Metabolic history and metabolic fitness as drivers of anabolic heterogeneity in isogenic microbial populations

Abstract

Microbial populations often display different degrees of heterogeneity in their substrate assimilation, that is, anabolic heterogeneity. It has been shown that nutrient limitations are a relevant trigger for this behaviour. Here we explore the dynamics of anabolic heterogeneity under nutrient replete conditions. We applied time-resolved stable isotope probing and nanoscale secondary ion mass spectrometry to quantify substrate assimilation by individual cells of Pseudomonas putida, P. stutzeri and Thauera aromatica. Acetate and benzoate at different concentrations were used as substrates. Anabolic heterogeneity was quantified by the cumulative differentiation tendency index. We observed two major, opposing trends of anabolic heterogeneity over time. Most often, microbial populations started as highly heterogeneous, with heterogeneity decreasing by various degrees over time. The second, less frequently observed trend, saw microbial populations starting at low or very low heterogeneity, and remaining largely stable over time. We explain these trends as an interplay of metabolic history (e.g. former growth substrate or other nutrient limitations) and metabolic fitness (i.e. the fine-tuning of metabolic pathways to process a defined growth substrate). Our results offer a new viewpoint on the intra-population functional diversification often encountered in the environment, and suggests that some microbial populations may be intrinsically heterogeneous.

Dynamic utilization of low-molecular-weight organic substrates across a microbial growth rate gradient

“…Thus far, we have assumed that the growth of these microbial populations relied on a relatively homogeneous metabolism within the population at any point in time. However, there could be anabolic heterogeneity within the population, as has been shown in single-cell studies (Calabrese et al, 2021), and this fluctuating heterogeneity could explain some of our divergence in substrate uptake between isolates. Phenotypic heterogeneity in terms of metabolism has been observed in clonal Saccharomyces cerevisiae populations under nutrient-limited conditions and is likely common in microbial populations (Ackermann, 2015;Varahan & Laxman, 2021).…”

Single-cell analysis reveals an active and heterotrophic microbiome in the Guaymas Basin deep subsurface with significant heterotrophic inorganic carbon fixation

“…The decreasing heterogeneity in activity with depth observed here might be a product of decreasing genetic heterogeneity; anoxic marine sediments generally decrease in taxonomic richness with subsurface depth globally (Hoshino et al 2020). Additionally, in some laboratory bacterial cultures, single-cell activity decreases in heterogeneity over time (Calabrese et al 2021), potentially due to metabolism optimization (Goel et al 2012). Sediment age increases with depth into the subsurface, and thus, metabolism optimization over time might explain the decrease in activity heterogeneity with depth and sediment age (Figure 4).…”

“…Cells in both isogenic, laboratory cultures (Kopf et al 2015;Schreiber et al 2016;Calabrese et al 2019Calabrese et al , 2021 and mixed community, environmental samples (Vasquez-Cardenas et al 2015;Arandia-Gorostidi et al 2017;Dekas et al 2019) can show a large range in singlecell activity. In pure cultures, the heterogeneity in activity might be a result of stochastic gene expression, cell growth stage, cell-to-cell interactions, and the co-occurrence of genotypes and ecotypes (Musat et al 2008;Woebken et al 2012;Berry et al 2013;Berthelot et al 2019;Calabrese et al 2021). In mixed cultures and natural samples, activity heterogeneity is also affected by genetic heterogeneity.…”

Stable Isotope Probing-nanoFTIR for Quantitation of Cellular Metabolism and Observation of Growth-dependent Spectral Features

“…These results are in accord with previous experimental observations using single cell SIP-nanoSIMS that also identified nutrient limitation as a driver of heterogeneous substrate assimilation. [53] Variation in glucose scavenging rates at the single-cell level means that individual outlier cells with elevated metabolic rates are significantly outcompeting the majority of the population, and as such may play a disproportionately large role in shaping the structure and function of growing microbial communities. SIP-nanoFTIR therefore provides a comprehensive means to link single-cell metabolic processes to population-wide changes originating in response to environmental fluctuations.…”