Printer Friendly

DNA sequencing by Microseq kit targeting 16S rRNA gene for species level identification of mycobacteria.

Background & objectives: Identification of mycobacteria to the species level is of therapeutic significance. Conventional methods are laborious and time consuming so we did 16S rRNA sequencing using a commercial MicroSeq sequencing kit, which includes DNA sequencing with software package for identification and phylogenetic analysis of clinical mycobacterial isolates.

Methods: A total of 47 mycobacteria were tested by both conventional and genotypic method using commercially available MicroSeq 500 amplification kit assay. The identification was determined by comparing the 500 bp amplified product of 16S rDNA sequence to the MicroSeq database.

Results: The phenotypic identification was concordant with genotypic identification in 33 (70.2%) isolates of 14 Mycobacterium tuberculosis, 11 M. fortuitum, 7 M. abscessus and 1 M. duvalii. For the discrepant isolates, identification was possible only by DNA sequencing in 14 (29.7%) isolates. The 14 discrepant isolates were 5 M. farcinogenes, 3 M. genavense, 2 M. species, nov and 1 each of M. fortuitum, M. immuogenum, M. simiae and M. wolinskyi. Of these, five were uncommon species that were difficult to identify by pbenotypic method.

Interpretation & conclusion: The MicroSeq DNA sequencing is an excellent tool for species identification of mycobacteria, which reduces the turn around time, makes repeat analysis easy as compared to phenotypic identification specially for mycobacterial isolates with ambiguous biochemical profiles.

Key words 16S rRNA--microSeq kit--Mycobacterium abscessus--M. duvalii--M. farcinogenes--M. fortuitum--M. genavense M. immuogenum--M. simiae--M. species nov--M. wolinskyi


Mycobacteria are aerobic, acid fast bacilli and currently include 92 established species (1) (http: // that cause serious human and animal diseases (2). Identification of mycobacteria to the species level is of paramount significance as the treatment regimen differs for each species (3). Traditional methods to identify mycobacteria based on biochemical tests are laborious and time consuming requiring 2-6 wk for completion. Moreover the rapid increase of newly described species, the repetition of tests in cases of ambiguous results and some species being biochemically inert or extremely slow growing have made the conventional method more complex and obsolete (2). The development and application of various molecular biology techniques in the last two decades has led to rapid identification of mycobacteria, universality of results which allows comparisons of identification among different laboratories (4). The rapid methods used for identification include high performance liquid chromatography (HPLC) (5), DNA probes (Accuprobe; Gen-Probe, Inc, San Diego, Calif.), restriction fragment length polymorphism (RFLP) using various target regions including hsp 65 (6), ITS (7), rpo B (8) and dna J gene (9). Though HPLC is used for identification in some research laboratories, it has limitations in the identification of mycobacteria with similar profiles particularly the rapid growers, which, now include many new species. The commercial probes have greatly reduced the identification time to 1 h but are limited to identification of few species including M. tuberculosis, M. avium, M. intracellulare, M. fortuitum, M. gordonae and M. kansasii (7). hsp 65 RFLP is the most widely used method of RFLP which can identify a wide range of mycobacteria but has not been applied for many of the new taxa with interpretation becoming problematic for species with unique patterns or multiple patterns. Genetic sequencing is the most efficient and acceptable method currently employed by many laboratories (10).

The 16S rRNA is an approximately 1500 nucleotides sequence encoded by the 16S ribosomal DNA (rDNA). The MicroSeq system utilizes a 500 bp region of the 16S rRNA common to all bacteria and has been used in many previous studies (10). Comparative sequence analysis utilizing the 16S rRNA gene sequence has been proven to be a reliable method for the identification of bacteria (11). The present study was undertaken to apply 16S rRNA sequencing technique using the Microseq 500 PCR and DNA sequencing kit to identify the clinical isolates of mycobacteria up to species level.

