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Phenotypic heterogeneity of microbial populations under nutrient limitation
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ScienceDirect Phenotypic heterogeneity of microbial populations under nutrient limitation Ana Gasperotti, Sophie Brameyer, Florian Fabiani and Kirsten Jung Phenotypic heterogeneity is a phenomenon in which genetically identical individuals have different characteristics. This behavior can also be found in bacteria, even if they grow as monospecies in well-mixed environments such as bioreactors. Here it is discussed how phenotypic heterogeneity is generated by internal factors and how it is promoted under nutrient-limited growth conditions. A better understanding of the molecular levels that control phenotypic heterogeneity could improve biotechnological production processes. Address Department of Microbiology, Ludwig-Maximilians-Universita¨t Mu¨nchen, 82152 Martinsried, Germany Corresponding author: Jung, Kirsten ([email protected])
Current Opinion in Biotechnology 2020, 62:160–167 This review comes from a themed issue on Environmental biotechnology Edited by David Johnson and Stephen Noack
bioreactors used in biotechnological applications. However, free-living bacterial communities are also found in natural environments – for example, in the ocean [1]. We begin by focusing on phenotypic heterogeneity caused by internal factors, such as physiological heterogeneity and stochasticity or global regulatory networks. We then describe several examples of heterogeneous responses of bacteria to the availability of nutrients, specifically lactose, pyruvate and nitrogen, and to the presence of chemical gradients. The primary drivers for these phenotypic differences are individual nutritional need — an internal factor — and the concentrations of the available nutrients, an external factor. Note that we will not address phenomena such as spatial and temporal heterogeneity within bacterial biofilms [2–5], persister cell formation [6] or the differentiation programs in Bacillus [7], Myxococcus [8] and Streptomyces [9], all of which are also affected by nutrient limitation. The interested reader is referred to the excellent reviews just cited.
https://doi.org/10.1016/j.copbio.2019.09.016 0958-1669/ã 2019 Elsevier Ltd. All rights reserved.
Phenotypic heterogeneity driven by internal factors Inherent physiological heterogeneity and stochasticity
Introduction It is now accepted that isogenic bacterial populations are phenotypically diverse, and that their members can follow individual strategies. Phenotypic heterogeneity is generated by internal factors, such as individual physiological state and stochastic gene expression, and is strongly influenced by external factors, such as availability of nutrients, microscale chemical gradients, cell density, physical and chemical stresses. It is conceivable that phenotypic heterogeneity not only promotes the survival of a species by permitting bet-hedging, but also sustains the fitness of a population by enabling division of labor. Consequently, phenotypic individuality is an important issue in various fields of microbiology, including medical microbiology, biotechnology and evolution. In this review, we will concentrate on selected aspects of phenotypic heterogeneity in bacteria living in a wellmixed environment, a condition which is typical for Current Opinion in Biotechnology 2020, 62:160–167
Even when exposed to the same environment, individual members of a clonal population can exhibit different phenotypes. One major factor responsible for this inherent variability is stochastic gene expression, often referred to as ‘noise’. Under otherwise constant conditions, gene expression, and thus levels of mRNAs and proteins fluctuate randomly within a population [10–12]. This noise can be divided into extrinsic and intrinsic noise (Figure 1). Variations in intracellular concentrations and binding rates of RNA polymerase, transcription factors, ribosomes and ribonucleases are the main sources of extrinsic noise that affect transcription/translation [13,14]. Variability of these factors can explain differences in protein levels between cells. Moreover, Elowitz et al. have shown that two identical promoters within the same cell are not always activated at the same time and to the same degree [11]. This phenomenon, termed intrinsic noise, is due to stochastic promoter activation and RNA degradation [14]. Noise also results from the fact that gene expression is not uniformly distributed over time, but is made up of periods of inactivity followed by bursts of both transcription [15] and translation [12]. www.sciencedirect.com
Phenotypic heterogeneity under nutrient limitation Gasperotti et al. 161
Figure 1
Extrinsic noise: - availabilty of metabolites
growth rate heterogeneity
- binding rates of RNA polymerase and transcription factors - mRNA and protein degradation
Global regulation networks
Enzyme DNA
protein
RNA
gene expression heterogeneity
metabolic heterogeneity
Intrinsic noise - stochastic promoter activation - mRNA degradation
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Cell-inherent heterogeneity can be divided into intrinsic and extrinsic noise and both are interconnected with gene expression, metabolism, growth rate and global regulation networks (dotted lines). Solid black arrows indicate transcription and translation.
