BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To construct a sizeable naive human Fab phage display antibody library to screen high-affinity specific antibodies in vitro. Methods:Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) of 126 healthy individuals, subsequently reverse-transcribed into cDNA, and used as a template. PCR amplification was performed to obtain the V H from IgG, IgM and light chain κ, λ, separately, with the initial PCR products serving as templates for a second round of PCR. Overlap extension PCR was employed to generate fragments of the κ and λ light chains. These fragments were ligated with the phage vector pNC3, which harbors the variable region 1 of the heavy chain, to construct a recombinant phage plasmid. This plasmid was then electroporated into competent Escherichia Coli TG1 cells to establish a naive human Fab phage display antibody library. One hundred clones were randomly selected for identification and sequencing, and antibody gene polymorphisms were analyzed using the IMGT database and MAFFT software. Recombinant α-hemolysin from Staphylococcus aureus was utilized to screen Fab antibody fragments through biopanning of the antibody library, followed by random selection of phage ELISA-identified clones. The positive clones (antigen A450∶blank control A450≥2.1) were sequenced. Results:Two large naive Fab phage display antibody libraries were successfully constructed, in which the capacity of κ and λ chain antibody libraries were 1.25×10 11 and 1.54×10 11, respectively. The titers for two antibody libraries were 6.04×10 13 CFU/ml and 3.50×10 13 CFU/ml. The positive transformation insertion rates for κ and λ chain antibody libraries were 96% (96/100) and 100% (100/100), respectively. Sequence analysis revealed that all antibody sequences were unique. The amino acid sequences in the skeletal region were relatively conserved. In contrast, significant variations in the length of the complementarity determining region (CDR) were found, and the diversity of amino acid sequence of the complementary determining region was high, especially the CDR3. Analysis using the IMGT database indicated that the sequences exhibited a broad distribution across variable-diversity-joining gene families. After six rounds of panning, specific phage antibodies enrichment targeting α-hemolysin were achieved. A total of 142 monoclonal antibodies were sequenced, yielding 8 distinct Fab antibody sequences. Conclusion:This study successfully constructed two naive human Fab phage display antibody libraries with large capacity and good diversity, which can be used for screening human antibodies for serum epidemiology.

BACKGROUND:Endoplasmic reticulum stress is closely associated with the occurrence and progression of osteoarthritis,but the key genes and regulatory mechanisms remain unclear.OBJECTIVE:Utilizing bioinformatics to identify crucial endoplasmic reticulum stress-related genes in osteoarthritis,followed by experimental validation in cell models,aiming to offer new strategies for the prevention and treatment of osteoarthritis from the perspective of endoplasmic reticulum stress.METHODS:Osteoarthritis-related dataset GSE55235 was downloaded from the GEO database.Differential genes in synovial tissue of osteoarthritis were obtained through WGCNA machine learning algorithm and intersected with endoplasmic reticulum stress-related genes from the GeneCard database to acquire differential endoplasmic reticulum stress-related genes in osteoarthritis(ERSDEGs).These genes underwent GO and KEGG enrichment analysis,construction of a protein-protein interaction network,and validation of diagnostic efficiency in external datasets.Human primary synovioblast model of osteoarthritis was constructed.The control group was not treated,and the experimental group received 20 ng/mL lipopolysaccharide to simulate osteoarthritic synoviocyte modeling.Real-time fluorescence quantitative PCR was then performed to validate the expression level of each differential gene followed by immune infiltration analysis.RESULTS AND CONCLUSION:A total of 27 key endoplasmic reticulum stress-related genes in osteoarthritis were identified.GO enrichment analysis revealed that these genes were mainly enriched in collagen metabolism,chemokine,antigen binding,and immunoglobulin receptor binding processes.KEGG analysis indicated that they were mainly enriched in pathways such as rheumatoid arthritis and relaxin signaling pathways.The protein-protein interaction network was constructed,and the top five genes with the highest scores were identified using the Degree algorithm in Cytoscape software,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,collagen type 1 alpha 1,and chemokine C-X-C motif ligand 12.Immune infiltration analysis showed that immune cells were mainly distributed in M2 macrophages,chemokine C-X-C motif ligand 12 showed a significant positive correlation with resting mast cells(r=0.70,P＜0.001)and a significant negative correlation with resting memory CD4+T cells(r=-0.72,P＜0.001).Matrix metallopeptidase 9 showed a significant positive correlation with MO macrophages(r=0.94,P＜0.001).Collagen type 1 alpha 1 was significantly positively correlated with resting NK cells(r=0.77,P＜0.001)and MO macrophages(r=0.76,P＜0.001).Receiver operator characteristic curve analysis in external datasets GSE77298 and GSE1919 showed that the five key genes had good disease prediction value.In vitro cell experiments demonstrated significant differences in the expression levels of matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12 in the osteoarthritic cell model compared to the control group.These results showed that the key genes related to endoplasmic reticulum stress in osteoarthritis,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12,influence the occurrence and development of osteoarthritis through the links of collagen degradation and immune regulation,which are expected to provide new insights into the targeted treatment of osteoarthritis.

