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Table of Contents

Protein Microarrays

The need for efficient, sensitive, and reliable protein-based microarrays to diagnose disease states early in their progression has become increasingly apparent over the last decade. While DNA-based microarrays are relatively ubiquitous, RNA may at times be a poor predictor of specific protein activity, indicating the need for direct arrays which can simultaneously survey levels of expression of multiple proteins in a given sample (1,2). Current proteomic microarrays have been successfully optimized to detect antigen-specific IgE levels with similar sensitivity to that of commercially available ELISA assays (2). These microarrays have great potential predictive clinical value, as shown by Quintana et al, who used an antigen-based microarray to establish patterns of IgG expression in nonobese diabetic mice which were strongly predictive of each mouse’s susceptibility to cyclophosphamide-accelerated diabetes (3).

One method of disease detection relies upon biomarkers: cellular molecules which have levels of expression that are characteristic of a current or future disease state or disease susceptibility. Biomarkers are often used in drug discovery and have obvious value in a clinical setting, allowing for advance treatment of a probable disease state before its more deleterious symptoms can manifest. For example, increased levels of IL-18 as detected by ELISA in urine following cardiopulmonary bypass were recently found to be predictive of future acute kidney injury, yielding the potential for physicians to begin appropriate treatment regimens earlier, resulting in improved patient outcomes (4). Biomarkers are assessed on the basis of both their specificity (the number of correctly established true negative results) and their sensitivity (number of correctly established true positive results) (5). Oftentimes, the specificity or sensitivity of a given biomarker may not be high; for example, a urine IL-18 level >50pg/mL 12 hours after cardiopulmonary bypass had a specificity of 94% but a sensitivity of only 50% as a predictor of acute kidney injury (4).This suggests that only 50% of cases of acute kidney injury would be correctly diagnosed (a high rate of false negatives), while only 6% of negative cases would be falsely diagnosed as positive. Thus, while urine IL-18 is a good predictor of acute kidney injury, it alone is insufficient to diagnose such a condition.

A solution to single biomarker sensitivity/specificity problems is the study of patterns of multiple biomarker activities in disease states. These biomarker signatures (biosignatures) of disease serve to increase both specificity and sensitivity of disease detection by simultaneously measuring levels of multiple biomarkers to provide a more precise indication of physiological states. A recent review on tuberculosis (TB) diagnosis and drug development stresses that the deleterious effects of the disease lack a single reliable biomarker, and as a result early diagnosis can be a challenging process. The authors instead propose the use of a panel of well-defined biomarkers of TB in order to permit for rapid diagnoses and drug discovery (6). Other biosignature discovery projects currently underway include establishing biosignatures of the prodromal stage of Alzheimer’s disease in order to allow for advance treatment, as this disorder currently cannot be diagnosed with certainty until autopsy (7). Other researchers are working to detect biosignatures of renal transplant tolerance in order to custom tailor treatment methods to individual transplant recipients (8).

The ability to predict disease onset from specific protein biosignatures has myriad applications in the current healthcare system. At birth, infants in the USA have blood drops collected on a Guthrie blood spot card that are screened for specific metabolic disorders, and are then stored for a period of time which is defined on a state-by-state basis. As initial PCR-based testing generally leaves the majority of these dried blood spot samples (DBSS) unused, numerous multiplexed protein- and DNA-based disease screens could be performed without requiring any additional sample collection from neonates (9). Janzi et al were able to use microarrays to detect C3 deficiency in Swedish DBSS which had been stored for up to 20 years, with a strong correlation between microarray and ELISA data, demonstrating the diagnostic value of these blood spots were they to be screened upon collection (10). While complement deficiency is a lack of a single defined protein, strategies are currently being developed to screen for biosignatures of much more complex diseases such as post-traumatic stress disorder; the ability to identify this and other debilitating conditions early in their course (or even before disease onset) would elucidate many potential early intervention treatments (11).

Many clinical applications of protein microarrays based on DBSS analysis can easily be imagined. DBSS-derived assays have already been developed which can test for the presence of specific toxins on blood collection cards, as demonstrated by Bottein et al. who were able to detect ciguatoxin in stored blood collection cards collected up to 12 hours post-toxicant exposure (12). Numerous occupations inevitably risk exposing employees to toxic compounds, with the potential to produce myriad negative health outcomes. At times, certain compounds may not be known to possess carcinogenic properties; for example, naphthalene was only classified as a “potential human carcinogen” in 2000 by the US Environmental Protection Agency despite having been a ubiquitous environmental and occupational compound for decades in certain industrial settings (13). If all employees of a particular company were to be screened for levels of a toxicant such as naphthalene after being exposed for a period of time, the relative values would be uninformative without a baseline reading from the beginning of each employee’s tenure, as preexisting exposure would vary on an individual basis. Storing a liquid serum or blood sample for each employee would be cost- and space-prohibitive; however storing DBSS samples would be affordable and would require very limited storage space. Stored DBSS would allow for retrospective baseline screens of compounds which were not known to be toxic at the onset of employment, providing a clear advantage over performing only specific screens after a hazard has been identified since some individuals may have had high levels of this toxicant in their systems prior to employment. A similar screening technique can be imagined for soldiers entering the armed forces, with DBSS being stored before each soldier goes to war in order to serve as a baseline to test toxicant exposure during wartime operations.

References

  • 1. LaBaer J, Ramachadran N. Protein microarrays as tools for functional proteomics. Current Opinion in Chemical Biology. 2005; 9:14-19
  • 2. Schweitzer B, Kingsmore SF. Measuring proteins on microarrays. Current Opinion in Biotechnology. 2002
  • 3. Quintana FJ, et al. Functional immunomics: Microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes. PNAS. October 5, 2004; 101(Suppl 2): 14615-14621.
  • 4. Parikh CR, et al. Urinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery. Kidney International. 2006; 70:199–203.
  • 5. Kozak KR, et al. Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis. PNAS. October 14, 2003; 100 (21): 12343-12348.
  • 6. Parida SK, Kaufmann SHE. The quest for biomarkers of tuberculosis. Drug Discovery Today. 2010; 15(3-4):148-157.
  • 7. Shaw LM, et al. Cerebrospinal Fluid Biomarker Signature in Alzheimer’s Disease Neuroimaging Initiative Subjects. Annals of Neurology.2009; 65(4): 403-413.
  • 8. Sagoo P, et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. The Journal of Clinical Investigation.2010; 120(6): 1848-61.
  • 9. Haak PT, et al. Archived unfrozen neonatal blood spots are amenable to quantitative gene expression analysis. Neonatology. 2009 ; 95(3): 210–216.
  • 10. Janzi M, et al. Screening for C3 Deficiency in Newborns Using Microarrays. PLoS ONE. 4(4): e5321.
  • 11. Zhang L, et al. A strategy for the development of biomarker tests for PTSD. Medical Hypotheses. 2009; 73(3):404-409.
  • 12. Bottein MY, et al. Biomonitoring of ciguatoxin exposure in mice using blood collection cards. Toxicon. 2005; 46(3):243-251.
  • 13. Preuss R, Angerer J, Drexler H. Naphthalene—an environmental and occupational toxicant. Int Arch Occup Environ Health. 2003; 76:556–576.

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