Molecular Origins of Transcriptional Heterogeneity in Diazotrophic Klebsiella oxytoca

Abstract

Phenotypic heterogeneity in clonal bacterial batch cultures has been shown for a range of bacterial systems; however, the molecular origins of such heterogeneity and its magnitude are not well understood. Under conditions of extreme low-nitrogen stress in the model diazotroph Klebsiella oxytoca, we found remarkably high heterogeneity of nifHDK gene expression, which codes for the structural genes of nitrogenase, one key enzyme of the global nitrogen cycle. This heterogeneity limited the bulk observed nitrogen-fixing capacity of the population. Using dual-probe, single-cell RNA fluorescent in situ hybridization, we correlated nifHDK expression with that of nifLA and glnK-amtB, which code for the main upstream regulatory components. Through stochastic transcription models and mutual information analysis, we revealed likely molecular origins for heterogeneity in nitrogenase expression. In the wild type and regulatory variants, we found that nifHDK transcription was inherently bursty, but we established that noise propagation through signaling was also significant. The regulatory gene glnK had the highest discernible effect on nifHDK variance, while noise from factors outside the regulatory pathway were negligible. Understanding the basis of inherent heterogeneity of nitrogenase expression and its origins can inform biotechnology strategies seeking to enhance biological nitrogen fixation. Finally, we speculate on potential benefits of diazotrophic heterogeneity in natural soil environments. IMPORTANCE Nitrogen is an essential micronutrient for both plant and animal life and naturally exists in both reactive and inert chemical forms. Modern agriculture is heavily reliant on nitrogen that has been "fixed" into a reactive form via the energetically expensive Haber-Bosch process, with significant environmental consequences. Nitrogen-fixing bacteria provide an alternative source of fixed nitrogen for use in both biotechnological and agricultural settings, but this relies on a firm understanding of how the fixation process is regulated within individual bacterial cells. We examined the cell-to-cell variability in the nitrogen-fixing behavior of Klebsiella oxytoca, a free-living bacterium. The significance of our research is in identifying not only the presence of marked variability but also the specific mechanisms that give rise to it. This understanding gives insight into both the evolutionary advantages of variable behavior as well as strategies for biotechnological applications.

Keywords: mathematical modeling; nitrogen fixation; transcriptional regulation.

FIG 1

(A) Regulatory pathway governing nifHDK expression. Transcription of nifHDK is subject to a hierarchical regulatory system. (B) Population size during transition to diazotrophy in wild-type K. oxytoca. Following run-out of ammonia, cultures displayed arrested growth during the diazotrophic transition, marked in gray. Growth from 10 h onwards was achieved through nitrogen fixation. (C) Distribution of nifHDK transcript abundance at 8 h, as illustrated by mRNA-FISH. Adapted from reference with permission from the publisher.

FIG 2

Mutual information analysis based on dual-probe measurements from biological replicate 1. For each case, the marginal distributions of the two mRNA abundances are shown, in addition to the calculated mutual information and the total entropy in nifHDK abundance. Mutual information was compared to a null distribution obtained by randomly shuffling the nifHDK data 100,000 times, thereby providing a P value for each pair. These values for the displayed data and a further biological replicate are displayed in Table 1. (A) WT cells in which glnK and nifH were simultaneously measured, indicating significant MI. (B) WT cells in which nifL and nifH were simultaneously measured, indicating no significant MI. (C) Cells from the ΔglnB mutant in which glnK and nifH were simultaneously measured, indicating significant MI. (D) Cells from the ΔglnK mutant in which nifL and nifH were simultaneously measured, indicating no significant MI.

FIG 3

Stochastic modeling of bursty transcription incorporating extrinsic noise. (A) nifHDK mRNA copy number distributions for each of the +nifA, WT, and ΔglnK strains. WT and ΔglnK data are from replicate 1 as displayed in Fig. 2. (B) Schematic of bursty transcription and its relation to the stochastic model. Extrinsic noise here is catered for by considering the burst frequency to be variable between cells. (C) Variation of the model parameters between mutants, plotted as a function of the mean expression level. Error bars denote 95% Bayesian credible intervals, obtained from the posterior distributions. Data are plotted for biological replicates 1 and 2.

FIG 4

(A) Calculated contributions of extrinsic noise to the variance in nifHDK transcript abundance. Contributions are calculated for 4,000 parameter triplets sampled from the posterior distributions, thereby providing the most probable value and 68% credible intervals. (B) Schematic showing propagation of noise through the signaling cascade. Only a contribution from glnK was supported directly by the experimental data, although contributions from nifLA and from ntrC could not be excluded. Global noise sources were determined to be undetectable.