1. Microbiology and Infectious Disease
  2. Physics of Living Systems
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Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies

  1. Francisco Díaz-Pascual
  2. Martin Lempp
  3. Kazuki Nosho
  4. Hannah Jeckel
  5. Jeanyoung K Jo
  6. Konstantin Neuhaus
  7. Raimo Hartmann
  8. Eric Jelli
  9. Mads Frederik Hansen
  10. Alexa Price-Whelan
  11. Lars EP Dietrich
  12. Hannes Link
  13. Knut Drescher  Is a corresponding author
  1. Max Planck Institute for Terrestrial Microbiology, Germany
  2. Department of Physics, Philipps-Universität Marburg, Germany
  3. Biozentrum, University of Basel, Switzerland
  4. Department of Biological Sciences, Columbia University, United States
  5. Interfaculty Institute for Microbiology and Infection Medicine, Eberhard Karls Universität Tübingen, Germany
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Cite this article as: eLife 2021;10:e70794 doi: 10.7554/eLife.70794

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

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
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.

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
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.

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 (sixfold for dadA, fivefold for dadX, and 50-fold for alaE), when comparing 72-hr-colonies to 12-hr-colonies (Figure 2D). These results indicate that colonies globally upregulate alanine export and degradation during development and that the anoxic region of the colony likely exports alanine, while the oxic region likely converts alanine into pyruvate and ammonium.

Alanine is exported in anoxic conditions and can be used as a carbon and nitrogen source in oxic conditions

The spatial transcriptomes suggest that alanine is primarily secreted in the anoxic region of the biofilm. To test this, we explored under which combination of carbon/nitrogen/oxygen availability E. coli secretes alanine in shaking liquid conditions. Mass spectrometry measurements from culture supernatants clearly showed that alanine is only secreted under anoxic conditions with glucose and ammonium (Figure 3A), which is an environment that corresponds to the anoxic base of the colony, where cells are in contact with the glucose- and ammonium-rich M9 agar. Oxic conditions with abundant glucose and ammonium did not result in significant alanine secretion. This finding suggests that alanine is secreted in the anoxic base of the colony, which is consistent with the spatial transcriptome results.

Alanine can be secreted in anoxic conditions and consumed in oxic conditions by E. coli.

(A) Extracellular alanine concentration in the supernatant of liquid cultures grown in presence or absence (indicated by+ or -) of glucose (glc), ammonium (NH4+), and molecular oxygen (O2). Data are mean ± s.d., n = 3 biological replicates. (B) Growth curves using M9 minimal salts medium with ammonium as nitrogen source and different carbon sources: Either glucose (5 g/L, as in our standard M9 medium), alanine (10 mM), or no carbon source. (C) Growth curves using M9 minimal salts medium with glucose as a carbon source and different nitrogen sources: Either ammonium (22.6 mM, as in our standard M9 medium), alanine (5 mM) or no nitrogen source. For panels B and C, continuous middle lines correspond to the mean and the dotted lines to the standard deviation, n = 3 biological replicates. The growth curves shown for alanine in panels B and C correspond to the alanine concentrations that resulted in the highest final optical density at 600 nm (OD600) in each condition. (D) E. coli cultures starting with OD600 = 0.03 were incubated for 96 hr in different anoxic media. The medium contained combinations of glucose, NH4+, and alanine as indicated. Alanine was provided either as the sole nitrogen source (middle bar) or sole carbon source (right bar) with a concentration of 5 mM and 10 mM, respectively. The final OD600 after 96 hr of incubation is shown, corresponding to a time point when the culture without alanine reached stationary phase. Continuous measurements of OD600 in anoxic conditions was not possible in our laboratory due to technical limitations. Data are mean ± s.d., n = 4 biological replicates.

The spatial transcriptomes also suggest that alanine is primarily consumed in the oxic region of the colony. It is well-known that E. coli can use extracellular alanine as a carbon or nitrogen source in oxic conditions (Franklin and Venables, 1976; Kornberg, 1965), which we confirmed for our strain and growth conditions by replacing either glucose or ammonium with alanine in the liquid shaking M9 medium (Figure 3B and C). We found that exogenous alanine can be utilized as a poor carbon source, but as a good nitrogen source in oxic conditions. Interestingly, we observed that under anoxic conditions, E. coli cannot utilize alanine as a carbon or nitrogen source (Figure 3D). Together, these results suggest that the alanine secreted in the glucose- and ammonium-rich anoxic region of the colony can be consumed in the oxic region of the colony as a cross-fed metabolite.

Bacterial survival in the oxic region of the colony is influenced by alanine export and consumption

To determine how interference with alanine export and consumption affects colony growth, we created individual and combinatorial deletions of known alanine transport and degradation genes. None of these deletions affected the cellular growth rates in liquid culture (Figure 4—figure supplement 1A). These deletions also did not cause clear phenotypes in colony height or diameter after 72 hr of incubation on M9 agar (Figure 4—figure supplement 1B,C), indicating that alanine export and consumption do not have large effects on global colony size. However, the mutants displayed substantial differences when we measured the fraction of dead cells in the oxic region (Figure 4A and Figure 4—figure supplement 2), using a fluorescent nucleic acid stain that can only penetrate the disrupted membranes of dead cells (SYTOX Green). We limited our measurements to the air-facing oxic region of the colony, because in this region the SYTOX Green fluorescence can be reliably quantified without microscopy artefacts that may occur deeper inside the colony due to poor laser penetration. We observed that the colonies displayed only a low fraction of dead cells at the bottom edges of the colony, but cell death increased towards the top of the colony. Interestingly, the oxic region at around 50 % of the maximum colony height displayed increased cell death when cells carried the ΔalaEΔdadAX deletion (Figure 4A), which is a strain that should have a strongly reduced capability for secreting and consuming alanine.

Figure 4 with 4 supplements see all
Alanine influences cell viability and growth.

(A) Measurement of the fraction of dead cells as a function of height in colonies grown for 72 hr on M9 agar. These measurements were performed only for cells located within 30 µm from the outer colony surface, which is a conservative measurement of the oxic region (Figure 2A). The table designates the genotype of strains that were investigated: ‘✓’ indicates that the gene is intact and ‘Δ’ that the gene was deleted. Data in the heatmaps shows means, n = 3 biological replicates. Errors are shown in Figure 4—figure supplement 2. (B) Extracellular alanine levels measured from colonies (mean ± s.d., n = 3 biological replicates). (C) Maximum liquid culture growth rate (in presence of glucose and ammonium) for different strains, as a function of the concentration of exogenously added alanine. Data are mean ± s.d., n = 3 biological replicates. (D) Fraction of dead cells, measured using SYTOX Green fluorescence normalized by OD600, in cultures grown with glucose and ammonium in presence or absence of 5 mM alanine. Fold-changes are calculated relative to the parental strain in exponential phase without alanine. Measurements were performed when cultures reached half of the maximum OD600 (for exponential phase measurements) or when they reached their maximum OD600 (for stationary phase measurements). Bars indicate mean ± s.d., n = 4 biological replicates.

To determine if the increased cell death displayed by the ΔalaEΔdadAX mutants could be caused by an impaired alanine cross-feeding, we first measured the extracellular alanine concentration in colonies of the relevant mutants using mass spectrometry (Figure 4B). As expected, mutants incapable of the major alanine degradation pathway (ΔdadAX) displayed substantially higher extracellular alanine than cells that are impaired for alanine secretion as well (ΔalaEΔdadAX). Amino acid transporters can often function as both importers and exporters, yet the higher extracellular alanine levels of the ΔdadAX strain compared with the ΔalaEΔdadAX strain (Figure 4B) indicate that AlaE acts as an alanine exporter inside colonies. Previous measurements have shown that AlaE also acts as an exporter in liquid cultures (Hori et al., 2011b). The high extracellular alanine levels of the ΔdadAX colonies are unlikely to be caused by permeable or lysed cells, as the ΔdadAX colonies do not display elevated levels of cell death (Figure 4A). The parental strain and the ΔalaE mutant displayed similarly low extracellular alanine levels, close to the detection limit of our mass spectrometry technique. Interestingly, and consistent with the results of Hori et al. in liquid cultures (Hori et al., 2011a; Hori et al., 2011b), our detection of extracellular alanine in colonies of the ΔalaE and ΔalaEΔdadAX mutants indicates that another mechanism for alanine export might exist that is currently unknown. Together, our measurements of extracellular alanine levels in colonies of different mutant show that alanine is primarily secreted via AlaE.

For cells that lack both the major alanine exporter AlaE and the major alanine degradation pathway via DadA and DadX, we hypothesized that the presence of extracellular alanine might lead to an accumulation of intracellular alanine to toxic levels. It has previously been shown that excess levels of intracellular alanine can inhibit growth (Katsube et al., 2019), yet the molecular mechanism underlying this process is still unclear. Indeed, we observed that all strains show a decreased growth rate in liquid media containing high levels of alanine, yet strains carrying the ΔalaEΔdadAX mutation were much more sensitive to exogenously added alanine than the parental strain (Figure 4C). This result was not due to unspecific effects of alanine (such as osmolarity changes), because no significant differences between the mutants and the parental strain were observed when serine was added exogenously instead of alanine (Figure 4—figure supplement 3). Therefore, extracellular alanine can modulate bacterial growth rates, particularly for strains that are deficient in alanine cross-feeding (ΔalaEΔdadAX).

Since colonies can accumulate alanine in their extracellular space (Figure 4B) and the cellular growth rate can be reduced by extracellular alanine (Figure 4C), we hypothesized that the increased cell death in the oxic region of the ΔalaEΔdadAX colonies (Figure 4A) is due to the accumulation of toxic extracellular alanine levels in this region, which arise from the impaired cross-feeding of this strain. To test this hypothesis, we measured cell viability for the parental strain and mutants in liquid cultures with and without exogenous alanine during mid-exponential phase and in stationary phase. We found that even though the parental strain displayed a reduced growth rate when exposed to extracellular alanine (Figure 4C), the parental strain did not display increased levels of cell death in such conditions (Figure 4D) – likely due to their ability to secrete and consume alanine, allowing them to control their intracellular levels of alanine. In contrast, we found that in the presence of high extracellular alanine concentrations, ΔalaEΔdadAX mutants displayed higher cell death levels than the parental strain, which was particularly strong in stationary phase conditions (Figure 4D). The increased cell death of the ΔalaEΔdadAX mutants was not accompanied by a reduction in optical density, indicating these cells did not lyse. If cells can still export alanine (ΔdadAX), cell viability in liquid cultures is significantly improved in the presence of extracellular alanine, compared with the ΔalaEΔdadAX mutant (Figure 4D). If cells can still degrade alanine (ΔalaE), cell viability is only slightly improved under stationary phase conditions, compared with the ΔalaEΔdadAX mutant (Figure 4D). These cell viability measurements are consistent with the effect of extracellular alanine on the growth rate of ΔalaEΔdadAX and ΔalaE mutants (Figure 4C). We speculate that the exponentially growing cultures resembled the aerobic periphery at the base of the colony that is in contact with glucose and ammonium, whereas the stationary phase cultures resemble the oxic region above, which is nutrient depleted if no cross-feeding is present.

Together, these results support the hypothesis that in ΔalaEΔdadAX mutant colonies, extracellular alanine accumulates due to impaired cross-feeding, which causes the increased cell death compared with the parental strain. Alanine cross-feeding should therefore have an important effect for the oxic region above the nutrient-rich base of the colony.

