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Year : 2017  |  Volume : 6  |  Issue : 2  |  Page : 82-87

Accurate identification of urinary isolates: Integration of conventional, automated, and molecular methods

1 Department of Microbiology, Sri Aurobindo Institute of Medical Sciences Medical College and PG Institute, Indore, Madhya Pradesh; Department of Biochemistry, SOS Indira Gandhi National Open University, New Delhi, India
2 Department of Microbiology, Sri Aurobindo Institute of Medical Sciences Medical College and PG Institute, Indore, Madhya Pradesh, India
3 Department of Biochemistry, SOS Indira Gandhi National Open University, New Delhi, India

Date of Web Publication18-May-2017

Correspondence Address:
Trupti Bajpai
Sri Aurobindo Institute of Medical Sciences Medical College and PG Institute, Indore, Madhya Pradesh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijhas.IJHAS_92_16

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BACKGROUND: Prompt and accurate detection of bacterial pathogens is essential for improving the management of infectious diseases.
AIMS: Our study aims at accurate identification of uropathogens through the integration of various methods, thereby highlighting each method of identification.
MATERIALS AND METHODS: The present prospective study was conducted in the department of microbiology of a teaching tertiary care hospital of the central India for 1 year 2014–2015. A total of 1202 urine samples were processed. Identification of different urinary isolates was done by conventional, automated, and molecular methods in few special cases. The study did not involve any statistical analysis.
RESULTS: A total of 509 samples were found to be culture positive out of the 1202 samples studied. Five hundred and fifty-four uropathogens were isolated from 509 culture positive samples. Among these, the bacterial isolates that could not be identified conventionally were processed through automated and/or 16S ribosomal ribose nucleic acid) (16S rRNA) sequencing.
CONCLUSIONS: The use of 16S rRNA gene sequences to study bacterial phylogeny and taxonomy has been the most common housekeeping genetic marker that provides reliability, reproducibility, and higher accuracy during identification of bacterial isolates. Our study has highlighted the importance of each method during the process of identification, thereby developing a high degree of confidence in diagnostic procedures.

Keywords: 16S ribosomal ribose nucleic acid sequencing, biochemical tests, phylogenetic tree, uropathogens, Vitek-2 Compact

How to cite this article:
Bajpai T, Varma M, Bhatambare G, Pandey M. Accurate identification of urinary isolates: Integration of conventional, automated, and molecular methods. Int J Health Allied Sci 2017;6:82-7

How to cite this URL:
Bajpai T, Varma M, Bhatambare G, Pandey M. Accurate identification of urinary isolates: Integration of conventional, automated, and molecular methods. Int J Health Allied Sci [serial online] 2017 [cited 2023 Jan 29];6:82-7. Available from: https://www.ijhas.in/text.asp?2017/6/2/82/206428

  Introduction Top

Prompt and accurate detection of bacterial pathogens is essential for improving the management of infectious diseases. Most of the current clinical microbiology laboratory practices in developing countries still continue to rely on the bacterial identification based on the use of conventional techniques. This phenotypic approach presents some inherent problems. Moreover, the characteristic database also lacks newly described species and unusual microorganisms. Moreover, it is well known, that in the case of urinary tract infection (UTI), the delay between specimen submission and diagnosis results in empiric and frequently inappropriate antimicrobial therapy.[1],[2],[3],[4]

Much progress has been made through the development of miniaturized identification (ID) systems followed by innovative automatic ID systems that provide accurate and reproducible identifications. In spite of the innovative results obtained from these automated systems, they are unable to identify microorganisms exhibiting biochemical features that do not fit into any known patterns of genus and species.[5]

The application of molecular approaches to clinical diagnostics has a number of advantages over standard microbiological techniques including sensitivity, speed, and ease of specimen processing. This has proved useful not only for slow-growing, unusual or fastidious bacteria but also for identification of rare bacteria having ambiguous profiles, which are poorly differentiated by conventional methods.[1],[2] An excellent example of these molecular methods is MicroSeq 500 (Applied Biosystems) Foster City, California, USA. 16S ribosomal ribose nucleic acid (rRNA) sequencing. Our study aims at identifying some “difficult” uropathogens through a combination of conventional, automated, and molecular method thereby highlighting the importance of all the three methods during our study.

