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1 3
Cellular and Molecular Life Sciences
https://doi.org/10.1007/s00018-019-03326-8
REVIEW
Quorum sensing forpopulation‑level control ofbacteria andpotential
therapeutic applications
ShengboWu1,2,3· JiahengLiu1,3,4· ChunjiangLiu1,2,3· AidongYang5· JianjunQiao1,3,4
Received: 4 July 2019 / Revised: 13 September 2019 / Accepted: 30 September 2019
© Springer Nature Switzerland AG 2019
Abstract
Quorum sensing (QS), a microbial cell-to-cell communication process, dynamically regulates a variety of metabolism and
physiological activities. In this review, we provide an update on QS applications based on autoinducer molecules includ-
ing acyl-homoserine lactones (AHLs), auto-inducing peptides (AIPs), autoinducer 2 (AI-2) and indole in population-level
control of bacteria, and highlight the potential in developing novel clinical therapies. We summarize the development in
the combination of various genetic circuits such as genetic oscillators, toggle switches and logic gates with AHL-based QS
devices in Gram-negative bacteria. An overview is then offered to the state-of-the-art of much less researched applications
of AIP-based QS devices with Gram-positive bacteria, followed by a review of the applications of AI-2 and indole based
QS for interspecies communication among microbial communities. Building on these general-purpose QS applications, we
highlight the disruptions and manipulations of QS devices as potential clinical therapies for diseases caused by biofilm forma-
tion, antibiotic resistance and the phage invasion. The last part of reviewed literature is dedicated to mathematical modelling
for QS applications. Finally, the key challenges and future perspectives of QS applications in monoclonal synthetic biology
and synthetic ecology are discussed.
Keywords Cell–cell communication· Signaling molecule· Microbial community· Population control· Genetic circuit·
Gut microbiota
List of symbols
[A] Intracellular AHL concentration (mM)
[C] Intracellular CI protein concentration (mM)
[E] Intracellular CcdB protein concentration (mM)
[L] Intracellular LacR concentration (mM)
[LuxR] Intracellular LuxR concentration (mM)
[R] Intracellular AHL/LuxR complex concentration
(mM)
N The cell density (CFUml−1)
Nm The maximum cell density (CFUml−1)
Fpfk The fractional Pfk-1 activity (U/mg)
Kd The cumulative dissociation constant
X Biomass concentration (gL−1)
n1, n2 Transcription factor cooperativity/
multimerization
αC CI protein synthesis rate constant (μMmin−1)
αL1, αL2 LacR protein synthesis rate constants
(μMmin−1)
βC CI repression coefficient (mM)
βL LacR repression coefficient (mM)
d Cell death rate (nM−1 h−1)
dA, dE AHL and CcdB protein decay constant (min−1)
dC CI protein decay constant (min−1)
dL, dR LacR and LuxR–AHL complex decay constants
(min−1)
k Growth rate (h−1)
kE CcdB protein production rate constant (h−1)
vA AHL production rate constant (nMmLh−1)
Cellular andMolecular Life Sciences
* Aidong Yang
aidong.yang@eng.ox.ac.uk
* Jianjun Qiao
jianjunq@tju.edu.cn
1 School ofChemical Engineering andTechnology, Tianjin
University, Tianjin300072, China
2 State Key Laboratory ofChemical Engineering, Tianjin
University, Tianjin300072, China
3 Collaborative Innovation Center ofChemical Science
andEngineering (Tianjin), Tianjin300072, China
4 Key Laboratory ofSystems Bioengineering, Ministry
ofEducation (Tianjin University), Tianjin300072, China
5 Department ofEngineering Science, University ofOxford,
OxfordOX13PJ, UK
S.Wu et al.
1 3
θR LuxR/AHL activation coefficient (mM)
ρR LuxR/AHL dimerization constant (μM−3min−1)
Introduction
Quorum sensing (QS) is a cell–cell communication process,
which is ubiquitous in fungi [1], bacteria and even viruses
[2]. QS regulates a series of physiological and biochemical
functions, such as biofilm formation, conjugation, compe-
tence, bacteriocin production and pathogenesis, achieved
by microbes producing, secreting, sensing and responding
to certain signal molecules which are called autoinducers
(AIs) [3]. Generally, various AIs can be roughly divided
into three types: (i) acylated homoserine lactones (AHLs)
and the diffusible signaling factors (DSFs) utilized by Gram-
negative bacteria; (ii) auto-inducing peptides (AIPs) utilized
by Gram-positive bacteria; and (iii) autoinducer 2 (AI-2) and
indole for interspecies communication of microbial com-
munities [4]. Combining these AIs and their relevant QS
devices with synthetic genetic circuits is of great importance
to the dynamic control of bacterial populations and to the
development of potential clinical therapies (Fig.1).
Dynamic control of bacterial populations usually includes
population size control, dynamic metabolic engineering for
desirable products and the regulation of various physiologi-
cal activities [5] (Fig.1). Metabolic control, a major issue
in dynamic control of metabolic engineering, can be divided
into static metabolic control and dynamic metabolic con-
trol [6]. Usually, static metabolic control involves natural or
slightly modified control systems with knockout, weakening
and overexpression of genes. Dynamic metabolic control
utilizes genetic circuits such as toggle switches and sensor-
regulator to achieve the dynamic adjustment of metabolic
production of microbes [5]. According to the type of genetic
circuits involved, either on–off or continuous dynamic meta-
bolic control can be achieved [7]. A common strategy via
introducing an on–off switch is to close the relevant competi-
tive pathways when the bacteria population reaches a certain
level. This type of genetic circuits has the disadvantages of
requiring proper induction time, increasing production costs
due to the addition of inducer and being incapable of sensing
the changing environment continuously. To overcome these
disadvantages, continuous dynamic metabolic control has
been developed to up-regulate the desirable product using
synthetic feedback loops, such as QS-based devices [7]. The
implementation of dynamic metabolic control can be either
pathway-specific or pathway-independent [8]. The pathway-
specific implementations are achieved by detecting input and
output changes of a relevant intermediate or byproduct [9],
while the pathway-independent implementations are through
nutrients in the medium or by QS [10, 11]. Compared to
the pathway-specific implementations which are restricted
to sense and dynamically control the metabolism of intra-
cellular pathways, pathway-independent implementations
allow microbes to respond to the changing extracellular
environment and adjust accordingly their metabolism and
physiological activities, with QS as an important enabler
[8]. What’s more, significant advances have been made in
synthetic biology which created synthetic pathways and cir-
cuits to control the expression levels of relevant genes, such
as overexpressing the genes for producing glycosides [12]
in engineered bacteria. Transcriptional toggle switches [13]
and transcriptional oscillators [14] are involved in transcrip-
tional regulation of genes, and genetic loops such as bista-
ble positive feedback loops and RNA-based anti-switches
are constructed into biological systems to control post-
transcriptional regulation [15], metabolic flux distribution
[16] and signaling proteins expression [17]. These QS-based
genetic circuits not only make the synthetic systems more
reliable and robust [18], but also provide new avenues to the
dynamic control of bacterial populations [19].
With the increasingly recognized importance of patho-
gens and microbiota for human health, the QS-based mono-
clonal synthetic biology and synthetic ecology have enor-
mous potential in promoting the development of potential
clinical therapies for curing devastating diseases, tackling
antimicrobial resistance [20] (Fig.1). Many bacteria have
been shown to have a tendency to organize in aggregates
generally to adhere to surfaces to form biofilms, and biofilm
formation is a principal virulence factor in many localized
Fig. 1 QS applications for dynamic control of bacteria populations
and its potential clinical therapies for diseases. Dynamic control of
bacterial populations includes three aspects, i.e., the population of
bacteria control, dynamic metabolic engineering control and regula-
tion of physiological activities. They mainly work on the combina-
tions of various genetic circuits such as genetic oscillators, genetic
toggle switches and genetic logic gates. Underpinned by the func-
tioning of autoinducer molecules, i.e., AHLs, DSFs, AIPs, AI-2
and indole, the disruptions and manipulations of QS in the dynamic
control of bacteria populations can be extended to be widely applied
in monoclonal synthetic biology and synthetic ecology to develop
potential clinical therapies
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
chronic infections [21, 22]. As shown by existing stud-
ies [23], QS and quorum quenching [4] can significantly
affect biofilm formation. One of the most important factors
causing the changes of microbiota is the use of antibiotics,
which does not only alter the microbiota but also promote
the emergence of antibiotics resistance [24]. It has recently
been demonstrated that QS inhibition including quorum
quenching and other QS-blocking approaches can decrease
the production of virulence factor [25]. Therefore, coupling
QS devices with microbial consortia in various environ-
ments such as the human gut has much therapeutic potential,
for example, treating chronic infections [26] and relieving
antimicrobial resistance [27]. Besides, as infections of phage
increase at high cell density [28], QS devices have much
potential to regulate the CRISPR-Cas (clustered regularly
interspaced short palindromic repeats; CRISPR-associated)
immune systems to monitor the development of diseases
[29].
