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FEMS Microbiology Reviews , 2024, 48 , fuae026
DOI: 10.1093/femsre/fuae026
Ad v ance access publication date: 4 November 2024
Re vie w Article
Assembly of functional microbial ecosystems: from
molecular circuits to communities
Shengbo Wu
1 ,2 ,‡
, Yo ngs hen g Zhou
1 ,2 ,‡
, Lei Dai
3 ,*
, Aidong Yang
4 ,*
, Jianjun Qiao
1 ,2 ,*
1
School of Chemical Engineering and Technology, Ti anjin University, Tia nji n, 300072, China
2
Zhejiang Institute of Tianjin Uni versity, Shao xing, 312300, China
3
CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy
of Sciences, Shenzhen, 518055, China
4
Department of Engineering Science, University of Oxfor d, Oxfor d, O X1 3PJ, UK
∗Corresponding authors . Lei Dai, C AS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Xili Shenzhen University Town , Nanshan District, Shenzhen, Guangdong, 518055, China. E-mail:
lei.dai@siat.ac.cn ; Aidong Yang, Department of Engineering Science, Wellington Squar e, Univ ersity of Oxfor d, Oxfor d, O X1 2JD, UK. E-mail:
aidong.yang@eng.ox.ac.uk ; Jianjun Qiao, School of Chemical Engineering and Technology, P eiy ang Park Campus, Ti anjin University, No.135 Ya guan Road, Haihe
Education Park, Tia nji n, 300072, China. E-mail: jianjunq@tju.edu.cn
‡
These authors contributed equally to this work
Editor: [Claudio Avignone Rossa]
Abstract
Microbes compete and cooperate with each other via a variety of chemicals and circuits. Recently, to decipher, sim ulate, or r econ-
struct microbial communities, many resear c hes have been engaged in engineering microbiomes with bottom-up synthetic biology
approaches for diverse applications. Howe ver, the y ha ve been separately focused on individual perspectives including genetic circuits,
comm unications tools, micr obiome engineering, or pr omising applications. The str ate gies for coordinating microbial ecosystems
based on different re gulation cir cuits have not been systematically summarized, which calls for a more comprehensive fr amew ork
for the assemb l y of micr obial comm unities. In this r e vie w, we summarize di v erse cr oss-talk and orthogonal r egulation modules for
de novo bottom-up assembling functional microbial ecosystems, thus promoting further consortia-based applications. F irst, w e re-
view the cross-talk communication-based regulations among various microbial communities from intra-species and inter-species
aspects. Then, orthogonal regulations are summarized at metabolites, tr anscription, tr anslation, and post-tr anslation lev els, r espec-
ti v el y. Furthermor e, to gi v e mor e details for better design and optimize v arious micr obial ecosystems, we pr opose a mor e compr e-
hensi v e design-build-test-learn pr ocedur e including function specication, chassis selection, interaction design, system build, per-
formance test, modeling analysis, and global optimization. F inally, current c hallenges and opportunities are discussed for the further
development and application of microbial ecosystems.
Ke yw ords: micr obiome engineering; signal cr osstalk; orthogonal r egulations; cell-cell comm unication; synthetic micr obial consortia;
synthetic ecology
Highlights:
r Cross-talk circuits should be considered as valuable toolk-
its in synthetic biology.
r Optimization of circuits and communities should be con-
ducted sim ultaneousl y.
r Pr opose a compr ehensiv e pr ocedur e to enhance the exist-
ing DBTL cycle.
r Pr ovide perspectiv es for de v eloping pr omising consortia-
based applications.
Introduction
Micr obes al ways exist as comm unities (Sepic h-Poor e et al. 2021 ),
where the use of diverse chemicals (Chen et al. 2018 ) and genetic
de vices (K enn y and Balskus 2018 ) is in competition among v ari-
ous cells. In recent decades, to decipher (Wu et al.
2020 ), simulate
(Shetty et al. 2022 ), or reconstruct (Cheng et al. 2022 ) natural mi-
cr obial comm unities, numer ous r esearc hers hav e been enga ged in
engineering microbiomes with the help of articial intelligence,
m ulti-omics tec hniques (Wu et al. 2022a ), and synthetic biology
a ppr oac hes (Kumar et al. 2022 ). Tr aditionall y, due to the exces-
sive metabolic burden, the unbalanced metabolic ux distribu-
tion limits the cell growth and productivity of a single-cultured
microbe (Zhou et al. 2015 ). With a better understanding of mi-
cr obial inter actions, ecology, and e volution, mor e r esearc hers ar e
focusing on engineering different microbiomes. Although some
consortia-based str ategies, suc h as the cr oss-feeding contr ol, can
ease the k e y bottleneck of metabolic labor to a certain extent
(Holtz and Keasling 2010 ), the imbalance of the resource alloca-
tion within the cell calls for new solutions (Hartline et al. 2021 ).
Due to the dynamic changes of biological systems and the com-
plexity of microbial communities, it is necessary to design corre-
sponding contr ol str ategies to r egulate , coordinate , and stabilize
Recei v ed 30 J an uar y 2024; revised 15 Au gust 2024; accepted 17 October 2024
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2 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
micr obiomes for differ ent a pplications (Li et al. 2022 ), suc h as eas-
ing the metabolic division of labor (Wang et al. 2022a ), increasing
genetic stability (Liao et al. 2019 ), r esisting envir onmental distur-
bances (Xiao et al. 2020 ), and increasing the efciency of biopro-
cesses (Shahab et al. 2020 ). The regulations for various microbial
consortia can be br oadl y divided into cr oss-talk and orthogonal
strategies (Wu et al. 2022b ).
Cr oss-talk r egulations exist widel y in natur al micr obial ecosys-
tems, such as cell-cell communications among human gut
ecosystems (Mour a-Alv es et al. 2019 ). Gut microbes participate
in a wide range of cross-talk communications through different
signaling molecules and hormones, and the analysis of r ele v ant
pathwa ys ma y serv e as potential ther a peutic tar gets (Li et al.
2019 , To da et al. 2019 ). For example, natural quorum sensing (QS)
cr osstalk, whic h includes signal cr osstalk, pr omoter cr osstalk, and
combined crosstalk (Scott and Hasty 2016 ), is common among
the communications of diverse gut microbes (Wu et al. 2022a ). It
is reported that QS language crosstalk is potentially helpful for
the stability of a microbial ecosystem by using synthetic quorum-
r egulated l ysis (Scott et al. 2017 ). Pr e viousl y, our r esearc h gr oup
also found that QS crosstalk would benet the impr ov ement
of isopropanol production in QS-based cocultivations (Wu et al.
2022b ).
Orthogonal regulations are often utilized in developing mul-
tiple intercellular comm unication c hannels for r epr ogr amming
cells with minimizing signal interference on information dissem-
ination, increasing the delity of signal transmission, and ul-
timatel y impr oving pr ogr amming efciency (Kylilis et al. 2018 ).
Note that the predictable expression of synthetic gene circuits
should reduce the internal and external noise as m uc h as possible
to ac hie v e the stability and contr ollability of the system. Further-
mor e, mor e and more designs of the congurations are being pro-
posed for engineering microbiomes by using different orthogonal
comm unication c hannels to obtain mor e pr edictiv e and pr ecise
population dynamics (Lopatkin and Collins 2020 ). For example,
Miano et al. have developed an inducible QS system mediated by
p-coumaric acid, which expands the range of population dynam-
ics, ac hie ving the contr ol of car go r elease and population death
(Miano et al. 2020 ). Ther efor e, man y r esearc hers hav e been en-
gaged in constructing diverse orthogonal circuits or engineering
microbiomes to perform expected cell beha viors , suc h as coa ggr e-
gation bridging pattern formation (Glass and Riedel-Kruse 2018 ),
population sync hr onization, and metabolic ux contr ol (Wu et al.
2021c ), etc.
Certainl y, the incr easing shifting of mono-cultur e synthetic bi-
ology to consortia-based synthetic ecology is leading to an in-
tense effort into the de v elopment of a systematic fr ame work to
guide the design and synthesis of different genetic circuits and
complex ecosystems sim ultaneousl y. While some typical r e vie ws
have been published, they have been separately focused on ge-
netic circuit design (Hirschi et al. 2022 , Yu et al. 2023 ), communi-
cation tools (Stallforth et al. 2023 ), microbiome engineering (van
Leeuwen et al. 2023 , Li et al. 2023b ), or summaries for various ap-
plications (Tan et al. 2021 , Jiang et al. 2023b ). Furthermore, exist-
ing studies have often focused on developing various orthogonal
strategies to construct circuits, while the quantitation or sum-
mary for cross-talk strategies is commonly neglected or ignored.
Importantl y, cr osstalk is widely present in biosystems and plays
an important role in the stability of the microbiome, which indi-
cates that cross-talk regulations are indispensable for the assem-
bly of microbial ecosystems.
This r e vie w aims to summarize div erse cr oss-talk and orthog-
onal regulation circuits for the de novo bottom-up assembling of
functional microbial ecosystems from molecular circuits to com-
m unities, thus pr omoting further consortia-based a pplications.
First, we introduce the development of cross-talk and orthogo-
nal regulation circuits in the advancing of synthetic biology tool-
bo xes, respecti vely. Then cross-talk regulations are summarized
at intra- and inter-species le v els. Orthogonal r egulations ar e r e-
vie wed fr om four aspects , i.e . metabolites , tr anscription, tr ansla-
tion, and post-tr anslation, r espectiv el y. Furthermor e, considering
the combinations of cross-talk and orthogonal k e y elements, we
propose a more comprehensive design-build-test-learn (DBTL) cy-
cle to pr ovide mor e details for the de novo synthesis and optimiza-
tion of various microbial ecosystems. Finally, we discuss current
challenges and opportunities for the further development of mi-
crobial ecosystems.
Cr oss-talk regula tion circuits
Micr obes hav e ada ptiv el y e volv ed complex and highl y inter activ e
network congurations to cope with the complex and changeable
environment (Faust and Raes 2012 ). Particularly, there are diverse
cr oss-talk r egulation circuits in the signaling processes mediated
by different components, such as metabolites, genetic operons,
and receptors (Zheng et al. 2022 ). In this section, because of space
limitations, we mainly summarize intra- and inter-species cross-
talk circuits based on different QS systems . T he crosstalk me-
diated by other substances and mechanisms will be briey dis-
cussed and we refer the reader to some other more comprehen-
siv e r e vie ws in “Other cr osstalk”.
Intra-species QS crosstalk
Ther e ar e man y binding and unintended binding e v ents in the
regulation of cell physiological activities due to the multitude of
genes and r egulators, whic h may lead to “pr omiscuous” cr osstalk
in the QS or some other communications . T here are some typ-
ical QS autoinducers, such as acyl-homoserine lactones (AHLs),
autoinducer 2 (AI-2), auto-inducing peptides (AIPs), and indole
(Wang et al. 2020 ), etc. QS crosstalk includes diverse combinations
of signals , receptors , and promoters . T he “promiscuous” crosstalk
could also be simply divided into three types , i.e . multiple signals
going through one pathway, one signal going through multiple
pathwa ys , and a mixture of both.
As for the multiple signals going through one pathway, re-
searc hers hav e found some QS autoinducers analogs that inhibit
the functions of the original QS signals, thus reducing the corre-
sponding virulence and drug resistance without affecting the cell
growths of pathogens (Ni et al. 2009 ). Specically, with the help
of high-throughput cell-free screening, Christensen et al. identi-
ed se v er al AHLs synthase inhibitors that could serv e as potential
ther a peutics for virulence and microbial infections (Christensen
et al. 2013 ). In order to better use QS receptor-based crosstalk, we
hav e de v eloped a nov el pipeline with similarity assessment and
molecular docking. We constructed a QS interference database
for different QS receptors termed as QSIdb ( http://www.qsidb.lbci.
net/), which includes 633 reported and 73 073 expanded Quorum
sensing interference molecules (QSIMs) (Wu et al. 2021a ).
For one signal going through multiple pathwa ys , there are also
complex QS networks including different QS signal transduction
pathwa ys . For example , Pseudomonas aeruginosa has three inter-
connected QS systems, namely, LasR/I, RhlR/I, and PqsR/ABCDH
(Fig. 1 a). The biolm formation of P. aeruginosa is cr oss-r egulated
by the above three QS systems. Note that even if some receptors
ar e m utated, the highl y interconnected m ulti-layer ed r egulatory
Wu et al. | 3
PqsE RhlI LasI AmbBCDE
PqsABCDH
RhlR LasR Unkonwn
?PqsR
Communication cooperation toxicity biofilms
motility collection responses
PqsE RhlI LasI AmbBCDE
PqsABCDH
RhlR LasR Unkonwn
?PqsR
Communication cooperation toxicity biofilms
motility collection responses
I1I2I3
R1R2R3
Signal A Signal B Signal C
(D)
(A)
BlpABBlpAB
BlpR
ComD
BlpH
BlpC
ComC
comE
comX comCDE blpT blpSRH blpCAB blpMNP
PP
PP
PP
PP
(B)
comE PP
CSP
ComAB
comX comCDE
ComC
PP
comE
P
CSP
PP
Self-sensing
ComAB ComD
Secreting Non-secreting
comE
P
P
comX comDE
ComD
(C)
Figure 1. Illustration for different intra-species QS crosstalk. (A) QS interaction network in P. aeruginosa . Different signals from the las , rhl , pqs , and amb
QS systems are recognized by the corresponding cytoplasmic transcription factors. (B) Diagram of QS crosstalk between ComQXP and Rap-Phr QS
systems. Competence-stimulating peptide (CSP) binds to the histidine kinase receptor ComD, thereby phosphorylating the response regulator ComE,
whic h incr eases tr anscription of blpC and the blp oper on as well as the com QS systems. (C) CS-based quorum- and self-sensing cr osstalk between
secreting and non-secreting strains. (D) QS regulation network diagram composed of various QS synthases (denoted by “I”), signals and receptors
(denoted by “R”).
pattern can generate alternative mechanisms to maintain the t-
ness of P. aeruginosa (Lee and Zhang 2015 ). The cross-talk activa-
tion or inhibition of the three aforementioned QS systems is ben-
ecial to the colonization resistance of P. aeruginosa (Welsh et al.
