Paper-Based Diagnostic for Assessing the Gut Microbiome

New studies are showing connections between the relative abundances of the bacterial species in the human gut to many health conditions including inflammatory bowel disease, obesity, autism, and cancer. In most cases, the precise mechanism by which alterations in the microbiome affect these various medical conditions is still being investigated. Current methods for profiling the microbiome typically involve deep sequencing of processed stool samples coupled with high throughput bioinformatics analysis of the sequences. These techniques are expensive, slow, and require significant technical expertise to design, run, and interpret. These limitations have severely restricted the large-scale prospective monitoring of patient cohorts necessary to determine the causal relationship between the microbiome and human health. Here we present a paper-based molecular diagnostic platform for simple and affordable analysis of the gut microbiome. The core of the platform is two technologies – RNA toehold switch sensors and an in vitro cell-free transcription-translation system freeze-dried onto small paper discs. When combined, these two technologies provide the ability to detect nearly any RNA sequence with a simple fluorescence or colorimetric output, for approximately $1 per reaction. Here we design toehold switch sensors to detect the V3 hypervariable region of the 16S ribosomal RNA (rRNA) of a panel of microbes found in the human gut. We demonstrate good specificity of the 16S rRNA sensors and show that specificity can be improved by targeting species specific mRNA transcripts found through bioinformatic analysis. In order to achieve the necessary sensitivity to analyze human fecal samples we employ NASBA (nucleic acid sequenced based amplification) to isothermally amplify specific RNAs prior to detection by our toehold switch sensors. Finally, we show that the toehold switch sensors can be used to quantify amplification by NASBA and validate our measurements of bacterial RNA from total RNA extracted from human fecal samples using RT-qPCR.