Strains impaired in alanine cross-feeding display a reduced fitness in colonies

If alanine cross-feeding has a significant impact on colony growth, we would expect that cross-feeding-impaired cells also have a fitness disadvantage compared to the parental strain during colony growth. To test if this is the case, we generated pairwise mixtures of different strains and inoculated these mixtures onto a membrane filter placed on M9 agar. It is important to note that this inoculation procedure using a liquid drop (leading to colonies that were inoculated by hundreds of cells) creates different colony morphologies compared to colonies grown from a single bacterium (which was the growth condition for all other experiments with colonies in this article). After 72 hr of incubation of the mixed-strain colonies, we compared the frequency of each strain at the growing front of the colony to the inoculation frequency (Figure 4—figure supplement 4). In a control experiment, we observed that for strains that were isogenic except for the fluorescent protein they express, the superfolder GFP (sfGFP) expressing strain slightly outcompeted the mRuby2 expressing strain – likely due to a difference in the energetic cost of the biosynthesis of each fluorescent protein. We also observed that the ΔalaEΔdadAX mutant was consistently strongly outcompeted by the parental strain (Figure 4—figure supplement 4B), despite having no liquid culture grow differences (Figure 4—figure supplement 1) and regardless of the fluorescent protein that they were carrying. These experiments indicate that cells capable of alanine cross-feeding have a fitness advantage during colony growth.

Alanine cross-feeding influences colony morphology

If the alanine molecules that are secreted in the anaerobic base of the colony are consumed in the oxic higher regions of the colony, alanine could serve as a carbon and nitrogen source and support growth in this region. From measurements of the colony height and diameter for mutants impaired in cross-feeding, we know that alanine cross-feeding does not have a major influence on colony size (Figure 4—figure supplement 1). These measurements of colony height and diameter also show that alanine cross-feeding does not contribute to aerobic growth at the very top of the colony or at the outer edge of the base. We therefore investigated effects of alanine cross-feeding on cellular growth in the oxic region at mid-height, where the fraction of dead cells was highest for the ΔalaEΔdadAX mutants (Figure 4A). Measurements of the colony morphology showed that ΔalaEΔdadAX and ΔalaE mutants displayed a significantly decreased colony curvature in the relevant region, in comparison to the parental strain (Figure 5A and B). The detection of this phenotypic difference in colony morphology was enabled by the high reproducibility of our E. coli colony biofilm growth assay. The ‘bulge’ of the parental colonies suggests that this region grows due to alanine cross-feeding. This interpretation is consistent with recent simulations of colony growth without cross-feeding, which resulted in nearly conical colony shapes with triangular xz-cross sections (Warren et al., 2019) that lacked the ‘bulge’ morphology we observed for the cross-feeding capable strains.

Figure 5 with 1 supplement see all
Colony morphology is influenced by alanine cross-feeding.

(A) Colony height as a function of the colony radius, for colonies grown for 72 hr. Central lines correspond to the mean and the shaded area to the s.d., n = 3 biological replicates. (B) Slope of curves in panel A at height 100 µm. Data are mean ± s.d., n = 3 biological replicates. (C) Fluorescence ratio of constitutively expressed unstable GFP and stable mRuby2, which is a measure for the cellular growth rate, as shown in Figure 5—figure supplement 1. The unstable GFP was constructed by adding the ASV-tag to superfolder GFP. The fold-change of this fluorescent protein ratio was calculated for the oxic colony region between the heights 100 µm and 0 µm. The fluorescent protein ratio was only measured for cells located within 30 µm from the outer colony surface, as a conservative measure of the oxic region. Data are mean ± s.d., n = 5–6 biological replicates. Statistical significances were calculated using one-way ANOVA with Dunnett’s correction. Non-significant differences are labeled ‘ns’ and the p-value is shown in brackets; **p < 0.01, ****p < 0.0001.

To further experimentally investigate the effect of alanine cross-feeding on cellular growth in the mid-height oxic region, we measured the fluorescence ratio of an unstable version of the superfolder GFP (Andersen et al., 1998) and the long-lived mRuby2, both of which were expressed constitutively using a Ptac promoter. The ratio of these fluorescent proteins serves as a measure of the cellular growth rate (Figure 5—figure supplement 1). These measurements showed that the parental strain grows faster in the mid-height oxic region than the cross-feeding impaired ΔalaEΔdadAX mutant (Figure 5C), which further supports the hypothesis that this region of the colony relies on alanine as a carbon and nitrogen source.

Discussion

We showed that alanine metabolism is spatiotemporally regulated inside E. coli colony biofilms. The high degree of control and reproducibility of our model system enabled us to determine that interference with the export and consumption of alanine causes phenotypes in cell viability and cell growth rate. These viability and growth rate phenotypes are localized in the region of the colony that depends on alanine as a nutrient source, but they affect the global morphology of the colony.

Based on our results, we propose the following model for the spatial organization of alanine metabolism in colonies that have grown for 72 hr (Figure 6A): Cells at the bottom periphery of the colony (red region in Figure 6A) have access to oxygen, glucose, and ammonium, and perform either aerobic respiration or fermentation by overflow metabolism (Basan et al., 2015; Cole et al., 2015) – these two possible metabolic states cannot be distinguished with our current approaches. Cells at the bottom center of the colony (orange region in Figure 6A) are anaerobic yet they have access to glucose and ammonium from the agar-solidified medium. These cells ferment glucose and secrete alanine, primarily via AlaE. Although many amino acid exporters have been described for E. coli (Pacheco et al., 2021; Prindle et al., 2015; Rani et al., 2007), their functions have remained elusive under regular physiological conditions. Our data now reveal a function for the alanine exporter AlaE during biofilm growth. Secreted alanine diffuses through the colony and can only be utilized by oxic nutrient-deprived cells (blue region in Figure 6A). Alanine consumption in the mid-height oxic region also has a detoxification effect, by reducing otherwise inhibitory levels of extracellular alanine. Alanine consumption at the oxic top of the colony is not significant, perhaps because the extracellular alanine is consumed before it reaches this region.

Model for alanine cross-feeding in E. coli colony biofilms.

This model applies to colonies grown for 72 hr on solid M9 agar containing glucose and ammonium. (A) In cross-feeding capable colonies, cells in the bottom layer of the biofilm have access to glucose and ammonium. Only cells in the outer periphery of the biofilm (green dashed line) have access to oxygen. Cells in the red region can use ammonium, glucose, and oxygen to perform aerobic respiration or fermentation by overflow metabolism. Cells in the orange region have access to glucose and ammonium, but no oxygen. These cells secrete alanine. The secreted alanine can be consumed by cells in the oxic region above this layer (depicted as blue), which perform aerobic respiration and convert alanine into pyruvate and ammonium that can be used for growth and to maintain cell viability. (B) Colonies of ΔalaEΔdadAX cells have a reduced ability to consume and export alanine. These colonies have a region performing aerobic respiration or overflow metabolism (red), similar to the parental strain. These colonies also have an anoxic fermentation region (orange), yet this region displays significantly less alanine secretion compared to the parental strain. Furthermore, these colonies lack an alanine consuming population in the oxic region. Due to the limited alanine secretion and alanine consumption of this strain, ΔalaEΔdadAX colonies display higher cell death and less growth in the otherwise cross-feeding-dependent oxic region, resulting in a more conical colony shape in comparison to parental colonies.

Colonies that are impaired in alanine cross-feeding because of a reduced ability to secrete and consume alanine display less growth and more cell death in the oxic glucose-deprived region, leading to a conical-shaped colony (Figure 6B). Furthermore, cross-feeding impaired cells are more susceptible to growth inhibition by high extracellular alanine levels (Figure 4C) and are outcompeted by the parental strain during colony growth.

The spatiotemporal organization of alanine metabolism during colony growth is unique among amino acids (Figure 1D and E), and is based on the secretion of alanine in the anaerobic, glucose-rich, and ammonium-rich base of the colony. Why is alanine secreted in this region? We speculate that this is not an altruistic trait evolved to support a starving oxic subpopulation at a different location, because such a trait would be highly susceptible to social cheaters in a multi-species community. Instead, alanine secretion in this region may be a necessity to avoid high intracellular alanine levels that presumably result from anaerobic fermentation of glucose in the presence of ammonium. High alanine levels are inhibitory (Figure 4C), so that secretion of alanine and transport of alanine away from this population is beneficial to this population.

On the other side of the cross-feeding interaction, the alanine-consuming subpopulation in the aerobic mid-height region of the colony strongly benefits from the secreted alanine originating from the base of the colony. We note that the alanine consumption in the aerobic mid-height region of the colony necessarily causes a steeper alanine concentration gradient between the two interacting subpopulations, compared with a case in which no alanine is consumed. Due to Fick’s law of diffusion, a steeper concentration gradient causes a higher diffusive flux. Therefore, the presence of the alanine-consuming subpopulation results in a benefit for the alanine-secreting subpopulation, by causing a higher diffusive flux of alanine away from the alanine-secreting population. It is unclear whether this benefit for the alanine-secreting subpopulation is significant, as it is not possible for us to measure local growth rates or cell viability in the anoxic base of the colony due to optical limitations of confocal fluorescence microscopy in this region. In summary, the interaction between the two cross-feeding subpopulations is likely mutualistic.

The spatial organization of alanine cross-feeding between two subpopulations we described in this study is analogous to the carbon cross-feeding in E. coli colonies via acetate, because it involves metabolite secretion in the anaerobic population and consumption in the carbon-starved oxic population (Cole et al., 2015; Dal Co et al., 2019; Wolfsberg et al., 2018). However, the location of the population that is proposed to consume acetate as a carbon source spans most of the oxic region of the colony (Cole et al., 2015), whereas alanine is primarily consumed only in the oxic mid-height region of the colony. Interestingly, our spatial transcriptomes did not reveal a signature for acetate cross-feeding between the anaerobic and oxic regions of the colony (Figure 1—figure supplement 4B), yet transcripts coding for enzymes involved in lactate, formate, and succinate metabolism display patterns that are indicative of spatially organized metabolism that could be the basis of carbon cross-feeding. Whether acetate, lactate, formate, and succinate are in fact cross-fed in our system remains to be tested in future work. In contrast to these metabolites, alanine can not only be used as carbon source, but also as a nitrogen source in the cross-feeding-dependent region, and we note that it is currently not clear what limits growth in the higher regions of the colony – whether it is carbon, nitrogen, or other elements such as iron, sulfur, or phosphorous. Very recently, it was shown that alanine can be shared in colonies of the Gram-positive bacterium Bacillus subtilis, and that this effect required three-dimensional colonies for unknown reasons (Arjes et al., 2021). In our E. coli model system, three-dimensional growth is required to create an anoxic region replete with carbon and nitrogen sources that causes alanine secretion, and we speculate that this effect may also be required in B. subtilis, and in other species.

Several cross-feeding interactions have been described in detail for multi-species communities (Henson et al., 2019; Kim et al., 2017; Moree et al., 2012; Pande et al., 2016; Røder et al., 2020; Smith et al., 2019; Watrous et al., 2013; Yang et al., 2009). Even though cross-feeding interactions are likely a ubiquitous process in single-species bacterial multicellular structures (San Roman and Wagner, 2018), only a few metabolic interactions between subpopulations have been documented for single-species biofilms (Arjes et al., 2021; Cole et al., 2015; Evans et al., 2020; Lin et al., 2018; Liu et al., 2015). For E. coli colonies grown on agar-solidified medium, and for bacterial communities in general, it is still unclear how many subpopulations interact metabolically, and on which length and time scales these interactions take place, and how significant these many interactions are for the community growth and stability.

