Skip to content

Advertisement

  • Research article
  • Open Access

Tracing the phylogenetic history of the Crl regulon through the Bacteria and Archaea genomes

BMC Genomics201920:299

https://doi.org/10.1186/s12864-019-5619-z

  • Received: 14 February 2019
  • Accepted: 18 March 2019
  • Published:

Abstract

Background

Crl, identified for curli production, is a small transcription factor that stimulates the association of the σS factor (RpoS) with the RNA polymerase core through direct and specific interactions, increasing the transcription rate of genes during the transition from exponential to stationary phase at low temperatures, using indole as an effector molecule. The lack of a comprehensive collection of information on the Crl regulon makes it difficult to identify a dominant function of Crl and to generate any hypotheses concerning its taxonomical distribution in archaeal and bacterial organisms.

Results

In this work, based on a systematic literature review, we identified the first comprehensive dataset of 86 genes under the control of Crl in the bacterium Escherichia coli K-12; those genes correspond to 40% of the σS regulon in this bacterium. Based on an analysis of orthologs in 18 archaeal and 69 bacterial taxonomical divisions and using E. coli K-12 as a framework, we suggest three main events that resulted in this regulon’s actual form: (i) in a first step, rpoS, a gene widely distributed in bacteria and archaea cellular domains, was recruited to regulate genes involved in ancient metabolic processes, such as those associated with glycolysis and the tricarboxylic acid cycle; (ii) in a second step, the regulon recruited those genes involved in metabolic processes, which are mainly taxonomically constrained to Proteobacteria, with some secondary losses, such as those genes involved in responses to stress or starvation and cell adhesion, among others; and (iii) in a posterior step, Crl might have been recruited in Enterobacteriaceae; because its taxonomical pattern constrained to this bacterial order, however further analysis are necessary.

Conclusions

Therefore, we suggest that the regulon Crl is highly flexible for phenotypic adaptation, probably as consequence of the diverse growth environments associated with all organisms in which members of this regulatory network are present.

Keywords

  • Crl regulon
  • Stress response
  • Transcription factors
  • Comparative genomics
  • Bacteria
  • Archaea

Background

Gene expression in bacteria is coordinated through the DNA-binding transcription factors (TFs), blocking or allowing the access of the RNA polymerase (RNAP)-sigma factor to the promoter and providing bacteria with the ability to activate or repress multiple genes under different metabolic stimuli or growth conditions. In the bacterium Escherichia coli K-12, seven sigma factors have been experimentally identified, together with around 300 different TFs responsible for recognizing and activating almost all of their genes [1]. Among these, RpoD, or σ70, regulates around 40% of the total gene repertoire, whereas alternative sigma factors such as RpoS (σS), the master regulator of the stationary-phase response [2], regulate between 5 and 10% of the total genes in E. coli K-12 [3].

Sigma factors and TFs regulate a large diversity of genes, hierarchically organized in regulons [4]. Previous comparative genomics studies have suggested that regulons exhibit considerable plasticity across the evolution of bacterial species [5]. In this regard, comparison of the gene composition of the PhoPQ regulon in E. coli and Salmonella enterica serovar Typhimurium revealed a very small overlap in both species, suggesting a low similarity in composition between the target genes that are regulated by PhoP in S. Typhimurium strains and in E. coli K-12 [6]. Incidentally, this plasticity in bacterial regulons is evidence of lineage-specific modifications [7].

We conducted an exhaustive analysis concerning the conservation of the Crl regulon in Bacteria and Archaea cellular domains, using as a reference the currently known system in E. coli K-12. Contrary to the most common regulatory mechanisms that involve the direct binding to operators or activators, Crl is an RNAP holoenzyme assembly factor that was originally identified in curli production. It is expressed at low temperatures (30 °C) [8] during the transition phase between the exponential and stationary phases, under low osmolarity, as well as in stationary phase [9]. In E. coli, Crl has a global regulatory effect in stationary phase, through σS, as it reorganizes the transcriptional machinery [10], stimulating the association of σS with the RNAP core, tilting the competition between σS and σ70 during the stationary phase in response to different stress conditions [11, 12] [8, 13]; its production is concomitant with the accumulation of σS [8].

Assembling the different pieces of the Crl regulon and its regulatory network into one global picture is one of our objectives in this work. The evaluation of this regulon in Bacteria and Archaea will provide clues about how the regulation of genes by Crl has been recruited in all the organisms, i.e., if the regulated genes were recruited similar to Crl or if they followed different pathways. To this end, 86 genes under the control of Crl in E. coli K-12 were compiled from exhaustive literature searches. To our knowledge, this is the first attempt to describe the genes regulated by Crl in E. coli K-12; in addition, few Crl homologs were identified among bacterial and archaeal genomes, constrained to Enterobacteriaceae species. Finally, members of the regulon were identified as widely distributed beyond enterobacteria, suggesting that Crl was recruited in a secondary evolutionary event to regulate a specific subset of genes, most likely genes involved in a functional response in enterobacteria to contend against starvation.

