Identifying microbially mediated transformations of DOC across season and tide from simultaneous changes in whole community gene expression and in mass spectra generated by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS)

Despite the advent of methods enabling high resolution characterization of metabolic activity and of organic matter, linking microbial metabolism to organic matter transformations remains a challenge. By sequencing metatranscriptomes and using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) to characterize organic matter (OM) at the beginning and at the end of incubations of estuarine water across tide and season, we sought to link observed a changes in OM composition to microbial metabolism. We used linear models and K means clustering to identify clusters of genes that responded coherently across season, which accounted for most of the variability in gene expression, over tidal regime, which explained the majority of the remaining variation, and over time during the 24 hour incubations. We used an approach from the field of signal processing, that to our knowledge has not been used to analyze FTICR-MS data, to identify formulae of compounds that changed in concentration during the incubations. This approach, based on the discrete wavelet transform (DWT), allowed us to overcome some of the challenges associated with analyzing FTICR-MS data: variable ionization of organic compounds, signal suppression by high concentration compounds, and uncertainty about how to normalize changes across spectra. We were able to link clusters of metabolic and transporter genes to changes in OM composition, and uniquely identify genes based on their cross correlation with changes in FTICR mass spectra. Our approach for analyzing FTICR- MS data enables more robust inference about OM transformations, and linking high resolution changes in gene expression and in OM data during incubations represents an important step toward formulating models of microbial metabolism relevant for predicting biogeochemically relevant C fluxes.