The cell cycle has been shown to be another significant source of noise, as the timing of chromosome replication affects gene copy number, and variable cell size causes protein concentrations to fluctuate [16]. Phenotypic heterogeneity also occurs at the metabolic level. Variability in metabolite concentrations arises from fluctuations in the synthesis of catabolically active enzymes. Adenosine triphosphate (ATP) is a critically important metabolite that powers many intracellular reactions. Quantitative measurements of absolute intracellular ATP concentrations in individual cells have yielded a broad range of values [17]. Furthermore, the shape of the distribution for bacteria grown in liquid culture was not Gaussian, but asymmetric. The heterogeneity of ATP levels probably reflects variability in enzyme content between cells, which is in turn attributable to the stochastic nature of the corresponding synthesis reactions. Overall, heterogeneous distribution of ATP may benefit the population by enabling individual cells to adopt different strategies under various conditions [17]. Moreover, the levels of other crucial metabolites, such as NAD (P)H [18], and second messengers, such as c-di-GMP [19], oscillate during the cell cycle, which also contributes to heterogeneity between cells. This metabolic diversity influences cell growth rates, which in turn affect the www.sciencedirect.com
concentrations of enzymes [20]. In addition, the variability of metabolites, many of which are co-factors for transcriptional regulators, can induce transcriptional feedbacks leading to a multimodal distribution of metabolic states [21]. Global regulation networks
Regulation networks, especially those that control central metabolic processes, are another source of heterogeneity, and have important physiological implications (Figure 1). In Escherichia coli, two subpopulations emerge after a glucose-to-gluconeogenic substrate shift [22]. This bistability between a growing and a non-dividing population is not attributable to stochastic switching before the substrate shift, but is generated by responsive diversification after the shift. This is driven by a flux-sensor system composed of the transcription factor Cra, a key regulator of the glycolysis/gluconeogenesis switch crucial for acetate utilization, and its inhibitor, the flux signaling metabolite fructose-1,6-biphosphate (FBP) [22]. FBP concentrations vary depending on the intracellular metabolic flux and Cra activity is modulated accordingly. This leads to responsive diversification, as cells with low glycolysis rates switch to acetate and form the growing population [23]. It should be noted that facultative anaerobic bacterial metabolism sensitively responds to even Current Opinion in Biotechnology 2020, 62:160–167
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Figure 2
External factors: - nutrients availabilty - environmental stress
sensor systems chemotaxis arrays Internal factors: gene expression
flagella
transport systems Current Opinion in Biotechnology
Effect of external and internal factors on phenotypic heterogeneity. Environmental alterations are transduced into cellular information by chemoreceptors, sensor and transport systems.
minor alterations in oxygen availability, which is influenced by culture volume, medium flow as well as shaking frequency [24]. Similarly, two stable subpopulations with different metabolic strategies emerge during diauxic shift between glucose and cellobiose in Lactococcus lactis [25]. The distribution between the two states is controlled by the combined effects of catabolic repression and the stringent response, mediated via the transcriptional regulator CcpA and (p)ppGpp-dependent inhibition of genes required for cellobiose utilization. Cells that switch during the short interval between the release of catabolite repression and the rise in (p)ppGpp concentration constitute the growing population, while cells which do not switch in time do not divide [25]. In Bacillus subtilis, the pleiotropic transcriptional regulator CodY, which senses internal GTP levels, was recently Current Opinion in Biotechnology 2020, 62:160–167
shown to be the source of heterogeneity in protein overproduction [26]. While wild-type cells produced heterogeneous amounts of the green fluorescent protein (GFP), an amino acid substitution in CodY was sufficient to induce synthesis of comparable levels of GFP in all cells, which effectively increased overall GFP production. Another example of bistability is the utilization of myoinositol by Salmonella enterica serovar Typhimurium strain 14028 (S. Typhimurium) [27]. Myo-inositol is a polyol that is abundant not only in the soil but also in the bloodstream of mammalian organisms, and is therefore an important food source for many pathogens. Enzymes, transporters, and a repressor responsible for the catabolism of myoinositol are encoded by a genomic island carrying the iol genes [27]. Growth of S. Typhimurium on myo-inositol as the sole carbon source is characterized by a long lag phase (40–60 h), which is followed by the emergence of large colonies against a background of slowly growing bacteria. www.sciencedirect.com
Phenotypic heterogeneity under nutrient limitation Gasperotti et al. 163
This bistability is reversible and is dependent on IolR, the transcriptional repressor of the iol genes. At the singlecell level, bistability is correlated with the heterogeneous expression of iolE and iolG, whose products catalyze the first steps in myo-inositol degradation. In addition, the inducer-dependent promoter activity of IolR is bistable as well [28]. Recently, the interaction of IolR with its cognate promoters was investigated in detail. Two different binding sites with high and low affinity for IolR are present in iol promoters. Rates of binding and release for the two binding sites are dependent on both the copy number of IolR and the concentration of the inducer, leading to a bistable, pulse-like, and readily reversible pattern of gene expression [29].
