Constructing high-quality genome-scale metabolic models (GEMs) for non-model organisms remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment, protein language models (PLMSearch), and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. AlphaGEM enhances homologous relationship identification compared to traditional sequence-based methods. Crucially, it employs an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine dark metabolic functions encoded by nonhomologous proteins, thereby expanding species-specific networks. We validated AlphaGEM across prokaryotes (Klebsiella pneumoniae, Bacillus subtilis), eukaryotes (Rhodosporidium toruloides, Pichia pastoris), and complex mammals (Mus musculus, Cricetulus griseus), achieving predictions comparable to manually curated models while outperforming existing tools. Furthermore, we demonstrated its scalability by automatically reconstructing high-fidelity GEMs for 332 distinct yeast species. In summary, AlphaGEM enables precise, rapid GEM construction across diverse domains, providing a solid foundation for universal functional analysis of organisms having genome sequences available.

Aim: To investigate the cellular composition and molecular mechanisms of periodontal granulation tissue formation using single‐cell RNA sequencing (scRNA‐seq), aiming to enhance the understanding of periodontal disease pathogenesis and identify potential targets for regenerative therapies.

Materials and Methods: Granulation tissue samples were collected from patients undergoing periodontal surgery ( n  = 3). Fresh tissues were processed into single‐cell suspensions and subjected to scRNA‐seq. The data were integrated and compared with existing datasets from healthy gingiva and periodontal ligament. Computational analyses were performed and validated through immunofluorescence staining.

Results: Ten distinct cell clusters were identified across the samples. Granulation tissue exhibited a higher abundance of immune cells compared to healthy tissues. A novel endothelial cell subpopulation, exclusive to granulation tissue, was discovered and characterised by NOTCH3 expression and involvement in ossification pathways. Additionally, granulation tissue fibroblast subpopulations demonstrated a progenitor‐like state, characterised by extracellular matrix reorganisation and low differentiation, similar to cancer‐associated fibroblasts.

Conclusion: This study identified a novel endothelial subpopulation offering new insights into the disease's pathogenesis and presenting potential targets for regenerative therapies. These findings will help advance the understanding of periodontal disease granulation tissue formation and provide information for the development of materials to modulate specific cellular pathways to improve periodontal disease management.

The interaction between a class I peptide-major histocompatibility complex (pMHC) and a T cell receptor (TCR) plays a central role in the elicitation of CD8+ T cell immune responses. As a result, considerable effort has been invested in understanding the structural, dynamic, and biophysical parameters that govern this recognition event, including designing altered peptide ligands (APLs) which seek to modulate the downstream signaling outcomes. However, dynamic links between modified peptide positions and distant residues have remained ill resolved until now. Using an integrative approach combining crystallographic ensemble and single models with atomistic molecular dynamics simulations and correlational analysis, we have established an approach that allows us to identify coupled dynamics between spatially distant residues at the pMHC interface. Furthermore, we constructed a network encoding the inter-residue couplings observed throughout the simulations. This computational workflow corroborates well with the functional and biophysical experimental data of our model system, and leads to novel insights regarding the differential immunogenicity of the closely related peptides analyzed in this study. Ultimately, we present an intuitive and comprehensive strategy for decoding the linked dynamics at the pMHC interface allowing for mechanistic insights into the biophysical bases governing immunogenicity.

Despite rapid advances in whole-genome sequencing (WGS), translating genomic findings into individualized insights remains challenging. We present GenRiskPro, a clinical decision-support and research platform, which automates WGS variant calling, annotation, prioritization, and reporting to deliver actionable findings and facilitate precision wellness. (To test the GenRiskPro platform, log on to https://www.phenomeportal.org/dashboard using the following credentials: Username: user@test.com; Password: test.) GenRiskPro integrates rare and common variant prioritization in a unified pipeline and in-house database, enabling both rare and complex disease and trait association analyses. Variant reporting is supported via LongevityCloud, which features a web portal for clinicians to review, adjust, and authorize the return of results in tabular and PDF formats, alongside a mobile app with artificial intelligence (AI) integration for sequenced individuals. Case studies using Turkish (TR, n = 275) and Swedish (SW, n = 101) WGS data assessed platform performance and variant prioritization: (a) predefined gene panels yielded a 1.82% positive rate for actionable findings per American College of Medical Genetics and Genomics (ACMG) secondary findings guidelines; (b) phenotype-driven support diagnosed cases including muscular dystrophy and microcephaly; (c) cohort-level ClinVar reassessment identified potentially misclassified pathogenic variants; (d) rare variant burden analysis revealed enrichment in ABCA4 for TR and SMPD1 in SW; and (e) population analysis highlighted carrier differences in trait-associated SNPs (rs12913832 and rs4988235) and PGx variants (CYP2B64 and CYP2B66). GenRiskPro unifies databases, literature, web development, and AI for rapid, user-friendly genomic analysis and reporting, which fosters collaboration among hospitals, researchers, clinicians, and patients.

