Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects

Abstract

The rising global demand for sustainable protein sources poses critical challenges across food, pharmaceutical, and industrial biotechnology sectors. Microbial expression systems provide scalable and versatile platforms for producing recombinant proteins, including enzymes, therapeutic molecules, and functional food ingredients. These platforms enable efficient biosynthesis of high-value proteins from renewable substrates often via precision fermentation, surpassing conventional methods in yield, cost-efficiency, and environmental sustainability. This review summarizes the genetic regulatory elements that govern gene expression in microbial hosts, with comparative coverage of prokaryotic and eukaryotic systems at transcriptional and translational levels. Key regulatory components, such as promoters, ribosome binding sites (RBS), untranslated regions (UTRs), signal peptides, and terminators, are discussed in the context of host-specific engineering strategies. We highlight advanced engineering approaches, including artificial intelligence (AI) assisted sequence design, CRISPR-Cas-based genome editing, and modular combinatorial optimization of genetic elements. Particular attention is given to the integration of high-throughput screening and predictive modeling tools that accelerate the rational design and optimization of microbial production systems. The review also discusses practical applications in food, pharmaceutical, and industrial enzyme production, emphasizing how genetic element engineering bridges fundamental research and biomanufacturing. Finally, key challenges and future prospects are analyzed to guide the development of next-generation microbial cell factories for sustainable protein production and industrial innovation.

Graphical abstract

Highlights

• •Genetic elements in microbes enable scalable recombinant protein production.
• •AI design and CRISPR tools enhance expression system optimization.
• •Prokaryotic vs. eukaryotic strategies compared at gene expression levels.
• •Modular parts and high-throughput tools accelerate system development.
• •Applications span food enzymes, functional proteins, and therapeutics.

1. Introduction

Protein is an essential biomolecule responsible for numerous physiological and structural functions in all living organisms [1]. A protein-rich diet plays a vital role in supporting human nutrition and contributes significantly to overall health and well-being [2]. However, the global protein supply shortage has become an important issue that needs to be addressed urgently. It is estimated that by 2050, in order to maintain the current per capita consumption level, the world will need to produce about 1.25 billion tons of meat and dairy products each year [3,4]. Moreover, due to the low efficiency of converting feed into meat and dairy products, it is difficult to meet the growing protein demand in a sustainable way by relying solely on traditional animal husbandry. Driven by environmental, safety, ethical and health factors, alternative proteins and microbial cell factories have emerged as scalable platforms to produce sustainable proteins [5,6]. Lignocellulose-based single-cell protein (SCP) production has recently gained attention as a viable approach to address protein scarcity while minimizing environmental impact. Microbial recombinant protein production, due to its high synthesis efficiency, wide source of raw materials and low environmental footprint, is particularly well-suited to meet global sustainability needs [7,8]. Recent advances in bioprocess engineering demonstrate that lignocellulosic residues can serve as efficient substrates for microbial protein production, offering an environmentally benign alternative to animal-derived feed [9]. Furthermore, the integration of AI-driven strain design and synthetic-biology optimization strategies accelerates the development of cost-effective SCP platforms, supporting the transition toward sustainable feed resources [10]. Beyond food applications, engineered microbial systems have also revolutionized biopharmaceutical and industrial enzyme production, enabling scalable synthesis of therapeutic proteins, biocatalysts, and functional bioactive [[11], [12], [13], [14]]. The versatility of microbial hosts, including Escherichia coli (E. coli), Bacillus, Streptomyces, and various yeasts, allows for precise control of gene expression, protein folding, and secretion, thereby enhancing yield and product quality across industries [[15], [16], [17], [18]].

Recombinant proteins are proteins produced by recombinant DNA technology, where a gene encoding a specific protein is inserted into a host organism (like bacteria, yeast, or mammalian cells) and the organism is then engineered to express and produce that protein in large quantities. This technology allows for the production of proteins that are difficult or impossible to obtain in sufficient amounts through natural means [19]. Microbial protein production normally uses microorganisms such as yeast, fungi and bacteria to synthesize protein-rich biomass using sugars or agricultural industrial waste as substrates. Compared with traditional animal protein production methods, this process has significant advantages in resource utilization efficiency, microbial growth rate and environmental impact, especially in reducing greenhouse gas emissions and land occupation [20]. In recent years, the rapid development of genetic engineering technology has provided precise control means for the efficient production of microbial proteins. Through the rational design and gene editing of microbial strains, the metabolic pathway can be finely regulated, thereby significantly improving protein yield and production efficiency [8]. Prokaryotic expression systems cover a variety of industrial microorganisms, such as E. coli, Streptomyces spp., Bacillus spp., Lactococcus lactis, and Corynebacterium glutamicum [21]. Yeast hosts commonly used for recombinant protein expression include Saccharomyces cerevisiae (S. cerevisiae), Komagataella phaffii (K. phaffii, formerly Pichia pastoris), Yarrowia lipolytica, and Kluyveromyces lactis [22]. Among, E. coli and S. cerevisiae have become indispensable production platforms due to their genetic tractability, rapid growth, and scalability, offering cost-effective solutions for large-scale protein synthesis [23,24]. Different expression hosts have their own advantages and limitations in terms of protein expression capacity, yield, modification methods, culture costs, and downstream processing convenience. They need to be reasonably selected and optimized according to the structural characteristics and application requirements of the target protein.

Although microbial protein expression systems have been widely used in the production of recombinant proteins due to their rapid growth, good scalability and clear genetic background, there are still many challenges in efficiently expressing high-yield functional proteins. The main bottlenecks include limited transcription and translation efficiency, incomplete protein folding, and insufficient secretion capacity. Overcoming these limitations and achieving efficient industrial production of recombinant proteins is of great significance for promoting their application in food, pharmaceuticals, industrial enzymes, and sustainable biomaterials. To this end, it is necessary to achieve precise regulation of gene expression and systematically optimize the metabolic pathways of host cells to support the efficient synthesis and accumulation of target proteins [21]. Nucleic acid elements encompass DNA or RNA sequences that play crucial roles in diverse biological processes, particularly in genetic regulation across four key microbial hosts (Table 1). They profoundly affect the synthesis efficiency of target products by regulating gene expression, optimizing metabolic pathways, and mediating gene editing, and have become a key factor in the efficient production of recombinant proteins. Different types of DNA elements (such as promoter, enhancer, transcription factor binding site (TFBS), RBS/Kozak sequence, terminator, etc.) and RNA elements (such as sgRNA, miRNA, siRNA, riboswitches, circRNA, etc.) regulate the expression performance of host cells at multiple levels such as transcription and translation [25]. The rational design and precise optimization of these nucleic acid elements are of great significance for improving the expression level and production efficiency of recombinant proteins in microbial cell factories.

Table 1.

Common regulatory elements in representative microbial hosts.

Category	E. coli	B. subtilis	K. phaffii	S. cerevisiae
Promoters	T7, lac, trc, araBAD, tac	P43, aprE, spoVG, srfA, xylA	AOX1 (inducible), GAP, TEF1 (constitutive)	GAL1, TEF1, ADH1, CUP1, PGK1
RBS/5′UTR	Shine-Dalgarno (SD) sequence, synthetic RBS	Native or synthetic RBS sequences, SD-like sequence	Kozak-like sequences or optimized synthetic 5′UTRs	Kozak consensus sequence (e.g., A/GCCATGG)
Terminators	rrnB T1, T7 terminator, synthetic terminators	amyE, spoVG terminators	AOX1 terminator, CYC1 terminator	CYC1, ADH1 terminators
Inducible Systems	IPTG (lac), arabinose (araBAD), rhamnose (rhaBAD)	Xylose (xylA), IPTG (LacI derivative), salt- or stress-inducible	Methanol (AOX1), glycerol depletion	Galactose (GAL1), copper (CUP1), estradiol-inducible systems
Secretion Signals	PelB, OmpA, DsbA, TolB, MalE	AmyQ signal peptide, SacB leader	S. cerevisiae α-factor, native PHO1 or SUC2 signal peptides	α-factor (MFα1), SUC2, mating factor signals
Vectors/Plasmids	pET, pUC, pBAD, pQE series	pUB110, pHT01, integrative vectors	pPICZ, pGAPZ, pAO815; integration at AOX1 or HIS4	pYES2, pRS series; centromeric or 2μ-based plasmids
Chromosomal Integration	Rare; mostly plasmid-based	Common (e.g., amyE locus, lacA)	Common at AOX1 or HIS4 sites	Common; URA3, LEU2, HIS3, or HO loci used
Codon Optimization	Based on E. coli codon usage	Based on B. subtilis codon usage	Strong preference for GC-rich codons; codon usage bias considered	Optimized for high AT content; codon bias varies by gene

