Objective To investigate the differential expression of related proteins in interosseous muscle tissue between patients with familial amyotrophic lateral sclerosis（FALS） and normal individuals in a family using the isobaric tags for relative and absolute quantitation （iTRAQ） technique， to identify the pathogenic proteins for this family， and to provide a basis for treatment. Methods Interosseous muscle tissue samples were collected from all subjects in this family， and the iTRAQ technique was used to perform qualitative and quantitative analyses for all proteins and obtain the expression profile of proteins in the disease group and the normal group. The bioinformatics methods were used to identify the proteins associated with the onset of FALS. A gene ontology（GO）analysis was performed for cell components， and a classification analysis was performed for related proteins. Results A total of 453 proteins were identified by mass spectrometry. The GO analysis obtained 14 differentially expressed proteins between the disease group and the normal group （P<0.05）， and compared with the normal group，the disease group had the low expression of 5 proteins （Ratio<1） and the high expression of 9 proteins （Ratio>1）. Conclusion This study identifies 8 proteins that are highly associated with FALS， i.e.， tripartite motif-containing protein 72， NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 1， annexin A1， decorin， glutathione peroxidase 3， collagen alpha-1 （Ⅻ） chain， collagen alpha-2 （Ⅰ） chain， and collagen type I alpha 1 isoform CRA-a. There are 6 proteins that might be associated with FALS， i.e.，26 S protease regulatory subunit 8， laminin subunit alpha-2，prolargin， fibrillin-1， myosin-8， and dermatopontin.
Proteomics

OBJECTIVE: To explore the serological characteristics and bioinformatics analysis results of 4 blood donors with RHCE*cE(281C, 282T) variant allele. METHODS: A total of 4 non-related blood donors with RHCE*cE (281C, 282T) variant allele (donors 1-4) were selected as the study objects. They donated blood at Shenzhen Blood Center from January 2022 to June 2023. The 4 blood donors were all Han. And 5 mL elbow venous blood was collected from these 4 blood donors. Regular serological assaying with 4 kinds of monoclonal antibody reagents was used for determination of the RhCcEe type. The nucleotide sequences of all 10 exons and adjacent flanking intron regions of RHCE gene in these 4 donors were analyzed by Sanger sequencing, and the full-length haplotype analysis of RHCE gene was performed by using the single-molecule real-time sequencing (SMRT) third-generation technology. DeepTMHMM software was used to analyze the structure of protein transmembrane region of wild type and variant RhCcEe protein and predict the location of amino acid substitution. The effects of mutations on RhCcEe protein function were analyzed using PolyPhen-2, SIFT and Mutation Taster bioinformatics software. Robetta and Swiss-PdbViewer v4.1.0 were used for modeling the tertiary structures of RhCcEe to analyze the difference between wild type and variant RhCcEe protein. The mutation was rated according to the standards and guidelines for the classification of genetic variants of the American College of Medical Genetics and Genomics (ACMG). This study has been approved by the Medical Ethics Committee of Shenzhen Blood Center (Approval No. SZBCMEC-2022-024). RESULTS: The RhCcEe phenotypes of the 4 blood donors were CCEweake by serological assaying. The RhE antigen were weakly expressed form 0 to 3+. The analysis of RHCE gene sequence indicated that all the 4 donors with RHCE*cE (281C, 282T) allele. The mutation caused the substitution of a single amino acid in the RhCcEe protein (p.Leu94 Pro) and the amino acid substitution was located in the transmembrane α3 chain resulted in significant changes in the 3D structure of the extracellular region of RhCcEe protein. The substitution was predicted to be "Probably damaging", "Damaging" and "Polymorphism" by PolyPhen-2, SIFT and Mutation Taster bioinformatics software. According to the guidelines of ACMG, the variant was rated to be likely pathogenic. CONCLUSION The RHCE*cE (281C, 282T) variant allele was first found in the Han Chinese population. The serological data of this allele were enriched. It provides an important guarantee for the safety of blood transfusion. Bioinformatics analysis provided evidences for further study of the structure and functions of RhCcEe protein.
Humans ; Blood Donors ; Computational Biology/methods* ; Alleles ; Rh-Hr Blood-Group System/genetics* ; Male ; Female ; Adult ; Exons

