In silico Approach to Retrieval of Multi-Drug Resistance Genes of Mycobacterium Tuberculosis Using Complete Genome Sequencing Technology

Introduction

Genomics and bioinformatics are increasingly contributing to our understanding of infectious diseases caused by bacterial pathogens such as Mycobacterium tuberculosis. Bioinformatics and WGS technology process and for optimal clinical Genomics and bioinformatics have contributed immensely to our understanding of infectious diseases: from disease pathogenesis, mechanisms and the spread of antimicrobial resistance, to host immune responses. The direct benefit of whole-genome sequencing (WGS) is the ability to provide organism identification, strain relatedness and drug resistance profile for resistance due to mutation. Comprehensive Antibiotic Resistance Database is a  biological database that collects and organizes reference information on  antimicrobial resistance  genes, proteins and phenotypes [1]. The database covers all types of drug classes and resistance mechanisms and structures its data based on an ontology. The CARD database was one of the first resources that covered antimicrobial resistance genes [2]. Owing to advances in technology and reductions in cost, whole-genome sequencing (WGS) has been transformed from a centralized service used by a select few to integrate single genome into a relatively decentralized laboratory technique used by many to detect and track infectious pathogens [3]. Drug-resistant tuberculosis poses a significant challenge to tuberculosis control programmes in high burden settings. Undiagnosed drug resistance leads to further transmission, poor patient outcomes and potential for amplification of drug resistance which are seeming impediment to the World Health Organization’s (WHO) strategy to end tuberculosis by 2035. The drug-resistant tuberculosis outbreaks in Tugela Ferry and other regions of South Africa highlight the need for early and accurate diagnosis of drug resistance [4]. Whole Genome Sequencing (WGS) has become the reference microbial typing method in outbreak studies and is increasingly being applied to national surveillance of infectious diseases in European Union/European Economic Area (EU/EEA) countries and beyond. In 2015, European Centre for Disease Prevention and Control (ECDC) developed an expert opinion on WGS in consultation with multidisciplinary experts. European Food Safety Authority (EFSA) and research and development projects to define the priorities and identify high impact diseases or drug resistance issues, where genome sequence information can make a difference for public health intervention [5, 6].

According to the recent WHO report, there were an estimated 10.0 million cases of TB and 1.3 million deaths during the year 2017. India alone accounted for 24% of global MDR-TB incidence and 27% of global TB incidence among HIV-negative individuals (Global Tuberculosis Who Report [GWTR] x, 2018). There is an urgent need for improved diagnosis of TB, such as identification of markers to monitor transmission and effective treatment to deal with this deadly disease. Whole genome sequencing (WGS) studies from across the globe have revealed genetic diversity of Mycobacterium tuberculosis and have provided significant insights into its evolution and transmission [7]. Several studies have shown association of the genetic variations with pathogenesis and drug resistance [8]. Global frontline molecular diagnostics such as line probe assays and Xpert MTB/RIF used for diagnosis of drug resistant TB, have been developed based on these genetic markers [9, 10, 11].

Current recommendations for the treatment of drug- susceptible TB include a 6-month course of a multi-drug regimen of rifampicin, isoniazid, pyrazinamide, and ethambutol. Historically, treatment of MDR- or XDR-TB involved the long-term use of second-line drugs, including injectable agents (WHO, 2016). More recently, the MDR-TB treatment landscape has changed with the introduction of multiple novel second-line drugs that can be administered orally. In 2012, bedaquiline, adiarylquinolone, became the first TB drug from a novel drug class to receive US Food and Drug Administration (FDA) approval in over 40 years [12]. Another oral agent, delamanid, a nitro-dihydro- imidazooxazole derivative, has also shown promise for TB treatment [13]. WHO published updated treatment guidelines for MDR/RR-TB [6], recommending fully oral MDR regimens for many patient groups. Recommended treatment strategies include both shorter, standardized MDR regimens (for 9 to 12 months) and longer, individualized treatment regimens (for 18 to 20 months). The updated guidelines group anti-tubercular drugs on the basis of how they should be combined to create individualized, longer MDR-TB regimen [6, 12, 13].

Prolonged treatment courses, leading to greater drug exposure, toxicity, and non-compliance; unacceptable side- effect profiles; logistics of drug access; and re-infection [14] are great treatment challenge to TB. The dawning of the new age of genome sequencing began to revolutionize our approach to human diseases, including TB. In 1998, [15] reported the complete genome sequence of the M. tuberculosis reference strain H37Rv, which was approximately 4.41 million base pairs in length and encoded approximately 4000 genes. The first sequencing of a clinical reference strain, CDC1551, quickly followed [16]. An accompanying editorial optimistically stated: “After several decades in the slow lane of classical microbiology, M. tuberculosis is once again at the cutting edge of science”. However, even at the time of these breakthroughs, there was recognition that translating these genomic data into clinical benefit would prove challenging [17]. Despite these challenges, it is clear, more than 20 years later, that M. tuberculosis genomic data have been remarkably useful in improving our understanding of how drug-resistant TB evolves and spreads and in helping to inform diagnostics and therapies [18, 19].