Previous reviews have extensively discussed strategies to enhance recombinant protein production, including host selection, expression system optimization, bioprocess engineering, and fermentation control [1,3,6,8,21,[26], [27], [28], [29], [30], [31], [32], [33]]. More recently, increasing focus has been placed on genetic engineering strategies that improve protein yield and quality in microbial expression systems. In particular, the rational design and fine-tuning of key genetic elements involved in transcriptional and translational regulation have emerged as critical factors in optimizing protein expression. This review highlights recent progress and challenges in the development and application of genetic tools for microbial protein expression, with an emphasis on the rational design and engineering of core genetic elements. Firstly, we outline the major genetic elements involved in recombinant protein expression, categorized by prokaryotic and eukaryotic microbial hosts. We then detail the regulatory roles played by these engineered genetic elements at the transcriptional and translational levels. Lastly, we summarize current methodologies for the design and modification of genetic elements, including emerging techniques such as AI-driven engineering. Together, these approaches offer promising avenues for advancing the efficiency, scalability, and versatility of microbial protein production systems.

2. Key genetic regulatory elements of microbial protein expression system

Microbial recombinant protein expression technology has been widely used in many fields such as modern food science, biopharmaceuticals, industrial enzyme preparations and agriculture, and has become a core tool to promote the efficient production and precise application of functional proteins [1]. With the development of genetic engineering and protein engineering, this technology can achieve large-scale controllable expression of target proteins. The basic process includes several specify steps (Fig. 1): Firstly, the target protein encoding gene is amplified by polymerase chain reaction (PCR), and then it is inserted into the selected expression vector through restriction endonuclease digestion and ligation reaction to construct a recombinant plasmid. The plasmid is then introduced into an adapted microbial host, such as bacteria, yeast or fungi, and relies on the host cell to complete transcription, translation and necessary post-translational modifications, and finally expresses a recombinant protein with biological activity. On this basis, the large-scale cultivation and protein accumulation of the microbial expression system are achieved by controlling the fermentation parameters, and then it is purified, characterized by structure and function, and evaluated for quality to ensure that it meets the relevant standards before it can be applied to actual production and market promotion [21]. Compared with the traditional method of extracting proteins from natural biological sources, microbial recombinant expression technology has significant advantages in terms of yield, purity, cost control, environmental friendliness and protein function customization.

Fig. 1.

Recombinant protein production process in microbial expression system.

The construction of microbial protein expression systems usually involves four key components: host strains, exogenous target genes, expression vectors, and genetic elements that regulate expression efficiency. Among them, the expression efficiency of recombinant proteins depends largely on the rational design and fine regulation of regulatory elements. These genetic regulatory elements mainly include promoters, RBS, UTRs, signal peptides, and terminators, which can be optimized through synthetic biology strategies to improve transcription and translation efficiency (Fig. 2). It is worth noting that there are significant differences in gene expression mechanisms between prokaryotes and eukaryotic microorganisms, such as transcription start site (TSS) recognition, mRNA structural stability, and translation initiation mechanisms. Therefore, the design of regulatory elements needs to be differentiated for different host systems to achieve optimal expression performance. In addition, precise regulation of gene expression at the molecular level is the key to constructing efficient engineered microbial expression systems. Transcriptional regulation mainly acts on the synthesis initiation, expression rate, and expression time of mRNA, while translational regulation fine-tunes the protein synthesis rate and efficiency, thereby achieving rational resource allocation and metabolic adaptation of cells. By coupling regulatory element design and systems biology analysis, the protein expression yield and quality can be further improved to meet the diverse needs in food, medicine and industrial applications.

Fig. 2.

Genetic architectures of microbial protein expression systems. Schematic of transcription and translation (left) and gene organization in eukaryotes and prokaryotes (right), highlighting regulatory elements and coding regions.

3. Regulatory roles of engineered genetic elements at different stages of protein expression

3.1. Genetic regulatory elements at the transcriptional level

In prokaryotic microbial expression systems, such as E. coli, B. subtilis, C. glutamicum and Streptomyces, the promoter is the core element that regulates the initiation of gene transcription. It mainly recognizes the conserved sequences in the −10 and −35 regions through the σ factor and forms a transcription initiation complex with RNA polymerase, thereby determining the initiation efficiency of transcription and the level of gene expression. The sequence characteristics of the promoter not only determine the transcription initiation site, but also have an important influence on the transcription intensity. It is the first checkpoint for regulating gene expression. Studies have shown that the promoter function not only depends on the core elements (−10, −35 regions), but is also regulated by the complex action of its surrounding sequences, DNA spatial configuration and cis-regulatory elements. Cara Deal systematically analyzed the three transcriptional stages related to promoters in prokaryotic systems, emphasizing the synergistic mechanism of cis-elements outside the core region of the promoter sequence in regulating transcription efficiency [34]. Based on this, the engineering design of promoters has become one of the key strategies to improve the expression efficiency of prokaryotic systems.

At present, the transcriptional regulation strategies for prokaryotic microorganisms mainly include the construction of constitutive, inducible or synthetic promoters to respond to different environmental signals or endogenous genetic clues. For example, Sun integrated the strong promoter P_tac from Enterobacter and 24 different fore-cistron sequences into C. glutamicum in a bicistronic design (BCD), and successfully improved its induced expression intensity through screening and sequence optimization, providing an expression module basis for efficient recombinant protein production in this strain [35]. Huang introduced point mutations in the promoter −10 box region of the transglutaminase (TGase) of S. mobaraensis by UV mutagenesis, which increased the gene transcription efficiency by 50 times and constructed an engineered strain TX1 with high TGase production [36]. In B. subtilis, the P_amyE-cdd dual promoter system was successfully used for the efficient expression of amidase in B. megaterium, providing a template for the construction of secretory proteins in strains of this genus [37]. In addition, Wang constructed an autoinducible expression module based on the LuxI/R quorum sensing (QS) system. By adjusting the copy number of the lux box on the promoter, they achieved the autoinducible extracellular expression of the target protein in B. subtilis for the first time, showing good scalability and industrial application potential [38]. In addition to the promoter, other regulatory elements such as the interaction between TFBS and specific transcription factors can further regulate the transcription rate [39]; and the terminator structure located in the 3′UTR can effectively terminate RNA polymerase extension by forming a stable hairpin structure, thereby regulating the stability and transcription efficiency of mRNA [40]. The coordinated optimization of the above regulatory elements is of great significance to improving the yield and quality of recombinant proteins in prokaryotic expression systems.

Previous studies have shown that yeast is a typical representative of eukaryotic microbial expression systems and plays an important role in the expression and production of functional recombinant proteins. Unlike prokaryotic systems, the transcription process of eukaryotic microorganisms is more complex, relying on three different RNA polymerases to synthesize different RNAs; its promoter structure is also more sophisticated, usually including TSS, TATA box, initiator element (Inr), downstream promoter element (DPE) and upstream regulatory element (URE) [41]. In 2019, Baghban systematically summarized the common yeast hosts used for recombinant protein expression, and divided them into methylotrophic (such as K. phaffii) and non-methylotrophic (such as S. cerevisiae) expression systems according to their metabolic characteristics, and analyzed their key genetic regulatory characteristics, especially the type and regulatory mechanism of promoters [42]. In recent years, K. phaffii has become an important host for recombinant protein production (such as industrial enzymes and biopharmaceutical proteins) due to its strong protein synthesis ability, regulatable expression system and suitability for large-scale fermentation [43,44].

