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PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗

PubMed2026

Integrated bulk and single-cell RNA sequencing reveals a prognostic neuro-mimicry signature in papillary thyroid carcinoma.

BACKGROUND: Cancer cells can acquire neuron-like characteristics ("neural mimicry") to promote progression. However, the role of specific ion channel genes in Papillary Thyroid Carcinoma (PTC) and their clinical significance remains unclear. METHODS: We included transcriptomic data from 521 PTC patients in the TCGA cohort. A neuron-specific gene set was used to screen for potential targets. We constructed a prognostic model using LASSO logistic regression. To verify the cellular origin of the signature, we performed single-cell RNA sequencing (scRNA-seq) analysis on the GSE184362 dataset. RESULTS: We established an 8-gene signature involving KCNN4, KCNN1, KCNT2, SNAP25, KCNK16, GABRG1, GABRG2, and GABRB2. The model demonstrated good predictive performance for lymph node metastasis, with an AUC of 0.721 (95% CI 0.677-0.765). Single-cell analysis of seven integrated tumor samples (N = 65,744 cells) confirmed that GABRB2 was specifically enriched in malignant thyrocytes (EPCAM+/KRT18+) at 200-fold higher detection rates than immune cells (20.0% vs. 0.1%, P ≈ 0), supporting tumor-intrinsic neural mimicry. High-risk patients showed immunosuppressive features with altered immune cell infiltration patterns. CONCLUSION: This study identifies a malignant cell-intrinsic signature for predicting PTC prognosis. Validated by single-cell data, our findings suggest that targeting ion channels may represent a potential therapeutic strategy for modulating neuro-immune interactions in thyroid cancer, pending experimental validation.

GABRB2↗

PubMed2003

Transcriptome analysis of sulfur depletion in Arabidopsis thaliana: interlacing of biosynthetic pathways provides response specificity.

Higher plants assimilate inorganic sulfate into cysteine, which is subsequently converted to methionine, and into a variety of other sulfur-containing organic compounds. To resist sulfur deficiency, plants must demonstrate physiological flexibility: the expression of an extensive set of genes and gene regulators that act in the affected pathways or signalling cascades must be delicately tuned in response to environmental challenges. To elucidate this network of interactions, we have applied an array hybridisation/transcript profiling method to Arabidopsis plants subjected to 6, 10 and 13 days of constitutive and induced sulfur starvation. The temporal expression behaviour of approximately 7200 non-redundant genes was analysed simultaneously. The experiment was designed in a way to identify statistically significant changes of gene expression based on sufficient numbers of repeated hybridisations performed with five uniform pools of plant material. The expression profiles were processed to select differentially expressed genes. Among the 1507 sulfur-responsive clones implicated in this way, 632 genes responded specifically to sulfur deficiency by significant over-expression. The sulfur-responsive genes were grouped according to functional categories or biosynthetic pathways. As expected, genes of the sulfur assimilation pathway were altered in expression. Furthermore, genes involved in flavonoid, auxin, and jasmonate biosynthesis pathways were upregulated in conditions of sulfur deficiency. Based on the correlative analysis of gene expression patterns, we suggest that a complex co-ordination of systematic responses to sulfur depletion is provided via integration of flavonoid, auxin and jasmonate pathway elements. Plait concept for transduction of specificity via the main non-specific signalling stream is proposed.

Adaptation, Physiological↗

PubMed2003

The role of HIF-1 alpha in transcriptional regulation of the proximal tubular epithelial cell response to hypoxia.

Epithelial cells of the kidney represent a primary target for hypoxic injury in ischemic acute renal failure (ARF); however, the underlying transcriptional mechanism(s) remain undefined. In this study, human proximal tubular epithelial cells (HK-2) exposed to hypoxia in vitro demonstrated a non-lethal but dysfunctional phenotype, closely reflective of the epithelial pathobiology of ARF. HK-2 cells exposed to hypoxia demonstrated increased paracellular permeability, decreased proliferation, loss of tight junctional integrity, and significant actin disassembly in the absence of cell death. Microarray analysis of transcriptomic changes underlying this response identified a distinct cohort of 48 genes with a closely shared hypoxia-dependent expression profile. Within this hypoxia-sensitive cluster were genes identified previously as hypoxia-inducible factor-1 (HIF-1)-dependent (e.g. vascular endothelial growth factor and adrenomedullin) as well as genes not previously known to be hypoxia-responsive (e.g. stanniocalcin 2). In hypoxia, HIF-1 bound to evolutionarily conserved hypoxia-response elements (HRE) in the promoters of these genes as well as to the HRE consensus motif. A further subset of these genes, not associated with transcriptional regulation by HIF-1, was also present, suggesting alternative HIF-1-independent pathways. Overexpression of HIF-1 alpha in normoxia induced the expression of a significant number of the hypoxia-dependent genes; however, it did not induce the pathophysiologic epithelial response. In summary, hypoxia-elicited alterations in renal proximal tubular epithelial cells in vitro closely resemble the epithelial pathophysiology of ARF. Our data indicate that although this event may rely heavily on HIF-1-dependent gene transcription, it is likely that separate hypoxia-dependent transcriptional regulators also play a role.