Phenotypic heterogeneity driven by internal and external factors Uptake of nutrients
Most microorganisms live in environments where nutrients are limited and levels vary over time. Cells generally respond to these fluctuations by monitoring and adapting their physiological state. Phenotypic heterogeneity in isogenic populations could serve as an alternative strategy in fluctuating environments [30,31], where substrate limitation often restricts the growth of microbes and can shape the degree of phenotypic heterogeneity in metabolism [32,33]. Organisms employ nutrient-responsive regulatory networks to monitor nutrient levels and adjust cellular processes accordingly [33,34] (Figure 2). Nutrient homeostasis, that is, the ability to maintain a relatively constant internal level of the limiting nutrient, is achieved by controlling the balance of nutrient uptake and utilization. Here we will discuss three examples to illustrate how microorganisms deal with nutrient limitation and uptake of lactose, pyruvate, and phosphate according to the needs of the individual cell. E. coli and many other bacteria use glucose as preferred carbon source, and the presence of this sugar prevents the use of other carbon sources. This phenomenon is termed carbon catabolic repression [35]. Only when glucose is depleted another sugar can be utilized. In the case of lactose, this requires the induction of the lac operon. In 1957, a groundbreaking study by Novick and Weiner demonstrated that when the lac operon is induced at a low level, two subpopulations appear, which either express lac genes at high levels or not at all [36]. The lac operon consists of the genes lacZ, lacY and lacA, encoding ß-galactosidase, lactose permease and transacetylase, respectively [37]. Expression of the operon is negatively regulated by a repressor (LacI), which dissociates from the DNA in the presence of an inducer, for example, allolactose or isopropyl-b-D-thiogalactopyranoside (IPTG), and subsequently transcription starts. Production of the lactose permease allows inducer influx, which results in positive feedback on the permease expression level. Above a certain threshold concentration www.sciencedirect.com
of permease molecules, a cell becomes capable of metabolizing lactose; below this threshold, the cell will remain unable to utilize lactose [38]. Using single-cell analysis in combination with modeling, it was shown that this bistable response can also be converted into a graded response [39]. While the lac operon remains the paradigmatic example of a bistable system in bacteria [40], similar systems have been described for the uptake of other nutrients, such as arabinose [41]. In an amino-acid-rich environment, E. coli and other ɣ-proteobacteria excrete pyruvate due to overflow metabolism [42]. Subsequently, these cells detect the externalized pyruvate by using the LytS/LytTR signaling systems BtsS/BtsR and YpdA/YpdB. Both systems respond specifically to pyruvate, but with different sensitivities, and respectively regulate the synthesis of the two transporters, BtsT and YhjX, thought to mediate the re-uptake of pyruvate [43,44]. Indeed, BtsT is a highaffinity pyruvate transporter [45] and YhjX is a secondary transporter of thus far unknown functionality. The promoters of these two genes are heterogeneously activated in single cells of the E. coli population, and expression of btsT is more variable than that of yhjX. The degree of heterogeneous behavior depends on the external pyruvate concentration and the individual nutritional need [46]. In addition, other factors known to affect the expression of both transporters could explain the heterogeneity observed. These factors include the regulation through CsrA, RNase E and the catabolite repression mechanism [43,47,48] (Figure 2). Sensing of external pyruvate by these systems and the tightly regulated synthesis of the two transporters in accordance with the needs of the individual cell ensure that the overall physiological state of the population is optimized to withstand impending metabolic stress — imposed by protein overproduction, for instance. In contrast to wildtype cells, cells lacking both sensing systems show a bimodal distribution of protein production, with a subpopulation of cells being unable to produce GFP or other proteins [46]. The evolved E. coli C41 (DE3) strain, which has been optimized for membrane protein overproduction, has a point mutation in btsS (previously yehU) that leads to stimulus-independent expression of btsT [49]. Constitutive expression of the pyruvate transporter guarantees a sufficient uptake of pyruvate independently of the nutritional needs of cells, and therefore reduces heterogeneous uptake. This can improve bioprocess performance by reducing heterogeneity in protein production. Another example of heterogeneous regulation of highaffinity and low-affinity transporters is the Saccharomyces cerevisiae phosphate-responsive signaling (PHO) pathway, which monitors cytoplasmic levels of inorganic phosphate. In response to a decrease in internal phosphate levels, cells activate the PHO pathway, triggering one of Current Opinion in Biotechnology 2020, 62:160–167
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two feedback elements, either negative or positive. Because the feedback loops are both controlled by the PHO pathway, they create mutually exclusive states in which wild-type cells either activate the PHO pathway, express high-affinity transporters, and downregulate the low-affinity transport system or, instead, keep the PHO pathway turned OFF and continue to utilize low-affinity transporters for phosphate uptake [50]. Acquisition of nitrogen
The nitrogen-fixing bacterium Klebsiella oxytoca converts N2 into NH4+ in the absence of oxygen, but this conversion is energetically costly. Therefore, when both compounds are available NH4+ is the preferred substrate. As NH4+ becomes a limiting factor, a subpopulation will switch to N2 fixation, and the extent of heterogeneity in N2 fixation varies with the degree of NH4+ limitation, being higher in the presence of larger amounts of NH4+ [51]. In E. coli, sustained N starvation generates a heterogeneous, metabolically diverse population, with a small subpopulation being metabolically inactive. This heterogeneity is dependent on the serine/threonine kinase YeaG, which acts upstream of rpoS, coding for the stationary phase sigma factor, in the regulatory cascade [52]. Motility and chemotaxis