In yeast expression systems, transcriptional regulation is usually achieved by combining engineered promoters with upstream activating sequences (UAS). For example, the combination of synthetic RNA polymerase II promoters (such as the modified AOX1 promoter), enhancer sequences, effective terminators and other elements can achieve efficient and controlled expression of exogenous genes. P_AOX1 is the most widely used methanol-inducible promoter in K. phaffii, and its structural design and functional optimization have long attracted attention. Liu constructed a high-intensity inducible promoter for expressing α-amylase by rationally designing its upstream regulatory sequence (URS), which significantly increased the enzyme activity yield [45]. In addition, in unconventional yeasts such as Y. lipolytica, researchers have developed a gene expression system with a wide range of regulation by connecting multiple UAS repeat sequences in series and combining them with a copper-inducible core promoter, demonstrating good controllability and engineering potential [46]. In addition to the yeast system, Zheng also constructed a synthetic promoter library in Aspergillus niger based on the strong P_gpdA promoter and fused efficient UAS elements upstream. The gradient regulation of expression intensity was achieved through the series connection of UAS, providing a new strategy for promoter engineering in eukaryotic systems [47].

In complex eukaryotic expression systems, gene expression is not only directly regulated by promoters, but also usually relies on a variety of cis-acting non-coding DNA elements, of which enhancers are the most representative regulatory factors. Enhancers can significantly improve the transcription efficiency of target genes by regulating the spatial conformation of chromatin and binding to specific transcriptional regulatory factors (such as activators and enhancement complexes) [48]. The long-range regulatory characteristics of enhancers make them widely used in synthetic biology to achieve spatial, temporal and dose-controlled expression regulation. In addition to enhancers, terminators also play a key role in transcription termination and mRNA stability maintenance in eukaryotic systems. Appropriate terminators can not only ensure the effective release of RNA polymerase and the precise termination of transcription, but also significantly affect the half-life and translation efficiency of post-transcriptional mRNA. Curran developed a set of short synthetic terminators with a length of 35–70 bp, which were successfully used to regulate gene expression levels in S. cerevisiae. In the optimal construction, the expression level of fluorescent protein increased by 3.7 times and the mRNA transcription level increased by 4.4 times [49]. It is worth noting that these synthetic terminators also showed good functionality in alternative yeasts such as Y. lipolytica, showing higher versatility and portability than natural terminators, providing an effective strategy for optimizing gene expression in multiple yeast platforms.

3.2. Genetic regulatory elements at the translational level

In addition to regulation at the transcriptional level, fine regulation at the translational level is also crucial for improving the expression efficiency of recombinant proteins. In prokaryotic expression systems, the efficiency and adjustability of gene expression are jointly regulated by multiple types of genetic elements, especially key regulatory elements in the translation initiation process, including RBS, translation initiation region (TIR), and codon usage preference. RBS in bacteria usually refers to the SD sequence, which guides mRNA to bind to the 30S subunit through complementary pairing with the ribosomal 16S rRNA and is a key factor in translation initiation. Designing and optimizing RBS sequences in synthetic biology has become a common strategy for improving recombinant protein expression.

Lu developed a method based on multi-level N-terminal engineering. By optimizing the SD sequence and its upstream and downstream regions in Comamonas testosteroni CNB-2 and combining it with N-terminal amino acid sequence reconstruction, they effectively improved the expression level of nicotinic acid dehydrogenase (NDHase) [50]. In Vibrio natriegens, researchers constructed and screened a series of constitutively synthesized regulatory modules containing different promoters and RBS elements with different translation strengths, providing a standardized toolbox for the rapid construction of high-expression systems [51]. In addition, codon optimization, as an important strategy for translation-level regulation, has also been widely used in heterologous protein expression. For example, Jin significantly increased the expression level of spider silk protein genes in C. glutamicum by synthesizing and optimizing the codons of the gene [52]; while Liu successfully expressed TGase from S. hygroscopicus in S. lividans through promoter engineering combined with targeted modification of rare codons (such as TTA), and provided a reference for the expression optimization of other proteins in this host system [53]. In addition, the BCD expression system that has emerged in recent years has also been proven to be an effective strategy to improve translation efficiency. This system has been successfully applied to optimize protein expression in C. glutamicum by introducing a leader peptide coding sequence upstream of the target gene to enhance the translation initiation efficiency of the downstream main coding sequence [35]. It is worth noting that RBS elements can also be coupled with other nucleic acid regulatory elements to give the expression system more complex regulatory capabilities and plasticity, providing a multi-level regulatory means for the translation regulation of engineered microorganisms.

In eukaryotic microorganisms, the translation regulation mechanism is more complex than that in prokaryotic systems due to the involvement of UTRs and introns. Studies have shown that the 5′- and 3′-UTRs of mRNA can significantly affect its stability and translation efficiency; among them, the cap structure at the 5′ end and the polyadenylic acid tail (poly-A tail) at the 3′ end are key structural elements required for mRNA maturity, stability and efficient translation. Translation initiation usually depends on the scanning mechanism of the ribosome, so optimizing the Kozak sequence around the start codon is particularly important for improving translation efficiency. The Kozak sequence (the conservative consensus sequence is GCCRCCAUGG) plays a core role in eukaryotic translation initiation by promoting ribosome binding and accurately identifying the start codon. Xu systematically evaluated and used specific Kozak sequence variants to effectively improve the translation efficiency of the minimal synthetic promoter in S. cerevisiae, expanding its application potential in synthetic biology [54]. In addition, post-translational modifications (such as glycosylation, disulfide bond formation, etc.) are widely present in eukaryotic expression systems and need to be considered in expression design. These modifications not only affect the function and stability of the protein, but also determine its potential for downstream industrial applications.

3.3. Genetic regulatory elements in other processes

For the recombinant expression of secretory proteins, the optimization of signal peptides is crucial for secretion efficiency and translocation process. Studies have shown that natural and artificially designed signal peptide libraries have been successfully applied to hosts such as K. phaffii and E. coli, significantly improving the secretion expression level of functional proteins such as antibodies and hormones [55,56]. In S. cerevisiae, a biosensor high-throughput screening platform based on G protein-coupled receptors (GPCRs) has also been developed to identify the optimal signal peptide combination, further promoting the construction of efficient secretion systems [57].

In summary, the design of gene regulatory elements in eukaryotic systems needs to fully combine the physiological characteristics and molecular mechanisms of the host to achieve the coordinated optimization of expression efficiency, protein quality and system scalability. In recent years, the development of synthetic biology and AI-driven design has further accelerated the modular and customized construction of translation regulatory elements, making it possible to build efficient expression systems in different eukaryotic hosts. It should be emphasized that due to the fundamental differences in structure and regulation mode between prokaryotic and eukaryotic expression mechanisms, the regulatory modules commonly used in prokaryotic systems (such as synthetic RBS calculators, σ factor-specific promoters, etc.) are often unable to be directly transferred and applied to eukaryotic systems. Therefore, host-specific genetic element engineering is the key to achieving efficient recombinant protein expression in eukaryotic microorganisms.

4. Conventional and emerging engineering strategies for genetic regulatory elements

Microbial protein expression systems are core tools in modern biotechnology, especially in the food industry, enzyme engineering, and functional protein production. In order to achieve efficient expression of recombinant proteins under industrial conditions, gene expression must be precisely regulated to support the expression of target proteins in appropriate time and space and correct folding and secretion. In recent years, many studies have reported a variety of strategies for these challenges, including the fine design of genetic elements, modular construction of expression systems, and comprehensive optimization of host cell metabolism and expression pathways (Table 2a, Table 2b). Thanks to the leap forward in AI, CRISPR-Cas gene editing technology, and synthetic biology toolboxes, the rational design of microbial protein expression systems has gradually surpassed the traditional trial-and-error method and entered a new era driven by system optimization and prediction. These emerging strategies allow for more precise engineering design of transcriptional and translational regulatory elements, providing higher predictability, adjustability, and system adaptability. In this section, we review and summarize the main strategies for the design of gene regulatory elements and optimization of expression systems in microbial hosts, covering aspects such as AI-driven design, CRISPR-Cas system regulation, genetic element combination optimization, and other engineering strategies (Fig. 3).