Material & Methods

Mycobacterial isolates selection: The 47 isolates included in the study were previously recovered during a three year period (2004-2006) in the L&T Microbiology Research Centre, Chennai, from clinical specimens received from various hospitals in and around Chennai. Of the 47 mycobacterial isolates, 19 were from respiratory specimens [13 sputum, 4 bronchoalveolar lavage (BAL), 1 each of pleural fluid and laryngeal wash] and 16 were from ocular specimens (5 corneal scraping, 3 corneal button, 1 intraocular lens (IOL), 4 vitreous aspirate (VA), 2 sub-retinal mass and 1 conjunctival scraping] and 12 were from miscellaneous specimens consisting of urine (6), one each of cerebrospinal fluid, bone marrow, ascitic fluid; pericardial fluid and two pus from cervical lymph nodes. The isolates were chosen from different specimens representing different species including isolates that were unidentifiable by phenotypic methods.

Phenotypic identification: The mycobacterial isolates were identified by the growth characteristics, which included growth at 25, 42 and 37[degrees]C, ability to grow on MacConkey agar without crystal violet, pigment production and colony morphology. The biochemical tests included niacin production, nitrate and tellurite reduction test, pyrazinamidase test, 68[degrees]C catalase test and semi quantitative catalase test, arylsulphatase A and B, tween 80 hydrolysis, urease test, tolerance to 5 per cent sodium chloride and iron uptake tests (12). Substrate utilization test for subspecies identification was carried out with mannitol, sorbitol and inositol for differentiation of rapidly growing mycobacteria (13).

The phenotypic identification tests were repeated whenever mycobacterial identification was doubtful. All tests were carried out inside a level III biosafety cabinet in the Mycobacteriology Research Laboratory, Microbiology Research Centre, Sankara Nethralaya, Chennai, India

Genotyping by 16S rRNA sequencing technique: The MicroSeq 500 system (Applied Biosystems, USA) includes separate kits for PCR and cycle sequencing. PCR and sequencing kits are designed with universal primers to cover all bacteria. DNA extraction was done using the PrepmanUltra protocol for Gram-positive bacteria supplied by Applied Biosystems, USA. A 500 bp 16S rDNA fragment was amplified from the 5' end of the gene in a reaction volume of 50 [micro]l using a GeneAmp PCR System 2700 (Applied Biosystems, USA). Purification of the amplified products was done using Qiagen PCR purification kit (Valencia, USA) according to the manufacturer's instructions prior to sequencing. The cycle sequencing was performed for forward and reverse primers for each amplified product according to instructions provided by the MicroSeq 500 kit. Sequencing reaction mixtures were purified using Montage [SEQ.sub.96] (Millipore, USA) following manufacturer's instructions. Sequence analyses were performed on an ABI PRISM 3100--Avant Genetic Analyzer (Applied Biosystems, USA).

Internally transcribed spacer (ITS) DNA sequencing: PCR and sequencing of the 16S-23S rRNA internally transcribed spacer region (ITS) was performed using primers SP1 (5'ACCTCCTTTCTAAGGAGCACC 3') and SP2 (5' GATGCTCGCAACCACTATCCA 3') (6). PCR was performed using AmpliTaq Gold PCR Master Mix (Applied Biosystems, USA) and 100 [micro]M each primer with thermal cycling conditions of 94[degrees]C 10 min, 38 cycles of 94[degrees]C 1 min, 59[degrees]C 30 seconds, 72[degrees]C for 45 sec followed by an extension at 72[degrees]C for 10 min. Cycle sequencing of the PCR products was performed with the primers used for PCR with Big Dye Terminator Cycle Sequencing Kit reagents (Applied Biosystems, California, USA) with the following thermal cycling conditions: 96[degrees]C 1 min, followed by 25 cycles of 96[degrees]C 1 min each, 50[degrees]C 5 sec and 60[degrees]C for 4 min.

Sequence data analysis: 16S rRNA gene sequences were assembled and edited using the MicroSeq microbial identification software version.1.0. The software was used to assemble each forward and reverse sequence into a consensus sequence, which was edited to resolve base pair ambiguities between the two strands by evaluation of electropherograms. Then comparisons were made between the consensus sequence and the entries in the MicroSeq database. The Alcon sequence database (Alcon Laboratories Inc., USA) was utilized in addition to the MicroSeq database for mycobacterial identification. ITS sequences were analyzed in the same manner using SeqScape sequence analysis software, version 1.0 (Applied Biosystems, USA). Comparative analysis of the ITS sequences was performed by searching the RIDOM (http: // The sequencing was carried out at Alcon Research Laboratories, Fort Worth, Texas, USA. The results were taken as concordant when the biochemical and the DNA sequencing result matched and discordant when they differed. When the results of biochemical tests were inconclusive the mycobacteria were assigned as unidentifiable. The 16S-23S rRNA internal transcribed spacer (ITS) sequence RFLP was carded out as described earlier (14). Mycobacterial protein (MPB) 64 PCR for identification of M. tuberculosis was performed on all isolates identified as M. tuberculosis by phenotypic tests (15).