Conclusion

In this study, we employed an unbiased approach based on temporal and spatial transcriptomes and metabolomes to reveal that a multitude of amino acids and mixed acid fermentation pathways display profiles that are consistent with cross-feeding. Particularly strong regulation was displayed by alanine metabolism, and we showed that alanine is a cross-fed metabolite inside E. coli colonies, between two spatially segregated subpopulations, with an interaction length scale of tens of microns. Alanine consumption supports growth in the cross-feeding-dependent region of the colony as a carbon and nitrogen source. Although many aspects of metabolism in biofilms are still unknown, methods for improved spatial and temporal analyses of metabolite profiles and transcriptome data promise the possibility to discover new metabolic interactions, and more generally understand the stability and functions of microbial communities.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Escherichia coli)KDE261Drescher lab stockStrain carrying plasmid pCP20Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE262Drescher lab stockStrain carrying plasmid pKD46Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE264Drescher lab stockStrain carrying plasmid pKD3Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE265Drescher lab stockStrain carrying plasmid pKD4Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE1361Drescher lab stockStrain carrying plasmid pNUT1361Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2338Drescher lab stockStrain carrying plasmid pNUT2338Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2658Drescher lab stock, Addgene #64,969Strain carrying plasmid pUC18R6KT-mini-Tn7-KmPlasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2659Drescher lab stock, Addgene #64,968Strain carrying plasmid pTNS2Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2674This studyStrain carrying plasmid pNUT2674Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2787This studyStrain carrying plasmid pNUT2787Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE2838This studyStrain carrying plasmid pNUT2838Plasmid information listed in Supplementary file 2
Strain, strain background (Escherichia coli)KDE474Serra et al., 2013aE. coli AR3110 wild-type
Strain, strain background (Escherichia coli)KDE679Vidakovic et al., 2018KDE474 (E. coli AR3110), Ptac-mRuby2-mRuby2 and KanR inserted at attB site (Ptac without operator).
Strain, strain background (Escherichia coli)KDE722Vidakovic et al., 2018KDE679 with ΔfliC::scar.
Strain, strain background (Escherichia coli)KDE1899This studyKDE474 (E. coli AR3110) with ΔfliC::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2007This studyKDE679 with ΔfliC::scar, ΔalaE::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2009This studyKDE679 with ΔfliC::scar, ΔdadAX::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2086This studyKDE679 with ΔfliC::scar, ΔalaE::scar, ΔdadAX::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2183This studyKDE679 with ΔfliC::scar, ΔcycA::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2185This studyKDE679 with ΔfliC::scar, ΔlivG::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2438This studyKDE679 with ΔfliC::scar, ΔyaaJ::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2242This studyAR3110, Ptac-sfgfp-sfgfp and KanR inserted at attB site (Ptac without operator), with ΔfliC::scar, ΔalaE::scar, ΔdadAX::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2445This studyAR3110, Ptac-sfgfp-sfgfp and KanR inserted at attB site (Ptac without operator), with ΔfliC::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2533This studyKDE679 with ΔfliC::scar ΔcycA::scar, ΔlivG::scar, ΔalaE::scar, ΔyaaJ::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2564This studyKDE679 with ΔfliC::scar, ΔcycA::scar, ΔdadAX::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2607This studyKDE679 with ΔfliC::scar, ΔyaaJ::scar, ΔdadAX::scar.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2937This studyKDE679 with ΔfliC::scar, Ptac-sfgfp(ASV) at the Tn7 insertion site, coding for an unstable superfolder GFP with an AANDENYAASV-tag.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2938This studyKDE679 with ΔfliC::scar, ΔalaE::scar, Ptac-sfgfp(ASV) at the Tn7 insertion site.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2939This studyKDE679 with ΔfliC::scar, ΔdadAX::scar, Ptac-sfgfp(ASV) at the Tn7 insertion site.This strain can be obtained from the Drescher lab upon request
Strain, strain background (Escherichia coli)KDE2940This studyKDE679 with ΔfliC::scar, ΔalaE ΔdadAX, Ptac-sfgfp(ASV) at the Tn7 insertion site.This strain can be obtained from the Drescher lab upon request
Sequence-based reagentKDO834This studyInsertions at the attB siteThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO894This studyfliC deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO895This studyfliC deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO1662This studyInsertions at the attB siteThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2562This studyalaE deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2563This studyalaE deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2566This studydadAX deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2567This studydadAX deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2845This studycycA deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2846This studycycA deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2841This studylivG deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO2842This studylivG deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3481This studyyaaJ deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3482This studyyaaJ deletionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3256This studypNUT2338 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3257This studypNUT2338 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3817This studypNUT2838 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3818This studypNUT2838 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3785This studypNUT2674 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO3786This studypNUT2674 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO4121This studypNUT2787 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO4121This studypNUT2787 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Sequence-based reagentKDO4127This studypNUT2838 constructionThe sequence of this oligonucleotide can be found in Supplementary file 3
Software, algorithmMatlabMathWorksVersion R2019b
Software, algorithmPrismGraphPad SoftwareVersion 9.2.0
Software, algorithmNIS-ElementsNikonVersion 4.5.2
Software, algorithmInkscapeInkscapeVersion 1.0.1
Software, algorithmCLC Genomics WorkbenchQiagenVersion 11.0
Chemical compound, drugNa2HPO4Carl RothP030.2
Chemical compound, drugCoCl2Carl Roth7095.1
Chemical compound, drugMnSO4Sigma-AldrichM8179
Chemical compound, drugCuCl2Sigma-Aldrich307,483
Chemical compound, drugZnSO4Sigma-AldrichZ0251
Chemical compound, drugthiamine-HClSigma-AldrichT4625
Chemical compound, drugFeCl3Sigma-Aldrich31,332
Chemical compound, drugMgSO4,Sigma-AldrichM2643
Chemical compound, drugCaCl2Sigma-AldrichC5670
Chemical compound, drugNaClCarl RothHN00.2
Chemical compound, drug(NH4)2SO4Carl Roth3746.2
Chemical compound, drugKH2PO4Carl Roth3904.1

Strains, strain construction, and media

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All E. coli strains used in this study are derivatives of the E. coli K-12 AR3110 strain (Serra et al., 2013a). For the construction of plasmids and bacterial strains, standard molecular biology techniques were applied (Sambrook et al., 1989), using enzymes purchased from New England Biolabs or Takara Bio. All AR3110 derivatives carried a constitutively expressed fluorescent protein expression system (based on the Ptac promoter without the lac operator) inserted in the chromosome at the attB site. To generate chromosomal deletions, the lambda red system was used to replace the target region with an antibiotic cassette flanked by FRT sites (Datsenko and Wanner, 2000). Then, the Flp-FRT recombination system was utilized to remove the antibiotic resistance cassette (Cherepanov and Wackernagel, 1995). To insert the unstable fluorescent protein sfGFP(ASV) (Andersen et al., 1998) expressed under the control of the Ptac promoter (without the lac operator) and a chloramphenicol cassette flanked by FRT sites into the Tn7 insertion site, we used the protocol described by Choi et al., 2005. After the fragment was inserted, the chloramphenicol resistance cassette was removed using the Flp-FRT recombination system. All strains, plasmids, and oligonucleotides that were used in this study are listed in Supplementary files 1-3, respectively.

Cultures were grown in LB-Miller medium (10 g L–1 NaCl, 10 g L–1 tryptone, 5 g L–1 yeast extract) for routine culture and cloning, or in M9 minimal salts medium supplemented with 5 g L–1 D-glucose (which we refer to as “M9 medium” throughout the article for simplicity). The M9 minimal salts medium consisted of the following components: 42.3 mM Na2HPO4 (Carl Roth P030.2), 22 mM KH2PO4 (Carl Roth 3904.1), 11.3 mM (NH4)2SO4 (Carl Roth 3746.2), 8.56 mM NaCl (Carl Roth HN00.2), 0.1 mM CaCl2 (Sigma-Aldrich C5670), 1 mM MgSO4, (Sigma-Aldrich M2643), 60 μM FeCl3 (Sigma-Aldrich 31332), 2.8 μM thiamine-HCl (Sigma-Aldrich T4625), 6.3 µM ZnSO4 (Sigma-Aldrich Z0251), 7 µM CuCl2 (Sigma-Aldrich 307483), 7.1 µM MnSO4 (Sigma-Aldrich M8179), 7.6 µM CoCl2 (Carl Roth 7095.1). M9 agar plates were made using 8 mL of M9 medium (as defined above) with 1.5 % w/v agar-agar, aliquoted into petri dishes with 35 mm diameter and 10 mm height (Sarstedt 82.1135.500).

Colony biofilm growth

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Samples from -80 °C frozen stocks were used to inoculate LB-Miller medium with kanamycin (50 µg mL–1) followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. At this point, 1 µL of the culture was used to inoculate 5 mL of M9 medium inside a 100-mL-Erlenmeyer flask and grown at 37 °C with shaking at 220 rpm for 16–22 hr. The cultures were continuously kept in exponential phase by regular back-dilutions, with optical density at 600 nm (OD600) always below 0.6. Aliquots from these cultures were passed through a sterile 0.45 µm pore size polyvinylidene fluoride membrane filter of diameter 5 mm, unless stated otherwise. High-resolution confocal fluorescence microscopy showed that this treatment resulted in spatially well-separated single cells on the filter membrane. Using clean and sterile stainless-steel tweezers, these filter membranes carrying the cells were immediately placed directly onto M9 agar plates and incubated at 37 °C for up to 72 hr.

Microscopy

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All imaging was performed using a Yokogawa spinning disk confocal unit mounted on a Nikon Ti-E inverted microscope. A Nikon 40 x air extra-long working distance objective with numerical aperture (NA) 0.60 was used for all imaging, except for mutant screening (Figure 4—figure supplement 1) and competition experiments (Figure 4—figure supplement 4) where a 4 x air objective (NA 0.13, Nikon) was used, and single-cell imaging (Figure 2—figure supplement 1A) were a 100 x oil objective (NA 1.45, Nikon) was used. All imaging was done inside a microscope incubator kept at 37 °C. Instead of imaging through the filter, colonies were imaged facing down and the Petri dish lid was removed (Figure 1—figure supplement 1A). To maintain a high humidity and avoid evaporation of the M9 agar, the space between the microscope objective and the petri dish was sealed with flexible plastic foil. To avoid condensation on the objective, the objective was heated using an objective heater at 37 °C.

Colony detection and biovolume measurements with adaptive microscopy

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In preliminary experiments, we observed that colony growth of the wild-type E. coli strain AR3110 resulted in heterogeneous shapes (Figure 1—figure supplement 1B) that were caused by flagella-based motility of cells on the filter membranes in the very early stages of incubation. To avoid the effects caused by cellular motility during early colony growth, all subsequently experiments were performed using a strain that lacks the flagellin FliC (∆fliC), which we used as parental strain in this study. This strain was incapable of swimming motility, and the resulting colonies were highly reproducible in shape and highly symmetric (Figure 1B and Figure 1—figure supplement 1C).

To determine the biomass of colonies, the colonies were grown on filter membranes on M9 agar as explained above, but with the addition of 0.2 µm dark red fluorescent beads (Invitrogen, F8807) to the bacterial suspension prior to filtering. This resulted in far-red fluorescent beads being located on the filter in addition to the red fluorescent cells. Using adaptive microscopy (Jeckel and Drescher, 2021) these beads were used to find the correct focal plane for imaging of the bacterial cells (or base of the colonies). After 12, 18, 24, 32, 42, 48, 60, and 72 hr of colony growth, the colonies were imaged in 3D using confocal microscopy. All colonies on the membrane filter were imaged using an adaptive microscopy approach (Jeckel and Drescher, 2021) as follows: The whole filter was scanned using 2D imaging, followed by an identification of all colonies on the filter, followed by a high-resolution 3D imaging of each colony. From the scans of the whole filter, the number of colonies was determined. From the 3D images of the colonies, the biovolume was calculated. To obtain the biovolume, the outline of the colony was identified by thresholding the image gradient in each z-slice. The convex area of this binary image was then used as a measure for the biomass present in this plane such that summation over all slices followed by multiplication with the appropriate µm³/voxel calibration yielded the biovolume of each colony. The code used for image analysis is available on Github, https://github.com/knutdrescher/colonymetabolism.

Quantification of mRuby2 fluorescence in colonies from images

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The colonies were grown for 72 hr and then imaged using the microscopy conditions described above, and colonies were detected in the images as described above. The colony surface was defined as the interface between the colony and the air. Measurements of fluorescence levels inside the colony at different locations was performed analogous to methods that are available within the BiofilmQ software (Hartmann et al., 2021), yet some modifications were performed as described below, which made it more convenient to use separate functions (available on Github, https://github.com/knutdrescher/colonymetabolism).

To measure the vertical and horizontal fold changes in mRuby2 fluorescence as a function of the distance to the colony surface (Figure 2A) two different approaches were applied. To measure the fluorescence in a vertical direction inside the colony, a vertical cylinder of radius 3.3 µm was defined in the center of the colony. The fluorescence in that cylinder was quantified and the values obtained were normalized to the fluorescence at the colony surface. To measure the fold change in fluorescence horizontally within the colony, a xy-horizontal plane at the base of the colony was selected. The mRuby2 fluorescence was quantified in the plane and normalized to the fluorescence at the colony-air interface within the same plane.

