Author response: Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies
2021-10-05

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Bacteria commonly live in spatially structured biofilm assemblages, which are encased by an extracellular matrix. Metabolic activity of the cells inside biofilms causes gradients in local environmental conditions, which leads to the emergence of physiologically differentiated subpopulations. Information about the properties and spatial arrangement of such metabolic subpopulations, as well as their interaction strength and interaction length scales are lacking, even for model systems like Escherichia coli colony biofilms grown on agar-solidified media. Here, we use an unbiased approach, based on temporal and spatial transcriptome and metabolome data acquired during E. coli colony biofilm growth, to study the spatial organization of metabolism. We discovered that alanine displays a unique pattern among amino acids and that alanine metabolism is spatially and temporally heterogeneous. At the anoxic base of the colony, where carbon and nitrogen sources are abundant, cells secrete alanine via the transporter AlaE. In contrast, cells utilize alanine as a carbon and nitrogen source in the oxic nutrient-deprived region at the colony mid-height, via the enzymes DadA and DadX. This spatially structured alanine cross-feeding influences cellular viability and growth in the cross-feeding-dependent region, which shapes the overall colony morphology. More generally, our results on this precisely controllable biofilm model system demonstrate a remarkable spatiotemporal complexity of metabolism in biofilms. A better characterization of the spatiotemporal metabolic heterogeneities and dependencies is essential for understanding the physiology, architecture, and function of biofilms. Editor's evaluation In this manuscript the authors characterize the temporal and spatial distribution of cells with different metabolic states within colony of biofilms of the model bacterium Escherichia coli using a combination of transcriptomics, metabolomics, and quantitative measurements of growth. They show that within the biofilm cells performing different metabolic functions are distributed in different regions of the colonies, and propose a model where nutrient cross-feeding through the amino acid alanine explains the phenotypic heterogeneity observed within the biofilm. Interestingly, they show that mutants impaired in cross-feeding have a fitness disadvantage. The finding reported and the innovative technical approaches used, can potentially be applied to identify novel metabolic interactions between communities of bacteria and understand how bacterial subpopulations interact spatially and metabolically within biofilms. https://doi.org/10.7554/eLife.70794.sa0 Decision letter Reviews on Sciety eLife's review process Introduction After bacterial cell division on surfaces, daughter cells often remain in close proximity to their mother cells. This process can yield closely packed populations with spatial structure, which are often held together by an extracellular matrix. Such spatially structured assemblages, called biofilms (Flemming et al., 2016), are estimated to be the most abundant form of microbial life on Earth (Flemming and Wuertz, 2019). The metabolic activity of cells inside these dense populations leads to spatial gradients of oxygen, carbon, and nitrogen sources, as well as many other nutrients and waste products (Ackermann, 2015; Evans et al., 2020; Pacheco et al., 2019; Stewart and Franklin, 2008). Cells in different locations within biofilms therefore inhabit distinct microenvironments. The physiological responses to these microenvironmental conditions result in spatially segregated subpopulations of cells with different metabolism (Barroso-Batista et al., 2020; D'Souza et al., 2018; Stewart and Franklin, 2008). Bacterial growth into densely packed spatially structured communities, and metabolic activity of the constituent cells, therefore naturally lead to physiological differentiation (Evans et al., 2020; Røder et al., 2020; Serra et al., 2013a; Stewart and Franklin, 2008). Metabolic and phenotypic heterogeneities are frequently observed in multi-species communities (Garg et al., 2016; Henson et al., 2019; Kim et al., 2020; Pacheco et al., 2021; Serra and Hengge, 2021) and in single-species biofilm populations (Cole et al., 2015; Dal Co et al., 2019; Lin et al., 2018; Moree et al., 2012; Rani et al., 2007; Serra