The phenotypic and genotypic identification was concordant in 33 of 47 (70.2%) isolates whereas identification was discordant in 14 (29.7%) of the isolates. The concordant identification included 14 M. tuberculosis identified by phenotypic method and confirmed by MPB 64 PCR (15) and identified as M. tuberculosis complex by 16S rRNA sequencing. Among the 33 atypical mycobacteria, identification was concordant for 19 mycobacteria, including 11 M. fortuitum, 7 M. abscessus and 1 M. duvalii by biochemical tests and genotypic methods including ITS RFLP and 16S rRNA sequencing identification. Of the phenotypically identified 7 M. abscessus, 16S rRNA sequencing identified 7 as M. chelonae/M. abscessus further. ITS sequencing was necessary to discriminate as 7 M. abscessus in concordance with phenotypic identification.

The identification was discordant for 9 of 47 (19.1%) isolates as the results of the phenotypic identification were incorrect. The 9 atypical mycobacteria included one each of M. wolinskyi (identified as M. fortuitum sorbitol positive third biovariant) and M. simiae (identified as M. abscessus), 2 M. genavense (identified as M. fortuitum), 2 M. species nov., (identified as M. fortuitum) and 3 M. farcinogenes (identified as M. fortuitum).

The identification was inconclusive for 5 atypical mycobacteria and these were unidentifiable based on the ambiguous profiles obtained by phenotypic identification. Of the 5 unidentified atypical mycobacteria, 2 were identified as M. farcinogenes, and one each as M. genavense, M. immunogenum and M. fortuitum based only on the results of 16S rRNA sequencing technique (Table).


The growing number of mycobacterial species included in the taxonomy and their varied treatment regimen necessitates rapid and accurate identification. The commercially available DNA probes allow timely but limited to the identification of the most commonly encountered species, M. tuberculosis complex, M. avium--intracellulare complex, M. kansasii and M. gordonae. Thin layer chromatography (TLC) and gas liquid chromatography (GLC) are techniques which need to be verified with another technique and cannot be performed alone as a number of species share a similar pattern. Even the discriminative and versatile high performance liquid chromatography (HPLC) have inherent problems especially in the identification of rapid growers whose numbers continue to expand rapidly due to similar profiles obtained. RFLP technique can be conveniently included in any clinical laboratory setting for the routine identification of mycobacterial isolates but fails to identify mycobacteria of the new taxa whose patterns have not been previously described and interpretation may at times be difficult for species with identical or multiple patterns (10).

The 16S rRNA universally present in bacteria has both highly conserved and more variable domains, which make it ideal, target for studying phylogenetic relationships (4). Identification based on the 16S rRNA gene sequence analysis has several advantages; it can identify all bacteria including those which are nonviable or uncultivable, it allows recognition and the reliable phylogenetic placement of species previously not described, more rapid turn around time with improved accuracy when compared to phenotypic identification (16-18). Sequencing of 16S rDNA not only is a useful technology for identification and phylogenetic analysis of bacteria but also may lead to the discovery of previously uncharacterized species. 16S rRNA sequencing has now become the new gold standard for defining a new genus and species (10).

Some of the disadvantages of 16S rRNA as a target for sequencing are its failure to discriminate M. tuberculosis complex, M. marinum/M. ulcerans, M. avium subspecies, M. abscessus and M. chelonae sequevar I, M. gastri/M. kansasii sequevars I and IV, M. farcinogenes/M. senegalense, M. peregrinum/M. septicum (16). Sequence variation in the rRNA internal transcribed spacer (ITS) has been demonstrated between M. abscessus and M. chelonae, hence we used ITS sequencing to discriminate between these two species (29). M. tuberculosis complex can be differentiated by DNA sequencing of the oxyR, pncA, gyrB, or hsp65 gene; analysis of spacers between direct repeats in the direct repeat (DR) region; and deletion analysis of the regions of difference (RD) provide a more rapid and accurate approach to the differentiation of the members of the M. tuberculosis complex (19-23). The major drawback of the MicroSeq 500 system is the high cost of the test. Considering the costs of the reagents, supplied technological time and repeat rate the relative costs per test for HPLC and the MicroSeq 500 systems are similar (approximately INR 2,500 and 2,700), Cook et al (24) did a cost analysis on conventional method and 16S rRNA gene sequencing for identification of nontuberculous mycobacteria and revealed that performing DNA sequencing saved one-third of the expense incurred with phenotypic methods for identification of probe negative nontuberculous mycobacteria.