Discussion

Comprehensive Antibiotic Resistance Database resistant genes identifier (CARD-RGI) have been successfully used in this study to understand antibiotic resistance genes in about 70 complete genome sequences and accessions of Mycobacterium tuberculosis retrieved from the NCBI. In this study, the NCBI database was used to retrieve 70 complete genome sequences of Mycobacterium tuberculosis and the CARD RGI was used to understand what antibiotic resistance genes are present in theses genome. The lowest number of complete genome sequences retrieved from Asia and Europe, (6% and 4% respectively) due to the limited number of the complete genome sequences of Mycobacterium tuberculosis deposited in the NCBI GenBank from these locations. Drug resistance in M. tuberculosis is the result of chromosomal mutations in existing genes that are passed along through vertical descent, which is, passed from mother to daughter cells. Unlike many other bacterial pathogens, M. tuberculosis rarely recombines via lateral exchange of DNA [1] and also lacks plasmids. Many of the resistance determinants were discovered before the sequencing of the M. tuberculosis genome was completed.

The resistance genes were sorted into two; which were perfect and strict. In all, there were thirty-seven (37) perfects and Eighty-one(81) stricts APH(6)-Id mutant, APH(3’’)-Ib mutant, sul2 mutant, tet(B) mutant, ANT(3’’)-Iic mutant, adeJ mutant, adeL mutant, AmvA mutant, adeN mutant, adeR mutant, AbaF mutant, KpnH mutant, gyrB mutant, bacA mutant, vanI mutant, farB mutant, mecR1 mutant, tet(45) mutant ,cat86 mutant, rpsL mutant, pncA mutant, soxRv mutant, patB mutant, oqxA mutant, cmH-1 mutant, FosA2 mutant, ramA mutant, parC mutant, KpnG mutant, OmpK37 mutant, FosA6 mutant, smeR mutant, iri mutant, mdfA mutant, AbaQ mutant, gyrA mutant, thyA mutant, kasA mutant, AAC(6’)-Iy mutant, emrB mutant, Bla2 mutant, Bla1 mutant, mphL mutant, arlR mutant, blaZ mutant, RbpA mutant, mepR mutant, marR mutant, folC mutant, acrB mutant, acrA mutant, kdpE mutant, KpnF mutant, MdtK mutant, folP mutant, adeF mutant, EF-Tu mutant, RbpA mutant, rpoB mutant, katG mutant, sdiA mutant, GlpT mutant, uhpT mutant, PmrF mutant, ampC1 mutant, CRP mutant embB mutant, FosB mutant, msbA mutant, emrR mutant, ampH mutant, KpnE mutant, marA mutant, H-NS mutant, baeR mutant, mecA mutant, norA mutant, murA mutant, rRNA mutant, LmrS mutant, AAC(6’)-Iaa mutant, mtrA mutant, AAC(2’)-Ic mutant, SHV-1 mutant, efpA mutant, tetM mutant, tet(K) mutant, FTU-1 mutant, BPU-1 mutant, ANT(4’)-Ib mutant, smeD mutant, mecI mutant, ADC-6 mutant, abeS mutant OXA-67 mutant, mdsB mutant, mdsA mutant, TEM-1 mutant, adeK mutant, adeI mutant, tetW mutant , ANT(4’)-Ib mutant, golS mutant, mecR1 mutant, ErmA mutant, mfpA mutant, have been successfully identified. The drug-resistant tuberculosis outbreaks in Tugela Ferry and other regions of South Africa highlighted the need for early and accurate diagnosis of drug resistance [7]. Several studies have shown association of the genetic variations with pathogenesis and drug resistance [8]. Global frontline molecular diagnostics such as line probe assays and Xpert MTB/RIF used for diagnosis of drug resistant TB, have been developed based on these genetic markers [9]. However, these tests rely on a limited number of mutations. There have been several instances where phenotypic resistance could not be explained by known mutations associated with drug resistance [10]. A recent study comparing the efficacy of Xpert MTB/RIF with line probe assay for detection of rifampicin mono-resistant M. tuberculosis reported the utility of country specific probes, to increase the sensitivity of Xpert MTB/RIF in India [11]. Since there is considerable genetic heterogeneity among M. tuberculosis isolates from different geographic regions, large-scale sequencing efforts are required to map genetic variations and identify the genotypes associated with drug resistance.

Conclusion

Whole Genome Sequence investigations have revealed that there is a specific order in which drug-resistance mutations are acquired: isoniazid resistance is almost always acquired before rifampicin resistance, which has significant implications for the design of diagnostic tests. Within individual patients, WGS studies have highlighted that mixed infections are common, and often represent important intermediates in the evolution of drug resistance. Whole Genome Sequence is expected to continue to advance our understanding of  M. tuberculosis  drug resistance. Furthermore, its practical use in clinical settings holds great potential to improve public health through real- time molecular epidemiology tracking, to identify global hotspots of drug-resistance emergence, and to facilitate the development of improved approaches for the diagnosis and treatment of drug-resistant Mycobacterium tuberculosis.