The fold-change in mRuby2 fluorescence as a function of the distance to the colony surface in whole colonies was also measured irrespective of the orientation of the measurement axis (Figure 2—figure supplement 1D). For this, the mRuby2 fluorescence intensity values of all locations inside the colony with a similar distance to the colony surface (see definition above) were averaged.

To determine the total fraction of fluorescent fraction of cells in a colony (insert of Figure 2B), colonies were grown as described above for either 12 h, 18h, 24 h, 32 h, 42 h, 48h, 60 h, or 72 h. Then, the mRuby2 fluorescence in the colonies was imaged using confocal microscopy, using the microscopy conditions described above. Inside the colony, 3D regions that were fluorescent in the mRuby2 channels were identified via thresholding. The fluorescent fraction was calculated as the fluorescent volume divided by the total volume of the colony.

Liquid growth assays

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Samples from -80 °C frozen stocks were used to inoculate LB-Miller medium, followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. Each culture was back-diluted 5,000-fold into 5 mL of M9 medium inside a 100-mL-Erlenmeyer flask, and grown at 37 °C with shaking at 220 rpm. At an OD600 of 0.3 each culture was washed three times in M9 minimal salts medium (lacking glucose and ammonium sulfate) and resuspended in the same volume of the medium of interest. These bacterial suspensions were diluted 10-fold and transferred into a 96-well plate (Sarstedt, 82.1581.001), and incubated at 37 °C with shaking in a microtiter plate reader (Epoch2, Biotek). The resulting growth curve data was analyzed using Matlab (version R2019b, Mathworks).

To simultaneously measure OD600 and the ratio between unstable GFP (with the ASV-tag) and mRuby2 as a function of time (Figure 5—figure supplement 1), a glycerol stock of the strain KDE2937 was used to inoculate LB-Miller medium, followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. The culture was then back-diluted 5000-fold into 5 mL of M9 medium inside a 100 mL Erlenmeyer flask and grown for 16 hr. This culture was used to inoculate 75 mL of M9 medium inside a 1 L Erlenmeyer flask with an adjusted OD600 of 0.05. This culture was grown at 37 °C with shaking at 220 rpm. Aliquots of the culture were taken every 30 min, to measure the OD600 and to determine the ratio between unstable GFP (with the ASV-tag) and mRuby2 using microscopy. To image the aliquots, they were placed between a cover slip and a M9 agar pad and imaged as described in the microscopy methods section.

Anoxic liquid growth assays

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Samples from -80 °C frozen stocks were used to inoculate LB-Miller medium, followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. Each culture was then back-diluted 5000-fold into 5 mL of M9 medium inside a 100 mL Erlenmeyer flask, and grown at 37 °C with shaking at 220 rpm. At OD600 = 0.7 each culture was washed three times in M9 minimal salts medium (lacking glucose and ammonium sulfate) and resuspended in 300 µL. These bacterial suspensions were used to inoculate 20 mL of the particular medium under investigation with a starting OD600 of 0.03, which was placed into a 50 mL bottle that was closed with a gas-tight lid containing a rubber septum (Duran, 292062803). Immediately after inoculation, the bottle and the culture were made anoxic using the following protocol: A needle connected to a system that can switch between applying a vacuum and gaseous N2 was introduced through the rubber septum. Then, vacuum was applied to remove the gas in the bottle for 1 min, followed by flushing the bottle with N2 at 1 bar for 1 min, while the medium in the bottle was continuously mixed using a magnetic stirrer. This process was repeated for 15 cycles. After this process, the needle was slowly removed from the bottle and the cultures were incubated at 37 °C with shaking at 220 rpm. After 96 hr of incubation, the OD600 was measured (Figure 3D).

Liquid culture conditions for measurements of extracellular alanine in supernatants

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Samples from -80 °C frozen stocks were used to inoculate LB-Miller medium with kanamycin (50 µg mL–1), followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. The cultures were then back-diluted 5000-fold into 5 mL of M9 medium inside a 100-mL-Erlenmeyer flask and incubated at 37 °C with shaking at 220 rpm. These cultures were kept in exponential phase by regular back-dilutions, with optical density at 600 nm (OD600) always below 0.6. At an OD600 of 0.3, the cultures were centrifuged at 14000 g for 2 min and the supernatant was removed. To investigate aerobic growth conditions, the pellets were suspended in M9 minimal salts medium supplemented with glucose and the OD600 was adjusted to 0.1 by dilution. For oxic starvation conditions, the pellets were suspended in same volume of M9 medium lacking ammonium sulfate but including glucose, M9 medium lacking glucose but including ammonium sulfate, or M9 medium lacking both glucose and ammonium sulfate. The cultures were then placed into 100-mL-Erlenmeyer flasks at 37 °C with shaking at 220 rpm as before. To investigate anoxic conditions, the pellets were suspended in the same media as described above, but the resulting cultures were transferred into a closed 15-mL-conical centrifuge tube (Sarstedt, 62.554.100) filled to the top. The tubes were oriented horizontally and incubated at 37 °C with shaking at 100 rpm. For the oxic and anoxic starvation conditions, the samples were taken after 2 hr of incubation. For aerobic and anaerobic conditions that permitted growth, samples were taken during exponential growth phase. Samples were processed and analysed using mass spectrometry as described below.

Sample processing for mass spectrometry-based metabolomics

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To measure metabolites in whole colonies over time, filter membranes that carried colonies were transferred into 150 µL of a mixture of 40:40:20 (v/v) acetonitrile:methanol:water at –20 °C for metabolite extraction. This suspension was vortexed with a glass bead to disrupt the colonies.

To measure extracellular metabolites from colonies, the filter membranes carrying the colonies were resuspended in 1 mL phosphate-buffered saline (PBS; 8 g l–1 NaCl, 0.2 g l-1 KCl, 1.44 g l–1 Na2HPO4, 0.24 g l–1 KH2PO4, pH 7.4) at 37 °C. In this case, no glass bead vortexing was needed, as the colonies readily dissolved. The suspension was immediately vacuum-filtered using a 0.45 µm pore size filter (HVLP02500, Merck Millipore) and 100 µL of the flow-through were mixed with 400 µL of a mixture of 50:50 (v/v) acetonitrile:methanol at –20 °C.

To measure extracellular metabolites from liquid cultures, 1 mL of grown cultures were filtered on a 0.45 µm pore size filter (HVLP02500, Merck Millipore) and 100 µL of the flow through were mixed with 400 µL of a mixture of 50:50 (v/v) acetonitrile:methanol at –20 °C.

All extracts were centrifuged for 15 min at 11000 g at –9 °C and stored at –80 °C until mass spectrometry analysis.

Mass spectrometry measurements

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For metabolomics, centrifuged extracts were mixed with 13C-labeled internal standards. Chromatographic separation was performed on an Agilent 1290 Infinity II LC System (Agilent Technologies) equipped with an Acquity UPLC BEH Amide column (2.1 × 30 mm, particle size 1.8 µm, Waters) for acidic conditions and an iHilic-Fusion (P) HPLC column (2.1 × 50 mm, particle size 5 µm, Hilicon) for basic conditions. The following binary gradients with a flow rate of 400 µl min–1 were applied. Acidic condition: 0–1.3 min isocratic 10% A (water with 0.1% v/v formic acid, 10 mM ammonium formate), 90% B (acetonitrile with 0.1% v/v formic acid), 1.3–1.5 min linear from 90% to 40% B; 1.5–1.7 min linear from 40% to 90% B, 1.7–2 min isocratic 90% B. Basic condition: 0–1.3 min isocratic 10% A (water with formic acid 0.2% (v/v), 10 mM ammonium carbonate), 90% B (acetonitrile); 1.3–1.5 min linear from 90% to 40% B; 1.5–1.7 min linear from 40% to 90% B, 1.7–2 min isocratic 90% B. The injection volume was 3.0 µl (full loop injection).

Ions were detected using an Agilent 6495 triple quadrupole mass spectrometer (Agilent Technologies) equipped with an Agilent Jet Stream electrospray ion source in positive and negative ion mode. The source gas temperature was set to 200 °C, with 14 L min–1 drying gas and a nebulizer pressure of 24 psi. Sheath gas temperature was set to 300 °C and the flow to 11 L min–1. Electrospray nozzle and capillary voltages were set to 500 and 2500 V, respectively. Metabolites were identified by multiple reaction monitoring (MRM). MRM parameters were optimized and validated with authentic standards (Guder et al., 2017). Metabolites were measured in 12C and 13C isoforms, and the data was analysed with a published Matlab code (Guder et al., 2017).

Data analysis for metabolomics

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For whole-colony metabolite measurements, the mass spectrometry measurements were normalized by the total biovolume measured with confocal microscopy. For measurements of the extracellular metabolites in colonies (Figure 4B), the mass spectrometry measurements were normalized by colony number and average colony volume determined by confocal microscopy. To create heatmaps of the metabolite dynamics, the Genesis software (Sturn et al., 2002) was used.

Whole-colony transcriptomes

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Colonies were grown on filter membranes as described above. After 12, 18, 24, 32, 42, 48, 60, and 72 hr of growth,the filters carrying the colonies were picked up using plastic forceps and transferred into 1.5 mL Eppendorf tubes, which were immediately placed into liquid nitrogen, followed by storage at –80 °C until further processing. To extract the RNA, a glass bead and 600 μL of cell lysis buffer were added during thawing of the sample at room temperature. Cell lysis buffer consisted of TE (10 mM Tris, adjusted to pH 8.0 with HCl, 1 mM EDTA) and 1 mg/mL of chicken egg lysozyme (Sigma, L6876). The colonies were disrupted by vortexing and the cell suspension was moved to a new 1.5 mL Eppendorf tube. Then, total RNA was extracted using the hot SDS/phenol method (Jahn et al., 2008) with some modifications as follows. Cells were lysed at 65 °C for 2 min in the presence of 1% (w/v) SDS, and the lysate was incubated with 750 µL of Roti-Aqua-Phenol (Carl Roth, A980) at 65 °C for 8 min, followed by the addition of 750 µL chloroform (Sigma, C2432) to the aqueous phase and centrifugation using a phase lock gel tube (VWR, 733–2478). RNA was purified from this suspension by ethanol precipitation and dissolved in 60 µL of RNase-free water. Samples were then treated with TURBO DNase (Thermo Fisher, AM2238) and rRNA depletion was performed using Ribo-Zero rRNA Removal Kit for bacteria (Illumina, MRZB12424). Sequencing library preparation was carried out using NEBNext Ultra II Directional RNA Library Prep with Sample Purification Beads (NEB, E7765S). Sequencing was carried out at the Max Planck Genome Centre (Cologne, Germany) using an Illumina HiSeq3000 with 150 bp single reads. All transcriptomic analyses were performed using the software CLC Genomics Workbench v11.0 (Qiagen). The E. coli K-12 W3310 genome (Hayashi et al., 2006), parental strain of E. coli AR3110 (Serra et al., 2013a), was used as reference for annotation. For creating heatmaps, clustering of transcripts was performed using the software Genesis (Sturn et al., 2002).

Spatial transcriptomes

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Filter membranes carrying colonies that were grown for 72 hr were transferred into 2 mL Eppendorf tubes (one filter per tube). Cells from the colonies on a filter were then quickly suspended in 1 mL PBS by vortexing and pipetting, followed by removal of the filter from the tube, and fixation of the cells by adding formaldehyde (Sigma, F8775) to a final concentration of 4%, for 10 min at room temperature. Formaldehyde fixation did not affect the transcriptomic profile or the fluorescence intensity (Figure 2—figure supplement 2). Then, the fixed cells were washed three times with PBS and the cell suspension was filtered with a 5 µm pore size filter (Sartorius, 17594) to remove aggregates. Cells were separated using fluorescence-activated cell sorting (BD FACSAria Fusion), using the mRuby2 fluorescence as a signal. After sorting, approximately 106 cells were collected in 10 mL of PBS for each bin. To concentrate the samples, they were vacuum-filtered using a 0.45 µm membrane filter (Millipore, HVLP02500). The filters containing the cells were suspended in 400 μL of the cell lysis buffer (same composition as above). The suspension was frozen in liquid nitrogen and stored at –80 °C until RNA extraction.

