Genome-wide screening of genes whose enhanced expression affects glycogen accumulation in Escherichia coli.

Using a systematic and comprehensive gene expression library (the ASKA library), we have carried out a genome-wide screening of the genes whose increased plasmid-directed expression affected glycogen metabolism in Escherichia coli. Of the 4123 clones of the collection, 28 displayed a glycogen-excess phenotype, whereas 58 displayed a glycogen-deficient phenotype. The genes whose enhanced expression affected glycogen accumulation were classified into various functional categories including carbon sensing, transport and metabolism, general stress and stringent responses, factors determining intercellular communication, aggregative and social behaviour, nitrogen metabolism and energy status. Noteworthy, one-third of them were genes about which little or nothing is known. We propose an integrated metabolic model wherein E. coli glycogen metabolism is highly interconnected with a wide variety of cellular processes and is tightly adjusted to the nutritional and energetic status of the cell. Furthermore, we provide clues about possible biological roles of genes of still unknown functions.

Introduction

Glycogen is a major intracellular reserve polymer consisting of α-1,4-linked glucose subunits with α-1,6-linked glucose at the branching points, which accumulates in Escherichia coli and other bacteria under conditions of limiting growth when an excess of carbon source is available and other nutrients are deficient.[1-3] The exact role of this polyglucan in bacteria is still not well-defined, but several works have linked glycogen metabolism to environmental survival, symbiotic performance and colonization and virulence.[4-12]

Bacterial glycogen is produced by the concerted action of glycogen synthase (GlgA) and branching enzyme (GlgB) using ADP-glucose (ADPG) as the sugar donor nucleotide.[1] Since the initial demonstration that ADPG serves as the precursor molecule for bacterial glycogen biosynthesis,[13] it has been considered that ADPG pyrophosphorylase (GlgC) is the sole enzyme catalyzing the production of ADPG in these organisms.[14] However, recent reports have provided evidence about the occurrence of other important sources of ADPG linked to glycogen biosynthesis in bacterial species such as E. coli, Salmonella, Streptomyces coelicolor and Mycobacterium tuberculosis.[11,15-17] Genes involved in glycogen metabolism in enterobacterial species, such as E. coli and Salmonella enterica, are clustered in two apparently independent transcriptional units designated as glgBX (encoding GlgB and debranching GlgX enzymes) and glgCAP [comprising genes coding for the glycogen anabolic enzymes GlgC and GlgA, and the catabolic glycogen phosphorylase (GlgP)].[1]

Regulation of E. coli glycogen metabolism involves a complex assemblage of factors that are adjusted to the physiological and energetic status of the cell,[2,3,18,19] and cell-to-cell communication.[20] At the level of enzyme activity, glycogen metabolism is subjected to the allosteric regulation of GlgC by different glycolitic intermediates.[14] Also, E. coli GlgP activity is regulated by the phosphorylation status of the carbohydrate phosphotransferase system (PTS) protein Hpr.[21] At the level of gene expression, several factors have been described to control E. coli glycogen accumulation. This includes negative regulation by the carbon storage regulator CsrA and by the still unidentified glgQ regulatory locus,[22-24] and positive regulation by guanosine 5′-triphosphate 3′-diphosphate and/or guanosine 5′-diphosphate 3′-diphosphate [(p)ppGpp] stringent response regulators[3,25-28] and by the PhoP–PhoQ regulatory system at low environmental Mg2+ concentration.[3] Different experimental evidences also indicate positive regulation of glgCAP expression by the cyclic AMP/cyclic AMP receptor protein complex[29-31] (however, for an opposite view, see Montero et al.[3] and Hengge-Aronis and Fischer[32]). The general stress regulator RpoS does not regulate glgCAP expression, but positively controls the expression of glgS, a gene whose product exerts a positive effect on glycogen accumulation.[32]