Recently, several QS-based reviews have been published
which focus on aspects including QS signals transduction
and architectures, dynamic control for metabolic engineer-
ing, applications of synthetic microbial consortia, socio-
microbiology based on cell–cell communications, the
applications of QS inhibitors in biofilm formation and the
mathematical modelling for QS, as listed in Table1. The
purpose of this current review is to provide an updated sum-
mary of the more recent achievements in applying QS to the
dynamic control of bacterial populations and in developing
potential clinical therapies. We start by presenting the recent
promising achievements, including bacteria population con-
trol, applications of the genetic toggle switches, synthetic
genetic oscillators and genetic logic gates that apply QS
devices which are based on AHLs signal transduction in
Gram-negative bacteria. We then provide an overview of the
AIP-based dynamic regulations of competence and virulence
in Gram-positive bacteria, followed by that of AI-2-based
and indole-based control of metabolism and physiological
activities in microbial communities. Building on the review
of these rather generic QS applications, we further highlight
the important progress in the disruptions and manipulations
of QS devices for therapeutic applications. The last part of
the reviewed literature is on the mathematical modelling
related to QS applications in the dynamic control of bacteria.
Finally, we identify key challenges and suggest directions for
future QS research.
QS applications inGram‑negative bacteria
QS forpopulation control
QS-dependent activities are the result of density-depend-
ent expression of both intra- and extracellular gene
products [47]. It is essential for population-level dynamics
and genetic-level regulation [19]. An etal. [48] and Goo
etal. [49] certified that nutrients are typically limited and
the environment is unfavorable for growth and metabolism
of microbes in a crowded environment. Therefore, it is of
much importance to control the cell density to optimize
metabolic production.
You etal. [50] proposed that cell–cell communication
can be used to programme the dynamics of a population.
Combining the cell survival and death genes to the LuxI/
LuxR QS circuit, they built and characterized a ‘popula-
tion control’ circuit that autonomously regulated the den-
sity of an Escherichia coli population (Fig.2a). Balagadde
etal. [51] applied two E. coli populations to construct a
synthetic ecosystem (predator and prey) (Fig.2b), which
was based on two QS mechanisms (LuxI/LuxR and LasI/
LasR). The predators will die following the expression of
a suicide gene (ccdB) when the density of prey is low. As
the prey density increases, AHL accumulates in the culture
and eventually reaches a sufficiently high concentration,
its combination with LuxR will then work to increase the
expression of an antidote gene (ccdA) to rescue the preda-
tors. However, the predators will produce and accumulate
Las AHL to a sufficient level to bind with LasR, which
will, in turn, activate the expression of ccdB gene to kill
the preys. The series of events leads to the oscillatory
behavior of the two E. coli populations, which is typi-
cal for a two-strain ecosystem. More recently, AHL-based
synthetic E. coli systems were used to test a general rule
deduced for predicting coexistence and productivity of
mutualistic communities [52]. Compared to the previous
work by the same group [51], new features of these experi-
mentally tested systems included the use of Isopropyl β-d-
1-thiogalactopyranoside (IPTG) to induce the expression
of CcdB (representing stress), and the application of anhy-
drotetracycline (aTc) to induce the QS module to impose
the cooperation cost to both strains.
With the development of genetic circuits, the lysis genes
can be coupled with new synthetic circuits such as genetic
oscillators to realize various functions. Din etal. [53] inte-
grated the lysis genes with a microbial drug delivery system
[54] to form a synchronized lysis circuit (SLC) for control-
ling population levels and facilitating drug delivery using
bacteria (Fig.2c). The circuit includes a luxI promoter which
promotes expression of LuxI, a therapeutic gene, a reporter
gene and a lysis gene ϕX174 E. When AHL reaches a target
threshold, the expression of the therapeutic gene and the
reporter gene will be promoted, and bacteria will produce
and release cytotoxic agents continually. At the same time,
the number of bacterium will decrease due to the expression
of the lysis gene. Then, a small number of remaining bacte-
ria will begin to produce AHL again to restart this process
in a cyclical fashion. Compared with existing drug delivery
S.Wu et al.
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strategies, SLC can be used as a novel therapeutic technique
to cure diseases through population control of bacteria.
In a separate study, two orthogonal QS devices and a
population control mechanism were combined together to
control the population densities of competitive microbes of
Salmonella typhimurium strains [55] (Fig.2d). lux and rpa
QS devices can be integrated with two lysis genes to form
SLCs in two bacterial strains to control bacteria population.
When two competitive microbes, the Lux-QS strain and the
Rpa-QS strain, are co-cultured, the latter has a significant
growth advantage over the former. It was observed that
from an initial population ratio of 100:1 between the Lux-
QS strain and the Rpa-QS strain with the lysis gene, the
population ratio became about 1:1 over 10h. Without the
lysis gene, the co-culture would be taken over by the Lux-QS
strain. This demonstrates that the integration of two orthogo-
nal QS devices to form an ‘ortholysis’ system is a potential
strategy to stabilize competitive strains in co-cultures.
Table 1 Recent reviews focusing on QS
Theme Core content References
QS signals transduction and architectures Reviewed various signal–response systems and their applications in
Gram-negative bacteria
[18]
Reviewed types of molecular mechanisms coupled with various QS
devices in Gram-negative and Gram-positive bacteria, and some
network architectures of QS circuits
[30]
Reviewed various signal–response systems in Gram-positive bacteria [31]
Reviewed the mechanisms of intracellular pathway and extracellular
pathway QS system, and the applications of regulating of conjugation,
competence, bacteriocin production, and biofilm formation in Gram-
positive bacteria
[32]
Reviewed the function of indole in bacterial pathogenesis and eukary-
otic immunity
[33]
QS and its applications in marine microbes [34]
Reviewed the diversity, functions, biosynthetic pathways, and turnover
systems for the diffusible signaling factors (DSF) family of QS signals
[35]
Dynamic control for metabolic engineering Reviewed various strategies such as QS system in the applications of
dynamic metabolic engineering
[7]
Reviewed some dynamic control strategies coupling with QS to syn-
chronize cellular activity
[36]
Applications of synthetic microbial consortia Reviewed some engineering cell–cell communication, mainly on QS,
and synthetic microbial consortia for community composition, divi-
sion of labor, and biofilm formation with QS system
[37]
Reviewed some typical synthetic microbial consortia by cell–cell com-
munications, mainly on QS
[38]
Discussed complex interactions and interplays in synthetic microbial
ecology based on QS-based cell–cell communication
[39]
Socio-microbiology based on cell-to-cell communications Discussed the complex signal network and the cooperation with QS
cheating phenotypes in bacterial. And reviewed various and feasible
mechanisms that have been certified to stabilize QS-based cooperation
in microbes
[40]
Reviewed the background and brief history of QS, and the applications
of QS in socio-microbiology
[4]
Applications of QS inhibitors in biofilm formation Reviewed natural and synthetic quorum sensing inhibitors (QSIs) in
various microbes
[25]
Reviewed applications of QS in biotechnology, especially for QSIs and
some other biosensors
[41]
Reviewed the mechanism for pathogenic biofilms formation, and dis-
cussed the current biofilm-targeting therapeutic strategies for disease
which caused by microbial biofilms and drug tolerance
[42]
Reviewed how bacteria deploy QS in realistic, complex and dynami-
cally changing scenarios
[43]
Mathematical modelling for QS Reviewed the modeling approaches on a systemic level [44]
Proposed the core principles of autoinducer systems in bacteria [45]
Reviewed various QS mathematical models [46]
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
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The above studies suggest that the combination of lysis
genes, such as ccdB, and various QS devices (LuxI/LuxR,
LasI/LasR, or RpaI/RpaR) can be applied to achieve the
control of bacterial populations in either mon-culture or
co-culture systems. Such control helps meet the essential
requirement on population size needed for realizing vari-
ous high-value metabolic production and microbial clinical
treatments.
QS‑based synthetic genetic oscillators
Using only a small amount of regulators in a large-scale
genetic regulatory network, a relatively large number of
genes and hence the complex cell behavior can be regu-
lated [56, 57]. Many metabolic activities, such as popu-
lation control, respiration, hormone secretion and circa-
dian rhythms, are closely associated with synchronized
oscillators [58–60]. Developing synthetic gene oscilla-
tors (SGOs) is one of the main research directions of the
research on synthetic genetic regulatory networks [61].
The first SGO, the repressilator, was proposed in 2000
and is illustrated in Fig.3a [14]. Unlike the repressilator
where only repression exists, a relaxation oscillator has
positive feedback loops which can promote the expression
of negative feedback loops [62]. Hasty etal. [63] applied
the common gene regulatory components (ci, lac and PRM)
to construct a SGO model (Fig.3b). Stricker etal. [64]
developed a fast, robust and persistent engineered genetic
oscillator in E. coli with the induction of IPTG and arab-
inose (Fig.3c), and confirmed that its oscillatory period
can be tuned by altering inducer levels, temperature and
the media source.
Due to the complexity of cellular interaction and vari-
ability, it is important to investigate population-level dynam-
ics of microbes, such as synchronization [65, 66] and pro-
grammed population interactions [39, 67, 68]. To avoid the
random phase drift and remove the effects of noise, it is
desirable to introduce measures to coordinate and synchro-
nize each cellular oscillator [69], and QS has been found to
offer an important means for this task [70].
Mcmillen etal. [71] firstly combined a genetic relaxation
oscillator, which is composed of promotor PRE, gene X (cii)
and gene Y (ftsh), with the lux QS mechanism (Fig.3d).
Protein CII can be degraded by protein FtsH, while the com-
plex of AHL and LuxR (LuxR-AHL) can activate the tran-
scription of CII. When the concentration of AHL reaches its
threshold, it will bind with another LuxR in other cells to
regulate their CII level.
Fig. 2 QS for the lysis and population control. a The signaling mol-
ecule 3OC6HSL (Lux AHL) is produced by the LuxI synthase. It will
accumulate in the culture with the increased cell density of E. coli.
