1.中国科学院过程工程研究所,生物药制备与递送全国重点实验室,北京 100190
2.中国科学院上海药物研究所,上海 201203
杜昱光(1963—),男,研究员,博士生导师。研究方向为糖链合成生物学与营养糖工程,推动功能糖产品在农业、食品及医药领域产业化应用。 E-mail:ygdu@ipe.ac.cn
收稿:2026-05-25,
修回:2026-07-26,
纸质出版:2026-08-31
移动端阅览
杜昱光, 李瑞莲, 王倬, 丁侃. 糖合成生物学:从糖科学基础解析到糖基产品生物制造的范式变革[J]. 合成生物学, 2026, 7(4): 703-721
DU Yuguang, LI Ruilian, WANG Zhuo, DING Kan. Synthetic glycobiology: paradigm shift from fundamental glycoscience interpretation to biomanufacturing of glycosyl products[J]. Synthetic Biology Journal, 2026, 7(4): 703-721
杜昱光, 李瑞莲, 王倬, 丁侃. 糖合成生物学:从糖科学基础解析到糖基产品生物制造的范式变革[J]. 合成生物学, 2026, 7(4): 703-721 DOI: 10.12211/2096-8280.2026-046.
DU Yuguang, LI Ruilian, WANG Zhuo, DING Kan. Synthetic glycobiology: paradigm shift from fundamental glycoscience interpretation to biomanufacturing of glycosyl products[J]. Synthetic Biology Journal, 2026, 7(4): 703-721 DOI: 10.12211/2096-8280.2026-046.
糖类作为自然界结构多样性最高的生物大分子类别,其糖链在细胞识别、信号转导和免疫调控中发挥不可替代的核心作用。然而,糖链的非模板驱动生物合成机制,糖苷键的连接位置、异头构型和分支模式均由定位在不同亚细胞区室的糖基转移酶协同决定,使其规模化制备长期受限于化学合成的高昂成本和天然来源的不均一性。合成生物学将糖基化系统重新定义为可设计、可重构的工程网络:通过糖基转移酶的基因组挖掘与蛋白质工程、糖核苷酸代谢网络的精细调控、微生物细胞工厂的系统优化,正在实现从单糖到复杂糖缀合物的精准生物制造。当前糖合成生物学面临三大核心瓶颈,包括糖基化元件库功能表征严重不完整、糖核苷酸胞内供给水平的精细调控精度不足、糖链的结构-活性关系远未系统解析。本文系统梳理了糖合成生物学四项核心使能技术(包括糖基转移酶元件工程、代谢途径重构、细胞工厂设计及无细胞合成体系)近二十年的关键突破,并讨论了在母乳寡糖、糖基化抗体、糖疫苗及功能多糖材料中的产业化进展。最后,本文分析AI驱动的糖链结构预测与糖密码解析对糖合成生物学智能化设计的推动作用,展望该领域从经验驱动到智能设计的范式跃迁前景。
Carbohydrates constitute the most structurally diverse class of biological macromolecules in nature
with their glycan chains playing irreplaceable roles in cell recognition
signal transduction
and immune regulation. Unlike the template-driven biosynthesis of nucleic acids and proteins
glycan biosynthesis operates without a direct molecular template. Glycosidic linkage positions (1→2
1→3
1→4
1→6)
anomeric configurations (α or β)
branching patterns
and modification groups are collectively determined by glycosyltransferases (GTs) localized across distinct subcellular compartments. This multi-dimensional structural freedom endows carbohydrates with the high information density among biomolecules
yet it has historically constrained scalable production of complex glycans: chemical synthesis of glycans demands laborious protecting-group manipulations and stringent stereochemical control
while natural sources yield structurally heterogeneous mixtures that preclude systematic structure-activity relationship studies. Synthetic biology fundamentally reframes this challenge by redefining glycosylation systems as engineerable
reconfigurable networks amenable to rational design. Through genome mining and protein engineering of GTs
precision tuning of sugar nucleotide metabolic networks
and systematic optimization of microbial cell factories
the field is advancing toward the programmable biomanufacturing of structurally defined glycans
from simple monosaccharides to complex glycoconjugates
with unprecedented efficiency and precision. The defining contribution of glycosynthetic biology is methodological. It transforms glycoscience from a discipline that catalogs what nature provides into one that engineers what we need. For decades
glycans stood as the
“dark matter” of molecular biology—an essential poorly understood component of cellular systems. They were structurally intractable
biosynthetically opaque
and functionally elusive
yet they sat at the center of development
immunity
infection
and tumorigenesis. Synthetic biology breaks this deadlock. By making homogeneous glycans accessible on demand
it enables the systematic elucidation of structure-function relationships that had been the missing foundation of the field. However
three bottlenecks remain. The first is functional annotation of GTs. Over 500
000 sequences are catalogued in the CAZy database; fewer than 5% have been experimentally validated for substrate specificity and kinetics. Glycosylation pathway design therefore remains a screening exercise
not a rational assembly of standardized parts. The second is sugar nucleotide supply control. These metabolic networks are deeply embedded in central carbon metabolism. A perturbation in one pathway
be it GDP-fucose or CMP-Neu5Ac
ripples unpredictably through the entire system. Independent
dynamic
and precise control of multiple sugar nucleotide pools remains unsolved. The third is glycan structure–activity relationships. These defy the frameworks built for small molecules and proteins. The same epitope elicits different responses depending on backbone context
whether
N
-glycan
O
-glycan
or glycolipid. Multivalency produces non-linear effects that cannot be extrapolated from monovalent affinities. This is not a data problem fixable by more measurements; it is an epistemological one requiring new conceptual tools. This review examines twenty years of progress through four enabling technologies. GT element engineering spans genomic mining against CAZy
structure-guided rational design
directed evolution
and ancestral sequence reconstruction. It has produced variants with orders-of-magnitude gains in catalytic efficiency
altered substrate specificity