Objective:To construct a sizeable naive human Fab phage display antibody library to screen high-affinity specific antibodies in vitro. Methods:Total RNA was extracted from peripheral blood mononuclear cells (PBMCs) of 126 healthy individuals, subsequently reverse-transcribed into cDNA, and used as a template. PCR amplification was performed to obtain the V H from IgG, IgM and light chain κ, λ, separately, with the initial PCR products serving as templates for a second round of PCR. Overlap extension PCR was employed to generate fragments of the κ and λ light chains. These fragments were ligated with the phage vector pNC3, which harbors the variable region 1 of the heavy chain, to construct a recombinant phage plasmid. This plasmid was then electroporated into competent Escherichia Coli TG1 cells to establish a naive human Fab phage display antibody library. One hundred clones were randomly selected for identification and sequencing, and antibody gene polymorphisms were analyzed using the IMGT database and MAFFT software. Recombinant α-hemolysin from Staphylococcus aureus was utilized to screen Fab antibody fragments through biopanning of the antibody library, followed by random selection of phage ELISA-identified clones. The positive clones (antigen A450∶blank control A450≥2.1) were sequenced. Results:Two large naive Fab phage display antibody libraries were successfully constructed, in which the capacity of κ and λ chain antibody libraries were 1.25×10 11 and 1.54×10 11, respectively. The titers for two antibody libraries were 6.04×10 13 CFU/ml and 3.50×10 13 CFU/ml. The positive transformation insertion rates for κ and λ chain antibody libraries were 96% (96/100) and 100% (100/100), respectively. Sequence analysis revealed that all antibody sequences were unique. The amino acid sequences in the skeletal region were relatively conserved. In contrast, significant variations in the length of the complementarity determining region (CDR) were found, and the diversity of amino acid sequence of the complementary determining region was high, especially the CDR3. Analysis using the IMGT database indicated that the sequences exhibited a broad distribution across variable-diversity-joining gene families. After six rounds of panning, specific phage antibodies enrichment targeting α-hemolysin were achieved. A total of 142 monoclonal antibodies were sequenced, yielding 8 distinct Fab antibody sequences. Conclusion:This study successfully constructed two naive human Fab phage display antibody libraries with large capacity and good diversity, which can be used for screening human antibodies for serum epidemiology.

BACKGROUND:Endoplasmic reticulum stress is closely associated with the occurrence and progression of osteoarthritis,but the key genes and regulatory mechanisms remain unclear.OBJECTIVE:Utilizing bioinformatics to identify crucial endoplasmic reticulum stress-related genes in osteoarthritis,followed by experimental validation in cell models,aiming to offer new strategies for the prevention and treatment of osteoarthritis from the perspective of endoplasmic reticulum stress.METHODS:Osteoarthritis-related dataset GSE55235 was downloaded from the GEO database.Differential genes in synovial tissue of osteoarthritis were obtained through WGCNA machine learning algorithm and intersected with endoplasmic reticulum stress-related genes from the GeneCard database to acquire differential endoplasmic reticulum stress-related genes in osteoarthritis(ERSDEGs).These genes underwent GO and KEGG enrichment analysis,construction of a protein-protein interaction network,and validation of diagnostic efficiency in external datasets.Human primary synovioblast model of osteoarthritis was constructed.The control group was not treated,and the experimental group received 20 ng/mL lipopolysaccharide to simulate osteoarthritic synoviocyte modeling.Real-time fluorescence quantitative PCR was then performed to validate the expression level of each differential gene followed by immune infiltration analysis.RESULTS AND CONCLUSION:A total of 27 key endoplasmic reticulum stress-related genes in osteoarthritis were identified.GO enrichment analysis revealed that these genes were mainly enriched in collagen metabolism,chemokine,antigen binding,and immunoglobulin receptor binding processes.KEGG analysis indicated that they were mainly enriched in pathways such as rheumatoid arthritis and relaxin signaling pathways.The protein-protein interaction network was constructed,and the top five genes with the highest scores were identified using the Degree algorithm in Cytoscape software,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,collagen type 1 alpha 1,and chemokine C-X-C motif ligand 12.Immune infiltration analysis showed that immune cells were mainly distributed in M2 macrophages,chemokine C-X-C motif ligand 12 showed a significant positive correlation with resting mast cells(r=0.70,P＜0.001)and a significant negative correlation with resting memory CD4+T cells(r=-0.72,P＜0.001).Matrix metallopeptidase 9 showed a significant positive correlation with MO macrophages(r=0.94,P＜0.001).Collagen type 1 alpha 1 was significantly positively correlated with resting NK cells(r=0.77,P＜0.001)and MO macrophages(r=0.76,P＜0.001).Receiver operator characteristic curve analysis in external datasets GSE77298 and GSE1919 showed that the five key genes had good disease prediction value.In vitro cell experiments demonstrated significant differences in the expression levels of matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12 in the osteoarthritic cell model compared to the control group.These results showed that the key genes related to endoplasmic reticulum stress in osteoarthritis,including matrix metallopeptidase 1,tumor necrosis factor ligand superfamily member 11,matrix metallopeptidase 9,and chemokine C-X-C motif ligand 12,influence the occurrence and development of osteoarthritis through the links of collagen degradation and immune regulation,which are expected to provide new insights into the targeted treatment of osteoarthritis.

BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

AIM: To investigate the mechanism by which Yijing Decoction antagonist high glucose-induced damage to the basement membrane(BM)in an in vitro inner blood-retinal barrier(iBRB)model.METHODS:Rat retinal microvascular pericytes(RMPs)and endothelial cells(ECs)were isolated and cultured to establish an in vitro iBRB model. The cells were randomly divided into four groups: low glucose group(LG), high glucose group(HG), minocycline group(MG)and Yijing Decoction group(YG). The LG group received 25 mmol/L glucose, the HG group received 60 mmol/L glucose, the MG group received 60 mmol/L glucose + 10 μg/mL minocycline, and the YG group received 60 mmol/L glucose + 10% Yijing Decoction-containing serum. Incubation for each group were terminated after intervention for 12 h. Next, the Western blot analysis was performed to assess the protein expression of BM-related proteins, including collagen Ⅳ(CⅣ)and laminin(LN), as well as matrix metalloproteinase(MMPs)/tissue inhibitor of matrix metalloproteinases(TIMPs)such as MMP-2, MMP-3, MMP-9, TIMP-1, TIMP-2.RESULTS:Compared to the LG group, the protein expressions of CⅣ increased in the HG, MG, and YG groups, as did LN in the HG and MG groups(all P<0.05). Both Yijing Decoction and minocycline effectively inhibited the elevated expression of CⅣ and LN induced by high glucose, and the difference between the YG, MG, and HG groups was statistically significant(all P<0.05). Futhermore, compared to the LG group, the protein expressions of MMP-2, MMP-3, and MMP-9 increased in the HG, MG, and YG groups(all P<0.05). Yijing Decoction specifically attenuated the high glucose-induced increase in MMP-2, MMP-3 and MMP-9 protein expression, and there were statistically significant differences between the YG and HG group(all P<0.05). No significant difference were observed in the expressions of TIMP-1 and TIMP-2 among the LG, HG, MG, and YG groups(all P>0.05).CONCLUSION:Yijing Decoction can potentially intervene in DR by modulating the protein expression of MMP-2, MMP-3, MMP-9, CⅣ, and LN, suppressing high glucose-induced BM remodeling, and mitigating damage to iBRB.

Objective:To illustrate the characteristics of the distribution of prophages among the Group A Streptococcus(GAS) by mining the existing whole genome sequencing of the GAS, performing bioinformatic analyses, extracting data about prophages, and analyzing the state of prophages in the genome and genetic composition of some prophages. Methods:It was a retrospective study.Genome assembly sequences of GAS reported in GenBank till May 2020 were collected, and the important background information of these strains was sorted out to create a local genomic database.A phylogenetic tree of the whole genome of GAS was conducted using the bioinformatics software.The core genome was analyzed, and potential prophages and their integrity in the genome were predicted to obtain the characteristics of the distribution of prophages.Genotype types, number of core genes, and number, length and carrying rate of prophages in the database for GAS were analyzed.Results:A database containing the genome sequence of 2 529 GAS strains was established, involving 140 emm genotypes.These strains were isolated from 19 countries from East Asia, Europe, America and Oceania.Stratified by the disease background, these strains were mainly divided into invasive infection, non-invasive infection and immune sequelae.Prophage analysis of 1 798 genomes showed that at least one complete prophage was detected in 1 366 (76.0%) genomes.The number of complete prophages of each strain ranged from 0 to 6, and the length ranged from 32.8 to 62.6 kb, which was mainly 30-40 kb in length.The phiHKUssa, phiHKUvir and phiHKU488 were the most common prophages present in dominant clones circulated in China in recent years, which mainly carried virulence genes like the speC, spd1 and ssa. Conclusions:Prophages are widely distributed in the genome of GAS, which are of great significance in the evolution and expansion of dominating clones and thus reshape the population structure within the emm genotype.The establishment of a local genome database provides important baseline data for molecular epidemiological surveillance.