With the advancement in big data and artificial intelligence technologies, the extensive application of omics technologies in traditional Chinese medicine(TCM) research has generated large experimental datasets, enabling the exploration of cross-scale correlations among massive data and thereby resulting in the shift toward a data-intensive research paradigm. The emerging approach of multi-omics data fusion analysis, emphasizing technical and computational tools, presents a potential breakthrough in this field. The holistic perspective of TCM aligns with the concept of multi-omics data fusion, yet the data types encountered exhibit high dimensionality with small sample sizes, necessitating data processing techniques such as dimensionality reduction. The current challenge lies in selecting suitable analytical methods for these data to enhance the systematic understanding of physiological functions and disease diagnosis/treatment processes. This paper explores the theories and frameworks of multi-omics data fusion, analyzes methods for fusing high-dimensional, small-sample multi-omics data in TCM, and aims to provide insights for advancing TCM research.
Medicine, Chinese Traditional/methods* ; Humans ; Computational Biology/methods* ; Genomics/methods* ; Sample Size ; Artificial Intelligence ; Multiomics

The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

Sjogren's syndrome (SS) is a common autoimmune disorder that primarily targets exocrine glands, leading to hallmark manifestations of xerostomia and xerophthalmia, with potential progression to multisystem involvement. The rapid advances in omics technologies-including metabolomics, proteomics, and transcriptomics-have yielded substantial insights into SS pathophysiology. This review consolidates current evidence on omics-derived biomarkers in SS. Studies consistently implicate aberrant glucose metabolism, neutrophil-derived enzyme activity, mitochondrial bioenergetic impairment, ferroptosis, and apoptotic pathways as central to SS development. These findings refine our understanding of disease mechanisms and the heterogeneity of therapeutic responses. Hydroxyproline has emerged as a candidate marker for distinguishing SS from IgG4-related disease, whereas distinct cytokine and chemokine signatures may enable earlier diagnosis. Genomic analyses demonstrate a robust association between expression of the rs11797 locus and SS-related lymphomagenesis, and several genes controlling DNA methylation represent promising therapeutic targets. Collectively, these findings lay the groundwork for personalized risk stratification and intervention in SS. The review concludes by summarizing existing progress and outlining priorities for future omics-based investigations.
Humans ; Sjogren's Syndrome/diagnosis* ; Biomarkers/analysis* ; Metabolomics/methods* ; Proteomics/methods* ; Genomics ; Multiomics

OBJECTIVES: Multiple myeloma (MM) is a hematologic malignancy characterized by clonal proliferation of plasma cells and remains incurable. Patients with primary refractory multiple myeloma (PRMM) show poor response to initial induction therapy. This study aims to develop a machine learning-based model to predict treatment response in newly diagnosed multiple myeloma (NDMM) patients, in order to optimize therapeutic strategies. METHODS: NDMM and post-treatment MM patients hospitalized in the Department of Hematology, Third Xiangya Hospital, Central South University, between August 2022 and July 2023 were enrolled. Post-treatment MM patients were categorized into PRMM patients and treatment-responsive MM (TRMM) patients based on therapeutic efficacy. Serum metabolites were detected and analyzed via metabolomics. Based on the metabolomics analysis results and combined with transcriptomic sequencing data of NDMM patients from databases, differentially expressed amino acid metabolism-related genes (AAMGs) among post-treatment NDMM patients with varying therapeutic outcomes were screened. Using bioinformatics analyses and machine learning algorithms, a predictive model for treatment response in NDMM was constructed and used to identify patients at risk for PRMM. RESULTS: A total of 61 patients were included: 22 NDMM, 23 TRMM, and 16 PRMM patients. Significant differences in metabolite levels were observed among the 3 groups, with differential metabolites mainly enriched in amino acid metabolism pathways. Follow-up data were available for 16 of the 22 NDMM patients, including 12 treatment responders (ND_TR group) and 4 with PRMM (ND_PR group). A total of 23 differential metabolites were identified between these 2 groups: 6 metabolites (e.g., tryptophan) were upregulated and 17 (e.g., citric acid) were downregulated in the ND_TR group. Transcriptomic data from 108 TRMM and 77 PRMM patients were analyzed to identify differentially expressed AAMGs, which were then used to construct a prediction model. The area under the receiver operating characteristic curve (AUC) for the model exceeded 0.8, and AUC values in 3 external validation cohorts were all above 0.7. CONCLUSIONS This study delineated the metabolic alterations in MM patients with different treatment response, suggesting that dysregulated amino acid metabolism may be associated with poor treatment response in PRMM. By integrating metabolomics and transcriptomics, a machine learning-based predictive model was successfully established to forecast treatment response in NDMM patients.
Humans ; Multiple Myeloma/drug therapy* ; Machine Learning ; Male ; Female ; Metabolomics/methods* ; Middle Aged ; Aged ; Treatment Outcome ; Transcriptome ; Computational Biology ; Adult ; Multiomics

