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PubMed2026

Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans↗

PubMed2026

Gut microbiota dysbiosis and host metabolite-immune crosstalk drives the pathogenesis of neonatal lupus erythematosus: a multi-omics analysis.

BACKGROUND: Neonatal lupus erythematosus (NLE) is a rare autoimmune condition triggered by the transplacental transfer of maternal antibodies. Despite its recognized clinical manifestations, the underlying pathogenesis remains incompletely understood. This study seeks to explore the disruption of the gut microbiota-host metabolism-immune axis in anti-Ro/La-positive neonates, and to assess its potential role in the development of NLE. METHODS: This multicenter, cross-sectional study included 90 neonates, divided into three groups: 30 with neonatal lupus erythematosus (NLE), 30 with positive antibodies but without clinical manifestations (No-NLE), and 30 healthy controls. We performed 16 S rRNA sequencing to analyze gut microbiota composition, untargeted plasma metabolomic profiling, and proteomic analysis to identify alterations associated with the pathogenesis of NLE. RESULTS: We identified significant alterations in the gut microbiota, plasma metabolome, and proteome profiles of anti-Ro/La-positive neonates. NLE infants exhibited marked enrichment of Enterobacteriaceae and depletion of Bifidobacterium and Clostridium butyricum. Metabolomic analysis revealed hyperactivation of β-alanine and purine metabolism, along with impaired α-linolenic acid metabolism and endocannabinoid signaling. Proteomic profiling indicated aberrant protein expression that modulated IFN signaling, particularly within the C-type lectin receptor pathway. Dysregulation of the spleen tyrosine kinase (SYK) and high-affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) decoupling was observed, correlating with elevated IFN-α and NF-κB p65 levels. Integrated correlation analysis revealed significant associations among differential microbial taxa, plasma metabolites, and proteins. Notably, E. coli-associated metabolites and proteins displayed inverse relationships with those associated with C. butyricum. CONCLUSIONS: These findings represent comprehensive evidence of dysregulation along the "gut microbiota-host metabolism-immune" axis in neonatal lupus erythematosus (NLE), providing novel insights into the disease's underlying heterogeneity.

Humans↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

PubMed2026

Recent advancements in exosomal content analysis: the future of liquid biopsy.

Exosomes are widely acknowledged as an essential agent that carries biomarkers for specific diseases, representing the molecular status of their parent cells and providing extremely useful diagnostic insights. They can be isolated from different body fluids and contain a range of cargo molecules, including proteins, lipids, metabolites, and nucleic acids. Recent advancements in technology have greatly accelerated exosome research. Proteomics provides protein signatures linked to many pathological conditions, enabling quick and clinically scalable diagnostic tools, whereas high-throughput RNA-sequencing can be used to perform detailed transcriptome profiling. Exosomal biomarkers are showing promising clinical results in early detection of neurological diseases, infectious and cardiovascular disorders, oncology, and other medical conditions, hence accelerating therapeutic monitoring. Despite these advances, several challenges continue to hinder clinical translation including the lack of standardized isolation protocol, variability in exosome yield and purity, biological heterogeneity, and limited large-scale clinical validation. Addressing these limitations will be critical for the successful integration of exosome-based liquid biopsy into routine clinical practice. Overall, exosomes having significant potential as diagnostic tool, represent a transformative horizon in biomedical liquid biopsy research to redefine the landscape of less-invasive diagnostics and tailored clinical applications.

Humans↗

PubMed2026

Integrated transcriptomic and metabolomic analyses reveal key regulators associated with lipid metabolic differences between subcutaneous and visceral adipose tissues in sheep.

The location of fat deposition has a significant impact on meat quality and body health, and different adipose tissues exhibit significant differences in lipid metabolism and immune regulation. This study aimed to systematically compare the phenotypic characteristics, transcriptome, and metabolome of subcutaneous adipose tissue (SAT) and two types of visceral adipose tissue (VAT) in sheep, in order to reveal the metabolic differences between SAT and VAT and their potential regulatory mechanisms. The results showed that compared with VAT, SAT had stronger triglyceride deposition ability and obvious cellular hypertrophy. Through integrative analysis, 15 key lipid metabolism genes and 12 differential metabolites were identified. Among them, ACACA, FASN, ELOVL6, SCD, as well as metabolites palmitic acid and glycerol-3-phosphate, may play a central role in SAT lipid synthesis and storage; whereas IGFBP2, ADRB3, LTA4H, and metabolites arachidonic acid and leukotriene B4 may be involved in the lipolysis regulation and inflammatory response of VAT. These findings may provide deeper insights into the regulatory mechanisms of fat deposition in sheep.

Animals↗

PubMed2026

Integrating transcriptomics and metabolomics reveals the molecular landscape of sperm maturation driven by regional differentiation in the epididymis of Guizhou-Guiqian semi-fine wool sheep.

Epididymal regionalized differentiation is crucial for sperm maturation. However, little is known about the synergistic remodeling mechanisms of different epididymal segments at the transcriptional and metabolic levels during sexual maturation in ruminants (especially sheep). We investigated the caput, corpus, and cauda epididymidis of pre-pubertal (2-month-old) and post-pubertal (7-month-old) Guizhou-Guiqian semi-fine wool sheep using histology, RNA sequencing, and metabolomics. Post-pubertal tissues exhibited increased luminal diameters, cilia lengths, and abundant cauda spermatozoa. Transcriptomic analysis revealed increasing differentially expressed genes (DEGs) along the caput-corpus-cauda axis (4642, 6103, and 7698 DEGs, respectively). Metabolomics detected 786 unique differentially accumulated metabolites (DAMs). Region-specific analysis showed that in the caput, up-regulated pathways (fructose/mannose metabolism; HK2, ALDOA, HKDC1) provide energy and substrates for initial sperm motility. In the corpus, down-regulated genes associated with extracellular matrix and tight junctions suggested epithelial barrier remodeling to establish an immune-tolerant microenvironment. The cauda specifically up-regulated the pentose phosphate pathway (FBP1, GPI) and glutathione metabolism, maintaining redox homeostasis for long-term sperm storage. Additionally, glycerophospholipid metabolism was enriched across all segments, where PEMT, AGPAT5, and LCAT likely regulate sperm plasma membrane fluidity. In conclusion, during sexual maturation, the caput drives energy metabolism and glycosylation, the corpus establishes immune tolerance, and the cauda maintains antioxidant homeostasis. The glycerophospholipid network throughout the across all epididymal segments synergistically remodels sperm membrane. This study reveals the underlying multi-omics regulatory mechanisms of epididymal functional differentiation, providing a theoretical basis for elucidating the molecular mechanisms of sperm maturation in this breed and for the molecular breeding of early reproductive performance in rams.

Animals↗

PubMed2026

Molecular biology and integrated strategies for activating cryptic biosynthetic gene clusters toward next-generation antibiotic discovery.

Antimicrobial resistance (AMR) has been identified as one of the 21st century's severest global public health crises. AMR led to an estimated 4.95 million deaths in 2019 and will claim 10 million lives a year by 2050 in the absence of targeted interventions. During the same period, the number of novel antibiotics discovered has decreased drastically as many researchers are rediscovering known antibiotics, non-model microorganisms are poorly understood or difficult to culture and antibiotic research and development investment has declined drastically. However, high-throughput whole genome sequencing and the subsequent application of bioinformatics in bacterial and fungal genomes have shown that a numerous of cryptic or silent biosynthetic gene clusters (BGCs) remain latent at ambient laboratory conditions since their genes are transcriptionally inactive. Cryptic BGCs represent a vast source of unique secondary metabolites, many of which may yield novel antibacterial, antifungal, anti-cancer and other potentially valuable natural products. This review discusses the biological relevance of cryptic BGCs, the major limiting factors that restricts their activation and novel strategies that have been employed to activate them and exploit their potential to produce novel natural products. The review focuses on biological approaches including CRISPR-Cas mediation for the activation of cryptic BGCs, promoter engineering, pathway refactoring, and heterologous expression; biochemical strategies such as Osman, OsMAC, Precursor Feeding, Chemical Elicitation, Epigenetic Regulation and Co-cultivation and technology-based strategies such as Genome mining, Microfluidic Cultivation systems, High-Throughput Screening, Metabolomics, Molecular Networking and Artificial Intelligence and Machine Learning based prediction of BGCs and their metabolites. The use of multi-omics technologies combined with synthetic biology to achieve better discovery, characterization and large-scale production of novel natural products is also discussed herein. Finally, we will talk about the ecological significance and evolutionary advantage of cryptic BGCs' role in interactions between microorganisms, such as competition, communication, symbiosis and environmental adaptability, so as to provide a useful background for accelerating next-generation antibiotics.

CRISPR-Cas activation↗

PubMed2026

Mechanistic analysis of rice caryopsis morphogenesis regulated by exogenous hormones and related precursor substances under blue light conditions.

Rice caryopsis morphogenesis is regulated by light signals and hormonal networks. However, the mechanism by which exogenous hormones and related precursor substances modulate rice caryopsis morphogenesis under blue light remains elusive. In the present study, we aimed to elucidate the molecular mechanisms underlying the regulatory effects of exogenous phytohormones and related precursor substances on caryopsis development at 10 days after pollination (10 DAP) in the japonica rice cultivar 'Chujing 27' under blue light conditions. Results showed that tryptamine treatment increased caryopsis cell volume, thereby significantly driving caryopsis expansion; meanwhile, it markedly enhanced the activities of TDC and TAA, the key rate-limiting enzymes mediating the conversion of tryptophan to auxin, leading to a significant elevation in endogenous auxin content (P < 0.05). In comparison, exogenous auxin treatment significantly boosted carbohydrate accumulation and the activities of associated metabolic enzymes (P < 0.05). Integrated transcriptomic and metabolomic analyses revealed that tryptamine treatment led to significant enrichment of the starch and sucrose metabolic pathway, and drove the coordinated enhancement of carbon metabolic flux and auxin biosynthesis by upregulating key auxin biosynthetic genes (e.g., TAA1) and repressing auxin oxidative degradation. Genes Os04g0531100, Os03g0266100 and Os11g0221200 identified by weighted gene co-expression network analysis (WGCNA) may serve as important candidate targets regulating rice caryopsis morphology and physiological traits under blue light conditions. This study first uncovers the critical function of the "tryptamine-auxin axis" in regulating rice caryopsis development under blue light, laying a theoretical foundation for regulating caryopsis morphogenesis via exogenous hormones and their precursors.

Oryza↗

PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗

PubMed2026

The domestication-associated WHP10 tandem cluster of amino acid transporter genes enhances whole-plant protein accumulation in maize.

Improving protein accumulation in maize is essential for sustainable agriculture, yet the regulatory mechanisms governing the intermediate "flow" of organic nitrogen remain elusive. Here, we show that the maize stem acts as a regulatory node for nitrogen allocation. By integrating spatial transcriptomics and metabolomics with quantitative genetics, we demonstrate that a transport-oriented stem program orchestrates the high-protein phenotype of the wild maize accession Ames21814. We identified a major locus, Whole-plant High Protein 10 (WHP10), that encodes a tandemly duplicated cluster of amino acid transporter genes. WHP10 exhibits strong vascular-biased expression, driven by promoter divergence that enhances the wild allele's activity. Functional assays and genetic validation support a model in which the WHP10 cluster facilitates the transport of multiple nitrogen-rich amino acids, thereby contributing to vascular-associated amino acid transport and post-uptake organic-nitrogen partitioning. Our findings establish stem flow as a regulatory layer for protein accumulation and identify WHP10 as a high-value target for precision breeding to enhance whole-plant protein accumulation without compromising grain yield.

Zea mays↗

PubMed2026

Systematic discovery of retina-enriched Rik genes identifies 1190005I06Rik as a novel modulator of visual signalling.

BACKGROUND: High‑throughput transcriptome projects have revealed thousands of mammalian genes with little or no functional annotation. Among these are hundreds of loci assigned provisional “Rik” identifiers following discovery in the RIKEN cDNA annotation effort. Although often dismissed as genomic dark matter, such genes may encode tissue‑restricted proteins that modulate physiologic functions and influence disease. The retina is a highly specialised neural tissue and a common site of inherited disorders; understanding its molecular repertoire could illuminate novel therapeutic avenues. METHODS: We integrated bulk RNA‑seq from ten adult mouse tissues, evolutionary and domain analysis, single‑cell RNA‑seq, and CRISPR/Cas9 gene disruption to systematically catalogue protein‑coding Rik genes enriched in the retina and test the function of a representative gene. RESULTS: A rigorous differential expression analysis identified 44 Rik genes with robust retina‑specific expression compared with nine non‑retinal tissues. Many of these genes lack orthologues beyond rodents, while others show broad conservation, illustrating a continuum from lineage‑restricted to conserved retinopathy candidates. Single‑cell transcriptomics revealed that these genes are expressed across retinal cell types, with the highest aggregate expression in cone photoreceptors and inner interneurons. To evaluate physiological significance, we generated a 1190005I06Rik knockout mouse. Although retinal architecture appeared normal, loss of 1190005I06Rik enhanced electroretinogram b‑wave amplitudes and altered light‑avoidance behaviour, indicating that this previously uncharacterised gene acts as a negative modulator of visual signalling. CONCLUSIONS: We present a curated atlas of retina‑enriched Rik genes and demonstrate that 1190005I06RIK modulates retinal circuit function. This resource expands the molecular landscape of the retina and provides new candidates for the genetic basis of inherited retinal disease. Our findings underscore that unannotated genes may exert measurable effects on sensory processing and warrant systematic exploration in the context of human ocular disorders.

Animals↗

PubMed2026

COVID-19 multi-omics reveal organ-specific responses and biomarkers.

OBJECTIVE: Post-COVID-19 syndrome is characterised by persistent immune dysfunction and multi-organ sequelae. This study aimed to characterise the systemic blood molecular landscape induced by SARS-CoV-2 infection and identify prognostic markers linked to skeletal muscle mass loss, a key driver of poor outcomes. METHODS: We enrolled 30 healthy controls and 307 COVID-19 patients, collecting 422 plasma samples for integrated proteomic and metabolomic profiling to investigate organ-specific molecular alterations in COVID-19. RESULTS: We comprehensively mapped the molecular landscape of COVID-19, encompassing immune, tissue-specific, and metabolic perturbations, and delineated their interactions. Focusing on organ-damage-related molecular patterns associated with disease progression and mortality, we found that skeletal muscle mass loss contributed to poor clinical outcomes of COVID-19 (p < 0.0001). Dysregulated arginine metabolism emerged as a key metabolic signature in fatal COVID-19 cases, with GLUL, GOT1, and citrulline showing significant correlation with skeletal muscle mass loss. Longitudinal analyses further revealed that reduced citrulline levels underlie the poor outcome of COVID-19 patients with muscle mass loss. These findings were robustly supported through multiple approaches: Mendelian randomization confirmed causal relationships between citrulline depletion, sarcopenia/fat-free mass loss, and COVID-19 mortality (p < 0.05), transcriptomic analyses of SARS-CoV-2-infected golden hamsters (GSE231910) provided additional support in enrichment of arginine biosynthesis (FDR < 0.05), and in vitro experiments further demonstrated that citrulline depletion promotes pro-inflammatory M1 macrophage polarisation — a key immunological feature of critical COVID-19. Leveraging these insights, we developed a skeletal muscle loss-specific prognostic prediction model for COVID-19 using GLUL, GOT1, and citrulline. This model effectively stratified patients into high- and low-risk groups (p = 0.035). CONCLUSION: Our study advances the understanding of COVID-19-induced organ pathophysiology and provides a foundation for developing targeted therapeutic strategies for post-COVID sequelae.

