Lee Ju Seok, Chen Junghuei, Deaton Russell, Kim Jin-Woo
Bio/Nano Technology Laboratory, Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Cell and Molecular Biology Graduate Program, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Department of Chemistry, Seoul National University, Seoul, Republic of Korea.
Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716 USA.
J Biol Eng. 2014 Nov 6;8(1):25. doi: 10.1186/1754-1611-8-25. eCollection 2014.
Genetic 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.
We 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.
This 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.
从原位微生物群落中提取的遗传物质极有可能作为生物系统状态的指标。然而,挑战在于在种群或群落规模上获取所有生物体的基因组信息,以监测生物系统的状态。因此,需要一种能提供生物系统基因组状态整体视图的更好的诊断工具。在此,我们介绍一种用于生物样品基因组模式分类的体外方法,该方法可从所有存在的基因中挖掘大量遗传信息,并利用这些信息检测基因组模式的变化并进行分类。
我们开发了一种称为“生物记忆”的生物传感协议,该协议具有体外计算能力,可直接从基因组样本中“学习”和“存储”基因组序列信息,而无需了解其明确序列,并且能够在体外发现先前未知输入与学习到的记忆分子之间的差异。“记忆”协议是基于以下两点设计和优化的:(1)使用20个碱基的随机探针进行常见的体外重组DNA操作,包括聚合、核酸酶消化和磁珠分离,以捕获生物样品基因组状态的DNA记忆快照;(2)新输入与记忆之间DNA双链体的热稳定性,以检测相似性和差异。为了实现高效读出,使用微阵列作为输出方法。当基于微阵列的“记忆”协议用于测试其使用两种模式细菌菌株(即大肠杆菌K12和枯草芽孢杆菌)的基因组DNA的能力和灵敏度时,结果表明“记忆”协议可以“学习”输入DNA,“召回”相似DNA,区分不同DNA,并检测样品中相对较小的浓度差异。
本研究不仅证明了DNA的体外信息处理能力,还证明了其作为基因组模式分类器的潜力,该分类器可以在没有明确基因组信息的情况下从生物系统中的所有生物体获取信息。“记忆”协议在许多应用中具有很高的潜力,包括生态系统的原位生物监测、疾病筛查、水和食品供应中病理特征的生物传感以及记忆装置的非生物信息处理等众多方面。