help button home button Biophys. J.
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Originally published as Biophys J. BioFAST on March 16, 2007.
doi:10.1529/biophysj.107.105320
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
biophysj.107.105320v1
92/10/L89    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Fish, D. J.
Right arrow Articles by Benight, A. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Fish, D. J.
Right arrow Articles by Benight, A. S.
Biophysical Journal 92:L89-L91 (2007)
© 2007 The Biophysical Society

Multiplex SNP Discrimination

Daniel J. Fish *, M. Todd Horne * {dagger}, Robert P. Searles {ddagger}, Greg P. Brewood * and Albert S. Benight * {dagger}

* Portland Bioscience, Portland, Oregon 97201; {dagger} Department of Chemistry and Department of Physics, Portland State University, Portland, Oregon 97207; and {ddagger} Spotted Microarray Core, Oregon Health and Science University, Beaverton, Oregon 97006

Correspondence: Address reprint requests and inquiries to Daniel Fish, E-mail: djf{at}pdxbio.com.


   ABSTRACT
TOP
 ABSTRACT
ACKNOWLEDGEMENTS
 REFERENCES
 
Multiplex hybridization reactions of perfectly matched duplexes and duplexes containing a single basepair mismatch (SNPs) were investigated on DNA microarrays. Effects of duplex length, G-C percentage, and relative position of the SNP on duplex hybridization and SNP resolution were determined. Our theoretical model of multiplex hybridization accurately predicts observed results and implicates target concentration as a critical variable in multiplex SNP detection.

Personalized medicine, medical diagnosis, and prognosis based upon the unique genotype of individuals require the diagnostic capability of detecting and discerning multiple genetic markers (targets) within the genome of each individual. Multiplex assays, involving the simultaneous hybridization and detection of multiple genetic markers, promise to be a cornerstone of modern genetic testing and personalized genotyping. However, before the potential of multiplex assays and the prospects for personalized medicine can be fully realized, a more complete understanding of the underlying physics and chemistry of multiplex hybridization must be established. Such an analytical understanding provides superior insight into design and interpretation of multiplex hybridization reactions (1Go). In this communication, our analytical approach has been applied to the design of a multiplex assay for the discrimination of single nucleotide polymorphisms.

Single nucleotide polymorphisms (SNPs) that occur in the sequences of the human genome constitute the most ubiquitous and subtle genetic variations between individuals and different populations of individuals. With rapid accumulation of personal genomic sequence information, the influence of SNPs on many important phenotypes, and the role of SNPs in disease susceptibility and the response to therapeutic treatment, are coming to light. This emergence of information makes it ever more important to develop high-throughput, and sensitive, reliable, and rapid SNP genotyping assays that can clearly discriminate and resolve SNPs from their perfectly matched duplex counterparts.

Nucleic acid diagnostic assays based on multiplex hybridization offer unprecedented capabilities for systematic high-throughput screening, discrimination, and analysis of large numbers of DNA (and RNA) sequences. However, multiplex hybridization reactions are much more complex than those composed of only two complementary single strands. Increased complexity in a multiplex hybridization environment arises from the vast number of mispairing interactions that can occur, leading to crosshybridization and mismatched duplex formation (1Go). Complexity of the multiplex environment and the resulting competition between perfect match and mismatch strands strongly influence both kinetic and equilibrium behaviors and severely alter amounts of overall reaction products. Several authors have attempted to describe effects of such competition and associated hybridization errors on multiplex reaction results (1Go–9Go).

Initial theoretical and numerical studies of multiplex hybridization kinetics have revealed various distinct temporal behaviors with unexpected consequences due to competition in reactions between perfect match and mismatch duplexes (1Go). These results suggested that the effects of competitive hybridization might be utilized for optimizing SNP detection. Experiments described below were performed to investigate conditions for observation of optimum discriminatory behavior of individual probe/target subsystems in a complex (multiplex) hybridization environment.

Experimental setup consisted of six subsystems and two controls spotted to a glass microarray slide with the general design and sequences shown in Fig. 1. Each subsystem was composed of different probe pairs. The targets for each subsystem were designed to form either a perfect match duplex, or a duplex containing a single basepair mismatch with each of the probes in the subsystem. Two control probes having the same G-C composition were also spotted at various positions on the microarray. Target strands labeled with Cy-3 were hybridized in multiplex fashion (i.e., all in the same mix) to microarrays containing 14 probes (six probe pairs and two controls) located in at least four different places on the microarray; in total, each array contained 234 individual probe spots. Experiments were conducted at 25°, 40°, and 55°C.