COVID-19↗

PubMed2026

Multi-omics panorama of glaucoma: Pathogenesis, biomarkers, and novel therapeutic strategies.

Glaucoma is a group of irreversible, blinding eye diseases characterized by progressive loss of retinal ganglion cells, leading to gradual visual field defects that severely impact patients' quality of life. Its complex pathophysiological mechanisms remain incompletely understood, limiting the development of early diagnostic and effective therapeutic strategies. Advances in omics technologies have provided new insights into elucidating the pathophysiology of glaucoma. We summarize specific alterations in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics associated with glaucoma. We emphasize the systematic analysis of disease mechanisms, identification of clinically applicable biomarkers, and discovery of novel therapeutic targets through the integration of these data. This approach paves new pathways for glaucoma subtype diagnosis and personalized treatment, while also outlining future research directions and challenges.

Humans↗

PubMed2026

Decoding the molecular basis of blue grain color codominance in Qingke: Integrative analysis of RNA-seq, DNA methylation, and miRNA-seq.

The grains on single spike of the F1 generation from the cross between blue- and white-grained Qingke (Hordeum vulgare L. var. nudum Hook. f.) are randomly distributed in blue and white colors. This study integrated data from RNA-seq, DNA methylation, and miRNA-seq to analyze this trait. The results showed that the HvF3'5'H gene is likely central to the development of this codominant phenotype. Through cross-validation of three omics approaches, it was found that the HvMYB gene targeted by miR858-z, as well as the WRKY24 and At3g44326 genes targeted by novel-m0152-5p, novel-m0153-5p, and novel-m0154-5p, are correlated with DNA methylation. qRT-PCR analysis confirmed that the four aforementioned genes exhibited variety-specific and developmental stage-specific expression patterns. This study dissects the regulatory network underlying the codominant blue and white grain color divergence on a single Qingke spike from a multi-omics perspective.

DNA Methylation↗

PubMed2026

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches. AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs. EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Humans↗

PubMed2026

Convergent methodologies in prosthetic joint infection research: integrating transdisciplinary approaches to understand and prevent biofilm-driven failure of orthopaedic prostheses.

Prosthetic joint infections (PJIs) remain among the most devastating complications of arthroplasty, imposing substantial clinical, economic and patient burdens. Although culture-based diagnostics underpin current clinical practice, PJIs are biofilm-driven infections shaped by taxonomic diversity, spatial organization, host responses and surface interactions, meaning conventional approaches provide only a partial and often decontextualized view of the infection process. We examine how convergent methodologies can transform PJI research by integrating approaches that have traditionally been studied in isolation, including sequencing, transcriptomics, metabolomics, advanced imaging and culture-based characterization. We discuss how whole-genome sequencing, shotgun metagenomics, transcriptomic and metabolomic approaches resolve pathogen identity, functional activity and adaptive persistence and how cross-scale imaging and spatial biology techniques reveal where microbes colonize, interact and survive across implant surfaces. We highlight emerging opportunities to unify these datasets into coherent frameworks that capture both the molecular and physical dimensions of PJIs. Integrating these complementary approaches will enable a multi-layered understanding of PJIs that link composition, function and spatial organization. Ultimately, this provides a foundation for predictive diagnostics, precision antimicrobial strategies and improved implant design and supports a shift towards more effective, mechanism-informed management of implant-associated infection.

Prosthesis-Related Infections↗