When the concentration of the Lux AHL reaches a certain thresh-
old, AHL will diffuse back into E. coli and be recognized by LuxR,
a specific protein receptor to activate the transcriptional expression
of the killer protein LacZa-ccdB to regulate the cell death of E. coli
and consequently control the population density. b Based on the two
QS mechanisms (LuxI/LuxR and LasI/LasR QS devices), two E.
coli strains are engineered to construct a synthetic predator and prey
ecosystem. c When the population reaches the critical threshold, the
AHL will bind with LuxR to become AHL–LuxR complex. It will
facilitate the expression of LuxI, gene ϕX174E for lysis, therapeutic
gene for cytotoxic agents, and sfGFP for reporter. d Genetic circuits
of a two-strain ecosystem including Lux-QS and Rpa-QS S. typhimu-
rium strains
S.Wu et al.
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Danino etal. [17] applied a QS-based approach to syn-
chronized oscillations at the colony level (Fig.3e). In this
circuit, luxI, aiiA and yemGFP genes were controlled by
the luxI promoter. The LuxR–AHL complex activated the
luxI promoter [72]. AHL was degraded by homoserine
lactonase (AiiA).
As the DNA copy number changes with environmental
pressures, it is a challenge to combine gene circuits with-
out predictable dynamic control of gene expression [73].
Treating the DNA copy number as a circuit control element,
Baumgart etal. [74] reported that the expression of some
cut site genes on a plasmid can be repressed by a targeted
Fig. 3 The applications of QS-based synthetic genetic oscillators. a
When the promoter PllacO1 is successfully promoted, the TetR pro-
tein will inhibit the downstream PltetO1 promoter, the CI protein will
not be expressed, the inhibition of the Pr promoter will be released,
LacI protein will be expressed, and the state of the PllacO1 promoter
will be changed from “turn on” to “turn off”. Then the expression of
TetR protein will be inhibited, and the inhibition of the PltetO1 pro-
moter will be released, CI protein will be expressed, Pr promoter
will be inhibited, LacI protein expression will be inhibited, and pro-
moter PllacO1 will resume its “turn on” state. This is the mechanism
of the first repressilator. b A relaxation oscillator with several positive
and negative feedback loops. CI protein promotes the expression of
itself and of LacI protein, while LacI protein inhibits the expression
of itself and of CI protein. c A relaxation oscillator with the induc-
tion of IPTG and arabinose. Arabinose promotes the expression of
AraC, GFP and LacI protein, while LacI protein inhibits the expres-
sion of itself, GFP and CI protein without IPTG. d Network architec-
ture of the proposed gene network. The relaxation oscillator includes
CII protein, FtsH protein LuxI/LuxR type QS system. e The network
architecture of a synchronized oscillation design with LuxI/LuxR
type QS system. f The circuit diagrams of two-plasmid circuit which
includes the activator and repressor plasmid. The activator plasmid
keeps activating luxI promoter to activate the expression of I-SceI in
the repressor plasmid. I-SceI can be used to negatively regulate the
expression of some cut site genes on the activator plasmid, which will
reduces its copy number
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
nuclease (I-SceI) to reduce the copy number. They combined
the negative feedback component with the positive feedback
component of lux QS system to form a synthetic gene oscil-
lator of the plasmid copy number (Fig.3f).
Most SGOs, such as those introduced above, have been
constructed to operate within single, isogenic cellular popu-
lations. Representing a step further, Prindle etal. [75] inte-
grated a genetic relaxation oscillator, lux QS system and
redox signaling (arsenite) to form coupled genetic ‘biopixels’
among different colonies (Fig.4a). This genetic circuit con-
sists of arsenite-responsive promoter, ArsR, lux QS system
and arsenite. When there is no arsenite, ArsR will repress
the expression of luxR, thus no fluorescence or oscillation
is generated. The repression will be removed when there
is a sufficient amount of arsenite, thereby the LuxR–AHL
complex will promote oscillations and the expression of the
fluorescence gene.
Compared to the synchronization of genetic oscillators by
means of coupling with standard transcription factor-based
methods such as QS devices, work on the delay times of
synchronization by competitive protein degradation is much
less [76]. Prindle etal. developed a post-translational cou-
pling platform which worked through shared degradation by
the ClpXP protease [77] to couple various synthetic genetic
modules rapidly and efficiently. This platform was used to
integrate intracellular genetic oscillators (including Plac/ara-1,
lacI and inducers) and the LuxI/LuxR-type QS system to
realize synchronization (Fig.4b).
As an example of synchronization with two QS devices,
Chen etal. [78] constructed a SGO with an “activator” strain
and a “repressor” strain to realize the emergent, population-
level oscillations of two genetically distinct E. coli (Fig.4c).
The activator produces Rhl AHL, which promotes the tran-
scription of target genes for both strains, while the repres-
sor produces Cin AHL, which inhibits the transcription for
both strains mediated by the LacI protein. Besides, there is
another negative feedback loop in which the AiiA protein
can degrade these two AHLs. These feedback loops were
divided into four types of topologies to investigate the pop-
ulation-level dynamics of these two strains.
These studies have demonstrated that incorporating QS
devices can indeed enrich the design and implementation of
SGOs. Such systems hold much potential in regulating the
synchronization of synthetic microbial consortia to benefit
real applications in metabolic engineering, particularly the
development and optimization of production pathways for
high-value metabolites, as well as some medical applica-
tions, such as the drug release process for some probiotic
therapies. On the other hand, these potential applications
may require further optimization of the QS-based SGOs
to achieve more accurate controls, beyond the feasibility
already demonstrated by the proof-of-concept studies.
QS‑based genetic toggle switches
The main objective of metabolic engineering is to increase
the yield and productivity of the desirable production
through genetic engineering [79]. As a synthetic genetic
circuit, a metabolic toggle switch (MTS) is often used to
meet this goal [13, 80]. Demonstrating a modular design
strategy, Kobayashi etal. [81] created four E. coli strains
that contained a genetic toggle switch (Fig.5a). Half of
the strains were interfaced with a transgenic QS signaling
pathway from Vibrio fischeri that detects AHLs. The genetic
circuit combined the QS mechanism and the artificial ON/
OFF genetic toggle switch. The QS circuit was composed
of the luxI, luxR and lacI genes, and would work as follows:
Fig. 4 QS for synchronization of genetic oscillators. a A genetic
relaxation oscillator consists of arsenite-responsive promoter, ArsR,
lux QS system, and arsenite. b Coupling (i) genetic oscillators which
includes promoter Plac/ara-1, lacI gene, arabinose and IPTG inducers
and (ii) LuxI/LuxR type QS system to realize the synchronization by
the ClpXP protease platform. c Genetic circuit diagrams of the activa-
tor with Rhl QS system and repressor strains with Cin QS system
S.Wu et al.
1 3
Initially, the concentration of the AHL is too low to function,
the expression of the target gene, such as gfp, is maintained
at the ‘OFF’ state; when AHL reaches a critical concentra-
tion, the gene will switch to the ‘ON’ state.
Anesiadis etal. [82] proposed an integrated computa-
tional model and showed that a genetic toggle switch can be
effectively employed in dynamic metabolic engineering to
increase bioprocess productivity and yield (Fig.5b). Anesi-
adis etal. [83] also applied the LuxI/LuxR-type QS system
to reconstruct E. coli to improve the productivity of serine
with an ON/OFF genetic toggle switch (Fig.5c). The highest
productivity of the final strain to produce serine was 29.6%
higher than that of the previous mutant strain. To further
the design, they integrated population response, dynamic
metabolic regulation, and the DFBA metabolic modeling
method to construct a mathematical model to analyze the
global sensitivity.
Although the aforementioned on–off two-stage control
strategies have taken into consideration the appropriate
cell density or sufficiently high concentrations of AHLs
into consideration to achieve the intended purposes, the
desirable gene expression often requires a more accurate
cell density to synchronize microbial growth and cellular
activity. To further improve the desirable production, novel
engineering strategies to manage the trade-off between
cell growth and desirable production are needed. Soma
etal. [10] constructed a synthetic lux system to achieve
a dynamic switch of the metabolic flux between the TCA
cycle and the isopropanol synthesis pathway (Fig.5d),
resulting in the yield and the conversion rate improved by
Fig. 5 QS for the dynamic metabolic control in Gram-negative bacte-
ria. a Genetic circuit diagram of a genetic toggle switch and QS sign-
aling pathway. b Genetic circuit diagram of the integration of LuxI/
LuxR type QS system, central carbon metabolism and a genetic tog-
gle switch in E. coli. The genetic toggle switch consists of LacI and
λCI proteins. Their expressions are inhibited mutually. The pta gene,
relevant to ethanol production, is at the downstream of λcI gene. At
low AHL concentration, λcI and pta genes express normally, while
lacI gene is inhibited. When the concentration of the Lux AHLs reach
a certain threshold, the inhibition of lacI gene will be released. Later
on, the expression of λcI and pta genes will be repressed. c Genetic
circuit diagram consists of the genetic controller for serine production
and the QS sensor. d Design of synthetic genetic circuit coupling QS
system and the genetic toggle switch. With the help of IPTG inducer,
this genetic circuit can realize the switch flexibly between two path-
ways: isopropanol synthesis and the TCA cycle. e Schematic of
dynamic control of cell growth and myo-inositol production. f Sche-
matic of two-layer dynamic control of cell growth and d-glucaric acid
production
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
3 and 2.3 times, respectively. The threshold cell density
was controlled by IPTG in this system.