Results

86 genes belong to the Crl regulon

Available information regarding the Crl regulon was gathered through an exhaustive review of the literature. In this regard, diverse experimental evidences were considered significant for determining the association between the regulated genes and Crl protein regulator, such as gene expression analysis (transcriptional fusions), mapping of signal intensities (RNA-seq or microarray analysis), and inferences made from a mutant phenotype (mutation of a TF with a visible cell phenotype), among other analyses. Therefore, 86 genes were included in this work as members of the σS sigmulon, of which 37 had already been reported in both RegulonDB and EcoCyc database; whereas, 49 genes identified by microarray data and crl rpoS double mutants [816], in previous works were also added (see Additional file 1). From the 86 genes identified as members of this regulon (see Table 1 and Fig. 1), 34 have a σS-type promoter experimentally determined and 8 genes have 13 σS-type promoters predicted by computational approaches [17]. These 86 genes are organized in 77 transcription units (TUs), where 52% are TUs with only one gene.
Table 1

Genes regulated by Crl in Escherichia coli

Gene

Bnumber

TU(s)

TFs

Effect of Crl

Evidence

Reference(s)

GO Terms

aat

b0885

aat

 

+

GEA and IMP

[11]

protein catabolic process, ubiquitin-dependent protein catabolic process via the N-end rule pathway

accB

b3255

accBC

AccB (−), FadR(+)

+

GEA and IMP

[11]

lipid metabolic process, fatty acid metabolic process, fatty acid biosynthetic process

accC

b3256

accBC

AccB (−), FadR(+)

+

GEA and IMP

[11]

lipid metabolic process, fatty acid metabolic process, fatty acid biosynthetic process, metabolic process, negative regulation of fatty acid biosynthetic process, malonyl-CoA biosynthetic process

acnB

b0118

acnB

CRP(+) ArcA(−), Cra(−), Fis (−)

IMP

[13]

regulation of translation, propionate catabolic process, 2-methylcitrate cycle, glyoxylate cycle, tricarboxylic acid cycle metabolic process

ada

b2213

ada- alkB

Ada(+/−)

+

GEA

[13]

DNA dealkylation involved in DNA repair, regulation of transcription, cellular response to DNA damage stimulus, metabolic process, methylation, DNA demethylation

allR

b0506

allR

 

+

MSI

[10]

regulation of transcription, cellular response to DNA damage stimulus, negative regulation of transcription

bfr

b3336

bfd -bfr

 

+

MSI, IMP

[40]MSI, [11]IMP

iron ion transport, cellular iron ion homeostasis, intracellular sequestering of iron ion, oxidation-reduction process

bglG

b3723

bglG

bglG FB

CRP (+), Fis (−), H-NS (−), LeuO (+), RcsB-BglJ (+), StpA (−)

MSI, IMP

[16]

regulation of transcription, positive regulation of transcription

bioB

b0775

bioB FCD

BirA (−)

IMP

[13]

biotin biosynthetic process

cbpA

b1000

cbpA M

Fis (−)

+

GEA

[13]

protein folding

crl

b0240

crl

Fur (−)

MSI, IMP

[12]

regulation of transcription, DNA-templated, cellular protein complex assembly, positive regulation of transcription

csgA

b1042

csgBAC

CpxR (−), CsgD (+), FliZ (−)

+

APPH, MSI, IMP, GEA, IMP

[8] APPH, MSI, IMP, [9] GEA, IMP

cell adhesion, single-species biofilm formation, amyloid fibril formation

csgB

b1041

csgBAC

CpxR(−), CsgD(+), FliZ(−)

+

APPH, MSI, IMP, GEA, IMP

[8] APPH, MSI, IMP, [9] GEA, IMP

cell adhesion, single-species biofilm formation, amyloid fibril formation

csgC

b1043

csgBAC

CpxR (−), CsgD (+), FliZ (−)

+

MSI

[10]

 

csgD

b1040

csgD EFG

BasR (+), Cra (+), CRP (+), CsgD (+), IHF (+), MlrA (+), OmpR (+), RcdA (+), CpxR(−), FliZ (−), RcsAB (−), RstA (−)

+

IMP

[13]

regulation of single-species biofilm formation

cstA

b0598

cstA

CRP (+)

+

GEA, IMP

[11]

cellular response to starvation

cysP

b2425

cysP UWAM

CysB (+), H-NS (−)

+

MSI

[10]

sulfur compound metabolic process, transport, sulfate transport, sulfate transmembrane transport

djlC (ybeV)

b0649

ybeU -djlC

 

+

MSI

[10]

positive regulation of ATPase activity

dps

b0812

dps

Fis(−), H-NS(−),IHF(+), MntR(−), OxyR(+)

+

GEA, IMP

[11]

cellular iron ion homeostasis, response to stress, chromosome condensation, response to starvation, oxidation-reduction process

fbaB

b2097

fbaB

Cra(−)

+

GEA, IMP

[11]

glycolytic process, transcription

flgM

b1071

flgMN,

flg A M N

CsgD(−)

GEA, IMP

[13]

regulation of transcription, bacterial-type flagellum organization, negative regulation of proteolysis, negative regulation of transcription

fliA

b1922

fliA Z -tcyJ

H-NS(+), MatA(−), SutR(−), NsrR(−), CsgD(−), FlhDC(+)

IMP

[13]

transcription initiation from bacterial-type RNAP promoter, sporulation resulting in formation of a cellular spore

fur

b0683

fur

fldA-uof -fur

uof -fur

CRP(+), Fur(−)

+

MSI, IMP

[12]

regulation of transcription, negative regulation of transcription

gadA

b3517

gadA X

AdiY(+), ArcA(+), CRP(−), FNR(−), Fis(−), GadE-RcsB(+), GadW(+−), GadX(+), H-NS(−), RcsB(−), TorR(−)

+

MSI

[10]

glutamate metabolic process, carboxylic acid metabolic process, intracellular pH elevation

gadB

b1493

gadBC

AdiY(+),CRP(−), Fis(−), FliZ(−), GadE(+), GadW(+−), GadX(+), RcsB(+)

+

MSI, GEA

[40]MSI, [13]GEA

glutamate metabolic process, carboxylic acid metabolic process, intracellular pH elevation

gadC

b1492

gadBC

AdiY(+), CRP(−), Fis(−), FliZ(−), GadE(+), GadW(+−), GadX(+), RcsB(+)

+

MSI

[10]

amino acid transmembrane transport, transport, amino acid transport, intracellular pH elevation

gadE

b3512

gadE- mdtEF

gadE

ArcA(+), CRP(−), EvgA(+), FliZ(−), GadE(+), GadW(+), GadX(+), H-NS(−), PhoP(+), YdeO(+)

