‘Omics’ in Microbiology


Vol. 23 • Issue 3 • Page 14

Cover Story

“Omics” is a shorthand term for a number of recent technologies used to examine the molecular composition of a cell, tissue, or organism at a particular point in time or under specific conditions. Among the list of such technologies, there are methods that analyze proteins (proteomics), RNA transcripts (transcriptomics), or genes (genomics). Additional methods, not covered here, include metabolomics, lipidomics, and pharmacogenomics, among others. We discuss how these dynamic technologies are changing the face of the clinical microbiology laboratory today.

Proteomics

The proteome is the set of proteins expressed; proteomics is the large-scale study of the proteins and their functions. In microbiology, proteomics are used to analyze the many proteins produced by an organism under specific culture conditions. MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) is an excellent example of a proteomics-based technology that is becoming more widely employed in clinical labs. Using MALDI-TOF for organism identification has been shown to increase the accuracy of bacterial and fungal identification, while decreasing turnaround time and cost, compared with traditional morphological and biochemical characterization.1-3

For the first step of analysis, a bacterial colony or a protein extract from bacteria is transferred onto a metal plate, then ionized with a laser. Ionized proteins are propelled away from the metal plate through a vacuum toward a detector. The mass spectrum of each bacterium is created based on the amount of time it takes the proteins to travel through the instrument, as smaller proteins fly faster than larger proteins. The spectrum of an unknown patient isolate is compared with a database of bacterial spectra to identify the organism.

As an expansion of this technology, researchers are exploring possible uses of this method for antimicrobial susceptibility testing and the detection of virulence factors and toxins.4

Laboratories that have incorporated this technology realize the major adjustment in workflow is the biggest change that has been implemented in many years. These approaches have the potential to significantly change the standard of care, allowing physicians to more quickly obtain critical information regarding specific pathogenic organisms.

Transcriptomics

The transcriptome is comprised of all of the RNA molecules produced during a particular time or condition. Transcripts derived from genomic exons display great variability, as the number of transcripts varies depending on environmental conditions. For instance, exposure to an antibiotic may cause an organism to upregulate the transcripts of genes that encode efflux pumps. Therefore, examining gene expression can illuminate mechanisms of stress management.

Technologies that examine the transcriptome of microbes are widely used in the research microbiology lab, but are not routinely seen in the clinical laboratory. cDNA microarrays are small chips or slides spotted with nucleic acid probes; they assess the relative abundance of various mRNAs in an organism. Although traditional microarrays have focused on transcriptomics of a single organism, targets from different organisms can be assessed using a single array. These techniques are used to understand virulence mechanisms, dynamics of polymicrobial infections, and optimal delivery of antimicrobial drugs, as well as to identify new drug targets.

Transcriptomics approaches have been combined with microfluidics to create “Lab on a Chip (LOC)” technologies. These miniature testing devices permit small-volume extraction, detection, and analysis without the aid of larger laboratory machinery. LOCs can be used to assess treatment biomarkers or distinguish if an infection is active or latent.5 The Gates Foundation’s Grand Challenge in Global Health is funding projects that focus on LOC technologies for the improvement of health diagnostics. They envision a future where only a few drops of blood are needed to detect the causative organisms of rapid onset fever, distinguishing malaria, typhoid, dengue, ricksettial diseases, and measles.5 LOCs that integrate DNA extraction and quantitative real-time PCR to detect bacterial and viral pathogens are also in development.

LOC technology is not limited to the detection of nucleic acids, but may also be used to antibodies or proteins.5,6 Point-of-care testing, testing immediately at the time and location of patient examination instead of later at a large core laboratory, is one major driver in LOC development, with the hope that this technology will allow for the implementation of globally applicable diagnostic assays in the future.

Genomics

Genomics is the study of the structure and function of genomes. Whole Genome Sequencing (WGS) is the process of sequencing the entire genome of an organism and is the cornerstone technique used in the study of genomics. WGS utilizes next-generation DNA-sequencing methods to determine the nucleotide arrangement of DNA.

Many short reads of DNA are acquired, then assembled into a longer continuous sequence using bioinformatics tools. These genome sequences can be used in both the research and clinical laboratory and have been instrumental in determining the phylogenetic relationships between families of clinically relevant organisms.