While single-omics analyses of Parkinson's Disease (PD) have demonstrated their ability in revealing the underlying molecular mechanisms, they often fail to provide a comprehensive view of the complete disease mechanisms. In this study, we leveraged multi-omics data from 64 heterogeneous, well-phenotyped PD patients, generated plasma metabolomics data and Olink proteomics data together with the gut and saliva metagenomics data, and investigated the altered molecular mechanisms and their interactions in association with the severity of motor function disorders in PD patients. Based on our multi-omics approach, we identified a panel of 58 biomarkers comprising one clinical variable, 10 proteins, and 17 metabolites from plasma, 26 gut species, and 4 saliva species for PD severity. These biomarkers exhibited superior predictive performance for assessing PD severity compared to those derived from single-omics datasets. The predictive power of our machine learning models based on these biomarkers was validated using additional multi-omics data from the same group of PD patients after a 3-month follow-up. The contribution of each omics dataset was evaluated by both supervised and unsupervised machine learning approaches, highlighting the importance of plasma metabolomics in disease stratification. Our study unveiled disease-related molecular alterations across multiple omics datasets, offering potential diagnostic and therapeutic insights for PD. Moreover, it underpinned the significance of employing multi-omics analyses when studying complex diseases like PD.

Achieving minimally invasive and rapid detection is a crucial goal in modern medicine. The comprehensive characterization of the blood proteome holds great promise in advancing our understanding of disease etiology, facilitating early diagnosis, risk stratification, and improved monitoring across various diseases and their subtypes. In this study, we collected plasma proteomes from over 3000 patients, representing 23 distinct diseases, encompassing a total of 1462 proteins. Based on histological knowledge, we developed a two-stage hierarchical multi-disease classifier and applied it to perform multi-disease classification on the collected proteomic data. Our results demonstrate that this empirically guided two-stage hierarchical multi-disease classifier outperforms traditional machine learning algorithms in terms of prediction performance, showing better balance and more meaningful feature selections. This finding highlights the positive role that domain expertise can play in machine learning-based disease detection, and underscores the potential of plasma proteomics for multi-disease screening.

Accurate interpretation of genomic variants remains a major bottleneck in precision oncology, due in part to fragmented knowledge across databases and limited integration between clinical evidence and population-scale genomic datasets. Here we present OncoRisk, a stand-alone, user-friendly web server that unifies data from over ten oncogenic databases and seven large-scale pan-cancer cohorts, enabling rapid multi-database queries and network-based exploration of genomic variants, gene-gene interactions, and therapy associations. The platform features a semi-automated reporting workflow that generates comprehensive, patient-specific clinical reports from raw tissue sequencing data and categorizes variants into actionable tiers. For translational research, OncoRisk provides modules for data-driven exploration, allowing users to validate findings by interrogating mutation frequencies and clinical associations across real-world patient data. Furthermore, an integrated suite of analytical tools enables comprehensive, cohort-level investigations of mutational landscapes, prognostic biomarkers, and oncogenic signaling pathways. By providing a unified ecosystem that bridges curated knowledge with large-scale cohort data, OncoRisk serves as an effective catalyst for both discovery research and clinical application in oncology. OncoRisk is publicly available at https://www.phenomeportal.org/oncorisk.