Gupta etal. [8] introduced the QS circuits to control
the expression of pfk-1 gene (which determines the carbon
flux to glycolysis and cell growth) to identify the optimal
point to switch off gene expression in terms of the desired
times and cell densities (Fig.5e). When there is no AHL,
the transcriptional regulator EsaRI70V will bind to the
PesaS promoter. As cell density increases, the accumula-
tion of AHL will reduce the activity of EsaRI70V and turn
off the expression of pfk-1 gene. As a result, most of the
glucose will be switched to the target pathway to increase
the titers of myo-inositol (MI).
Doong etal. [11] combined pathway-independent and
pathway-specific strategies to form a control mechanism
that involved two orthogonal and tunable dynamic regula-
tion strategies, for the purpose of improving the produc-
tion of d-glucaric acid (Fig.5f). The pathway-independent
strategy was based on the QS system to turn glucose utili-
zation from the glycolysis to the production of d-glucaric
acid at the threshold of AHL concentration. The pathway-
specific strategy used myo-inositol as the intermediate
metabolite sensor to achieve dynamic and autonomous
control to improve the production of d-glucaric acid.
More recently, Honjo etal. [84] constructed an engi-
neered microbial community composed of an E. coli strain
producing beta-glucosidase (BGL) and the other E. coli
strain producing isopropanol (IPA) using QS-dependent
cell lysis circuits. Specifically, the BGL-producing strain
will produce BGL to convert cellobiose to glucose as the
carbon source when the desired cell density is reached
by lysing itself. The IPA-producing strain grows with the
help of the BGL release and can detect AHL produced
by the BGL-producing strain to induce the expression of
IPA. With a three-species consortium (Gluconobacter oxy-
dans–Ketogulonicigenium vulgare–Bacillus megaterium)
for vitamin C fermentation, Wang etal. [85] applied the
LuxI/LuxR QS system to control the lysis of G. oxydans as
the population level QS-based metabolic toggle switches
for l-sorbose production and for the relieve of l-sorbose
competition with K. vulgare, thus realizing one-step fer-
mentation for vitamin C.
The above studies commonly feature the combination
of synthetic QS systems and metabolic toggle switches,
allowing the synthetic genetic circuit to be activated at the
targeted cell density, and hence resulting in self-induced
metabolic states switching. With the gradual maturation of
the application of metabolic division of labor in metabolic
engineering [86], the combined applications of engineered
microbial communities and QS-induced metabolic toggle
switches appear to offer a potentially promising direction
for metabolic engineering and synthetic biology.
QS‑based logic gates
Positive and negative feedback control loops are prevalent in
physical systems. Analogously, various positive and negative
feedback regulatory structures have been found in biologi-
cal systems [87]. The amazing similarities and sophisticated
connections between the two types of systems have attracted
many researchers to develop biomolecular computing sys-
tems [88] which mainly include the design and simulation
of various genetic circuits such as logic gates [89, 90]. Logic
gates, such as Boolean logic gates, are the basic content and
computing units of digital electronic circuits. Boolean logic
gates for genetic circuits that have been involved in various
applications mainly include AND, OR, NOR, NAND and
XOR [91]. As early as 2002, an AND logic gate based on
exogenously added signals has been established, activating
the expression of GFP as the output based on the addition of
two inputs, IPTG and aTc [92]. Step by step, various logic
gates were constructed in microbes, such as AND logic gates
in Pseudomonas aeruginosa [93] and Shewanella oneidensis
[94]. Due to the ability of coordinating cell behavior at the
population level, the QS devices such as lux, las and rhl have
been combined with other genetic circuits to form various
QS-based logic gates.
QS devices can be used as “wires” to combine genetic
circuits to produce more complex computations in space, as
shown by Tamsir etal. [95]. Firstly, based on previous work
[96], they constructed the simplest NOR gates from NOT
gates with the addition of a repressor. The inputs and the
outputs of the NOR gates were designed to act as promoters
to form multiple gates. Secondly, three NOR gates and a
buffer gate in four separate E. coli cells were wired together
to form an XOR gate via Las AHL and Rhl AHL from P.
aeruginosa PAO1 (Fig.6a).
Different from the logic gates which solely rely on
two exogenously added signals such as IPTG and aTc
[97], Shong etal. [98] developed a synthetic AND gate in
response to the endogenous AHL signal and exogenously
added IPTG or aTc (Fig.6b). The esa QS system from Pan-
toea stewartii was applied to obtain the endogenous Esa
AHL signal to avoid the disadvantages of the lux QS system.
They showed that the downstream gene would not express
without a second exogenous signal, hence demonstrating the
function of this QS-dependent AND gate.
An AND logic gate combined with the lux QS system was
constructed in Shewanella oneidensis to realize the appli-
cation of a logic gate in microbial fuel cells (MFCs) [99].
They firstly integrated the IPTG responding module, a QS
module and an output module (reporter or target gene mtrA)
to form a synthetic AND gate (Fig.6c) to control extracel-
lular electron transfer in S. oneidensis. When the Lux AHL
concentration reaches a critical threshold and binds with
LuxR protein to facilitate the activation of promoter Plux,
S.Wu et al.
1 3
accompanied by the IPTG addition to release the inhibition
of Ptac by LacI protein, this AND logic gate will be switched
on to start extracellular electron transfer.
As stated earlier in “QS-based logic gates”, QS devices
have been widely integrated with the synthetic genetic toggle
switches in metabolic engineering to dynamically regulate
and control the gene expression responsible for the desired
production. As the cell’s physiological state affects meta-
bolic regulation, the stationary phase sensing system and
a QS system were combined by He etal. [100] to obtain
an auto-induced AND gate for monitoring cell growth and
polyhydroxybutyrate (PHB) production (Fig.6d). PrpoS regu-
lates gene expression in the stationary phase [101] and was
chosen here to control the transcription of HrpS, forming
one of the inputs of the AND gate. The other input was from
a QS system that controlled the expression of HrpR. Pro-
moter PhrpL, controlled by the complex of HrpR and HrpS,
was the output.
As a broadly shared paradigm, the LuxI/LuxR-type QS
system has been integrated with synthetic genetic oscillators,
genetic toggle switches, and logic gates, revealing a wide
range of possibilities and potential applications. It should be
emphasized that, although some success has been achieved
in applying QS-based engineering to specific microbes
within the scope of monoclonal synthetic biology, complica-
tions arising from factors such as metabolic load and toxicity
of metabolites could seriously limit what a single microbe
can achieve. Therefore, we anticipate that, following some
of the existing studies reviewed in this section, more explo-
rations will be published on the engineering of microbial
consortia with QS-based synthetic genetic circuits, as a prac-
tice of synthetic ecology, which realize the population-level
synchronization in synthetic communities to overcome the
limit of single species.
QS applications inGram‑positive bacteria
Gram-positive bacteria can also apply their own QS mech-
anism to regulate gene expression at the population level
dynamically. Different from the AHLs adopted in the QS
systems in Gram-negative bacteria, QS of Gram-positive
bacteria is dependent on AIPs, also known as pheromones
[3, 102]. The Gram-positive bacteria can thus sense AIPs to
regulate their own metabolism in a changing environment
[103]. Under certain conditions, AIPs of Gram-positive bac-
teria are produced in the cytoplasm and then secreted by the
oligopeptide transport system to the extracellular medium.
Thereafter, they are either detected at the bacterial surface
by the extracellular pathway or re-internalized by the intra-
cellular pathway [32].
As shown in “QS applications in Gram-negative bac-
teria”, the QS mechanisms in Gram-negative bacteria
(including lux, las QS system and so on) are relatively well
Fig. 6 The applications of QS-based logic gates. a Genetic circuit of
an XOR gate with three NOR gates and a buffer gate in four separate
E. coli colonies. Arabinose (Ara) and anhydrotetracycline (aTc) are
inputs and expression of LasI is the output for the first NOR gate in
cell 1. Based on the first LasI input, Ara and aTc are regarded as the
second inputs for the cell 2 and cell 3. The output of cell 2 and cell 3
is the expression of RhlI. The buffer gate responses to the RhlI input
to express the reporter gene (YFP). b Schematic diagram of QS-
dependent AND logic gate genetic circuits in P. stewartii. c Genetic
circuits schematic diagram of the AND logic gate in S. oneidensis.
The promoter Ptac is inhibited by LacI protein, and it can be relieved
by IPTG addition in IPTG responding module. The AND logic gate
functions by the combination of the IPTG responding module and QS
regulation of LuxR–AHL complex (QS module). d Schematic dia-
gram of the AND logic gate with the control of HrpR and HrpS in
E. coli
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
understood and have been shown to be applicable to the
design and construction of genetic toggle switches, oscil-
lators and logic gates. In contrast, QS in Gram-positive
bacteria still has a number of unknown mechanisms [104],
and there is much less research on the application of QS in
Gram-positive bacteria than that of Gram-negative bacte-
ria [41]. Recently, Marchand etal. [105] reported the first
construction of a synthetic QS system to realize cell–cell
communication among Gram-positive bacteria. They incor-
porated the agr QS system of Staphylococcus aureus into
Bacillus megaterium to synthesize a genetic circuit for
monitoring cell growth. In this circuit, the original P2 pro-
moter for the expression of agrB, agrD, agrC, and agrA
genes was replaced with the PxylA promoter. The AgrD pro-
tein was transported by ArgB and SipM protein to the cul-
ture medium to become mature AIPs, which would then be
recognized by a two-component system (TCS). The TCS
consisted of the AgrC receptor and the AgrA transcriptional
activator which were used to regulate the P3 promoter and
the expression of the target gene such as gfp reporter gene.
