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Computational and Structural Biotechnology JournalSystems BiologyPeer-reviewed journal article

Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness

Eric V. PatridgeiD, Anmol GorakshakariD, Matthew M. MoluskyiD, Oyetunji OgundijoiD, Angel JanevskiiD, Cristina Julian, Lan HuiD, Momchilo VuyisichiD, Guruduth S. BanavariD

PDFDOIPreprint

Abstract

It is increasingly recognized that an important step towards improving overall health is to accurately measure biomarkers of health from the molecular activities prevalent in the oral cavity. We present a general methodology for computationally quantifying the activity of microbial functional pathways using metatranscriptomic data. We describe their implementation as a collection of eight oral pathway scores using a large salivary sample dataset (n=9,350), and we evaluate score associations with oropharyngeal disease phenotypes within an unseen independent cohort (n=14,129). As clinical validation, we show that the relevant oral pathway scores are significantly worse in individuals with periodontal disease, acid reflux, and nicotine addiction, compared with controls. Given these associations, we make the case to use these oral pathway scores to provide molecular health insights from simple, non-invasive saliva samples, and as molecular endpoints for actionable interventions to address the associated conditions. HighlightsO_LIMicrobial functional pathways in the oral cavity are quantified as eight oral scores C_LIO_LIScores are significantly worse for individuals with oropharyngeal disease phenotypes C_LIO_LIThis methodology may be generalized to other pathways and other sample types C_LIO_LIThese scores provide longitudinal health insights in a precision wellness application C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/565122v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@12fb7bdorg.highwire.dtl.DTLVardef@1c7dbf3org.highwire.dtl.DTLVardef@39700forg.highwire.dtl.DTLVardef@ebf63c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Citations
9
FWCI
3.68
Field percentile
top 8%

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