Table 2a.

Promoter engineering strategies and performance outcomes.

Regulatory Element	Host & System	Engineering Strategy	Key Outcome	Reference
phi15 promoter	Pseudomonas putida SEM11	Build phi15 RNAP/promoter with RBS variants and GTG start codon	Robust single-copy integration platform for fluorinase expression	[61]
vp39 late promoter	Baculovirus Expression Vector System (BEVS)	Insert extra vp39 promoter to drive overexpress IE0/IE1	Elevated IE0/IE1 levels to improved late‐gene expression in BEVS	[63]
SPluxI & RPluxIR6 promoter modules	B. subtilis	Mutate −10/−35, UP/spacer sequences and adjust lux box copy number	Autoinducible extracellular expression via LuxI/R; tight QS-driven induction	[38]
Signal peptide/AOX1 promoter	K. phaffii GS115	Screen native/synthetic SPs under AOX1 promoter	Universal workflow for efficient nanobody secretion	[55]
Inducible/T7 hybrid promoter	E. coli	Combine orthogonal translational control combined with inducible promoter	Alcohol dehydrogenase (ADH) yield increased ∼9.82-fold vs. control	[64]
Characterized endogenous promoters + T7	E. coli	Screen 16 native promoters, combine with T7 promoter to build CEIES_Ecoli	GFP expression 1.98-fold plasmid level; stable antibiotic-free expression	[65]
PUTR (promoter-5′UTR) library	E. coli K-12; P. chlororaphis GP72	Construct & characterize promoter-5′UTR combinations	Tuned multi-gene expression; improved pathway flux and product titer in P. chlororaphis	[66]
Non-conventional yeast promoters	Y. lipolytica, K. marxianus, K. phaffii, E. coli, C. glutamicum	Build promoter libraries & shuttle vectors across hosts	Panel of expression systems with graded strengths across hosts	[67]
Promoter	S. mobaraensis TX1	Induce UV mutagenesis of −10 box	∼50-fold increase in TGase transcription; TGase yield 37.5 U/mL	[36]
Promoter kasOp; signal peptide sigcin*	S. lividans SBT5	Constitutive promoter kasOp* + PLD (G215S) mutation, fuse to sigcin signal peptide, codon optimization and targeted mutation	First thiostrepton-free PLD production in Streptomyces	[68]
Synthetic hybrid promoter (Pcc)	Trichoderma reesei	Fuse Pcbh1 activation region to core of constitutive Pcdna1	Improved expression of specific cellulase components	[69]
Synthetic GAL promoter library (UAS + core)	S. cerevisiae (GAL system)	Combine UAS elements with core promoters	∼2-fold activity vs. native PGAL1 under varied carbon sources	[70]
Dual promoter + signal peptide	B. megaterium (pBSHdd2-20)	Assemble PamyE-cdd + Pac SP	Highly active secretory system for amidase secretion	[37]
Bicistronic design (BCD)/Promoter (tac)	C. glutamicum	Insert optimized fore-cistron upstream of target gene	Enhanced recombinant protein production	[35]
Promoter/SD sequence	C. testosteroni CNB-2	Select & optimize promoter; modify N-terminal SD sequence and amino acid rearrangement	3-fold expression of green fluorescent reporter protein, NDHase activity increased from 90.6 to 165 U/L	[50]
Synthetic promoter library/variable RBS	V. natriegens	Design & screen combinations of promoter variants and RBS elements	Panel spanning broad constitutive strength for tuning expression in V. natriegens	[51]
Core‐promoter redesign (-10 to TSS)	Y. lipolytica	Create 30-nt artificial core promoter variants with altered base-composition	Identify motifs that modulate promoter strength in Y. lipolytica	[71]
Enhanced amy15A(p) promoter	Penicillium oxalium	Overexpress transcription activator AmyR + delete repressor CreA	Δ13A-OamyR-ΔCreA strain: high-purity, high-titer secretion; robust production platform	[72]
Promoter & signal peptide combination	E. coli	High-throughput combinatorial screening of promoters and signal peptides	Enhanced periplasmic scFv BL1 and hGH yields	[56]
rDNA-mediated multi-copy promoter (pGAP9-pro/pPICZα)	K. phaffii	Construct rDNA‐mediated multi‐copy mtg expression vectors	Directional copy-number increase of MTG for boosted enzyme expression	[73]
Promoter deletion & codon optimization	S. lividans TK24	Delete mapping of −693 to −48 region and optimize the site-directed codon TTA	High-yield, high-productivity TGase production via promoter minimization + codon tuning	[53]
Promoter‐exchange cassettes	S. cerevisiae	Design orthogonal promoter cassettes for modular swap	Scalable yeast promoter‐reengineering platform enabling rapid circuit assembly	[74]

Table 2b.

Other regulatory elements engineering strategies in microbial protein expression systems.

Regulatory Element	Host & System	Engineering Strategy	Key Outcome	Reference
Terminators
Terminator (hybrid)	E. coli HMS174(DE3)	Combine T7 terminator + rrnBT1 terminators	Reduced read-through; increased plasmid stability and protein yield	[75]
Synthetic short terminators (38–75 bp)	Yeast (p413 centromeric plasmid)	Create panel of minimal terminators varying in length	Enabled fine-tuning of termination; improved heterologous enzyme expression	[49]
Upstream Activating Sequences (UAS)
Synthetic UAS library	A. niger	Fuse varied UASs upstream of PgpdA core promoter	5.4-fold higher cexA expression vs. native PgpdA	[47]
Engineered UAS copy number	Y. lipolytica	Assemble BioBrick to tandem UAS elements (UAS2-UAS64)	Extended dynamic range; enabled C32–C36 wax-ester biosynthesis	[46]
Rewired URS	K. phaffii	Rational redesign URS to generate PA13, P0688, PsynIV-5 high-strength inducible promoters	α-Amylase activity 1.6-fold, 2.6-fold and 4.5-fold higher than PAOX1 benchmarks	[45]
Signal peptides
Signal peptide	S. cerevisiae	One-pot Golden Gate assemble >6000 expression SP cassettes + biosensor‐based secretion assay	Identified optimal SPs for high-throughput secretion optimization	[57]
Signal peptide	K. phaffii GS115	SP engineering, gene-dosage optimization, co-express molecular chaperones, hybrid signal peptide (cSP3)	cSP3 improved secretion of E2-Spy antigen protein, higher yields than native SP	[76]
Signal peptide	C. glutamicum	Codon-optimized synthetic gene encoding 16 repeats of MaSpI consensus; signal-peptide fusion	Secreted MaSpI at 554.7 mg/L in culture supernatant	[52]
Signal peptides	Lactobacillus plantarum & B. subtilis	Construct 12 vectors using SPs from ABC transporters, cell-wall, and secreted proteins	Versatile secretion platforms for heterologous protein production in both hosts	[77]
Codon optimization
Stop‐codon	Genomically recoded organisms (GRO), E. coli	Whole‐genome replacement of synonymous stop codons to compress translational termination into UAA	Demonstrated single‐codon control of stop signals across genome	[78]
Codon‐optimized base editor (dCas9-CDA-ULstr)	S. coelicolor & S. rapamycinicus	Deaminase‐assisted base editor with codon optimization	High-efficiency multiplex editing in Streptomyces	[79]
Codon-optimized gene & protein-engineering steps	S. rimosus	Codon-optimize gene + iterative protein engineering	Active extracellular pernisine yields comparable to E. coli	[80]
Copy number
High‐copy number vector	E. coli	Use high‐copy plasmid + autoinduction	∼83.5 mg/L active Taq polymerase in shake‐flask cultures	[81]
Copy number	K. phaffii X-33	In vitro multimerization to generate multicopy integrants	hIL-3 protein extracellular product yield 2.23 g/L; volumetric productivity 27.31 mg/L/h	[62]
Others
PURE transcription/translation system	E. coli	Couple expression of 20 AARS genes with DNA replication machinery	Sustained regeneration of 20 AARS for ≥20 reaction cycles	[82]
Kozak sequence	S. cerevisiae	Systematically vary sequence around AUG, generate a chimeric promoter library	Translational strengths spanning a 500-fold range; 8.5-fold and 3.3-fold increases in fluorescence intensity	[54]
Copper‐inducible GAL‐regulator system (CuIGR)	S. cerevisiae	Place GAL transcription regulators under native CUP1 promoter control for signal amplification	∼72-fold induction of EGFP expression rise under CuIGR	[83]
Transcriptional biosensor (TF-based)	S. cerevisiae	Engineer repressive biosensor responding to ligand-dependent transcriptional deactivation	Optimized repressive regulation; modular biosensor for pathway control	[84]

Fig. 3.