Clonal populations can also exhibit cell-to-cell variability in their chemotactic behavior. Non-genetic individuality was first identified in 1976 by measuring the amount of time that individual cells needed to adapt to specific stimuli [53]. Other parameters of the chemotaxis pathway also produce variability in isogenic populations, including tumble bias [54,55], input gain [56], number of flagella [57] or chemotaxis proteins [53,58], and cell velocity [59]. As described above, variations in the abundances of proteins can arise from noise in gene expression [60] as well as from the partitioning of cellular components during cell division [61] (Figure 2). Modulation of swimming speed by manipulation of intracellular levels of cyclic di-GMP [62] and modification of the proton-motive force by adjustment of environmental pH [63] also affect the heterogeneity observed in chemotaxis. The behavioral differences in navigation could have important effects on processes like colonization or exploration of the environment. In a phenotypically heterogeneous population, better chemotaxers will be the first responders to nutrient hotspots and less strongly chemotactic cells will remain more evenly distributed in space, potentially decreasing the impact of negative events such as predation. More broadly, recent findings suggest that bet-hedging strategies are important in chemotaxis [64]. Current Opinion in Biotechnology 2020, 62:160–167
Biotechnological implications and perspectives The various examples described here reveal that the inherent cell-to-cell variability of a population is greatly enhanced when one or more nutrients become limiting. This has obvious implications for bacterial gene expression, metabolite and protein production during industrial bioprocessing. A better understanding of the molecular switches will certainly help to diminish phenotypic heterogeneity of production strains [65]. The lac operon is a good example for minimizing phenotypic heterogeneity. Thus deletion of lacY in the frequently used E. coli BL21 (DE3) strains resulted in homogeneous expression responses [66]. Similarly, a point mutation (F227V) in the high-affinity pyruvate sensor BtsS leads to constitutive production of the high-affinity pyruvate transporter BtsT [49]. The BtsS/BtsR signaling system was found to be critical for homogeneous protein overproduction [46], but the point mutation was originally selected in the E. coli strain C41(DE3), which had undergone repeated mutagenesis and selection for the overexpression of membrane proteins [67]. Alternatively, homogeneous production of L-valine by Corynebacterium glutamicum was achieved via physiological manipulation using different media [68]. The integration of sophisticated synthetic genetic circuits helps to limit cell-to-cell variations and increase biosynthetic performance. For example, the implementation of in vivo population control enabled the selection of high performing, non-genetic variants and improved the production of free fatty acids or tyrosine [69]. In a further study, a synthetic product addition circuit was generated that prevents the occurrence of genetic variants of E. coli that impair biosynthetic capacity [70]. Although our knowledge of the many facets of heterogeneous behavior within microbial communities is constantly increasing, we still don’t understand all of the underlying molecular principles. Identification and characterization of the molecular switches that cause phenotypic heterogeneity remain challenging, as there is no standardized analytical framework for such investigations. Phenotypic heterogeneity in microbial metabolism is more pronounced in nutrient-limited, dynamic habitats than in nutrient-saturated, stable habitats. Since multiple metabolic activities are controlled by internal metabolite concentrations [71,72], biologically relevant phenotypic heterogeneity can potentially be generated in many other pathways that translate nutrient-limited, flux-controlled growth into intermediate inducer concentrations. Once these switches are recognized, the generation of production strains with alterations in the relevant promoters, mRNA lifetimes, and plasmid or transport systems becomes feasible. In addition, novel multiplex biosensors are needed to monitor the degree of heterogeneous www.sciencedirect.com
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behavior of more than one phenotype in single cells. Finally, large-scale bioreactors exhibit inhomogeneities, in concentrations of nutrients and dissolved oxygen, for example. Novel microfluidic systems offer new opportunities to scale down bioreactors and facilitate systematic studies at single-cell level [73]. Understanding and overcoming phenotypic heterogeneity in bioreactors should boost the production of useful biomolecules, both lowmolecular-weight compounds and recombinant proteins.
Funding This work was supported by the Deutsche Forschungsgemeinschaft (SPP1617, Project JU270/13-2 to K.J.).
Conflict of interest statement Nothing declared.
Acknowledgements We thank all current and former members of the research group for their important contributions to this research.
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52. Figueira R, Brown DR, Ferreira D, Eldridge MJG, Burchell L, Pan Z, Helaine S, Wigneshweraraj S: Adaptation to sustained nitrogen starvation by Escherichia coli requires the eukaryote-like serine/ threonine kinase YeaG. Sci Rep 2015, 5:1-14. 53. Spudich JL, Koshland DE: Non-genetic individuality: chance in the single cell. Nature 1976, 262:467-471. 54. Dufour YS, Gillet S, Frankel NW, Weibel DB, Emonet T: Direct correlation between motile behavior and protein abundance in single cells. PLoS Comput Biol 2016, 12:e1005041-25. Here, the authors combine automated microscopy with in situ hydrogel polymerization to measure single-cell protein expression after tracking swimming behaviour. With this strategy, they were able to map motile phenotype to protein number, and found that changes in numbers of proteins affect the population mean tumble bias and its variance independently. Therefore, it is possible to adjust the degree of phenotypic diversity of a population by adjusting the global level of expression. 