Hepatocellular carcinoma (HCC) remains difficult to treat due to its limited targets. Hence, we introduced phosphorylated c-Jun N-terminal kinase (p-JNK) as an anti-HCC target protein and investigated JNK-IN-5A and six derivatives (SET135, SET156, SET158, SET159, SET171, and SET172) which stabilize p-JNK. In vitro, these compounds outperformed sorafenib and regorafenib, inducing stronger p53-mediated cell-cycle arrest, autophagy, apoptosis, and reduced invasiveness via JNK/c-Jun pathways. RNA-seq profiling revealed distinct mechanisms: SET135 triggered autophagic necrosis via p62/SQSTM1, while SET171 induced reactive oxygen species (ROS)-driven necrosis. Systems biology analysis confirmed their enhanced efficacy. A 7-day GLP-like rat toxicity study showed SET135 and SET171 were well-tolerated. In vivo study performed with 21-day treatment of SET135 or SET171 showed superior anti-tumor effects compared to sorafenib via apoptotic mechanisms in HCC-transplanted mice. These findings highlight JNK-IN-5A derivatives as promising HCC therapeutic candidates capable of inducing both apoptotic and necrotic cell death.

Background and Aim: While liver biopsy remains the reference standard for assessing hepatic fibrosis, the major prognostic factor in metabolic dysfunction-associated steatotic liver disease (MASLD), its inherent limitations have driven the search for innovative non-invasive diagnostic tests. In this study, we sought to evaluate the diagnostic accuracy of plasma PDGFRβ and PSD4 methylation levels, integrated within machine learning algorithms, for non-invasive staging of hepatic fibrosis in patients with MASLD, and to compare its performance against established non-invasive biomarkers. Materials and Methods: Patients with biopsy-proven MASLD and healthy controls were recruited from the three institutions. Quantitative methylation of circulating cell-free DNA was assessed using bisulfite modification and pyrosequencing. The resulting data were used to develop linear discriminant analysis, random forest, and support vector machine algorithms to identify patients at different stages of fibrosis. Results: The study included 234 patients with histologically confirmed MASLD and 43 healthy controls. Each dataset was validated using an independent cohort. Advanced fibrosis was associated with elevated plasma PDGFRβ and PSD4 methylation levels in both the discovery and validation cohorts. The integration of plasma DNA methylation markers with clinical parameters demonstrated performance comparable to that of existing noninvasive biomarkers for detecting both significant and advanced fibrosis, achieving area under the curve scores exceeding 0.75 across all models. Conclusion: Supervised machine learning algorithms incorporating plasma PDGFRβ and PSD4 DNA methylation levels show promise as a non-invasive approach for assessing hepatic fibrosis in MASLD.

Background Pyruvate kinase liver and red blood cells (PKLR) is linked to metabolic dysfunction-associated steatotic liver disease (MASLD). Previous study, we identified JNK-IN-5A, a c-Jun N-terminal kinase (JNK) inhibitor that suppresses PKL expression in HepG2 cells using computational drug repurposing and screened out four hit JNK-IN-5A derivatives (SET-151, SET-152, SET-162, SET-130).Materials and Methods We validated therapeutic efficacy of JNK-IN-5A and four derivative (SET-151, SET-152, SET-162, SET-130). HepG2 de novo lipogenesis (DNL) steatosis model was used in vitro validation. RNA sequencing data were analysed using systems biology approaches, including transcriptomic profiling and COMPASS analysis. GLP-like toxicity assessment in rat model shows in vivo safety and MASLD rat model revealed in vivo therapeutic effect to MASLD and MASH.Results In a HepG2 DNL steatosis model, all compounds reduced intracellular triacylglycerol (TAG) and inhibited key DNL proteins (PKL, FASN, ACACA, SCD1, SREBP1-c, ChREBP). Transcriptomic profiling revealed stronger anti-steatotic effects with SET-151, SET-152, and SET-162, which uniquely downregulated genes in pyruvate metabolism, bile acid synthesis, fatty acid metabolism, and glycolysis. Compass analysis showed these derivatives significantly altered lipid-related metabolic reactions, unlike JNK-IN-5A. In a high-sucrose, high-fat diet-induced MASLD rat model, JNK-IN-5A and SET-152 reduced hepatic lipid accumulation, liver stiffness, and MASLD biomarkers.Conclusion Our findings identify PKLR as a promising therapeutic target for MASLD and MASH. SET-152 suppressing PKLR through JNK inhibition highlights its potential as a new drug for MASLD and MASH therapy.