Advanced genetic elements optimization strategies for microbial protein expression systems. The optimization strategies for microbial protein expression systems include four main categories: (A) AI-based gene design, exemplified by synthetic promoter generation via GANs and deep learning-driven enhancer optimization [58,59]; (B) CRISPR-based modifications enabling precise gene regulation, illustrated by CRISPRi-FlowFISH for high-throughput identification of regulatory elements [60]; (C) Engineering of modular and inducible regulatory systems, such as orthogonal transcriptional machinery (phi15 expression system) [61] and quorum-sensing circuits (lux-type QS system) [38]; (D) Combinatorial methods, utilizing strategies like Combinatorial Golden Gate cloning to generate large libraries of expression constructs and multicopy expression platforms [57,62].

4.1. AI-based gene element design

AI and deep learning models have shown great potential in the computational design of gene regulatory elements and are reshaping the design paradigm of different expression systems (Table 3). The engineering of gene expression not only depends on the nucleotide sequence itself, but is also affected by the complex dynamic interactions between regulatory elements and host systems. Therefore, building modeling tools that can accurately predict system behavior has become a key link in the design of gene elements. At the system level, analysis tools based on genome-scale metabolic models (GEMs), such as the COBRA toolbox, can be used to simulate metabolic flux distribution to identify metabolic bottlenecks that limit protein synthesis efficiency under specific expression conditions [85]. At the molecular level, molecular dynamics simulation (MD) and protein structure prediction software (such as Rosetta and AlphaFold2) are used to predict the folding path and structural stability of recombinant proteins, guide codon optimization and mRNA secondary structure regulation, and enhance the solubility and expression efficiency of target proteins [86]. Besides, AI-driven frameworks have been applied to predict gene circuit behavior and optimize host-microbe interactions, demonstrating broad utility in synthetic biology and even complex ecosystems such as the human gut microbiome [87]. Deep-learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been trained on large-scale promoter and RBS libraries to improve prediction accuracy. These approaches enable in silico optimization of sequence motifs that control transcription initiation, translation efficiency, and secretion signals. Recent integration of AI and synthetic biology for gene editing further demonstrates this convergence: machine learning (ML) algorithms are now used to predict CRISPR guide-RNA performance and editing specificity, marking a new generation of AI-augmented gene-editing systems [88]. This synergy bridges computational design and experimental implementation, forming the basis for intelligent expression-system engineering.

Table 3.

AI-driven frameworks for regulatory element design.

AI Model/Framework	Target Element	Organism/Domain	Function & Application	Reference
DeepCROSS	5′ Regulatory Sequences	Bacteria/Eukaryotes	Cross-species de novo regulatory design via deep learning-based optimization	[89]
DeepTFBU	TF Binding Context Sequences	Human genome	Enhancer modulation via context-aware TF binding unit modeling	[90]
lentiMPRA/MPRA-LegNet	Candidate cis-Regulatory Elements	Human	Expanded MPRA analysis of promoter-enhancer orientation across ENCODE cell lines	[91]
CNNs	Enhancers	Drosophila embryo/Mammalian	Sequence-based enhancer prediction and de novo design	[92]
CODA	Cis-regulatory Elements (CREs)	Mouse, Zebrafish	Synthetic CRE design enabling cell-type-specific gene regulation	[93]
Evo (Genomic Foundation Model)	Multi-scale genomic features	Cross-species	Foundation model for genome-wide sequence prediction and generation	[94]
Deep learning framework (unspecified)	Enhancer	Human, Drosophila	Synthetic cell type-specific enhancer design at single-nucleotide resolution	[58]
Cis-regulatory models (ML + MPRAs)	Regulatory Sequences	Human	Sequence-to-function modeling for deciphering cis-regulatory code	[95]
ZFDesign	Zinc Finger-DNA Interactions	Human	Attention-based model for target-specific ZF protein engineering	[96]
DeePromClass	Promoter	S. cerevisiae, C. elegans, D. melanogaster, etc.	Deep learning classifier distinguishing promoter vs. non-promoter sequences	[97]
DeepSTARR	Enhancer	Drosophila melanogaster S2 cells	Enhancer activity prediction directly from sequence	[98]
MPRAs	Gene Regulatory Elements	Human	High-throughput transcriptional activity profiling across large sequence libraries	[99]
BPNet	TF Binding Motifs	Mammalian	Predicts base-resolution TF binding from DNA sequence	[100]
WGAN-GP/DCGAN/PSSM (GANs)	Promoter	E. coli	De novo promoter generation guided by learned sequence features	[59]
lentiMPRA/MPRAflow	Candidate Regulatory Sequences	Lentiviral/Mammalian	Quantitative screening of thousands of CRSs in parallel	[101]
CRISPRi-FlowFISH	Enhancer	Human/Mammalian	High-throughput enhancer perturbation and expression quantification	[60]

Regarding the optimization of transcription and translation start regions, several public tools have achieved sequence-based performance predictions. For example, RBS Calculator can perform structural modeling of the ribosome binding site RBS sequence in the 5′ untranslated region (5′UTR) to predict its translation start rate; while tools such as UNAFold simulate RNA secondary structure to evaluate its stability and post-transcriptional processing potential. In recent years, supervised ML models have performed well in predicting the function of gene elements. Neural networks trained on large-scale omics data or high-throughput experimental data (such as MPRAs) can predict promoter activity, RBS strength, and the effect of codons on translation efficiency. For example, models inspired by natural language processing (NLP) such as DNABERT and DeepCROSS have been successfully used to analyze the regulatory effects of promoter syntax and sequence context on expression [89]. In terms of synthetic element design, methods such as generative adversarial networks (GANs) and reinforcement learning (RL) are widely used to design promoters and enhancers with specific expression strengths or cell state responsiveness from scratch. In E. coli, models such as WGAN-GP and DCGAN have been trained on the basis of natural promoter sequences to generate synthetic promoters that are highly matched to the target expression intensity, breaking through the design limitations of traditional motif models [59]. At the same time, new generation design platforms such as TIDAL (Targeted Inference and Design through Adaptive Landscapes) and DeepSynth deeply couple performance prediction with optimization algorithms to achieve a closed-loop design process of sequence-to-function mapping [94]. Similarly, the DeepCROSS model allows the use of sequence representation and optimization in prokaryotic and eukaryotic systems to design 5′ regulatory elements across species [89]. For enhancer elements, models such as DeepTFBU and DeepSTARR can predict the effects of upstream and downstream sequence variations on transcription factor binding and enhancer activity, guide synthetic enhancer design with cell type-specific resolution, and are suitable for expression system optimization of eukaryotic microorganisms such as yeast [90,98]. In addition, models such as DeePromClass have been successfully used to distinguish functional promoters from non-regulatory sequences in different species, providing auxiliary decision-making tools for the screening of regulatory elements in multi-species expression systems [97]. A recent study integrated an AI and knowledge-based approach for rational design of E. coli sigma70 promoters, combining deep learning with mechanistic understanding to guide promoter construction [102]. By coupling high-throughput screening using eGFP reporters with a trained deep-learning model, the authors identified strong sigma70 promoters that increased collagen and microbial transglutaminase (mTG) expression by 81.4 % and 33.4 %, respectively. Moreover, these constitutive promoters enabled soluble expression of the mTG-activating protease, resulting in highly active enzyme production in E. coli. This case exemplifies how AI-guided promoter design can deliver quantifiable improvements in recombinant protein yield and functional activity, bridging computational prediction with practical bioprocess enhancement.

