Monastrol, a DHPM-based Eg5 inhibitor with well-known antiproliferative activity but limited therapeutic potential due to poor solubility and low bioavailability, was selected as the lead compound for the design of styryl-modified 3,4-dihydropyrimidin-2(1H)-ones with an improved pharmaceutical profile. Twelve derivatives (10–21) were synthesized via the Biginelli reaction and evaluated for cytotoxicity in HeLa and MCF-7 cells. Styryl derivatives 16 and 17 emerged as the most active. In HeLa cells, derivatives 17 (IC50 = 1.3 µM) and 16 (IC50 = 3.7 µM) were approximately 85-fold and 30-fold more potent than monastrol (IC50 = 111 µM), respectively. In MCF-7 cells, derivatives 16 and 17 displayed 18- to 20-fold higher potency than monastrol, respectively. Biological results also indicate that styryl derivatives 16 and 17 induce apoptosis in both HeLa and MCF-7 cells. In HeLa cells, activation of caspase-8, -9, and -3 suggests the involvement of both intrinsic and extrinsic pathways. In contrast, in MCF-7 cells, the increased expression of p53 and p21, together with PARP cleavage, suggests a p53-dependent apoptotic response. Derivatives 16 and 17 emerged as promising Eg5 inhibitors from docking studies, but their poor aqueous solubility (0.2–0.7 µM), despite high biological stability, highlights the need for formulation strategies to improve drug-like properties.

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.

Ischemic heart disease (IHD) involves coordinated molecular changes across heart; yet, their interplay remains poorly understood. Here, we investigated transcriptomic alterations in two heart tissue subtypes, left ventricle (LV) and epicardial adipose tissue (EAT), from age-and BMI-matched healthy and IHD individuals, including diabetic and non-diabetic patients. We performed transcriptomic profiling and systems-level network analysis to identify disease-associated gene expression changes. Our analysis revealed (1) stronger transcriptional responses in EAT than LV, particularly in diabetic individuals, and (2) widespread dysregulation of inflammatory and metabolic pathways, including oxidative phosphorylation, cytokine signaling, and fatty acid degradation, across tissue subtypes. Co-expression network analysis uncovered shared gene modules, with SDHA and OGDH emerging as central, downregulated genes linked to mitochondrial function and inflammation, important processes in IHD pathophysiology. These findings were validated in independent human and mouse datasets. Overall, our integrative analysis identifies conserved molecular signatures across cardiac tissue subtypes, suggesting therapeutic potential in IHD.

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.

Microbiome perturbations are associated with advanced chronic liver disease (ACLD), but how microorganisms contribute to disease mechanisms is unclear. Here we analysed metagenomes of paired saliva and faecal samples from an ACLD cohort of 86 individuals, plus 2 control groups of 52 healthy individuals and 14 patients with sepsis. We identified highly similar oral and gut bacterial strains, including Veillonella and Streptococcus spp., which increased in absolute abundance in the gut of patients with ACLD compared with controls. These microbial translocators uniquely share a prtC gene encoding a collagenase-like proteinase, and its faecal abundance was a robust ACLD biomarker (area under precision-recall curve = 0.91). A mouse model of hepatic fibrosis inoculated with Veillonella and Streptococcus prtC-encoding patient isolates showed exacerbation of gut barrier impairment and hepatic fibrosis. Furthermore, faecal collagenase activity was increased in patients with ACLD and experimentally confirmed for the prtC gene of translocating Veillonella parvula. These findings establish mechanistic links between oral–gut translocation and ACLD pathobiology.

Clear cell Renal Cell Carcinoma (ccRCC) is the most common subtype of renal malignancy and remains a major clinical challenge. This challenge underscores the urgent need to identify novel molecules with potential as therapeutic targets in ccRCC. Therefore, in this study, four novel pyrimidine-based derivatives of TG-101,209 (3a-d) were designed and synthesized as potential inhibitors of BUB1B (Budding Uninhibited by Benzimidazoles 1 Mitotic Checkpoint Serine/Threonine Kinase B), a mitotic checkpoint kinase involved in ccRCC pathogenesis. Complete structural characterization of all synthesized compounds was achieved using Fourier-transform infrared (FT-IR) spectroscopy, nuclear magnetic resonance (1H and 13C NMR), and mass spectrometry (MS). The crystalline architecture and intermolecular interactions of compound 3a were further elucidated by single-crystal X-ray diffraction. Theoretical computations were performed using Density Functional Theory (DFT) at the B3LYP/6–311++G(d,p) level of theory to investigate the electronic and structural properties of the most active compound. The computed IR spectrum of compound 3a showed excellent agreement with experimental data, supporting the structural findings from X-ray analysis. Hirshfeld surfaces (HS) analyses were carried out to visualize the intermolecular interactions in the crystal packing of 3a. This analysis highlighted key N—H···O and N—H···N hydrogen bonds, which were found to be in excellent agreement with the experimental single-crystal X-ray diffraction data. Multiple noncovalent interactions (N–H···O/N, C–H···O, C–H···π) were identified in the solid state and quantitatively examined using Independent Gradient Model based on Hirshfeld partition (IGMH) analysis, confirming N–H···N as the strongest interaction. In vitro cytotoxicity assays on Caki-1 cells revealed that compounds 3a, 3c, and 3d exerted significant antiproliferative effects. Compound 3a exhibited superior BUB1B inhibition compared to the other derivatives, eliciting a robust apoptotic response as evidenced by enhanced PARP cleavage and caspase activation. Molecular docking and dynamics studies revealed stable and favorable binding affinity of the compound 3a within the BUB1B active site, consistent with experimental observations. The combined experimental and computational findings indicate that these pyrimidine derivatives, particularly compound 3a, act as promising BUB1B inhibitors with potential therapeutic relevance in ccRCC therapy.

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.

Aims: This study aims to investigate the efficacy of drug repurposing using a corneal organoid model developed from patient-derived iPSCs and to elucidate the pathophysiology of Aniridia-Associated Keratopathy (AAK). Materials and methods: A 90-day stepwise differentiation protocol was used to generate corneal organoids from iPSC cell lines developed from aniridia patients and healthy control. The corneal organoids produced were characterized using histology, immunofluorescence, qPCR, western blot, and transcriptomics. Two known agents, Duloxetine and Ataluren, were tested for corneal organoids for the restoring PAX6 protein expression. Key findings: Histological analyses showed that the corneal organoids had a similar architecture to the native corneal tissue. Corneal epithelial, stromal, and endothelial cell biomarker staining showed positive expressions. AAK corneal organoids exhibited features that indicate the AAK disease phenotype, such as thickening of the epithelial cell layers and decrease in expressions of PAX6, ΔNP63, and keratocan genes. An increase in PAX6 protein was observed in organoids produced from AAK1 after duloxetine treatment and in organoids produced from AAK2 following ataluren treatment. AAK3 did not respond to either agent, indicating that there was no mutation-specific drug activity. Transcriptomic analyses showed clear corneal differentiation and absence of retina or lens profile in organoids. Significance: This study presents patient-specific organoid models for AAK using iPSCs and offers insight into mutation effects and PAX6 restoration following drug repurposing. The findings form the basis of personalized treatments for congenital aniridia.

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.