+

MSI

[10]

regulation of transcription

gadW

b3515

gadW

GadW (+), GadX (−), H-NS(−), PhoP (+), SdiA (+), YdeO (+)

+

MSI

[10]

regulation of transcription, cellular response to DNA damage stimulus

glgS

b3049

glgS

CRP(+)

+

GEA

[13]

glycogen biosynthetic process, positive regulation of cellular carbohydrate metabolic process, negative regulation of single-species biofilm formation on inanimate substrate, negative regulation of bacterial-type flagellum-dependent cell motility

glnH

b0811

glnH PQ

IHF(+), NtrC(+/−)

+

GEA, IMP

[11]

transport, amino acid transport

gltA

b0720

gltA

ArcA(−), CRP(+), IHF(+)

+

GEA, IMP

[11]

tricarboxylic acid cycle, metabolic process, cellular carbohydrate metabolic process

grxB

b1064

grxB

 

+

GEA, IMP

[11]

cell redox homeostasis, oxidation-reduction process

hdeA

b3510

hdeAB- yhiD

FliZ(−), GadE(+), GadW(+/−), GadX(+/−), H-NS(−), Lrp(−), MarA(−), PhoP(+), RcsB(+), TorR(+)

+

MSI, GEA

[40]MSI, [13]GEA

cellular response to stress, cellular response to acidic pH

hdeB

b3509

hdeAB- yhiD

FliZ(−), GadE(+), GadW(+/−), GadX(+/−), H-NS(−), Lrp(−), MarA(−), PhoP(+), RcsB(+), TorR(+)

+

MSI

[10]

response to pH change, cellular response to stress

hdeD

b3511

hdeD

GadE(+), GadX(+), H-NS(−), PhoP(+),RcsB(+)

+

MSI

[10]

response to pH change

hdhA

b1619

hdhA

 

+

GEA, IMP

[11]

lipid metabolic process, metabolic process, steroid metabolic process, lipid catabolic process, bile acid, catabolic process, protein homotetramerization, oxidation-reduction process

iadA

b4328

yjiH G-iadA

 

+

MSI

[10]

proteolysis

luxS (ygaG)

b2687

luxS

 

+

MSI, GEA, IMP

[10]MSI, [11] GEA, IMP

cell-cell signaling involved in quorum sensing, L-methionine biosynthetic process from S-adenosylmethionine, quorum sensing

malE

b4034

malE FG

CRP(+), CreB(−), Fis(+), MalT(+)

+

GEA, IMP

[11]

cellular response to DNA damage stimulus, carbohydrate transport, maltose transport, detection of maltose stimulus, maltodextrin transport, cell chemotaxis

msrB

b1778

msrB

 

IMP

[13]

protein repair, response to oxidative stress

narU

b1469

narU

 

+

MSI

[10]

nitrate transport, nitrite transport, nitrate assimilation

ompF

b0929

ompF

CRP(+), CpxR(−), EnvY(+), Fur(+), IHF(+/−), OmpR(+/−), PhoB(+), RstA(−)

GEA, IMP

[41]

transport, ion transport, ion transport, drug transmembrane transport, bacteriocin transport

ompT

b0565

ompT

envY -ompT

PhoP(+)

IMP

[13]

proteolysis

ompX

b1482

ompX

FNR(−)

+

GEA, IMP

[11]

 

osmC

b4376

osmC

H-NS(−), Lrp(+/−)

+

GEA, IMP

[11]

hyperosmotic response, response to oxidative stress, response to hydroperoxide, oxidation-reduction process

osmY

b1388

osmY

CRP(−), Fis(−), FliZ(−), IHF(−), Lrp(−)

+

GEA, IMP

[11]

response to osmotic stress

paaA

b1389

paaAB C D E F G H IJ K

CRP(+), IHF(+), PaaX(−)

+

MSI

[10]

phenylacetate catabolic process, oxidation-reduction process

paaB

b1391

paaAB C D E F G H IJ K

CRP (+), IHF (+), PaaX (−)

+

MSI

[10]

phenylacetate catabolic process

paaD

b1393

paaAB C D E F G H IJ K

CRP (+), IHF (+), PaaX (−)

+

MSI

[10]

phenylacetate catabolic process

paaF

b1395

paaAB C D E F G H IJ K

CRP (+), IHF (+), PaaX (−)

+

MSI

[10]

lipid metabolic process, fatty acid metabolic process, phenylacetate catabolic process

paaH

b1398

paaAB C D E F G H IJ K

CRP(+), IHF(+), PaaX(−)

+

MSI

[10]

fatty acid metabolic process, phenylacetate catabolic process, oxidation-reduction process

paaK

b3916

paaAB C D E F G H IJ K

CRP(+), IHF(+), PaaX(−)

+

MSI

[10]

metabolic process, phenylacetate catabolic process

pfkA

b0871

pfkA

Cra(−)

+

GEA, IMP

[11]

fructose 6-phosphate metabolic process, glycolytic process

poxB

b4226

poxB,

poxB-l taE-ybjT

Cra(+), MarA(+), SoxS (+)

+

GEA, IMP

[11]

pyruvate metabolic process, oxidation-reduction process

ppa

b0384

ppa

 

+

GEA, IMP

[11]

phosphate-containing compound metabolic process

psiF

b1676

phoA -psiF

PhoB(+)

+

MSI

[10]

 

pykF

b1235

pykF

Cra(−)

+

GEA, IMP

[11]

glycolytic process, metabolic process, response to heat, phosphorylation

rssB

b0721

rssB

 

+

IMP

[10]

protein destabilization, positive regulation of proteolysis, regulation of nucleic acid-templated transcription (phosphorelay signal transduction system)

sdhC

b4719

sdhC DAB -sucA BC D

CRP(+), Fur(+), ArcA(+/−), Fnr(−)