  Materials and Methods Top

The present prospective study was conducted from March 2014 to February 2015 in the Department of Microbiology of a teaching tertiary care hospital located in the central India. It was approved by the institutional ethical committee. Clean catch, midstream urine samples from 1202 patients clinically suspected of UTI were subjected to microscopy and culture on blood agar, MacConkey agar, and UTI chromogenic media (HiMedia, Mumbai). The urine samples from patients already on antibiotic or those suspected of renal tuberculosis and leptospirosis were not included in the study. Furthermore, the samples from patients with the sample episode of UTI were not repeated again. The uropathogens isolated from the culture-positive samples were identified up to species level by conventional method (biochemical tests) (HiMedia, Mumbai).[6],[7] The isolates were further confirmed by automated method (Vitek 2-Compact System, BioMérieux Inc., France). If required, then, further confirmation was done by molecular method (16S rRNA sequencing) (Yaazh Xenomics, Mumbai and Chennai). In 16S rRNA sequencing, genomic DNA was extracted, followed by its amplification (polymerase chain reaction). The amplified fragments were purified before sequencing. Bidirectional sequencing was performed for each amplified product by an automated sequencer.[2],[5] The analysis of sequencing data was performed by MicroSeq 500 software. The consensus sequences were compared (online) with the published sequences available in GenBank at the website (http://www.ncbi.nlm.nih.gov/) using nucleotide Basic Local Alignment Search Tool of National Center for Biotechnology Information. The phylogenetic tree of the identified bacterial isolates was analyzed. Data for the phylogenetic analysis were obtained from sequences contained in the GenBank nucleotide sequences database.[8],[9] The data were entered into MS Excel sheet, and simple percentages were used for the analysis of the data.

  Results Top

A total of 509 (42.3%) urine samples were found to be culture positive out of 1202 patient samples processed. Five hundred and fifty-four uropathogens were isolated from the 509 culture positive samples (45 samples had mixed flora, i.e., two pathogens per sample). Among these 554 isolates, 364 (65.7%) isolates were Gram-negative bacilli, 113 (20.3%) were Gram-positive cocci, and 77 (13.8%) were Candida species. Among the 364 Gram-negative isolates, 328 (90.1%) were members of Enterobacteriaceae, 30 (8.2%) were Pseudomonas aeruginosa, 5 (1.3%) were Acinetobacter species, and 1 (0.27%) isolate was a member of nonfermenter. Out of the 328 members of Enterobacteriaceae, 248 (75.6%) were confirmed as  Escherichia More Details coli. Among these 248 E. coli, 6 (2.4%) isolates were confirmed as E. coli only through automated and molecular methods. Among the five Acinetobacter species, two isolates were confirmed as Acinetobacter baumannii and one isolate was confirmed as Acinetobacter lwoffii by both conventional and automated methods. One isolate that was unidentified by conventional method was confirmed as Acinetobacter junii by automated and molecular methods. One more isolate that was unidentified by conventional method and identified as A. baumannii-Acinetobacter calcoaceticus complex by automated method was confirmed as A. baumannii by molecular method. One nonlactose fermenter isolate that could not be identified conventionally was confirmed as Citrobacter koseri by both automated and molecular methods. Moreover, one member of nonfermenter that could not be identified by conventional method and identified as  Moraxella More Details lacunata by automated method was confirmed as  Providencia rettgeri Scientific Name Search 6S rRNA sequencing method [Table 1].
Table 1: 16S rRNA sequencing results

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  Discussion Top

The partially identified or completely unidentified bacterial uropathogens isolated from the patient samples were subjected to 16S rRNA sequencing. Such isolates could not be confirmed biochemically and/or through automated method; and16S rRNA sequencing was a necessity because the isolated bacteria were exhibiting some ambiguous biochemical features. Furthermore, there were some “difficult” strains that automated system failed to characterize either by furnishing an inconclusive ID or by exhibiting an unlikely (implausible) profile.

