RapTOR Being Adapted

Stan Langevin optimizes DNA hybridization conditions to suppress high-abundance human DNA using time-consuming standard benchtop methods. Microfluidic-based normalization in RapTOR will eliminate tedious benchtop protocols combining DNA hybridization and hydroxyapatite chromatography on a single platform. (Photo by Randy Wong).

Finding a collection of slightly different needles in a haystack of human DNA. This slightly inadequate analogy characterizes the problem of discovering novel, heretofore unknown or uncharacterized pathogens in a human blood or tissue sample. With the vast majority of DNA from such a sample being of human origin, Sandia National Laboratories’ RapTOR (Rapid Threat Organism Recognition) LDRD is pursuing an automated method of eliminating or minimizing the effect of this human DNA, in order to use existing molecular biology methods, such as ultrahigh-throughput DNA sequencing (UHTS) to characterize the DNA of these new pathogens. Such pathogens may arise from genetic engineering of existing threat organisms, or like Ebolavirus, when initially found, may be newly discovered pathogenic agents. In any case, using an older technology known as hydroxyapatite chromatography, the RapTOR LDRD has been microfluidically automating a process known as “normalization,” which removes abundant human DNA sequences, so that less numerous DNA of potential pathogens can be sequenced.

Recently, the RapTOR team, led by Principal Investigator Todd Lane, received validation in the form of an $800,000 grant over two years from the DOE Biomass Program for the proposal “Pond Crash Forensics.” Using pathogen detection and characterization technologies developed under the RapTOR LDRD Grand Challenge, the team will compare the environmental conditions and metagenomes of algal samples taken from normal ponds to those taken from ponds that have undergone population collapse. Since algae are commonly grown in raceway ponds, large, shallow, artificial ponds that serve as fields for algae crops, bacterial, fungal, and viral pathogens can enter these ponds and cause algal populations to precipitously crash. Given that there has been little work done on the microbiological agents that can provoke these crashes, RapTOR becomes a logical tool to weed through the majority algal DNA, normalizing it, so that the DNA (and if relevant, RNA) of potential crash-provoking pathogens (to algae) can be sequenced and characterized.

Since algae are widely regarded as a potential source of renewable biofuels (biodiesel), any technology to mass-produce fuel-grade algae must be capable of sustaining high growth rates and avoiding such population crashes. Hopefully RapTOR can begin to point the way toward discerning algal pathogens that, if characterized, can be thwarted in order to minimize such crashes.

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