After the initial start up costs, nucleotide-sequencing costs are low considering the cost and time saved during the patient's hospital stay and initiation of appropriate therapy, poses no safety hazard for personnel since only DNA is used (unlike phenotypic methods which involve handling of live culture), offers high throughput, low consumable costs and good inter- and intra-laboratory reproducibility (25). In this study, phenotypic method failed to correctly identify 13 of the 47 mycobacterial isolates. Delayed or misidentification of mycobacteria have a direct impact on the patient awaiting chemotherapy (26).

The MicroSeq 500 identification system has the distinct advantage of providing identification for approximately 1400 different bacterial species in its extensive database of the sequences. In addition, the MicroSeq assay provides commercially prepared reagents and a proprietary, validated database. The premade amplification and sequencing master mixes decrease time in reagent preparation and quality control. Further, the MicroSeq kit has been used in the identification of slow growing, biochemically inert bacteria, and those with ambiguous biochemical profiles (17,26-29). Phylogenetic trees and per cent difference reports are generated using the query sequence and the top matches to the sequence libraries.

MicroSeq database consists mainly of a single type strain for each species and clearly lacks some important species. The public databases such as Genbank ( and European molecular biology laboratory ( embl/) are not monitored. Hence monitored databases like RIDOM ( that has peer-reviewed entries along with extensive phenotypic and sequence data are recommended to confirm MicroSeq results which do not result in species level matches (2,25).

Comparative sequencing analysis utilizing a high quality database is a reproducible and accurate means of species identification of mycobacteria. Sequencing reduces the turn around time and eliminates the necessity of confirmation with biochemicals tests. Accurate identification and taxonomic placement of the causative mycobacterium should assist in the culture requirements in the future, thereby facilitating effective prevention, treatment and control of the disease through tracing of its ecological niche.


The authors from L&T Microbiology Research Centre, Chennai, acknowledge the Research grant by Indian Council of Medical Research (ICMR) to carry out part of the study.

Received June 14, 2007


(1.) LPSN. List of prokaryotic names with standing in nomenclature formerly list of bacterial names standing in nomenclature (LPSN). J.P. Euzeby: Genus Mycobacterium. October 2003 [cited 2004 June 11]. Available from: http: //

(2.) Turenne CY, Tschetter L, Wolfe J, Kabani A. Necessity of quality-controlled 16S rRNA gene sequence databases: identifying nontuberculous Mycobacterium species. J Clin Microbiol 2001; 39: 3637-48.

(3.) [No author listed]. Diagnosis and Treatment of Disease caused by nontuberculous mycobacteria. This official statement of the American thoracic Society was approved by the Board of Directors March 1997. Medical Section of the America Lung Association. Am J Respir Crit Care Med 1997; 156: S1-25.

(4.) Devulder G, Peroused Montclos M, Flandrois JP. A multigene approach to phylogenetic analysis using the genus Mycobacterium as a model. Int J Syst Evol Microbiol 2005; 55 : 293-302.

(5.) Butler WR, Guthertz LS. Mycolic acid analysis by high-performance liquid chromatography for identification of Mycobacterium species. Clin Microbiol Rev 2001; 14: 704-26.

(6.) Telenti A, Marchesi F, Balz M, Bally F, Bottger EC, Bodmer T. Rapid identification of mycobacteria to the species level by polymerase chain reaction and restriction enzyme analysis. J Clin Microbiol 1993; 31: 175-8.

(7.) Roth A, Reischl U, Streubel A, Naumann L, Kroppenstedt RM, Habicht M, et al. Novel diagnostic algorithm for identification of mycobacteria using genus-specific amplification of the 16S-23S rRNA gene spacer and restriction endonucleases. J Clin Microbiol 2000; 38: 1094-104.