Total RNA was extracted using the same protocol as described above (based on the hot SDS/phenol method), but with the following modifications to minimize the number of steps and loss of RNA: after treatment with phenol, the aqueous and organic phases were both directly transferred to a phase lock gel tube (VWR, 733–2478) without centrifugation. Chloroform was added and centrifuged at 15000 rpm for 15 min at 12 °C. After centrifugation, RNA in the aqueous phase was purified and collected in 10 µL of RNase-free water using Agencourt RNAClean XP (Beckman Coulter, A63987) according to the manufacturer’s recommendations. Samples were then treated with TURBO DNase (Thermo Fisher, AM2238) and rRNA depletion was performed using Ribo-Zero rRNA Removal Kit for bacteria (Illumina, MRZB12424). Library preparation was carried out using NEBNext Ultra II Directional RNA Library Prep with Sample Purification Beads (NEB, E7765S). Sequencing was carried out at the Max Planck Genome Centre (Cologne, Germany) using an Illumina HiSeq3000 with 150 bp single reads.

Spatial and temporal transcriptomic data were uploaded to the Gene Expression Omnibus (GEO) repository with the accession code GSE175768.

Measurements of the fraction of dead cells

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To measure the fraction of dead cells within the oxic region of the colonies, the colonies were grown on filter membranes on M9 agar as described above, but with the exception that SYTOX Green (Thermofisher, S7020) with a final concentration of 2.5 µM was added to the M9 agar plates. SYTOX Green is a nucleic acid stain that can only diffuse into dead cells with a compromised cell membrane. After incubation for 72 hr, the mRuby2 fluorescence and the SYTOX Green fluorescence in the colonies was imaged using confocal microscopy. Inside the colony, 3D regions that were fluorescent in both the mRuby2 and SYTOX Green channels were identified via thresholding. The fraction of dead cells was calculated as the thresholded volume present in both the mRuby2 and SYTOX Green channels, divided by the thresholded volume of the mRuby2 channel. For the quantification of the fraction of dead cells in the oxic region of the colony, only cells located within 30 µm from the outer surface of the colony were considered, because only in this region oxygen penetration was high enough to generate a sufficiently strong mRuby2 signal.

To measure the fraction of dead cells in liquid cultures, cultures were grown in M9 medium, which was supplemented with SYTOX Green (2.5 µM), incubated at 37 °C inside 96-well plates with continuous shaking. The OD600 and SYTOX Green fluorescence were measured in a microtiter plater reader (Spark 10 M, Tecan). The fraction of dead cells was calculated as the SYTOX Green fluorescence normalized by the OD600.

Strain competition experiments

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Samples from -80 °C frozen stocks were used to inoculate LB-Miller medium with kanamycin (50 µg mL–1), followed by incubation for 5 hr at 37 °C with shaking at 220 rpm. The cultures were then back-diluted 5000-fold into 5 mL of M9 medium inside a 100-mL-Erlenmeyer flask and incubated at 37 °C with shaking at 220 rpm until exponential phase. Then, the cultures were adjusted to an OD600 of 0.05 and two strains were mixed in a 1:1 ratio and the solution was diluted 100-fold. For each experiment, one strain expressed the fluorescent protein mRuby2 and the other strain the fluorescent protein sfGFP. The exact ratio of the strains in the inoculation suspension was measured using flow cytometry. Then, 1 µL of this mixture was filtered on a membrane filter, which it was placed on solid M9 agar and incubated at 37 °C. After 72 hr, the colonies were imaged and the ratio of each strain was measured in a ring with a width of 35 µm and at a distance of 350 µm from the edge of the inoculation spot using basic image analysis and image thresholding.

Oxygen measurements

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Oxygen concentrations were measured in 72-hr-old colonies (grown on M9 agar as described above) using a 25-µm-tip oxygen microsensor (Unisense OX-25) according to the manufacturer’s instructions. Briefly, the oxygen microsensor was calibrated using a two-point calibration. The microsensor was first calibrated to atmospheric oxygen using a calibration chamber (Unisense CAL300) containing water continuously bubbled with air. The microsensor was then calibrated to a ‘zero’ oxygen point using an anoxic solution of 0.1 M sodium ascorbate and 0.1 M sodium hydroxide in water. Oxygen measurements were then taken through the top ≥100 µm of the colony biofilm in 5-µm-steps using a measurement time of 3 s at each position, and a wait time between measurements of 5 s. A micromanipulator (Unisense MM33) was used to move the microsensor within the colony. Profiles were recorded using a multimeter (Unisense) and the SensorTrace Profiling software (Unisense).

Image analysis scripts

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All Matlab scripts used for image analysis are available on Github, https://github.com/knutdrescher/colonymetabolism (copy archived at swh:1:rev:cfc8d212071e634b6b7aa8462b745a14761193e9, Drescher, 2021).

Statistical analysis

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Statistical analysis was carried out using GraphPad Prism v8 (GraphPad Software), except for the statistical analysis for transcriptomic data, which was performed using the software CLC Genomics Workbench 11.0 (Qiagen). All statistical tests performed and sample sizes are reported in the corresponding figure legend. Each biological replicate was performed on a different day.

Data availability

Spatial and temporal transcriptomic data were uploaded to the Gene Expression Omnibus (GEO) repository with the accession code GSE175768. All data generated or analysed during this study are included in the manuscript and supporting files. All Matlab scripts used for image analysis are available on Github, https://github.com/knutdrescher/colonymetabolism (copy archived at https://archive.softwareheritage.org/swh:1:rev:cfc8d212071e634b6b7aa8462b745a14761193e9).

The following data sets were generated
    1. Diaz-Pascual F
    2. Nosho K
    3. Drescher K
    (2021) NCBI Gene Expression Omnibus
    ID GSE175768. Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies.

References

    1. Kornberg HL
    (1965) Anaplerotic Sequences in Microbial Metabolism
    Angewandte Chemie International Edition in English 4:558–565.
    https://doi.org/10.1002/anie.196505581
  1. Book
    1. Sambrook J
    2. Fritsch E
    3. Maniatis T
    (1989)
    Molecular Cloning: A Laboratory Manual
    Cold Spring Harbor Laboratory Press.

Decision letter

  1. Karina B Xavier
    Reviewing Editor; Instituto Gulbenkian de Ciência, Portugal
  2. Gisela Storz
    Senior Editor; National Institute of Child Health and Human Development, United States
  3. Karina B Xavier
    Reviewer; Instituto Gulbenkian de Ciência, Portugal

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Spatial alanine metabolism determines local growth dynamics of Escherichia coli colonies" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Karina B Xavier as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Gisela Storz as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

In general, all three reviewers found the work technical very sound with potential impact for thinking about the spatial structure of bacterial species in biofilm in all bacterial species. While they recognize the interest of the proposed nutrient cross-feeding model for explaining the observed phenotypic spatial heterogeneity within the biofilm the reviewers think that additional experiments or arguments are needed to support the proposed model. Additionally, the reviewers also propose a couple of additional simple experiments which could help improve the significance of the finding reported in the paper.

The major points to be addressed are listed in the 3 essential revisions below. Additionally, you can also find in the end of this letter the sperate "recommendations for the authors" written by each of the reviewers which should help you to improve the manuscript and to better understand the rationale used by each of the reviewers that support for the proposed essential reviews. We recommend that you take the recommendations written by each of the reviewers into consideration while preparing the revised version, but you do not need to reply to each of the individual comments from the reviewers, you will only need to reply the essential points summarized here by the reviewing editor:

1) We propose that the authors revise the model considering the following point: Specifically, we are concern about the fact that the authors are assuming that the cells are respiring, or fermenting based on whether they are in aerobic (oxygen expose) or anaerobic areas of the biofilm or not. In liquid cultures it is well known that bacteria use fermentative metabolism to promote fast growth, irrespective of oxygen levels, if the glucose levels in the medium are high enough. This could happen at the edge of the biofilm; cells are in close contact to the glucose of the agar medium. Thus, we question that the fast-growing bacteria at the edge of the biofilm are probably fermenting cells and not respiring cells.

Therefore, we recommend that the authors revisit their model or include data that support the correlation between oxygen/respiration. One possibility that we recommend to the authors is to correlate the fermenting or respiring biofilm regions to glucose (and or alanine) levels instead of oxygen levels (this point is discussed in more detail in the specific recommendation of reviewer #3).

2) Also related to the cross-feeding model, we recommend that the model described in Figure 6 also addresses what happens with the alaE dadAX mutant (which is impaired in cross-feeding), as this could help in understanding the overall proposed model (see major comment 1 from reviewer 1).

3) To improve the broader relevance and overall implication of the findings described in the paper the reviewers propose the following two set of experiments: A) to determine if there are potential fitness advantages of the proposed alanine cross-feeding mechanism the authors could ask if there is a fitness advantage for the WT versus the mutant impaired in cross-feeding within the biofilm (see major comment 2 from reviewer 1), B) to shed some light on potential selective pressures related to alanine metabolism the authors could look at biofilms in colonies composed of a mixtures of WT and the alaE dadAX mutant (which is impaired in cross-feeding) and look for potential spatial sorting of the two strains within the biofilm community (see major comment 4 of reviewer 2).

Reviewer #1 (Recommendations for the authors):

I have a couple major comments/questions that could be important for strengthening the model proposed in the paper:

1 – The authors present a cross-feeding model in Figure 6 based on alanine cross-feeding explaining with the proposed model for the WT biofilm colonies. But, I think that the discussion about what happens in the alaE dadAX mutant is also important. I think it is important to also discuss what happens with the mutant, namely regarding cell death within the biofilm. Perhaps also have a scheme for the mutant, but at least to address it in the discussion.

2 – I think it is not clearly demonstrated that there is a benefit for the overall fitness of the population within colony biofilm for having the proposed cross-feeding mechanism. But because there is more cell death in the alaE dadAX mutant it is likely that there is a fitness benefit. This could be tested by scrapping WT and mutant biofilms, disaggregating them and then determine colony forming units. This experiment should allow to address this question.

3 – Alanine secretion under high nitrogen and high carbon conditions resemble what happens with acetate secretion, namely also because both metabolites are then consumed by starved cells. The authors mention this in the discussion. I think that the fact that cells are secreting alanine and not acetate is interesting. Perhaps it is relevant to comment on the fact that the cells secreting alanine in the biofilm have an excess of both Carbon and Nitrogen, as opposed to carbon only. Perhaps cells secrete acetate when Carbon is in excess but Nitrogen and secrete alanine when both are in excess, no?

Reviewer #2 (Recommendations for the authors):

Most of my suggestions focus on the broader relevance of these findings, which I think the authors could extend quite substantially with a few more experiments.

1. Line 100: the study suggests that the shift in the transcriptome after 24 h is due to oxygen depletion, but the authors don't actually show this (I don't think). They could monitor oxygen directly over time to address this point directly, e.g. through oxygen-sensitive nanoparticles.

2. Line 154: I'm confused: couldn't the mRuby2 fluorescence increase due to moving the colony, which would expose it to more oxygen? Also, there's a typo: "in for formerly".

3. In terms of why the mutant cells are dying (line 207), the authors suggest that it is due to the accumulation of alanine, which they can recapitulate through live/dead staining in liquid culture. It could be fascinating to track cells at the single-cell level – do the cells lyse? (if so, this could impact surrounding cells in a colony, by releasing more alanine and thereby increasing the local concentration) Or do the cells simply halt growth and become permeable?

4. In a colony that is made up of a mixture of wild-type and ∆alaE∆dadAX cells, do the two strains spatially sort based on their differential ability to grow in aerobic regions of the colony? This simple experiment could shed some light on the selective pressures on alanine metabolism.