The International System for Human Cytogenomic Nomenclature (ISCN) is a standardized international nomenclature system established by the International Standing Committee on Human Cytogenomic Nomenclature (ISCN SC). It is designed for describing chromosomal or genomic abnormalities detected by commonly used genetic and genomic techniques including but not limited to karyotyping, fluorescence in situ hybridization, microarray, genome mapping, various region-specific assays, and high-throughput sequencing. With a history spanning over six decades, the ISCN was revised by the ISCN SC in 2024 and officially published in September 2024. This article provides a summary for the updates introduced in the 2024 edition of the International System for Human Cytogenomic Nomenclature.
Humans ; Terminology as Topic ; Genomics ; Genome, Human/genetics*

OBJECTIVE: To explore the effect mechanism of electroacupuncture (EA) for ameliorating diabetic peripheral neuropathy (DPN) based on the analysis of gut microbiota and metabolomics. METHODS: Thirty SPF-grade male SD rats were randomly divided into a normal group, a model group, and an EA group, with 10 rats in each one. Except in the normal group, the intraperitoneally injection with streptozotocin was used to induce diabetes mellitus model in the rest groups. In the EA group, acupuncture was delivered at bilateral "Zusanli" (ST36), "Sanyinjiao" (SP6), "Pishu" (BL20) and "Shenshu" (BL23), and electric stimulation was attached to "Zusanli" (ST36)-"Sanyinjiao" (SP6) and "Pishu" (BL20)-"Shenshu" (BL23), on the same side, with continuous wave and a frequency of 2 Hz, for 10 min in each intervention. The intervention measure of each group was delivered once every 2 days, 3 times a week, for 8 consecutive weeks. Body weight, random blood glucose (RBG), thermal withdrawal latency (TWL), and mechanical withdrawal threshold (MWT) before intervention, and in 4 and 8 weeks of intervention, separately, as well as sensory nerve conduction velocity (SCV) and motor nerve conduction velocity (MCV) of the sciatic nerve after intervention were measured. Metagenomic sequencing (MS) was used to analyze gut microbiota and screen for differential species. Liquid chromatography-mass spectrometry (LC-MS) was employed to detect the differential metabolites in plasma, and the metabolic pathway enrichment analysis was performed on the differential metabolites. Spearman correlation analysis was adopted to assess the relationship between gut microbiota and metabolomics. RESULTS: After 4 and 8 weeks of intervention, when compared with the model group, the EA group showed the increase in body weight, TWL, MWT (P<0.01), and the decrease in RBG (P<0.01). Compared with the normal group, SCV and MCV, as well as Chao1 index were dropped in the model group (P<0.01), and those were elevated in the EA group when compared with those in the model group (P<0.01). The dominant bacterial phyla of each group were Firmicutes (F) and Bacteroidota (B), the ratio of them (F/B) in the model group was lower than that of the normal group (P<0.05), and F/B in the EA group was higher when compared with that in the model group (P<0.05). In comparison with the normal group, the relative abundance increased in Prevotella, Segatella, Prevotella-hominis and Segatella-copri (P<0.05); and it decreased in Ligilactobacillus, Eubacterium, Pseudoflavonifractor, Ligilactobacillus-murinus (P<0.05) in the model group. Compared with the model group, the relevant abundance of the above mentioned gut bacteria was all ameliorated in the EA group (P<0.05, P<0.01). Among the three groups, 120 differential metabolites were identified and enriched in 28 key metabolic pathways, such as glycerophospholipid and linoleic acid, of which, glycerophospholipid was the most significantly affected pathway in EA intervention. Spearman correlation analysis showed that 6 phosphatidylcholine metabolites were significantly positively correlated with Pseudoflavonifractor and were negatively with Prevotella, Segatella, Prevotella-hominis, Segatella-copri; 5 phosphatidylethanolamine metabolites were significantly negatively correlated with Pseudoflavonifractor and positively correlated with Prevotella, Segatella, Prevotella-hominis, Segatella-copri. CONCLUSION EA may regulate metabolic pathways such as glycerophospholipid, modulate specific gut microbiota such as Pseudoflavonifractor, Prevotella, and Segatella, and the co-expressed differential metabolites like phosphatidylcholine and phosphatidylethanolamine, thereby reducing blood glucose and protecting nerve function, so as to relieve the symptoms of DPN of rats.
Animals ; Electroacupuncture ; Male ; Gastrointestinal Microbiome ; Diabetic Neuropathies/microbiology* ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Humans ; Acupuncture Points

BACKGROUND: Hepatic inflammatory cell accumulation and the subsequent systematic inflammation drive acute-on-chronic liver failure (ACLF) development. Previous studies showed that the vagus nerve exerts anti-inflammatory activity in many inflammatory diseases. Here, we aimed to identify the key molecule mediating the inflammatory process in ACLF and reveal the neuroimmune communication arising from the vagus nerve and immunological disorders of ACLF. METHODS: Proteomic analysis was performed and validated in ACLF model mice or patients, and intervention animal experiments were conducted using neutralizing antibodies. PNU-282987 (acetylcholine receptor agonist) and vagotomy were applied for perturbing vagus nerve activity. Single-cell RNA sequencing (scRNA-seq), flow cytometry, immunohistochemical and immunofluorescence staining, and clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9) technology were used for in vivo or in vitro mechanistic studies. RESULTS: The unbiased proteomics identified C-X-C motif chemokine ligand 9 (CXCL9) as the greatest differential protein in the livers of mice with ACLF and its relation to the systematic inflammation and mortality were confirmed in patients with ACLF. Interventions on CXCL9 and its receptor C-X-C chemokine receptor 3 (CXCR3) improved liver injury and decreased mortality of ACLF mice, which were related to the suppressing of hepatic immune cells' accumulation and activation. Vagus nerve stimulation attenuated while vagotomy aggravated the expression of CXCL9 and the severity of ACLF. Blocking CXCL9 and CXCR3 ameliorated liver inflammation and increased ACLF-associated mortality in ACLF mice with vagotomy. scRNA-seq revealed that hepatic macrophages served as the major source of CXCL9 in ACLF and were validated by immunofluorescence staining and flow cytometry analysis. Notably, the expression of CXCL9 in macrophages was modulated by vagus nerve-mediated cholinergic signaling. CONCLUSIONS Our novel findings highlighted that the neuroimmune communication of the vagus nerve-macrophage-CXCL9 axis contributed to ACLF development. These results provided evidence for neuromodulation as a promising approach for preventing and treating ACLF.
Animals ; Mice ; Chemokine CXCL9/metabolism* ; Vagus Nerve/physiology* ; Acute-On-Chronic Liver Failure/metabolism* ; Humans ; Male ; Mice, Inbred C57BL ; Proteomics ; Flow Cytometry ; Receptors, CXCR3/metabolism*

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*

BACKGROUND: Diabetic foot is a complex condition with high incidence, recurrence, mortality, and disability rates. Current treatments for diabetic foot ulcers are often insufficient. This study was conducted to identify potential therapeutic targets for diabetic foot. METHODS: Datasets related to diabetic foot and diabetic skin were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software. Enrichment analysis was conducted to screen for critical gene functions and pathways. A protein interaction network was constructed to identify node genes corresponding to key proteins. The DEGs and node genes were overlapped to pinpoint target genes. Plasma and chronic ulcer samples from diabetic and non-diabetic individuals were collected. Western blotting, immunohistochemistry, and enzyme-linked immunosorbent assays were performed to verify the S100 calcium binding protein A9 (S100A9), inflammatory cytokine, and related pathway protein levels. Hematoxylin and eosin staining was used to measure epidermal layer thickness. RESULTS: In total, 283 common DEGs and 42 node genes in diabetic foot ulcers were identified. Forty-three genes were differentially expressed in the skin of diabetic and non-diabetic individuals. The overlapping of the most significant DEGs and node genes led to the identification of S100A9 as a target gene. The S100A9 level was significantly higher in diabetic than in non-diabetic plasma (178.40 ± 44.65 ng/mL vs. 40.84 ± 18.86 ng/mL) and in chronic ulcers, and the wound healing time correlated positively with the plasma S100A9 level. The levels of inflammatory cytokines (tumor necrosis factor-α, interleukin [IL]-1, and IL-6) and related pathway proteins (phospho-extracellular signal regulated kinase [ERK], phospho-p38, phospho-p65, and p-protein kinase B [Akt]) were also elevated. The epidermal layer was notably thinner in chronic diabetic ulcers than in non-diabetic skin (24.17 ± 25.60 μm vs. 412.00 ± 181.60 μm). CONCLUSIONS S100A9 was significantly upregulated in diabetic foot and was associated with prolonged wound healing. S100A9 may impair diabetic wound healing by disrupting local inflammatory responses and skin re-epithelialization.
Calgranulin B/therapeutic use* ; Diabetic Foot/metabolism* ; Humans ; Datasets as Topic ; Computational Biology ; Mice, Inbred C57BL ; Animals ; Mice ; Protein Interaction Maps ; Immunohistochemistry

