Pathogen Genotype in Molecular Epidemiology of Waterborne Illness

IN Epoxy slides

Single nucleotide polymorphism (SNP) microarray produced using Grace Bio-Labs Epoxy Slides




The little-known parasites of the Cryptosporidium genus are responsible for the leading waterborne illness in the United States.1 The symptoms are unpleasant and an acute disease state can become life-threatening for immunocompromised individuals. In 1993, the largest recorded outbreak made 400,000 people sick in Milwaukee, WI.2 From January 2017 – March 2017, the city of Portland, OR detected Cryptosporidium in the Bull Run water source at levels requiring infrastructure overhaul.3 Cryptosporidium or “Crypto” are protected from chlorine and alcohol-based sanitizers by a thick protein-lipid-carbohydrate matrix making contaminated water difficult to treat. Construction of water filtration facilities is the only solution for Bull Run. The project is not slated for completion until 2027 and will cost an estimated $500 million US dollars.4  To better understand Cryptosporidiosis epidemiology, the Centers for Disease Control has developed a molecular tracking system, “CryptoNet”, to encourage genetic analysis of Crypto.Only molecular methods can distinguish Crypto species due to morphological similarities in clinical lab tests. Here, application of a DNA microarray using Grace Bio-Labs Epoxy Microarray Slides is employed to genotype subspecies of Crypto based on single base variations in DNA sequence, or SNPs, which occur across the genome. Known SNP variants at specific genomic loci describe a population and serve as markers to identify species, genotypes, and subtypes.6 Cyrptosporidium species and types vary in pathogenic strength, host (human vs. animal), and geographic distribution. Genetic analysis of Crypto is used to identify outbreak source (human-to-human vs. animal-to-human) and mitigate risk factors in public water supply.

Figure 1 -Genotyping assay using Grace Bio-Labs Epoxy Microarray Slides in three steps. Left panel, step 1, amino-modified oligonucleotide probes spotted on the slide surface. Middle panel, step 2, Cyrpto sample DNA was hybridized to probes. Right panel, step 3, bound sample DNA detected by Cy3 (green) or Cy5 (red) labeled

SNP Detection in Cryptosporidium Typing Array

Epoxy Microarray slides were used to distinguish C. parvum genotype I from C. parvum genotype II due to robust attachment chemistry coupled with high signal-to-noise ratio. Discrimination between five SNP variants was sufficient to unambiguously identify C. parvum genotype I, known to transmit between humans, from C. parvum genotype II, known to transmit from animals to humans.7 A SNP array is a rigorous test of microarray performance as it requires discrimination between 1 or 2 mismatches in DNA sequence. By comparison, arrays that use longer DNA based probes only need 80-85% sequence homology with sample DNA to yield a positive result.9 Two pairs of SNP probes were selected from Straub et al. 20027 to design an assay that could discriminate up to two Crypto genotype SNP variants (Figure 2). Each SNP pair contained one probe that matches genotype I DNA and one probe that matches genotype II DNA. A binary red-green output was designed as a genotyping readout. Green spots highlight DNA matching genotype I and red spots highlight DNA matching genotype II. Figure 3 shows fluorescent images of replicate arrays after hybridization and detection using a mixture of both genotype I and II Crypto DNA. Well resolved signal indicated SNP discrimination of variants from both pairs. Grace Bio-Labs ProPlate slide modules were used to optimize conditions in a high-throughput, rapid assay format. In a rapid format, DNA was hybridized for 1 hour compared with 18-hour average hybridization time in a typical assay.10 Multi-well ProPlate configurations allow replicate arrays to be assayed from the same slide surface, reducing intra-assay variation from microarray printing. Screening sample DNA concentration and hybridization conditions in multi-well ProPlates simplifies high-throughput workflows, reducing assay optimization time.

Figure 2 – Crypto genotyping assay. Replicate arrays resolve green spots of genotype I sample DNA and red spots genotype II sample DNA. Each array contains a row of control spots and four rows of SNP probes, two rows for each SNP pair. Cy3 directly labeled oligo (top row) was used as a control to show reproducible spot morphology between replicate spots. Spots were printed using non-contact piezo-electric arraying. Synthetic sample DNA was hybridized to the array followed by secondary hybridization of Cy3 and Cy5 labeled detectors. Sample DNA sequence was derived from NCBI accession AF221535.1 for genotype I modified with Iowa and GCH1 isolate SNP variants for genotype II.8