Gene sequencing is an accurate and reproducible method to identify microorganisms and has increased our ability to capture the diversity of microbial taxa. This new technology has resulted in the identification of unusual microorganisms and detection of novel, difficult-to-cultivate microorganisms. The gene target that is most commonly used for bacterial identification is 16S rRNA or 16S ribosomal deoxyribose nucleic acid, a ~1500 base pair gene that codes for a portion of the 30s ribosome.[10] One of the most attractive potential uses of 16S rRNA gene sequence informatics is to provide genus and species identification for isolates that do not fit any recognized biochemical profiles, for strains generating only a “low likelihood” or “acceptable” identification according to commercial systems. The cumulative results from a limited number of studies to date suggest that 16S rRNA sequencing provides about ≥90% genus and 65%–83% species identification with about 1%–14% isolates remaining unidentified after testing. A higher percentage (62%–91%) of species identifications for routine isolates can be obtained using sequencing results than with either conventional or commercial method. Identification rates of 62%–83% are usually obtained for bacteria that are difficult to grow or identify conventionally. Difficulties encountered in obtaining a genus, and species identification includes the recognition of novel taxa, too few sequences deposited in nucleotide databases, species sharing similar and/or identical 16S rRNA sequences, or nomenclature problems arising from multiple genomovars assigned to single species or complexes.[11]

The variants of E. coli that are termed as “inactive” are nonmotile, anaerogenic, nonlactose fermenters. Biochemically, they are very similar to Shigella species, and such isolates pose a significant diagnostic challenge.[12],[13] Phenotypically, they share many common characteristics, but genotypically they could be considered as same species.[13] Studies have reported that it is impossible to differentiate between E. coli and Shigella species on the basis of 16S rRNA sequencing. Actually, E. coli and Shigella are closely related species and phylogenetic relationship among them cannot be established since 16S rRNA sequences of bacteria contain highly conserved regions.[8],[10] Even during our study, these isolates showed ambiguous results biochemically and hence could not be confirmed as E. coli by conventional methods. The automated method could identify it as E. coli, but again 16S rRNA sequencing identified it as either Shigella sonnie or E. coli. However, the source of organism and the results of other identification methods helped to reach the final confirmation. It was finally confirmed as E. coli on the basis of the clinical sample from which it was isolated. Being a urinary isolate, it was finally confirmed as enteroinvasive strain of E. coli resembling Shigella species [Figure 1].
Figure 1: Phylogenetic tree of  Escherichia coli Scientific Name Search  the nucleotide sequences of 16S ribosomal ribose-nucleic acid genes

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The taxonomy of the genus Acinetobacter has a long history of debate. Recent molecular studies of these catalase positive and oxidase negative nonmotile coccobacilli.[14] have shown 31 distinct Acinetobacter species with valid names among the genus Acinetobacter.[15],[16] Besides, the genus comprises a number of taxa including species with published names. Of these, A. calcoaceticus, A. baumannii, Acinetobacter Pitti,, and Acinetobacter nosocomialis (formerly referred to as genomic species 1, 2, 3, and 13TU) are phenotypically and genotypically similar.[16] Only few phenotypic techniques (based on 19 biochemical test pattern developed by Bouvet and Grimont)[17] have been validated to identify 12 clinically important Acinetobacter species. Therefore, in our study, it was possible to identify two isolates as A. baumannii by a conventional method with further confirmation by an automated system (Vitek-2 Compact). However, these techniques are unable to identify rest of the Acinetobacter species that have already been identified by DNA-DNA hybridization.

Therefore, in our study, one of the Acinetobacter isolates that could not be identified by conventional method up to species level was identified as A. junii by automated method and was further confirmed by molecular method (16S rRNA sequencing) [Figure 2]. Apart from these, one of the isolates that was identified as Acinetobacter spp. conventionally was identified as A. baumannii - A. calcoaceticus complex by our automated system. It was finally confirmed as A. baumannii by 16S rRNA sequencing [Figure 3]. Studies have proved that differentiation of genetically closely related species in an A. calcoaceticus-baumannii (ACB) complex is not possible using conventional methods. In addition, commercially available platforms are also insufficient and inaccurate to reach the confirmation.[18]
Figure 2: Phylogenetic tree of  Acinetobacter junii Scientific Name Search the nucleotide sequences of 16S ribosomal ribose-nucleic acid genes

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Figure 3: Phylogenetic tree of Acinetobacter baumanni based on the nucleotide sequences of 16S ribosomal ribose-nucleic acid genes

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Associated with the A. baumannii is a group of phenotypically and genotypically closely related Gram-negative bacteria that can hardly be distinguished by phenotypic and chemotaxonomic criteria. These particular species have been named as A. calcoaceticus, A. pitti and A. nosocomialis. Collectively, they are now referred as ACB complex, having a 16S rRNA sequence identity value between 97% and 99% and interspecies values between 65% and 75%. Although ACB complex is considered to be an important nosocomial agent, the clustering of these species together in the ACB complex is unsatisfactory for clinical reasons because it obscures possible differences in the biology and pathology of the individual species. For example, A. calcoaceticus is predominantly an environmental isolate while other three are clinically important species with A. baumannii especially involved in infection and epidemic spread.[14],[17],[18],[19]