However, different from the aforementioned work [105],
most of the other reported AIPs systems of Gram-positive
bacteria are based on the up-regulation or down-regulation
of their own native QS devices as opposed to “borrowing”
one from a different species, as summarized in Table2.
These QS systems are disrupted and manipulated in their
own specific bacteria. Compared with the AHLs, which can
diffuse through the cell membrane freely, the secretion of
AIPs requires the aid of the oligopeptides transport system
[103]. Besides, it should be taken into consideration that the
rates of diffusion of the larger oligopeptides are slower than
the smaller AHLs, especially in a solid culture [31]. These
differences between AHLs and AIPs are arguably among the
reasons for fewer studies on the QS application with Gram-
positive bacteria.
QS forpopulation‑level control based
oninterspecies communication
QS signals can either be used by bacteria to form coopera-
tion or exploited by individuals which do not secrete them;
the latter is termed as cheating phenotypes [129]. QS cheat-
ing in microbes is one of the most important parts of QS
research. As listed in Table1, achievements on cheating
phenotypes and AHL-based social interactions have been
Table 2 Recent QS applications in Gram-positive bacteria with native devices
Pathways AIPs Bacteria species Function controlled References
Extracellular pathway Agr type peptides Clostridium botulinum Neurotoxin production and sporulation [106]
Clostridium acetobutylicum Granulose formation, sporulation [107]
Listeria monocytogenes Population dynamics in soil [108]
Clostridium perfringens Virulence and toxin production [109]
Staphylococcus epidermidis Biofilms and infection [110]
Clostridium perfringens Toxin production and virulence [111]
peptides that contain Gly–Gly motifs Streptococcus thermophilus Production of Blp Bacteriocins [112]
Streptococcus pneumoniae Competence development [113]
Streptococcus pneumoniae Competence control [114]
Streptococcus mutans Competence control [115]
Streptococcus pneumoniae Competence control [116]
Intracellular pathway Rap/NprR/PlcR/PrgX (RNPP family) Bacillus Competence control (Rap) [117]
Bacillus cereus group protease production in sporulation
(NprR)
[118]
Bacillus cereus Necrotrophism (NprR) [119]
Bacillus cereus Virulence regulation (PlcR) [120]
Bacillus cereus and Bacil-
lus thuringiensis Virulence and necrotrophic properties
(NprR)
[121]
Enterococcus faecalis Regulation of conjugation (PrgX) [122]
Rgg-like family Streptococci mutans Competence control [123]
Streptococci genus cross-talk between these different
SHP/Rgg systems
[124]
Streptococcus genus Competence control [125]
Streptococcus thermophilus Competence control [126]
Streptococci genus Competence control [127]
Streptococcus mutans Genetic competence [128]
S.Wu et al.
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extensively reviewed by Whiteley etal. [4] and Asfahl etal.
[40]. Different from the intra-species signal responses which
are mainly dependent on AHLs and AIPs (as reviewed in
“QS applications in Gram-negative bacteria” and “QS appli-
cations in Gram-positive bacteria”), the signals for inter-
species communication are mainly autoinducer 2 (AI-2)
[130–132] and indole [133, 134] that regulate cooperation
and competition in microbial communities, which is the
focus of this section.
AI‑2‑based communication
AI-2 is a product of the LuxS enzyme, which widely exists
in Gram-positive and Gram-negative bacteria, and even in
fungi [3]. LuxS enzymes synthesize 4,5-dihydroxy-2,3-pen-
tanedione (DPD), which can be regarded as the precursor
of AI-2 [135]. DPD is a by-product of the S-adenosyl-
methionine (SAM) metabolism, which is included in the
activated methyl cycle (AMC). SAM can transfer methyl
groups to methyl-transferases and substrates to produce
S-adenosylhomocysteine (SAH). Catalyzed by a series
of relevant protease, SAH can be converted to homocyst-
eine and DPD [136]. DPD is a highly active molecule that
can spontaneously cyclize into different DPD derivatives,
which can be identified as a signal molecule, AI-2, by dif-
ferent bacteria [137]. Chen etal. [131] certified that AI-2
produced by Vibrio harveyi contains boron. In contrast,
the AI-2 signals of S. typhimurium and E. coli [138] are
non-borated cyclized. There are existing reviews [136, 137]
which explain the mechanism of the AI-2 signaling systems
at length.
Xavier etal. [139] testified that AI-2 can mediate two-
way communication between E. coli and Vibrio harveyi in
co-culture. The AI-2 produced by E. coli can be sensed by
V. harveyi to induce bioluminescence, and reciprocally, the
AI-2 produced by V. harveyi can be detected by E. coli to
regulate its Lsr system. Armbruster etal. [140] found that
Haemophilus influenzae and Moraxella catarrhalis, which
are responsible for one of the common childhood infections
named otitis media, have reciprocal effects on biofilm for-
mation via the AI-2 QS signal. They pointed out that the
former promotes the biofilm formation of the latter. This
and the other studies show the potential of the exploration on
AI-2-based control to further the understanding of interac-
tions between microbes and hosts and to develop strategies
to influence the physiological and biochemical functions of
various pathogenic bacteria for curing the relevant diseases.
Indole‑based communication
With l-tryptophan as the reactant, indole is synthetized
by tryptophanase (TnaA) in many bacteria [141]. More
recently, indole is regarded as a signaling molecule which
is relevant to various bacterial physiology, such as bio-
film formation [142], plasmid stability [143], popula-
tion-based resistance [144], virulence [145], persister
formation [146], spore formation [147], and cell division
[148] in indole-producing bacteria. Yee etal. [149] had
studied biofilm formation with indole-producing bacteria
(E. coli) and non-indole-producing bacteria (P. fluores-
cens), where indole was converted to isoindigo by toluene
o-monooxygenase (TOM), with the TOM gene introduced
from the soil bacterium Burkholderia cepacia G4. Their
results indicated that E. coli was present in a higher den-
sity when co-cultured with P. fluorescens which expressed
TOM. Han etal. [150] proposed that indole oxidation by
TOM will increase the electricity generation in an E. coli-
catalyzed microbial fuel cell. Lee etal. [142] reported that
indole increases the biofilm formation of P. aeruginosa
that does not synthesize indole. Also, indole derivatives
(Indole-3-acetaldehyde) from pathogen Rhodococcus sp.
BFI 332 have been verified for inhibition to biofilm forma-
tion of E. coli O157:H7 [151]. Indole and its derivative
7-benzyloxyindole (7BOI) were investigated for their inhi-
bition to the virulence of S. aureus [152]. Lee etal. [153]
found that indole influences the growth, biofilm formation,
antibiotic tolerance, and motility of Agrobacterium tume-
faciens. Chu etal. [154] investigated the interaction and
competitiveness between E. coli and P. aeruginosa in a
mixed culture. They concluded that the major indole-based
protection for the growth of E. coli in the mixed culture
was due to direct inhibition of QS-based virulence factors,
such as pyocyanin and elastase, in P. aeruginosa. Focus-
ing on the host-microbe interactions of various bacteria
and Caenorhabditis elegans, Lee etal. [155] concluded
that indole and its derivatives will influence the egg-lay-
ing behavior, chemotaxis, and the survival of C. elegans.
Further details and examples of indole-based QS can be
found in several reviews, such as the one by Lee etal.
[133] which covered at length indole-producing bacteria
and the mechanisms and applications of the signaling sys-
tems based on indole and its derivatives. Recently, the per-
spectives on indole signaling systems have been expanded
from interspecies to inter-kingdom. For example, Lee etal.
[33] and Tomberlin etal. [156] provided overviews on
how indole and its derivatives affect various physiological
activities in fungi, insects, plants, and animals.
The functioning of AI-2 and Indole, as two main sign-
aling molecules facilitating the inter-species communica-
tion, greatly expand the presence of QS in the microbial
world. As the understanding of their mechanisms deepens,
one would expect that engineering and medical innova-
tions leveraging such knowledge will start to emerge,
despite their current relative insignificance.
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
QS applications inpotential clinical
therapies
Biolm formation andinhibition
Many bacteria have a tendency to be organized in aggre-
gates, commonly adhering to surfaces to form biofilms
[157]. Compared to the free-living counterparts, there are
some unique properties in bacteria forming biofilms, such
as antimicrobial tolerance [158]. In fact, biofilms forma-
tion has been widely regarded as one of the most impor-
tant virulence factors for microbial toxicity and infections
[159]. When there is a biofilm for bacteria within the
human host, the infections will be hard to treat.