Acute Kidney Injury↗

PubMed2025

Genetic and transcriptional insights into immune checkpoint blockade response and survival: lessons from melanoma and beyond.

BACKGROUND: Integration of immune checkpoint inhibitors (ICIs) with non-immune therapies relies on identifying combinatorial biomarkers, which are essential for patient stratification and personalized treatment. METHODS: We analyzed genomic and transcriptomic data from pretreatment tumor samples of 342 melanoma patients treated with ICIs to identify mutations and expression signatures associated with ICI response and survival. External validation and mechanistic exploratory analyses were conducted in two additional datasets to assess generalizability. RESULTS: Responders were more likely to have received anti-PD-1 therapy rather than anti-CTLA-4 and exhibited a higher tumor mutation burden (both P < 0.001). Mutations in the dynein axonemal heavy chain (DNAH) family genes, specifically DNAH2 (P = 0.03), DNAH6 (P < 0.001), and DNAH9 (P < 0.01), were enriched in responders. The combined mutational status of DNAH 2/6/9 effectively stratified patients by progression-free survival (hazard ratio [HR]: 0.69; 95% confidence interval [CI] 0.51-0.92; P = 0.013) and overall survival (HR: 0.58; 95% CI 0.43-0.78; P < 0.001), with consistent association observed in the validation cohort (HR: 0.28; 95% CI 0.12-0.61; P < 0.001). DNAH-altered melanomas exhibited upregulation of chemokine signaling, cytokine-cytokine receptor interaction, and cell cycle-related pathways, along with elevated expression of immune-related signatures in interferon signaling, cytolytic activity, T cell function, and immune checkpoints. Using LASSO logistic regression, we identified a 26-gene composite signature predictive of clinical response, achieving an area under the curve (AUC) of 0.880 (95% CI 0.825-0.936) in the training dataset and 0.725 (95% CI 0.595-0.856) in the testing dataset. High-risk patients, stratified by the expression levels of a 13-gene signature, demonstrated significantly shorter overall survival in both datasets (HR: 3.35; P < 0.001; HR: 2.93; P = 0.002). CONCLUSIONS: This analysis identified potential molecular determinants of response and survival to ICI treatment. Insights from melanoma biomarker research hold significant promise for translation into other malignancies, guiding individualized anti-tumor immunotherapy.

Humans↗

PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗

PubMed2026

Allelochemical signaling and phytohormone crosstalk in plants: molecular mechanisms and implications for sustainable weed management.

Phytotoxic effects from allelopathy occur due to signaling pathways that induce alterations in hormonal balance within the plants, thereby hindering weed growth. Signaling crosstalk between various hormones and signaling pathways (Ca2⁺, MAPK, ROS) is involved in the molecular response mechanisms found through omics. Utilizing such mechanisms would help develop new environmentally friendly methods for sustainable weed management. Allelopathy serves as an essential component of plant-plant interaction via controlling the secretion of secondary metabolites (allelochemicals), which affect the growth, development, and physiological activity of nearby plants. The latest findings indicate that allelochemicals disturb phytohormone balance and signaling pathways resulting in oxidative stress, metabolism dysfunctions, cellular processes disturbances, and eventually inhibiting the growth of target weed species. Molecular biology progress and omics techniques brought information about the sophisticated regulation processes involved in allelopathic interactions. This review summarizes the information about the molecular mechanism of weed suppression mediated by allelopathy with the emphasis on allelochemical perception, phytohormone signaling, ROS responses, and evidence obtained by the application of transcriptomics, proteomics, metabolomics, and other omics-based studies. In addition, it introduces novel approaches, such as rhizosphere engineering, nanotechnologies, and genome editing, which may improve the effectiveness and reliability of allelopathic weed suppression. Overall, these achievements provide prospects for creating a new generation of weed control technologies that are sustainable, environmentally friendly, and climate-adaptive.