Figure 1
View larger version (13K):
[in this window]
[in a new window]

  		FIGURE 1  Design of probes (left) and sequences (right). Target strands were synthesized on the 100 nM scale with Cy-3 on the 5' end. DNA probe and target strands were synthesized, with standard purification, and used directly as obtained from the commercial supplier (IDT, Coralville, IA). Probes were spotted using a 50 µM DNA solution (the actual concentration on the spot is not precisely known, but is assumed to be the same at each spot). Sequences contained 18 or 24 bases and varied in G-C% from 30% to 60%. SNP positions (indicated by brackets) were either in the middle or on the 3' end.

 
At incubation times of 5, 10, 15, 20, 25, 30, 45, 60, 90, and 120 min, a microarray was removed, washed, fixed, and read. The average fractional intensities were determined and are displayed in Fig. 2. Solid lines drawn through the data points are scaled predictions of our theoretical (numerical) model. For each subsystem, the predicted curve for the perfect match duplex was first normalized, and then uniformly scaled to the equilibrium value, averaged over all temperatures, of the perfect match intensity of that subsystem. Scaling factors varied from 0% to 40%. Temperature in the fitting was allowed to vary from 25° to 55°C, with best results achieved at an average of 35 ± 10°C. As seen in the figure, a reasonably good fit of the data is obtained for both perfect match and SNP duplexes.


Figure 2
View larger version (16K):
[in this window]
[in a new window]

  		FIGURE 2  Plots of intensity versus hybridization time for each of the targets shown in Fig. 2 with the probe pairs (a–f) and two controls (g). The six target strands and two controls were hybridized by adding a 10 µM solution of target to microarrays incubated at 25°, 40°, and 55°C (diamonds, triangles, and squares, respectively) and allowed to react for varying times from 5 min to 2 h before washing, fixing, and reading. Solid curves depict scaled best fits of simulated kinetics from the theoretical model; theoretically predicted curves were fit to experimental data for each subsystem (see text). Blue colors represent perfect match duplex intensities, green represents SNP intensities. Average standard deviation is approximately the size of the symbols.

 
Clear resolution between perfect match and SNP duplexes, where the perfect match duplex intensity was significantly higher than that of the SNP duplex, was seen in nearly every case. In those cases where resolution was less clear, the SNP (mismatch) occurred on the ultimate 3' end of the probe molecule. Collectively, results in Fig. 2 indicate that better discrimination occurs for SNPs that lie in the middle of shorter duplexes. Recall, these reactions took place in a multiplex hybridization environment where all targets were present simultaneously in the reaction mixture. Such consistent resolution further indicates that for the probe/target subsystems that were designed and utilized, crosshybridization between subsystem components was not a significant factor. Under these conditions, the effects of temperature from 25° to 55°C are minimal.

Although SNP discrimination was evident, optimum discriminatory behavior was not observed in these initial experiments. With the goal of achieving enhanced SNP discrimination, kinetic simulations of the multiplex system were performed and calibrated with experimental data. Results for a representative target-probe set are shown as solid curves in Fig. 3. The upper blue and green curves were calculated assuming a relatively high theoretical target-probe concentration ratio. These curves fit the data in Fig. 2, and are shown as they appear in the model calculation (before scaling). The lower pair of solid curves in Fig. 3 shows results of the simulated experiment at significantly lower target concentration. The model predicts enhanced discriminatory behavior should be observed if the target-probe concentration ratio used in the calculations is reduced by a factor of 104. This enhanced resolution suggests that a lower abundance of available target leads to a competitive dynamic between perfect match and SNP probes, which in turn leads to displacement of less favorable SNP reactions.


Figure 3
View larger version (19K):
[in this window]
[in a new window]

  		FIGURE 3  Theoretical and experimental results of SNP discrimination experiments. Solid curves display predicted behavior at a high concentration ratio (top set of curves) and a lower ratio (bottom set). Data points show best fit (within one standard deviation) of experimental results to numerical prediction. Triangle markers indicate high concentration ratio, square markers indicate lower ratio. Blue color refers to perfect match duplex intensity, green to mismatch.

 
To test this prediction, an identical series of hybridization experiments was performed with the same microarray design and same target strand mixture added, except that the concentration of added targets was reduced to 400 pM, a 2.5 x 104 lower relative concentration of target. Representative results are shown as square symbols in Fig. 3. A complete analysis and report of these, and additional relevant microarray experiments that have been performed, will be described in a subsequent publication (D. J. Fish, M. T. Horne, G. P. Brewood, and A. S. Benight, unpublished).