Many studies have reported that the QS of AI-2/luxS and
DSF-based QS systems play an important role in biofilm
development and disassembly [4, 42, 160, 161]. From the
studies using flow-cell systems [160], QS and biofilms have
been shown to be inextricably linked. Lebeer etal. [162]
conducted the first investigation on the relationship between
the LuxS and biofilm formation in Lactobacillus rhamnosus
GG, which is one of the probiotics for human. They found
that LuxS enzyme is crucial for the gastric stress resistance
and the metabolism of L. rhamnosus GG. Sun etal. [163]
found that the over-expression of luxS or AI-2 supplementa-
tion enhanced biofilm formation of Bifidobacterium longum
NCC2705 by about 50%. Furthermore, exogenously addition
of signaling molecule AI-2 was found to promote the biofilm
formation of P. aeruginosa PAO1 [164], Helicobacter pylori
[165], and Staphylococcus epidermidis [166]. Laganenka
etal. [167] showed that self-produced AI-2 can mediate
autoaggregation of E. coli to enhance bacterial stress resist-
ance and promote biofilm formation. Papenfort etal. [168]
discovered 3, 5-dimethylpyrazin-2-ol (DPO) in Vibrio chol-
erae which can be regarded as a new QS signal that regulates
biofilm formation and virulence. Liu etal. [169] identified
that d-Ribose can be applied to decrease the activity of AI-2
to inhibit biofilm formation of Lactobacillus paraplantarum
L-ZS9. Besides, based on the DSF QS systems, Ryan etal.
[170] proposed that DSF underpins the interspecies signal-
ing between Stenotrophomonas maltophilia and P. aerugi-
nosa, with the latter influencing the former’s biofilm forma-
tion. Dean etal. [171] demonstrated that the Burkholderia
diffusible signal factor (BDSF) plays an important role in the
inhibition and dispersion of biofilm formed by Francisella
novicida. It has also been shown that DSF signaling regu-
lates many functions that contribute to biofilm formation
in Stenotrophomonas maltophilia [172] and Helicobacter
pylori [173]. What’s more, DSF-based QS systems can be
used to regulate antibiotic tolerance (e.g., [174]) and the
production of virulence factors (e.g., [175]); this area has
been reviewed at length [176].
Given the close connections between QS and biofilm
formation, the development of novel antimicrobial thera-
pies by QS inhibitors has attracted extensive attention from
researchers [177]. QS inhibitors are functioned by the deg-
radation of QS signals (quorum quenching) [178] or some
other QS-blocking approaches. Shen etal. [179] synthesized
a series of structural analogues of the substrate S-ribosyl-
homocysteine (SRH) and a 2-ketone intermediate to inhibit
LuxS enzyme. Zhang etal. [180] proved that two small pep-
tides, 5411 and 5906, could inhibit the AI-2 activity and
influence biofilm formation and virulence of Edwardsiella
tarda. Ni etal. [181] reviewed seven approaches to inhibit-
ing QS pathways. Brackman etal. [182] certified that cinna-
maldehyde and cinnamaldehyde derivatives can interact with
the AI-2 QS signal by reducing the DNA-binding ability
of LuxR. Brackman etal. [183] investigated the relation-
ship between the susceptibility of biofilms to antibiotics and
the antibiofilm effect of quorum sensing inhibitors (QSI)
invitro and invivo model systems. Christensen etal. [184]
discovered, by an high-throughput cell-free screen, three
AHLs inhibitors which can be used as potential therapeutic
agents for virulence and microbial infections. O’Loughlin
et al. [185] reported that the meta-bromo-thiolactone
(mBTL) can not only inhibit the production of pyocyanin
and biofilm formation but also reduce the activity of two
QS receptors, LasR and RhlR, in P. aeruginosa. Starkey
etal. [186] identified several compounds as QSI (all with
the structure of benzamide-benzimidazole) which can inhibit
the expression of the mvfR QS system which is one of the
key reasons for multidrug-resistant and antibiotic-tolerant
infections. Ouyang etal. [187] also reported that quercetin
can be applied to inhibit biofilm formation and virulence
factors in P. aeruginosa.
Consortia‑based therapies forP. aeruginosa
infection
Pseudomonas aeruginosa, a multidrug resistant pathogen,
can cause disease in plants and animals, including humans
[188]. Biofilms of P. aeruginosa can cause chronic oppor-
tunistic infections, especially for immunocompromised
patients and the elderly. These biofilms also appear to pro-
tect the bacteria from traditional antibiotic therapies [189].
Therefore, research on the discovery of new treatments, such
as consortia-based therapies against P. aeruginosa is much
needed. In particular, engineering microbial consortia with
genetic circuits based on QS devices to inhibit biofilm for-
mation offers a potentially attractive therapeutic technique
to deal with the infectious pathogens.
Taking the potential applications in bioremediation
(among other applications) into consideration, Hong
etal. [23] combined the LasI/LasR-type QS system from
P. aeruginosa with biofilm dispersal genes to control
S.Wu et al.
1 3
biofilm displacement in a microfluidic device. The engi-
neered microbial consortia consisted of disperser cells
and initial colonizer cells. In a disperser cell, the LasI
protein which is the precursor of the signaling molecule
Las AHL is produced continuously, and the biofilm-dis-
persing Hha13D6 protein is encoded when the relevant
gene is induced by IPTG. In an initial colonizer cell, the
LasR protein is encoded, which couples with Las AHL
to form a complex to promote the expression of another
biofilm-dispersing protein, BdcAE50Q (Fig.7a).
Saeidi etal. [190] integrated the LasI/LasR-type QS
system, a lysing device for engineered E. coli, and pyocin
for killing P. aeruginosa. The Las AHL is produced by P.
aeruginosa and detected by the engineered E. coli. When
the Las AHL concentration reaches a threshold, the Las
LasR–AHL complex promotes the transcription of PluxR
and then facilitates the expression of pyocin S5 and lysis
E7 genes. E7 lysis protein will accumulate and then lyse
E. coli cell to release S5 pyocin to inhibit biofilm forma-
tion and kill P. aeruginosa (Fig.7b).
In a further study, Gupta etal. [191] integrated a secre-
tion module to the genetic circuit “sense-kill” system for
P. aeruginosa. They applied a novel pathogen-specific
bacteriocin CoPy, the combination of Colicin E3 and
Pyocin S3 to kill P. aeruginosa. What’s more, they uti-
lized a secretion tag, FlgM, to transport the bacteriocin
CoPy into the culture to kill P. aeruginosa (Fig.7c).
Hwang etal. [192] combined the LasI/LasR-type QS
system, motility control and a killing device to form a
novel genetic circuit engineered into E. coli to kill P.
aeruginosa. The Las LasR–AHL complex promotes the
expression of gene lasI, gene cheZ (controlling the motil-
ity of E. coli toward P. aeruginosa), gene Dnasel (control-
ling biofilm degradation of P. aeruginosa), and gene mcsS
(killing P. aeruginosa) (Fig.7d).
Based on a previous work [190], Hwang etal. [193]
introduced an auxotrophic marker in E. coli to avoid the
horizontal gene transfer of the antibiotic resistance to
other bacteria. The alr and dadX genes which help the
interconversion of d-alanine and l-alanine were knocked
out in the novel engineered E. coli. To construct a modi-
fied “sense-kill” system for P. aeruginosa, they first com-
plemented the auxotrophic E. coli with an alr + plasmid
(pEaak) to ensure the growth and other physiological
activities and then added the dspB gene to the previous
genetic circuits (Fig.6b) to inhibit the biofilm forma-
tion more efficiently. The dspB gene encodes dispersin B
(DspB), an anti-biofilm enzyme for degrading mature bio-
films. It was proven that combining the dspB and pyoS5
genes together to disassemble biofilm formation is an
efficient strategy to kill P. aeruginosa (Fig.7e).
Fig. 7 The applications of QS devices relevant to biofilm formation. a
Diagram of genetic circuits of disperser cell and initial colonizer cell.
b Schematic of genetic circuit coupling QS, killing, and lysing sys-
tems to kill P. aeruginosa. c Genetic circuit architectural of “sense-
kill” system of P. aeruginosa with transport system of the bacteriocin
CoPy. d Schematic of engineering E. coli to sense, migrate and kill P.
aeruginosa. e Diagram of novel “sense-kill” genetic circuit coupling
QS, dspB and pyoS5 gene (for killing), and lysing systems to kill P.
aeruginosa
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
Probiotic therapies fordiseases relevant togut
microbiota
Gut microbiota has been shown to clearly relate to a range
of diseases and conditions of human, such as type 2 diabe-
tes [194], cardiovascular disease [195], clostridium difficile
infection (CDI) [196], epithelial tumors [197] and obesity
[198]. With the large quantities of antibiotics widely used
in the past decades, antibiotic resistance is currently ubiqui-
tous and hard to deal with, especially in the human gut [20,
199, 200]. Much research has thus been focusing on finding
alternative antimicrobial therapies. Taking S. typhimurium,
enterohaemorrhagic E. coli (EHEC) and Clostridium difficile
as representative microbes, Bäumler etal. [60] reviewed the
interactions among the microbiota, the host and these three
pathogenic bacteria when treated by antibiotics. Antibiot-
ics treatment appears to increase free sialic acid (from the
host) and succinate (from the microbiota) level. The elevated
sialic acid and succinate in turn promote the expansion of
the S. typhimurium and C. difficile, which will do harm to
the intestinal epithelium cells (IECs). Besides, EHEC was
found to use a fucose-sensing signaling-transduction QS
system [201] to avoid the nutrient competition with com-
mensal E. coli. To avoid the defect of antibiotics treatment
leading to antibiotic resistance, multiple attempts have been
made to develop probiotic therapies which utilize probiotic
bacteria such as lactic acid bacteria [54] to serve as vectors
for delivery of drug and signaling molecules [202]. As one
of the most important ways for cell–cell communication,
QS devices hold enormous potential in sensing the patho-
gens (stated in “Consortia-based therapies for P. aeruginosa
infection”) for probiotic therapies.
Diarrhoeal diseases, caused by the invasion of Vibrio
cholera, are nightmares for both children and adults [203].
