, EMB: Ethambutol). The nucleotide base for both reference (wild type) and mutant alleles for variants in the coding region, corresponds to that of the (+) strand. For insertions and deletions upstream of coding genes or in rRNA genes, the nucleotide base is of the respective strand. Numbers 1) and 2) mentioned in the column “WHO Final confidence grading” are based on WHO nomenclature.

[0029] Table 2: Drug list and their respective gene targets (9, 13)

[0030] Example 2:

[0031] Assay designed using IDT rhAmpSeq technology: a. The genomic sequences of the 17 genes (table 2) were extracted from Mycobacterium tuberculosis H37Rv, complete genome NC000962.3 and the genomic coordinates of the SNP / indel reported in WHO were verified. Briefly, the nucleotide base call (A / T / G / C) at the respective genomic coordinate of the reference genome was cross-checked to that listed in the WHO catalogue under ref nt. b. The verified coordinates were provided to IDT to design amplicon panel based on proprietary IDT rhAmpSeq PCR chemistry. c. An in- silico based verification for assay specificity for the primers & assay design for Mtb genome was performed to minimize non-specific amplification from the host DNA and other non-tuberculous bacteria that may be present in respiratory infections. Non-specificity was cross checked with Human genome sequence and genome sequences from the following microbes: Streptococcus pneumoniae, Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Haemophilus influenzae, Moraxella catarrhalis, Neisseria meningitidis and Veillonella parvula d. The rhAmpSeq system leverages RNase H-dependent PCR (rhAmp PCR), a novel technology developed at IDT, to improve target specificity and reduce both off-target amplification and primer-dimer formation. rhAmp primers contain a 3’ blocking modification and a single RNA base. When rhAmp primers anneal to their specific target, a thermostable RNase H2 enzyme cleaves the RNA base, activating the primer and allowing extension to occur (Fig 1). e. After a few iterations of in-silico designing, the final design comprising of 41 assays (table 3) in two pools of 31 assays and 10 assays respectively, was synthesized and assembled for validation.

[0032] Table 3: M. tuberculosis H37Rv genomic coordinates for the 41 assays and the respective primer sequences. *i) prefix rhSeq-f and rhSeq-r represent the overhang stretch of nucleotides added for each primer for compatibility with the adapter sequence of the Illumina sequencing platform ii) In the sequence for every primer, the ribonucleotide stretch added at the 3 ’end is denoted by rNNNNN, where r indicates ribonucleotide and N indicates the ribonucleotide at that position

[0033] Example 3:

[0034] Template preparation for detection of wild type and mutant alleles in the targeted genomic coordinates:

[0035] Two samples were used for the study:

[0036] 1. Commercially available wild type genomic DNA from Mycobacterium tuberculosis strain H37Rv (ATCC-25618D-2)T Range of 103- 107copies of TB genome (0.005-50 ng of genomic DNA) was taken as input for each PCR reaction.

[0037] 2. TB synthetic construct (TSC): For representation of mutant alleles, we designed and synthesized 13 DNA fragments at Twist Bioscience using sequences corresponding to Mtb genome coordinates (Table 4). These fragments were cloned into a plasmid vector at Twist Biosciences and the purified DNA supplied to us. 1.52 x 108copies of TB genome (or 5ng) were taken as input.

[0038] Table 4: Details of synthetic construct

[0039] Example 4: Assay workflow and library preparation

[0040] The two samples of example 3 were taken as input DNA to prepare libraries according to rhAmpSeq workflow instructed by IDT. The workflow involves 2 PCR amplifications that generate sequence-ready libraries (Figure 1). In the first PCR step, miBiome rhAmpSeq Panel (31 and 10 primer pools) were combined with other components at final concentrations given in parentheses [IX rhAmpSeq Library Mix 1, IX forward primer pool and IX reverse primer pool] to amplify regions of interest using the PCR cycling parameters given below in a volume of 20 pl.