and improved process stability. Metabolic pathway rewiring
and cell factory design have delivered landmark achievements: the first transplantation of prokaryotic
N
-glycosylation into
E. coli
the humanization of yeast for complex sialylated
N
-glycans
the CRISPR-enabled glycoform customization of CHO cells
and the de novo microbial biosynthesis of glycosaminoglycans including heparin and hyaluronic acid. Cell-free synthetic systems bypass the constraints of living chassis entirely. There are no membrane barriers
no metabolic cross-talk
no product toxicity. They uniquely enable the incorporation of non-natural sugar nucleotides for bioorthogonal applications. AI and machine learning are accelerating glycan structure prediction
GT function annotation
and pathway optimization. Yet their defining handicap is structural: glycan databases are orders of magnitude smaller than those for proteins or nucleic acids. This places a hard ceiling on what data-hungry models can currently deliver. Four product categories illustrate the translational arc from concept to commerce. Human milk oligosaccharides
led by 2′-fucosyllactose
represent the most mature success. Fermentation titers have surpassed 100 g/L; the global market exceeds one billion USD. Multiple species
including 3-FL
LNT
LNnT
3′-SL
and 6′-SL
have reached industrial production. Glycoengineered antibodies demonstrate glycan-level pharmacology. Removal of core fucose from IgG1 Fc enhances FcγRIIIa binding by approximately 50-fold
boosting ADCC from therapeutically marginal to clinically decisive. The FDA approval of afucosylated obinutuzumab for chronic lymphocytic leukemia validated this principle. Terminal galactosylation tunes complement activation; α2
6-sialylation extends serum half-life. These parameters are independently controllable
making the Fc glycan a multi-dimensional tuning interface. Glycoconjugate vaccines are being reshaped by recombinant bioconjugation. Engineered
E. coli
enzymatically transfers target oligosaccharides to carr
ier proteins
eliminating the chemical coupling and multi-step purification that dominate traditional conjugate vaccine manufacturing. Functional polysaccharides have fulfilled the promise of metabolic engineering at scale. Hyaluronic acid
produced at hundred-ton scale in engineered Bacillus subtilis
now serves ophthalmic surgery
joint injection
and aesthetic medicine. Its success validated the general design principle of push-pull UDP-sugar flux control. Underlying all four cases is a common trajectory: from empirical iteration toward predictive
computation-guided design
powered by the modular assembly of standardized biological parts. Three grand challenges define the next decade. The first is deciphering the glyco-code
the rules that translate glycan structure into biological function. Unlike the genetic code
this mapping is non-linear
multivalent
and context-dependent. Progress demands the convergence of glycan microarray technology
high-resolution mass spectrometry
and AI-driven pattern recognition. It also demands training data that currently does not exist in sufficient quantity. The second is transitioning from trial-and-error to intelligent design. This requires comprehensive
standardized GT kinetics databases. It requires embedding AI prediction models within automated biofoundry workflows. It requires low-resource machine learning methods that can learn from sparse
heterogeneous datasets
a fundamentally different challenge from the data-rich ecosystems of proteomics and genomics. The third is bridging the lab-to-plant gap. Cell factory robustness must withstand industrial fermentation conditions
including temperature swings
pH drift
and osmotic stress. Phage contamination remains an existential threat in bacterial chassis. Downstream purification still consumes 50 to 70 percent of total production costs. Beyond these challenges
emerging frontiers are reshaping the boundaries of the field. C
1
feedstock-driven glycan biosynthesis
using CO
2
methanol
or formate a
s sole carbon sources
promises to decouple carbohydrate production from arable land and plant photosynthesis. Non-natural glycan therapeutics
including fluorinated antivirals
thio-sugar glycosidase inhibitors
and amino-glycoside antibiotic potentiators
extend the accessible chemical space beyond nature’s monosaccharide repertoire. Personalized glycan vaccines designed against patient-specific tumor glycoforms and CRISPR-like programmable glycan editing tools point toward a future where glycan structures can be edited as precisely as genes. Glycosynthetic biology is completing a fundamental transformation. It is moving from discovering what nature provides to designing and manufacturing what we desire. Carbohydrates are emerging from the neglected periphery of molecular biology to a frontier at the intersection of discovery and industry. The destination is clear: a standardized glycan operating system for the programmable
on-demand biomanufacturing of structurally defined glycans.
2
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