et al., 2013b; Teal et al., 2006). Identifying the origins of these heterogeneous subpopulations, and how they interact with each other, is important for understanding the development and function of biofilms (Cole et al., 2015; Lin et al., 2018; Liu et al., 2015; Prindle et al., 2015). Multi-species biofilms predominate in the environment, yet they are highly complex and feature many concurrent intra-species and inter-species interaction processes, which may be organized in space and time (Brislawn et al., 2019; Garg et al., 2016; Kim et al., 2020). Due to this complexity of multi-species biofilms, it is often difficult to disentangle whether spatiotemporal physiological differentiation or a particular interaction between subpopulations is caused by the relative position of the species, or the position of each species in the context of the entire community, or the mutual response of different species to each other. In contrast, single-species biofilms offer precisely controllable model systems with reduced complexity for understanding basic mechanisms of metabolic differentiation and the interaction of subpopulations. Investigations of phenotypic heterogeneity in single-species assemblages have already revealed fundamental insights into metabolic interactions of subpopulations with consequences for the overall fitness and growth dynamics of the assemblages (Arjes et al., 2021; Cole et al., 2015; Evans et al., 2020; Lin et al., 2018; Liu et al., 2017; Liu et al., 2015; Wolfsberg et al., 2018). However, even for single-species biofilms, the extent of metabolic heterogeneity and metabolic dependencies of subpopulations are unclear. To obtain an unbiased insight into the spatial organization of metabolism inside biofilms, we measured metabolome and transcriptome dynamics during the development of E. coli colony biofilms on a defined minimal medium that was solidified with agar. Our model system enabled highly reproducible colony growth and precise control of environmental conditions, which allowed us to detect phenotypic signatures of subpopulations. The temporally and spatially resolved data revealed that alanine metabolism displays a unique pattern during colony growth. We determined that secretion of alanine occurs in a part of the anoxic region of the colony, where the carbon and nitrogen sources are abundant. The secreted alanine is then consumed in a part of the oxic region of the colony, where glucose and ammonium from the minimal medium are lacking. This spatially organized alanine cross-feeding interaction occurs over a distance of tens of microns, and has important consequences for the viability and growth of the localized cross-feeding-dependent subpopulation, and for the global colony morphology. Results Colony growth transitions and global metabolic changes To investigate the spatiotemporal organization of metabolism inside E. coli colonies, we first characterized the basic colony growth dynamics and morphology on solid M9 minimal medium, which contained glucose and ammonium as the sole carbon and nitrogen sources, respectively (Figure 1A). For all measurements performed with colonies, including microscopy-based measurements, the colonies were grown on top of filter membranes that were placed on M9 agar (Figure 1—figure supplement 1A). These membranes enabled rapid transfer of the colonies into extraction buffers, for downstream transcriptome and metabolome analyses. When we grew E. coli wild-type colonies in these conditions, we observed motility on the filter membranes on M9 agar, which led to heterogeneous colony development and an asymmetric colony morphology (Figure 1—figure supplement 1B). However, to study the spatiotemporal metabolism in E. coli colonies as precisely as possible, and to resolve even small phenotypic differences between strains and conditions, we required a colony growth behavior that is as reproducible as possible. Therefore, a strain lacking flagella due to the deletion of the flagellin (ΔfliC) was used as the parental strain for all following experiments, which resulted in highly reproducible and axially symmetric colony morphologies (Figure 1—figure supplement 1C). Colonies generally displayed two growth phases: colonies showed exponential growth in volume, height, and diameter for up to ~24 hr, followed by linear growth (Figure 1A and B). This transition in colony growth dynamics has previously been observed, and was hypothesized to be caused by a change in metabolism due to altered nutrient