Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

Proteins are the basic building blocks of life. Studying the protein expression mechanism is essential for understanding the cellular organization principles and the development of biotechnology. Protein expression, involving transcription, translation, folding, and post-translational modification, is a complicatedly regulated process affected by various cellular components and sequence features of the expressed protein. Establishing protein expression models based on expression data is of great significance for probing into the regulatory factors and mechanisms of protein expression. Here we review the recent research progress in the mechanism models for quantitatively simulating the protein expression process and the prediction algorithms based on artificial intelligence for analyzing the regulatory factors. Chemical reaction network models have been developed to mathematically describe the elementary processes in protein expression and simulate the influences of various cellular components such as RNA polymerase and tRNA. However, the experimental determination of the huge number of model parameters is a big challenge. The main objective of data-driven AI models is to study the effects of protein/DNA sequences of the target protein on its expression, and subsequently optimize the sequences to improve protein expression. Methods combining mechanism models and AI models have the potential to deepen our understanding of protein expression processes, providing theoretical and technical support for the efficient production of high-value proteins and coordinate the regulation of different proteins.
Artificial Intelligence ; Proteins/metabolism* ; Algorithms ; Protein Biosynthesis

BACKGROUND:Transplantation of stem cell-derived islet β cells has been considered effective for the treatment of type 1 diabetes.Human umbilical cord mesenchymal stem cell is an ideal cellular source,but with a low differentiation efficiency to islet β cells. OBJECTIVE:To explore the possibility of human umbilical cord mesenchymal stem cells modified by MAFA and PDX1 to differentiate into insulin-producing cells. METHODS:MAFA-PDX1 lentivirus expression vectors were constructed.The efficiency and potentiality of human umbilical cord mesenchymal stem cells differentiated into insulin-producing cells with three methods were compared by cell morphology,RT-qPCR,and dithizone staining[protocol A:Simple lentivirus group;protocol B:Drug(nicotinamide β-mercaptoethanol)induction followed by lentivirus group;protocol C:lentivirus and drug induction group]. RESULTS AND CONCLUSION:(1)Morphological change of cells:Cell morphology was all altered after the induction of three protocols.At day 11,human umbilical cord mesenchymal stem cells induced by protocol B showed the most cell clusters among the three protocols,appearing aggregated islet-like cell clusters.(2)Islet-related gene expression detected by RT-qPCR:Horizontal comparison of the three protocols at the same induction time point showed that the expression levels of MAFA and PDX1 genes were the highest in protocol C on day 5 of induction,and those in protocol B were the highest on day 11 of induction.Human umbilical cord mesenchymal stem cells induced by protocol B had the greatest expression of GCG gene at day 5,INS and GLUT2 genes at day 11.(3)Dithizone staining to identify zinc ions:parts of the post-induced cells were stained brownish red by dithizone on day 11.The partial small island cells were stained brownish red with a darker color(positive expression)in protocol B.(4)It is concluded that the overexpression of MAFA and PDX1 can promote the differentiation of human umbilical cord mesenchymal stem cells into insulin-producing cells.The combination of MAFA-PDX1 gene modification and drug induction is superior to the single gene modification.

Objective To investigate the effect and mechanism of IL-17 on heart failure(HF)in hy-pertensive rats based on NF-κB/sarco-endoplasmic reticulum calcium ATPase 2(SERCA2)signa-ling pathway.Methods Thirty SPF male spontaneously hypertensive rats(SHR)aged 6-8 weeks were divided into control group,model group,phosphate buffer salt(PBS)solution injec-tion group(PBS group),IL-17 protein injection group(IL-17 group)and IL-17 and antibody injec-tion group(IL-17+IgG group),with 6 animals in each group.Hypertensive HF model was estab-lished,and corresponding agents were applied to the PBS group,IL-17 group and IL-17+IgG group intraperitoneally,respectively.The role of IL-17,NF-κB and SERCA2 in hypertensive HF was studied with HE staining,immunohistochemical assay,Western blotting,RT-qPCR and ELISA.Results Significantly higher serum levels of NT-proBNP and IL-17,enhanced myocardial expression of IL-17 mRNA and NF-κB protein,lower serum VEGF level,and down-regulated pro-tein level of SERCA2 in heart tissue were observed in the model group and the PBS group when compared with the control group(P＜0.01).The IL-17 group had obviously higher serum NT-proBNP and IL-17 levels and myocardial expression of IL-17 mRNA and NF-κB protein,and reduced serum VEGF level and SERCA2 protein level in heart tissue than the model group(P＜0.01).IL-17+IgG treatment resulted in notably lower serum IL-17 level and myocardial NF-κB protein level when compared with those of model group(8.98±1.20 vs 11.19±1.22,0.88±0.03 vs 0.93±0.03,P＜0.01),and also resulted in remarkably reduced serum levels of NT-proBNP and IL-17 and myocardial expression of IL-17 mRNA and NF-κB protein but increased serum VEGF level and SERCA2 protein level in heart tissue when compared with the IL-17 group(P＜0.01).The heart rate,SBP,IVSd,LVPWd,LVEDD and LVESD were significantly lower,while LVFS was notably higher in the IL-17+IgG group than the model group and IL-17 group(P＜0.01).The IL-17+IgG group had obviously higher LVEF than the IL-17 group[(70.81±6.50)％vs(62.77±5.43)％,P＜0.01].Conclusion IL-17/NF-κB/SERCA2 signaling pathway is involved in the regulation of inflammatory response after hypertensive HF,and inhibiting IL-17 can effective-ly improve the cardiac dysfunction caused by hypertensive HF.