Co-culturing the Ruminococcus obeum and Vibrio cholera
in AKI medium, Hsiao etal. [204] found that the patho-
genicity of V. cholerae was reduced by AI-2 of R. obeum.
The results of experiments in the gnotobiotic mice model
illustrated that the virulence of V. cholerae was regulated via
a novel regulatory pathway in R. obeum. It relates to a new
mechanism based on the VqmA virulence regulator rather
than the known pathway based on HapR.
Considering that the gut microbiota mainly includes
Bacteroidetes and Firmicutes, Thompson etal. [27] chose
these two microbes to investigate the influence of AI-2 on
the ratio of them. Firstly, they used streptomycin to induce
gut dysbiosis, which had previously been applied to inves-
tigate the relationship between streptomycin treatment and
colonization resistance in intestinal E. coli [205]. They sub-
sequently engineered E. coli to manipulate the AI-2 level in
the mouse intestine and investigated the influence on strep-
tomycin-induced dysbiosis. By increasing the level of AI-2,
the ratio of Firmicutes and Bacteroidetes was increased so
as to relieve the strong effect of the antibiotic and restore the
dysbiosis (Fig.8a). Analogously, Xavier etal. also proposed
that AI-2 can make some difference on the composition of
gut microbiota in mouse [206].
Lactococcus lactis and Enterococcus faecalis are ubiq-
uitous in human gastrointestinal tract [207]. Borrero etal.
[208] proposed using L. lactis, generally recognized as safe
(GRAS) for human, to kill E. faecalis which is responsible
for hospital-acquired infections such as enterococcal infec-
tions [209]. In this bi-directional system, L. lactis produces
three antimicrobial peptides (AMPs), enterocin A, hiracin
JM79 and enterocin P, to inhibit the growth of E. faeca-
lis, including vancomycin-resistant enterococcus (VRE)
strains. E. faecalis produces and secrets the sex pheromone
cCF10 (as an AIP) to be detected by the engineered L.
lactis (Fig.8b). When the concentration of the sex phero-
mone cCF10 expressed by E. faecalis reaches the thresh-
old, it will be imported by PrgZ protein and oligopeptide
permease (Opp) system of the engineered L. lactis into its
cytoplasm. cCF10 will then integrate with PrgX protein to
form a PrgX–cCF10 complex which increases RNA poly-
merase access to PQ [104] and enhances the expression of
the downstream antimicrobial peptides to kill E. faecalis.
As concluded by Coyte etal. [210], understanding the
interactions between microbes, especially for the competi-
tion and cooperation among pathogens and probiotics, is
key to revealing the mechanisms of gut microbiota-related
diseases. To understand these complex processes, the studies
reviewed above show that it is important to recognize the
Fig. 8 The applications of QS devices in the gut microbiota. a Bal-
ance of the gut microbiota. Once treated by antibiotics such as strep-
tomycin, Firmicutes and AI-2 producing will reduce, while metabo-
lite such as sialic acid, succinate and fucose will increase in the gut
microbiota. Artificially increasing the levels of AI-2 produced by
engineered E. coli reliefs the dysbiosis, increases the ratio of Firm-
icutes, and reverses state partially. b Diagram of genetic circuits of
applying Lactococcus lactis to sense and kill Enterococcus
S.Wu et al.
1 3
QS signaling molecules as the language or bonds that link
together the members of the microbiome, and also as the
bridge for between the bacteria and the host [211].
Therapies based onCRISPR‑Cas immune systems
Bacteria often suffer from invasion by foreign mobile genetic
elements such as bacteriophage infestation [212] and plas-
mids conjugation [213] which forms a basis for developing
potential therapies to treat human diseases caused by patho-
genic bacteria. Bacteria possess natural defense systems,
named CRISPR-Cas immune systems [76], to reject foreign
phages or plasmids (Fig.9). The regulation of CRISPR-Cas
immune systems can occur during different stages of an inte-
grated network which involves pre-emptive warning, first
contact, breaking the silence, detecting infection, and dedi-
cated regulators [214]. Due to infections of phage increase at
high cell density [28], QS devices have much potential to be
applied to monitor the infections [29]. It is costly to continu-
ously defend the whole CRISPR-Cas immune systems with
an integrated network of various sensors [14]. Therefore,
combining with QS devices, microbes can be “instructed”
to regulate CRISPR-Cas immune systems only at high cell
density, hence minimizing the cost. This is in line with the
work of many researchers.
In response to phage and plasmids invasion, this is in line
with the work of Rossmann etal. [56] which proposed that
AI-2 regulates enterococcal pathogenicity and induces the
horizontal gene transfer (HGT) of virulence genes by con-
jugation or transformation from phages to the commensal
enterococci.
Høyland-Kroghsbo etal. [59] demonstrated that the
CRISPR-Cas activity can be regulated by QS devices in the
pathogen P. aeruginosa. The type I-F CRISPR-Cas system,
LasI/LasR- and RhlI/RhlR-type QS devices were utilized
to analyze the consequence of QS regulations on CRISPR-
Cas activity. When lasI and rhlI genes were knocked out,
the mutant exhibited obvious decreases in the expression of
CRISPR-Cas relative to the wild type. Also, CRISPR-Cas
activity restored to wild type levels when certain auto-induc-
ers were artificially replenished. Therefore, it is possible to
suppress the CRISPR-Cas immune system by QSIs as a cost-
effective way to promote the killing of the pathogens by the
phage.phage therapies.
Patterson etal. [215] combined the SmaI/smaR-type QS
system and homologs of the LuxI/LuxR-type QS system
with type I-E, I-F, and III-A CRISPR-Cas systems, respec-
tively, to investigate the regulation effect of QS on HGT in
Serratia sp. ATCC39006. The signaling molecule Sma AHL
is produced by smaI gene and bonded with SmaR protein
to form a SmaR–AHL complex. When the concentration
of Sma AHL, Lux AHL homologs, is low at low cell den-
sity, the SmaR transcriptional regulator will act as a DNA-
binding repressor for the CRISPR-Cas operon. As the cell
density increases, Sma AHL accumulates in the culture and
eventually reaches sufficiently high concentrations, and the
SmaR–AHL complex will then inhibit the DNA binding
activity of SmaR, which causes the expression of CRISPR-
Cas to increase (Fig.9).
The population-level resistance of bacteria upon invasion
by foreign phages or pathogenic bacteria is commonly con-
sidered as important for maintaining the healthy state of the
microbiome [214]. The development QS-based CRISPR-Cas
technologies such as those reviewed above thus have the
potential to bring useful additions to the toolbox for realizing
population-level resistance.
Mathematical modelling forQS applications
Complementing experimental explorations and supported by
the richness of biological information, a variety of synthetic
genetic circuits and established databases [216], mathemati-
cal modelling has been widely applied in systems biology
and synthetic biology to achieve systematic understandings
of cellular behavior [217] and to optimize TYR for desir-
able products in engineering applications [218]. In particu-
lar, various modeling methods, such as flux balance analysis
(FBA) [219], dynamic flux balance analysis (DFBA) [220],
and sensitivity analysis have been combined with ordinary
differential equations (ODE) [221–223] to construct math-
ematical models for QS. As listed in Table1, there exist
comprehensive reviews of deterministic and stochastic mod-
els and modelling approaches for QS devices, particularly
of their molecular mechanisms. Not repeating these exist-
ing reviews, this section is intended to focus on the typi-
cal approaches and recent achievements of mathematical
Fig. 9 The schematic of QS regulation on CRISPR-Cas systems in
Serratia. Mechanisms have been described in the text. The more spe-
cific introduction for the mechanisms of CRISPR-Cas immune sys-
tems can be found in the review paper (Patterson etal. [214])
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
modelling of the applications of QS for bacterial population
control as reviewed earlier in this work.
QS applications modelling inmonoclonal synthetic
biology
As a commonly shared paradigm for QS modelling, the
LuxI/LuxR-type QS has been modelled by a number of
researchers. To predict the function of the circuit illustrated
earlier in Fig.2a (and explained in “QS-based synthetic
genetic oscillators”), You etal. built a mathematical model
which includes cell growth, cell death, production and deg-
radation of CcdB protein, and change of AHL concentration
in this system [50]. The equations of the model are shown in
Eqs.1a–1c, which are derived following five assumptions:
(a) Without IPTG induction, cell density changes will fol-
low the logistic model;
(b) When induced by IPTG, the rate of cell death will be
proportional to the concentration of CcdB protein;
(c) The generation rate of CcdB protein is proportional
to the concentration of AHL, and the intracellular and
extracellular AHL concentrations are equal;
(d) The generation rate of AHL is proportional to cell den-
sity;
(e) Degradation of CcdB protein and AHL follows a first-
order kinetics.
A number of QS models similar to the above have sub-
sequently been developed, making predictions based on the
capture of the interactions between cell density, concentra-
tion of AIs, complex of AIs and their correspond receptors
[44].
In principle, QS is not only dictated by reactions of vari-
ous molecules but also affected by diffusion. Basu etal.
[224] designed a synthetic multicellular system with QS
devices, which consists of “sender” and “receiver” cells. Due
to the diffusion of AHL, the concentration of AHL gener-
ated by the “sender” decreases gradually from the cell to
the periphery. Consequently, the “receiver” cells in different
regions respond to different concentrations of AHLs and
express different colors of fluorescent protein, thus forming
different colors and different ring-like patterns.
(1a)
dN
dt
=kN
(
1−
N
Nm)
−d[E]N
,
(1b)
d[A]
dt
=vAN−dA[A]
,
(1c)
d[E]
dt
=kE[A]−dE[E]
.