55. Waite AJ, Frankel NW, Dufour YS, Johnston JF, Long J, Emonet T: Non-genetic diversity modulates population performance. Mol Syst Biol 2016, 12 895–14. 56. Keegstra JM, Kamino K, Anquez F, Lazova MD, Emonet T, Shimizu TS: Phenotypic diversity and temporal variability in a bacterial signaling network revealed by single-cell FRET. eLife 2017, 6:708. 57. Mears PJ, Koirala S, Rao CV, Golding I, Chemla YR: Escherichia coli swimming is robust against variations in flagellar number. eLife 2014, 3:e01916. 58. Levin MD, Morton-Firth CJ, Abouhamad WN, Bourret RB, Bray D: Origins of individual swimming behavior in bacteria. Biophys J 1998, 74:175-181. 59. Taute KM, Gude S, Tans SJ, Shimizu TS: High-throughput 3D tracking of bacteria on a standard phase contrast microscope. Nat Commun 2015, 6:8776. 60. Tanguchi J, Murata H, Okamura Y: Analysis of aggregation and dispersion states of small particles in concentrated suspension by using diffused photon density wave spectroscopy. Colloids Surf B Biointerfaces 2010, 76:137-144. 61. Huh D, Paulsson J: Non-genetic heterogeneity from stochastic partitioning at cell division. Nat Genet 2010, 43:95-100. 62. Boehm A, Kaiser M, Li H, Spangler C, Kasper CA, Ackermann M, Kaever V, Sourjik V, Roth V, Jenal U: Second messengermediated adjustment of bacterial swimming velocity. Cell 2010, 141:107-116. 63. Hu B, Tu Y: Behaviors and strategies of bacterial navigation in chemical and nonchemical gradients. PLoS Comput Biol 2014, 10:e1003672-8. 64. Salek MM, Carrara F, Fernandez V, Guasto JS, Stocker R: Bacterial chemotaxis in a microfluidic T-maze reveals strong phenotypic heterogeneity in chemotactic sensitivity. Nat Commun 2019, 10:1877. In this paper, the authors adopt a classic tool from animal ecology (the Tmaze) and implement it at the microscale by using microfluidics to expose bacteria to a sequence of decisions, each consisting of migration up or down a chemical gradient, showing strong heterogeneity in the chemotactic sensitivity even within clonal populations of bacteria. 65. Binder D, Drepper T, Jaeger K-E, Delvigne F, Wiechert W, Kohlheyer D, Gru¨nberger A: Homogenizing bacterial cell factories: analysis and engineering of phenotypic heterogeneity. Metab Eng 2017, 42:145-156. 66. Binder D, Probst C, Gru¨nberger A, Hilgers F, Loeschcke A, Jaeger K-E, Kohlheyer D, Drepper T: Comparative single-cell analysis of different E. coli expression systems during microfluidic cultivation. PLoS One 2016, 11:e0160711. 67. Kwon S-K, Kim SK, Lee D-H, Kim JF: Comparative genomics and experimental evolution of Escherichia coli BL21(DE3) strains reveal the landscape of toxicity escape from membrane protein overproduction. Sci Rep 2015, 5:16076. 68. Mustafi N, Gru¨nberger A, Mahr R, Helfrich S, No¨h K, Blombach B, Kohlheyer D, Frunzke J: Application of a genetically encoded www.sciencedirect.com
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biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains. PLoS One 2014, 9:e85731. 69. Xiao Y, Bowen CH, Liu Di, Zhang F: Exploiting nongenetic cellto-cell variation for enhanced biosynthesis. Nat Chem Biol 2016, 12:339-344. 70. Rugbjerg P, Sarup-Lytzen K, Nagy M, Sommer MOA: Synthetic addiction extends the productive life time of engineered Escherichia coli populations. Proc Natl Acad Sci U S A 2018, 115:2347-2352. In this paper, the authors highlight how synthetic networks can be designed to control genetic population heterogeneity by using the concept of synthetic product addiction. Thereby, costly and high-yield
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metabolite production is linked to cellular growth by a product-responsive biosensor regulating nonconditionally essential genes. 71. Kochanowski K, Volkmer B, Gerosa L, van Rijsewijk BRH, Schmidt A, Heinemann M: Functioning of a metabolic flux sensor in Escherichia coli. Proc Natl Acad Sci U S A 2013, 110:1130-1135. 72. Chubukov V, Gerosa L, Kochanowski K, Sauer U: Coordination of microbial metabolism. Nat Rev Microbiol 2014, 12:327-340. 73. Ho P, Westerwalbesloh C, Kaganovitch E, Gru¨nberger A, Neubauer P, Kohlheyer D, Lieres EV: Reproduction of largescale bioreactor conditions on microfluidic chips. Microorganisms 2019, 7:105.
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ScienceDirect Phenotypic heterogeneity of microbial populations under nutrient limitation Ana Gasperotti, Sophie Brameyer, Florian Fabiani and Kirsten Jung Phenotypic heterogeneity is a phenomenon in which genetically identical individuals have different characteristics. This behavior can also be found in bacteria, even if they grow as monospecies in well-mixed environments such as bioreactors. Here it is discussed how phenotypic heterogeneity is generated by internal factors and how it is promoted under nutrient-limited growth conditions. A better understanding of the molecular levels that control phenotypic heterogeneity could improve biotechnological production processes. Address Department of Microbiology, Ludwig-Maximilians-Universita¨t Mu¨nchen, 82152 Martinsried, Germany Corresponding author: Jung, Kirsten ([email protected])
Current Opinion in Biotechnology 2020, 62:160–167 This review comes from a themed issue on Environmental biotechnology Edited by David Johnson and Stephen Noack
bioreactors used in biotechnological applications. However, free-living bacterial communities are also found in natural environments – for example, in the ocean [1]. We begin by focusing on phenotypic heterogeneity caused by internal factors, such as physiological heterogeneity and stochasticity or global regulatory networks. We then describe several examples of heterogeneous responses of bacteria to the availability of nutrients, specifically lactose, pyruvate and nitrogen, and to the presence of chemical gradients. The primary drivers for these phenotypic differences are individual nutritional need — an internal factor — and the concentrations of the available nutrients, an external factor. Note that we will not address phenomena such as spatial and temporal heterogeneity within bacterial biofilms [2–5], persister cell formation [6] or the differentiation programs in Bacillus [7], Myxococcus [8] and Streptomyces [9], all of which are also affected by nutrient limitation. The interested reader is referred to the excellent reviews just cited.
https://doi.org/10.1016/j.copbio.2019.09.016 0958-1669/ã 2019 Elsevier Ltd. All rights reserved.