Despite the promising advances achieved through AI-based sequence design, several persistent challenges remain in terms of data availability, quality, and reproducibility. Most predictive models are trained on datasets derived from a limited number of host organisms, primarily E. coli and S. cerevisiae, which restricts their generalizability to other microbial systems. Furthermore, data heterogeneity in promoter-activity assays, fluorescence reporters, and experimental protocols often leads to inconsistencies in labeling and performance evaluation across studies. To improve reproducibility and model reliability, the establishment of open-access standardized datasets and cross-host benchmarking frameworks is essential, accompanied by transparent model documentation and publicly available repositories for trained parameters. The adoption of community standards such as the FAIR (Findable, Accessible, Interoperable, Reusable) data principles would further facilitate reproducibility of AI-predicted genetic elements across independent laboratories and diverse microbial contexts. Notably, AI-driven design methods are being highly integrated with high-throughput experimental verification platforms (such as MPRA and lentiMPRA), thus forming a closed-loop optimization path of design-prediction-verification-redesign. This strategy is gradually being applied to the development of microbial expression systems in the food industry to accelerate the expression optimization and process scale-up of target products such as functional proteins, bioactive peptides, and food enzymes [99,101].

4.2. CRISPR-cas-based approaches for expression system modification

The emergence of CRISPR-Cas system has greatly expanded the ability of precise genome manipulation in microorganisms and has become one of the most revolutionary genetic tools in expression system design. Its high targeting specificity, flexible programmability and support for simultaneous editing of multiple sites provide strong technical support for the reconstruction of transcriptional regulatory elements, functional enhancement of cis-acting elements and precise intervention of non-coding regions. At present, this system is not only widely used in traditional gene knockout and insertion operations, but also applied to promoter strength regulation, enhancer function optimization and programmable reconstruction of transcriptional networks, becoming one of the core strategies for building efficient, stable and regulatable microbial protein expression systems. Novel reporter-coupled CRISPR systems such as the SpCas9M platform for Caulobacter crescentus have enhanced editing speed and accuracy, providing powerful tools for non-model bacteria [103]. Beyond simple genome editing, CRISPR-based regulators can act as transcriptional switches and epigenetic modifiers. These emerging applications extend the technology's scope far beyond DNA cutting, transforming CRISPR into a versatile synthetic biology platform [104]. At the molecular level, high-resolution structural analyses of Cas9 mismatch recognition have revealed how off-target effects arise and how engineered variants improve fidelity [105]. In yeast systems such as K. phaffii, CRISPR-mediated rDNA integration combined with fluorescence screening has enabled rapid pathway optimization for heterologous protein production [106]. Furthermore, compact TnpB-derived editing systems like the STAGE toolkit offer a versatile alternative to canonical Cas nucleases and expand the applicability of CRISPR-like tools in Streptomyces and other actinomycetes [107]. These developments illustrate how CRISPR technologies are becoming increasingly adaptable to diverse microbial hosts and industrial applications.

Moreover, CRISPR interference (CRISPRi) technology has been shown to effectively reconstruct metabolic networks and redirect energy fluxes. Taking B. subtilis as an example, researchers used CRISPRi to target and inhibit the expression of odhA (encoding α-ketoglutarate dehydrogenase) and overexpress ATP synthase subunit genes such as atpC, atpD and atpG, which significantly improved the energy metabolism capacity of chassis cells, thereby increasing the secretion expression of recombinant proteins (such as lactoferrin) by 75 % [108]. Furthermore, CRISPR-derived base editing tools (such as dCas9-CDA-ULstr) allow precise modification of multiple target sites in the genome without introducing double-strand breaks, avoiding the risks of nonspecific repair and cytotoxicity that may be caused by traditional CRISPR-Cas9 technology. In Streptomyces, this system has been successfully used to activate silent biosynthetic gene clusters, significantly increasing the production of natural products such as rapamycin, and providing a feasible path for the microbial synthesis of functional active substances [79]. In addition, the CRISPRa/CRISPRi system consisting of dCas9 or dCas12a fused with activating or repressing factors (such as VP64 or KRAB) can achieve reversible regulation of target gene expression without changing the DNA sequence. This strategy is particularly suitable for the expression regulation of target proteins (such as food enzymes and bioactive peptides) that require fine control of expression intensity and timing, and shows significant application potential in metabolic engineering and functional protein manufacturing. In eukaryotic microbial systems, such as K. phaffii, CRISPR tools also show wide applicability. By targeting the inhibition of endogenous protease expression or regulating methanol metabolism regulatory factors, complex recombinant proteins such as glycosylated proteins, cytokines or antibody fragments that are sensitive to degradation or energy can be stably expressed [33]. It is worth mentioning that the CRISPRi-FlowFISH technology combines CRISPRi with fluorescence in situ hybridization (FISH) to achieve parallel functional screening of hundreds of non-coding regulatory elements. This high-throughput method not only helps to analyze the natural enhancer network, but also provides an efficient platform for the introduction of synthetic regulatory elements in a site-specific manner, opening up a new path for the system-level optimization of the expression system [60].

While CRISPR-Cas systems are powerful, several limitations must also be acknowledged for a rigorous and balanced assessment. Editing efficiency and off-target control vary substantially among microbial hosts due to differences in DNA-repair pathways and chromatin accessibility, and accurate prediction of guide RNA specificity remains challenging even with AI-driven design tools. In addition, many non-model industrial microorganisms, including filamentous fungi and lactic acid bacteria, still lack efficient transformation and selection systems, which constrains the practical application of CRISPR technologies. Beyond technical barriers, regulatory and public acceptance issues also persist, particularly in food-grade or probiotic applications where genome-edited strains are subject to stricter approval and labeling requirements. Moreover, large-scale deployment raises biosafety concerns, underscoring the need for orthogonal or self-limiting CRISPR circuits to minimize unintended horizontal gene transfer. Addressing these challenges will require integrative efforts that combine bioinformatics-driven optimization, advanced molecular engineering, and transparent policy frameworks to ensure safe and responsible implementation.

As CRISPR technology continues to evolve, its deep integration with AI-driven design platforms (such as DeepCRISPR, DeepCas9) and high-throughput phenotypic screening technology will accelerate the expression system into the closed-loop design stage of “prediction-editing-verification-iteration”. This strategy can not only significantly improve the expression efficiency and regulation accuracy of microbial expression systems, but also provide key support for the realization of the next generation of intelligent fermentation and customized protein production.

4.3. Optimization of the combination of different gene elements

In the construction of microbial protein expression systems, a significant trend in recent years is to achieve fine control of expression intensity, temporal dynamics and compartmental localization through combinatorial optimization of multi-level regulatory elements. These regulatory elements include promoters, RBS, signal peptides and terminators. Their synergistic effects have been shown to be far superior to the optimization of single elements and have become the core strategy for the design of efficient expression systems.

For example, in K. phaffii, researchers constructed and screened combinatorial libraries of promoters and signal peptides, from which a variety of efficient constructs suitable for the expression of nanoantibodies and therapeutic proteins were identified [55,62]. In E. coli and the fast-growing V. natriegens, a library of regulatory modules with adjustable strength and suitable for a variety of biosynthetic tasks was established by synthetically combining endogenous and exogenous promoters and RBS variants [51]. BCD and polycistronic expression systems have also been used to enhance translation efficiency. In C. glutamicum, the BCD system effectively improved the expression level of downstream genes by designing leader peptides and optimizing translation coupling, providing a reference for the design of expression systems for non-model microorganisms [35]. In B. subtilis, researchers constructed a ternary expression module by jointly optimizing the promoter P566, the fusion peptide (EAAAK)₃, and the screened efficient signal peptide. In combination with CRISPRi technology to inhibit non-essential energy consumption pathways in the chassis, the expression level of extracellular lactoferrin was greatly improved, demonstrating the synergistic potential of regulatory element combination optimization and chassis engineering [108]. In addition, although terminator engineering is often overlooked, its impact on transcription termination efficiency and expression stability cannot be ignored. Taking E. coli as an example, synthetic hybrid terminators (such as T7 + rrnBT1) can effectively prevent transcriptional readthrough and increase protein production [75]. At the design level, Tietze and Lale (2021) pointed out that modular synthetic biology platforms (such as Golden Gate and BioBrick) provide a technical basis for standardized assembly and high-throughput combinatorial screening of regulatory elements [85]. Combined with ML-driven feedback optimization, it is possible to achieve systematic reconstruction of multiple sites such as promoter-RBS-signal peptide-terminator, greatly improving the customizability and performance ceiling of the expression system.