Plant Growth Regulators↗

PubMed2026

Genomic and Transcriptomic Correlates of Deep PSA Response in Patients with Metastatic Androgen Pathway Modulation-Sensitive Prostate Cancer.

BACKGROUND: Despite advances in metastatic androgen pathway modulation-sensitive prostate cancer (mAPMS) treatment, outcomes remain heterogeneous. Achieving a post-treatment undetectable prostate specific antigen (PSA) is a strong prognostic marker. We aimed to identify genomic and transcriptomic determinants of PSA response in a real-world clinical-genomic cohort. PATIENTS AND METHODS: Patients with mAPMS who underwent DNA (Tempus xT) and, in a subset, RNA (Tempus xR) sequencing were identified from the Tempus Lens database. Inclusion required stage IV disease within 90 days of sample collection and samples obtained within 12 months before or 3 months after treatment initiation. Patients with PSA at 6 months (n = 525) were classified as PSA-low (<0.1 ng/mL, n = 240) or PSA-high (≥0.1 ng/mL, n = 285). Overall survival (OS) was assessed by 6-month landmark analysis with delayed-entry adjustment. Logistic and Cox models were adjusted for clinical variables. Sensitivity analyses used a relative definition of > 95% PSA decline from baseline. RESULTS: Baseline PSA was lower in PSA-low versus PSA-high patients (24 vs 36 ng/mL, p = 0.01). SPOP (17% vs 11%) and ZFHX3 (2.5% vs 6%) alterations differed between groups, but neither persisted after adjustment. Using the relative definition, ZMYM3 and JAK1 alterations were independently associated with failure to achieve a deep PSA response. Expression of PSMA, TROP2, B7-H3, and STEAP1 did not differ between groups. PSA-low status was independently associated with improved OS, as was deep relative response. CONCLUSION: Deep PSA response at 6 months correlates with improved OS in mAPMS. Integrating molecular markers with PSA response may inform treatment intensification or de-escalation strategies.

Biomarkers↗

PubMed2001

Microarray analysis of hippocampal gene expression in global cerebral ischemia.

The brain's response to ischemia, which helps determine clinical outcome after stroke, is regulated partly by competing genetic programs that respectively promote cell survival and delayed cell death. Many genes involved in this response have been identified individually or systematically, providing insights into the molecular basis of ischemic injury and potential targets for therapy. The development of microarray systems for gene expression profiling permits screening of large numbers of genes for possible involvement in biological or pathological processes. Therefore, we used an oligodeoxynucleotide-based microarray consisting of 374 human genes, most implicated previously in apoptosis or related events, to detect alterations in gene expression in the hippocampus of rats subjected to 15 minutes of global cerebral ischemia followed by up to 72 hours of reperfusion. We found 1.7-fold or greater increases in the expression of 57 genes and 1.7-fold or greater decreases in the expression of 34 genes at 4, 24, or 72 hours after ischemia. The number of induced genes increased from 4 to 72 hours, whereas the number of repressed genes decreased. The induced genes included genes involved in protein synthesis, genes mutated in hereditary human diseases, proapoptotic genes, antiapoptotic genes, injury-response genes, receptors, ion channels, and enzymes. We detected transcriptional induction of several genes implicated previously in cerebral ischemia, including ALG2, APP, CASP3, CLU, ERCC3, GADD34, GADD153, IGFBP2, TIAR, VEGF, and VIM, as well as other genes not so implicated. We also found coinduction of several groups of related genes that might represent functional modules within the ischemic neuronal transcriptome, including VEGF and its receptor, NRP1; the IGF1 receptor and the IGF1-binding protein IGFBP2; Rb, the Rb-binding protein E2F1, and the E2F-related transcription factor, TFDP1; the CACNB3 and CACNB4 beta-subunits of the voltage-gated calcium channel; and caspase-3 and its substrates, ACINUS, FEM1, and GSN. To test the hypothesis that genes identified through this approach might have roles in the pathophysiology of cerebral ischemia, we measured expression of the products of two induced genes not heretofore implicated in cerebral ischemia-GRB2, an adapter protein involved in growth-factor signaling pathways, and SMN1, which participates in RNA processing and is deleted in most cases of spinal muscular atrophy. Western analysis showed enhanced expression of both proteins in hippocampus at 24 to 72 hours after ischemia, and SMN1 was localized by immunohistochemistry to hippocampal neurons. These results suggest that microarray analysis of gene expression may be useful for elucidating novel molecular mediators of cell death and survival in the ischemic brain.

Animals↗

PubMed2001

What we could do now: molecular pathology of bladder cancer.

There is much information on the genetic alterations that contribute to the development of bladder cancer. Because it is hypothesised that the genotype of the cancer cell plays a major role in determining phenotype, this genetic information should impact on clinical practice. To date however, this has not happened. Some of the alterations identified in bladder cancer have clear associations with outcome-for example, mutational inactivation of the cell cycle regulator proteins p53 and the retinoblastoma protein (Rb). However, as single markers, these events have insufficient predictive power to be applied in the management of individual patients. The use of panels of markers is a potential solution to this problem. Examples of suitable panels include those genes/proteins with known impact on specific cell cycle checkpoints or with impact on cellular phenotypes, such as immortalisation, invasion, or metastasis. To evaluate such marker panels, large tumour series will be needed-for example, archival samples from completed clinical trials. The use of these valuable resources will require coordination of sample provision. This might involve central collection and distribution of tissue blocks, sections, or tissue arrays and the provision of patient follow up information to laboratories participating in a study. With the availability of microarray technologies, including cDNA and comparative genomic hybridisation arrays, the transcriptome and genome of transitional cell carcinomas of different phenotypes can be compared and will undoubtedly provide a wealth of information with potential diagnostic and prognostic uses. Although these studies can be initiated using small local tissue collections, high quality collection of fresh tissues from new clinical trials will be crucial for proper evaluation of associations with clinical outcome. Funding for molecular pathological studies to date has been poor. To begin to translate molecular information from the laboratory to the clinic and to make maximum use of valuable urological patient resources in the UK, adequate funding and scientific energy are required. Whereas the latter is not in doubt, present funding for this type of translational research is inadequate.

Carcinoma, Transitional Cell↗

PubMed2026

Beyond Canonical Neoantigens: Emerging Technologies for Identification of Noncanonical Antigens and Implications for Personalized Cancer Vaccines.

Over the past decade, advances in sequencing technologies and computational pipelines enabled the development of personalized cancer vaccines (PCVs). Current PCV strategies primarily target cancer neoantigens generated by non-synonymous DNA mutations, which can result in altered amino acid sequences capable of eliciting tumor-specific immune responses. More recently, a distinct class of tumor-specific antigens (TSA), termed noncanonical or cryptic antigens, has emerged as an additional source of immunogenic targets. Unlike canonical neoantigens, noncanonical antigens typically cannot be identified by tumor/normal whole-exome sequencing, as they do not arise from classical DNA mutations. Instead, they are often associated with less well recognized and/or aberrant processes in the pathways from DNA to human leukocyte antigen (HLA)-presented peptides. Examples include transposable elements, circular RNA, translation of alternative open reading frames and/or long non-coding RNA, among others. Emerging evidence suggests that noncanonical antigens represent a substantial portion of the tumor-specific immunopeptidome and, similar to canonical neoantigens, are absent during thymic selection and can evade central tolerance and elicit T cell responses. Technological advances have increasingly facilitated the identification of noncanonical antigens. Long-read RNA sequencing reveals noncanonical transcripts by improving transcriptome assembly, while ribosome profiling provides genome-wide maps of actively translated regions, facilitating the discovery of peptides from aberrant translation events. Specialized molecular approaches enable enrichment and sequencing of circular RNAs, and immunopeptidomics using mass spectrometry allows for direct characterization of HLA-presented peptides. Together, these technological advances have led to an increasing interest in prioritizing and targeting noncanonical antigens in the next generation of PCVs. This review provides an overview of the diverse origins of TSAs beyond classical neoantigens and discusses emerging approaches that may enable the integration of these antigens in future clinical trials.

circular RNA↗

PubMed2026

Construction and validation of a β-hydroxybutyrylation-related molecular model for predicting prognosis of papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) usually has a favorable prognosis, yet a subset of patients develops persistent, recurrent, or biologically aggressive disease. The clinical relevance of lysine β-hydroxybutyrylation (Kbhb)-related transcriptional programs in PTC remains unclear. Accordingly, this study aimed to characterize Kbhb-related molecular heterogeneity in PTC, construct a prognostic signature, and explore its association with the tumor microenvironment (TME). METHODS: Transcriptomic and clinical data from PTC samples within The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort were analyzed to identify Kbhb-related differentially expressed genes (DEGs), define molecular subtypes, construct a prognostic signature, and characterize tumor microenvironmental features. Single-cell RNA-sequencing data from PTC were further used to explore the cellular distribution of representative genes. RESULTS: We identified 51 Kbhb-related DEGs in PTC and defined two Kbhb molecular subtypes. The Kbhb_C2 subtype showed shorter progression-free interval (PFI) and a more immune- and stroma-enriched microenvironment. A six-gene prognostic signature comprising TARID, CDSN, PIMREG, KLRC1, SYT13, and NPR3 was then established. High-risk patients had significantly worse PFI in the full, training, and testing cohorts, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.715, 0.793, and 0.771, respectively, in the full cohort. High-risk tumors also exhibited higher stromal, immune, and ESTIMATE scores, altered immune infiltration, and increased expression of multiple immune checkpoint molecules. Single-cell analysis confirmed distinct cell-type-specific expression patterns of representative genes. CONCLUSIONS: Kbhb-related transcriptional programs define clinically relevant molecular heterogeneity in PTC and are closely associated with prognosis and TME remodeling. The identified six-gene signature provides a biologically interpretable framework for risk stratification in PTC.

Papillary thyroid carcinoma (PTC)↗

PubMed2026

LymphGen-Sig: Integrating Genetic and Transcriptional States to Predict Therapeutic Response in Diffuse Large B-Cell Lymphoma.

PURPOSE: Genetic classification may advance precision medicine in diffuse large B-cell lymphoma (DLBCL), but existing tools like LymphGen (LG) are limited by complexity and incomplete classification and do not incorporate nongenetic features that affect disease biology and therapeutic outcomes. To address these limitations, we developed LG-sig (LGsig), a gene expression-based platform that classifies all DLBCLs and harmonizes both genetic and nongenetic dimensions of the disease. METHODS: LGsig was built on the distinct subtype-specific gene expression signature of each LG class using paired genomic and transcriptomic data (National Cancer Institute/British Columbia Cancer Agency; N = 764). Model development was restricted to DLBCLs classified into MYD88L265P and CD79B mutations (MCD), BCL6 translocation and NOTCH2 mutations (BN2), EZH2 mutations and BCL2 translocation (EZB), or SGK1 and TET2 mutations (ST2). Gene features were selected by differential gene expression, with 294 genes being optimal for classification using a nearest shrunken centroid classifier. LGsig classifications were designated as MCDsig, BN2sig, ST2sig, and EZBsig. The final model was applied to RNAseq from archival samples from the POLARIX trial (N = 678) to assess outcomes after polatuzumab vedotin-R-CHP (pola-R-CHP) or rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) for each LGsig subtype. RESULTS: LGsig accurately identified LG subtypes using transcriptional data alone and extended assignments to all previously LG-unclassified cases. Importantly, LG-unclassified DLBCLs reassigned by LGsig mirrored the transcriptional and clinical features of their corresponding LG counterparts, supporting their reclassification. In addition, LGsig reassigned LG A53 DLBCLs, characterized by aneuploidy and TP53 alterations, into more biologically and therapeutically relevant LGsig clusters. Finally, LGsig improved the performance of LG as a biomarker in the POLARIX study, by identifying distinct DLBCL subtypes exhibiting a survival benefit with pola-R-CHP over R-CHOP in both LG-classified and LG-unclassified cases. CONCLUSION: LGsig expands molecular classification beyond current genetic classifiers in DLBCL by integrating both genetic and transcriptional dimensions of the disease to better inform subtype-specific therapeutic strategies.

Journal Article↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed2026

Establishment of a CRISPR-Cas9 Library for Indica Rice and Identification of OsOPR5 (LOC_Os06g11210) as a Regulator of Root Architecture.

Functional characterization of a large number of rice genes remains a major challenge despite the availability of genome sequences and large-scale transcriptomic datasets. CRISPR-Cas9 library is a powerful approach for high-throughput targeted mutagenesis; however, its application in indica rice cultivars remains limited due to low transformation and regeneration efficiencies. In this study, we developed a CRISPR-Cas9 library targeting 12,000 rice genes and evaluated its utility for functional genomics in the indica cultivar MTU-1010. Sanger sequencing and NGS analysis of the plasmid library revealed high sgRNA coverage and more than 80% accuracy. Transformation of the developed library into the indica cultivar MTU-1010 resulted in a high target editing efficiency, with 90% of analyzed transgenic plants carrying mutations at the intended target site. Functional analysis of one homozygous mutant identified a previously uncharacterized role for OsOPR5 (LOC_Os06g11210), a member of the 12-oxophytodienoate reductase family in root architecture. The opr5 mutants exhibited significant reductions in lateral root number, seminal and crown root number, and root length, demonstrating that OsOPR5 positively regulates root system architecture in rice. Notably, endogenous jasmonic acid (JA) and JA-isoleucine levels were not significantly altered in the mutant, suggesting potential functional specialization or redundancy among rice OPR family members for JA accumulation. The root system architecture is a key determinant of water and nutrient acquisition; our results suggest that OsOPR5 may play an important role in adaptation under adverse environmental conditions. Collectively, this study establishes an efficient genome-editing platform for indica rice and identifies OsOPR5 as a novel regulator of root development.

Oryza↗

PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗

PubMed2025

The correlation of DPM1 overexpression with immune infiltration and poor prognosis in hepatocellular carcinoma.

BACKGROUND: The DPM1 gene, crucial for glycosylation processes, has shown abnormal expression in various cancers, raising interest in its potential oncogenic role and as a biomarker in hepatocellular carcinoma (HCC). METHODS: Transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. DPM1 expression levels were compared between HCC tissues and adjacent normal tissues. Clinical correlations were assessed using statistical analyses, including survival analysis and multivariate Cox regression. Immune microenvironment profiling was conducted to evaluate associations between DPM1 expression and immune cell infiltration patterns. RESULTS: Elevated DPM1 levels were associated with advanced tumor stages (P < 0.001), higher pathologic T stage (P < 0.001), increased histologic grade (P < 0.001), tumor positivity (P < 0.001), tissue inflammation (P < 0.001), and elevated alpha-fetoprotein levels (AFP > 400 ng/mL, P < 0.05). Multivariate Cox regression analysis identified DPM1 as an independent prognostic factor for reduced overall survival (HR = 1.990, 95% CI 1.390-2.848). Immunological analysis revealed that DPM1 expression was positively correlated with T helper cells (R = 0.268, P < 0.001) and Th2 cells (R = 0.295, P < 0.001), and negatively correlated with plasmacytoid dendritic cells (R=-0.291, P < 0.001) and cytotoxic cells (R=-0.284, P < 0.001). CONCLUSIONS: DPM1 serves as a promising prognostic biomarker in HCC, with its expression correlating with unfavorable clinical outcomes and immune landscape alterations. Future studies should further validate DPM1's impact on ferroptosis and immune evasion in HCC, and explore its potential as a therapeutic target.

DPM1↗

PubMed2026

Comparative Transcriptomics Reveals Shared Downstream Pathways in Craniofacial Pathology.

Treacher Collins syndrome and Nager syndrome are craniofacial developmental disorders caused by defects in ribosome biogenesis and RNA splicing, respectively, yet they exhibit overlapping abnormalities affecting neural crest cell-derived craniofacial structures. To investigate shared downstream pathogenic mechanisms, we performed a comparative transcriptomic analysis of zebrafish polr1c and sf3b4 mutant models from our previous studies. Comparative analysis identified 17 shared differentially expressed genes (DEGs) between polr1c and sf3b4 mutants, with the majority of shared genes dysregulated in the same direction, indicating a coordinated rather than random transcriptional response. Gene ontology analysis identified ATP-dependent protein folding chaperone activity as the only shared molecular function, driven in part by upregulation of hsp90aa1.2, indicating a common proteostasis response. Because chaperone activity is linked to extracellular matrix (ECM) protein processing, we cross-referenced DEGs from both mutants against the curated zebrafish matrisome. Three of the 17 shared DEGs (serpinh1b, il11a, and lepa) were matrisome-associated and upregulated in both mutants. Serpinh1b, a collagen-specific chaperone, was strongly expressed in craniofacial cartilage and mesenchymal populations during pharyngeal arch development and exhibited nearly identical fold changes in both mutants. Il11a is of particular interest because its receptor, IL11RA, is known to be associated with human craniosynostosis, suggesting potential relevance to craniofacial development. Together, it is possible to hypothesize that shared chaperone-associated transcriptional changes, together with altered ECM-related gene expression, may contribute to polr1c- and sf3b4-associated craniofacial disorders, warranting further functional validation.

Extracellular Matrix↗