Phenotypic heterogeneity driven by internal factors Inherent physiological heterogeneity and stochasticity
Introduction It is now accepted that isogenic bacterial populations are phenotypically diverse, and that their members can follow individual strategies. Phenotypic heterogeneity is generated by internal factors, such as individual physiological state and stochastic gene expression, and is strongly influenced by external factors, such as availability of nutrients, microscale chemical gradients, cell density, physical and chemical stresses. It is conceivable that phenotypic heterogeneity not only promotes the survival of a species by permitting bet-hedging, but also sustains the fitness of a population by enabling division of labor. Consequently, phenotypic individuality is an important issue in various fields of microbiology, including medical microbiology, biotechnology and evolution. In this review, we will concentrate on selected aspects of phenotypic heterogeneity in bacteria living in a wellmixed environment, a condition which is typical for Current Opinion in Biotechnology 2020, 62:160–167
Even when exposed to the same environment, individual members of a clonal population can exhibit different phenotypes. One major factor responsible for this inherent variability is stochastic gene expression, often referred to as ‘noise’. Under otherwise constant conditions, gene expression, and thus levels of mRNAs and proteins fluctuate randomly within a population [10–12]. This noise can be divided into extrinsic and intrinsic noise (Figure 1). Variations in intracellular concentrations and binding rates of RNA polymerase, transcription factors, ribosomes and ribonucleases are the main sources of extrinsic noise that affect transcription/translation [13,14]. Variability of these factors can explain differences in protein levels between cells. Moreover, Elowitz et al. have shown that two identical promoters within the same cell are not always activated at the same time and to the same degree [11]. This phenomenon, termed intrinsic noise, is due to stochastic promoter activation and RNA degradation [14]. Noise also results from the fact that gene expression is not uniformly distributed over time, but is made up of periods of inactivity followed by bursts of both transcription [15] and translation [12]. www.sciencedirect.com
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Figure 1
Extrinsic noise: - availabilty of metabolites
growth rate heterogeneity
- binding rates of RNA polymerase and transcription factors - mRNA and protein degradation
Global regulation networks
Enzyme DNA
protein
RNA
gene expression heterogeneity
metabolic heterogeneity
Intrinsic noise - stochastic promoter activation - mRNA degradation
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Cell-inherent heterogeneity can be divided into intrinsic and extrinsic noise and both are interconnected with gene expression, metabolism, growth rate and global regulation networks (dotted lines). Solid black arrows indicate transcription and translation.
The cell cycle has been shown to be another significant source of noise, as the timing of chromosome replication affects gene copy number, and variable cell size causes protein concentrations to fluctuate [16]. Phenotypic heterogeneity also occurs at the metabolic level. Variability in metabolite concentrations arises from fluctuations in the synthesis of catabolically active enzymes. Adenosine triphosphate (ATP) is a critically important metabolite that powers many intracellular reactions. Quantitative measurements of absolute intracellular ATP concentrations in individual cells have yielded a broad range of values [17]. Furthermore, the shape of the distribution for bacteria grown in liquid culture was not Gaussian, but asymmetric. The heterogeneity of ATP levels probably reflects variability in enzyme content between cells, which is in turn attributable to the stochastic nature of the corresponding synthesis reactions. Overall, heterogeneous distribution of ATP may benefit the population by enabling individual cells to adopt different strategies under various conditions [17]. Moreover, the levels of other crucial metabolites, such as NAD (P)H [18], and second messengers, such as c-di-GMP [19], oscillate during the cell cycle, which also contributes to heterogeneity between cells. This metabolic diversity influences cell growth rates, which in turn affect the www.sciencedirect.com
concentrations of enzymes [20]. In addition, the variability of metabolites, many of which are co-factors for transcriptional regulators, can induce transcriptional feedbacks leading to a multimodal distribution of metabolic states [21]. Global regulation networks
Regulation networks, especially those that control central metabolic processes, are another source of heterogeneity, and have important physiological implications (Figure 1). In Escherichia coli, two subpopulations emerge after a glucose-to-gluconeogenic substrate shift [22]. This bistability between a growing and a non-dividing population is not attributable to stochastic switching before the substrate shift, but is generated by responsive diversification after the shift. This is driven by a flux-sensor system composed of the transcription factor Cra, a key regulator of the glycolysis/gluconeogenesis switch crucial for acetate utilization, and its inhibitor, the flux signaling metabolite fructose-1,6-biphosphate (FBP) [22]. FBP concentrations vary depending on the intracellular metabolic flux and Cra activity is modulated accordingly. This leads to responsive diversification, as cells with low glycolysis rates switch to acetate and form the growing population [23]. It should be noted that facultative anaerobic bacterial metabolism sensitively responds to even Current Opinion in Biotechnology 2020, 62:160–167
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Figure 2
External factors: - nutrients availabilty - environmental stress
sensor systems chemotaxis arrays Internal factors: gene expression
flagella
transport systems Current Opinion in Biotechnology
Effect of external and internal factors on phenotypic heterogeneity. Environmental alterations are transduced into cellular information by chemoreceptors, sensor and transport systems.