5. Line 229: can it simply be cell death that is providing the exogenous alanine?

6. Figure 4D: why is there not increased cell death in the parent at high concentrations (5 mM) since the growth rate is lower at this concentration? And more generally, is the reduction in growth rate in the mutant quantitatively explained by the death rate, or do you have to invoke a second mechanism?

7. Line 275: For the bulge morphology, can this be restored in a mixed colony of wild-type and the mutant? Or do you get an intermediate phenotype?

8. How is the spatial patterning that they see affected by the ability of cells to produce matrix?

9. Their model seems to predict what you would see in a more two-dimensional text, when colonies are sandwiched between a cover glass. This could be a neat means to further investigate the significance of alanine export for growth rate of the colony.

10. What about the presence of another species that consumes alanine, will it colocalize preferentially at the aerobic region?

Reviewer #3 (Recommendations for the authors):

I recommend the authors to revisit the idea of linking oxic/anoxic regions with alanine producers/consumers. Maybe is true but it needs stronger evidence-support because is different from what is described in liquid cultures. In the light of the data presented, I think is better idea to link alanine metabolism to glucose availability; it makes a stronger case,

1) The authors equate the aerobic or anaerobic areas of the biofilm with respiring (fast-growing) or fermenting (slow-growing) cells, respectively. Is this already known? I am confused because, in liquid cultures, cells use fermentation to grow when carbohydrates are abundant and rely on respiration when the concentration of carbohydrates decreases. Hence, the regions with fast cell growth should show fermentative growth (alanine production) whereas the regions with slow growth undergo respiration and consume alanine as a non-preferred catabolite. Notice that these two biofilm areas are equally exposed to oxygen. This does not really fit with the model in figure 6 in which fast growing cells respire at high carbohydrate concentration. I can see a correlation between alanine metabolism and glucose abundance but the connection to oxygen levels is not clear to me.

2) I am not convinced that maturation of the fluorescent protein mRuby2 is indicative of respiring bacteria. I think it is a very elegant way to identify the aerobic areas of the biofilm but again, bacteria can ferment even in the presence of oxygen, if the concentration of carbohydrates is high enough (i.e. crabtree effect in yeast, overflow metabolism in bacteria or Warburg effect in cancer cells). The subpopulation of fluorescence cells likely combine fermenting (closer edge to the agar) and respiring (middle-height edge of the colony) bacteria. Moreover, the authors removed glucose from the medium to alter the metabolic processes that consume oxygen (line 151) but this will severely impact fermentative growth as well.

3) I do not understand how these extracellular alanine concentrations are toxic to the cells. The paper cited is about intracellular alanine (Katsube et al., 2019). Is there anything known about how extracellular alanine levels are toxic to the cells?

Line 114: "Lactate, formate and succinate biosynthesis, however, displayed interesting dynamics during colony growth". This sentence has no meaning as it does not describe the data. In what way are these dynamics interesting?

Line 125: "All amino acid abundances decreased during colony growth, except for alanine, which remained relatively constant with a peak abundance at 32 h". Several aminoacids derive from alanine metabolism. I wonder how the level of these aminoacids did not remain closer to that of alanine

Line 129: I do not understand how the transcriptomic or metabolomic data invited the authors to explore whether alanine metabolism is spatially heterogeneous during biofilm growth.

Lines 217-260: these two paragraphs describing the phenotypes of different mutants in alanine metabolism are hard to digest. I read them several times and I do not think I understood.

https://doi.org/10.7554/eLife.70794.sa1

Author response

Essential revisions:

1) We propose that the authors revise the model considering the following point: Specifically, we are concern about the fact that the authors are assuming that the cells are respiring, or fermenting based on whether they are in aerobic (oxygen expose) or anaerobic areas of the biofilm or not. In liquid cultures it is well known that bacteria use fermentative metabolism to promote fast growth, irrespective of oxygen levels, if the glucose levels in the medium are high enough. This could happen at the edge of the biofilm; cells are in close contact to the glucose of the agar medium. Thus, we question that the fast-growing bacteria at the edge of the biofilm are probably fermenting cells and not respiring cells.

Therefore, we recommend that the authors revisit their model or include data that support the correlation between oxygen/respiration. One possibility that we recommend to the authors is to correlate the fermenting or respiring biofilm regions to glucose (and or alanine) levels instead of oxygen levels (this point is discussed in more detail in the specific recommendation of reviewer #3).

We thank the reviewers for this comment. We agree that our statements equating oxic conditions to respiration were inaccurate and that the cells in the glucose-, ammonium-, and oxygen-rich region of the colony (i.e. the edge of the base of the colony) could perform overflow metabolism instead of aerobic respiration. We currently cannot distinguish these two metabolic states in the relevant region of the colony, yet such a distinction would not have a major impact on our conclusions. To correct our inaccurate statements, we have therefore made the following changes to the manuscript:

– In Figure 6, we labeled the relevant region of the colony with “overflow metabolism or aerobic respiration”, and we also updated the caption of Figure 6 accordingly.

– We also added a new statement in the main text where the model (i.e. Figure 6) is described, as follows (main text, lines 344-348):

“Based on our results, we propose the following model for the spatial organization of alanine metabolism in colonies that have grown for 72 h (Figure 6A): Cells at the bottom periphery of the colony (red region in Figure 6A) have access to oxygen, glucose and ammonium, and perform either aerobic respiration or overflow metabolism (Basan et al., 2015; Cole et al., 2015) – these two possible metabolic states cannot be distinguished with our current approaches.”

– Throughout the entire manuscript, we also revised all mentions of the terms “aerobic”, “anaerobic”, “respiration”, and “fermentation”, and, where appropriate, rephrased them.

2) Also related to the cross-feeding model, we recommend that the model described in Figure 6 also addresses what happens with the alaE dadAX mutant (which is impaired in cross-feeding), as this could help in understanding the overall proposed model (see major comment 1 from reviewer 1).

We agree that this would be a helpful addition to the manuscript. In response, we have made the following changes to the manuscript:

– We expanded the model description in the Discussion section, to include the ΔalaEΔdadAX mutant (in lines 358-362).

– We have included a new panel in Figure 6 to highlight the differences between the parental strain and the ΔalaEΔdadAX mutant.

3) To improve the broader relevance and overall implication of the findings described in the paper the reviewers propose the following two set of experiments: A) to determine if there are potential fitness advantages of the proposed alanine cross-feeding mechanism the authors could ask if there is a fitness advantage for the WT versus the mutant impaired in cross-feeding within the biofilm (see major comment 2 from reviewer 1), B) to shed some light on potential selective pressures related to alanine metabolism the authors could look at biofilms in colonies composed of a mixtures of WT and the alaE dadAX mutant (which is impaired in cross-feeding) and look for potential spatial sorting of the two strains within the biofilm community (see major comment 4 of reviewer 2).

We thank the reviewers once more for their helpful suggestions.

Regarding proposed experiment A:

The reviewers are asking for an experiment to test if there is a fitness benefit for the whole colony if the colony is capable of the spatially organized alanine cross-feeding process we are describing in this manuscript. To investigate this question, we have measured the colony diameter and colony height of the cross-feeding capable strain (parental strain) and the cross-feeding impaired strain (ΔalaEΔdadAX). The results show that the colony size is not significantly different between these two strains (panels B,C in Figure 4—figure supplement 1). We expect that CFU counts of such colonies (as suggested by reviewer 1, comment 2) would have a larger measurement uncertainty than microscopy-based measurements of colony sizes so that we performed microscopy measurements instead of CFU measurements. To be able to resolve the expected small fitness benefit of cross-feeding for the whole colony, a very large number of biological replicates would be needed, given the natural variation of colony sizes between biological replicates shown in Figure 4—figure supplement 1.

We also note that in our system the cell death of the ΔalaEΔdadAX colonies reaches only 4% of the total biomass in the cross-feeding dependent region of the colony (shown in Figure 4A). We therefore expect that for the whole colony, the global fitness benefit of alanine cross-feeding is relatively small. To highlight this point in the manuscript, we have made the following changes:

– We revised the section of the manuscript where the effects of alanine secretion and consumption on colony growth are described (lines 224-230):

“To determine how interference with alanine export and consumption affects colony growth, we created individual and combinatorial deletions of known alanine transport and degradation genes. None of these deletions affected the cellular growth rates in liquid culture (Figure 4—figure supplement 1A). These deletions also did not cause clear phenotypes in colony height or diameter after 72 h of incubation on M9 agar (Figure 4—figure supplement 1B,C), indicating that alanine export and consumption do not have large effects on global colony size. However, the mutants displayed substantial differences when we measured the fraction of dead cells in the oxic region”

– We also expanded the section of the manuscript on the effect of alanine cross-feeding on colony morphology (lines 315-321):

“From measurements of the colony height and diameter for mutants impaired in cross-feeding, we know that alanine cross-feeding does not have a major influence on colony size (Figure 4—figure supplement 1). These measurements of colony height and diameter also show that alanine cross-feeding does not contribute to aerobic growth at the very top of the colony or at the outer edge of the base. We therefore investigated effects of alanine cross-feeding on cellular growth in the oxic region at mid-height,”

– Figure 4—figure supplement 1 and caption have been updated.

Regarding proposed experiment B:

We agree that this experiment would provide helpful new insights into the impact of alanine cross-feeding on colony biofilm growth. We have performed this experiment and added a new supplemental figure and a new section in the manuscript to describe the results.

It is important to note that the proposed experiment required that two strains were mixed together and a drop of the resulting culture suspension was spotted onto a filter membrane on agar. This inoculation condition is different from all other colonies grown and investigated for this study – these colonies were exclusively grown from a single bacterial cell seeded onto the filter membrane on agar. The resulting 3D morphologies of colonies grown from a single cell and colonies grown from a 1 µL drop of culture suspension are quite different. In particular, the colonies grown from a drop of culture suspension were flatter and wider compared to the colonies grown from single cells. Therefore, the results from the two-strain competition experiments are not completely comparable to the other experiments in this study, yet they provide some new insights that we believe are valuable.

In our implementation of the two-strain competition experiments, we mixed two strains together at approximately 1:1 ratio, measured the exact strain ratio using flow cytometry, and inoculated a small drop of this suspension on a filter membrane on M9 agar. We then measured the strain frequency at the growing front of the colony using microscopy.

Our results show that the cross-feeding impaired ΔalaEΔdadAX strain was outcompeted by the parental strain in colonies (new Figure 4—figure supplement 4), despite having no liquid culture growth difference (Figure 4—figure supplement 1).

In response to this comment by the reviewers, we have made the following changes to the manuscript:

– We added a new paragraph in the Results section of the manuscript (lines 296-312).

– We also briefly mention these new results in the discussion (lines 358-362).

– We also added a new figure: Figure 4—figure supplement 4.

Reviewer #1 (Recommendations for the authors):

I have a couple major comments/questions that could be important for strengthening the model proposed in the paper:

1 – The authors present a cross-feeding model in Figure 6 based on alanine cross-feeding explaining with the proposed model for the WT biofilm colonies. But, I think that the discussion about what happens in the alaE dadAX mutant is also important. I think it is important to also discuss what happens with the mutant, namely regarding cell death within the biofilm. Perhaps also have a scheme for the mutant, but at least to address it in the discussion.

This comment is reflected in the Essential Revision #2, and we provide a detailed answer to this comment above.

2 – I think it is not clearly demonstrated that there is a benefit for the overall fitness of the population within colony biofilm for having the proposed cross-feeding mechanism. But because there is more cell death in the alaE dadAX mutant it is likely that there is a fitness benefit. This could be tested by scrapping WT and mutant biofilms, disaggregating them and then determine colony forming units. This experiment should allow to address this question.

This comment is reflected in the Essential Revision #3 (“Experiment A”), and we provide a detailed answer to this comment above.

3 – Alanine secretion under high nitrogen and high carbon conditions resemble what happens with acetate secretion, namely also because both metabolites are then consumed by starved cells. The authors mention this in the discussion. I think that the fact that cells are secreting alanine and not acetate is interesting. Perhaps it is relevant to comment on the fact that the cells secreting alanine in the biofilm have an excess of both Carbon and Nitrogen, as opposed to carbon only. Perhaps cells secrete acetate when Carbon is in excess but Nitrogen and secrete alanine when both are in excess, no?