Probe Design – Number of SNP Variants Impacts Sensitivity and LOD

Performance of probes on a microarray drives assay sensitivity and the limit of detection. Factors that influence SNP probe performance include nucleotide length, position of SNP variant along the probe, number of SNP variants per probe, and modification for surface attachment.11 Two pairs of SNP probes were used to evaluate performance relative to the number of SNP variants per probe. SNP pair A contained two SNP variants per probe while SNP pair B contained one SNP variant per probe. Probe pair B had a lower limit of quantification compared to probe pair A. Both probe pairs gave a linear response to DNA concentration in support of probe design and demonstrate robust performance using Grace Bio-Labs Epoxy Microarray slides. SNP analysis on a small number of probes is shown as a genotype detection tool. A single probe to detect species or genotypes can be reliable when probes target unique regions of the genome. Rational design will allow the detection of a broad spectrum of closely related species in parallel. Epoxy microarray slides are a dependable substrate for fabrication of high-quality detection arrays. Epoxy-activated surfaces support long print runs and stable storage after printing by leveraging high reactivity and covalent immobilization of epoxy. Next-generation sequencing methods have increased the available pool of sequences to design unique diagnostic probes. While DNA sequencing remains the highest resolution genomic technique, future microarray designs will utilize newfound genetic understanding to create a user-friendly platform. Microarray technology will leverage the advantage of a high throughput, parallel, and multiplex format.

Figure 3 –SNP probes show linear response in Crypto Genotyping assay. Mean Signal-to-noise ratio (SNR) ± SD (N = 2) calculated using (signal-background) / SD background from replicate spots at each concentration of sample DNA.

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Catalog #

Product name

405278

Epoxy Microarray Slides

246824

ProPlate® Multi-Array Slide System

244864

ProPlate® Multi-Well Chambers

470638

ProPlate® Multi-Well Chambers

106109

Vytal™ PBS

106108

Vytal™ 10 X PBS

115601

NanoParticle Fluorescent Calibration Slide

References

  1. Centers for Disease Control (CDC). (2017, November 8). Cyrptosporidiosis Surveillance, United States, 2011-2012. Morbidity and Mortality Weekly Report, Surveillance Summaries. 2015; 64(3): 1-24
  2. Gradus, S. (2014). Milwaukee, 1993: The Largest Documented Waterborne Disease Outbreak in US History. Retrieved from https://waterandhealth.org/safe-drinking-water/drinking-water/milwaukee-1993-largest-documented-waterborne-disease-outbreak-history/
  3. Portland Water Bureau, City of Portland, OR. (2018). Information on Cryptosporidium. Retrieved from https://www.portlandoregon.gov/water/75112
  4. Portland Water Bureau, City of Portland, OR (2018). Bull Run LT2 Interim Measures Watershed Inspection and Monitoring Plan. Retrieved from https://www.portlandoregon.gov/water/article/669108
  5. Centers for Disease Control and Prevention (CDC). (2015, January 28). CryptoNet: Molecular-based Tracking to Better Understand U.S. Cryptosporidiosis Transmission. Retrieved from https://www.cdc.gov/parasites/crypto/cryptonet.html
  6. Kitts, A. et al. (2014). The Database of Short Genetic Variation (dbSNP). Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK174586/
  7. Straub TM, Daly DS, Wunshel S, Rochelle PA, DeLeon R, Chandler DP. Genotyping Cryptosporidium parvumwith an hsp70 Single-Nucleotide Polymorphism Microarray. Applied and Environmental Microbiology. 2002;68(4):1817-1826. doi:10.1128/AEM.68.4.1817-1826.2002.
  8. Peng, M.M. et al. Genetic polymorphism Among Cryptosporidium parvum Isolates: Evidence of Two Distinct Human Transmission Cycles. Emerging Infectious Diseases. Centers for Disease Control. October – December 1997; 3(4): 567-573.
  9. Kostić, T. and Sessitsch, A. Microbial Diagnostic Microarrays for the Detection and Typing of Food-and Water-Borne (Bacterial) Pathogens. Microarrays. 2012; 1:3-24. doi:10.3390/microarrays1010003
  10. Chagovetz, A. and Blair, S. Real-time DNA microarrays: reality check. Biochem Soc Trans. 2009 April; 37(Pt 2): 471-475. doi: 10.1032/BST0370471
  11. Dufva, M. Fabrication of high quality microarrays. Biomolecular Engineering. 22(5-6):173-184. doi: 10.1016/j.bioeng.2005.09.003
  12. Miller, M.B. and Tang Y. (2009). Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology. Clinical Microbiology Reviews. 2009; 22(4): 611-633.
  13. Li, B et al. Advancements in Microarray Utility for Detection and tracking of Foodborne Microbes in the Genomic Era. Advanced Techniques in Biology and Medicine. 2017; 5(3). Doi: 10.4172/2379-1764.1000239.