[0041] Cleanup of the two rhAmpSeq PCR products (31pool and lOpool) was carried out with 1.5X ratio of AMPure XP beads to reaction volume. Equal volume of purified eluted products (5.5 pl) were combined and taken for second PCR setup. In the second PCR step, rhAmpSeq Index Primers (final cone 0.5pM) and Library Mix 2 (IX) was added to append sample indexes and P5 / P7 sequences to the PCR 1 amplicon to generate final libraries using the PCR cycling parameters as described below: The rhAmpSeq library was purified with IX AMPure XP beads. The final purified rhAmpSeq library was quantified using Qubit and appropriate dilutions loaded on a High sensitivity DI 000 screen tape to determine the size range of the fragments and the average library size checked using Tapestation 4200.

[0042] Example 5: Sequencing of libraries generated on Illumina platform

[0043] For sequencing on Illumina platform, using 2x150 chemistry and targeting minimum 700x coverage, the libraries prepared in example 4 were diluted quantified using Qubit, loaded on HSD1000 screen tape of a Tapestation 4200 (Agilent Technologies) to check the average library size. Libraries that passed the QC criteria were pooled in accordance with the Illumina platform protocol and loaded on an appropriate flowcell supporting 2X150 read chemistry. Libraries were sequenced for 151 cycles for both read 1 and read 2.

[0044] Example 6: Secondary and tertiary analysis of sequence data

[0045] The flowchart for the analysis has been depicted in figure 2. Raw data were generated using NGS based platform followed by demultiplexing to separate sequenced reads from different samples based on specific index sequences incorporated during library preparation. These segregated raw data were then adaptor trimmed and quality filtered using fastp tool (14). In this filtration process, all low-quality bases (Phred score<15), polyG and polyX bases were removed from the 3’end of the reads and only reads with read length of >=50bp were selected. Further these reads were mapped to Mycobacterium tuberculosis genome H37Rv

[0046] NC_000962.3) downloaded from NCBI using BWA aligner (15) to generate BAM formatted output file. The BAM files were then quality checked, for mapping percentage >95%, to proceed with the downstream steps.

[0047] Before proceeding for variant calling, the targeted amplicons were assessed to ensure adequate coverage of >20x, using BAMtools (16). Further variant calling was performed in 2 stages using 2 different variant callers; GATK standard pipeline (17) and VarScan2 (18). All called variants were merged together using an in-house custom script. The final list of variants were annotated with drug sensitivity information using a custom script. In the final step, all information from VCF file format were extracted in a TSV format. The wild type and mutant allele % were calculated based on their frequency of occurrence in respective samples. The wild type allele for 321 mutations were checked with wild type control (ATCC 25618D-2) and mutant allele for a few selected mutations (mentioned above in Table 4) with Twist Synthetic Control (TSC) as shown in Table 5.

[0048] Table 5: List of 321 mutations with genomic coordinates (within the assay) for the prediction of drug resistance to various drugs (LFX: Levofloxacin, MFX: Moxifloxacin, RIF: Rifampicin, BDQ: Bedaquiline, CFZ: Clofazamine, STM: Streptomycin, LZD: Linezolid, AMK: Amikacin, KAN: Kanamycin, CAP: Capreomycin, ETO: Ethionamide, INH: Isoniazid, PZA: Pyrazinamide, DLM: Delaminid, EMB: Ethambutol). Presence of drug resistance conferring mutations in the sequence data analysis have been denoted by (R) in the mutant allele % column. Wild type allele % was determined in the control sample with 103genome equivalents.

[0049] The results from the above table indicate that, the accuracy of calling wildtype alleles is 99.37% and that of mutant alleles is 99.37%.

[0050] Example 7: Specificity of drug resistance panel design for M. tuberculosis complex using in silica approach

[0051] To estimate specificity, a total of 82 primer pairs were tested in silica against 7 Mycobacterial genomes and 9 other bacterial genomes from common respiratory pathogens (Table 7). MFEprimer 3.1. was used to determine the possibility of primer binding with no mismatch and Tm >55 °C in the region of complementarity, generating an amplicon >100bp but <350 bp. Further all such potential amplicon sequences were aligned against Mycobacterium tuberculosis genome (NC_000962.3) downloaded from NCBI website. All hits with identity >99% and query coverage of 100% were considered for specificity calculations.

