The activity level of a microbial community function can be predicted from its metatranscriptome

Abstract

The objective of this work was to improve our understanding of the quantitative predictive capabilities of metatranscriptomics. To meet this objective, we investigated whether we can predict the activity level of a specific biochemical function based on the abundance of the corresponding gene transcript within measured community metatranscriptomes. In addition, we investigated the lower limit of a microorganism's abundance that still allows detection of its transcripts within a metatranscriptome and prediction of the activity levels of the enzyme encoded by the transcript. To do this, we amended an undefined microbial community with varying fractions of an Escherichia coli strain that can catalyze a specific transformation reaction for the herbicide atrazine. We observed a linear and proportional relationship between the activity level of the transformation reaction and the abundance of its associated encoding transcript down to an E. coli cell density of 0.05% of the population.

Figure 1

atzA gene transcripts detected by (▴) qPCR and (♦) metatranscriptome sequencing versus the rate of 2-hydroxy atrazine formation for each of the six E. coli titration levels (given as percentage of the total cell population). Error bars in the formation rate represent the 90% confidence interval from parameter estimation as described in the Supporting Information. The dashed lines are fit to the data based on model 1 linear regression by minimizing the sum of the squared residual errors and forcing a zero intercept. The coefficients of determination (r²) for the model fits were 0.987 for the qPCR data and 0.970 for the metatranscriptome sequencing data. The inset plots show the data for the reactors with low E. coli cell densities plotted on the same axes (with a reduced scale) as the main plots.