(8.) Lee H, Park HJ, Cho SN, Bai GH, Kim SJ. Species identification of mycobacteria by PCR-restriction fragment length polymorphism of the rpoB gene. J Clin Microbiol 2000; 38: 2966-71.

(9.) Takewaki S, Okuzumi K, Manabe I, Tanimura M, Miyamura K, Nakahara K, et al. Nucleotide sequence comparison of the mycobacterial dnaJ gene and PCR-restriction fragment length polymorphism analysis for identification of mycobacterial species. Int J Syst Bacteriol 1994; 44: 159-66.

(10.) Tortoli E. Impact of genotypic studies on mycobacterial taxonomy: the new mycobacteria of the 1990s. Clin Microbiol Rev 2003; 16: 319-54.

(11.) Clarridge JE 3rd. Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 2004; 17: 840-62.

(12.) Diane MC, Martha AC, Lynne SG, Glenn DR, Richard BT, John AW. Mycobacteria. In: Betty AF, Daniel FS, Alice SW, editors. Bailey and Scott's, diagnostic microbiology, 9th ed. Missouri, St. Louis: Mosby Publications; 1990. p. 590-633.

(13.) Mycobacterium. In: Lennette EH, Balows A, Hausler WJ Jr, Shadomy HJ, editors. Manual of clinical microbiology, 4th ed. Washington DC: American Society for Microbiology; 1985. p. 214-30.

(14.) Deepa P, Therese KL, Madhavan HN. Application of PCR-based restriction fragment length polymorphism (RFLP) for the identification of mycobacterial isolates. Indian J Med Res 2005; 121: 694-700.

(15.) Madhavan HN, Therese KL, Gunisha P, Jayanthi U, Biswas J. Polymerase chain reaction for detection of Mycobacterium tuberculosis in epiretinal membrane in Eales disease. Invest Ophthalmol Vis Sci 2000; 41: 822-5.

(16.) Harmsen D, Dostal S, Roth A, Niemann S, Rothganger J, Sammeth M, et al. RIDOM: Comprehensive and public sequence database for identification of Mycobacterium species. BMC Infect Dis 2003; 3: 26 [online: http:// [Pubmed].

(17.) Bosshard PP, Abels S, Zbinden R, Bottger EC, Altwegg M. Ribosomal DNA sequencing for identification of aerobic gram-positive rods in the clinical laboratory (an 18 month evaluation). J Clin Microbiol 2003; 41: 4134-40.

(18.) Cloud JL, Neal H, Rosenberry R, Turenne CY, Jama M, Hillyard DR, et al. Identification of Mycobacterium spp. by using a commercial 16S ribosomal DNA sequencing kit and additional sequencing libraries. J Clin Microbiol 2002; 40: 400-6.

(19.) Goh KS, Fabre M, Huard RC, Schmid S, Sola C, Rastogi N. Study of the gyrB gene polymorphism as a tool to differentiate among Mycobacterium tuberculosis complex subspecies further underlines the older evolutionary age of 'Mycobacterium canettii'. Mol Cell Probes 2006; 20: 182-90.

(20.) Goh KS, Legrand E, Sola C, Rastogi N. Rapid differentiation of "Mycobacterium canettii" from other Mycobacterium tuberculosis complex organisms by PCR-restriction analysis of the hsp65 gene. J Clin Microbiol 2001; 39: 3705-8.

(21.) Niemann S, Harmsen D, Rusch-Gerdes S, Richter E. Differentiation of clinical Mycobacterium tuberculosis complex isolates by gyrB DNA sequence polymorphism analysis. J Clin Microbiol 2000; 38: 3231-4.

(22.) Scorpio A, Zhang Y. Mutations in pncA, a gene encoding pyrazinamidase/nicotinamidase, cause resistance to the antituberculous drug pyrazinamide in tubercle bacillus. Nat Med 1996; 2: 662-7.

(23.) Sreevatsan S, Escalante P, Pan X, Gillies DA 2nd, Siddiqui S, Khalaf CN, et al. Identification of a polymorphic nucleotide in oxyR specific for Mycobacterium bovis. J Clin Microbiol 1996; 34: 2007-10.