Acenetobacters are occasionally known to cause UTI's especially related to indwelling Foley's catheters. These infections are usually benign and can be either community acquired or hospital acquired.[14] In our study also, a very small percentage (1.3%) of uropathogens were detected as Acinetobacter spp. Even 1.3% Acinetobacter isolated as uropathogens in our clinical setting should attract the attention of clinicians and microbiologists because they are a matter of concern having the enormous potential to spread its notorious characteristics among other strains through horizontal transmission.

Although not identified biochemically, C. koseri [Figure 4], A. junii and E. coli were confirmed both by Vitek-2 Compact Automated System and 16S rRNA sequencing.[3],[5] Thus, in our case, 60% strains identified by Vitek-2 were in concordance with sequencing. One of the bacterial isolate that could not be identified biochemically but was identified as M. lacunata by Vitek-2 was identified as P. rettgeri by sequencing [Figure 5]. Such unusual or “difficult” strains are quite common when isolated from patients that have undergone long-term antimicrobial therapy (such as hematological patients and those in intensive care units) can lose their typical biochemical characteristics and sometimes become extremely difficult to identify.[2],[5],[11]
Figure 4: Phylogenetic tree of  Citrobacter koseri Scientific Name Search on the nucleotide sequences of 16S ribosomal ribose-nucleic acid genes

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Figure 5: Phylogenetic tree of Providencia rettgeri based on the nucleotide sequences of 16S ribosomal ribose-nucleic acid genes

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Challenges arise when microorganisms cannot be discriminated by 16S rRNA gene sequencing. Although 16S rRNA sequencing is highly useful in regards to bacterial classification, it has low phylogenetic power at the species level, and poor discriminatory power for some genera, and DNA relatedness studies are necessary to provide absolute resolution to these taxonomic problems. Many investigators have found resolution problems at the genus and/or species level with 16S rRNA gene sequencing data. These groups include (but not restricted to) the family Enterobacteriaceae (in particular Enterobacter and Pantoea), rapid growing Mycobacteria, the A. baumannii–A. calcoaceticus complex, Achromobacter, Stenotrophomonas, Actinomyces, few species of Bacillus, Bordetella, Burkholderia,  Neisseria More Details, Edwardsiella, Pseudomonas, and Streptococcus, Aeromonas verona,[11] and in between E. coli and Shigella species [8],[10] and non-jejuni-coli group of Campylobacter. The resolution problems are known to be related to bacterial nomenclature and taxonomy, sequence identity or very high similarity scores. In certain instances, 16S rRNA gene sequence data cannot provide a definitive answer since it cannot distinguish between recently diverged species. In other instances, the difference between the closest and next closest match to the unknown strain is <0.5% divergence (<99.5% similarity). In these circumstances, such small differences cannot justify choosing match as a definitive identification.[11] Such resolution problem was faced by our E. coli isolate during 16S rRNA sequencing. However, ABC complex was detected as A. baumannii without any controversies.

  Conclusions Top

Microbial identification by gene sequencing plays an important role in the identification of unknown isolates or those with ambiguous biochemical profiles and has improved our ability to recognize poorly described, rarely isolated, biochemically and phenotypically aberrant or new, emerging pathogens. 16S rRNA gene sequencing traditionally played a limited role in the identification of microorganisms in clinical microbiology laboratories, mainly due to high costs, requirements for great technical skill, and the lack of user-friendly comparative sequencing analysis software and validated databases. However, the availability of improved DNA sequencing techniques, vastly increased databases and more readily available kits and software, makes this technology a competitive alternative to routine microbial identification techniques.[2],[4],[10],[11],[20] However, on considering the cost-benefit ratio, the automated and molecular methods are less better choices but should be preferred if conventional methods could not provide any clues, and uropathogen identity plays a greater role in benefiting the health of the patient. Our study with urinary isolates has proved that proper integration of all the three methods can help us to identify the pathogenic isolates, which will be further helpful in administering appropriate antibiotics and in understanding the trend of pathogens in various clinical samples.


The authors would like to thank the management, technical and clinical staff of SAIMS Medical College and PG Institute for their kind support.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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