Abstract
BackgroundGenetic material extracted from in situ microbial communities has high promise as an indicator of biological system status. However, the challenge is to access genomic information from all organisms at the population or community scale to monitor the biosystem’s state. Hence, there is a need for a better diagnostic tool that provides a holistic view of a biosystem’s genomic status. Here, we introduce an in vitro methodology for genomic pattern classification of biological samples that taps large amounts of genetic information from all genes present and uses that information to detect changes in genomic patterns and classify them.ResultsWe developed a biosensing protocol, termed Biological Memory, that has in vitro computational capabilities to “learn” and “store” genomic sequence information directly from genomic samples without knowledge of their explicit sequences, and that discovers differences in vitro between previously unknown inputs and learned memory molecules. The Memory protocol was designed and optimized based upon (1) common in vitro recombinant DNA operations using 20-base random probes, including polymerization, nuclease digestion, and magnetic bead separation, to capture a snapshot of the genomic state of a biological sample as a DNA memory and (2) the thermal stability of DNA duplexes between new input and the memory to detect similarities and differences. For efficient read out, a microarray was used as an output method. When the microarray-based Memory protocol was implemented to test its capability and sensitivity using genomic DNA from two model bacterial strains, i.e., Escherichia coli K12 and Bacillus subtilis, results indicate that the Memory protocol can “learn” input DNA, “recall” similar DNA, differentiate between dissimilar DNA, and detect relatively small concentration differences in samples.ConclusionsThis study demonstrated not only the in vitro information processing capabilities of DNA, but also its promise as a genomic pattern classifier that could access information from all organisms in a biological system without explicit genomic information. The Memory protocol has high potential for many applications, including in situ biomonitoring of ecosystems, screening for diseases, biosensing of pathological features in water and food supplies, and non-biological information processing of memory devices, among many.Electronic supplementary materialThe online version of this article (doi:10.1186/1754-1611-8-25) contains supplementary material, which is available to authorized users.
Highlights
Nucleic acid technology has become an indispensible tool in medical diagnosis, microbial ecology, environmental microbiology, etc. by providing specific, sensitive detection of genes in chemically and biologically complex backgrounds
Principle of the Biological Memory An underlying hypothesis of the DNA-computing-inspired Biological Memory is that the products, which are learned in vitro, represent the entire DNA population in the input genomic sample, and that the differences between input sets could be discriminated by separating the output hybridization patterns between the learned products
As the complexity of genomic samples increases, which would likely happen in real-world samples, for example from the environment or a living being, the Memory protocol could be adapted by using longer random sequences (i.e., >R20) to capture more information in the complex samples
Summary
Nucleic acid technology has become an indispensible tool in medical diagnosis, microbial ecology, environmental microbiology, etc. by providing specific, sensitive detection of genes in chemically and biologically complex backgrounds. Studies have estimated that there are approximately 4 × 103 to 104 microbial species per gram of soil, but only less than 1% of microorganisms in nature are observable with the standard culturing techniques [2] These in turn generated a renewed demand for innovative approaches that can quickly, exhaustively, and intelligently detect and classify gene expression profiles. A challenge is to discover more efficient and effective ways to tap the large amounts of genetic information from a biological sample (i.e., gDNA) or all expressed genes in the biological sample (i.e., cDNA from mRNA), and to use that information to detect changes in genomic patterns and classify them. We introduce an in vitro methodology for genomic pattern classification of biological samples that taps large amounts of genetic information from all genes present and uses that information to detect changes in genomic patterns and classify them
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