Overall, this iterative design concept of “module combination + data feedback” not only enhances the portability and adaptability of the expression system, but also accelerates the development of target product expression platforms such as enzyme preparations, nutritional peptides, and metabolites in the food industry.

4.4. High-throughput tools for combinatorial genetic engineering

Recent developments in high-throughput screening and analysis technologies have transformed the way genetic elements are designed and optimized. Traditional trial-and-error approaches are being replaced by automated, data-driven workflows that enable parallel testing of thousands of variants within a single experiment. Fluorescence-activated cell sorting (FACS) and microfluidic droplet systems have become indispensable for rapidly screening promoter and signal peptide libraries based on reporter expression or secretion efficiency. These platforms allow single-cell resolution measurement of expression dynamics, facilitating the identification of variants with superior strength, tunability, or stability. Moreover, next-generation sequencing (NGS)–based deep mutational scanning (DMS) enables quantitative mapping of sequence–function landscapes for regulatory elements such as ribosome-binding sites (RBSs) and transcriptional terminators. This approach provides rich datasets that feed into AI-guided sequence optimization, closing the loop between experiment and prediction. In combination, these high-throughput technologies accelerate combinatorial optimization of genetic parts, enabling the systematic design of promoter–RBS–signal peptide combinations for improved translation efficiency and secretion performance across multiple microbial hosts. The integration of AI-driven predictive modeling with FACS/DMS platforms is expected to further enhance the precision and scalability of genetic element engineering in next-generation expression systems.

4.5. Other engineering strategies

In addition to the optimization of transcription and translation regulatory elements, the overall performance of microbial protein expression systems is also affected by a combination of many “external factors”. These factors include the correct folding and secretion of proteins, vector construction and gene copy number, the adaptability and metabolic stability of chassis strains, and the matching degree between expression systems and process parameters. In recent years, these strategies have gradually been incorporated into system design considerations to build more adaptable and scalable expression platforms.

In heterologous expression, highly expressed target proteins are prone to form inclusion bodies, especially when expressing transmembrane proteins, multi-domain enzymes or exogenous polypeptide chains. In order to improve their solubility and biological activity, the co-expression molecular chaperone strategy is often used. For example, the GroEL/GroES and DnaK/DnaJ systems widely used in bacteria, and endoplasmic reticulum chaperones such as Kar2p and PDI used in yeast have been shown to significantly improve protein folding efficiency [86]. In addition, signal peptide engineering is also key to achieving effective secretory expression. By constructing and screening signal peptide libraries, precise adaptation can be achieved for the secretion pathways of different hosts. For example, in E. coli, S. cerevisiae and B. subtilis, a variety of signal peptide combinations with high compatibility and good expression have been developed to guide the efficient secretion of proteins to the extracellular or periplasmic secretion [56,57].

During the expression of heterologous genes, the difference in codon usage preference between the host and the exogenous gene often leads to translation delay or termination. Codon optimization can significantly improve translation efficiency and protein folding quality. For example, when expressing spider silk proteins in C. glutamicum or overnisinase in Streptomyces, optimizing codons has become a key link in improving expression performance [52,80]. At the same time, copy number regulation is used to achieve a controllable increase in expression intensity. In K. phaffii, gene dosage-dependent expression enhancement can be achieved through multi-copy integration, tandem expression cassette design or in vitro polymerisation strategy, while avoiding excessive burden on host metabolism [62].

In recent years, the regulatory logic of expression systems has also been significantly expanded, and the construction of self-induction and orthogonal expression systems has brought new ideas for expression controllability and host adaptability. For example, the Lux system based on QS was developed in B. subtilis as a self-regulatory expression platform without exogenous inducers, while in P. putida, the orthogonal phage RNA polymerase system achieved expression control without cross-interference with host expression [38,61]. In addition, synthetic promoter libraries have constructed an expression module system with adjustable strength and sensitive response through promoter module recombination and artificial insertion of UAS elements. They are widely used in fungal systems such as A. niger and T. reesei to promote their industrial expression in food enzymes and biotransformation enzymes [47,69].

The industrial feasibility of the expression system also depends on the adaptability of the chassis strain and its synergistic matching with the fermentation process conditions. De Brabander emphasized that controlling key parameters such as dissolved oxygen, pH, carbon source flow rate and temperature, and coordinating the reconstruction of chassis metabolic pathways (such as enhancing energy synthesis and inhibiting byproducts) can significantly improve the stability and repeatability of the expression system in large-scale production [109]. In terms of raw material adaptability, Cuadrado-Osorio proposed to develop engineered strains that can efficiently utilize low-cost food by-products (such as pomace and molasses), which can effectively reduce production costs and enhance the sustainability of expression systems in the context of green manufacturing and circular economy [20]. In summary, although these engineering strategies at the non-core regulatory element level are not directly involved in the transcription and translation process, they play a decisive role in the efficiency, stability and industrial adaptability of protein expression. Together with the gene element design strategy, they constitute a multi-dimensional, programmable, iteratively optimized expression system ecology, which provides a solid foundation for the production of functional proteins, precision fermentation and development of high-value nutritional factors in the food industry.

4.6. Comparative overview of conventional vs. emerging strategies

Conventional approaches to optimize microbial expression, such as random mutagenesis, native promoter modification, and empirical fermentation parameter tuning, have contributed greatly to the early development of industrial protein production. However, these methods are often time-consuming, low in predictability, and limited by host-specific responses. Recent advances in synthetic biology and ML-guided design have redefined this landscape. For example, combinatorial promoter-RBS libraries screened by high-throughput assays can identify optimal expression cassettes within days rather than months. AI-based sequence design platforms predict promoter strength or signal peptide efficiency with over 80 % accuracy, greatly improving design predictability. Similarly, CRISPR-Cas systems allow multiplexed genome edits to modulate gene dosage and eliminate competing pathways, achieving over 10-fold yield improvements in some enzyme production cases compared with classical iterative mutagenesis.

In terms of scalability and versatility, emerging tools such as microfluidic droplet screening, modular expression vectors, and automated fermentation systems enable parallel optimization across multiple strains and conditions. These strategies collectively transform protein expression from trial-and-error to data-driven design.

5. Application of microbial protein expression systems

The practical application of engineered microbial expression systems covers multiple key areas, including biopharmaceuticals, industrial enzymes, food nutrition, synthetic biology and intelligent fermentation (Fig. 4). In different application scenarios, microbial systems have become the core platform for protein manufacturing due to their scalability, expression controllability and industrial adaptability.

Fig. 4.

Diverse applications of microbial protein expression systems through genetic element engineering. Microbial protein expression systems, enhanced by genetic element engineering strategies, support diverse applications in biopharmaceutical production (e.g., therapeutic proteins, vaccines), food and nutrition enhancement (e.g., protein-rich foods, nutritional supplements), and industrial enzyme manufacturing (e.g., cellulase, recombinant enzymes), facilitated by synthetic biology tools (e.g., transcriptomics, target identification) and intelligent fermentation techniques.

5.1. Biopharmaceuticals

Microbial systems provide a rapid and controllable expression platform for the large-scale production of biological agents such as therapeutic proteins, antibodies and vaccines. Recent reviews highlight how microbial species engineering offers improved scalability and reduced biosafety risk for pharmaceutical applications [110]. Engineered yeast represented by K. phaffii is widely used in the production of glycosylated proteins such as interleukins, growth hormones and antibodies due to its excellent secretory expression ability and mammalian-compatible glycosylation modification ability [33]. The optimization of expression strategies depends on promoter screening, folding auxiliary system design and secretion pathway regulation. For example, the yield and activity of recombinant proteins were significantly improved, achieving high-density fermentation (approximately 150 g/L dry cell weight) within a short period, by combining the inducible expression system with chaperone protein co-expression [86].

