Oral squamous cell carcinoma (OSCC) is the most common manifestation of oral cancer. It has been proposed that periodontal pathogens contribute to OSCC progression, mainly by their virulence factors. However, the main periodontal pathogen and its mechanism to modulate OSCC cells remains not fully understood. In this study we investigate the main host-pathogen pathways in OSCC by computational proteomics and the mechanism behind cancer progression by the oral microbiome. The main host-pathogen pathways were analyzed in the secretome of biopsies from patients with OSCC and healthy controls by mass spectrometry. Then, functional assays were performed to evaluate the host-pathogen pathways highlighted in oral cancer. Host proteins associated with LPS response, cell migration/adhesion, and metabolism of amino acids were significantly upregulated in the human cancer proteome, whereas the complement cascade was downregulated in malignant samples. Then, the microbiome analysis revealed large number and variety of peptides from Fusobacterium nucleatum (F. nucleatum) in OSCC samples, from which several enzymes from the L-glutamate degradation pathway were found, indicating that L-glutamate from cancer cells is used as an energy source, and catabolized into butyrate by the bacteria. In fact, we observed that F. nucleatum modulates the cystine/glutamate antiporter in an OSCC cell line by increasing SLC7A11 expression, promoting L-glutamate efflux and favoring bacterial infection. Finally, our results showed that F. nucleatum and its metabolic derivates promote tumor spheroids growth, spheroids-derived cell detachment, epithelial-mesenchymal transition and Galectin-9 upregulation. Altogether, F. nucleatum promotes pro-tumoral mechanism in oral cancer.
Humans ; Fusobacterium nucleatum/metabolism* ; Mouth Neoplasms/metabolism* ; Disease Progression ; Proteomics ; Carcinoma, Squamous Cell/metabolism* ; Host-Pathogen Interactions ; Metabolic Networks and Pathways ; Case-Control Studies ; Mass Spectrometry

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

The pursuit of healthy aging has long rendered aging and senescence captivating. Age-related ailments, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, pose significant threats to individuals. Recent studies have shed light on the intricate mechanisms encompassing genetics, epigenetics, transcriptomics, and metabolomics in the processes of senescence and aging, as well as the establishment of age-related pathologies. Amidst these underlying mechanisms governing aging and related pathology metabolism assumes a pivotal role that holds promise for intervention and therapeutics. The advancements in metabolomics techniques and analysis methods have significantly propelled the study of senescence and aging, particularly with the aid of multiscale metabolomics which has facilitated the discovery of metabolic markers and therapeutic potentials. This review provides an overview of senescence and aging, emphasizing the crucial role metabolism plays in the aging process as well as age-related diseases.
Humans ; Aging/metabolism* ; Metabolomics/methods* ; Neurodegenerative Diseases/metabolism* ; Cardiovascular Diseases/metabolism*

Ankylosing spondylitis (AS) is linked to an increased prevalence of myocardial infarction (MI). However, research dedicated to elucidating the pathogenesis of AS-MI is lacking. In this study, we explored the biomarkers for enhancing the diagnostic and therapeutic efficiency of AS-MI. Datasets were obtained from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and machine learning models to screen hub genes. A receiver operating characteristic curve and a nomogram were designed to assess diagnostic accuracy. Gene set enrichment analysis was conducted to reveal the potential function of hub genes. Immune infiltration analysis indicated the correlation between hub genes and the immune landscape. Subsequently, we performed single-cell analysis to identify the expression and subcellular localization of hub genes. We further constructed a transcription factor (TF)-microRNA (miRNA) regulatory network. Finally, drug prediction and molecular docking were performed. S100A12 and MCEMP1 were identified as hub genes, which were correlated with immune-related biological processes. They exhibited high diagnostic value and were predominantly expressed in myeloid cells. Furthermore, 24 TFs and 9 miRNA were associated with these hub genes. Enzastaurin, meglitinide, and nifedipine were predicted as potential therapeutic agents. Our study indicates that S100A12 and MCEMP1 exhibit significant potential as biomarkers and therapeutic targets for AS-MI, offering novel insights into the underlying etiology of this condition.
Humans ; Spondylitis, Ankylosing/complications* ; Systems Biology/methods* ; Myocardial Infarction/diagnosis* ; Biomarkers/metabolism* ; MicroRNAs/genetics* ; Gene Regulatory Networks ; Gene Expression Profiling ; Machine Learning

Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by diffuse alveolar and interstitial edema caused by damage to alveolar-capillary and epithelial cells, often induced by infection, sepsis, trauma, and other factors. It is marked by progressive hypoxemia and respiratory distress. Due to the diverse causes of ARDS, the unclear pathogenesis, and the absence of effective predictive markers or biomarkers, there are no effective treatment measures available, resulting in a high mortality rate. ARDS is increasingly recognized for its heterogeneity, biomarkers, and the emergence of new opportunities for the development of diagnostic tools and personalized treatment strategies provided by omics technologies. A single omics analysis cannot fully reveal the heterogeneity and complexity of ARDS, while multi-omics analysis can provide a more systematic and comprehensive understanding of ARDS. Using clinical samples is closer to the actual disease situation compared to animal models. Multi-omics studies based on clinical samples have achieved significant progress in elucidating the pathophysiology of ARDS, identifying ARDS subtypes, and identifying biomarkers related to ARDS. This review focuses on the current applications of genomics, transcriptomics, metabolomics, and proteomics analyses based on clinical samples in the ARDS field, with a focus on the application of these omics methods in ARDS heterogeneity, potential biomarkers, and pathogenesis. It also introduces the differences in the application of different clinical samples in ARDS omics research, in order to gain a deeper and more comprehensive understanding of the pathogenesis of ARDS and explore new strategies for its prevention and treatment.
Respiratory Distress Syndrome/diagnosis* ; Humans ; Metabolomics ; Proteomics ; Genomics ; Biomarkers ; Multiomics

Sepsis is a life-threatening organ dysfunction syndrome caused by a dysregulated host response to infection. Due to different infection sources, pathogens and basic conditions of patients, there is significant heterogeneity in clinical manifestations, response to treatment and prognosis of patients with sepsis. Accurate classification and individualized treatment of sepsis will help to further improve the prognosis of patients with sepsis. In recent years, the integration of artificial intelligence and bioinformatics has brought new opportunities for the research of sepsis classification. This review systematically introduces a variety of sepsis classification methods and their clinical application value. The clinical data in the electronic medical record, such as the dynamic changes of vital signs such as body temperature, can be used as the basis for sepsis classification. Different subtypes of body temperature trajectories have differences in physiological characteristics and prognosis, which contributes to predict the prognosis of patients and guide fluid management strategies. Biomarker classification can more comprehensively reflect the pathophysiological state of patients. Immune index classification is helpful to identify immunocompromised patients so as to carry out targeted immunotherapy. Transcriptome data and genotyping reveal the heterogeneity of sepsis at the molecular level and provide a new perspective for precision medicine. In addition, a detailed systematic review of sepsis-related organ function damage, such as acute respiratory distress syndrome (ARDS), acute kidney injury (AKI), and acute liver injury, has also been conducted, which is helpful to develop targeted organ protection and treatment strategies. These typing methods have shown good application prospects in clinical practice. However, there are still limitations in the current research, such as typing stability and biomarker selection, which need to be further explored. Future research should focus on the development of stable and efficient typing tools to achieve precise treatment of sepsis and improve the prognosis of patients.
Humans ; Sepsis/classification* ; Multiple Organ Failure/classification* ; Prognosis ; Artificial Intelligence ; Biomarkers ; Computational Biology ; Respiratory Distress Syndrome

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

This study aims to investigate the therapeutic effect and mechanism of Daotan Xixin Decoction on APP/PS1 mice. Twelve APP/PS1 male mice were randomized into four groups: APP/PS1 and low-, medium-, and high-dose Daotan Xixin Decoction. Three C57BL/6 wild-type mice were used as the control group. The learning and memory abilities of mice in each group were examined by the Morris water maze test. The pathological changes of hippocampal nerve cells were observed by hematoxylin-eosin staining and Nissl staining. Immunohistochemistry was employed to detect the expression of β-amyloid(Aβ)_(1-42) in the hippocampal tissue. The high-dose Daotan Xixin Decoction group with significant therapeutic effects and the model group were selected for high-throughput sequencing. The differentially expressed gene(DEG) analysis, Gene Ontology(GO) analysis, Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and Gene Set Variation Analysis(GSVA) were performed on the sequencing results. RT-qPCR and Western blot were conducted to determine the mRNA and protein levels, respectively, of some DEGs. Compared with the APP/PS1 group, Daotan Xixin Decoction at different doses significantly improved the learning and memory abilities of APP/PS1 mice, ameliorated the neuropathological damage in the CA1 region of the hippocampus, increased the number of neurons, and decreased the deposition of Aβ_(1-42) in the brain. A total of 1 240 DEGs were screened out, including 634 genes with up-regulated expression and 606 genes with down-regulated expression. The GO analysis predicted the biological processes including RNA splicing and protein folding, the cellular components including spliceosome complexes and nuclear spots, and the molecular functions including unfolded protein binding and heat shock protein binding. The KEGG pathway enrichment analysis revealed the involvement of neurodegenerative disease pathways, amyotrophic lateral sclerosis, and splicing complexes. Further GSVA pathway enrichment analysis showed that the down-regulated pathways involved nuclear factor-κB(NF-κB)-mediated tumor necrosis factor-α(TNF-α) signaling pathway, UV response, and unfolded protein response, while the up-regulated pathways involved the Wnt/β-catenin signaling pathway. The results of RT-qPCR and Western blot showed that compared with the APP/PS1 group, Daotan Xixin Decoction at different doses down-regulated the mRNA and protein levels of signal transducer and activator of transcription 3(STAT3), NF-κB, and interleukin-6(IL-6) in the hippocampus. In conclusion, Daotan Xixin Decoction can improve the learning and memory abilities of APP/PS1 mice by regulating the STAT3/NF-κB/IL-6 signaling pathway.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Male ; Alzheimer Disease/metabolism* ; Computational Biology ; Mice, Inbred C57BL ; High-Throughput Nucleotide Sequencing ; Amyloid beta-Protein Precursor/metabolism* ; Hippocampus/metabolism* ; Mice, Transgenic ; Presenilin-1/metabolism* ; Humans ; Memory/drug effects* ; Maze Learning/drug effects* ; Amyloid beta-Peptides/genetics* ; Disease Models, Animal