IMP

[13]

aerobic respiration. Cytochrome complex assembly, tricarboxylic acid cycle, oxidation-reduction process

sdsN

b1646

sdsN

 

+

GEA

[42]

small RNA

sodC

b4059

sodC

 

+

GEA

[13]

superoxide metabolic process, removal of superoxide radicals, oxidation-reduction process

ssb

b0726

ssb

ArcA(−), LexA(−)

+

GEA, IMP

[11]

recombinational repair, DNA replication, cellular response to DNA damage stimulus, SOS response

sucA

b0729

sucA B

sucA BC D

ArcA(+/−), FNR(−), IHF(−)

+

GEA, IMP

[11]

glycolytic process, tricarboxylic acid cycle, metabolic process, oxidation-reduction process

sucD

b2464

sucA B

sucA BC D

ArcA(+/−),FNR(−),IHF(−)

+

GEA, IMP

[11]

tricarboxylic acid cycle, metabolic process, protein autophosphorylation

talA

b1886

talA- tktB

CreB(+)

+

GEA, IMP

[11]

carbohydrate metabolic process, pentose-phosphate shunt

tar

b1920

tar- tap-cheRBYZ

Fnr(+)

IMP

[13]

chemotaxis, signal transduction

tcyJ (fliY)

b3116

tcyJ

fliA Z -tcyJ

H-NS(+), MatA(−), SutR(−), NsrR(−), CsgD(−), FlhDC(+)

IMP

[13]

L-cystine transport

tdcC

b3708

tdc AB C DEFG , tdc B C DEFG

CRP(+), FNR(+), IHF(+), TdcA(+), TdcR (+)

+

MSI

[10]

L-serine transport, threonine transport, proton transport, serine transport

tnaA

b3453

tna C A B

CRP(+), TorR (+)

+

GEA, IMP

[11]

cellular amino acid metabolic process, aromatic amino acid family metabolic process

ugpB

b3495

ugpB AECQ

CRP(+), PhoB(+/−)

+

GEA, IMP

[11]

glycerophosphodiester transport, transport, glycerol-3-phosphate transport

uspA

b0607

uspA

FadR(−), IHF(+)

+

GEA, IMP

[11]

response to stress

uspG (ybdQ)

b1004

uspG

 

+

GEA, IMP

[11]

response to stress, protein adenylylation, protein autophosphorylation, nucleotide phosphorylation, regulation of cell motility

wrbA

b0453

wrbA- yccJ

CsgD(+)

+

MSI, GEA, IMP

[13]MSI, [11] GEA, IMP

response to oxidative stress, negative regulation of transcription

ybaY

b0753

ybaY

 

+

MSI

[10]

 

ybgS

b0897

ybgS

 

+

MSI

[10]

 

ycaC

b1674

ycaC

BaeR(+), Fnr(−)

+

MSI, GEA, IMP

[10]MSI, [11] GEA, IMP

metabolic process

ydhY

b1784

ydhY VWXUT

FNR (+), NarL (−), NarP (−)

+

MSI

[10]

oxidation-reduction process

yeaH

b2013

yea G H

NtrC (+)

+

MSI

[10]

 

yeeE

b2665

yeeE D

 

+

MSI

[10]

 

ygaU

b3535

ygaU

CpxR (+)

+

GEA, IMP

[11]

 

yhjR

b3555

yhjR

 

+

MSI

[10]

bacterial cellulose biosynthetic process

yiaG

b4045

yiaG

 

+

MSI

[10]

regulation of transcription

yjbJ

b4329

yjbJ

FliZ (−)

+

MSI

[10]

 

yjiG

b1044

yjiH G-iadA

 

+

MSI

[10]

 

ymdA

b1138

ymdA

 

+

MSI

[10]

 

ymfE

b0885

ymfED

 

+

MSI

[10]

 

Genes regulated by Crl, Bnumbers, TUs to which they belong (in bold are possible candidates regulated by Crl, since they are controlled by Crl and σS, but they did not have a change of expression in the data we evaluated), TFs regulating the TU, the effect of Crl, evidences, references, and associated GO terms. Experimental evidence types supporting regulation by Crl: APPH Assay of protein purified to homogeneity, GEA Gene expression analysis, transcriptional fusions (lacZ), MSI Mapping of signal intensities, such as RNA-seq or microarray analysis; IMP Inferred from mutant phenotype (such as a mutation of a TF that has a visible cell phenotype and it is inferred that the regulator might be regulating the genes responsible for the phenotype). Growth conditions were 30 °C, as the stationary phase was induced for all experiments. All experiments were done with E. coli K-12 or derivative strains. This information, including regulatory interactions can be accessed at RegulonDB (http://regulondb.ccg.unam.mx/) by consulting the Crl regulon

Fig. 1
Fig. 1

Crl regulatory network in E. coli K-12. In green are those genes regulated positively and in red those regulated negatively. The regulatory effects of additional TFs are shown as green solid lines for activation and red solid lines for repression

Previously, genes under the control of Crl were classified in four main categories depending on their role(s) in the cell: DNA metabolism, central metabolism, response to environmental modifications, and miscellaneous [11]. Based on Gene Ontology (GO) annotations, multifunctional classification, and KEGG pathway maps to categorize functions, Crl-regulated genes appear to be involved in metabolic processes such as energy metabolism, amino acid, carbohydrate, and lipid metabolism, and biosynthetic processes such as glycan biosynthesis and biosynthesis of other secondary metabolites, among other metabolic processes. These functions correlate with results of the enrichment analysis using PANTHER, which showed that catabolic processes, metabolic processes, and cellular responses to xenobiotic stimuli were overrepresented among the functions associated with genes under the control of Crl (See Fig. 2).
Fig. 2
Fig. 2

Functions associated with genes under the control of Crl. GOs and Multifun-associated genes under Crl control and enrichment analysis with the PANTHER classification system and Multifun. Categories of KEGG used to classify GOs and Multifun terms are shown on the X-axis, and the number of GOs associated with each category are shown on the Y-axis

In general, genes under Crl control are involved in regulating many aspects of cellular metabolism through Crl’s interaction with a subset of genes of the σS regulon [8] in addition to quorum sensing playing a major role in cell-to-cell communication during stationary phase and in different processes such as biofilm formation or virulence, transporters [11] and genes involved in the uptake and utilization of β-glucosides [16].