We have recently initiated a series of studies aimed to uncover mechanisms regulating bacterial glycogen metabolism and its connection with other biological processes. Using a systematic and comprehensive gene-disrupted mutant collection of E. coli (the Keio collection[33]), we carried out genome-wide screenings of genes affecting glycogen metabolism in this bacterial species.[2,3] Our studies revealed that bacterial glycogen metabolism is highly interconnected with a wide variety of cellular processes and proposed an integrated metabolic model wherein glycogen metabolism is influenced by the stringent and general stress responses, end-turnover of tRNA, intracellular AMP levels, nutrient transport and metabolism, low extra-cellular Mg2+ availability and energy production.[3] To further investigate the mechanisms regulating bacterial glycogen metabolism and its connection with other biological processes, in this work, we have carried out a genome-wide analysis of glycogen content using the ASKA library, a set of 4123 clones expressing all predicted ORFs of an E. coli K-12 derivative.[34] The overall data presented in this work reinforce the idea that glycogen metabolism is highly interconnected with a wide variety of cellular processes and is adjusted to the bacterial energy and nutritional status. Furthermore, we provide evidence showing that glycogen metabolism is also affected by factors determining intercellular communication, aggregative and social behaviour modes.

Materials and methods

Bacterial strains and culture conditions

We used the ASKA library, a set of 4123 different clones of the AG1 E. coli K-12 strain (recA1 endA1 gyrA96 thi-1 hsdR17(rK_mKþ) supE44 relA1), each expressing one of all predicted E. coli K-12 ORFs.[34] For quantitative measurement of glycogen content, cells were grown at 37°C with rapid gyratory shaking in liquid Kornberg medium (1.1% K2HPO4, 0.85% KH2PO4, 0.6% yeast extract from Duchefa, Haarlem, the Netherlands) supplemented with 50 mM glucose and 1 mM MgCl2 after inoculation with 1 vol. of an overnight culture per 50 volumes of fresh medium. The culture medium was not supplemented with IPTG. Cultures entering the stationary phase were centrifuged at 4400g for 15 min, and the collected cells were rinsed with fresh Kornberg medium, resuspended in 40 mM Tris–HCl (pH 7.5) and disrupted by sonication as described previously.[2] Solid culture medium was prepared by adding 1.8% bacteriological agar to liquid Kornberg medium before autoclaving.

Screening of ASKA clones with altered glycogen content

A first screening of glycogen in the different bacterial clones of the ASKA collection after growth on solid glucose Kornberg medium was carried out employing the iodine staining method. In the presence of iodine vapours, ‘glycogen-excess’ clones stained darker than its brownish parent cells, whereas ‘glycogen-deficient’ clones stained yellow.[16] Clones identified using this procedure were subsequently cultured in liquid glucose Kornberg medium and subjected to the quantitative measurement of glycogen content at the onset of the stationary phase using an amyloglucosidase/hexokinase/glucose-6P dehydrogenase-based test kit from Sigma. Intracellular glycogen content was referred to protein, which was measured using a Bio-Rad (USA) prepared reagent. The function of each gene whose enhanced expression affects glycogen accumulation was assigned by referring to the EchoBASE (http://ecoli-york.org/)[35] and EcoCyc (http://www.ecocyc.org/)[36] databases.

Morphotype evaluation

To monitor the expression of curli and cellulose biosynthesis, 10 µl of a bacterial overnight culture suspended in water to an absorbance at 600 nm of five were spotted onto TY agar plates (1% Bacto Tryptone, 0.5% yeast extract, 1.5% bacteriological agar) supplemented with 40 µg ml−1 Congo red and 20 µg ml−1 Coomassie brilliant blue.[37] Plates were incubated at 28°C for 5 days, and dye binding was evaluated by red colour intensity. The multicellular rdar morphotype is characterized by a red, dry and rough aspect on Congo red agar plates, which is determined by the expression of extracellular matrix components such as cellulose and adhesive curli fimbriae.[38] The appearance of a pink colony (pdar morphotype) is indicative of cellulose biosynthesis.[39] Capacity for cellulose production was also qualitatively analyzed by assessing the level of calcofluor white (Fluorescent brightener 28; Sigma) binding of colonies grown on TY agar plates supplemented with 50 µg ml−1 of this dye. Fluorescence of the cells was observed under a 366 nm UV light source and compared with the wild-type (WT) strain.