This is an interesting suggestion. We agree that there are some analogies between alanine secretion and acetate secretion. Although our spatial transcriptome data show no characteristic signature of acetate cross-feeding, it is not impossible that acetate cross-feeding takes place in our conditions (perhaps no spatially organized transcriptional regulation is required for this). Cole et al., 2015 (https://doi.org/10.1186/s12918-015-0155-1) present data that is consistent with acetate cross-feeding in E. coli colonies, using fluorescent reporters. In our system fluorescent protein reporters for acetate secretion at the base of the colony would not be conclusive because of the oxygen requirement for fluorescent protein folding – which was the basis of our method for obtaining spatial transcriptomes.

To the best of our knowledge acetate secretion does not rely on low nitrogen levels, and we speculate that cells in the anoxic region are perhaps secreting acetate as well as alanine (and perhaps also succinate, formate, and lactate, and likely even more compounds). We are currently planning a follow-up study, where we will investigate more broadly whether these (and other) metabolites are cross-fed.

In response to this comment, we have added a statement in the discussion (lines 390-395):

“Interestingly, our spatial transcriptomes did not reveal a signature for acetate cross-feeding between the anaerobic and oxic regions of the colony (Figure 1— figure supplement 4B), yet transcripts coding for enzymes involved in lactate, formate, and succinate metabolism display patterns that are indicative of spatially organized metabolism that could be the basis of carbon cross-feeding. Whether acetate, lactate, formate and succinate are in fact cross-fed in our system remains to be tested in future work.”

Reviewer #2 (Recommendations for the authors):

Most of my suggestions focus on the broader relevance of these findings, which I think the authors could extend quite substantially with a few more experiments.

1. Line 100: the study suggests that the shift in the transcriptome after 24 h is due to oxygen depletion, but the authors don't actually show this (I don't think). They could monitor oxygen directly over time to address this point directly, e.g. through oxygen-sensitive nanoparticles.

We have performed such measurements, which are summarized below, including the context:

The temporal transcriptome data we acquired (Figure 1C) is an average of the entire colony and does not resolve heterogeneity inside the colony. The transcriptome shift that occurs at around 24 h shows that the average transcriptome of the colony has a shift towards anaerobic metabolism, which is a state in which the average transcriptome remains until the end of our experiments (72 h).

For 72 h colonies we performed direct measurements of oxygen levels with spatial resolution using an oxygen microsensor. These measurements of oxygen levels inside the colony closely align with mRuby2 fluorescence levels (mRuby2 requires oxygen to fold into a fluorescent conformation), indicating that mRuby2 levels in our system correlate with oxygen abundance (Figure 2A), and that mRuby2 fluorescence can be used to measure spatial heterogeneity of oxygen levels in our system.

By measuring mRuby2 levels in space and time during colony growth, and by computing the ratio between mRuby2 fluorescent/non-fluorescent cells we can compute the ratio of cells in the oxic/anoxic regions of the colony (Figure 2B, inset). Indeed, these measurements of heterogeneity of oxygen levels show a shift towards a largely anoxic colony around 24 h.

To highlight these measurements, we expanded the description of Figure 2B, resulting in the following changes to the manuscript (lines 148-152):

“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 h, 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 lead to a thin layer of fluorescent cells in the air-facing part of the colony (Figure 2B).”

The reviewer’s suggestion of an alternative measurement method for oxygen levels is interesting, but we feel that the existing data we described above are already unambiguous regarding the change in metabolism related to a spatially segregated anaerobic population.

2. Line 154: I'm confused: couldn't the mRuby2 fluorescence increase due to moving the colony, which would expose it to more oxygen? Also, there's a typo: "in for formerly".

We thank the reviewer for pointing out that our description was not clear. Indeed, there are two effects when the filter carrying the colonies is moved to a fresh M9 agar plate without glucose:

– Effect 1: The fresh agar plate initially has some oxygen underneath the colonies.

– Effect 2: The cells in the colony are now starved of glucose and therefore consume a lot less oxygen, which enables oxygen to diffuse into the colony to enable mRuby2 folding throughout the entire colony.

Effect 2 is shown in Figure 2—figure supplement 1B,C,D. Effect 1 will only cause an initial folding of mRuby2 near the bottom surface of the colony (similar to the thin mRuby2 fluorescent zone in the air-exposed region of the colony), but this effect cannot explain that mRuby2 fluorescence emerges throughout the entire depth of the colony after transfer to the M9 agar plate without glucose (which is shown in the newly added Figure 2 – supplement 1C).

To clarify these two effects in the manuscript, we have revised the relevant section of the control experiment description (lines 163-172):

“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).”

We have also corrected the typo mentioned by the reviewer.

3. In terms of why the mutant cells are dying (line 207), the authors suggest that it is due to the accumulation of alanine, which they can recapitulate through live/dead staining in liquid culture. It could be fascinating to track cells at the single-cell level – do the cells lyse? (if so, this could impact surrounding cells in a colony, by releasing more alanine and thereby increasing the local concentration) Or do the cells simply halt growth and become permeable?

This is an interesting idea. Optical imaging inside the colonies on agar is unfortunately at lower resolution than our imaging of biofilms on glass surfaces in microfluidic chambers, where single-cell resolution is achievable. In microfluidic chambers, we can use oil-immersion objectives which have a very high resolution. The lower imaging resolution in colonies on agar is due to the requirement to either use air immersion objectives or water-immersion objectives that don’t have the required resolution for single-cell imaging in dense E. coli aggregates (not even near the edges).

Therefore, we cannot see whether the cells definitely lyse, although their membrane is compromised, as inferred from the live/dead stain. However, we know from liquid culture experiments (Figure 4D) in which extracellular alanine was added that the optical density did not substantially decrease (an OD decrease would be expected following lysis), even though the live/dead stain showed an increase. This suggests that the majority of dead cells in the colony don’t lyse.

Moreover, when the concentration of extracellular alanine was determined for the different mutant colonies (Figure 4B), we observed that the ΔdadAX strain presented an increased level of extracellular alanine. However, the strain does not display an increased cell death phenotype in colonies (Figure 4A) so that it is unlikely that the increased levels of extracellular alanine originate from lysed or dead cells.

To highlight these points in the manuscript, we made the following changes:

– Lines 248-250:

“The high extracellular alanine levels of the ΔdadAX colonies are unlikely to be caused by permeable or lysed cells, as the ΔdadAX colonies do not display elevated levels of cell death (Figure 4A).”

– Lines 279-283:

“we found that in the presence of high extracellular alanine concentrations, ΔalaEΔdadAX mutants displayed higher cell death levels than the parental strain, which was particularly strong in stationary phase conditions (Figure 4D). The increased cell death of the ΔalaEΔdadAX mutants was not accompanied by a reduction in optical density, indicating these cells did not lyse.”

4. In a colony that is made up of a mixture of wild-type and ∆alaE∆dadAX cells, do the two strains spatially sort based on their differential ability to grow in aerobic regions of the colony? This simple experiment could shed some light on the selective pressures on alanine metabolism.

This comment is related to Essential Revision #3 (“Experiment B”), and we provide a detailed answer to this comment above.

5. Line 229: can it simply be cell death that is providing the exogenous alanine?

Once again, we thank the reviewer for this important comment that we addressed above (in the answer to Major questions/suggestions #3). Additionally, we added a sentence in the manuscript in lines 248-250 to clarify this.

6. Figure 4D: why is there not increased cell death in the parent at high concentrations (5 mM) since the growth rate is lower at this concentration? And more generally, is the reduction in growth rate in the mutant quantitatively explained by the death rate, or do you have to invoke a second mechanism?

It is indeed thought provoking that the parental strain displays a reduction in growth rate but not an increased cell death at 5 mM extracellular alanine (Figure 4C, D) – even though 5 mM extracellular alanine does lead to both a reduction in growth rate and increased cell death for the ΔalaEΔdadAX mutant. The results from the parental strain show that apparently the reduction in growth rate is not caused by cell death. We speculate that a reduced growth rate is measurable before the stress is large enough to cause cell death and that a second mechanism for these two phenomena does not need to be invoked. We note that the parental strain still has the capability to decrease their intracellular alanine levels via the proteins encoded by dadAX and alaE. Our data in Figure 4C, D suggests that these proteins help the parental strain to survive the increased alanine levels, and under some conditions this strain even uses extracellular alanine to grow (Figure 4D).

To clarify our results and interpretation on this topic, we have now included additional explanations in lines 274-281:

“To test this hypothesis, we measured cell viability for the parental strain and mutants in liquid cultures with and without exogenous alanine during midexponential phase and in stationary phase. We found that even though the parental strain displayed a reduced growth rate when exposed to extracellular alanine (Figure 4C), the parental strain did not display increased levels of cell death in such conditions (Figure 4D) – likely due to their ability to secrete and consume alanine, allowing them to control their intracellular levels of alanine. In contrast, we found that in the presence of high extracellular alanine concentrations, ΔalaEΔdadAX mutants displayed higher cell death levels than the parental strain”

7. Line 275: For the bulge morphology, can this be restored in a mixed colony of wild-type and the mutant? Or do you get an intermediate phenotype?

This would be nice, but unfortunately, is not possible to mix 2 (or more) strains and obtain a colony morphology that is directly comparable to colonies that were grown from a single cell. In all of our experiments (except for the mixed-strain experiments that were performed in response to your comment #4, our new Figure 4—figure supplement 4) we grew colonies from a single bacterial cell. To obtained mixed-strain colonies, it is necessary to inoculate the colonies with a small drop of culture suspension (containing the different strains), which necessarily results in a different initial inoculation condition. The resulting colonies in mixed-strain experiments are wider and flatter compared with colonies grown from a single bacterium. Therefore, the experiment proposed by the reviewer would unfortunately not be interpretable.

To clarify the difference between colonies grown from a mixture of strains and colonies grown from a single bacterium, we included an explanatory sentence in lines 298-303:

“To test if this is the case, we generated pairwise mixtures of different strains and inoculated these mixtures onto a membrane filter placed on M9 agar. It is important to note that this inoculation procedure using a liquid drop (leading to colonies that were inoculated by hundreds of cells) creates different colony morphologies compared to colonies grown from a single bacterium (which was the growth condition for all other experiments with colonies in this article).”

8. How is the spatial patterning that they see affected by the ability of cells to produce matrix?

Although matrix genes are expressed (mentioned in lines 110-111), we speculate that during the first 72 h of colony growth (which is the time window we study in this article), and for colonies grown from a single bacterial cells at 37°C, the matrix does not yet have a major influence on the colony morphology. The pyramid-like colony morphology we observed for the ΔalaEΔdadAX mutant resembles the simulations from Warren et al., 2019 (Terence Hwa´s lab), in which colony growth was simulated without the presence of an extracellular matrix. We also note that the primary matrix component responsible for wrinkling of E. coli colonies (curli amyloid fibers) are not strongly expressed at 37°C (mentioned initially by Olsén et al., 1989, and corroborated by several groups).

Studies by Regine Hengge et al., have revealed that for the same strain we use, but cultured at lower temperatures (which induces stronger production of curli amyloid fibers), and inoculated from a drop of culture suspension, the matrix does have a very large effect on colony morphology after longer incubation times.

9. Their model seems to predict what you would see in a more two-dimensional text, when colonies are sandwiched between a cover glass. This could be a neat means to further investigate the significance of alanine export for growth rate of the colony.

In fact, we think that our experiments show that the three-dimensionality of the colonies is important for the alanine cross-feeding, because we need the oxygen-gradient and the opposing glucose and ammonium gradients (only the anoxic glucose- and ammonium-rich cells secrete alanine). We now explicitly mention this in the Discussion section (in lines 400-403).

To replicate such a spatially organized alanine metabolism in a two-dimensional microfluidic system, we predict that it would require a microfluidic chip that produces opposing gradients in oxygen and glucose and ammonium.

10. What about the presence of another species that consumes alanine, will it colocalize preferentially at the aerobic region?