2015 ). Bacteriocin production of Streptococcus pneumoniae is also
regulated by blp and com QS systems. Miller et al. demonstrated
that pol ymor phic QS leads to mismatc hes between AIPs and six
differ ent blp QS r eceptors, r esulting in QS cr osstalk, whic h is fur-
ther demonstrated by in vitro experiments (Miller et al. 2018 ). Tar-
geting c har acteristics of the combination of ComQXP and Rap-Phr
QS systems (Fig. 1 b) in Bacillus subtilis , Bareia et al. found that the
autoinducer-secreting had a stronger QS response than the non-
secreting cells (Fig. 1 c) (Bareia et al. 2018 ).
Furthermor e, “pr omiscuous” cr osstalk includes different sig-
nals and receptors and is perhaps the most common phenomenon
in micr obial comm unications. Specicall y, taking lux , tra , las , and
rpa QS systems as examples, r esearc hers hav e systematicall y in-
v estigated the dose-r esponse c har acteristics of differ ent QS cir-
cuits . T he crosstalk of six common QS systems ( lux , las , tra , rpa , rhl ,
and cin ) was systematically investigated, and a software tool was
de v eloped for their quantitative analysis and design (Kylilis et al.
2018 ). Inducing by se v en exogenousl y added AHLs, the activities
of the corresponding QS receptors (RhlR, LuxR, LasR, BtaR1, QscR,
CviR, BtaR2) have been measured in their native organisms and
heter ologous expr ession in Esc heric hia coli (Wellington and Gr een-
berg 2019 ). Results sho w ed that most of the QS receptors were re-
sponsive to at least one non-self AHL. Tek el et al. also conducted
dose-dependent experiments to test the responses of LuxR, LasR,
TraR , BjaR, and AubR receptors to a series of AHLs . T he nal re-
sults sho w ed that most of them have a certain degree of crosstalk.
To sum up, the corresponding QS responses are determined
by differ ent QS cr osstalk, whic h is contr olled by complex QS
networks that include differ ent signal-r eceptor combinations
(Fig. 1 d).
Inter-species QS crosstalk
Micr obial comm unities ada pt to differ ent envir onmental c hanges
thr ough differ ent QS langua ges (Blac kwell and Fuqua 2011 ). The
inter-species QS crosstalk based on different signaling molecules
mostly belongs to the signal crosstalk that is relevant to different
phenotypes. In this section, we will provide a summary of inter-
species crosstalk based on some typical QS signals, such as AHLs,
AIPs, AI-2, indole, and diffusion signal factors (DSFs).
AHLs-mediated inter-species crosstalk
AHLs vary in their sensitivity to receptor activation and inhi-
bition not only for crosstalk between QS systems within a sin-
gle organism, but also for inter-species crosstalk, thus leading to
the intricate AHL-based communications in natural communi-
ties (Decho et al. 2011 ). Specically, Dulla and Lindow found that
3-oxohexanoyl-homoserine lactone (3OC6) from other species
would induce the QS crosstalk for r elie ving the virulence of Pseu-
4 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
domonas syringae pv. syringae ( Pss ), thus leading to fewer lesions
for leaves (Dulla and Lindow 2009 ). Hosni et al. also identied that
AHLs produced by two other bacteria ( Pantoea agglomerans and Er-
winia toletana ) would activate the inter-species QS crosstalk that
a ggr av ated the knot disease caused by P. savastanoi pv. savastanoi
( Psv ) (Hosni et al. 2011 ). Similarly, Chandler et al. demonstrated
that AHLs-based eav esdr opping guided the inter-species compe-
tition between Burkholderia thailandensis and Chromobacterium vio-
laceum (Chandler et al. 2012 ). Furthermore, AHL-based eav esdr op-
ping through promiscuous receptors can also be applied to me-
diate various inter-species sense-kill systems for the r emov al of
pathogens . For instance , as illustrated in Fig. 2 a, the LasI/LasR-
type QS system was often used to construct the sense-kill circuits
for the r emov al of P. aeruginosa . The engineer ed E. coli was modied
to specically sense QS signal molecules of P. aeruginosa . When QS
signal molecules r eac hed a certain density, they would induce the
activation of the lysis protein to release p y osin S5, which effec-
tiv el y inhibited the density of P. aeruginosa (Hwang et al. 2017 ).
AIPs-mediated inter-species crosstalk
The AIP-mediated cr osstalk r egulation of the agr QS system is also
of great signicance in an inter-species niche competition and
the expression of density-dependent virulence factors. AIPs can
specicall y activ ate cognate r eceptors and compete for binding
to non-homologous r eceptors, whic h is pr omising for the de v el-
opment of probiotic therapies (Piewngam and Otto 2020 ). Speci-
call y, Borr er o et al. constructed a heterologous monitoring circuit
in the probiotic Lactococcus lactis by sensing the concentration of
the pheromone cCF10, a typical AIP, for inhibiting the cell growth
of the Enterococcus f aecalis , whic h is responsible for enterococcal
infections (Borr er o et al. 2015 ). Piewngam et al. explor ed the mec h-
anism of the probiotic Bacillus sp. inhibiting the agr QS system of
Staphylococcus aureus (Piewngam et al. 2018 ). They found that the
fengycin and AIP from Bacillus have similar chemical structures,
so they competed for binding to the extracellular domain of AgrC,
thus leading to the fengycin-mediated inhibition of QS-related
virulence and colonization (Fig. 2 b). Piewngam et al. utilized the
spores of the probiotic Bacillus subtilis to inhibit the activity of fecal
str eptococci r egulator ( fsr ) in E. f aecalis . Note that the fsr QS sys-
tem can be used by E. faecalis to produce protease GelE that dis-
rupts the integrity of the intestinal epithelium, thereby blocking
its translocation from the gut to the bloodstream and subsequent
systemic infection (Piewngam et al. 2021 ).
AI-2-mediated inter-species crosstalk
AI-2, a common inter-species microbial language, can mediate
v arious micr obial comm unications (Gallo w ay et al. 2011 , Meng et
al. 2022 ), such as the interaction between E. coli and Vibrio harveyi
(Xavier and Bassler 2005 ). Hsiao et al. discov er ed the AI-2-based
crosstalk between Ruminococcus obeum and Vi brio cholera , and the
pathogenicity of the latter would be reduced by the former (Hsiao
et al. 2014 ). Inter estingl y, AI-2 fr om engineer ed E. coli can also be
manipulated to r estor e the stre ptom ycin-induced imbalance of
the cell density ratio between Bacteroidetes and Firmicutes , thus re-
lieving gut dysbiosis (Fig. 2 c) (Thompson et al. 2015 ). Recently, in
addition to the LuxP and LsrB families, Zhang et al. have also iden-
tied a third type of AI-2 receptor that includes a dCACHE domain,
such as PctA and TlpQ from P. aeruginosa , KinD from B. subtilis , and
diguanylate cyclase rpHK1S-Z16 from Rhodopseudomonas palustris
(Zhang et al. 2020 ). Note that the widespread existence of dCACHE
domains in various receptors indicates that AI-2-based crosstalk
is among a large number of prokaryotic species . Furthermore ,
ther e ar e mor e than 680 micr obes in the rumen including AI-2
synthase- or r eceptor-encoding genes, whic h suggests the univer-
sal intra- and inter-species crosstalk among rumen microbes (Liu
et al. 2022d ).
Indole-mediated inter-species crosstalk
Indole, another common QS signal, plays an important role in di-
v erse inter-species inter actions, whic h ar e r ele v ant to biolm de-
velopment (Sethupathy et al. 2020 ) and persister formation (Vega
et al. 2012 ), etc. Note that gut micr obes ar e often exposed to indole
whether they produce indole or not, which indicates the univer-
sal indole-based crosstalk among human gut microbiota (Lee and
Lee 2010 ). It is reported that the indole-based crosstalk could be
either benecial or detrimental for the corresponding cell activi-
ties. Specically, with the help of indole produced by E. coli , Nicole
et al. found that the antibiotic tolerance of Salmonella typhimurium
(which does not natively produce indole) would increase in re-
sponse to indole-based crosstalk (Vega et al. 2013 ) (Fig. 2 d). Simi-
larl y, Lee et al. r ealized that indole-based cr osstalk would inhibit
cell growth and decrease the motility of the non-indole-producing
Agrobacterium tumefaciens (Lee et al. 2015 ). To gain a better under-
standing of the role of indole in microbial communities, please
refer to a good review of indole-related mechanisms and physi-
ologies, as well as the major gaps and contradictions in this eld
(Zarkan et al. 2020 ).
DSFs-mediated inter-species crosstalk
DSFs, another common QS signal, not onl y mediate intr a-species
crosstalk but also play an important role in inter-species interac-
tions (Deng et al. 2011 ). Ther e ar e man y types of DSFs with dif-
fer ent c hain lengths and br anc hing, whic h ar e often fr om Xan-
thomonas campestris . DSFs were also produced by many other mi-
cr obes, suc h as P. aeruginosa, Burkholderia cenocepacia , Xylella fas-
tidiosa , Lysobacter enzymogenes , and Streptococcus mutans (Zhou et
al. 2017 ). Ther efor e, it is not surprising that there are many DSF-
mediated inter-species crosstalk in microbial communities. For
example, when coculturing Stenotrophomonas maltophilia and P.
aeruginosa , the DSFs from the former can be sensed by the ki-
nase PA1396 of the latter by affecting its biolm formation and
pol ymyxin toler ance (Ryan et al. 2008 ). It is also reported that DSF-
r elated cr osstalk might lead to pol ymicr obial infections including
Burkholderia, Stenotrophomonas, and P. aeruginosa (Twomey et al.
2012 ). There is DSF-mediated inter-species crosstalk between Xan-
thomonas and Bacillus species bacteria in biological ecosystems,
where DSFs from the former interfere with morphological transi-
tion and sporulation of the latter (Deng et al. 2016 ). Furthermore,
DSFs-mediated crosstalk can also target HilD, an AraC-type tran-
scription regulator, to repress the virulence of Salmonella enterica
(Golube v a et al. 2016 ). Specicall y, An et al. discov er ed that two of
v e tr ansmembr ane helices of PA1396 are required for DSFs sens-
ing, and de v eloped synthetic DSF analogs to mediate cr osstalk for
modulating or inhibiting biolm formation and antibiotic toler-
ance for P. aeruginosa (Fig. 2 e) (An et al. 2019 ).
Other crosstalk
Except for QS signals, some other substances and mechanisms
can also mediate the intra-species or inter-species crosstalk. For
example, the interactions between transcription factors (TFs) and
corr esponding tar gets hav e a certain non-specicity, whic h may
lead to various intra-species crosstalk (Todeschini et al. 2014 ).
To investigate unintended crosstalk, Maerkl and Quake devel-
oped a high-throughput microuidic platform to measure prop-
erties and molecular interactions . T hey took adv anta ge of the
Wu et al. | 5
Engineered E. coli
PtetR PluxR
P. aeruginosa AI-1
Lysing
LasI
lasR Pyosin S5 LysisE7
Sensing Killing
agrB agrD agrC
hld
P3 P2
RNAIII
agrA
AgrA PP
Staphylococcus aureus
AgrB
AgrC
fenA
Bacillus subtilis
Fengycin
AgrD
PP
(A) (B)
Streptomycin
Dysbiosis
Firmicutes
Bacteroidetes
Indole
E. coli
S. typhimurium
(Susceptible)
S. typhimurium
(Tolerant)
Dysbiosis relieving
(C)
Indole
(D)
DSF analogues
rpfF
DSFs
(E)
P. aeruginosa
HisKA HATPase c REC
HisKA HATPase c REC
Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ
DSF sensor PA1396
Biofilm
formation
Antibiotic
tolerance
Xanthomonas campestris
DSFs
PCadBA
J23113 PNanA
Bile salt
deconjugation
Sialic acid (dysbiosis signal)
nanR cadC Cbh
Escherichia coli Nissle 1917
Growth Germination
C. difficile
endospore
(F)
Clostridioides
difficile
Figure 2. Illustr ation for differ ent inter-species QS cr osstalk. (A) AHL-based inter-species sense-kill systems for the r emov al of P. aeruginosa . Engineer ed
E. coli was designed to sense the QS signaling molecule unique to P. aeruginosa , lysing to release the toxin, p y ocin S5, when a specic density was
r eac hed. (B) Fengycin-mediated inhibition of the agr QS system (agrABCD) of Staphylococcus aureus . Fengycin produced by the probiotic B. subtilis can
antagonize the Agr system, inhibiting the virulence and the ability of S. aureus to colonize in mice. (C) Esc heric hia coli -deriv ed AI-2 can r estor e the
stre ptom ycin-induced imbalance of the cell density ratio between Bacteroidetes and Firmicutes. (D) Pathogenic S. typhimurium encountering indole
from E. coli to enhance its own tolerance to antibiotics. (E) Histidine kinase PA1396 auto-phosphorylation to DSFs and analogs to inhibit the biolm
formation and antibiotic tolerance of P. aeruginosa . (F) Bile salt-based crosstalk inhibits the germination and growth of C. difcile with an engineered E.
coli Nissle 1917. Sialic acid can r emov e the r epr ession of the inducible promoter P
CadBA
by the nanR repressor and induce the efcient expression of the
activ ation ther a peutic module thr ough the CadC signal amplication module.