General molecular techniques

Routine DNA manipulations were performed following standard procedures.[40] Plasmids were extracted by Quantum Prep plasmid mini-prep kit (Bio-Rad). ΔrelA cells of the Keio collection[33] expressing relA in trans were obtained by incorporation of relA- expression vector of the ASKA library. DNA sequencing was carried out in Secugen (Madrid). Sequence homologies to genes in the GenBank database were determined by using the BLAST algorithm of the National Center for Biotechnology Information at the National Library of Medicine.

Analytical procedures

Bacterial growth was followed spectrophotometrically by measuring the absorbance of cultures at 600 nm. Protein contents in bacterial extracts were measured by the Coomassie G dye-binding method using a Bio-Rad prepared reagent.

Results and discussion

Screening, identification and classification of genes whose enhanced expression affects glycogen accumulation

Clones of ASKA collection were first screened for altered glycogen content in solid glucose Kornberg medium. In the presence of iodine vapours, ‘glycogen-excess’ clones stained darker than their brownish parent cells, whereas ‘glycogen-deficient’ clones stained yellow. On inspecting the ASKA library, 28 clones (0.7% of the library) displayed ‘glycogen-excess’ phenotypes, whereas 58 clones (1.4% of the library) displayed yellow, ‘glycogen-deficient’ phenotypes. Subsequent quantitative glycogen measurement analyses on cells entering the stationary phase confirmed that the 86 selected clones accumulate altered levels of glycogen (Fig. 1).

Figure 1

Glycogen content (referred as percentage of glycogen accumulated by WT cells) of glycogen-excess and glycogen-deficient clones of the ASKA library. Averaged glycogen content in WT cells was 45 nmol glucose mg protein−1. Cells were grown at 37°C with rapid gyratory shaking in liquid Kornberg medium (1.1% K2HPO4, 0.85% KH2PO4, 0.6% yeast extract) supplemented with 50 mM glucose. Because some yeast extracts are deficient in Mg2+,[3] and because Mg2+ is a major determinant of cell metabolic and energetic status and of expression of genes affecting glycogen metabolism,[91,92] the Kornberg medium was also supplemented with 1 mM MgCl2.

The 86 genes whose enhanced expression showed modified glycogen accumulation were classified into clusters of orthologous groups (COGs).[41] Tables 1 and 2 show the genes whose enhanced expression leads to glycogen-excess and glycogen-deficient phenotypes, respectively, whereas Supplementary Table S1 shows the function of each gene product. In some cases, the families are clearly meaningful, with the presence of multiple genes of related function, reinforcing the validity of their identification in the survey. Yet, a large group of 28 clones, representing one-third of the clones identified, express genes about which little or nothing is known such as glgS, gspD, mdtG, ppdB, rutF, smg, ucpA, yabI, yafV, ybcV, ycbJ, yciN, ydcJ, yegH, yfaY, yfdN, yfeD, yfjR, yhcE, yjcC, yjcQ, ylcG, ymgC, ynbD, yncC, yncG, yoaE and yqjA.

Table 1

Escherichia coli genes whose enhanced expression caused a ‘glycogen-excess’ phenotype in cells of the ASKA library entering the stationary phase

Metabolism
E	Amino acid transport and metabolism (1/432): putP
F	Nucleotide transport and metabolism (1/94): hyuA
G	Carbohydrate transport and metabolism (3/395): glgA, glgC, ptsI
P	Inorganic ion transport and metabolism (3/273): ppx, pspE, ssuA
Cellular processes
M	Cell wall/membrane/envelope biogenesis (1/227): ddg
O	Posttranslational modification, protein turnover, chaperones (1/144): yncG
U	Intracellular trafficking (1/116): ppdB
Information, storage and processing
K	Transcription (5/321): rbsR, rpoS, tdcA, yfeD, yncC
T	Signal Transduction (2/186): dos, yjcC
Poorly characterized
R	General function prediction only (3/510): mdtG, rutF, yifJ
S	Function unknown (3/315): erfK, ydcJ, yqjA
	No COG assignment (4/590): glgS, ycbJ, yciN, ymgC

Genes are classified into COG categories.[35,36,41] The numbers in parentheses represent the number of glycogen-related genes to the number of genes belonging to each of COG category.