Composition of the Crl regulon

In order to determine whether additional TFs also regulate the genes under the control of Crl, RegulonDB was used to evaluate how genes associated with Crl are also regulated by alternative TFs or sigma factors. A total of 24 genes were identified as exclusively controlled by Crl, whereas 62 are regulated by additional TFs (See Additional file 2). In this regard, 55 different TFs are involved in the regulation of genes associated with Crl, including Crp, IHF, H-NS, Fis, FNR, ArcA, GadX, GadW, GadE, and CsgD (Table 1), suggesting that all genes regulated by Crl are also involved in multiple functions beyond the stationary phase, or, alternatively, phase transition has to regulate genes involved in large number of different functions. It is interesting that six of seven global regulators identified in the regulatory network of E. coli are also associated with the set regulated by Crl. Another way to look at this small network is that 19 genes of the total of Crl-regulated genes are regulated by one TF, 11 by two TFs, and 14 by three different TFs. Therefore, Crl is regulating positively 73 (85%) genes, whereas 12 (15%) genes are regulated negatively (Table 1). The predominance of positive regulation suggests that genes associated with this regulon are in high demand according the demand theory suggested by Savageau [18], and the activities of their proteins are enhanced to contend with varied environmental stimuli. Thirty-four of the 86 genes have a σS-type promoter that was experimentally determined (RegulonDB). Finally, the promoters of 49 genes identified as members of Crl and of the σS sigmulon, based on transcriptional fusions and microarray analysis data, remain to be experimentally determined.

Phylogenetic analysis of Crl

In order to evaluate the phylogenetic history of Crl across the bacterial and archaeal cellular domains, its homologs were identified as described in the Methods section, and a phylogenetic tree with maximum likelihood was generated (Fig. 3 and Additional file 3). From this analysis, we found that Crl and its homologs are distributed almost exclusively among Gammaproteobacteria but do not share homology with proteins from other taxonomical divisions, as has been previously noted for E. coli, Vibrio spp., Citrobacter spp., Salmonella spp., and Enterobacter aerogenes [16]. Additional information suggests that Crl is less widespread and less conserved at the sequence level than σS [19]. In this regard, four conserved residues (Y22, F53, W56, and W82) are important for Crl activity and for Crl-σS interaction but not for Crl stability in S. Typhimurium [19]. On one hand it is probable that Crl homologs exist in some σS-containing bacteria; however, some species might use alternative strategies to favor σS interaction with the core of the RNAP [19]. Therefore, our phylogenetic analysis suggests that Crl is a protein conserved and constrained to Gammaproteobacteria, such as in Vibrio spp., Klebsiella spp., Enterobacter spp., and Escherichia coli. Contrary to Crl, several other of the TFs that co-regulate the Crl regulon, are present beyond the gamma-proteobacterial, probably pre-dating regulation of some of the target genes, which have been more recently subject to Crl regulation.
Fig. 3
Fig. 3

Phylogenetic tree of Crl. Phylogenetic tree based on Crl of E. coli and homologs in other organisms generated via maximum likelihood analysis, with 1000 replicates. Species with bootstrap values higher than 60% are displayed. The black triangles to the right of the branches indicate multiple species for those genera

In addition, homologous of Crl were found in low copy numbers, i.e., one Crl-like protein per genome. This information, together with the distribution of σS, suggests that the regulator was recruited as an element to regulate a subset of σS-regulated genes in Gammaproteobacteria. In this regard, it is interesting that genes under the control of Crl contain an UP element with A/T-rich DNA sequence upstream of a poorly conserved − 35 promoter which may serve for alpha subunit binding of RNAP; suggesting that Crl could play a fundamental role in the contacts between RNAP and its promoter [13]. In addition, Crl would increase transcription rate during the transition from growing to stationary phase at low temperatures [8], using indole as an effector molecule. In summary, this result opens the question explored in the follow section of whether genes regulated by Crl are also constrained to this taxonomical division.

Taxonomical distribution of Crl-regulated genes

Based on the identification of orthologs of 86 Crl-regulated genes, we evaluated their taxonomical distribution across archaea and bacteria sequence genomes, as described in Methods (See Fig. 4 and Additional file 4). Based on a taxonomical profile, we determined that the evolution of the Crl regulon seems to have involved diverse losses and gains of regulatory interactions. It is possible that large portions of the regulatory network associated with Crl evolved through extensive genetic changes during the evolution of the species studied. Indeed, we suggest three main events modeled the evolution of this regulon: (i) the regulation of a large number of genes widely distributed among Bacteria and Archaea, such as those genes involved in ancient metabolic processes such as glycolysis (fbaB, pykF, pfkA, and sucA) and those involved in the tricarboxylic acid cycle (gltA and sucD) [20]; (ii) the regulation of genes with a distribution pattern mainly constrained to Proteobacteria, with some secondary losses in other organisms, such as those genes involved in response to stress and starvation (cstA and hdcA) or cell adhesion (csgA and csgB), among others; and (iii) the recruitment of Crl as a consequence of its emergence in Enterobacteriales. It is interesting that Crl-regulated genes are also part of the σS sigmulon, where there are no essential genes [2124]. All these elements suggest that the Crl regulon is highly flexible for phenotypic adaptation, probably as a consequence of the diverse growth environments associated with the organisms in which members of this regulatory network are present.
Fig. 4
Fig. 4