6 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
high-thr oughput natur e of the micr ouidic platform to measur e
the DNA binding ener gy landsca pe of four eukaryotic TFs us-
ing molecular interaction mechanisms (Maerkl and Quake 2007 ).
With the global analysis of protein phosphorylation in yeast,
Ptacek et al. found that there is extensive crosstalk in the two-
component signaling systems, a microbial dominant signaling
modality including a sensor histidine kinase and a response reg-
ulator (Ptacek et al. 2005 ).
Certainly, some other metabolites can also mediate inter-
species cr osstalk, whic h is meaningful for the stability mainte-
nance of microbial communities and the analysis of microbe–host
inter actions. Specicall y, the cr osstalk mediated by indole deriv a-
tives can also affect the development of microbial biolm and vir-
ulence. 7-hydroxyindole (Lee et al. 2009 ), Indole-3-acetaldehyde
(Kim et al. 2011 ), and 7-benzyloxyindole (Lee et al. 2013 ) have been
veried for the inhibition of the virulence of P. aeruginosa , E. coli
O157:H7, and S. aureus , r espectiv el y. Note that human-deriv ed pri-
mary bile salts can also be sensed by various gut microbes, and
transformed into secondary bile acids, the dysregulation of which
will lead to Clostridium difcile infection (CDI).
To sum up, the intra- and inter-species crosstalk mediated by
diverse signals or metabolites are universal in various microbial
ecosystems, and can be utilized for virulence r eduction, gr owth
interfer ence, antibiotic r esistance elimination, and pathogens in-
hibition. For example, to help patients with CDI, Koh et al. have
engineered a genetic circuit to encode a sensor , amplier , and ac-
tuator in E. coli Nissle 1917 (Fig. 2 f) to r estor e the metabolism of
bile salt to limit the germination of endospores and v egetativ e cell
growth of C. difcile (Koh et al. 2022 ). Note that further details can
be r eferr ed to in some excellent r e vie ws or databases (Sam et al.
2017 ) that have summarized the extensive crosstalk in diverse sig-
naling networks of prokaryotes (Qin et al. 2020 ), eukaryotes, and
e v en micr obe–host inter actions (Zheng et al. 2022 ).
Orthogonal regulation circuits
The modularization of synthetic toolboxes r equir es independent
functions of each component to pr e v ent the unwanted interac-
tions that may cause malfunction (Lu et al. 2019 ). Ther efor e, ac-
cum ulating r esearc h has been de voted to de v eloping v arious or-
thogonal channels for the construction of stable and multifunc-
tional gene regulation networks (Costello and Badran 2021 ). In this
section, to give a broad illustration of different orthogonal regu-
lation strategies, we will outline the circuits or elements based
on the le v els of metabolites, tr anscription, tr anslation, and post-
tr anslation modication, r espectiv el y.
Metabolite response biosensors
Micr obes hav e e volv ed complex r egulatory mec hanisms to sense
the changes in the concentration of diverse metabolites to adapt
to differ ent envir onments. This indicates that metabolites play an
important role in the regulation of different genes expression and
phenotypes (Rinschen et al. 2019 ). T herefore , many metabolite-
based orthogonal sensors were developed by introducing inter-
mediate metabolites or by-products . T he responses of targets and
their corresponding homologous promoters were often applied to
ada ptiv el y adjust the activation or inhibition of genes, with easy-
detection phenotypes (such as the uorescence intensity) as the
outputs for various sensors (Jung et al. 2021 ). Note that biosensors
were often developed to balance the metabolic ux and improve
the efciency of production (Jung et al. 2021 ). Recently, in order
to further expand the versatility and orthogonality of metabolite-
based biosensors in microbial communication, Du et al. also ex-
ploited some metabolites, such as 2,4-Diacetylphophloroglucinol
(DAPG), methylenomycin furan, and naringenin to design a ge-
netic toolbox for orthogonal m ulti-c hannel comm unications and
biological computations (Du et al. 2020 ). Similarl y, man y r e-
searc hers hav e also constructed div erse metabolite-based orthog-
onal sensors, such as naringenin, isopr ene, and tyr osine, etc.
To sum up, diverse metabolite response biosensors were devel-
oped in different microbes for the design of applications . Here , in
Table 1 , we summarize some metabolite-based orthogonal sen-
sors for different functions.
Orthogonal transcription regulation
Var ious regulation strategies often require the expression of mul-
tiple active components in a single cell at different time scales
(Rollié et al. 2012 ). The prosperity and de v elopment of synthetic
biology has provided a series of eng ineered biolog ical elements
for gene transcription regulations. In this section, we will focus on
the summary of orthogonal circuits at the transcription level, in-
cluding RNA pol ymer ases (RN APs), RN A-based s witches , CRISPR-
based, and DNA-binding regulations.
RNAPs-based regulations
It is one of the important c hec kpoints to control the function
of gene circuits and cell behaviors b y RN APs . T he orthogonal T7
RNAP has pr e viousl y been scr eened to de v elop a set of r esource
allocators that can attract RNAP to specic promoters to produce
orthogonal gene expr ession (Segall-Sha pir o et al. 2014 ). The out-
put of each controller was presented under the control of ho-
mologous promoters to express orthogonal σfragments, which
wer e a pplied to contr ol the tr anscription intensity of the system.
T7 RNAP has also been applied to construct a modular and pro-
grammable system for the activation of multiple orthogonal tran-
scription regulations in E. coli (Hussey and McMillen 2018 ), as well
as semi-synthetic organisms (Zhang et al. 2017 , Feldman et al.
2019 ). Furthermore, the essential components of RNAPs ( σfactors
and anti- σ-factors) were often combined to conduct transcrip-
tion regulations. For example, based on the extensive investiga-
tion of the extracytoplasmic function σs and the corresponding
anti- σs, Rhodius et al. studied their orthogonality on the -35 and
-10 binding domains of different promoters, which were applied
to construct diverse synthetic genetic switches in E. coli (Rhodius
et al. 2013 ). Similarly, Bervoets et al. found that three heterolo-
gous σfactors ( σB
, σF
, and σW
) from Bacillus subtilis show mutual
and host orthogonality in E. coli strains (Fig. 3 a). By creating pro-
moter libraries, they developed a sigma factor toolbox for orthog-
onal gene expressions with a wide range of transcription initia-
tion frequencies, tunable multiple outputs in response to differ-
ent signals (Bervoets et al. 2018 ). Note that more details about the
advances and potential applications of RNAPs can be r eferr ed to
in another good r e vie w (Wang et al. 2022b ).
RNA-based switch
Toehold switches are a highly orthogonal and specic new type
of RNA nano-device that can detect any trigger RNA sequence to
activ ate downstr eam gene tr anslation (Gr een et al. 2014 ). Toehold
switches can be used to construct multi-input cellular logic gates
to perform biological calculations. Recentl y, some r esearc hers fur-
ther deplo y ed the scalability of toehold switc h v ariants and de-
veloped an orthogonal paper-based diagnostic method for sens-
ing various viruses and gut microbiota (Cao et al. 2021 ). Further-
more, Kim et al. designed some po w erful translation-repressing
Wu et al. | 7
Tab le 1. Details of different orthogonal metabolite-based biosensors.
Metabolites Functions References
4-hydroxybenzoic acid (pHBA) Detection of benzoic acid deri vati ves (Castaño-cerezo et al. 2020 )
3-hydr oxypr opionic acid (3-HP) Biosensor for accelerating evolution (Seok et al. 2021 )
3-hydr oxypr opionic acid (3-HP) Impr ov e bioc hemical pr oduction (Kang et al. 2022 )
p-coumaroyl-CoA Biosensor for dynamic regulation (Liu et al. 2022a )
L-2-Hydr oxyglutar ate (L-2-HG) Biosensor for carbon starvation response (Kang et al. 2021 )
2,4-Diacetylphophloroglucinol (DAPG) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Cuminic acid (Cuma) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
3-oxohexanoyl-homoserine lactone (3OC6) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Vanillic acid Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Isopropyl- β-d-thiogalactoside (IPTG) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Anhydr otetr acycline HCl (aTc) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
aTC Monitor the quantitative relationship between
sialic acid and its corresponding enzyme
(Lim et al. 2017 )
l-Arabinose Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Choline chloride Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Naringenin Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
3,4-Dihydroxybenzoic acid (DHBA) Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Sodium salicylate Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
N-(3-hydr oxytetr adecanoyl)-l-homoserine lactone
(3OHC14)
Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Acrylic acid Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
Erythromycin Orthogonal optimized small-molecule sensors (Meyer et al. 2019 )
D-2-hydr oxyglutar ate Quantitative detection of D-2-HG as a biomarker
in many cancers
(Xiao et al. 2021 )
riboregulators (also termed as toehold r epr essors and thr ee-way
junction r epr essors) with sensing and logic ca pabilities . T he high-
performance translation repressors were designed to induce the
opening of the hairpin structure variants (Fig. 3 b). The dynamic
range of the toehold repressor was improved by the forward au-
tomation process, and the mechanism of the three-way junc-
tion r epr essor w as determined b y high-throughput RN A structure
analysis . Using the abo ve two highly specic and highly orthogo-
nal inhibitors, a four-input logic gate was realized and corr ectl y
e v aluated in E. coli (Kim et al. 2019 ). As summarized by other re-
sear chers, w e also believe that the sensitivity , stability , and com-
patibility of the ecosystems based on the RNA switches will be
impr ov ed gr aduall y.
CRISPR-based regulations
The bacterial CRISPR-CAS defense was massiv el y a pplied to the
exible and modular transcription regulations . T he n uclease-n ull
CRISPR-Cas (dCas) proteins were validated to regulate the corre-
sponding process orthogonally. Note that the dCas9 protein has a
RNA-guided gene targeting property, which is very important for
the regulation of externally induced genes (Yu et al. 2021 ). dCas9
protein can also bind to the downstream of RNAP, thus blocking
the corresponding transcription process. Ligand-activated or in-
hibited single-guide RN A (sgRN A) can be used for constructing a
m ulti-layer ed CRISPR loop to provide an orthogonal and modular
tr anscription r egulation str ategy for pr ogr amming bacterial cells
(Dong et al. 2018 ). Note that the working principle of the intra-
cellular controller is to respond to a burden increase by reducing
the gene expression rate of the synthetic target proteins. Speci-
call y, Cer oni et al. constructed a genetic cir cuit b y using a dCas9-
based feedback controller to respond to burden (Fig. 3 c). Results
sho w ed that microbes equipped with the dCas9-based feedback
contr oller could r ealize r obust gr owth and higher pr otein yield in
batc h pr oduction (Cer oni et al. 2018 ). Yang et al. have established
a dual-function dynamic r egulation str ategy by using CRISPR in-
terference (CRISPRi) to couple with antisense RNAs . T hey used
muconic acid (MA) as the input signal to couple antisense RNA
to construct a sensor. Then MA sensor was applied to dynami-
call y r egulate the phosphoenolpyruv ate metabolic node (EP mod-
ule) in E. coli to distribute the carbon ux for native metabolism
(such as cell growth and maintenance) and MA production (NC
module) (Fig. 3 d) (Yang et al. 2018 ). Some other r esearc hers hav e
also de v eloped some m ulti-stable and dynamic CRISPRi-based cir-
cuits with high predictability , orthogonality , and low metabolic
burden (Santos-Moreno et al. 2020 ). Another powerful tool based
on CRISPR activation (CRISPRa) has also emerged for creating or-
thogonal synthetic gene circuits for diverse functions in micro-
bial cells (Liu et al. 2019 ). Furthermore, orthogonal CRISPRa and
CRISPRi systems can be further coupled for simultaneous tran-
scription upregulation of a subset of target genes while downreg-
ulating another subset, thus gaining the control of gene regulatory
netw orks, signaling pathw ays, and cellular processes (Martella et
al. 2019 ). As a po w erful tool for microbial regulations, researchers
have also conducted more comprehensive summaries and discus-
sions for CRISPR-based regulations (McCarty et al. 2020 , Nishida
and Kondo 2021 ).