This study explored the action mechanism of Jiawei Xionggui Decoction in the treatment of Alzheimer's disease(AD) by integrating mouse brain tissue metabolomics and network pharmacology. Six-month-old amyloid precursor protein/presenilin 1(APP/PS1) mice were selected and divided into the APP/PS1 group and Jiawei Xionggui Decoction intervention group, with age-matched C57BL/6 mice serving as controls. Cognitive abilities and pathological damage in the mice were observed. Gas chromatography-mass spectrometry/mass spectrometry(GC-MS/MS) technology was utilized to analyze the metabolic profiles of mice brain tissue. Differential metabolites were screened, and relevant metabolic pathways were enriched. Network pharmacology was adopted to screen the active components of Jiawei Xionggui Decoction, so as to construct a protein-protein interaction network of its core targets for AD treatment and conduct Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis of potential targets for Jiawei Xionggui Decoction in treating AD. Finally, a "metabolite-reaction-enzyme-gene" network was constructed for combined analysis of metabolomics and network pharmacology. The results showed that Jiawei Xionggui Decoction significantly reversed the trends of 18 differential metabolites involved in 15 metabolic pathways such as glyoxylate and dicarboxylate metabolism, glycine, serine, and threonine metabolism, pyruvate metabolism, alanine, aspartate, and glutamate metabolism, and tricarboxylic acid cycle(TCA) in mouse brain tissue. Furthermore, 383 core targets of Jiawei Xionggui Decoction were implicated in pathways like the phosphoinositide 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway and calcium signaling pathway. Overall analysis indicated that energy metabolism, amino acid metabolism, and fatty acid metabolism were crucial metabolic pathways for Jiawei Xionggui Decoction in treating AD. The findings suggest that Jiawei Xionggui Decoction can protect neuronal cells in mouse brain tissue, thus improving cognitive impairment.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Network Pharmacology ; Metabolomics ; Cognitive Dysfunction/genetics* ; Alzheimer Disease/genetics* ; Mice, Inbred C57BL ; Amyloid beta-Protein Precursor/metabolism* ; Male ; Brain/drug effects* ; Humans ; Presenilin-1/metabolism* ; Protein Interaction Maps/drug effects* ; Mice, Transgenic ; Disease Models, Animal

This study aimed to explore the pharmacological mechanism of Yourenji Capsules(YRJ) in improving osteoporosis by combining network pharmacology and proteomics technologies. The SD rats were randomly divided into a blank control group and a 700 mg·kg~(-1) YRJ group. The rats were subjected to gavage administration with the corresponding drugs, and the blank serum, drug-containing serum, and YRJ samples were compared using ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) to analyze the main components absorbed into blood. Network pharmacology analysis was conducted based on the YRJ components absorbed into blood to obtain related targets of the components and target genes involved in osteoporosis, and Venn diagrams were used to identify the intersection of drug action targets and disease targets. The STRING database was used for protein-protein interaction(PPI) network analysis of potential target proteins to construct a PPI network. Gene Ontology(GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment were performed using Enrichr to investigate the potential mechanism of action of YRJ. Ovariectomy(OVX) was performed to establish a rat model of osteoporosis, and the rats were divided into a sham group, a model group, and a 700 mg·kg~(-1) YRJ group. The rats were given the corresponding drugs by gavage. The femurs of the rats were subjected to label-free proteomics analysis to detect differentially expressed proteins, and GO functional enrichment and KEGG pathway enrichment analyses were performed on the differentially expressed proteins. With the help of network pharmacology and proteomics results, the mechanism by which YRJ improves osteoporosis was predicted. The analysis of the YRJ components absorbed into blood revealed 23 bioactive components of YRJ, and network pharmacology results indicated that key targets involved include tumor necrosis factor(TNF), tumor protein p53(TP53), protein kinase(AKT1), and matrix metalloproteinase 9(MMP9). These targets are mainly involved in osteoclast differentiation, estrogen signaling pathways, and nuclear factor-kappa B(NF-κB) signaling pathways. Additionally, the proteomics analysis highlighted important pathways such as peroxisome proliferator-activated receptor(PPAR) signaling pathways, mitogen-activated protein kinase(MAPK) signaling pathways, and β-alanine metabolism. The combined approaches of network pharmacology and proteomics have revealed that the mechanism by which YRJ improves osteoporosis may be closely related to the regulation of inflammation, osteoblast, and osteoclast metabolic pathways. The main pathways involved include the NF-κB signaling pathways, MAPK signaling pathways, and PPAR signaling pathways, among others.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Osteoporosis/metabolism* ; Proteomics ; Rats ; Rats, Sprague-Dawley ; Network Pharmacology ; Female ; Protein Interaction Maps/drug effects* ; Capsules ; Humans ; Signal Transduction/drug effects*