Table 2

Escherichia coli genes whose enhanced expression caused a ‘glycogen-deficient’ phenotype

Metabolism
C	Energy production and conversion (2/301): gor, napF
E	Amino acid transport and metabolism (5/432): gltI, metH, serB, thrB, tnaA
F	Nucleotide transport and metabolism (3/94): cpdB, gpp, pfs
G	Carbohydrate transport and metabolism (10/395): glgB, glgP, gntT, malP, nagB, nagD, ptsN, rpiB, talA, xylG
I	Lipid transport and metabolism (1/104): ynbD
P	Inorganic ion transport and metabolism (4/273): cysI, cysP, ppK, pstC
Cellular processes
M	Cell wall/membrane/envelope biogenesis (1/227): wzc
O	Posttranslational modification, protein turnover, chaperones (2/144): clpA, cydC
T	Signal transduction mechanisms (3/186): csrA, csrD, yeaP
U	Intracellular trafficking, secretion, and vesicular transport (1/116): gspD
Information, storage and processing
J	Translation, ribosomal structure and biogenesis (2/188): pnp, prfB
K	Transcription (6/321): exuR, galS, malT, mlc, spoT, yfjR
L	DNA replication, recombination and repair (3/224):holC, phr, recQ
Poorly characterized
R	General function prediction only (7/510): ucpA, yafV, ybcV, yegH, yfaY, yoaE, aspP
S	Function unknown (4/315): smg, yabI, yjcQ, yoeB
	No COG assignment (4/590): hokA, yfdN, yhcE, ylcG

Genes are classified into COG categories.[35,36,41] The numbers in parentheses represent the number of glycogen-related genes to the number of genes belonging to each of COG category.

The general trend observed after this analysis indicates that glycogen metabolism of E. coli cells cultured in glucose Kornberg medium is affected by genes whose products can be embodied in the following groups:

Carbon sensing, transport and metabolism;

general stress response;

stringent response;

factors determining intercellular communication, aggregative and social behaviour;

nitrogen metabolism;

energy status.

Carbon sensing, transport and metabolism

As expected from the glycogen synthetic roles of GlgA and GlgC, glgA and glgC over-expressing bacteria of the ASKA library displayed glycogen-excess phenotypes (Fig. 1). In fact, these bacteria presented the highest levels of glycogen accumulation of the whole collection. In agreement also with the assigned function of GlgP and AspP in E. coli glycogen breakdown,[42,43] AG1 cells over-expressing glgP and aspP showed reduced glycogen accumulation (Fig. 1). Noteworthy, although GlgB is a glycogen anabolic enzyme, glgB over-expressing cells of the ASKA library displayed a glycogen-deficient phenotype (Fig. 1). This could be ascribed to the fact that glycogen granule architecture is the result of the highly orchestrated actions of GlgB and other glycogen enzymes, which may collapse under GlgB overproduction conditions.[44]

The global regulator of carbon metabolism CsrA is an RNA-binding protein, which is thought to prevent glycogen biosynthesis by both promoting glgCAP decay and translation.[24,45] Consistently, csrA over-expressing bacteria of the ASKA library displayed a glycogen-deficient phenotype (Fig. 1). CsrA activity is antagonized by the two CsrB and CsrC non-coding RNAs,[46-48] which in turn are targeted by CsrD for RNase E degradation.[49] Thus and consistent with the assigned role of CsrD as relieving CsrA function from CsrB and CsrC, csrD over-expressing cells of the ASKA library displayed a glycogen-deficient phenotype (Fig. 1).

malP and malT over-expressing bacteria of the ASKA collection displayed glycogen-deficient phenotypes (Fig. 1). MalT is a transcriptional regulator of genes involved in maltose/maltodextrin transport and metabolism.[50,51] MalP, over which MalT exerts a positive control, catalyzes the phosphorolytic breakdown of maltodextrins. However, MalP poorly recognizes large and highly branched polyglucans such as glycogen,[42] suggesting an indirect rather than a direct effect of malP over-expression on glycogen accumulation. In this respect, previous studies have indicated tight, albeit still not well characterized, links between glycogen and maltodextrin metabolisms.[52,53]