DNA-binding regulations
Ther e ar e some engineer ed r egulators based on orthogonal DNA
binding elements, such as transcription acti vator-lik e effectors
(TALEs) (Leben et al. 2022 ), selected TFs, and engineered promot-
ers . For example , Segall-Sha pir o et al. r edesigned pr omoters for
maintaining constant le v els of expr ession at an y copy number,
due to the metabolic burden being affected by the copy num-
ber of the vector and the location of the genome. In order to
ac hie v e stable gene expression intensity and minimize the im-
pact of foreign perturbation, an incoherent feedforw ar d loop (iFFL)
of a TALE was engineered into E. coli pr omoters, whic h had near-
identical expression in different genome locations and plasmids
(Fig. 3 e) (Segall-Sha pir o et al. 2018 ). Some selected TFs ar e also
8 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Figure 3 Illustrations for orthogonal transcription regulations based on different tools. (A) Orthogonal regulations of E. coli RNAP and Bacillus subtilis
sigma factors without cr osstalk. (B) illustr ation for tr anslation-r epr essing ribor egulators. Tr anslation r egulation by the binding of ribosome binding site
(RBS) and start codon and two single-stranded domains a
∗and b
∗. (C) Illustration for a biomolecular feedback controller. The htpG1 promoter drives
the expression of CRISPR sgRNA, which in turn directs binding of dCas9 to target pBAD promoter to inhibit transcription of VioB–mCherry. (D) Muconic
acid (MA) promotes the expression of the phosphoenolpyruvate metabolic node (EP module) and decreases the carbon ux into the TCA cycle via
RNAi. (E) The DAPG-induced PhIF r epr essor was regulated using either the constitutive promoter (Pconst) or the TALEsp1 stabilized promoter. (F)
Molecular implementation of tr anscription incoher ent-feedforw ar d-loop netw orks, in which the LacI is induced by IPTG; cI434, T7 RNAP, and sfGFP are
the r epr essor, activ ator, and the output, r espectiv el y.
used as DNA-binding regulators and are regarded as “gatek ee p-
ers” for various genes expression (Collins et al. 2006 ). Mutation of
ligand binding domain residues can effectively enhance the speci-
city of inducers without changing the function of allosteric TFs.
The method of coupling computer prediction models and high-
thr oughput scr eening can impr ov e the design and selection of
high-quality TFs (Jha et al. 2015 ). For example, Meyer et al. de-
v eloped a dir ected e volution and scr eening str ategy, and obtained
12 high-performance sensors with low bac kgr ound and cr osstalk,
and amplied the available orthogonal transcription regulators
(Meyer et al. 2019 ). Furthermore, Zong et al. conducted ratio-
nal engineering on biological systems at the transcription level
by insulated promoter design and operator optimization. Results
sho w ed that the combinatorial promoters with insulated tran-
scription elements had good performances on mean errors and
success rate for encoding NOT-gate functions. They also com-
bined their optimized insulated transcription elements to be a
four-node network (Fig. 3 f) with iFFL topology, and veried its com-
plex functions (Zong et al. 2017 ). Ther e ar e some other excellent
r e vie ws that hav e pr ovided mor e details for the tr anscriptional
regulation circuits (Soutourina 2018 , Cramer 2019 , Maucourt et al.
2020 ).
Orthogonal tr ansla tion regula tions
Synthetic biological regulations design can also be conducted at
the translation level, which has a shorter time scale than the
tr anscription le v el (Fink et al. 2019 ). In this section, we pr ovide
an ov ervie w of differ ent orthogonal tr anslation r egulations, in-
cluding ribosomes , ribos witches , and aminoac yl-tRN A synthetase
(aaRS)-tRNA pairs based on non-canonical amino acids (ncAAs).
Ribosomes
Orthogonal ribosomes have become a new mode of translation
le v el r egulation str ategy.
By redesigning the specic recognition between the small ri-
bosomal subunit ribosomal mRNA and the corresponding mRNA
leader sequence, the modied orthogonal ribosomal machinery
Wu et al. | 9
can only translate specic mRNAs, achieving independent par-
allelism with the strain’s endogenous translation mechanism.
Specically, the orthogonal 16S subunit and wild type 50S subunit
of orthogonal ribosomal tr anslation onl y r ecognize the particular
mRNA, which can be used as a completely orthogonal transla-
tion. An and Chin de v eloped an orthogonal transcription by T7
RNA pol ymer ase and orthogonal ribosomes (O-ribosomes) to con-
struct a simple logic gate, which realizes the adjustment of the
response time of different input systems in the similar AND gate
system in the natural regulatory network (An and Chin 2009 ). It
was also reported that the covalent linkage of circularly permu-
tated rRNAs could eliminate the heterogenous ribosome, and im-
pr ov e the cellular orthogonality for ribosomes (Fried et al. 2015 ).
Furthermore, the use of screening and engineering a ppr oac hes,
as well as combinations with some other tools, can increase the
orthogonality and the application scope of ribosomes. For exam-
ple, Carlson et al. applied an evolutionary approach to engineer
ribosomes with tethered subunits to minimize the association of
cov alentl y linked ribosomal subunits with their native counter-
parts (Fig. 4 a), thus obtaining orthogonal ribosomes that enable
faster cell growth and protein expression (Carlson et al. 2019 ). It
is reported that the combination of orthogonal ribosomes and dy-
namic resource allocators could reduce heterologous expression
and gene crosstalk (Darlington et al. 2018 ). There is a more com-
pr ehensiv e r e vie w, whic h can be r eferr ed to obtain mor e details for
the assembly and repair of ribosomes (Yang and Karbstein 2024 ).
Riboswitches
With the deepening of the understanding of the regulation and
structural principles of ribosomal s witches , a series of orthogo-
nall y r esponsiv e nativ e and non-nativ e ribosomal switc hes con-
tinue to be applied to gene regulations in different bacteria. For ex-
ample, thr ough tar geted m uta genesis and compr ehensiv e scr een-
ing strategies, Dixon et al. successfully created an orthogonal RNA
r egulatory element-riboswitc h, whic h was a pplied to construct
a m ulti-component dual-pr omoter co-expr ession system and a
synthetic operator system (Dixon et al. 2012 ). By combining con-
sistent E. coli ribosome binding site (RBS) and anti-RBS libraries,
Kent and Dixon adopted a high-throughput method based on
uor escence-activ ated cell sorting to identify riboswitches with
the maximal protein expression (Kent and Dixon 2019 ). Then four
endogenous E. coli stress response promoters were designed as ri-
boswitc h r egulators, whic h wer e induced by orthogonal ribosome-
specic ligands (Fig. 4 b). Furthermore , ribos witches can be used
as RNA-based intracellular sensors that control gene expression
(Hossain et al. 2020 ). The rate or opening of translation is con-
tr olled by cleav a ge or bloc king of the corr esponding RBS. For ex-
ample, Xiu et al. de v eloped the rst naringenin-r esponsiv e biosen-
sor based on the RNA riboswitc h, whic h was scanned by ow cy-
tometer and used to screen E. coli strains with real-time detection
of metabolite products (Xiu et al. 2017 ). In view of the crosstalk in
the induced expression system, Robinson et al. used a structure-
guided chemical genetic screening method to successfully iden-
tify excellent chimeric ligands for orthogonal riboswitches (Robin-
son et al. 2014 ). They used the corresponding ligand to regulate
the dose-dependent expression of the physiologically important
c heZ gene r equir ed for the mov ement of E. coli . Riboswitc hes can
also be used to specically respond to exogenous small molecules
to ac hie v e self-cleav a ge r egulation of articial cells. Specicall y,
the histamine-specic corresponding ribosome switches were iso-
lated by in vitro screening, then encapsulated in articial cells of
phospholipid vesicles for the characterization of a self-destructive
kill-switch (Fig. 4 c) (Dwidar et al. 2019 ). Ther efor e, orthogonal
riboswitc hes can pr ecisel y r egulate the expr ession of bacterial
genes and provide new modular and orthogonal regulatory com-
ponents for functions verication and synthetic biology toolboxes
(Kavita and Breaker 2023 , Salvail and Breaker 2023 ).
ncAAs-based aaRS/tRNA pairs
Note that ensuring the orthogonality of ncAAs-based aaRS/tRNA
pairs is the k e y to their translation level regulation, which in-
cludes orthogonal tRNA engineering and orthogonal aaRS en-
gineering (Hammerling et al. 2020 ). The orthogonality of tRNA
r equir es that tRNA can only be specicall y r ecognized by the
corresponding tool enzyme and cannot cross-talk with other
endogenous aaRS. Ther efor e, the aaRS/tRNA pairs can be se-
lected and modied from evolutionarily distant organisms to re-
duce the probability of cross-reactivity. For example, tRNAPyl
has been r ationall y e volv ed with six nucleotide c hanges to im-
pr ov e the efciency for incor por ating ncAAs at the termina-
tion codon, such as U A G codons in uorescence protein (Fan et
al. 2015 ). Note that pyrr ol ysyl-tRNA synthetase/pyrr olidine tRNA
pairs (PylRS/tRNAPyl) have high orthogonality in a variety of bio-
logical systems and are suitable as a universal codon expansion
tool for modication in different microbial ecosystems (Suzuki et
al. 2017 ). To impr ov e the orthogonality of tRNA, r ational design
and a dir ected e volution str ategy hav e been effectiv el y used to
modify and optimize tRN A b y the manipulation of the variable
loop region (Willis and Chin 2018 ), such as elongation factor-Tu
(EF-Tu) (Fan et al. 2015 ) and elongation factor P (EF-P) (Hammer-
ling et al. 2020 ) (Fig. 4 d).
Natur all y, the amino-acid binding pocket of aaRS can speci-
call y r ecognize the corr esponding amino acid, so as to ensur e the
orthogonality of aaRS to the substrate. To improve orthogonality
and efciency, different types of aaRS have been used for modi-
cation, and se v er al tool enzymes for gene codon extension, and
diverse aaRS/tRNA pairs engineering has been successfully devel-
oped, enabling the specic introduction of more than 200 ncAAs
into biological proteins (Vargas-Rodriguez et al. 2018 ). The orthog-
onality of aaRS/tRNA pairs r equir es that the heterologous aaRS
tools can specicall y r ecognize exogenousl y ad ded non-nati ve
amino acids (Fig. 4 e) (de la To rre and Chin 2021 ). Furthermore,
through the in-depth analysis and mining of bioinformatics, some
new PylRS/tRNAPyl pairs have been discovered, which can be used
to de v elop the same type of m utuall y orthogonal non-natur al
amino acid coding tools . For example , Willis and Chin created
se v er al ne w PylRS/tRNA
Pyl
pairs that ar e m utuall y orthogonal to
other existing aaRS/tRNA pairs (Willis and Chin 2018 ). Based on
the computation and analysis of millions of sequences, Cervettini
et al. identied 243 candidate tRNAs in E. coli , obtained 71 orthog-
onal tRNAs , disco v er ed v e orthogonal pairs, and c har acterized a
matrix of 64 orthogonal aaRS/tRNA specicities with the help of
their proposed approach (Fig. 4 f) (Cervettini et al. 2020 ).
To sum up, genetic code expansion has been engaged in the
use of orthogonal aaRS that can specically recognize ncAAs and
tRNA in different microbes (Sisila et al. 2022 ). Recently, massive
genomic and metagenomic data have become an important re-
source for the de v elopment of new orthogonal aaRS/tRNA pairs
(de la To rr e and Chin 2021 ). Future efforts will be engaged in inte-
grating codon expansion using the afor ementioned m utuall y or-
thogonal aaRS–tRNA pairs, synthetic bases (Fischer et al. 2020 ), or-
thogonal mRNA design (Dunkelmann et al. 2021 ), and some other
strategies to decode diverse ncAAs to be incorporated into a pro-
tein. More details of the advances and applications of genetic code
expansion and ncAAs can be r eferr ed to in some other good re-
views (Ishida et al. 2024 , Yi et al. 2024 ).
10 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Figure 4. Illustrations for orthogonal translation regulations based on different tools. (A) Orthogonal function evolved for the wild type ribosome and
fully orthogonal ribosome (Ribo-T) system. (B) Schematic overview of [4,5-d] pyrimidine-2,4-diamine (PPDA) responsive orthogonal riboswitch. (C)
Illustration of the histamine-specic corresponding ribosome s witches . Histamine binding to aptamer disrupts the secondary structure of RNA and
activates the translation state. (D) Techniques to engineer tRNA including aaRS identity elements, EF-Tu binding, EF-P binding, and four loops. (E)
Sc hematic ov ervie w of the orthogonal pairs of ncAA, aaRS, and tRNA. ncAAs ar e r ecognized b y orthogonal aaRSs, loaded onto orthogonal tRN As. (F) A
pipeline to identify orthogonal aaRS–tRNA pairs. First, the method of calculation or experiment was used to produce a series of candidate tRNAs, and
then orthogonal tRN As w er e experimentall y conrmed. Subsequentl y, the activ e homologous synthases wer e scr eened and their orthogonality to eac h
other was conrmed.
Orthogonal protein regulations
In addition to genetic circuits based on transcription and trans-
lation le v els, ther e ar e man y biomolecular r egulations based on
post-tr anslation modication, suc h as pr otein circuits. For ex-
ample, the viral proteases that specically recognize and cleave
short peptide targets provide the basic elements for protein cir-
cuits (Chung and Lin 2020 ). Specically, using the diversity and
pr ogr ammability of vir al pr oteases, a combinable protein sys-
tem (circuits of hacked orthogonal modular proteases, CHOMP)
was de v eloped (Fig. 5 a) for the construction of different syn-
thetic logics (Gao et al. 2018 ). In order to r egulate pr otein circuits
mor e quic kl y and accur atel y, Fink et al. obtained split-pr otease-
cleavable orthogonal-CC-based (SPOC) logic circuits, which could
quic kl y r espond to small molecule inducers within a few min-
utes (Fink et al. 2019 ). The complete activation of functional en-
zymes can be ac hie v ed when the other recombinant enzyme has
str onger matc hing ability and paired implementation (Fig. 5 b).
The CHOMP and SPOC logic circuits are both modular and ex-
pandable; the former relies on protein degradation mechanisms,
while the latter logic circuits r el y on pr otease cleav a ge, whic h
has a faster response rate. A cooperatively induced protein het-
erodimer (CIPHR), used as the basic input of the protein circuit,
was constructed by adjusting the binding strength of protein het-
erodimers (Fig. 5 c). These elements are based on the afnity be-
tween proteins and do not depend on the intracellular environ-
ment. Ther efor e, CIPHR was pr ogr ammable and portable, whic h
has been veried in yeast, T cells, cell-free, and other systems
(Chen et al. 2020 ).
With the enhancement of computing and design capabilities,
protein circuits will have a wider range of applications. As one of
the important circuits, the de v elopment of pr otein sensors and
switches is still limited and challenging (Yu et al. 2018 ). Note
that Quijano-Rubio et al. have designed and constructed a gen-
eral class of protein biosensor from scratch (Quijano-Rubio et al.
2021 ). The protein sensor consists of two protein components:
the “lucCage” part and the “lucKey” part. The “lucCage” part con-
sists of a cage-like domain and a luciferase fragment containing a
target-binding motif and cleavage (similar to latch structure), and
the “lucKey” part consists of a fragment complementary to the
open state lucCage binding bond peptide and luciferase (similar
Wu et al. | 11
Figure 5. Orthogonal regulatory strategies based on protein circuits. (A) Illustration of the CHOMP circuit. SOS activates Ras, causing it to bind RBD,
r econstituting RasTEVP. TVMVP cleav a ge detac hes Casp3 and r educes its ability by membr ane-localized TEVP. (B) Sc heme of c hemicall y inducible split
proteases with rapamycin based on a coiled-coils (CC) interaction module . T he complementary split fragments of the protease were fused to a domain
pair, the pr oteol ytic acti vity was obtained after the ad dition of the inducer r a pamycin, and the leucine zipper was r emov ed to activ ate the lucifer ase
r econstitution. (C) Sc hematic ov ervie w of the basic input of the pr otein cir cuit b y adjusting the binding str ength of the dimer. The inactiv ation
operation was performed by separating the formed dimer through competitive binding, and the activation operation was performed by connecting two
non-interacting monomers and recombining the fused split protein domains. (D) Schematic mechanism of multi-state biosensors . T he binding of the
target and k e y allows the reconstitution of SmBiT and LgBiT for luciferase activity. (E) Illustration of the LOCKR system. The protein switch is
composed of a cage and latch with a functional motif, which is in thermodynamic equilibrium state. (F) Schematic diagram of the
Degr onLOCKR-induced degr adation, whic h consists of the designer degr onSwitc h and inducer pr otein (the "k e y").
to k e y structure) (Fig.
5 d). T he s witc h state of the sensor onl y de-
pends on the thermodynamic coupling of the analyte and sensor
acti vation, and its sensiti vity de pends on the target binding do-
main and the change of free energy after target binding. Similarly,
Langan et al. cle v erl y used the adjustable spiral structure in a
wide dynamic range to design a protein switch with a model of the
lock-k e y structure (Langan et al. 2019 ). The external k e y competes
with the door latch to bind to the ca ge. Befor e unloc king, the func-
tional peptide of the door latch is in a state of inactivation, and
the external k e y is inserted (by adjusting its thermodynamic pa-
rameters) to activate the function peptide (Fig.
5 e). Furthermore,
in another work, the degradation determinant (degron) was de-
signed as a cage structure, and the degradation process of the
protein was coupled with the switch state of the protein molecule
(Ng et al. 2019 ). Different functional peptides can be adjusted to
design different LOCKRs, indicating that LOCKR has plug-and-
pla y modularity. T he feedbac k loop mediated by Degr onLOCKR
not only can realize the control of the endogenous pathway
and synthesis circuit, it can also change the afnity of the k e y
(Fig. 5 f).
12 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Compared with regulation at the DNA level, protein-based cir-
cuits have the advantages of fast response, direct coupling to en-
dogenous pathwa ys , and no need to integrate the genome of cells
(Gao et al. 2018 ). In this section, we only conduct a brief browsing
of protein circuits; for more details of the design of different pro-
tein circuits, please refer to an extensive review (Chen and Elowitz
2021 ).
To sum up, although the engineering ability of a single species
has made extraordinary advances, the engineering of microbial
ecosystems often fails to ac hie v e their stability and robustness.
Whether the abov e-mentioned cr oss-talk and orthogonal regula-
tory tools and circuits are suitable for the targeting behavior of
cells mainly depends on the application elds and related require-
ments, such as the sensiti vity, selecti vity, d ynamic range, host
ada ptability, and other factors. Futur e efforts will be engaged in
de v eloping a compr ehensiv e cycle for the de novo design and opti-
mization of various microbial ecosystems from the assembling of
v arious cr oss-talk and orthogonal r egulatory tools by considering
the global c har acteristics, contr ollability, and r obustness, etc. All
the above efforts introducing the cross-talk and orthogonal cir-
cuits based on various levels, such as metabolites, transcription,
tr anslation, and post-tr anslation, pr ovide the experimental basis
(a summary of the different circuits is listed in the following “Build
module”) for the pr ecise r egulations of differ ent micr obial ecosys-
tems.
Assembling of microbial ecosystems
Natur all y occurring micr obial ecosystems ar e functional and sta-
ble (Giri et al. 2020 ). Members of micr obial comm unities exhibit
r obustness and emer gent pr operties in r esponse to envir onmental
c hanges. Although m ulti-omics and high-thr oughput tec hniques
hav e made r a pid pr ogr ess in ca pturing micr obiota phenotypes
and metabolic functions, little is known about the corresponding
inter action mec hanisms . T he use of bottom-up construction of
microbial ecosystems is expected to be used to explore the eco-
logical knowledge of micr obiota thr ough modular rational design
and assembly. To design microbial ecosystems, the DBTL cycle
has been proposed to guide and promote microbial community
engineering (Lawson et al. 2019 ). Ho w e v er, mor e details for the
guidance of designing microbial ecosystems based on DBTL cy-
cles ar e lac king. Considering the complexity of various practical
a pplication conditions, suc h as the human gut and soil envir on-
ment, the construction of controllable , stable , and r obust micr o-
bial ecosystems must be the basis and direction of future micro-
bial applications. To better design and optimize various microbial
ecosystems, we propose a more comprehensive DBTL (cDBTL) pro-
cedure that includes function specication, chassis selection, in-
teractions design, system build, performance test, modeling anal-
ysis, and global optimization (Fig. 6 ). Herein, to gain a better un-
derstanding of microbial interaction mechanisms and optimized
design of microbial ecosystems, thus achieving the desired en-
gineering functions, we provide an introduction (with details of
each step) to the design and optimization of functional microbial
ecosystems.
Design module
Synthetic microbial ecosystems can function as low complexity
systems to further understanding of the composition and interac-
tions of the microbiota. To further realize their stability and func-
tional activities, we propose that the design module of the micro-
bial ecosystems should specically include the steps of function
specication, chassis selection, and interaction design.
Function specication
Because of their unique adv anta ge of division of labor, microbial
ecosystems have been extensively used in bio-computing, bio-
manufacturing, bio-ther a py, and bio-r emediation, etc. In this sec-
tion, we will only give a brief introduction for the updated and
specic function.
The integration of circuits can realize different bio-computing
with accurate perception and calculation for various biologi-
cal networks (Liu et al. 2020c ). For example, Müller et al. com-
bined the sensor-sender and r eceiv er-digitizer cells to de v elop
the fr a gr ance-pr ogr ammable analog-to-digital conv erter for r e-
mote control of digital gene expression (Fig. 7 a) (Müller et al.
2017 ). With the expansion of orthogonal communication toolkit,
Du et al. deplo y ed a three-input AND-XOR logic gate in se v en
E. coli strains based on the signal transmission of 3OC6, sal-
ic ylate, p -coumaro yl-HSL (pC), and DAPG (Fig. 7 b) (Du et al.
2020 ). Shin et al. distributed a biocomputing process into seven
E. coli strains that acquire four different signals , i.e . 3OC6, N-
(3-hydr oxytetr adecanoyl)-l-homoserine lactone (3OHC14), anhy-
dr otetr acycline (aTc), and isopr opyl- β-D-1- thiogalactopyr anoside
(IPTG), to realize the binary-coded digit to 7-segment decoder for
clocks and calculators (Fig. 7 c) (Shin et al. 2020 ). In another study,
inspired by structural similarity between multi-cellular networks
and articial neural networks, Li et al. conducted a neural-like
calculation in microbial consortia to sense the weights of various
group induction signals and recognize patterns (Fig. 7 d) (Li et al.
2021 ). These studies sho w ed the pr ogr ammability of neur on-like
bio-computing in different microbial ecosystems.
Recentl y, se v er al studies have been carried out among various
microbial ecosystems to achieve various bio-manufacturing prod-
ucts, such as biofuels (Zhang et al. 2021 ), short-chain fatty acids
(SCFAs), and other value-added chemicals (Sgobba and Wendisch
2020 ). For example, Li et al. distributed the two upstream branches
(the synthesis pathway of the caffeic acid and salvianic acid A)
and downstream modules of the synthetic pathway of rosmarinic
acid to three strains of E. coli , and optimized the external condi-
tions such as mixed carbon source and inoculation ratio to in-
crease the stability and synthesis ability of the co-culture system
(Fig. 8 a) (Li et al. 2019 ). Compared with single-strain strategy, the
yield of rosmarinic acid produced by co-culture of three strains of
E. coli increased by 38 times (172 mg/L). Recently, Li et al. have also
de v eloped a stable system of co-culture of three strains by using
a carbon source to reduce competition independently and cross-
feeding multiple metabolites to strengthen internal connection
(Li et al. 2022 ). DmpR (r esponsiv e to caffeic acid), a self-regulating
biosensor, was introduced to dynamically regulate GdhA that suc-
cessfull y r egulated the composition of the micr obial comm unity,
which was demonstrated by the biosynthesis of silybin/isosilybin
(Fig. 8 b). Furthermore, with the help of the design for metabolic
nic he, v arious SCFAs wer e pr oduced under the synergistic effect
of v arious heter ogeneous consortia with the lignin as input car-
bon source, as well as the acetic acid and lactic acid being the
intermediate carbon source (Shahab et al. 2020 ) (Fig. 8 c). Jiang et
al. constructed a synergistic consortium including Trichoderma as-
perellum and Lactobacillus paracasei to boost lactic acid conversion
from lignocellulose (Fig. 8 d) (Jiang et al. 2023a ). As stated abo ve ,
the mechanism understanding of metabolic function accelerates
the applications of microbial ecosystems, and provides feasible
solutions for global problems of en vironmental go vernance and
energy crisis.
Wu et al. | 13
Figure 6. Illustration of the comprehensive DBTL (cDBTL) procedure including Design module (function specication, chassis selection, and
interactions design), Build module (system build), Te st module (performance test), and Learn module (modeling analysis and global optimization).
With the deepening of the microbial community, many stud-
ies have found that combinations of well characterized strains
for bio-ther a pies not onl y can r educe side effects (suc h as the r e-
mov al of drug-r esistant bacteria), the y also mak e full use of the
adv anta ges of each strain (Tan et al. 2021 ). For example, lactic acid
bacteria have the advantages of biological safety and metabolic
diversity (Liu et al. 2022c ), and have been used to construct syn-
thetic microbial ecosystems in recent years. Specically, Mao et
al. designed the gene loop of L. lactis , so that it can be applied to
the diagnosis and colonization resistance of V. cholerae (Mao et al.
2018 ). With the help of hydrogel consisting of the poly (ethylene
glycol) diacrylate and chitosan (CS), Li et al. have also developed
a two-strain consortium ( Synechococcus elongatus and L. lactis ) as a
photoautotrophic living material to promote skin wound healing
(Li et al. 2023a ). In the S. elongatus - L. lactis consortium, S. elonga-
tus PCC7942 was provided to produce sucrose by photosynthesis,
while the L. lactis was designed to use the intermediate sucrose for
cell growth and secreting the functional biomolecules . T herefore ,
exploiting some more consortia-based bio-therapies for different
diseases will be an important area for future medical applications
of microbial communities.
