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

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

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

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

OBJECTIVE: To screen out the key metabolites related to diabetic foot (DF) by integrating genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS). METHODS: The literature databases such as PubMed and China national knowledge infrastructure(CNKI), as well as genomics databases such as PAN UKBB, FinnGen, and IEU Open GWAS were systematically retrieved from database estobilishment to November 2024 on DF-related single nucleotide polymorphisms and genome-wide association studies. DF-single nucleotide polymorphism-metabolite network was constructed by mGWAS package and mGWAS-Explorer platform. The causal relationship between key factors was evaluated by two-sample Mendelian randomization. The genetic correlation between DF and 575 metabolites (source:IEU Open GWAS) was evaluated by linkage disequilibrium score regression. In vitro experiments were conducted to induce injury of human umbilical vein endothelial cells with 30 mM glucose and intervene with 20 μM γ-tocopherol. Changes in cell migration, scratch healing and tube formation function were detected. RESULTS: Twenty-senen literatures on single nucleotide polymorphism literatures and 3 studies on GWAS were included. Genetic analysis results showed DF-related single nucleotide polymorphisms were enriched in vascular endothelial dysfunction-related pathways (such as fluid shear stress and atherosclerosis). The results of metabolic network analysis screened out 19 associated metabolites, among which 12 such as γ -tocopherol and pyruvate had significant genetic correlations with DF. Mendelian randomization suggested matrix metalloproteinase-9(MMP-9) might be a potential driver of DF (β=0.658, P=0.063 8), and the occurrence of DF could reduce the level of high-density lipoprotein (β=-0.002, P=0.015 2). The results of in vitro experiments confirmed that γ -tocopherol could improve endothelial dysfunction induced by high glucose, specifically manifested as an increase in the number of cell migrations, improvement in the scratch healing rate, and recovery of tubule formation ability (P<0.05). CONCLUSION DF has a genetic basis centered on vascular endothelial dysfunction, and its occurrence can lead to further metabolic disorders. The key single nucleotide polymorphism loci integrated provided molecular markers for the risk stratification of foot ulcers in diabetic patients. In addition, γ -tocopherol has demonstrated clinical application potential as a therapeutic drug for DF by significantly improving the function of vascular endothelial cells in a high-glucose environment.
Humans ; Diabetic Foot/drug therapy* ; Polymorphism, Single Nucleotide ; Genome-Wide Association Study ; Genomics ; Metabolomics ; Metabolome

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

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*

This study explored the growth-promoting effect and mechanism of the endophytic bacterium Kocuria rosea on Rehmannia glutinosa, aiming to provide a scientific basis for the development of green bacterial fertilizer. R. glutinosa 'Jinjiu' was treated with K. rosea, and the shoot parameters including leaf length, leaf width, plant width, and stem diameter were measured every 15 days. After 120 days, the shoots and roots were harvested. The root indicators(root number, root length, root diameter, root fresh weight, root dry weight, root volume, and root vitality) and secondary metabolites(catalpol, rehmannioside A, rehmannioside D, verbascoside, and leonuride) were determined. The R. glutinosa growth-promoting mechanism of K. rosea was discussed from the effect of K. rosea on the nutrient element content in R. glutinosa and rhizosphere soil and the genome information of this plant. After application of K. rosea, the maximum increases in leaf length, leaf width, plant width, and stem diameter were 35.67%(60 d), 25.39%(45 d), 40.17%(60 d), and 113.85%(45 d), respectively. The root number, root length, root diameter, root volume, root fresh weight, root dry weight, and root viability increased by 41.71%, 45.10%, 48.61%, 94.34%, 101.55%, 147.61%, and 42.08%, respectively. In addition, the content of rehmannioside A and verbascoside in the root of R. glutinosa increased by 76.67% and 69.54%, respectively. K. rosea promoted the transformation of nitrogen(N), phosphorus(P), and potassium(K) in the rhizosphere soil into the available state. Compared with that in the control, the content of available N(54.60 mg·kg~(-1)), available P(1.83 μmol·g~(-1)), and available K(83.75 mg·kg~(-1)) in the treatment with K. rosea increased by 138.78%, 44.89%, and 14.34%, respectively. The content of N, P, and K in the treatment group increased by 293.22%, 202.63%, and 23.80% in the roots and by 23.60%, 107.23%, and 134.53% in the leaves of R. glutinosa, respectively. K. rosea carried the genes related to colonization(rbsB, efp, bcsA, and gmhC), N, P, and K metabolism(narG, narH, narI, nasA, nasB, GDH2, pyk, aceB, ackA, CS, ppa, ppk, ppk2, pstS, pstA, pstB, and pstC), and indole-3-acetic acid and zeatin synthesis(iaaH and miaA). Further studies showed that K. rosea could colonize the roots of R. glutinosa and secrete indole-3-acetic acid(3.85 μg·mL~(-1)) and zeatin(0.10 μg·mL~(-1)). In summary, K. rosea promotes the growth of R.ehmannia glutinosa by enhancing the nutrient uptake, which provides a theoretical basis for the development of plant growth-promoting microbial products.
Rehmannia/metabolism* ; Endophytes/metabolism* ; Plant Roots/growth & development* ; Micrococcaceae/genetics* ; Data Mining ; Plant Leaves/metabolism* ; Genomics ; Rhizosphere

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.
Humans ; Proteome/metabolism* ; Proteomics ; Prospective Studies ; Obesity/genetics* ; Genomics ; Genome-Wide Association Study

OBJECTIVE: To explore the genetic basis for a child with Aspartylglucosaminuria (AGU). METHODS: Clinical data of the patient was analyzed. The child was subjected to trio-whole exome sequencing (WES) and copy number variation sequencing (CNV-seq), and candidate variant was verified by Sanger sequencing. RESULTS: The child was found to harbor homozygous c.319C>T (p.Arg107*) nonsense variant of the AGA gene, for which both of his parents were heterozygous carriers. No abnormality was found by CNV-seq analysis. The c.319C>T (p.Arg107*) variant was not found in population database, HGMD and other databases. Based on guidelines of the American College of Medical Genetics and Genomics, the variant was predicted to be pathogenic (PVS1+PM2+PP3). CONCLUSION The c.319C>T variant of the AGA gene probably underlay the autosomal recessive AGU in this child. Above finding has enabled genetic counseling and prenatal diagnosis for his parents.
Female ; Pregnancy ; Humans ; Child ; Aspartylglucosaminuria ; DNA Copy Number Variations ; Genetic Counseling ; Genomics ; Heterozygote ; Mutation