In summary, results that are presented reveal several interesting features of multiplex SNP hybridization and detection: 1), Hybridization is essentially complete, and SNP resolution is obtained in 1–2 h. 2), Better resolution is obtained when the SNP is located near the middle of the strand versus the ultimate end. 3), Better SNP resolution is obtained when shorter sequence fragments are employed. 4), Better resolution is obtained in higher GC% sequences. 5), Target concentration is a critical experimental parameter in achieving optimal discrimination of SNPs.

This study clearly demonstrates how our rigorous theoretical model provides guidance in experimental design as well as interpretation. Other experimental observations of enhanced discrimination of SNP duplexes due to competitive dynamics were also recently reported (10Go). These, along with results reported here, validate the efficacy of our theoretical approach to experimental design. Such analysis should prove invaluable in the future design of specific genotype assays, and find a central role in enhancing microarray performance and advancing personalized medicine.


   ACKNOWLEDGEMENTS
TOP
 ABSTRACT
 ACKNOWLEDGEMENTS
REFERENCES
 
This work was supported in part by grant No. R44GM064299 from the Small Business Innovation Research program of the National Institutes of Health.

Submitted on January 25, 2007; accepted for publication March 8, 2007.


   REFERENCES
TOP
 ABSTRACT
 ACKNOWLEDGEMENTS
 REFERENCES
 
1. Horne, M. T., D. Fish, and A. S. Benight. 2006. Statistical thermodynamics and kinetics of DNA multiplex hybridization reactions. Biophys. J. 91:4133–4153.[Abstract/Free Full Text]

2. Rose, J., and R. Deaton. 2001. Simulations of statistical mechanical estimates of hybridization error. Preliminary Proc. of the 6th International Meeting on DNA Based Computers, Leiden Center for Natural Computing. 251.

3. Deaton, R., H. B. Chen, and J. Rose. 2003. A software tool for generating non-crosshybridizing libraries of DNA oligonucleotides. DNA Computing, 8th International Workshop on DNA Based Computers (DNA8). Springer LNCS. 2568:252–261.

4. Nakao, M., Y. K. Okuji, and M. Kaneshisa. 2000. Quantitative estimation of cross-hybridization in DNA microarrays based on a liner model. Genome Informatics. 11:231–232.

5. Zhang, Y., D. A. Hammer, and D. J. Graves. 2005. Competitive hybridization kinetics reveals unexpected behavior patterns. Biophys. J. 89:2950–2959.[Abstract/Free Full Text]

6. Bishop, J., S. Blair, and A. Chagovetz. 2006. A competitive model of nucleic acid surface hybridization in the presence of point mutants. Biophys. J. 90:831–840.[Abstract/Free Full Text]

7. Chechetkin, V. R. 2007. Two-compartment model for competitive hybridization on molecular biochips. Phys. Lett. A. 360:491–494.[CrossRef]

8. Sorokin, N. V., V. R. Chechetkin, S. V. Pan'kov, O. G. Somova, M. A. Livshits, M. Y. Donnikov, A. Y. Turygin, V. E. Barsky, and A. S. Zasedatelev. 2006. Kinetics of hybridization on surface oligonucleotide microchips: theory, experiment, and comparison with hybridization on gel-based microchips. J. Biomol. Struct. Dyn. 24:57–66.[Medline]

9. Sorokin, N. V., V. R. Chechetkin, M. A. Livshits, S. V. Pan'kov, M. Y. Donnikov, D. A. Gryadunov, S. A. Lapa, and A. S. Zasedatelev. 2005. Discrimination between perfect and mismatched duplexes with oligonucleotide gel microchips: role of thermodynamic and kinetic effects during hybridization. J. Biomol. Struct. Dyn. 22:725–734.[Medline]

10. Bishop, J., C. Wilson, A. Chagovetz and S. Blair. 2006. Competitive displacement of DNA during surface hybridization. Biophys. J. 92:L10–L12.[CrossRef][Medline]




This article has been cited by other articles:


Home page
 Biophys. JHome page
J. Bishop, A. M. Chagovetz, and S. Blair
Kinetics of Multiplex Hybridization: Mechanisms and Implications
Biophys. J., March 1, 2008; 94(5): 1726 - 1734.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
D. J. Fish, M. T. Horne, G. P. Brewood, J. P. Goodarzi, S. Alemayehu, A. Bhandiwad, R. P. Searles, and A. S. Benight
DNA multiplex hybridization on microarrays and thermodynamic stability in solution: a direct comparison
Nucleic Acids Res., December 18, 2007; 35(21): 7197 - 7208.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
biophysj.107.105320v1
92/10/L89    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Fish, D. J.
Right arrow Articles by Benight, A. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Fish, D. J.
Right arrow Articles by Benight, A. S.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Copyright © 2007 by the Biophysical Society.