Building on the two models mentioned above, Anesiadis
etal. [82] constructed a mechanistic model (Eqs.2a–2e)
for investigating the dynamics of the genetic circuit includ-
ing a genetic toggle switch (introduced earlier in Fig.5a).
The same research group [83] further integrated the QS
model (Eqs.2a–2e) and a DFBA model to maximize serine
production.
As shown earlier in Fig.5e and explained in “QS-based
genetic toggle switches”, a synthetic genetic toggle switch
can be designed and applied for MI production (Eqs.3a–3c).
Gupta etal. [8] modified the population control equation
Eq.1a by removing the lysis term and adding a variable
which denotes the fractional Pfk-1 activity to predict the
circuit’s function.
QS applications modelling insynthetic ecology
Based on the aforementioned relatively simple models
for monoclonal synthetic biology, several more complex
models for combinatorial quorum sensing [70] in synthetic
ecology have been reported. Building on the model of You
etal. [50], Balagadde etal. [51] modelled the dynamics of
a synthetic E. coli predator–prey system, which includes
two QS devices (lux QS and las QS) (introduced earlier in
Fig.2b). Further, Song etal. [225], from the group of You,
expanded their model to investigate the spatiotemporal
(2a)
dX
dt
=kX
,
(2b)
d[A]
dt
=vAX−dA[A]
,
(2c)
d[R]
dt
=𝜌R[LuxR]2[A]2−dR[R]
,
(2d)
d
[L]
dt=𝛼L1
1+
(
[C]
∕𝛽C
)
n1+𝛼L2 ⋅R
n2
(𝜃R)n2+Rn2−dL[L]
,
(2e)
d[C]
dt=𝛼C
1+
(
[L]
∕𝛽L
)
n1−dC[C]
.
(3a)
d
N
dt
=FPfk ⋅kN
(
1−N
FPfk ⋅Nm
),
(3b)
d[A]
dt
=vAN−dA[A]
,
(3c)
d
FPfk
dt=
(
−Kd
(Kd +[A])
2
)
×(vAN−dA[A])
.
S.Wu et al.
1 3
dynamics of the predator–prey system by adding the fac-
tors of chemical diffusion, cellular motility, and nutrient
consumption. To study the dynamics of emergent genetic
oscillations in a synthetic microbial consortium (intro-
duced earlier in Fig.4c), Chen etal. [78] developed a
model depicting the system with three compartments,
namely the intracellular space of the activator strain, the
intracellular space of the repressor strain, and the extra-
cellular space, to reduce the difficulty of modelling. Scott
etal. [55] combined agent-based modeling and determin-
istic modeling to describe the population-level dynamics
of synchronized oscillations (introduced earlier in Fig.2d).
Based on the models derived from two-strain consortia,
Kong etal. [226] developed models to investigate the
dynamics of three- and four-strain ecosystems induced by
nisin, a QS molecule of Gram-positive bacteria.
QS stochastic modelling
Stochastic models for QS are needed to account for the
natural stochasticity in gene expression. In particular,
when the rates of gene expression are relatively low
and the amount of the reactants is relatively small, the
effects of stochasticity on the system’s behavior cannot
be ignored [227]. It thus becomes necessary to adopt a
stochastic model to more faithfully capture the nature of
genetic circuits such as synthetic genetic oscillators [56,
58]. Tian etal. [228] recoganized the importance of noise
in the switching of bistable systems, such as the QS-based
genetic toggle switch (Fig.5a). They developed quantita-
tive stochastic models for large-scale genetic regulatory
networks by introducing Poisson random variables into
deterministic models to analyze the influence of noise. The
model developed by Baumgart etal. [74] considered the
dynamics of the concentrations of LuxI, I-SceI and GFP,
and was used for conducting robustness analysis of a sto-
chastic process that involved the positive feedback of QS,
negative feedback of plasmid copy number, and intracel-
lular delay in feedback, which were integrated to describe
the oscillator in DNA copy number control (mechanism
shown in Fig.3f).
The modelling studies reviewed in this section show that
lumped-parameter (i.e., well-mixed), deterministic models,
which are relatively simple, can already predict the effect
of applications of QS-based genetic circuits in metabolic
engineering and to offer useful insights on the dynamics of
such systems. On the other hand, more advanced modelling
schemes, such as those taking into account spatial heteroge-
neity and stochasticity, could offer more faithful and more
detailed representation, which would become particularly
important when such complexities play a decisive role in
shaping the behavour and function of a QS-based system.
Summary andfuture perspectives
QS devices have been shown to be able to play a key role
in engineered dynamic control of metabolism and various
microbial physiological activities, such as the TYR (titer,
yield and rate) increase of desirable metabolites, microbiota
synchronization, and regulation of sporulation, virulence,
competence and toxin production. These applications of QS
have been realized by five main types of signaling molecules,
namely AHLs, DSFs, AIPs, AI-2 and indole. Genetic oscil-
lators, toggle switches and logic gates have been constructed
by coupling with AHL-based QS devices in Gram-negative
bacteria. With Gram-positive bacteria, both extracellular
and intracellular pathway AIP-based QS devices have been
developed for controlling physiological activities. AI-2 and
indole, on the other hand, can be regarded as languages
of communication in microbial communities. Engineered
applications of QS in microbes can be either “constructive”
(through introducing new/foreign or enhancing existing QS
mechanisms) or “destructive” (through inhibiting existing
QS mechanisms). In constructive cases, the QS devices
could be either natural or synthetic. Furthermore, a synthetic
QS device can be either constructed partly from naturally
occurring QS modules or synthesized from scratch. Among
other areas, both constructive and destructive QS applica-
tions have found increasing potential in developing clinical
therapies for a range of diseases caused by biofilm forma-
tion, antibiotic resistance and phage invasion.
For QS to be more effective and more widely applicable,
further work is needed to address several general challenges.
First, the target QS modulators must recognize precisely the
corresponding signals in an extremely miscellaneous pool.
Second, compared to the long-range effective electrical sig-
nals [229, 230], quenching problems are common in chemi-
cal signals in QS devices; answering questions such as how
to realize the spatiotemporal and tempo control of signal-
ing molecules is essential, too. Third, there are couplings or
crosstalk between different QS signals and receptors (e.g.,
AHL-based QS and indole-based QS affect each other);
understanding how to decouple these pathways is necessary
and important to achieve their applications with larger scale
genetic oscillators in demanding tasks such as investigat-
ing the population-level dynamics of microbes. Fourth, the
cheating behaviors aggravate the complexity of microbial
communities; the questions of how to constrain cheaters
when desirable and how to exploit cheating behaviors to
inhibit QS are not only theoretically interesting for microbial
ecology but also practically important for the development
of QSIs. Finally, nonlinearities and stochasticity need to be
considered and resolved appropriately in QS modelling.
With synthetic biology making great leaps forward,
the future perspectives of QS applications are expected
Quorum sensing forpopulation-level control ofbacteria andpotential therapeutic…
1 3
to keep pace with it. Currently, most of the engineered
dynamic regulation mechanisms build on AHL-based QS
devices of Gram-negative bacteria. In contrast, existing
QS-based interventions in Gram-positive bacteria are
mostly in the form of up-regulating or down-regulating
naturally occurring processes, and there is very limited
research on synthetic QS in this type of bacteria. On the
other hand, Gram-positive bacteria, such as Lactococ-
cus lactis have been viewed as important candidates for
building engineered microbial cell factories and vaccine
delivery systems, and they play an irreplaceable role in
metabolic engineering and medical applications, such as
producing dairy fermentations [231, 232], industrial prod-
ucts and various vaccines [233]. Given their significance,
more research is needed to discover, study and apply the
QS mechanisms in Gram-positive bacteria. In particular,
one may expect that there is great potential to couple cer-
tain synthetic TCS-based or AIP-based QS devices with
the original pathways in Gram-positive bacteria to improve
the TYR of desirable products in Gram-positive cell facto-
ries and to develop future therapeutic systems.
As a broad perspective for future research, the explora-
tion of QS for population-level control of bacteria and its
potential in therapeutic applications may proceed in hori-
zontal and vertical dimensions, respectively. Horizontally,
QS applications can be centered on multi-circuit systems
(the combination of various QS-based genetic circuits),
multi-production (two or more products from one single
cell), and microbial communities (engineering microbiota
to produce one or more products). Vertically, QS applica-
tions in medical therapeutics have the potential to advance
by addressing the interactions between microbes (virus,
bacterium and fungi) and the host [212, 234–236]. The
understanding of these interactions will accelerate the
development of more reliable strategies for manipulating
the microbiota against infectious diseases and antibiotic
resistance, which is of great significance for human health.
In the future horizontal and vertical developments of QS
applications, mathematic models are expected to continue
their supporting role to construct QS networks, by not only
embracing nonlinearities and stochasticity, but also inte-
grating across component, circuit, cellular and community
levels and addressing the interactions between microbes
and their hosts and other environments, to offer the power
of making holistic predictions.
Acknowledgements This study was supported by the National Key
Research and Development Project of China (2017YFD0201400), the
National Natural Science Foundation of China (31570089, 31170076),
and the Funds for Creative Research Groups of China (21621004), Dr.
Jianjun Qiao was supported by The New Century Outstanding Talent
Support Program, Education Ministry of China.
Compliance with ethical standards
Conflict of interest The authors declare no competing financial inter-
ests.
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