minor alterations in oxygen availability, which is influenced by culture volume, medium flow as well as shaking frequency [24]. Similarly, two stable subpopulations with different metabolic strategies emerge during diauxic shift between glucose and cellobiose in Lactococcus lactis [25]. The distribution between the two states is controlled by the combined effects of catabolic repression and the stringent response, mediated via the transcriptional regulator CcpA and (p)ppGpp-dependent inhibition of genes required for cellobiose utilization. Cells that switch during the short interval between the release of catabolite repression and the rise in (p)ppGpp concentration constitute the growing population, while cells which do not switch in time do not divide [25]. In Bacillus subtilis, the pleiotropic transcriptional regulator CodY, which senses internal GTP levels, was recently Current Opinion in Biotechnology 2020, 62:160–167
shown to be the source of heterogeneity in protein overproduction [26]. While wild-type cells produced heterogeneous amounts of the green fluorescent protein (GFP), an amino acid substitution in CodY was sufficient to induce synthesis of comparable levels of GFP in all cells, which effectively increased overall GFP production. Another example of bistability is the utilization of myoinositol by Salmonella enterica serovar Typhimurium strain 14028 (S. Typhimurium) [27]. Myo-inositol is a polyol that is abundant not only in the soil but also in the bloodstream of mammalian organisms, and is therefore an important food source for many pathogens. Enzymes, transporters, and a repressor responsible for the catabolism of myoinositol are encoded by a genomic island carrying the iol genes [27]. Growth of S. Typhimurium on myo-inositol as the sole carbon source is characterized by a long lag phase (40–60 h), which is followed by the emergence of large colonies against a background of slowly growing bacteria. www.sciencedirect.com
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This bistability is reversible and is dependent on IolR, the transcriptional repressor of the iol genes. At the singlecell level, bistability is correlated with the heterogeneous expression of iolE and iolG, whose products catalyze the first steps in myo-inositol degradation. In addition, the inducer-dependent promoter activity of IolR is bistable as well [28]. Recently, the interaction of IolR with its cognate promoters was investigated in detail. Two different binding sites with high and low affinity for IolR are present in iol promoters. Rates of binding and release for the two binding sites are dependent on both the copy number of IolR and the concentration of the inducer, leading to a bistable, pulse-like, and readily reversible pattern of gene expression [29].
Phenotypic heterogeneity driven by internal and external factors Uptake of nutrients
Most microorganisms live in environments where nutrients are limited and levels vary over time. Cells generally respond to these fluctuations by monitoring and adapting their physiological state. Phenotypic heterogeneity in isogenic populations could serve as an alternative strategy in fluctuating environments [30,31], where substrate limitation often restricts the growth of microbes and can shape the degree of phenotypic heterogeneity in metabolism [32,33]. Organisms employ nutrient-responsive regulatory networks to monitor nutrient levels and adjust cellular processes accordingly [33,34] (Figure 2). Nutrient homeostasis, that is, the ability to maintain a relatively constant internal level of the limiting nutrient, is achieved by controlling the balance of nutrient uptake and utilization. Here we will discuss three examples to illustrate how microorganisms deal with nutrient limitation and uptake of lactose, pyruvate, and phosphate according to the needs of the individual cell. E. coli and many other bacteria use glucose as preferred carbon source, and the presence of this sugar prevents the use of other carbon sources. This phenomenon is termed carbon catabolic repression [35]. Only when glucose is depleted another sugar can be utilized. In the case of lactose, this requires the induction of the lac operon. In 1957, a groundbreaking study by Novick and Weiner demonstrated that when the lac operon is induced at a low level, two subpopulations appear, which either express lac genes at high levels or not at all [36]. The lac operon consists of the genes lacZ, lacY and lacA, encoding ß-galactosidase, lactose permease and transacetylase, respectively [37]. Expression of the operon is negatively regulated by a repressor (LacI), which dissociates from the DNA in the presence of an inducer, for example, allolactose or isopropyl-b-D-thiogalactopyranoside (IPTG), and subsequently transcription starts. Production of the lactose permease allows inducer influx, which results in positive feedback on the permease expression level. Above a certain threshold concentration www.sciencedirect.com
of permease molecules, a cell becomes capable of metabolizing lactose; below this threshold, the cell will remain unable to utilize lactose [38]. Using single-cell analysis in combination with modeling, it was shown that this bistable response can also be converted into a graded response [39]. While the lac operon remains the paradigmatic example of a bistable system in bacteria [40], similar systems have been described for the uptake of other nutrients, such as arabinose [41]. In an amino-acid-rich environment, E. coli and other ɣ-proteobacteria excrete pyruvate due to overflow metabolism [42]. Subsequently, these cells detect the externalized pyruvate by using the LytS/LytTR signaling systems BtsS/BtsR and YpdA/YpdB. Both systems respond specifically to pyruvate, but with different sensitivities, and respectively regulate the synthesis of the two transporters, BtsT and YhjX, thought to mediate the re-uptake of pyruvate [43,44]. Indeed, BtsT is a highaffinity pyruvate transporter [45] and YhjX is a secondary transporter of thus far unknown functionality. The promoters of these two genes are heterogeneously activated in single cells of the E. coli population, and expression of btsT is more variable than that of yhjX. The degree of heterogeneous behavior depends on the external pyruvate concentration and the individual nutritional need [46]. In addition, other factors known to affect the expression of both transporters could explain the heterogeneity observed. These factors include the regulation through CsrA, RNase E and the catabolite repression mechanism [43,47,48] (Figure 2). Sensing of external pyruvate by these systems and the tightly regulated synthesis of the two transporters in accordance with the needs of the individual cell ensure that the overall physiological state of the population is optimized to withstand impending metabolic stress — imposed by protein overproduction, for instance. In contrast to wildtype cells, cells lacking both sensing systems show a bimodal distribution of protein production, with a subpopulation of cells being unable to produce GFP or other proteins [46]. The evolved E. coli C41 (DE3) strain, which has been optimized for membrane protein overproduction, has a point mutation in btsS (previously yehU) that leads to stimulus-independent expression of btsT [49]. Constitutive expression of the pyruvate transporter guarantees a sufficient uptake of pyruvate independently of the nutritional needs of cells, and therefore reduces heterogeneous uptake. This can improve bioprocess performance by reducing heterogeneity in protein production. Another example of heterogeneous regulation of highaffinity and low-affinity transporters is the Saccharomyces cerevisiae phosphate-responsive signaling (PHO) pathway, which monitors cytoplasmic levels of inorganic phosphate. In response to a decrease in internal phosphate levels, cells activate the PHO pathway, triggering one of Current Opinion in Biotechnology 2020, 62:160–167