PTS is a major determinant of transport and phosphorylation of a large number of carbohydrates including glucose.[21,54,55] PTS mutants impaired in sensing and transport of glucose accumulate low glycogen content.[3] It is therefore conceivable that over-expression of some PTS components would result in enhanced glycogen content in cells cultured in glucose Kornberg medium, whereas cells over-expressing the Mlc transcriptional repressor of PTS genes[56] would display a glycogen-deficient phenotype. Confirming this presumptions ptsI over-expressing bacteria of the ASKA library displayed a glycogen-excess phenotype (Fig. 1), whereas cells ectopically expressing the Mlc transcriptional repressor of PTS genes displayed a glycogen-deficient phenotype (Fig. 1).

General stress response

Different genetic studies indicate a requirement of the general stress regulator RpoS as a positive modulator of glycogen biosynthesis.[2,3,57] In agreement, rpoS over-expressing cells of the ASKA library displayed a glycogen-excess phenotype (Fig. 1). It has been shown that RpoS up-regulates the expression of glgS, a gene whose product exerts a positive effect on glycogen accumulation.[2,32] Consistently, glgS over-expressing cells of the ASKA library displayed a glycogen-excess phenotype (Fig. 1).

Stringent response

During nutrient starvation, E. coli elicits the so-called ‘stringent response’ that switches the cell from a growth-related mode to a maintenance/survival mode.[58,59] The hallmark of this pleiotropic physiological response is the accumulation of the alarmones pppGpp and ppGpp.[58-60] Although ppGpp is more abundant than pppGpp, the relative effects of these two regulatory nucleotides have not been thoroughly examined, their levels depending on the synthesis of pppGpp by RelA and SpoT, the hydrolysis of pppGpp to ppGpp by Gpp, and the breakdown of ppGpp by the bifunctional enzyme SpoT.[58-62] (p)ppGpp binds bacterial RNA polymerase to increase transcription of amino acid biosynthesis genes during amino acid starvation and to down-regulate the transcription of ‘stable’ RNAs (rRNAs and tRNAs) genes.[58,59] As transcription of genes coding for components of the translation apparatus account for a large percentage of transcription in exponentially growing cells, the liberation of RNA polymerase from these genes is thought to passively allow up-regulation of diverse promoters activated at the onset of stationary phase.[63]

Different in vivo and in vitro experimental evidences have linked the E. coli stringent response and (p)ppGpp accumulation with increased glycogen contents and enhanced expression of glg genes at the onset of the stationary phase.[25-28] Consistent with the involvement of (p)ppGpp in regulatory aspects of glycogen metabolism, and also consistent with the assigned functions of SpoT and Gpp in (p)ppGpp degradation,[64,65] both spoT and gpp over-expressing cells of the ASKA collection displayed glycogen-deficient phenotypes (Fig. 1).

We recently found that ΔrelA cells of the E. coli Keio collection[33] display reduced glycogen contents and restricted expression of glgC::lacZ transcriptional fusions[3] (see also Fig. 2), which further fortifies the view that (p)ppGpp plays an important role in glycogen accumulation in E. coli. AG1 strain used in the ASKA library as plasmids recipient has been annotated as a K-12 derivative relA1 mutant.[34] relA1 mutants possess little or residual pppGpp synthase activity, which is due to an IS2 insertion between the 85th and 86th codons of the WT relA structural gene.[66] It is thus conceivable that relA over-expressing cells of the ASKA collection would display a glycogen-excess phenotype. Surprisingly, however, these cells displayed glycogen levels similar to those of control AG1 cells (Fig. 2A and B). To understand why the relA over-expressing cells of the ASKA library accumulate glycogen levels comparable to those of control cells, we sequenced the relA gene of AG1 cells. This analysis revealed that relA of AG1 does not contain any mutation (not shown). To explore whether this phenomenon could be ascribed to the possible occurrence of secondary mutations in AG1 cells or to defects of the ASKA library relA expression vector, we analyzed the glycogen contents in ΔrelA cells of the Keio mutant collection transformed with the ASKA library relA expression vector. These cells were constructed on a K-12 derivative BW25113 strain, which is normal for the relA function. As shown in Fig. 2C and D, ectopic expression of relA complemented the glycogen-deficient phenotype of ΔrelA cells, the overall data thus showing that (i) AG1 cells are not relA1 mutants, (ii) the relA expression vector of the ASKA library codes for an active RelA form and (iii) ectopic expression of relA does not lead to enhancement of glycogen accumulation.