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics

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

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.
Fermentation ; Genomics/methods* ; Biotechnology/methods* ; Proteomics/methods* ; Models, Biological ; Metabolomics/methods* ; Bioreactors ; Multiomics

Hormesis effect has been observed in the secondary metabolite synthesis of microorganisms induced by rare earth elements. However, the underlying molecular mechanism remains unclear. To analyze the molecular mechanism of the regulatory effect of Rhodotorula mucilaginosa in the presence of lanthanum chloride, different concentrations of lanthanum chloride were added to the fermentation medium of Rhodotorula mucilaginosa, and the carotenoid content was subsequently measured. It was found that the concentrations of La³⁺ exerting the promotional and inhibitory effects were 0-100 mg/L and 100-400 mg/L, respectively. Furthermore, the expression of 33 genes and the synthesis of 55 metabolites were observed to be up-regulated, while the expression of 85 genes and the synthesis of 123 metabolites were found to be down-regulated at the concentration range of the promotional effect. Notably, the expression of carotenoid synthesis-related genes except AL1 was up-regulated. Additionally, the content of β-carotene, lycopene, and astaxanthin demonstrated increases of 10.74%, 5.02%, and 3.22%, respectively. The expression of 5 genes and the synthesis of 91 metabolites were up-regulated, while the expression of 35 genes and the synthesis of 138 metabolites were down-regulated at the concentration range of the inhibitory effect. Meanwhile, the content of β-carotene, lycopene, and astaxanthin decreased by 21.73%, 34.81%, and 35.51%, respectively. In summary, appropriate concentrations of rare earth ions can regulate the synthesis of secondary metabolites by modulating the activities of various enzymes involved in metabolic pathways, thereby exerting the hormesis effect. The findings of this study not only contribute to our comprehension for the mechanism of rare earth elements in organisms but also offer a promising avenue for the utilization of rare earth elements in diverse fields, including agriculture, pharmaceuticals, and healthcare.
Lanthanum/pharmacology* ; Rhodotorula/genetics* ; Carotenoids/metabolism* ; Hormesis/drug effects* ; Fermentation ; Multiomics

Bacillus velezensis DJ1 exhibits broad-spectrum antagonistic activity against diverse phytopathogenic fungi, while its biocontrol mechanisms against Fusarium graminearum, the causal agent of maize stalk rot, remain poorly characterized. In this study, we integrated genomics and transcriptomics to elucidate the antifungal mechanisms of strain DJ1. The results demonstrated that DJ1 inhibited F. graminearum with the efficacy of 64.4%, while its polyketide crude extract achieved the control efficacy of 55% in pot experiments against this disease. Whole-genome sequencing revealed a single circular chromosome (3 929 792 bp, GC content of 47%) harboring 12 biosynthetic gene clusters for secondary metabolites, six of which encoded known antimicrobial compounds (macrolactin H, bacillaene, difficidin, surfactin, fengycin, and bacilysin). Transcriptomic analysis identified 243 differentially expressed genes (152 upregulated and 91 downregulated, P < 0.05), which were potentially associated with the antagonistic activity against F. graminearum. KEGG enrichment analysis highlighted activation (P < 0.05) of cysteine/methionine metabolism, pentose phosphate pathway, and polyketide biosynthesis pathways, indicating that DJ1 employed synergistic strategies involving antimicrobial compound synthesis, energy metabolism enhancement, and nutrient competition to suppress pathogens. This study provides a theoretical foundation for developing novel microbial resources and application technologies to combat phytopathogenic fungi.
Fusarium/drug effects* ; Bacillus/metabolism* ; Plant Diseases/prevention & control* ; Antifungal Agents/pharmacology* ; Genomics ; Zea mays/microbiology* ; Transcriptome ; Gene Expression Profiling ; Antibiosis ; Multigene Family ; Multiomics