In addition to providing accurate identification of an isolate, WGS can be used to identify novel organisms, identify slow-growing isolates sooner than traditional methods, detect organisms that do not grow well in culture, and identify organisms in mixed cultures.7-9

WGS also shows tremendous potential as a tool for the tracking of hospital and community outbreaks. These data are beneficial from both a hospital epidemiology and infectious disease perspective, as they provide actionable data for the improvement of hospital tracking and decontamination procedures.10

Similar techniques have been employed to retrospectively track outbreaks of ­drug-resistant Enterobacter and ­Enterococcus species.9 A large outbreak in Europe in 2011 involved over 3,500 cases of entero-aggregative-haemorrhagic E. coli (EAHEC), likely due to consumption of contaminated sprouts.11,12 Several groups used WGS to determine important genetic determinants of outbreak strains, including presence of the Shiga-toxin gene.13,14 This approach was coined “prospective genomics epidemiology” by one source; the authors hope that information gained from WGS would lead to information regarding treatment, prevention, and source tracking.12,14

The lack of robust WGS databases for many organisms is a limiting factor for the implementation of this technology, and scientists are actively working to expand the databases. Other limitations to WGS include lack of accessibility, the difficulty of integrating these technologies into the clinical workflow, and need for the bioinformatics expertise required to analyze the complex and voluminous data obtained through WGS.

Metagenomics is the culture-independent analysis of microbial communities. Analysis involves shotgun, or random, sequencing of the genomic DNA of a sample taken directly from the environment.15,16 Organisms can be examined in their natural environment, and these methods are used for environmental, microbiome, and drug discovery studies. The term “microbiome” was coined in 2001 and refers to the collective genomes of the microorganisms that inhabit a specific organ of the human body, such as the skin or intestines. This sequencing approach has allowed for the identification of new pathogens that would not be detectable by normal culture methods.17

Through automated platforms and multi-organism approaches, the microbiologist will face new interpretation challenges different than those encountered through culture-based methods. The old and new are not incongruous, however, as both will be required to determine the clinical impact of microorganisms in today’s increasingly complex patient population.

Dr. Blosser is a medical microbiology fellow in a CPEP-accredited fellowship; and Dr. Frank is the chief of Microbiology, Microbiology Service, Department of Laboratory Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland.

References

1. K. Alby, P. H. Gilligan, M. B. Miller, Comparison of matrix-assisted laser desorption ionization-time of flight (maldi-tof) mass spectrometry platforms for the identification of gram-negative rods from patients with cystic fibrosis. J Clin Microbiol 51, 3852 (Nov, 2013).

2. T. C. Dingle, S. M. Butler-Wu, Maldi-tof mass spectrometry for microorganism identification. Clinics in laboratory medicine 33, 589 (Sep, 2013).

3. S. S. Richter et al., Identification of Enterobacteriaceae by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using the VITEK MS system. Eur J Clin Microbiol Infect Dis 32, 1571 (Dec, 2013).

4. A. van Belkum, M. Welker, M. Erhard, S. Chatellier, Biomedical mass spectrometry in today’s and tomorrow’s clinical microbiology laboratories. J Clin Microbiol 50, 1513 (May, 2012).

5. P. Yager et al., Microfluidic diagnostic technologies for global public health. Nature 442, 412 (Jul 27, 2006).

6. C. M. Parry, L. Wijedoru, A. Arjyal, S. Baker, The utility of diagnostic tests for enteric fever in endemic locations. Expert review of anti-infective therapy 9, 711 (Jun, 2011).

7. H. Hasman et al., Rapid whole-genome sequencing for detection and characterization of microorganisms directly from clinical samples. J Clin Microbiol 52, 139 (Jan, 2014).

8. S. W. Long et al., A genomic day in the life of a clinical microbiology laboratory. J Clin Microbiol 51, 1272 (Apr, 2013).

9. S. Reuter et al., Rapid bacterial whole-genome sequencing to enhance diagnostic and public health microbiology. JAMA internal medicine 173, 1397 (Aug 12, 2013).

10. E. S. Snitkin et al., Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci Transl Med 4, 148ra116 (Aug 22, 2012).

11. C. Frank et al., Epidemic profile of Shiga-toxin-producing Escherichia coli O104:H4 outbreak in Germany. The New England journal of medicine 365, 1771 (Nov 10, 2011).

12. M. Turner, Microbe outbreak panics Europe. Nature 474, 137 (Jun 9, 2011).

13. E. Brzuszkiewicz et al., Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: Entero-Aggregative-Haemorrhagic Escherichia coli (EAHEC). Archives of microbiology 193, 883 (Dec, 2011).

14. A. Mellmann et al., Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology. PLoS One 6, e22751 (2011).

15. L. Bragg, G. W. Tyson, Metagenomics using next-generation sequencing. Methods Mol Biol 1096, 183 (2014).

16. M. Kim et al., Analytical tools and databases for metagenomics in the next-generation sequencing era. Genomics & informatics 11, 102 (Sep, 2013).

17. A. S. Bhatt et al., Sequence-based discovery of Bradyrhizobium enterica in cord colitis syndrome. The New England journal of medicine 369, 517 (Aug 8, 2013).

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