This is an interesting idea and we agree with the reviewer’s prediction – for species that can only consume alanine in aerobic conditions (which is true for E. coli, Figure 3D). Such a mixed-species experiment would require detailed characterization of the other species’ preference for alanine over glucose in oxic and anoxic conditions, to interpret results from mixed-species colony growth experiments. Although we agree that this would be an interesting direction to dissect interactions in a simple multi-species community, our opinion is that such an experiment would go beyond the scope of the current study. We also note that the first author of this study has departed from the lab now, which means that such an experiment would not be possible within a reasonable time frame.

Reviewer #3 (Recommendations for the authors):

I recommend the authors to revisit the idea of linking oxic/anoxic regions with alanine producers/consumers. Maybe is true but it needs stronger evidence-support because is different from what is described in liquid cultures. In the light of the data presented, I think is better idea to link alanine metabolism to glucose availability; it makes a stronger case,

1) The authors equate the aerobic or anaerobic areas of the biofilm with respiring (fast-growing) or fermenting (slow-growing) cells, respectively. Is this already known? I am confused because, in liquid cultures, cells use fermentation to grow when carbohydrates are abundant and rely on respiration when the concentration of carbohydrates decreases. Hence, the regions with fast cell growth should show fermentative growth (alanine production) whereas the regions with slow growth undergo respiration and consume alanine as a non-preferred catabolite. Notice that these two biofilm areas are equally exposed to oxygen. This does not really fit with the model in figure 6 in which fast growing cells respire at high carbohydrate concentration. I can see a correlation between alanine metabolism and glucose abundance but the connection to oxygen levels is not clear to me.

This is a valuable comment. In our initial submission the description of the metabolic state of the oxic region of the base of the colony (which has high glucose levels) was inaccurate – it is certainly possible that the cells in this region ferment through overflow metabolism. We have therefore corrected our description of this region in Figure 6 and in the manuscript main text. This comment is related to Essential Revision #1 above, where we provide a detailed answer including the specific changes we made to the manuscript in response to this comment.

Regarding the reviewer’s comment about alanine secretion and glucose availability: In liquid cultures we only detected alanine secretion in anoxic conditions (Figure 3A). The oxic, fast-growing liquid culture conditions did not result in measurable extracellular alanine levels in our conditions. These results are consistent with the interpretation that only anoxic fermenting cells secrete alanine. However, from our current data we cannot conclude whether or not cells in our fast-growing liquid culture conditions performed overflow metabolism, because our metabolite measurements did not include acetate for technical reasons. In response to this comment, we carefully revised the paragraph in which Figure 3A is described (lines 205213):

“The spatial transcriptomes suggest that alanine is primarily secreted in the anoxic region of the biofilm. To test this, we explored under which combination of carbon/nitrogen/oxygen availability E. coli secretes alanine in shaking liquid conditions. Mass spectrometry measurements from culture supernatants clearly showed that alanine is only secreted under anoxic conditions with glucose and ammonium (Figure 3A), which is an environment that corresponds to the anoxic base of the colony, where cells are in contact with the glucose- and ammonium-rich M9 agar. Oxic conditions with abundant glucose and ammonium did not result in significant alanine secretion. This finding suggests that alanine is secreted in the anoxic base of the colony, which is consistent with the spatial transcriptome results.”

2) I am not convinced that maturation of the fluorescent protein mRuby2 is indicative of respiring bacteria. I think it is a very elegant way to identify the aerobic areas of the biofilm but again, bacteria can ferment even in the presence of oxygen, if the concentration of carbohydrates is high enough (i.e. crabtree effect in yeast, overflow metabolism in bacteria or Warburg effect in cancer cells). The subpopulation of fluorescence cells likely combine fermenting (closer edge to the agar) and respiring (middle-height edge of the colony) bacteria. Moreover, the authors removed glucose from the medium to alter the metabolic processes that consume oxygen (line 151) but this will severely impact fermentative growth as well.

We are grateful that the reviewer pointed out this inaccuracy in our terminology. We agree that fluorescence of mRuby2 is not indicative of respiration. We used mRuby2 fluorescence levels as an indication of oxic regions. To correct this inaccuracy in our terminology, we have carefully gone through every mentioning of the words “respiration”, “fermentation”, “aerobic”, and “anaerobic” and, where appropriate, rephrased the relevant sentences based on the terms “oxic” and “anoxic” throughout the manuscript.

3) I do not understand how these extracellular alanine concentrations are toxic to the cells. The paper cited is about intracellular alanine (Katsube et al., 2019). Is there anything known about how extracellular alanine levels are toxic to the cells?

The experiments in which we varied the extracellular alanine level and observed growth inhibition (Figure 4C) show that the extracellular alanine concentration has a large effect. In particular, cells that were unable to export alanine (ΔalaE) were more affected by extracellular alanine compared to the parental strain, but the double mutant (ΔdadAXΔalaE) which has an impaired ability to secrete or degrade alanine, was most affected. These results support the idea that the extracellular alanine levels can affect the intracellular alanine levels.

As far as we are aware, the molecular mechanism for how alanine can be inhibitory to the cells is currently not described in the literature. Perhaps alanine toxicity is linked to misloading of tRNAs or the modification of the direction of some enzymatic reactions due to an excess of alanine.

In response to this comment, we added the following clarifications in the manuscript (lines 258-262):

“For cells that lack both the major alanine exporter AlaE and the major alanine degradation pathway via DadA and DadX, we hypothesized that the presence of extracellular alanine might lead to an accumulation of intracellular alanine to toxic levels. It has previously been shown that excess levels of intracellular alanine can inhibit growth (Katsube, Ando, and Yoneyama, 2019), yet the molecular mechanism underlying this process is still unclear.

Line 114: "Lactate, formate and succinate biosynthesis, however, displayed interesting dynamics during colony growth". This sentence has no meaning as it does not describe the data. In what way are these dynamics interesting?

We agree that this sentence lacked meaning. We have now modified the sentence to (lines 120-122):

“However, transcripts of the lactate, formate, and succinate biosynthesis pathways displayed differential regulation during colony growth (Figure 1—figure supplement 4A).”

Line 125: "All amino acid abundances decreased during colony growth, except for alanine, which remained relatively constant with a peak abundance at 32 h". Several aminoacids derive from alanine metabolism. I wonder how the level of these aminoacids did not remain closer to that of alanine

We thank the reviewer for this interesting comment. We are not completely sure why is this the case. We offer two speculations:

– Speculation 1: Figure 1E reports measurements of the total amino acid pool in the colony (the sum of intracellular and extracellular levels of each amino acid) – it is possible that only alanine is secreted and therefore the measured alanine levels primarily reflect the extracellular levels, whereas the other amino acids remain intracellular and they decrease during colony growth because cells adapt to an increasingly nutrient-poor environment.

– Speculation 2: Several amino acids can be made from pyruvate. These reactions generally require multiple enzymes, except for alanine, which can be converted to and from pyruvate in a simple enzymatic reaction. Furthermore, several amino acids can be converted into alanine. The close link of alanine levels to central metabolism (and in particular to pyruvate levels) combined with its secretion during anaerobic fermentation could be the origin of the difference between the alanine profile and the profile of other amino acids.

Because these arguments are quite speculative, we did not include them in the manuscript.

Line 129: I do not understand how the transcriptomic or metabolomic data invited the authors to explore whether alanine metabolism is spatially heterogeneous during biofilm growth.

We thank the reviewer for highlighting that there was a logical gap at this point in the manuscript. We have now added an additional sentence in lines 136-139 to bridge the logical gap in the argument, as follows:

“Biofilms are expected to be metabolically heterogeneous so that we hypothesized that alanine metabolism could be spatially organized inside biofilms. To test this hypothesis, we developed a method to measure transcriptomes with spatial resolution in the colonies.”

Lines 217-260: these two paragraphs describing the phenotypes of different mutants in alanine metabolism are hard to digest. I read them several times and I do not think I understood.

We carefully read these paragraphs again, and discussed them with colleagues who are not co-authors of the manuscript and detected a logical break in the mentioning of the -alanine exporter, which we have now corrected by rearranging the sentences and addition additional text. We have also added a summary sentence at the end of the first paragraph, to improve the readability. We also made numerous smaller edits in these paragraphs aimed at improving the readability.

https://doi.org/10.7554/eLife.70794.sa2

Article and author information

Author details

  1. Francisco Díaz-Pascual

    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing, Project administration, Visualization
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8947-682X
  2. Martin Lempp

    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    Contribution
    Data curation, Investigation, Methodology, Validation
    Competing interests
    No competing interests declared
  3. Kazuki Nosho

    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    Contribution
    Investigation, Methodology, Validation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4811-1397
  4. Hannah Jeckel

    1. Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    2. Department of Physics, Philipps-Universität Marburg, Marburg, Germany
    3. Biozentrum, University of Basel, Basel, Switzerland
    Contribution
    Data curation, Formal analysis, Investigation, Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7080-4907
  5. Jeanyoung K Jo

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1543-1148
  6. Konstantin Neuhaus

    1. Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    2. Department of Physics, Philipps-Universität Marburg, Marburg, Germany
    3. Biozentrum, University of Basel, Basel, Switzerland
    Contribution
    Methodology, Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9877-5765
  7. Raimo Hartmann

    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    Contribution
    Methodology, Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4924-6402
  8. Eric Jelli

    1. Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    2. Department of Physics, Philipps-Universität Marburg, Marburg, Germany
    Contribution
    Software
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6202-2449
  9. Mads Frederik Hansen

    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9283-304X
  10. Alexa Price-Whelan

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7587-7534
  11. Lars EP Dietrich

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Funding acquisition, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2049-1137
  12. Hannes Link

    1. Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    2. Interfaculty Institute for Microbiology and Infection Medicine, Eberhard Karls Universität Tübingen, Tübingen, Germany
    Contribution
    Funding acquisition, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  13. Knut Drescher

    1. Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    2. Department of Physics, Philipps-Universität Marburg, Marburg, Germany
    3. Biozentrum, University of Basel, Basel, Switzerland
    Contribution
    Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    knut.drescher@unibas.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7340-2444

Funding

H2020 European Research Council (StG-716734)

  • Knut Drescher

Deutsche Forschungsgemeinschaft (SFB987)

  • Hannes Link
  • Knut Drescher

Deutsche Forschungsgemeinschaft (DR 982/5-1)

  • Knut Drescher

Heineman Foundation

  • Knut Drescher

Bundesministerium für Bildung und Forschung (TARGET-Biofilms)

  • Knut Drescher

Max-Planck-Gesellschaft

  • Knut Drescher

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (NCCR AntiResist)

  • Knut Drescher

National Science Foundation (CAREER Award)

  • Lars EP Dietrich

National Institute of Allergy and Infectious Diseases (R01AI103369)

  • Lars EP Dietrich

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to Thorben Schramm and Nicole Paczia for performing additional metabolite measurements. We thank Gabriele Malengo for helping with the cell sorting procedure. We also thank Miriam Bayer, Praveen K Singh, Daniel Rode, Lucia Vidakovic, Sanika Vaidya, and all members of the Drescher lab for their comments and fruitful discussions. This work was supported by grants from the European Research Council (StG-716734), Deutsche Forschungsgemeinschaft (SFB987 and DR 982/5–1), Minna James Heineman Foundation, Bundesministerium für Bildung und Forschung (TARGET-Biofilms), Max Planck Society, and the Swiss National Science Foundation NCCR “AntiResist” (to K.D.), and an NSF CAREER award and NIH/NIAID grant R01AI103369 (to L.E.P.D.).

Senior Editor

  1. Gisela Storz, National Institute of Child Health and Human Development, United States

Reviewing Editor

  1. Karina B Xavier, Instituto Gulbenkian de Ciência, Portugal

Reviewer

  1. Karina B Xavier, Instituto Gulbenkian de Ciência, Portugal

Publication history

  1. Preprint posted: February 28, 2021 (view preprint)
  2. Received: May 28, 2021
  3. Accepted: October 18, 2021
  4. Version of Record published: November 9, 2021 (version 1)

Copyright

© 2021, Díaz-Pascual et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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