(24.) Cook VJ, Turenne CY, Wolfe J, Pauls R, Kabani A. Conventional methods versus 16S ribosomal DNA sequencing for identification of nontuberculous mycobacteria: cost analysis. J Clin Microbiol 2003; 41: 1010-5.

(25.) Hall L, Doerr KA, Wohlfiel SL, Roberts GD. Evaluation of the MicroSeq system for identification of mycobacteria by 16S ribosomal DNA sequencing and its integration into a routine clinical mycobacteriology laboratory. J Clin Microbiol 2003; 41: 1447-53.

(26.) Patel JB, Leonard DG, Pan X, Musser JM, Berman RE, Nachamkin 1. Sequence-based identification of Mycobacterium species using the MicroSeq 500 16S rDNA bacterial identification system. J Clin Microbiol 2000; 38: 246-51.

(27.) Fontana C, Favaro M, Pelliccioni M, Pistoia ES, Favalli C. Use of the MicroSeq 500 16S rRNA gene-based sequencing for identification of bacterial isolates that commercial automated systems failed to identify correctly. J Clin Mierobiol 2005; 43 : 615-9.

(28.) Woo PC, Ng KH, Lau SK, Yip KT, Fung AM, Leung KW, et al. Usefulness of the MicroSeq 500 16S ribosomal DNA-based bacterial identification system for identification of clinically significant bacterial isolates with ambiguous biochemical profiles. J Clin Microbiol 2003; 41: 1996-2001.

(29.) Clarridge JE 3rd. Impact of 16S rRNAgene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 2004; 17: 840-62.

Reprint requests: Dr K. Lily Therese, L &T Microbiology Research Centre, Vision Research Foundation, Sankara Nethralaya 18, College Road, Chennai 600 006, India e-mail:

K. Lily Therese, John Bartell *, P. Deepa, S. Mangaiyarkarasi, Diedra Ward *, Joseph Dajcs * H.N. Madhavan & David Stroman *

L & T Microbiology Research Centre, Vision Research Foundation, Sankara Nethralava, Chennai, India & * Alcon Research Laboratories, Fort Worth, Texas, USA
Table. Comparison of mycobacterial isolates identification by
conventional methods and Microseq nucleic acid sequencing technique

Identification No. of Identification by
by phenotypic isolates genotypic method
method (N= 47) by [greater than or
 equal to] 1% match score
 and MicroSeq 500 database

 MicroSeq ID

Concordant 33
M. tuberculosis 14 M. tuberculosis complex
M. duvalii 1 M. duvalii
M. fortuitum 11 M. fortuitum
M. abscessus 7 M. abscessus / M. chelonae
Discordant 9
M. fortuitum
sorbitol positive 1 M. wolinskyi
third biovariant
M. abscessus 1 M. simiae
M. fortuitum 7 M. genavense - 2
 M. species nov - 2
 M. farcinogenes - 3
Unidentifiable 5 M. immunogenum - 1
mycobacteria M. fortuitum - 1
 M. genavense - 1
 M. farcinogenes - 2

Identification Identification by
by phenotypic genotypic method
method by [greater than or
 equal to] 1% match score
 and MicroSeq 500 database

 Additional tests
 (/ITS seq/
 MPB 64 PCR)

M. tuberculosis MPB 64 PCR
M. duvalii
M. fortuitum -
M. abscessus ITS Seq
M. fortuitum
sorbitol positive -
third biovariant
M. abscessus -
M. fortuitum -

Unidentifiable -

MPB 64 PCR, Mycobacterial protein; ITS, internal transcribed spacer
COPYRIGHT 2009 Indian Council of Medical Research
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2009 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Therese, K. Lily; Bartell, John; Deepa, P.; Mangaiyarkarasi, S.; Ward, Diedra; Dajcs, Joseph; Madhav
Publication:Indian Journal of Medical Research
Article Type:Report
Geographic Code:9INDI
Date:Feb 1, 2009
Previous Article:Organic anion transporter protein (OATP 1B1) encoded by SLCO1B1 gene polymorphism (388A>G) & susceptibility in gallstone disease.
Next Article:Application of 16S rDNA based seminested PCR for diagnosis of acute bacterial meningitis.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters |