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PubMed2026

Time-dependent effects of rapid-acting antidepressants in iPSC-derived neurons from treatment-resistant depression and healthy volunteers.

Rapid-acting antidepressants like ketamine and serotonergic psychedelics show promise for treatment-resistant depression (TRD), but the molecular mechanisms that contribute to their therapeutic effects remain unclear. Induced pluripotent stem cells (iPSCs) offer a platform to model human cortical neurons and investigate drug effects in a human-relevant system. Here, iPSCs from individuals with TRD and healthy volunteers (HVs) were differentiated into mature cortical-like neurons and treated for six and 24 h with agents being investigated as rapid-acting antidepressants, including (2 R,6 R)-hydroxynorketamine (HNK), psilocybin, lysergic acid diethylamide (LSD), and 2,5-Dimethoxy-4-iodoamphetamine (DOI). Bulk and single-cell RNA sequencing assessed global and cell-type-specific transcriptomic responses. Synaptic proteins were evaluated via Western blotting and immunocytochemistry. To validate translational relevance, transcriptomic results were compared to CSF proteomics from ketamine-treated HVs. Despite differing initial pharmacological targets, overall gene expression across all compounds was highly correlated at matched timepoints compared to vehicle control, suggesting shared downstream effects. Both glutamatergic and serotonergic drugs converged on pathways involving inflammation, mTORC1 signaling, and cellular growth. At the single-cell level, (2 R,6 R)-HNK showed distinct cell-type specific alterations: upregulation in excitatory neurons and concomitant downregulation of inhibitory neuron populations. Differentially expressed genes from (2 R,6 R)-HNK-treated neurons also overlapped with CSF proteomic signatures from ketamine-treated individuals, supporting the model's translational relevance. This study is the first to assess multiple putative rapid-acting antidepressants in parallel using an iPSC-derived neuron model. Both convergent and drug-specific changes in gene expression and pathway enrichment were observed across diverse compounds, supporting the use of human iPSC-derived neurons in antidepressant drug discovery. Clinical Trial Registry: www.clinical trials.gov, NCT02484456.

Journal Article↗

PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗

PubMed2024

Cross-platform proteomics signatures of extreme old age.

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Humans↗

PubMed2025

Dual-patterned pluripotent stem cells self-organize into a human embryo model with extended anterior-posterior patterning.

Human gastruloids are a powerful class of stem cell-derived models that recapitulate key features of early embryonic development, including symmetry breaking and the emergence of three germ layers1-3. However, they lack anterior embryonic structures and coordinated axial organization4-6. To address this limitation, we pre-patterned human pluripotent stem cells (hPSCs) by exposing them to either anterior (FGF2) or posterior (CHIR99021 [CHIR] & retinoic acid [RA]) cues. Upon mixing, these dual-patterned hPSCs interacted and self-organized into elongated structures with both anterior and posterior features-which we term anterior-posterior (AP) human gastruloids. Anteriorly pre-treated cells robustly intercalated into posteriorly pre-treated cells, collectively giving rise to a continuum of neural tissues-including a brain-like domain, a neural tube-like structure, and neuro-mesodermal progenitors (NMPs)-with segmented somites arrayed bilaterally. Single cell RNA sequencing (scRNA-seq) revealed that human AP gastruloids contain cell types resembling the midbrain-hindbrain boundary (MHB), regionalized hindbrain structures (i .e. rhombomeres 1-8), regionalized neural crest (i.e. cranial, vagal, trunk)7,8 and head mesoderm. Transcriptomic comparisons to primate embryos revealed that human AP gastruloids most closely resemble Carnegie stage 11 (CS11) embryos. While they lack a notochord and full dorsal-ventral polarity, human AP gastruloids recapitulate key spatial and temporal features of early neurulation and somitogenesis. Perturbation of folic acid metabolism or rho-associated kinase (ROCK) signaling induced spinal cord defects, phenocopying aspects of spina bifida and other neural tube defects, highlighting this model's potential for studying congenital disorders9. AP gastruloids may serve as a simple, robust, scalable platform for modeling coordinated human AP body axis development. More broadly, our results suggest that controlled interactions between differentially prepatterned progenitors can initiate self-organization of complex body axis features. The "pattern-and-mix" strategy may serve as a generalizable framework for assembling spatially organized stem cell models of mammalian development.

Journal Article↗

PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗

PubMed2026

Epithelial tumor suppressor deletion promotes neuroendocrine differentiation in bladder cancer and reveals homoharringtonine as a candidate vulnerability.

Neuroendocrine bladder carcinoma (NEBC) is a highly aggressive malignancy with unresolved lineage determinants and limited preclinical models, hindering mechanistic investigation and therapeutic development. Here, we sought to assess whether bladder epithelial-derived models are competent to acquire neuroendocrine lineage programs under defined tumor suppressor alterations and to identify candidate therapeutic vulnerabilities in these systems. We integrated genomic and transcriptomic analyses of human NEBC with genetically engineered mouse models, epithelial-derived bladder organoids, and patient-derived NEBC models. Human NEBC exhibited dominant RB1 and TP53 alterations and an epithelial transcriptional continuum consistent with lineage plasticity. In vivo, intravesical Adeno-Cre-mediated tumor suppressor deletion predominantly generated sarcoma-like tumors, whereas epithelial-restricted organoid models recapitulated the molecular and neuroendocrine features of human NEBC, supporting epithelial lineage competence for neuroendocrine differentiation. Patient-derived models and human NEBC specimens further supported epithelial identity in NEBC. Using these complementary platforms, drug screening identified homoharringtonine (HHT) as a candidate therapeutic vulnerability in the tested NEBC systems. HHT suppressed neuroendocrine marker expression, induced apoptosis, and attenuated IL6-JAK-STAT3 signaling. Together, these findings describe complementary epithelial-derived NEBC models and support further investigation of HHT as a candidate therapeutic vulnerability.

Journal Article↗

PubMed2025

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

Algorithms↗

PubMed2022

CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states.

Astrocytes become reactive in response to insults to the central nervous system by adopting context-specific cellular signatures and outputs, but a systematic understanding of the underlying molecular mechanisms is lacking. In this study, we developed CRISPR interference screening in human induced pluripotent stem cell-derived astrocytes coupled to single-cell transcriptomics to systematically interrogate cytokine-induced inflammatory astrocyte reactivity. We found that autocrine-paracrine IL-6 and interferon signaling downstream of canonical NF-κB activation drove two distinct inflammatory reactive signatures, one promoted by STAT3 and the other inhibited by STAT3. These signatures overlapped with those observed in other experimental contexts, including mouse models, and their markers were upregulated in human brains in Alzheimer's disease and hypoxic-ischemic encephalopathy. Furthermore, we validated that markers of these signatures were regulated by STAT3 in vivo using a mouse model of neuroinflammation. These results and the platform that we established have the potential to guide the development of therapeutics to selectively modulate different aspects of inflammatory astrocyte reactivity.

Humans↗

PubMed2026

Long-term rock dove (Columba livia) primordial germ cell culture: A tool towards avian conservation.

Primordial germ cells (PGCs) are critical tools for genome engineering and conservation in birds. Although culture systems for chicken PGCs have been well established for nearly two decades, efforts to propagate PGCs from other avian species have proved exceptionally challenging, limiting the broader application of artificial reproductive technologies in birds. Here we report the first successful derivation and long-term culture of PGCs from the rock dove, or common pigeon (Columba livia). Guided by transcriptomic profiling of PGCs, we developed a species-specific medium that supports PGC maintenance and expansion. We identify insulin signaling as a requirement for survival under the conditions tested, and demonstrate that inhibition of the retinoic acid receptor, in the presence of vitamin A, supports propagation. Supplementation with bone morphogenetic protein 4, leukemia inhibitory factor (LIF), glial cell line-derived neurotrophic factor, and pleiotrophin further enhances PGC proliferation. Cultured cells express canonical germline markers and migrate to the gonads following injection into both rock dove and chicken embryos, confirming functional migratory competency. These findings establish a platform for germline manipulation and biobanking in Columbidae, broadening the potential applicability of reproductive technologies to conservation efforts.

Animals↗

PubMed2026

An Instrumental Optimization of a Label-Free Proteomic Method for Trace Protein Input.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics of trace-level samples, such as tens of cells or spatially resolved tissue regions, offers unique biological insights but is often constrained by the requirement for specialized, costly instrumentation. In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.

Proteomics↗

PubMed2026

A 3D in vitro co-culture model to investigate tumor-endothelial interactions in Neurofibromatosis type 2-associated meningiomas.

BACKGROUND: Neurofibromatosis type 2 (NF2)-associated meningiomas and schwannomas are vascular tumors, and while vascular endothelial growth factor (VEGF) inhibition with bevacizumab has benefited some NF2-related schwannomas, most NF2-associated meningiomas remain nonresponsive. METHODS: Leveraging our transcriptomic data, we performed Gene Ontology (GO) analysis comparing NF2-deficient meningioma cells with NF2-expressing arachnoid cells (ACs). We then established a 3D in vitro angiogenesis model by co-culturing NF2-null meningioma cells with human umbilical vein endothelial cells (HUVECs). Endothelial sprouting was assessed by CD31/PECAM immunostaining. Effects of third-generation mechanistic target of rapamycin complex 1 (mTORC1)-selective inhibitor RMC-6272 as well as APLN knock-out using CRISPR-Cas9 gene editing were also examined. RESULTS: GO analysis identified vascular development among the top significantly upregulated pathways in NF2-deficient cells. In 3D co-culture, ECs formed radially sprouting tube-like networks from the spheroid surface, and our data supports an angiogenesis phenotype driven by meningioma cells. Given these results along with hyperactivation of mTORC1 upon NF2-deficiency, we examined whether RMC-6272 disrupts meningioma-driven angiogenesis. RMC-6272 potently suppressed EC sprouting. Cross-referencing baseline transcriptomic data, we identified Apelin (APLN), the ligand for APLN receptor (APLNR), as a basally upregulated angiogenic factor in NF2-deficient meningiomas. Quantitative RT-PCR (qRT-PCR) confirmed increased APLN expression in NF2-null immortalized and patient-derived meningioma lines, with reduced expression upon mTORC1 inhibition. Apelin-13 stimulation enhanced sprouting, whereas APLN deletion reduced endothelial sprouting. CONCLUSIONS: Here we establish a 3D-tumoroid model and implicate tumor-derived Apelin as an important contributor to NF2-associated meningioma angiogenesis. Our data also suggest that APLN expression is regulated, at least in part, by mTORC1. Together, these results provide a preclinical platform for investigating angiogenic vulnerabilities beyond VEGF in NF2-deficient meningiomas.

3D tumoroid model↗

PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

PubMed2026

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

Caesalpinia↗

PubMed2026

Systems genetics approaches model the heritable architecture of polyendocrine metabolic ovarian syndrome.

Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is the most common endocrine disorder in women and is closely associated with complex diseases such as cardiovascular disease and type 2 diabetes. However, the mechanistic links between PMOS and its comorbidities remain poorly understood. Here, we present an integrative systems genetics platform that leverages genetic diversity in both mice and humans to dissect the drivers of PMOS and its associated complications. This framework uncovered conserved genetic and environmental factors underlying PMOS, identified susceptible cell types and organs, and elucidated mechanisms linking PMOS to subsequent pathologies. For instance, we showed that increased ovarian area contributes to both PMOS susceptibility and ovarian cancer progression, while specific ovary-heart signaling circuits modulate cardiac function with aging. We further identified ovarian SF3B1-mediated alternative splicing as a key mechanistic link between PMOS and metabolic traits. Pharmacologic inhibition of SF3B1 in mice reduced circulating testosterone, insulin, and glucose levels as well as fat mass expansion. Transcriptomics analysis of ovaries from mice and experiments using human cell lines localized these effects to exon skipping events in granulosa cells. Together, this study offers a mechanistic framework for modeling the diversity of PMOS pathologies and uncovers SF3B1-mediated splicing as a link between ovary function and systemic metabolism.

Female↗

PubMed2025

Identification of Potential Therapeutic Agents for Type I Interferonopathy Using iPSC-Based Disease Modeling.

PURPOSE: Type I interferonopathy encompasses disorders marked by systemic inflammation and neurological involvement, arising from genetic mutations that result in the upregulation of type I IFN signaling through various mechanisms. Currently, therapeutic options are limited, and no standard therapy exists. This study aims to develop a strategy for identifying new therapeutic targets for type I interferonopathy using induced pluripotent stem cells (iPSCs). METHODS: The IFIH1 R779H variant was introduced into iPSCs through genome editing. RNA sequencing of iPSC-derived dendritic cells (DCs) was performed, and differentially expressed genes (DEGs) were identified. IFN-α secretion, reactive oxygen species (ROS), and mitochondrial oxygen consumption rate (OCR) were analyzed in iPSC-derived DCs. An in silico prediction of compounds binding to the OAS-like domain was conducted. Candidate compounds were evaluated for their ability to inhibit IFN secretion from IFIH1 R779H-mutated iPSC-derived DCs. RESULTS: Transcriptome analysis indicated upregulation of the IFN-related and metabolic pathways. IFIH1 R779H-mutated iPSC-derived DCs exhibited increased OCR and ROS generation, and blocking mitochondrial metabolism significantly reduced excessive IFN-α secretion. Among the DEGs, PML was upregulated, and targeting this gene with arsenic trioxide (ATO), a PML antagonist, suppressed IFN-α secretion from IFIH1 R779H-mutated iPSC-derived DCs. Additionally, bisantrene, phthalylsulfathiazole and ganaplacide were predicted to bind to the RNA binding groove of OAS-like domain of human OASL in silico, effectively inhibiting IFN-α secretion from IFIH1 R779H-mutated DCs. CONCLUSION: Our iPSC-based disease modeling and drug investigation approach provides a robust platform for validating the efficacy and toxicity of candidate therapeutic agents for rare and intractable human diseases such as type I interferonopathy.

Humans↗

PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗

PubMed2000

Conceptual modelling of genomic information.

MOTIVATION: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Computational Biology↗

PubMed2025

Multi-Omics Analyses Reveal the Red and Far-Red Light Combination Enhancing Heterologous Protein and Metabolite Production in Nicotiana benthamiana.

Transient expression of exogenous protein in Nicotiana benthamiana leaves via agroinfiltration offers a rapid and efficient platform for functional gene discovery and heterologous production of valuable eukaryotic proteins and metabolites. Though light quality is an important factor for plant photomorphogenesis, its impact on the efficiency of transient expression remains unexplored. In this study, we examined the influence of five representative light qualities with varying wavelength mix on the N. benthamiana growth and recombinant green fluorescent protein (GFP) production. Plants with red and far-red light treatment (LED-red) showed the highest GFP expression, 57.4% higher than white light. Further study showed that a higher dosage of post-infiltration Agrobacterium and the resulting increase in the number of transcripts contribute to the expression rate enhancement. Moreover, as for exogenous metabolites, a 76.5% increase of accumulated taxadiene was also observed in LED-red group. Integrated transcriptomic, proteomic and metabolomic revealed that LED-red plants reduced the resistance pathways before infiltration, inducing a higher dosage of post-agroinfiltration Agrobacterium. Our results suggest that N. benthamiana grown under LED-red creates a more favorable environment for Agrobacterium growth, enhancing heterologous protein and metabolite production. This study highlights the potential utilization of light quality as an implementable tool in plant synthetic biology.

Nicotiana↗