Bio-remediation engineering can be guided by synthetic biol-
ogy and engineering principles to realize bio-remediation, such
as plastic bio-degradation and w astew ater treatment (Wei et al.
2020 ). Man y r esearc hers hav e conducted bio-degr adation by top-
down screening or engineered synthetic microbial communities.
For example, Wang et al. studied the community succession
of plastisphere of polyethylene lm (Wang et al. 2023 ). Results
sho w ed that the community combination of Rhodobacter sp. Rs
and Bacillus aryabhattai 5–3 had high polyethylene mulching lm
(PMF) degradation efciency (Fig. 9 a). Phenanthrene is a polycyclic
ar omatic hydr ocarbon with carcinogenic effects. Zhang et al. ra-
tionally distributed 17 k e y genes of phenanthrene degradation
into three E.coli strains, thus contributing to degrading consor-
tium, which had synergistic functions and clear division of labor
(Fig. 9 b) (Zhang et al. 2022 ). After optimizing the degradation con-
ditions, 90.66% phenanthr ene degr adation was ac hie v ed within 21
da ys . Furthermore , microbial communities pro vide an econom-
ical and eco-friendly option, as well as an excellent paradigm
for pollutant treatment capacity. For instance, to realize the bio-
degradation of acetoacetanilide (AAA) in hypersaline w astew a-
ter, a synthetic consortium, including P aenarthr obacter, Rhizo-
bium, Rhodococcus, Delftia, and Nitr atir eductor, was de v eloped
to tr eat AAA waste water with differ ent water quality c har acteris-
tics (Fig. 9 c) (Zhang et al. 2023 ). To bio-degrade the palm oil mill
efuent (POME), Islam et al. utilized the response surface method-
ology to e v aluate the performances of the two-strain inoculated
microbial fuel cells, including Klebsiella variicola and P. aeruginosa
14 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Figure 7. Illustrations of the assembling of microbial ecosystems for bio-computing. (A) Diagram of the fragrance-programmable analog-to-digital
converter with Boolean expression logic, including sampling-and-quantization module, gas-to-liquid transducer, and the digitizer module with signal
amplier. (B) Dia gr am of 7-str ain AND–XOR biocomputing circuit with thr ee inputs and four orthogonal c hannels , i.e . anhydr otetr acycline HCl (aTc),
cuminic acid (Cuma), N-(3-Oxohexanoyl)-L-homoserine lactone (3OC6), and p-coumaroyl-HSL (pC). (C) Schematic diagram of the 7-strain digital
display with four signal inputs, i.e. IPTG, N-(3-hydr oxytetr adecanoyl)-l-homoserine lactone (3OHC14), aTc, and 3OC6. (D) Sc hematic dia gr am of a
QS-based perceptron network among two E. coli strains that based on the sending and receiving 3OC6 and 3OHC14 QS molecules.
(Fig.
9 d). Ther efor e, differ ent micr obial ecosystems will provide us
with important selections and strategies to face the major chal-
lenges of environmental safety.
As stated abo ve , the r a pid de v elopment of biotec hnology in
synthetic biology has expanded the engineering ability of micro-
bial ecosystems, which can effectively realize diversied func-
tions in different elds, including but not limited to the afore-
mentioned bio-computing, bio-manufacturing, bio-ther a py, and
bio-remediation (Sgobba and Wendisch 2020 , Jiang et al. 2023b ).
Ho w e v er, most of the applications were based on the cocktail
of microbes, and metabolic division of biological pathways for
the distribution of functions . T he dened microbial communities
should be designed and optimized mor e r ationall y based on some
other str ategies, suc h as QS de vices , metabolite-based sensors ,
and chassis selection.
Chassis selection
It is reported that the diversity of synthetic biology circuits and
the applicability of different tools are suitable for different chas-
sis cells (Calero and Nikel 2019 ). With the de v elopment of syn-
thetic biology, E. coli , S. cerevisiae , Bacillus subtilis , Lactococcus lactis ,
Corynebacterium glutamicum , and Streptom yces hav e acted as com-
mon microbial chassis to produce a bounty of products (Liu et al.
2020b ). Ther e ar e man y efcient tools to engineer the aforemen-
tioned chassis strains and they have well-studied genomes and
fast growth rates. Note that the understanding of chassis strains
is still limited, and m uc h r emains unclear in the dynamics and
pr operties of heter ologous modules, thus leading to the difculty
in ac hie ving the a pplications of optimal c hassis. An ideal c hassis
should be an organism harboring the availability of genomic se-
quences and a r elativ el y compr ehensiv e metabolic network (Vick-
ers et al. 2010 ). Mor eov er, a good c hassis supports v arious genetic
manipulations and modications to scale up its application elds
(Adams 2016 ). Chassis selection relies on microbial physiological
c har acteristics, suc h as the tolerance to heat and high concen-
tr ations of pr oducts (Choi et al. 2019 ). Especiall y in the process of
c hemical pr oduction using toxic substrates or intermediates, the
host’s tolerance to environmental stress is an important condi-
tion for efcient pr oduction. In addition, man y conv entional c has-
sis str ains ar e not ideal hosts for bioproduction due to the lack
of synthetic tools, low yields, slow growth rates, or vulnerabil-
ity to envir onmental c hanges. In light of the inherent drawback
and bottleneck of model chassis strains in heterologous expres-
sion of complex products, engineering non-model microbes to ef-
ciently biosynthesize metabolites has attracted increasing atten-
tion. Ther efor e, the engineering and selection of c hassis micr oor-
ganisms ar e pr acticall y indispensable for metabolic engineering
(Volk et al. 2023 ). To have a better design for microbial ecosys-
tems, it is essential to select and deal with the basic problems
of c hassis cells, suc h as identifying optimal genetic modules, se-
lecting a suitable host, investigating the metabolic network, and
mastering genetic editing tools. More details can be r eferr ed to
in other r e vie ws that focus on the engineering and selection of
c hassis str ains for natur al pr oducts (Xu et al. 2020 ) or secondary
metabolites (Liu et al. 2022b ).
Interactions design
T he in v estigation of micr obial inter actions of a comm unity is a
k e y issue in understanding the stability maintenance. Note that
micr obial inter actions can be divided into six types: m utualism
Wu et al. | 15
(D)
(A) (B)
(C)
Figure 8.
Illustrations of the assembling of microbial ecosystems for bio-manufacturing. (A) Diagram of division of labor for rosmarinic acid (RA)
production, including caffeic acid (CA), salvianolic acid A (SAA), and RA module. (B) Schematic of the carbon metabolic ow in the three-strain
ecosystem to produce silybin/isosilybin. glpK gene encoding glycerol kinase and glutamate synthesis genes were knocked out in strains 1 and 2, while
genes involved in the glucose utilization pathway ( glK ptsG manXYZ ) and entry into the TCA cycle were knocked out in strain 3. (C) Schematic of the
carbon metabolic ow in a microbial consortium-based consolidated bioprocessing strategy for the production of short-chain fatty acids from
lignocellulose using lactate and acetate as central intermediates. (D) Schematic of the compartmentalization strategy on carbon metabolic and
nitrogen metabolic ow for boosting lactic acid conversion from lignocellulose via consolidated bioprocessing.
( + / + ), favoritism ( + /0) or favoritism (-/0), parasitism or predation
( ±), competition (-/-) and neutrality (0/0) (Faust and Raes 2012 ).
These interactions can have different impacts on related species,
thus determining the corresponding community morphology (Liu
et al. 2020a ). For example, Zhou et al. redesigned the compet-
itiv e r elationship thr ough carbon source allocation (xylose and
acetate) as a mutualistic interaction between the two species
(Fig. 10 a) (Zhou et al. 2015 ). Specically, glucose was initially used
as the common substrate for the co-culture of E. coli and Saccha-
romyces cerevisiae , but the growth of E. coli was seriously affected by
the ethanol produced by yeast. Ther efor e, xylose and acetate were
added as carbon sources for E. coli and S. cerevisiae , r espectiv el y,
and the co-culture interaction between them was changed, and
the yield of target products was effectively improved. To inves-
tigate how interaction variability shapes succession of synthetic
micr obial comm unities, Liu et al. designed synthetic micr obial
consortia with three strains of Lactococcus lactis (named as Kp, C α,
and C β) induced by environmental pH to quantitatively capture
the dynamic changes of different communities (Fig. 10 b) (Liu et
al. 2020a ). Liao et al. designed r ecipr ocal inhibitory circuits medi-
ated by to xin-antito xin (TA) pairs in thr ee E. coli str ains, with eac h
str ain pr oducing its own TA pair and the specic toxin a gainst the
next strain, similar to the rock-paper-scissors interaction model
(Fig. 10 c) (Liao et al. 2019 ). The same QS signaling molecules were
a pplied to sync hr onize and coordinate the cell gr owth of differ-
ent populations . T his unique inter action pattern ensur es genetic
stability and species diversity of gene circuit function (Liao et al.
2020 ). Kong et al. conducted a systematic work for the design of
microbial consortia with six dened social interactions to estab-
lish social-inter action engineering, whic h was r ecognized as an ef-
fective and valuable route for microbial ecosystem programming
(Fig. 10 d) (Kong et al. 2018 ). Ther efor e, designing specic interac-
tion relationships for synthetic microbial consortia will contribute
to exploring the underl ying mec hanisms and potential applica-
tions.
Build module
The diverse toolbox of synthetic biology with new genetic ele-
ments enables us to engineer micr oor ganisms with incr easingl y
complex functional le v els and incr easing numbers of species . T he
biotechnology of modular assembly of gene regulatory elements
and encoded synthetic enzymes is also constantly being updated.
Building experiments for the regulations of microbial ecosystems
is mainly based on various biological cross-talk or orthogonal cir-
cuits (such as genetic and protein circuits; more specic details
have been listed in the aforementioned “Cross-talk regulation cir-
cuits” and “Orthogonal regulation circuits”) to ac hie v e the corr e-
sponding regulations based on metabolic utilizations or commu-
nications. Note that, in order to better select and a ppl y differ ent
circuits for various functions, we have summarized the character-
istics, such as the chassis cells , advantages , dra wbacks , and the
potential applications of the aforementioned toolkits, in Table 2 .
For more of the latest regulations and synthesis pathways of as-
sembl y str ategies, r eaders can r efer to an extensiv e r e vie w (Young
et al. 2021 ).
16 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Tab le 2. Summarization of the c har acteristics of the cross-talk and orthogonal regulation strategies.
Types Str a tegies Chassis Ad v antages Limits Applications
Cross-talk
regulation
AHLs Prokaryotic
(G + /G-)
r Diversied and modular
circuits
r Easy to transform and
utilize
r The intensity of crosstalk is
affected by external culture
conditions and internal
expression intensity
r Time and intensity of
activ ation r egulation ar e
difcult to optimize
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
r Bio-remediation
r Others
AIPs Prokaryotic (G + )
r The mechanism is clear
r Modular communication
circuit
r Not portable
r Exogenous natural products
may block the AIP binding
pathway
r Bio-computing
r Bio-ther a py
AI-2 Prokaryotic and
Eukaryotic
r Clear transduction
pathway
r Regulate many phenotypes
r Unstable structure of signals
r It is difcult to quantitativ el y
measure and add exogenously
r Bio-ther a py
r Bio-manufacturing
Indole Prokaryotic
(G + /G-)
r Use indole to gain survival
adv anta ge
r Easy access
r Concentr ation gr adients lead
to pleiotropic effects
r Mechanism is not yet fully
understood.
r Bio-ther a py
r Bio-remediation
DSFs Prokaryotic and
Eukaryotic
r Diversity
r Clear signal transduction
pathway
r The interaction mechanism
with plants is not yet fully
understood
r Fe w pr actical a pplications
r Bio-ther a py
r Bio-manufacturing
Others Prokaryotic and
Eukaryotic
r Diversity
r Helpful for the analysis of
micr obe–host inter actions
r Mechanism is not yet fully
understood
r Most of them are only
qualitativ e anal yses but not
quantitativ e measur ements
r Bio-ther a py
r Bio-manufacturing
Orthogonal
regulation
Biosensors Prokaryotic and
Eukaryotic
r Portable
r User-dened
r Diversity
r Poor versatility
r Time-consuming and
labor-intensive optimization
process
r Difcult to accur atel y
quantify, and highly
dependent on metabolic
pathways
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
r Bio-remediation
r Others
Wu et al. | 17
Tab le 2. Continued
Types Str a tegies Chassis Ad v antages Limits Applications
RNAPs Prokaryotic
(G + /G-)
r High expression level
r Strict regulation of genes
r Simple operation
r Irrational distribution will
lead to high metabolic burden
on the host
r Relativ el y narr ow r ange
r Biocomputing
r Bio-manufacturing
RNA-based
switches
Prokaryotic
(G + /G-)
r High specicity
r High afnity binding to
various metabolites
r Time-consuming and costly
for screening
r Relativ el y low compatibility
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
CRISPRs Prokaryotic and
Eukaryotic
r Versatility
r Wide host applicability
r High compatibility
r Realize multi-stable and
dynamic control
r Potential off-target effects
r High expression causing
cytotoxicity
r Restricted target genes and
lar ger pr oteins
r Increase metabolic burden
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
r Bio-remediation
r Others
DNA-binding
regulations
Prokaryotic and
Eukaryotic
r Modularization
r Stabilize genes expression
r High compatibility
r Lack of rational guidance from
models
r Trial-and-error optimization
design is time-consuming and
laborious
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
Ribosomes Prokaryotic
(G + /G-)
r Strong orthogonality
r Ra pid r egulation
r Limited to specic chassis
organisms
r Low screening efciency
r Bio-manufacturing
Riboswitches Prokaryotic and
Eukaryotic
r High dynamic range
r Low crosstalk
r Composability
r Single input type (trigger RNA)
r Strictly designed nucleic acid
sequence
r Bio-computing
r Bio-ther a py
ncAAs-based
aaRS/tRNA
pairs
Prokaryotic and
Eukaryotic
r Strong specicity
r Diverse functions
r High sensitivity
r Difcult to design and screen
r ncAAs are costly and difcult
to synthesize
r Limited dynamic range of
regulation
r Bio-manufacturing
r Bio-ther a py
r Bio-safety.