[0052] Table 6: List of genomes tested for specificity

[0053] Based on the analysis, among all mycobacterial and non-mycobacterial genomes tested, >99% specificity was displayed for MTB complex compared to non-MTB complex mycobacterial genomes, except for the rrs targets which are highly conserved in all mycobacterial genomes.

[0054] Example 8: Sensitivity determination for assay coordinate coverage

[0055] For determining the sensitivity of the assay in covering all coordinates of the assays, serial dilutions of the M. tuberculosis genomic DNA was used as an input for the assays, corresponding to 107, 105and 103copies of Mtb genome respectively. Library preparation was completed as described in earlier sections and sequenced for a targeted 700X coverage using PEI 50 on Illumina platform. The data was analyzed to determine the coverage for the variant positions. The default coverage / depth for the pipeline to qualify the coordinate for variant calling is >=20. All variants had depth of > 20 reads.

[0056] Table 7: Depth of coverage for variant positions with variable wild type genomic DNA input

[0057] Example 9: Incorporation of new targets based on future validation

[0058] WHO released it’s second edition for the catalog of mutations in 2023 (19). Some examples of the mutations that have now appeared / been upgraded in the category of “Associated with resistance” or “Associated with resistance-interim” but are within the coordinates of the assays described in example 1 are listed below.

[0059] Such new targets can be included to predict the drug resistance disposition in addition to the 321 included in the initial design.

[0060] The above examples describe the design of a panel for targeted amplicon based Next-Gen sequencing, encompassing 321 targets, across 17 genes of the Mycobacterium tuberculosis H37Rv genome, for the detection of variants associated with drug resistance of MTB complex to 15 drugs. The sequence data analytics are comprising of read trimming, mapping, variant calling and variant annotation, results in list of variants with annotations like gene name, variant position in the gene and drug sensitivity prediction. The in-silico based specificity of detection for targets belonging to 16 genes of the MTB complex was >99%. This panel also allows the incorporation of new targets based on future validation.

[0061] Definition of terms and full form of abbreviations used in this specification

[0062] Variant calling: It refers to the use of high-throughput sequencing technology to sequence and analyze the differences in the entire genome or targeted region of an individual or population of a species, to obtain a large amount of genetic variation information, such as Single Nucleotide Polymorphism (SNP), Insertion and deletion sites (InDei) and structural variation sites (SV), copy number variation (CNV) and other information. Variant calling can provide the most basic and comprehensive data foundation for subsequent functional gene fine mapping and quickly, accurately and efficiently analyse the differences between genomes / targeted region, analyze each base of the whole genome, and obtain the most extensive molecular markers.

[0063] IDT: Integrated DNA Technologies rhAmpSeq: It is a design tool for deep, targeted amplicon sequencing with highly multiplexed panels for sequencing on Illumina® platforms.

[0064] TB: Tuberculosis:

[0065] MDR-TB: Multi drug-resistant tuberculosis

[0066] MDR: Multi drug-resistant

[0067] MTBC: Mycobacterium tuberculosis complex:

[0068] Mtb: Mycobacterium tuberculosis

[0069] DRTB:Drug resistanct tuberculosis

[0070] XDR: Extensively drug-resistant

[0071] NGS: Next-generation sequencing

[0072] WGS: Whole genome sequencing

[0073] Assoc w R: Associated with resistance

[0074] References:

[0075] 1. Votintseva AA, Pankhurst LJ, Anson LW, Morgan MR, Gascoyne -Binzi D, Walker TM, Quan TP, Wyllie DHDel Ojo Elias C, Wilcox M, Walker ASPeto TEA, Crook DW 2015. Mycobacterial DNA Extraction for Whole -Genome Sequencing from Early Positive Liquid (MGIT) Cultures. J Clin Microbiol 53zzz https.7 / doi.org / 10.1128 / jcm.03073-14

[0076] 2. SPIT SEQ by Medgenome: htps: / / diagnostics.medgenome.com / spit-seq /

[0077] 3. QTB® by Haystack Analytics: httgs;7haxs£^

[0078] 4. Deeplex® Myc-TB - A new Mycobacterium tuberculosis drug resistance prediction assay:

[0079] 5 • €N 11732731.3 Composition for detecting mycobacterium tuberculosis complex group and drug-resistant genotype based on NGS

[0080] 6. CN112342307; Mycobacterium tuberculosis drug resistance detection kit and method based on NGS technology

[0081] 7. US20220145366: Early detection of drug-resistant mycobacterium tuberculosis

[0082] 8. EP3038649: Single molecule-overlapping read analysis for minor variant mutation detection in pathogen samples Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance: . Andries K, Villellas C, Coeck N, Thys K, Gevers T, Vranckx L, Lounis N, de Jong BC, Koul A. Acquired resistance of Mycobacterium tuberculosis to bedaquiline. PLoS One. 2014 Jul 10;9(7):el02135. doi:

[0083] 10.1371 / joumal.pone.0102135. PMID: 25010492; PMCID: PMC4092087. 1. Lindsay Sonnenkalb, Joshua Carter, Andrea Spitaleri, Zamin Iqbal, Martin Hunt, Kerri Malone, Christian Utpatel, Daniela

[0084] Maria Cirillo, Camilla Rodrigues, Kayzad S. Nilgiriwala, Philip

[0085] W. Fowler, Matthias Merker, Stefan Niemann. Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach. . Indian catalogue of Mycobacterium tuberculosis mutations and their association with drug resistance - 2022: . WHO consolidated guidelines on drug -resistant tuberculosis treatment. Geneva: World Health Organization; 2019. Licence: CC BY-NC-SA 3.0 IGO. . Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1 ;34(17):i884-i890. doi: 10.1093 / bioinformatics / bty560. PMID: 30423086; PMCID: PMC6129281. . Li H, Durbin R. Fast and accurate short read alignment with Burrows -Wheeler transform . Bioinformatics. 2009 Jul 15;25(14): 1754-60. doi:

[0086] 10,I093 / bioinfonnatics / btp324. Epub 2009 May 18. PMID: 19451168; PMCID: PMC2705234. . BAMTools: Barnett DW, Garrison EK, Quinlan AR, Stromberg MP, Marth GT. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics. 2011 Jun 15;27(12): 1691 -2. doi: I0.1093 / bioinformatics / btrl74. Epub 2011 Apr 14. PMID: 21493652; PMCID: PMC3106182. . GATK: McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kemytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010 Sep;20(9): 1297-303. doi:

[0087] 10.1101 / gr.107524. 110. Epub 2010 Jul 19. PMID: 20644199; PMCID: PMC2928508. VarScan2: Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin I.,, Miller CA, Mardis ER, Ding L, Wilson RK. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012 Mar;22(3):568-76. doi: 10. 1101 / gr.129684.111. Epub 2012 Feb 2. PMID: 22300766; PMCID: PMC3290792. Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance, second edition. Geneva: World Health Organization; 2023. Licence: CC BY-NC-SA 3.0 IGO.

Claims

We claim,1. A composition for the small amplicon, short read, RNA-primed targeted Next-Generation sequencing based detection of drug resistance conferring genotype of Mycobacterium tuberculosis complex, comprising of a mixture of the following components: a) Multiplexed primer pairs with ribonucleotides, b) RNaseH2, High-fidelity DNA polymerase, dNTPs, buffer, c) Illumina index - P5 / P7adapter molecules2. A cost-effective method for the detection of multiple drug resistant Mycobacterium tuberculosis complex genotype using the composition of claim 1 that comprises of, compact selection of specific targets for drug resistant panel, assays using IDT rhAmpSeq technology, library preparation, sequencing and data analysis to provide high sensitivity and specificity of detection.

3. A method for the detection of multiple drug resistant Mycobacterium tuberculosis complex genotype using the composition of claim 1 that uses ribonucleotide primers based multiplexing of PCR and ultra-high depth of sequencing to obtain highest accuracy in variant calling technologies4. A computer based data analysis tool to analyse the data of claims 2 and 35. A cloud-based reporting software / system for TB drug resistance mapping using t-NGS, for decentralisation of TB drug resistance reporting.

6. A method for identifying new targets using the methods of claim 2-5.

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