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

OBJECTIVES: To identify potential molecular targets of quercetin in the treatment of clear cell renal carcinoma (ccRCC). METHODS: The therapeutic targets of quercetin were screened from multiple databases by network pharmacology analysis, and the targets significantly correlated with ccRCC were screened from 4907 plasma proteins using a Mendelian randomization method. The drug-disease network model was constructed to screen the potential key targets. The functions of these targets were evaluated via bioinformatics analysis, and the screened targets were verified in cultured ccRCC cells. RESULTS: Network pharmacology analysis combined with Mendelian randomization identified TP53 (OR=3.325, 95% CI: 1.805-6.124, P=0.0001), ARF4 (OR=0.173, 95% CI: 0.065-0.456, P=0.0003), and DPP4 (OR=0.463, 95% CI: 0.302-0.711, P=0.0004) as the core targets in quercetin treatment of ccRCC. Bioinformatics analysis showed that TP53 was highly expressed in ccRCC, and patients with high TP53 expressions had worse survival outcomes. Molecular docking studies showed that the binding energy between quercetin and TP53 was -5.83 kcal/mol. In cultured 786-O cells, CCK-8 assay and wound healing assay showed that treatment with quercetin significantly inhibited cell proliferation and migration. Quercetin treatment also strongly suppressed the expression of TP53 at both the mRNA and protein levels in 786-O cells as shown by RT-qPCR and Western blotting. CONCLUSIONS TP53 may be the key target of quercetin in the treatment of ccRCC, which sheds light on potential molecular mechanism that mediate the therapeutic effect of quercetin.
Humans ; Quercetin/pharmacology* ; Carcinoma, Renal Cell/genetics* ; Cell Proliferation/drug effects* ; Kidney Neoplasms/genetics* ; Cell Movement/drug effects* ; Tumor Suppressor Protein p53/metabolism* ; Cell Line, Tumor ; Computational Biology

OBJECTIVES: To explore the role of oxidative stress and immune infiltration in rheumatoid arthritis (RA). METHODS: RA datasets GSE55235 (10 RA vs 10 normal samples) and GSE55457 (13 RA vs 10 normal samples) from the GEO database were merged as the test set to identify the differentially expressed genes (DEGs) in RA using R. The DEGs were intersected with oxidative stress-related genes to obtain oxidative stress-associated DEGs. KEGG and GO enrichment analyses of the DEGs were performed, and the RA-related pathways and biological processes were analyzed using GSEA. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, and the top 10 key genes were obtained using the Degree algorithm. The validation dataset GSE1919 from GEO database was used for ROC analysis of the key genes to obtain the core genes, and their correlations with infiltrating immune cells were analyzed using CIBERSORT. The results were verified by RT-qPCR for detecting expression levels of the core genes in RA and normal joint samples. RESULTS: We identified 89 oxidative stress-associated DEGs. Enrichment analysis suggested that these DEGs were involved in the biological processes including oxidative stress, chemical stress response, reactive oxygen species response, and lipopolysaccharide response. ROC analysis showed that the 5 core genes (STAT1, MMP9, MYC, CCL5, and JUN) all had AUC values >0.7, indicating their high diagnostic sensitivity and specificity for RA. These genes were closely correlated with immune cells, particularly T cells. RT-qPCR confirmed significant differential expressions of the core genes between RA and normal samples. CONCLUSIONS Oxidative stress and diverse immune responses are features of RA, and the immune responses contribute to activation of oxidative stress. The identified core genes can potential serve as new diagnostic markers for RA.
Arthritis, Rheumatoid/genetics* ; Oxidative Stress/genetics* ; Humans ; Computational Biology ; Protein Interaction Maps ; Gene Expression Profiling ; Gene Regulatory Networks