Protein
circuits
Prokaryotic and
Eukaryotic
r Versatility
r Flexibility
r Ra pid r esponse (minutes)
r Lack composability
capabilities
r r equir e de novo design or
optimization
r Poor portability
r Bio-computing
r Bio-manufacturing
r Bio-ther a py
18 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
Agricultural Soil Soil microbial
community
Microbial
ecosystem
Polyethylene mulching film (PMF)
(A)
Bacillus aryabhattai 5-3
Rhodo bacter sp . Rs
E. coli 1
E. coli 3
E. coli 2
HO
HO
O
HO
HO
HOOC SCoA
O
SCoA
O
H3C
H2O+CO2
(B)
phdF phnD ph dG
nidD phnF
nahC nahD nahE
nahF nahG
pcaI pcaJ pcaF
catA catB catC catD
Aromat ic rin g cleava ge module Salicy lic acid module
Catechol meta bolism module
H2O+CO2
(C)
H
N
OO
NH2
AAA NaCl AAA NaCl AAA NaCl
High salinity
High concentration
Soil River Marine
sediment Indus try WWTP
1,3-propanediol
Palm oil mill e ffluent (POME)
pyocy anin
Oil contents
Trig lyceride
By-product
Ethanol
Glycerol
3-hydroxypropionaldehyde (3-HPA)
1,3-propanediol
Enhan ced elec tron shuttle mediators
Enhanced power generation
Klebsiella variicola
P. aeruginosa
(D)
Acetoacetanilide (AAA)
Wastewat er treatme nt pl ant (WWTP)
Figure 9.
Illustrations of the assembling of microbial ecosystems for bio-remediation. (A) Schematic diagram of polyethylene mulching lm (PMF) with
the combination of Rhodobacter sp . Rs and Bacillus aryabhattai 5–3. (B) Illustration of division of labor in the degradation of phenanthrene in a
consortium consisting of three E. coli strains. (C) Diagram of consortia-based bio-degradation of acetoacetanilide (AAA) in the treatment of w astew ater,
which is from industry, using a w astew ater treatment plant (WWTP), etc. (D) Schematic of coculture of Klebsiella variicola and P. aeruginosa in
bio-degrading palm oil mill efuent (POME) for a better performance of microbial fuel cells.
Test module
The test module for the assembly of microbial ecosystems in-
volv es using differ ent tools to measur e tar get phenotypes and
e v aluating c har acteristics to determine the efcacy of the design
and build modules. Note that the tools for measuring target phe-
notypes have been comprehensively summarized in an exten-
siv e r e vie w (Lawson et al. 2019 ), whic h includes differ ent high-
thr oughput phenotypic scr eening tec hnologies, suc h as m ulti-
omics integr ation, isotopic tr acers, mass spectr ometry ima ging,
and microuidics , etc. T herefore , in this section, we only introduce
the summary for e v aluating c har acteristics, including system sta-
bility , productivity , functional exibility , and species diversity, etc.,
to determine the engineering efcacy.
Resilience is an important index of the stability of the microbial
community, including elasticity and ecological amplitude . T he for-
mer refers to the recovery rate of the microbial community, while
the latter refers to the maximum deviation that the consortia can
r ecov er. Coyte et al. e v aluated the stability of the microbial ecosys-
tem fr om thr ee aspects, namel y, the possibility of the consortia
returning to the original state after a small disturbance, dynamic
c har acteristics when facing specic disturbance, and the response
time r equir ed for r ecov ery (Coyte et al. 2015). A ne w micr obial in-
teraction model was developed by combining the consumption
and production of the corresponding resources . T hen the corre-
sponding Jacobian matrix was used for determining whether the
equilibrium was locally stable according to the eigenvalues of the
matrix (Butler and O’Dwyer 2018 ). Di and Yang discussed the ef-
fects of different factors on the stability of microbial ecosystems
in combination with substrate utilization and microbial interac-
tions, and e v aluated the effects fr om thr ee aspects (Di and Yang
2019 ). The rst is how quic kl y the consortium can r ecov er and
conv er ge to the corresponding steady state after deviating from
the stable state . T he second is the range of the corresponding op-
er ating par ameters when the consortium r eac hes a steady state.
The third is to e v aluate whether ther e will be m ultiple steady
states or whether there will be stable oscillation behavior.
Except for the stability test, some other c har acteristics, suc h
as div ersity, pr oductivity , and exibility , were also focused on by
Wu et al. | 19
Figure 10. Micr obial inter actions design for differ ent synthetic micr obial consortia. (A) Inter action design of E. coli –S. cerevisiae consortium for
production of oxygenated taxanes through the utilization of xylose and acetate. (B) Interaction design of the L. lactis C α-C β-Kp community in different
pH conditions . T he pH-r esponse pr omoter dynamicall y r ealizes its switc hes fr om inactiv ation to activ ation with pH r eduction. (C) Inter action topology
of a r oc k-pa per-scissors consortium. Eac h E. coli str ain could kill or be killed b y one of the other tw o strains b y producing its o wn to xin-antito xin pair
and the toxin, such as the colicin E3, E, and V, against the next str ain. (D) Inter action dia gr am of thr ee- and four-strain consortia composed of different
L. lactis strains. For example, ther e ar e commensalism r elationships among CmA, CmB, and CmBn, as well as the complex interaction consisting of the
two commensalism strains (CmA and CmBn), a predation strain (PrB), and a cooperation strain (CoAg).
some other r esearc hers . For example , r ecentl y, Hu et al. pr oposed
that the behavior of microbial ecosystems can be predicted by
mastering only two community-scale control variables , i.e . eco-
logical diversity and microbial interactions (Hu et al. 2022 ). They
found that an increase in the number of species and the av er a ge
interspecies interactions caused the microbial ecosystem to un-
dergo phase transitions among three distinct dynamical phases,
from a stable equilibrium of all species to a stable coexistence
of some species, and nally to a continuous oscillation of species
numbers o ver time . Productivity is one of the important indica-
tors of interest in the eld of metabolic engineering (Anesiadis
et al. 2008 ). Ther efor e, Di and Yan g have conducted a compre-
hensiv e anal ysis of pr oductivity and stability for two-str ain and
thr ee-str ain comm unities, and found that ther e is a certain trade-
off between the two objectives in synthetic microbial consortia (Di
and Yang 2019 ). Besides, Libusha K ell y pr oposed that micr obes can
tune their functional output based on signals from surroundings,
which was termed as functional exibility (Segrè et al. 2023 ). They
pointed out that understanding of the functional exibility of the
micr obial comm unity will help us identify the desired functions.
In short, when assembling a specic consortium, we need to test
as m uc h as possible about its comm unity-le v el c har acteristics in
order to gain more knowledge for better optimizing the microbial
ecosystems and realizing the specic function.
Learn module
The Learn module of the cDBTL cycle is essential for increasing
the efciency of design, build, and test modules . T he new knowl-
edge from the Learn module is evolved from the modeling analy-
sis and specic optimization, which will be incorporated into the
subsequent DBTL cycle.
Modeling analysis
The quantitativ e c har acterization and anal ysis of micr obial
ecosystems by mathematical models in the Learn module pro-
vide a unique perspective for deepening the understanding of
the underlying physical and molecular mec hanisms (Goryac he v
2011 ). Recentl y, man y r esearc hers hav e r eported that genome-
scale metabolic models (GEMs) can pr ovide mor e compr ehensiv e
perspectives for the deciphering and design of microbial commu-
nities . T he combination of GEMs and a large amount of avail-
able omics data will provide better analysis for processing and de-
signing microbial communities (Esvap and Ulgen 2021 , Kim et al.
2022 ). Flow balance analysis (FBA), the specic modeling method
of the GEMs, is usually used to predict the ux distribution and
gene phenotype, which is often realized based on constraint re-
construction and analysis (or COBRA) (Heirendt et al. 2019 ). For
example , Duko vski et al. developed a platform for the computa-
tion of microbial ecosystems in time and space (COMETS), which
compartmentalizes and encapsulates metabolic models of differ-
ent species into a given metabolic en vironment (Duko vski et al.
2021 ). Specically, COMETS uses dynamic FBA to simulate the spa-
tial distribution of a microbial community, and also introduces a
diffusion model to predict the proportion of species, as well as the
temporal and spatial dynamics of the community (Harcombe et
al. 2014 ). OptCom, a metabolic model used to optimize the produc-
tion of consortia, was de v eloped to r ealize the co-cultiv ation of D.
vulgaris and M. maripaludis to pr oduce hydr ogen ener gy (Zomor-
rodi and Maranas 2012 ). SteadyCom, a metabolic model used
to impr ov e the stability of micr obial comm unities, could be a p-
plied to solve the problem of unbalanced metabolic ux distri-
bution (Chan et al. 2017 ). Mor eov er, FLYCOP pr ovides a universal
fr ame work and high-precision kinetic metabolism models, which
20 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
could exibly achieve multi-objective optimization, predict bac-
terial population ratio and amino acid secretion rate of different
strains (García-Jiménez et al. 2018 ). For mor e details r egarding the
application of GEMs in understanding and designing interactions
within microbial communities, please refer to our recent review
(Wu et al. 2024).
Except for the GEMs, some other ecosystem modeling ap-
pr oac hes, suc h as Lotka-Volterra (GLV), individual-based, and
consumer–r esource models, ar e also imper ativ e for establishing
the quantitative link between community structure and func-
tion. For example, the classical GLV, a minimal dynamical systems
model of microbial communities, is a re presentati ve species-only
population-le v el models with pairwise interactions. Note that the
GLV model is r elativ el y simple to construct because its par ameters
can be inferred from temporal or steady-state data of the com-
munity (Liu 2023 ). The construction of compr ehensiv e metabolic
models, such as individual-based and consumer-resource models,
is insepar able fr om the a pplication of micr obiome tools, dynamic
pr ocess contr ol and monitoring. Specicall y, v an den Ber g et al.
provided a more detailed summary of how to select the appropri-
ate model according to the c har acteristics of the need to predict
(v an den Ber g et al. 2022 ). It should be pointed out that there is a
trade-off between the number of parameters in the model of mi-
crobiota and the difculty of obtaining experimental c har acteri-
zation parameters . T he curr ent m ulti-omics tec hnology supple-
ments and enriches the quantity and quality of existing models
to a certain extent. The transcriptomics and proteomics provide
model data of the gene expression process for building accurate
models based on individuals, and enhancing the predictability of
the assembling of various microbial ecosystems. Metabolomics
under different environmental factors is conducive to the regu-
lation of gene expression intensity and impr ov es the long-term
stability of the consortia. Due to limited space, for more details of
mathematical modeling on the analysis of microbial community,
readers can refer to some other excellent reviews (Colarusso et al.
2021 , Esv a p and Ulgen 2021 , San León and Nogales 2022 , van den
Berg et al. 2022 ).
Global optimization
Certainly, the assembling of microbial ecosystems is not a sim-
ple combination of strains, but a compr ehensiv e construction and
optimization for different objectives (such as stability, robustness,
div ersity, pr oductivity, and so on) with considerion of both internal
interaction rules and external environmental changes. For exam-
ple, Wortel et al. used the enzyme-ux cost minimization model
to show that the trade-off between growth and yield is jointly
determined by internal metabolic kinetics and external environ-
mental conditions, so the global optimization of the system re-
quir es compr ehensiv e consider ation of m ulti-dimensions (Wortel
et al. 2018 ). It should be pointed out that temporal characteris-
tics (Ronda and Wang 2022 ), and spatial distribution (Ozgen et
al. 2018 ), would affect the stability of the microbial community,
which calls for optimization globally for the assembly of the mi-
cr obial comm unity. Ther efor e, optimization of the internal and
external factors of the micr obial comm unity can be reected by
the common microbial composition, time , space , and their com-
bination (Grandel et al. 2021 ) (more details in Fig. 11 ). The control
mechanisms of the three dimensions were usually highly corre-
lated, interdependent, and could be used in combination. Speci-
cally, the QS-based consortium is time- and density-dependent in
essence; the corresponding functions will only be activated when
the population r eac hes a certain density. This indicates that the
timing and composition optimization of the QS-based synthetic
consortium is a basic r equir ement for the assembling of different
micr obial ecosystems. Recentl y, we pr oposed and constructed QS
langua ge “inter pr eter” ecosystems in the linear and circular thr ee
E. coli strains, as well as optimizing them by combining strain-
le v el micr oscopic and bulk-le v el macr oscopic measur ements in
response to dramatic environmental changes (Wu et al. 2024 ).