penetration and consumption for colonies above a certain size (Pirt, 1967; Warren et al., 2019). Figure 1 with 5 supplements see all Download asset Open asset Transcriptomes and metabolomes during E. coli colony biofilm growth reveal metabolic transition and a unique role of alanine. (A) Cross-sectional profiles of representative E. coli colonies grown on membrane filters placed on solid M9 agar at different time points. (B) Volume (top panel), radius and height (bottom panel) of colonies as a function of time indicate a growth rate transition around 24–32 hr. All replicates are shown as individual data points, with a line connecting the mean values, n = 3 biological replicates. (C) Dynamics of expression profiles of 4231 genes during colony growth reveal physiological transition between 24 and 32 hr, n = 3 biological replicates. (D) Mean expression fold-changes of known amino acid exporters during colony growth, calculated in comparison to 12 hr colonies, n = 3 biological replicates. (E) Comparison of amino acid profiles in whole colonies, measured with mass spectrometry during colony growth; n = 3 biological replicates. Levels of amino acids are shown in Figure 1—figure supplement 5. Figure 1—source data 1 Source data for Figure 1. https://cdn.elifesciences.org/articles/70794/elife-70794-fig1-data1-v1.xlsx Download elife-70794-fig1-data1-v1.xlsx To further characterize the phenotypic changes that occur during colony growth, we performed time-resolved whole-colony transcriptome measurements (Figure 1C and Figure 1—figure supplement 2A). The transcriptomes revealed a major change after 24 hr of growth (Figure 1C), which reflects the change in growth dynamics that was apparent in the morphological parameters (Figure 1B). After 72 hr of growth, 966 out of 4231 detected genes were differentially expressed in comparison to the 12 hr time point (log2-fold-changes > 1 or <-1, and FDR-adjusted p-value < 0.05). The whole-colony transcriptomes also showed that biofilm matrix biosynthesis genes are expressed continuously during colony growth (Figure 1—figure supplement 3). Furthermore, the transcriptomes confirmed that well-known pathways such as mixed acid fermentation and tricarboxylic acid (TCA) cycle were differentially regulated after 24 hr of growth (Figure 1—figure supplement 4A,C). These results are consistent with the hypothesis that above a certain colony size, which is reached between 24 and 32 hr in our conditions, the consumption of oxygen by cells in the outer region of the colony causes a large anoxic region inside the colony. Although it is commonly assumed that E. coli subpopulations cross-feed acetate (Cole et al., 2015; Dal Co et al., 2019; San Roman and Wagner, 2020; Wolfsberg et al., 2018), the temporal transcriptomes did not reveal strong regulation of acetate metabolism transcripts during the major transition in colony metabolism described above (Figure 1—figure supplement 4A). However, transcripts of the lactate, formate, and succinate biosynthesis pathways displayed differential regulation during colony growth (Figure 1—figure supplement 4A). Apart from these metabolites, we noticed peculiar patterns in the expression of amino acid pathways. The gene expression levels for some amino acid transporters remained unchanged during colony growth, while a few amino acid transporters were >2 fold up- or down-regulated after 24 hr or 32 hr of growth (Figure 1D). Interestingly, the gene coding for the alanine exporter AlaE (Hori et al., 2011b) showed strong expression changes (~50 fold increase) in 72-hr-colonies relative to 12-hr-colonies (Figure 1D). To further characterize the observed metabolic transition between 24 and 32 hr of colony growth, we measured the amino acid profiles of the colonies using mass spectrometry and normalized them by the colony biomass. To obtain sufficient biomass for these measurements, the colonies needed to be grown for at least 18 hr. All amino acid abundances decreased during colony growth – except for alanine, which remained relatively constant with a peak abundance at 32 hr (Figure 1E, Figure 1—figure supplement 2B, and Figure 1—figure supplement 5). Thus, both transcriptome and metabolome data suggest a unique change of alanine metabolism during E. coli colony growth, which led us to investigate the role of alanine metabolism in colony morphogenesis. Spatial regulation of alanine transport and degradation Biofilms are expected to be metabolically heterogeneous so that we hypothesized that alanine