Taxonomic distribution of orthologs from the perspective of E. coli K-12. A single linkage-clustering algorithm with no leaf order optimization was applied with Pearson distance as the similarity measure. The display clustering results were obtained using the MeV program [39]. The conserved groups across the different taxonomic groups are indicated. Each column denotes Crl-regulated genes, whereas rows denote taxonomic groups. The bar at the top of the figure indicates the relative abundance of orthologs per group, represented as a percentage, where a value of 1 corresponds to 100% presence and 0% indicates a division without any ortholog of the Crl regulon in the taxonomic group

Conclusions

Crl stimulates -but can also repress- the association of σS with the RNAP core in E. coli K-12 through direct and specific interactions, increasing, or decreasing, the transcription rate of a subset of genes of the σS sigmulon. This TF has been described during the transition to stationary phase at low temperatures. In our work, based on an exhaustive literature search, we found 86 genes under the control of Crl in E. coli. We considered that the quality of the experiments could compensate the few number of papers where evidences associated to the rpoS-crl and its target genes; such as microarray analysis, mutations, assay with purified proteins, among others; however, a large number of records and sources were evaluated to consider the dataset as significant. Indeed, all interactions reported in this work meet the same criteria to be considered in RegulonDB. These protein-coding genes were retrieved mainly from microarray and mutation analyses, among other experimentally supported evidence. Gathering this regulon offers a wider physiological role than previously assumed for Crl. Certainly, these genes are associated with multiple functions, including xenobiotic processes, biofilm formation, metabolic, catabolic, and biosynthetic processes, responses to different stress conditions, and protein assembly, amino acid transport, and transcriptional processes, among others. The diverse functions regulated by Crl suggest that these genes play a fundamental role in multiple functions to respond to environmental changes, mainly those associated with stationary-phase growth at low temperatures. In addition, we conducted an exhaustive analysis concerning the conservation of the regulon Crl among the Bacteria and Archaea genomes, using as starting point, the knowledge gathered for E. coli K-12. From this analysis, Crl was identified in low copy numbers and constrained to the Enterobacteriales order, whereas the homologs of all regulated genes were found to be widely distributed beyond enterobacteria, suggesting that Crl was recruited in a secondary event to regulate a specific subset of genes for which the regulation (stimulation or repression) of Crl and σS helps bacteria in the phase transition.

Methods

Identification of Crl-regulated genes

We performed an exhaustive search of the literature related to Crl (GI: 114152792) in E. coli K-12 in PubMed [25] under the following search strategy: coli in the title (to exclude spurious articles) and crl, rpoS (ID: NP_417221) and regulation both in title and in all fields in their different combinations (coli[all field] crl[all field] rpos[all field] regulation[all field]; coli[ti] rpoS[ti] regulation[ti]; coli[ti] crl[all field] rpos[all field] regulation[all field]; coli[ti] crl[ti] regulation[all field]; coli[ti] rpoS[ti] crl[all field] regulation[all field]; coli[ti] rpoS[ti] crl[ti] regulation[all field]). Fifty nine unique articles with different search profiles were obtained and exhaustively revised, from which, 10 were selected since they contained information on regulatory interactions in E. coli on rpoS-crl and its target genes, such as microarray analysis, mutations, and assay with purified proteins, among others (Table 1). Finally, we searched for gene/operon notes in RegulonDB and EcoCyc [3, 26] for Crl interactions and σS promoters, for assembling the network of regulation of Crl.

The regulatory network generated was displayed using the Cytoscape program, version 3.3.0 [27], with information obtained in the identified papers as well as information contained in RegulonDB [3]. Genes under Crl control were classified based on Gene Ontology (GO) annotations (http://www.geneontology.org/) using the Gene Association Format (GAF 2.0) as well as the Multifun classification scheme [28]. An enrichment analysis was carried out to find overrepresented annotations, using the PANTHER Classification system program, version 12.0; selecting biological processes and E. coli as parameters [29, 30]. In addition, we used KEGG to categorize the functions of GOs (http://www.genome.jp/kegg-bin/show_organism?menu_type=pathway_maps&org=eco) [31].

Identification of Crl homologs

The Crl protein sequence of E. coli K-12 (ID: 114152792) was used as the seed to scan all the bacterial and archaeal genomes via a BLASTp search (BLAST version 2.2.30+) [32] (E-value ≤10− 3 and coverage ≥60%). All proteins were compared and aligned using the Muscle algorithm [33] with default parameters, and results were manually edited with the program Jalview. Finally, a phylogeny was inferred by the maximum likelihood method with 1000 replicates by using the program MEGA [34] and the Tamura-Nei model.

Identification of orthologous genes

Orthologous genes have been classically defined as encoding proteins in different species that evolved from a common ancestor via speciation [35] and have retained the same function. In this work, orthologs were identified by searching for bidirectional best hits (BDBHs) in other organisms [36] considering the same conditions as [36] an E-value ≤ of 1e− 6; database size fixed (−z 5e+ 8), soft filtering of low information content (the −F ‘m S’ option), the Smith–Waterman alignment (−s T), and a coverage of at least 60%.

Taxonomical distribution of orthologous genes

In order to evaluate the taxonomical distribution of the genes belonging to the Crl regulon, 5321 complete genomes were downloaded from the NCBI’s Refseq genome database [37] and open reading frames (ORFs) that encode predicted proteins were considered. Redundancy was excluded using a web-based tool [38] considering a Genome Similarity Score GCCa≥0.95 [38]. In this representative genome dataset, orthologs were traced along 18 archaeal and 69 bacterial cellular divisions. To this end, the relative abundance of the orthologs was calculated as the fraction of genomes in the group that had one ortholog, divided by the total number of genomes per phylum, i.e., the ratio (total number of orthologs in a phylum) / (total number of organisms in phylum). The corresponding matrix was analyzed with a hierarchical complete linkage-clustering algorithm with correlation uncentered as the similarity measure. We used the program MeV to perform the analyses (http://mev.tm4.org/) [39].