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two feedback elements, either negative or positive. Because the feedback loops are both controlled by the PHO pathway, they create mutually exclusive states in which wild-type cells either activate the PHO pathway, express high-affinity transporters, and downregulate the low-affinity transport system or, instead, keep the PHO pathway turned OFF and continue to utilize low-affinity transporters for phosphate uptake [50]. Acquisition of nitrogen
The nitrogen-fixing bacterium Klebsiella oxytoca converts N2 into NH4+ in the absence of oxygen, but this conversion is energetically costly. Therefore, when both compounds are available NH4+ is the preferred substrate. As NH4+ becomes a limiting factor, a subpopulation will switch to N2 fixation, and the extent of heterogeneity in N2 fixation varies with the degree of NH4+ limitation, being higher in the presence of larger amounts of NH4+ [51]. In E. coli, sustained N starvation generates a heterogeneous, metabolically diverse population, with a small subpopulation being metabolically inactive. This heterogeneity is dependent on the serine/threonine kinase YeaG, which acts upstream of rpoS, coding for the stationary phase sigma factor, in the regulatory cascade [52]. Motility and chemotaxis
Clonal populations can also exhibit cell-to-cell variability in their chemotactic behavior. Non-genetic individuality was first identified in 1976 by measuring the amount of time that individual cells needed to adapt to specific stimuli [53]. Other parameters of the chemotaxis pathway also produce variability in isogenic populations, including tumble bias [54,55], input gain [56], number of flagella [57] or chemotaxis proteins [53,58], and cell velocity [59]. As described above, variations in the abundances of proteins can arise from noise in gene expression [60] as well as from the partitioning of cellular components during cell division [61] (Figure 2). Modulation of swimming speed by manipulation of intracellular levels of cyclic di-GMP [62] and modification of the proton-motive force by adjustment of environmental pH [63] also affect the heterogeneity observed in chemotaxis. The behavioral differences in navigation could have important effects on processes like colonization or exploration of the environment. In a phenotypically heterogeneous population, better chemotaxers will be the first responders to nutrient hotspots and less strongly chemotactic cells will remain more evenly distributed in space, potentially decreasing the impact of negative events such as predation. More broadly, recent findings suggest that bet-hedging strategies are important in chemotaxis [64]. Current Opinion in Biotechnology 2020, 62:160–167
Biotechnological implications and perspectives The various examples described here reveal that the inherent cell-to-cell variability of a population is greatly enhanced when one or more nutrients become limiting. This has obvious implications for bacterial gene expression, metabolite and protein production during industrial bioprocessing. A better understanding of the molecular switches will certainly help to diminish phenotypic heterogeneity of production strains [65]. The lac operon is a good example for minimizing phenotypic heterogeneity. Thus deletion of lacY in the frequently used E. coli BL21 (DE3) strains resulted in homogeneous expression responses [66]. Similarly, a point mutation (F227V) in the high-affinity pyruvate sensor BtsS leads to constitutive production of the high-affinity pyruvate transporter BtsT [49]. The BtsS/BtsR signaling system was found to be critical for homogeneous protein overproduction [46], but the point mutation was originally selected in the E. coli strain C41(DE3), which had undergone repeated mutagenesis and selection for the overexpression of membrane proteins [67]. Alternatively, homogeneous production of L-valine by Corynebacterium glutamicum was achieved via physiological manipulation using different media [68]. The integration of sophisticated synthetic genetic circuits helps to limit cell-to-cell variations and increase biosynthetic performance. For example, the implementation of in vivo population control enabled the selection of high performing, non-genetic variants and improved the production of free fatty acids or tyrosine [69]. In a further study, a synthetic product addition circuit was generated that prevents the occurrence of genetic variants of E. coli that impair biosynthetic capacity [70]. Although our knowledge of the many facets of heterogeneous behavior within microbial communities is constantly increasing, we still don’t understand all of the underlying molecular principles. Identification and characterization of the molecular switches that cause phenotypic heterogeneity remain challenging, as there is no standardized analytical framework for such investigations. Phenotypic heterogeneity in microbial metabolism is more pronounced in nutrient-limited, dynamic habitats than in nutrient-saturated, stable habitats. Since multiple metabolic activities are controlled by internal metabolite concentrations [71,72], biologically relevant phenotypic heterogeneity can potentially be generated in many other pathways that translate nutrient-limited, flux-controlled growth into intermediate inducer concentrations. Once these switches are recognized, the generation of production strains with alterations in the relevant promoters, mRNA lifetimes, and plasmid or transport systems becomes feasible. In addition, novel multiplex biosensors are needed to monitor the degree of heterogeneous www.sciencedirect.com
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behavior of more than one phenotype in single cells. Finally, large-scale bioreactors exhibit inhomogeneities, in concentrations of nutrients and dissolved oxygen, for example. Novel microfluidic systems offer new opportunities to scale down bioreactors and facilitate systematic studies at single-cell level [73]. Understanding and overcoming phenotypic heterogeneity in bioreactors should boost the production of useful biomolecules, both lowmolecular-weight compounds and recombinant proteins.
Funding This work was supported by the Deutsche Forschungsgemeinschaft (SPP1617, Project JU270/13-2 to K.J.).
Conflict of interest statement Nothing declared.
Acknowledgements We thank all current and former members of the research group for their important contributions to this research.
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