metabolism is spatially organized inside biofilms. To test this hypothesis, we developed a method to measure transcriptomes with spatial resolution in the colonies. The method is based on the oxygen-dependence of chromophore maturation of the fluorescent protein mRuby2 (Lam et al., 2012; Tsien, 1998). Using a strain that constitutively expresses mRuby2 from a chromosomal locus, so that all cells in the colony produce mRuby2, we observed that only the air-facing region of the colony was fluorescent in colonies that had grown for 72 hr. Quantification of the mRuby2 fluorescence showed a similar, but slightly steeper decrease of fluorescence in the horizontal xy-direction into the colony, compared with the vertical z-direction (Figure 2A). By using an oxygen microsensor to directly measure oxygen levels inside the colony in the z-direction (Jo et al., 2017), we determined that the mRuby2 fluorescence was a reliable indicator of oxygen levels (Figure 2A). During colony growth, the fraction of fluorescent cells in the colony decreased (inset in Figure 2B), and the majority of the colony became non-fluorescent (i.e. anoxic) around 24 hr, which coincides with the time at which the whole-colony transcriptome shifted towards anaerobic metabolism (Figure 1C). This decrease in the mRuby2-fluorescent population during colony growth ultimately led to a thin layer of fluorescent cells in the air-facing part of the colony (Figure 2B). Figure 2 with 4 supplements see all Download asset Open asset Alanine transport and degradation are spatially regulated within colony biofilms. (A) Measurement of oxygen penetration into colonies grown for 72 hr on filter membranes on M9 agar using two different methods (for strain KDE722). Left axis (red): Intensity of mRuby2 fluorescence within a vertical cylinder of radius 3.3 μm in the center of the colony (filled circles), and intensity of mRuby2 fluorescence within a horizontal plane at the base of the colony (open circles, mean ± s.d., n = 3 biological replicates). Right axis (blue): Direct measurement of oxygen levels acquired by vertical scanning of an oxygen microsensor at the center of the colony (blue triangles, mean ± s.d., n = 10 replicates). (B) Confocal image of a representative 72 hr colony of a strain that constitutively expresses mRuby2 (KDE722). Insert: Fraction of fluorescent biovolume inside colonies grown for different times. Data are mean ± s.d., n = 3 biological replicates. (C) Scheme of the sorting procedure: Cells from a 72 hr colony are separated, fixed using formaldehyde, and sorted according to their mRuby2 fluorescence, followed by RNA-seq, as described in the methods section. (D) Heatmaps showing the mean fold-change (n = 3 biological replicates) of expression levels of genes involved in alanine metabolism, from whole-colony measurements. Fold-changes are computed relative to the 12 hr timepoint. (E) Spatial transcriptome results, quantified as fold-changes between the inner region (no mRuby2 fluorescence) and outer region (high mRuby2 fluorescence) regions of colonies grown for 72 hr. Blue color in the heatmap indicates genes with higher transcript levels in the inner region of the colony, red color indicates higher transcripts in the outer region of the colony. Data are means, n = 4 biological replicates. Non-significant differences between spatial regions are labeled 'ns'. The p-values correspond to false discovery rate (FDR)-adjusted p-values: * p < 0.05, ** p < 0.01, *** p < 0.001. Figure 2—source data 1 Source data for Figure 2. https://cdn.elifesciences.org/articles/70794/elife-70794-fig2-data1-v1.xlsx Download elife-70794-fig2-data1-v1.xlsx To eliminate the possibility that the observed mRuby2 fluorescence profile was due to imaging artefacts, such as insufficient laser penetration into the colony, we disrupted 72-hr-colonies and imaged the resulting, well-separated single cells. The time between colony disruption and imaging for this control experiment was less than 2 min, which is substantially shorter than the mRuby2 fluorophore maturation time (~150 min Lam et al., 2012). In these images, only some cells displayed fluorescence (Figure 2—figure supplement 1A), indicating that the fluorescence gradient we observed in Figure 2B is not an imaging artefact. In an additional control experiment, we sought to test if the fluorescence gradient was caused by the oxygen gradient. If the oxygen gradient in the colony is created by oxygen consumption by cells in the air-facing region (Klementiev et al., 2020; Stewart and Franklin, 2008), the oxygen gradient (and therefore the mRuby2 fluorescence gradient) should disappear when metabolic processes that consume oxygen are prevented. To test this, we starved the colonies by transferring the filter membrane carrying the colonies to an M9 agar plate lacking glucose, which strongly decreases the colony capacity to consume oxygen. We observed that in this case, mRuby2 proteins that are located in the formerly dark anoxic region of the colony became fluorescent (Figure 2—figure supplement 1B,C,D). The oxygen in the fresh agar plate is not able to cause the entire colony to become fluorescent without the reduced oxygen consumption in the colony caused by the lack of glucose. The finding that the entire colony becomes fluorescent after the transfer is consistent with the interpretation that in the absence of glucose, cells consume less oxygen so that molecular oxygen can penetrate into the colony to enable chromophore maturation of mRuby2 in the formerly anoxic region (Figure 2—figure supplement 1B,C,D). Together, these control experiments and the direct measurements of oxygen levels inside the colonies show that the difference in fluorescence levels in our system reflect the spatial position of the cells in the colony. To obtain spatial transcriptomes, we then separated colonies grown for 72 hr into individual cells, immediately fixed them with formaldehyde (which prevents mRuby2 fluorophore maturation in the presence of oxygen without altering the transcriptomes; Figure 2—figure supplement 2), and subjected them to fluorescence-activated cell sorting (FACS) to separate the oxic (fluorescent) and anoxic (not fluorescent) populations (Figure 2C and Figure 2—figure supplement 3). The resulting two cell populations were then analyzed using RNA-seq. The spatial transcriptome comparison showed that, as expected, genes involved in the TCA cycle and mixed acid fermentation were differentially expressed between the inner and outer regions of the colony (Figure 1—figure supplement 4B,D). This result demonstrates that the method successfully separated the fermenting population inside the colonies from the respiring population in the outer layer, which serves as a qualitative verification of the experimental methodology. In both the spatially and temporally resolved transcriptomes, we observed changes in alanine transport and degradation, but not in alanine biosynthesis (Figure 2D and E; a schematic diagram of alanine metabolism pathways is shown in Figure 2—figure supplement 4). In particular, the spatial transcriptomes showed that the expression of the alanine exporter gene alaE was significantly upregulated in the inner (non-fluorescent) region of the colony, compared with the outer (fluorescent) region (Figure 2E). Alanine conversion into pyruvate and ammonium was also spatially regulated. Two pathways for converting alanine to pyruvate are known: The reversible conversion by enzymes involved in the alanine biosynthetic pathways, and the irreversible conversion mediated by the dadAX operon (Figure 2—figure supplement 4B). The latter encodes a racemase (dadX) and a dehydrogenase (dadA) (Lobocka et al., 1994). In colonies grown for 72 hr, the dadAX operon was down-regulated in the inner region, compared with the outer region (Figure 2E). Interestingly, in the whole-colony temporal transcriptomes, the dadAX operon and the gene alaE were the only strongly upregulated alanine-related genes for for and for when 72-hr-colonies to 12-hr-colonies (Figure These results indicate that colonies alanine and degradation during development and that the anoxic region of the colony alanine, while the oxic region alanine into pyruvate and Alanine is in anoxic conditions and can be used as a carbon and nitrogen source in oxic conditions The spatial transcriptomes suggest that alanine is secreted in the anoxic region of the biofilm. To test this, we which combination of availability E. coli alanine in spectrometry measurements from showed that alanine is only secreted anoxic conditions with glucose and ammonium (Figure which is an that to the anoxic base of the colony, where cells are in with the and M9 agar. conditions with abundant glucose and ammonium did not result in alanine This finding that alanine is secreted in the anoxic base of the colony, which is consistent with the spatial transcriptome Figure 3 Download asset Open asset Alanine can be secreted in anoxic conditions and consumed in oxic