Abbreviations

APPH: 

Assay of protein purified to homogeneity

BDBHs: 

Bidirectional best hits

BLASTp: 

Basic Local Alignment Search Tool (proteins)

Crl: 

Curling genes regulatory protein

Ecocyc: 

E. coli metabolic database

GAF: 

cGMP-specific phosphodiesterase, adenylyl cyclase and FhlA domain

GCCa: 

Genome Similarity Score

GEA: 

Gene expression analysis, transcriptional fusions (lacZ)

GO: 

Gene onthology

IMP: 

Inferred from mutant phenotype (such as a mutation of a TF that has a visible cell phenotype and it is inferred that the regulator might be regulating the genes responsible for the phenotype)

Jalview: 

Java alignment editor

KEGG: 

Kyoto encyclopedia of genes and genomes database

MEGA: 

Molecular Evolutionary Genetics Analysis

MSI: 

Mapping of signal intensities, such as RNA-seq or microarray analysis

Muscle: 

Multiple sequence comparison by Log-expectation

ORFs: 

Open reading frames

PANTHER: 

Protein analysis through evolutionary relationships classification system

PhoPQ: 

Two-component system of virulence and adaptation

RegulonDB: 

Regulon database

RNAP: 

RNA polymerase

RNA-seq: 

RNA sequencing

RpoD: 

RNA polymerase sigma factor D

RpoS: 

RNA polymerase sigma factor S

TFs: 

Transcription factors

TUs: 

Transcription units

Declarations

Acknowledgements

We acknowledge contributions by César Bonavides-Martínez for his technical help; and Joaquin Morales, Sandra Sauza, and Israel Sánchez are very much appreciated for their computational support.

Funding

Publication of this article was funded by National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers [R01GM110597, U24GM077678]; and DGAPA-UNAM IN-201117.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Authors’ contributions

ASZ, JCV and EPR conceived and designed the research. EPR and ASZ designed and drafted the manuscript, contributed equally in the development of this work and they are the major contributors in writing the manuscript. MSP and EPR performed the bioinformatics analysis. MSP and DAVR gathered the regulation of the network. ASZ and DAVR contributed to the data extraction, and curation of the processed data of the articles. EPR performed the phylogenetic analyzes. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, 62210 Cuernavaca, Morelos, Mexico
(2)
Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Sede Mérida, Universidad Nacional Autónoma de México, Unidad Académica de Ciencias y Tecnología, 97302 Mérida, Yucatán, Mexico
(3)
Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile

References

  1. Perez-Rueda E, Tenorio-Salgado S, Huerta-Saquero A, Balderas-Martinez YI, Moreno-Hagelsieb G. The functional landscape bound to the transcription factors of Escherichia coli K-12. Comput Biol Chem. 2015;58:93–103.View ArticleGoogle Scholar
  2. Landini P, Egli T, Wolf J, Lacour S. sigmaS, a major player in the response to environmental stresses in Escherichia coli: role, regulation and mechanisms of promoter recognition. Environ Microbiol Rep. 2014;6(1):1–13.View ArticleGoogle Scholar
  3. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muniz-Rascado L, Garcia-Sotelo JS, Alquicira-Hernandez K, Martinez-Flores I, Pannier L, Castro-Mondragon JA, et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 2016;44(D1):D133–43.View ArticleGoogle Scholar
  4. Dufour YS, Kiley PJ, Donohue TJ. Reconstruction of the core and extended regulons of global transcription factors. PLoS Genet. 2010;6(7):e1001027.View ArticleGoogle Scholar
  5. Lozada-Chavez I, Janga SC, Collado-Vides J. Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 2006;34(12):3434–45.View ArticleGoogle Scholar
  6. Monsieurs P, De Keersmaecker S, Navarre WW, Bader MW, De Smet F, McClelland M, Fang FC, De Moor B, Vanderleyden J, Marchal K. Comparison of the PhoPQ regulon in Escherichia coli and Salmonella typhimurium. J Mol Evol. 2005;60(4):462–74.View ArticleGoogle Scholar
  7. Liu R, Ochman H. Origins of flagellar gene operons and secondary flagellar systems. J Bacteriol. 2007;189(19):7098–104.View ArticleGoogle Scholar
  8. Bougdour A, Lelong C, Geiselmann J. Crl, a low temperature-induced protein in Escherichia coli that binds directly to the stationary phase sigma subunit of RNA polymerase. J Biol Chem. 2004;279(19):19540–50.View ArticleGoogle Scholar
  9. Arnqvist A, Olsen A, Pfeifer J, Russell DG, Normark S. The Crl protein activates cryptic genes for curli formation and fibronectin binding in Escherichia coli HB101. Mol Microbiol. 1992;6(17):2443–52.View ArticleGoogle Scholar
  10. Typas A, Barembruch C, Possling A, Hengge R. Stationary phase reorganisation of the Escherichia coli transcription machinery by Crl protein, a fine-tuner of sigmas activity and levels. EMBO J. 2007;26(6):1569–78.View ArticleGoogle Scholar
  11. Lelong C, Aguiluz K, Luche S, Kuhn L, Garin J, Rabilloud T, Geiselmann J. The Crl-RpoS regulon of Escherichia coli. Mol Cell Proteomics. 2007;6(4):648–59.View ArticleGoogle Scholar
  12. Lelong C, Rolland M, Louwagie M, Garin J, Geiselmann J. Mutual regulation of Crl and Fur in Escherichia coli W3110. Mol Cell Proteomics. 2007;6(4):660–8.View ArticleGoogle Scholar
  13. Dudin O, Lacour S, Geiselmann J. Expression dynamics of RpoS/Crl-dependent genes in Escherichia coli. Res Microbiol. 2013;164(8):838–47.View ArticleGoogle Scholar
  14. Olsen A, Arnqvist A, Hammar M, Sukupolvi S, Normark S. The RpoS sigma factor relieves H-NS-mediated transcriptional repression of csgA, the subunit gene of fibronectin-binding curli in Escherichia coli. Mol Microbiol. 1993;7(4):523–36.View ArticleGoogle Scholar
  15. Pratt LA, Silhavy TJ. The response regulator SprE controls the stability of RpoS. Proc Natl Acad Sci U S A. 1996;93(6):2488–92.View ArticleGoogle Scholar
  16. Schnetz K. Silencing of the Escherichia coli bgl operon by RpoS requires Crl. Microbiology (Reading, England). 2002;148(Pt 8):2573–8.View ArticleGoogle Scholar
  17. Huerta AM, Collado-Vides J. Sigma70 promoters in Escherichia coli: specific transcription in dense regions of overlapping promoter-like signals. J Mol Biol. 2003;333(2):261–78.View ArticleGoogle Scholar
  18. Savageau MA. Demand theory of gene regulation. I. Quantitative development of the theory. Genetics. 1998;149(4):1665–76.PubMedPubMed CentralGoogle Scholar
  19. Monteil V, Kolb A, D'Alayer J, Beguin P, Norel F. Identification of conserved amino acid residues of the Salmonella sigmaS chaperone Crl involved in Crl-sigmaS interactions. J Bacteriol. 2010;192(4):1075–87.View ArticleGoogle Scholar
  20. Dandekar T, Schuster S, Snel B, Huynen M, Bork P. Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J. 1999;343(Pt 1):115–24.View ArticleGoogle Scholar
  21. Chen G, Patten CL, Schellhorn HE. Positive selection for loss of RpoS function in Escherichia coli. Mutat Res. 2004;554(1–2):193–203.View ArticleGoogle Scholar
  22. Dong T, Schellhorn HE. Control of RpoS in global gene expression of Escherichia coli in minimal media. Mol Gen Genomics. 2009;281(1):19–33.View ArticleGoogle Scholar
  23. Zambrano MM, Siegele DA, Almiron M, Tormo A, Kolter R. Microbial competition: Escherichia coli mutants that take over stationary phase cultures. Science. 1993;259(5102):1757–60.View ArticleGoogle Scholar
  24. Cavaliere P, Norel F. Recent advances in the characterization of Crl, the unconventional activator of the stress sigma factor sigmaS/RpoS. Biomol Concepts. 2016;7(3):197–204.PubMedGoogle Scholar
  25. Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH. The NCBI BioSystems database. Nucleic Acids Res. 2010;38(Database issue):D492–6.View ArticleGoogle Scholar
  26. Keseler IM, Mackie A, Santos-Zavaleta A, Billington R, Bonavides-Martinez C, Caspi R, Fulcher C, Gama-Castro S, Kothari A, Krummenacker M, et al. The EcoCyc database: reflecting new knowledge about Escherichia coli K-12. Nucleic Acids Res. 2017;45(D1):D543–50.View ArticleGoogle Scholar
  27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.View ArticleGoogle Scholar
  28. Serres MH, Riley M. MultiFun, a multifunctional classification scheme for Escherichia coli K-12 gene products. Microb Comp Genomics. 2000;5(4):205–22.View ArticleGoogle Scholar
  29. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–9.View ArticleGoogle Scholar
  30. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13(9):2129–41.View ArticleGoogle Scholar
  31. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.View ArticleGoogle Scholar
  32. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.View ArticleGoogle Scholar
  33. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.View ArticleGoogle Scholar
  34. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30(12):2725–9.View ArticleGoogle Scholar
  35. Fitch WM. Distinguishing homologous from analogous proteins. Syst Zool. 1970;19(2):99–113.View ArticleGoogle Scholar
  36. Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics. 2008;24(3):319–24.View ArticleGoogle Scholar
  37. Haft DH, DiCuccio M, Badretdin A, Brover V, Chetvernin V, O'Neill K, Li W, Chitsaz F, Derbyshire MK, Gonzales NR, et al. RefSeq: an update on prokaryotic genome annotation and curation. Nucleic Acids Res. 2018;46(D1):D851–60.View ArticleGoogle Scholar
  38. Moreno-Hagelsieb G, Wang Z, Walsh S, ElSherbiny A. Phylogenomic clustering for selecting non-redundant genomes for comparative genomics. Bioinformatics. 2013;29(7):947–9.View ArticleGoogle Scholar
  39. Saeed AI, Bhagabati NK, Braisted JC, Liang W, Sharov V, Howe EA, Li J, Thiagarajan M, White JA, Quackenbush J. TM4 microarray software suite. Methods Enzymol. 2006;411:134–93.View ArticleGoogle Scholar
  40. Weber H, Polen T, Heuveling J, Wendisch VF, Hengge R. Genome-wide analysis of the general stress response network in Escherichia coli: sigmaS-dependent genes, promoters, and sigma factor selectivity. J Bacteriol. 2005;187(5):1591–603.View ArticleGoogle Scholar
  41. Pratt LA, Silhavy TJ. Crl stimulates RpoS activity during stationary phase. Mol Microbiol. 1998;29(5):1225–36.View ArticleGoogle Scholar
  42. Hao Y, Updegrove TB, Livingston NN, Storz G. Protection against deleterious nitrogen compounds: role of sigmaS-dependent small RNAs encoded adjacent to sdiA. Nucleic Acids Res. 2016;44(14):6935–48.View ArticleGoogle Scholar

Copyright

© The Author(s). 2019

Advertisement