Figure 2

AG1 is not a relA1 mutant. In (A) and (B), relA over-expressing cells of the ASKA library accumulate WT glycogen content. In (C) and (D), relA expression vector of the ASKA library complements the glycogen-deficient phenotype of ΔrelA cells of the Keio collection. ΔglgA cells of the Keio collection are used as negative control for glycogen accumulation.

Factors determining intercellular communication, aggregative and social behaviour modes

We have recently shown that glycogen metabolism may also be subjected to regulation by cell-to-cell communication.[20] In E. coli, swimming, swarming and adherence of cells to surfaces or to one another by biofilm formation are fundamental modes to communicate and to coordinately regulate metabolic processes. Communication, aggregative and social behaviour modes are highly determined by environmental cues and act as major determinants of the nutritional status of the cell, which as discussed above is a major determinant of glycogen accumulation. The following data provided evidence that factors determining intercellular communication, aggregative and social behaviour (Supplementary Fig. S1) are important determinants of glycogen content in E. coli, although further studies are required to get a clear picture of the link between the different factors involved.

First, enhanced expression of the poorly characterized yncC, yncG and ymgC genes (all down-regulated in the mqsR biofilm deficient mutants[67]) resulted in increased glycogen content (Fig. 1). yncC encodes a transcription factor that positively affects biofilm formation by repressing production of the biofilm matrix component colanic acid,[68] whereas ymgC is an orphan gene belonging to the ymgABC operon whose transcription is repressed in young and mature biofilms, but is induced in the intermediate, developed biofilms.[69] Although the function of ymgC is still unknown, ymgA and ymgB strongly promote the synthesis of colanic acid, and virtually eliminate the expression of adhesive curli fimbriae genes.

Second, gaining-of-function of GGDEF and EAL domain enzymes controlling the intracellular levels of cyclic diguanylate (a secondary messenger that regulates the transition from the motile, planktonic state to sessile, community-based behaviours in different bacteria[39,70,71]) resulted in changes in the intracellular glycogen content. For instance, up-regulation of YeaP (a diguanylate cyclase that positively regulates the expression of csg genes involved in curli and cellulose production[72]) exerted a negative effect on glycogen accumulation (Fig. 1). In contrast, up-regulation of YjcC (a predicted cyclic diguanylate phosphodiesterase that down-regulates the expression of the CsgD central regulator of extra-cellular matrix components[37,38,73]) and Dos (a cyclic diguanylate phosphodiesterase[74]) resulted in enhanced glycogen content (Fig. 1).

Third, some ASKA clones ectopically expressing functions that participate in the synthesis of biofilm components and/or precursors displayed glycogen deficient phenotypes. Thus, ectopic expression of Wzc (an autophosphorylating protein-tyrosine kinase that prevents the production of colanic acid[75,76]), GalS (a repressor of metabolism of galactose linked to the synthesis of colanic acid and other exopolysaccharide components[77,78]), and NagB (a d-glucosamine 6-P isomerase that prevents the synthesis of major components of the cell envelope such as peptidoglycans and lipopolysaccharides[79]) resulted in a glycogen-deficient phenotype (Fig. 1).

Nitrogen metabolism

It is known that carbon metabolism is subject to regulation by nitrogen availability, although the mechanisms involved are still obscure. PtsN is a member of the nitrogen-related PTS, which has been associated with balancing of nitrogen and carbon metabolism,[80] and regulation of the RpoE-dependent cell envelope stress response and potassium uptake.[81,82] Most recently, evidence has been provided suggesting the occurrence of cross-talk between sugar-PTS and nitrogen-related PTS.[83] Consistent with this view, the ptsN overproducing cells of the ASKA library displayed a marked glycogen-deficient phenotype when cultured in glucose Kornberg medium (Fig. 1).