conditions by E. (A) alanine in the of grown in presence or absence or of glucose ammonium and molecular oxygen Data are mean ± s.d., n = 3 biological replicates. (B) using M9 minimal medium with ammonium as nitrogen source and different carbon glucose as in our M9 alanine or carbon (C) using M9 minimal medium with glucose as a carbon source and different nitrogen ammonium as in our M9 alanine or nitrogen For and correspond to the mean and the to the n = 3 biological replicates. The growth shown for alanine in and correspond to the alanine that resulted in the at in each (D) E. coli with = were for hr in different anoxic media. The medium contained of glucose, and alanine as Alanine was as the sole nitrogen source or sole carbon source with a of 5 and 10 The after hr of is to a time point when the without alanine reached measurements of in anoxic conditions was not in our due to technical Data are mean ± s.d., n = 4 biological replicates. Figure data 1 Source data for Figure Download The spatial transcriptomes also suggest that alanine is consumed in the oxic region of the colony. is well-known that E. coli can use extracellular alanine as a carbon or nitrogen source in oxic conditions and which we confirmed for our strain and growth conditions by glucose or ammonium with alanine in the M9 medium (Figure and We that alanine can be as a carbon but as a nitrogen source in oxic Interestingly, we observed that anoxic conditions, E. coli utilize alanine as a carbon or nitrogen source (Figure Together, these results suggest that the alanine secreted in the and anoxic region of the colony can be consumed in the oxic region of the colony as a Bacterial in the oxic region of the colony is by alanine and consumption To how with alanine and consumption colony growth, we created individual and of known alanine transport and degradation of these the cellular growth in (Figure supplement 1A). These also did not cause in colony height or diameter after 72 hr of on M9 agar (Figure supplement indicating that alanine and consumption not have large on global colony However, the mutants displayed differences when we measured the fraction of cells in the oxic region (Figure and Figure supplement 2), using a fluorescent acid that can only penetrate the disrupted membranes of cells We our measurements to the air-facing oxic region of the colony, in this region the fluorescence can be quantified without that may occur inside the colony due to laser We observed that the colonies displayed only a fraction of cells at the of the colony, but cell towards the top of the colony. Interestingly, the oxic region at around of the colony height displayed cell when cells the deletion (Figure which is a strain that should have a strongly reduced for and alanine. Figure 4 with 4 supplements see all Download asset Open asset Alanine influences cell viability and growth. (A) Measurement of the fraction of cells as a function of height in colonies grown for 72 hr on M9 agar. These measurements were performed only for cells located within μm from the outer colony which is a measurement of the oxic region (Figure 2A). The the of strains that were indicates that the gene is and that the gene was Data in the means, n = 3 biological replicates. are shown in Figure supplement 2. (B) alanine levels measured from colonies ± s.d., n = 3 biological replicates). (C) growth rate presence of glucose and for different as a function of the of alanine. Data are mean ± s.d., n = 3 biological replicates. (D) Fraction of cells, measured using fluorescence normalized by in grown with glucose and ammonium in presence or absence of 5 alanine. Fold-changes are calculated relative to the parental strain in exponential without alanine. were performed when reached of the (for exponential or when they reached their (for indicate mean ± s.d., n = 4 biological replicates. Figure data 1 Source data for Figure Download To if the cell displayed by the mutants be caused by an impaired alanine we first measured the extracellular alanine in colonies of the mutants using mass spectrometry (Figure 4B). expected, mutants of the major alanine degradation displayed substantially higher extracellular alanine than cells that are impaired for alanine secretion as well acid transporters can often function as both and yet the higher extracellular alanine levels of the strain compared with the strain (Figure indicate that AlaE as an alanine exporter inside colonies. measurements have shown that AlaE also as an exporter in