Microbial ecosystems can be also dynamically manipulated
and constructed with spatial distribution and multiple interac-
tion topologies. For example, Shahab et al. designed a reasonable
distribution of different strains according to different oxygen de-
mands to ac hie v e efcient pr oduction of lignocellulosic to SCFAs
(Shahab et al. 2020 ). The study sho w ed that the self-assembly of
microbial ecosystems based on adhesion (Glass and Riedel-Kruse
2018 ) and the diffusion gradient of signal molecules (Boehm et al.
2018 ) would have a certain inuence on the spatial control of dif-
fer ent str ains. Note that the r a pid de v elopment of biological ma-
terials (An et al. 2023 ) has gr eatl y enric hed the regulations of syn-
thetic microbial consortia, such as the use of hydrogels, 3D print-
ing materials, and micr o-enca psulation r eactors, etc. In addition,
the cultivation of the initial conditions (such as nutrition distri-
bution, proportion of bacteria and total biomass, etc.) will interact
with external conditions, by affecting the metabolism and growth
tness of community members . T he initial conditions of the com-
m unity ar e also important to impr ov e the genetic stability of the
micr obial comm unity so that different gene circuits can function
better at different time scales (Ronda and Wang 2022 ). Further-
mor e, the differ ences in the external envir onment will cause dif-
ferent populations to use different resources in the system, thus
enhancing the local internal interactions and avoiding the over-
all collapse of the ecosystem. Ther efor e, in the optimization of
synthetic micr obial consortia, differ ent inter actions should be de-
signed, and environmental adaptation and spatial coordination of
microbes should be carried out to improve their environmental
adaptability. In the Learn module, efcient prediction of possible
interactions and the potential structure of the community with
the aid of different models are important for guiding the optimiza-
tion of the function and stability of microbial ecosystems.
To sum up, the unclear internal interaction rules among mi-
crobes and the evolutionary mechanism of external environment
ada ptation gr eatl y limit the r ational design and wider a pplica-
tion of microbial ecosystems (Lopatkin and Collins 2020 ). Relevant
r esearc hers hav e de v eloped some good c hassis str ains, and con-
structed se v er al co-cultur e systems to ac hie v e the corr esponding
objectives and specic functions. Ho w ever, the studies w ere rel-
ativ el y independent and scattered, and most of them only car-
ried out simple combinations of strains through metabolic inter-
actions or QS-based communications, without conducting quan-
titativ e, contr ollable, and stability anal ysis, let alone the optimiza-
tion of the corr esponding systems. Mor eov er, the inuence of ex-
ternal environment on the dynamic c har acteristics of micr obial
ecosystems was not consider ed. Ther efor e, it is a good choice to
assemble microbial ecosystems b y follo wing our proposed cDBTL
pr ocedur e to select chassis strains, design internal interactions,
build the corresponding ecosystems with cross-talk or orthogonal
circuits , test performances , anal yze r ele v ant dynamics, and opti-
mize ecosystems from composition, time, and space dimensions.
Concluding remarks and future
perspectives
In summary, the design and optimization of complex ecosystems
not only greatly improve the understanding of microbial inter-
Wu et al. | 21
Space
dimension
Time
dimension
Time
Density
Time
Density
Time
Density
Time
Density
Environmental
perturbation
Microbiome diversity
Proteins
Peptides
Enzymes
Metabolites
Composition
dimension
Combination
Adhesion
Spatial
separation
Stability Robustness
Productivity Diversity
Populations
optimization
Figure 11. Schematics of global optimization for different objectives, such as productivity , stability , r obustness, and div ersity fr om time dimension,
space dimension, composition dimension, and their combination. The time-based optimizations are often conducted using different activation
moments (white left). The space-based optimizations are often conducted using adhesion and spatial separation (white right). The composition-based
optimizations are often conducted by controlling different initial conditions for different populations (white below).
actions, but also transform them into various environmentally
friendl y biological pr oducts that can meet the needs of society. We
envision that the de novo assembly of user-dened functional mi-
cr obial ecosystems fr om molecular circuits to comm unities will
be more widely used under the guidance of our proposed cDBTL
pr ocedur e. Furthermor e, the principle of bottom-up modular as-
sembly contributes to a deep understanding of the deep-seated
mechanisms between the structure and function of synthetic mi-
cr obial comm unities. Finall y, in vie w of the fact that ther e ar e
man y av ailable cr oss-talk and orthogonal circuits for the design
and construction of microbial ecosystems, various performances
and modelings for the testing and learning of their dynamics and
c har acteristics, futur e consortia-based biological applications will
gr aduall y enter the r a pid de v elopment sta ge . T her e will be mor e
challenges for further developing bottom-up assembly of func-
tional ecosystems.
T he de velopment of a cross-talk toolkit
Compared with the traditional single-species culture, the micro-
bial community has higher resistance and recovery ability in deal-
ing with the inv asion of heter ogeneous str ains and envir onmental
disturbance (Lindemann et al. 2016 ). To obtain a predictable man-
ner, the microbial consortia need to be rationally assembled by
reducing the retrospective effect among modules, minimizing the
inter action between heter ogeneous loops and the host, and en-
suring maximum signal transmission stability and delity. There-
fore, in the past decades, most r esearc hes on microbial engineer-
ing were conducted based on the de v elopment and optimization
of orthogonal channels to regulate different systems, without or
avoiding considering the existence of signal crosstalk. Ho w ever,
signal crosstalk is abundant in natural microbial communities.
Mor eov er, bacteria hav e r etained a lar ge number of highl y orthog-
onal signal transduction systems in the long-term evolution but
also produced a large number of crosstalk regulation mechanisms
(more details in “Cross-talk regulation circuits” and “Orthogonal
r egulation circuits”). Pr e viousl y, our theor etical sim ulations for QS
cr oss-talk comm unication sho w ed that the combined application
of most QS systems can effectiv el y pr omote the r ational allocation
of metabolic ux among m ultiple str ains (Wu et al. 2022b ). How-
e v er, compar ed with the ubiquitous QS system in nature, the num-
ber of well-c har acterized cr oss-talk elements is still the tip of the
iceber g, whic h calls for more mining and quantitative characteri-
zation. At present, the molecular mechanisms of cross-talk regu-
lations have not been fully and clearly characterized and under-
stood. We propose some limitations hindering the de v elopment of
a cross-talk toolkit: (i) the challenges involved in solving the cross-
talk mechanisms and inuencing factors behind the diversity of
natur al comm unities; (ii) quantifying the str ength of cr osstalk un-
der given environmental conditions; and (iii) how to introduce and
e v aluate cr oss-talk r egulation circuits for a specic function.
Simultaneous optimization of gene circuits and
communities
The continuousl y de v eloping orthogonal and cr oss-talk contr ol
systems are being rationally designed for constructing various
functional microbial ecosystems with high stability and strong
predictability. As a medium for sensing and transmitting sig-
nals, orthogonal and cross-talk modules with clear functions and
22 | FEMS Microbiology Reviews , 2024, Vol. 48, No. 6
stable properties can be used to design and assemble different
kinds of circuits to encode the functions of cells, just as electronic
computers use strippable, modular standard components to per-
form complex logic operations. Note that most circuits in the
past were designed, constructed, and optimized based on single-
cultur e a pplications . At the same time , div erse micr obial ecosys-
tems were also designed, constructed, and optimized for many
consortia-based applications at the community level. Ho w ever,
the synthesis of different genetic circuits and complex ecosys-
tems was curr entl y separ ated, whic h should be a unied whole.
The optimization of gene circuits in single strains (local optimiza-
tion) may not be suitable for the goals of the whole microbial
comm unity (global optimization). Ther efor e, synthetic ecosys-
tems with reduced complexity and predictable circuit functions
should be established from scratch with the optimization of gene
circuits and communities simultaneously . Accordingly , we provide
some difculties for the simultaneous synthesis of genetic circuits
and communities: (i) what principle should be adopted to model
and design the synthesis of circuits and community; (ii) the chal-
lenges involved in determining the ov er all optimization objectiv e,
stability, productivity or both; and (iii) difculties in the experi-
mental tting for some genetic circuits and chassis cells of micro-
bial ecosystems.
The execution of the cDBTL cycle
Certainly, we need to select suitable chassis cells and design the
specic interaction types to construct the microbial ecosystems
based on the expected functional r equir ements . For example , the
non-model strain Yarrowia lipolytica , with strong lipid accumula-
tion capacity and excellent physiological tolerance, can be se-
lected according to different functional demands acting as the tai-
lor ed str ain in differ ent micr obial ecosystems (P ark and Ledesma-
Amaro 2023 ). When constructing a synthetic microbial consor-
tium for metabolic engineering, we tend to introduce symbiotic
or cooper ativ e inter actions into the tar get ecosystem to maintain
maximum metabolic ux ow to the target product, as well as the
system stability. At the same time, the assembling of microbial
comm unities m ust also take into account the inseparable natu-
r al e volution and external controllable conditions on the time and
space scale . T he predictable output of the expected function can
be ac hie v ed to a certain extent thr ough the optimization and ex-
pansion of the model. Ther efor e, to obtain quantitativ e r elation-
ships in ecology, different mathematical models should be com-
bined with experiments to get better performances. According to
the expected functional r equir ements, after designing and select-
ing specic components, the assembly of microbial ecosystems
needs to meet the controllability of the systems, as well as system
biosafety. Biosafety-controlled methods include the use of toxin-
antitoxin systems, regulation of responses to specic environmen-
tal factors, the addition of amino acids essential for growth or un-
natural amino acids, and gene mutation suppression.
Further applications of microbial ecosystems
In general, consortia-based applications in various elds are just
emer ging, and man y cases are at a preliminary stage . T he research
of bio-computing focuses on the logic gates encoded by cells with
m ulti-le v el inputs, whic h onl y car es about the performances of
the accurate functional outputs, but not the specic molecu-
lar mechanism. Consortia-based bio-manufacturing has been ex-
tended into the production of natural products , biofuels , com-
modity chemicals, and so on. Ho w ever, there are still great chal-
lenges in controlling the coordination of microbial ecosystems to
maintain the stability, density contr ol desir abl y, r espond to envi-
r onmental c hanges timel y, and ac hie v e efcient pr oduction. Gr eat
pr ogr ess has been made in the diagnosis of engineering bacteria
in vivo , while the functional stability and genetic stability of chas-
sis, as well as the necessary biological contr ol, ar e difcult to be
widely used in clinic. Because of the highly complex metabolic
environment in the organism, the robust design of the sensor
against the change of homeostasis and the interference of ana-
log molecules must be considered. Note that there are few cases
of interaction-based design and construction of synthetic consor-
tia for corresponding medical applications . T his ma y be because
microbial stabilization and editing are inherently challenging, not
to mention carrying them out in the complex environment of
gut system. Similarly, most of the microbial ecosystems were not
pr operl y designed and optimized for the bio-remediation, and
wer e constructed simpl y based on coc ktail composition. In order
to expand application elds, it is necessary not only to increase the
efciency of gene or protein circuits, but also to understand and
realize the coordination of the corresponding microbial commu-
nity. Indeed, the activity and abundance of protein can be quickly
adjusted by signal processing and tr ansduction thr ough highl y
orthogonal combinable and detachable protease elements (Chen
and Elowitz 2021 ). Furthermore, some communication-based reg-
ulations, such as QS molecular mechanisms, can realize the dy-
namic control of various microbial ecosystems (Wu et al. 2021b ).
To r ationall y engineer and design the complex communications,
the QS systems in non-model micr oor ganisms (e.g. human gut mi-
cr obes) ar e not well studied experimentall y, leading to another ga p
that needs to be addressed. Thanks to the excellent performance
of effective orthogonal and cross-talk circuits, as well as diverse
inter action networks [suc h as the QS-based comm unication net-
work (QSCN)], consortia-based applications will be quickly devel-
oped in the futur e. Finall y, we envision that future understanding
and applications for microbial ecosystems lie in the de v elopment
of co-cultur e tec hniques (Cao et al. 2023 ), genome editing of non-
model gut micr obiota, suc h as Bacteroides (Zheng et al. 2022 ), con-
struction of compr ehensiv e inter action networks (suc h as QSCN)
(Wu et al. 2022a ), the combination of top-down deciphering ap-
pr oac hes and some other data-driven techniques.
Ac kno wledgements
The present study was supported by grants from the China Post-
doctoral Science Foundation (2023M732599); the National Natu-
ral Science Foundation of China (32300022), the National Key Re-
search and Development Program of China (No. 2019YFA0905600,
2020YFA0907900), and the Funds for Cr eativ e Researc h Gr oups of
China (21621004).
Conict of interest : The authors declare no conicts of interest.
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Recei v ed 30 J an uar y 2024; revised 15 August 2024; accepted 17 October 2024
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