Yeast extract (the amino acid source of the Kornberg medium employed in this work) is deficient in various amino acids.[2,84] Mutants impaired in cysteine biosynthesis entering the stationary phase display a glycogen-excess phenotype when they are cultured in the Kornberg medium, which is ascribed to stringent response-mediated up-regulation of glg genes occurring when the culture medium is deficient in cysteine precursors.[2] Consistent with the view that the stringent response favourably affects glycogen accumulation in E. coli, serB, cysI and cysP over-expressing cells of the ASKA library (all ectopically expressing genes involved in cysteine biosynthesis) accumulated lower levels of glycogen than WT cells at the onset of the stationary phase (Fig. 1). tdcA positively regulates the expression of tdcB and tdcC genes, which code for biodegradative proteins involved in threonine and serine metabolism.[85,86] Also consistent with the view that the lack of internal amino acid provision positively affects glycogen accumulation, tdcA over-expressing cells of the ASKA library displayed a glycogen-excess phenotype (Fig. 1).

Energy status

Because intracellular ATP level is a major determinant of glycogen accumulation,[3,18] it is conceivable that any factor affecting ATP availability will also affect glycogen accumulation. In fact, deletion mutants lacking components required for the proper functioning of the aerobic electron transport chain and ATP generation displayed a glycogen-deficient phenotype.[2,3] Consistent with this view, bacteria with enhanced expression of cytosolic enzymes likely competing with GlgC for the same ATP pool, such as RecQ (an ATP-dependent DNA helicase[87]), NagD (a promiscuous ribo and deoxyribonucleoside tri-, di- and monophosphatase[88]), and Ppk (an ATP requiring enzyme that catalyzes the production of polyphosphate[89]) displayed glycogen-deficient phenotypes (Fig. 1).

Glutathione is a major determinant of cell redox status, playing a prime role in maintaining the correct assembly of electron transport chain components.[90] It is therefore conceivable that factors altering the intracellular glutathione levels will also affect ATP and glycogen formation. In agreement with this presumption, bacteria with enhanced expression of GorA (a glutathione reductase) and CydC (a protein involved in the transport of glutathione from the cytosol to the periplasm[90]) displayed glycogen deficient phenotypes (Fig. 1).

Proposal of an integrated model for the regulation of glycogen metabolism in E. coli

Results presented in this work further strengthen the view that glycogen metabolism is highly interconnected with a wide variety of cellular processes.[2,3] Figure 3 illustrates a suggested model of glycogen metabolism in E. coli wherein major determinants of glycogen accumulation include intracellular concentration and availability of ATP for ADPG synthesis, levels of ppGpp (which accumulates in a RelA- SpoT- and/or Gpp-dependent manner under conditions of limited provision of nutrients such as amino acids, sulphur, Mg2+, iron, etc.), factors determining intercellular communication, aggregative and social behaviour modes (which in turn determine the nutritional status of the cell), expression levels of the general stress regulator RpoS and of the global regulator CsrA, availability of a carbon source and less well-defined systems sensing the cell energy status through the activity of the electron transport chain.

Figure 3

Suggested model of glycogen metabolism in E. coli wherein major determinants of glycogen accumulation include availability of ATP for ADPG synthesis, levels of (p)ppGpp (which accumulates in a RelA- SpoT- and/or Gpp-dependent manner under conditions of limited provision of nutrients such as amino acids, sulphur, Mg2+, iron, etc.), factors determining intercellular communication, aggregative and social behaviour modes (which in turn determine the nutritional status of the cell), expression levels of the general stress regulator RpoS and of the global regulator CsrA, availability of a carbon source, redox status of the cell and less well-defined systems sensing the cell energy status through the activity of the electron transport chain. CsrB and CsrC small non-coding RNAs, represented by stem-loops, are likely involved in the regulation of functions strongly affecting glycogen accumulation through interaction with CsrA (Adk, adenylate kinase; c-di-GMP, cyclic diguanylate; ETC, electron transport chain).

Supplementary data

Supplementary data are available at www.dnaresearch.oxfordjournals.org.

Funding

This research was partially supported by the grant BIO2007-63915 from the Comisión Interministerial de Ciencia y Tecnología and Fondo Europeo de Desarrollo Regional (Spain) and by Iden Biotechnology S.L.