5.2. Industrial enzymes and specialty compounds

Industrial enzyme production has benefited from metabolic pathway optimization and fine-tuning of transcriptional regulation. Engineering bacterial systems including B. subtilis, C. glutamicum and Streptomyces are widely used in the production of enzyme preparations in scenarios such as food processing, fermentation manufacturing and biorefining. The target enzymes include transglutaminases, proteases, amylases, etc. Fungal platforms such as T. reesei and A. niger have strong extracellular secretion capabilities and are suitable for large-scale expression of lignocellulose degrading enzymes such as cellulases and xylanases. They show important potential in the transformation of food by-products and pretreatment of plant-based proteins [22]. Moreover, technological advancements have established K. phaffii as a next-generation platform for sustainable biomanufacturing, combining genome-scale engineering, CRISPR editing, and process automation [10]. These examples demonstrate how synthetic and systems biology tools translate into improved enzyme titers and more eco-friendly production processes.

5.3. Food proteins and sustainable sources of nutrition

The food field is becoming one of the most promising application direction for microbial expression systems, covering multiple levels such as food enzymes, bioactive peptides, functional proteins and alternative proteins. As the core tool for flavor formation, texture regulation and functional enhancement, the expression system of food enzymes must meet the standards of high yield, stability and controllability. B. subtilis and E. coli have achieved self-induced efficient secretory expression through QS expression system and signal peptide engineering [38]; A. niger and T. reesei have expanded the range of expression intensity regulation through synthetic promoter library and UAS regulatory elements [47]. Active peptides have multiple nutritional functions such as antioxidant, antibacterial, and immunomodulatory. The engineered K. phaffii system has achieved secretory expression of antimicrobial peptides and ACE inhibitory peptides, and enhanced the stability and efficiency of translation initiation [33]. Actinomycetes such as Streptomyces also show the advantages of natural small molecule synthesis and are suitable for efficient expression of low molecular weight peptides.

In nutritional supplements and future protein manufacturing, the demand for high value-added proteins such as lactoferrin, immunoglobulin fragments, and plant-derived functional proteins is increasing. Wang and colleagues achieved efficient extracellular expression of lactoferrin in B. subtilis by reprogramming metabolic networks and optimizing expression modules through CRISPRi, which is suitable for scenarios such as functional beverages and infant nutrition formulas [108]. Methanol-inducible expression systems such as K. phaffii have also been widely used to produce functional enzyme preparations such as glutathione transferase and peroxidase, and have broad prospects in healthy food, nutritional intervention and green preservation [62]. Microbial systems including yeast and algae have become solutions for “landless, high-efficiency” protein manufacturing. The application of SCP technology in animal feed and human nutrition has gradually expanded. Its high nitrogen utilization rate, carbon neutrality potential and short-cycle production advantages make it an important supplement to replace livestock protein [3].

5.4. Scale-up and industrial adaptation in intelligent synthetic biology platforms

The transition from laboratory-scale microbial expression systems to robust industrial biomanufacturing platforms remains a critical step in translating synthetic biology innovations into practical applications. While advanced gene circuit design and metabolic control strategies have enabled fine-tuned expression at the cellular level, scalability, process stability, and regulatory compliance continue to be major challenges. To address these bottlenecks, intelligent synthetic biology platforms increasingly integrate in silico modeling, biosensor feedback, and AI-driven fermentation control. For example, signal peptide screening frameworks coupled with biosensor-based regulation have achieved automated identification of secretion-efficient variants, enabling rapid adaptation to process fluctuations [57]. Similarly, CRISPR-based programmable genetic circuits are being repurposed as dynamic regulators of metabolism or controlled-release systems for recombinant products, enhancing productivity and system resilience during long-term fermentation [111].

During scale-up, variations in oxygen transfer, pH, substrate feeding, and metabolic flux distribution can significantly impact recombinant protein yield and quality. To mitigate these issues, ML- and AI-powered process analytics are being employed for real-time monitoring and adaptive control. A representative study demonstrated the use of ML/AI algorithms for continued process verification (CPV) in K. phaffii fed-batch fermentation [112]. Using multivariate anomaly detection and random-forest predictive models, the system successfully controlled respiratory quotient (RQ) under hypoxic conditions and optimized the production of recombinant Candida rugosa lipase 1 (Crl1). This work provided quantitative evidence that ML-based process intelligence can bridge molecular-level engineering and large-scale process reliability. These advances exemplify how AI-enabled process analytics, digital-twin modeling, and process analytical technology (PAT) can complement synthetic biology to create adaptive, self-optimizing fermentation platforms. By integrating genetic regulation, systems biology, and real-time data science, microbial cell factories are evolving into autonomous biomanufacturing systems capable of maintaining productivity and quality from lab to industrial scale.

6. Challenges and future directions

Despite remarkable progress in engineering genetic elements for microbial protein expression, several fundamental challenges still limit large-scale and commercial implementation. At the cellular level, metabolic burden and resource competition often arise during high-level heterologous expression, leading to growth inhibition, protein misfolding, and yield reduction. Integrating dynamic regulation strategies, such as feedback-controlled promoters, tunable ribosome-binding sites, and adaptive circuits, with systems biology and flux balance modeling can help balance host fitness and productivity.

From an engineering perspective, scalability and process stability remain critical bottlenecks. Expression systems optimized under laboratory conditions frequently exhibit instability in pilot or industrial bioreactors due to fluctuations in aeration, nutrient gradients, and stress responses. The combination of genetic circuit optimization, adaptive laboratory evolution, and process control engineering offers potential solutions to improve robustness and reproducibility. Equally important are biosafety and regulatory challenges, especially for food-grade and industrial applications involving genetically modified microorganisms. Establishing standardized GRAS chassis strains, transparent safety assessment frameworks, and clear regulatory guidelines will be essential for ensuring public acceptance and compliance. As AI-driven design and high-throughput screening increasingly shape genetic engineering, the lack of standardized datasets and evaluation metrics has become a major barrier to reproducibility and model transferability. The creation of open-access databases, benchmark models, and transparent validation pipelines will be critical for bridging computational predictions with experimental performance. Finally, the transition to commercial-scale production must address cost and sustainability constraints, particularly when using alternative or waste-derived feedstocks. Integration of automation, real-time monitoring, and digital-twin fermentation systems can enhance efficiency and enable greener, low-carbon biomanufacturing.

Overall, addressing these intertwined challenges will require a systems-level, interdisciplinary approach that combines synthetic biology, bioinformatics, process optimization, and regulatory science to construct safe, scalable, and intelligent microbial expression platforms.

7. Conclusion and perspective

Microbial protein expression systems are evolving from basic expression platforms into intelligent and programmable biofactories. Enabled by advances in synthetic biology, protein engineering, and AI-driven design, future developments will focus on integrated sequence-to-product workflows that couple promoter prediction, sequence optimization, protein folding, and regulatory circuit engineering.

The construction of food-grade microbial chassis with modular, standardized regulatory elements and optimized metabolic pathways will accelerate the industrial deployment. Simultaneously, coupling these systems with novel carbon sources, circular feedstocks, and data-driven fermentation control will advance green and low-carbon protein production. Moreover, the establishment of biosafety and functionality evaluation standards will bridge laboratory innovations with market and regulatory requirements, ensuring reliable application in food, pharmaceutical, and industrial contexts.

In summary, microbial expression systems now stand at the frontier of sustainable biomanufacturing, serving not only as efficient production platforms for enzymes and functional proteins but also as a cornerstone technology for valorizing renewable resources and advancing the circular bioeconomy.

CRediT authorship contribution statement

Xiaoqian Li: Writing – review & editing, Writing – original draft, Investigation, Data curation, Conceptualization. Cuifang Ye: Writing – review & editing, Investigation. Tao Liu: Writing – review & editing, Investigation. Shiyu Li: Visualization, Resources. Mengyu Zhang: Visualization, Formal analysis. Yao Zhao: Resources, Formal analysis. Yuanxiang Jin: Writing – review & editing, Validation. Jintao Cheng: Supervision, Project administration, Conceptualization. Guiling Yang: Supervision, Funding acquisition. Peiwu Li: Validation, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.