Objectives:To evaluate the safety and feasibility of simultaneous bilateral adrenal vein sampling(AVS)via the basilic vein approach. Methods:21 consecutive patients with primary aldosteronism(PA)who underwent simultaneous bilateral AVS via the basilic vein in Fuwai Hospital between July 2023 and November 2023 were enrolled in this study.The puncture site,catheter used in AVS,operation time,fluoroscopy time,contrast agent dosages,success rate of bilateral sampling,adverse events,and complications were recorded and analyzed.Successful sampling was determined by a selectivity index(cortisol in the adrenal vein/cortisol in inferior vena cava)greater than or equal to 2. Results:The average age of 21 patients was(49.3±7.7)years,with 13 male patients.The first 5F sheath was successfully inserted into the right basilic vein in all patients,the second 5F sheath insertion failed in two patients and switched to the ipsilateral cephalic vein approach.The 5F MPA1 catheter was inserted into the right adrenal vein and the 5F TIG catheter into the left adrenal vein in all patients.Operation time was 17.50(12.00,22.00)min,fluoroscopy time was 5.90(4.75,10.55)min,and contrast agent dosage was 25.00(25.00,35.00)ml.Bilateral AVS was successful in all patients.Two patients experienced adverse events,one case was catheter entanglement,which resulted in 5F TIG catheter slipped from adrenal vein,and another case was vascular spasm.No complications were recorded. Conclusions:Simultaneous bilateral AVS via basilic vein approach is safe and feasible in most PA patients,further researches with larger patient cohort are needed to validate the results from this study.

BACKGROUND: Despite the progress in perinatal-neonatal medicine, complications of extremely preterm infants continue to constitute the major adverse outcomes in neonatal intensive care unit. Human umbilical cord Wharton’s Jellyderived mesenchymal stem cells (HUMSCs) may offer new hope for the treatment of intractable neonatal disorders. This study will explore the functional differences of HUMSCs between extremely preterm and term infants. METHODS: UMSCs from 5 extremely preterm infants(weeks of gestation: 22+5 w,24+4 w,25+3 w,26 w,28 w) and 2 term infants(39 w,39+2 w) were isolated, and mesenchymal markers, pluripotent genes, proliferation rate were analyzed.HUVECs were injured by treated with LPS and repaired by co-cultured with HUMSCs of different gestational ages. RESULTS: All HUMSCs showed fibroblast-like adherence to plastic and positively expressed surface marker of CD105,CD73 and CD90, but did not expressed CD45,CD34,CD14,CD79a and HLA-DR; HUMSCs in extremely preterm exhibited significant increase in proliferation as evidenced by CCK8, pluripotency markers OCT-4 tested by RT-PCR also showed increase. Above all, in LPS induced co-cultured inflame systerm, HUMSCs in extremely preterm were more capable to promote wound healing and tube formation in HUVEC cultures, they promoted TGFb1 expression and inhibited IL6 expression. CONCLUSIONS Our results suggest that HUMSCs from extremely preterm infants may be more suitable as candidates in cell therapy for the preterm infants.

Novel coronavirus Omicron variant infection can cause severe illness and even death in certain populations. Omicron variant infection may lead to systemic inflammatory response, coagulation disorder, multi-organ dysfunction and other pathophysiological changes, which are different from other Novel coronavirus variants to a certain extent, so therapeutic strategies should not be the same. The National Medical Center for Major Public Health Events invited experts in fields of infectious diseases, respiratory medicine, intensive care, pediatrics and fever clinic to develop this quick guideline based on the current best evidence and extensive clinical practices. This quick guideline aims to standardize the diagnosis and treatment of novel coronavirus Omicron infection, and to improve the disease management abilities of clinicians.

Since 2010, the incidence of severe fever with thrombocytopenia syndrome (SFTS) has been increased. Owing the progress in diagnosis and treatment, the overall mortality of SFTS in China has decreased, while the mortality in critical SFTS patients is still high. In order to provide guidance and working procedures for clinicians to diagnose and treat critical SFTS, the National Medical Center for Major Public Health Events invited experts to discuss and formulate this consensus based on their experience and up-to-date knowledge on SFTS.

Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.
Genome ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Models, Biological ; Thermodynamics