Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.06.22.733885

Ginsenoside Ro ameliorates diabetic cardiomyopathy by maintaining ciliary homeostasis and enhancing antioxidation

Abstract

ObjectiveTo investigate and clarify the role of Ginsenoside Ro (GRo) in diabetic cardiomyopathy (DiaCM) and to elucidate the molecular mechanism by which GRo ameliorates DiaCM. Methods[circled1] The construct of type 2 diabetic mouse model. The bought C57BL/6 male mice were housed in a specific pathogen-free (SPF) animal facility and randomly divided into control, STZ (model), STZ + GRo, and control+GRo groups. The STZ (model) and STZ + GRo groups were fed a high-fat and high-glucose diet combined with intraperitoneal injection of streptozotocin (STZ). The control and control + GRo groups were fed a normal diet, while the control + GRo and STZ + GRo groups were treated with GRo via oral gavage. Then, all groups were evaluated for cardiac function and structure by small animal echocardiography and histological staining including hematoxylin and eosin (HE) and Massons trichrome staining to screen and confirm diabetic cardiomyopathy in mice. Finally, immunofluorescence staining of cilia in mouse heart tissue was performed to determine whether GRo inhibits abnormal ciliary growth. [circled2] The construct of cell models. First, the CCK-8 (Cell Counting Kit-8) assay was used to separately evaluate the cytotoxicity of GRo and the combination of TGF-{beta}1 and PA in myocardial fibroblasts and cardiomyocytes. Subsequently, mouse myocardial fibroblast lines (MCFs) were treated with transforming growth factor-beta 1 (TGF-{beta}1), and H9c2 cardiomyocytes were treated with palmitic acid (PA). Both cell types then received the GRo treatment. [circled3] Molecular and cellular testing. Firstly, we measured serum levels of cardiac injury markers (CK-MB, MYO, and TNNI3), glutathione (GSH), and malondialdehyde (MDA). Secondly, we examined the expression of myocardial fibrosis-related genes (Col1a1, etc.), myocardial hypertrophy markers (Nppa, etc.), cilia-specific genes (Pkd1, etc.), and oxidative stress-related genes (Nrf2, etc.) in both animal and cell samples by Western blotting and RT-qPCR. Finally, we used immunofluorescence staining of myocardial fibroblasts to detect cilia length and phalloidin staining of cardiomyocytes to measure their cross-sectional area. [circled4] The correlation mechanism. Firstly, the cilia-specific inhibitory drug HIP-4 was used to disrupt cilia homeostasis by inhibiting cilia growth. Secondly, small activating RNA (saRNA) was used to upregulate the Pkd1 gene to verify whether GRo exerts its anti-fibrotic effects through the inhibition of PC1. Results[circled1] Animal level. A diabetic cardiomyopathy mouse model was successfully established by combining STZ injection with a high-fat and high-glucose diet, and treatment with GRo significantly ameliorated the associated symptoms. [circled2] Cellular level. We successfully established a myocardial fibrosis model by treating myocardial fibroblasts with TGF-{beta}1, and a myocardial hypertrophy model by treating cardiomyocytes with PA. Immunofluorescence staining demonstrated that GRo significantly decreased cilia length in the fibrosis model, while phalloidin staining showed that GRo significantly attenuated the increase in cardiomyocyte cross-sectional area. [circled3] Molecular level. Compared with the model group, GRo treatment significantly reduced serum levels of cardiac injury markers (CK-MB, MYO and TNNI3), glutathione (GSH) and malondialdehyde (MDA). Western blotting and RT-qPCR analyses of both animal and cell samples revealed that GRo markedly alleviated indicators of myocardial fibrosis and hypertrophy, while also suppressing cilia-specific genes and oxidative stress-related genes. Overall, GRo significantly ameliorated the markers associated with myocardial fibrosis and hypertrophy, and inhibited cilia-specific protein expression as well as oxidative stress parameters. [circled4] The correlation mechanism. The cilia-specific drug hedgehog pathway inhibitor 4 (HPI-4) was used to revealed that cilia homeostasis is closely linked to myocardial fibrosis and shortened cilia inhibit the fibrosis progression. Furthermore, upregulation of the Pkd1 gene by small activating RNA demonstrated that PC1 overexpression abrogates the therapeutic effect of GRo. Finally, GRo can alleviate DiaCM.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yang, Z., Guo, Y., Guan, B., Guo, X., Shang, Y., Tang, Y., Zhao, C., Wang, P., Ren, Z.. 2026-06-26. Ginsenoside Ro ameliorates diabetic cardiomyopathy by maintaining ciliary homeostasis and enhancing antioxidation. https://doi.org/10.64898/2026.06.22.733885

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Functional primary human 3D skeletal muscle organoids enable exercise and metabolic research

Human skeletal muscle is the principal site of insulin-stimulated glucose disposal and a major mediator of exercise-induced metabolic benefits, yet human models that preserve metabolic and exercise responsiveness remain limited. We generated primary human skeletal muscle organoids from donor-derived CD56+ myoblasts using a collagen-based extracellular matrix and serum-free IGF1-guided differentiation. The organoids formed aligned contractile tissues containing oxidative and glycolytic fiber type-like myotubes, displayed enhanced mitochondrial respiration, insulin-stimulated glucose uptake, and reproducible force generation. Electrical pulse stimulation induced AMPK activation, increased glucose utilization and lactate production, and upregulated canonical exercise-responsive genes including NR4A3 and PPARGC1A. Notably, transcriptional responses to in vitro exercise overlapped with acute exercise responses observed in skeletal muscle biopsies from the same donors. The organoids further detected functional impairments of skeletal muscle performance induced by TGF-{beta}1 and metformin and increased speed generation by testosterone treatment. These findings establish a donor-specific human skeletal muscle platform that recapitulates key features of insulin action and exercise adaptation and may enable mechanistic studies of skeletal muscle metabolism, exercise responsiveness, and therapeutic interventions relevant to diabetes.

Molecular Biology↗

TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces): An Optimized Algorithm for Vertebrate Taxonomic Assignments in eDNA Metabarcoding, Integrating Molecular, Taxonomic, and Ecological Criteria

Environmental DNA (eDNA) metabarcoding has become a powerful approach for large-scale biodiversity assessment, yet taxonomic assignment remains one of its most critical error-prone steps. Current bioinformatic pipelines rely on molecular similarity searches against reference databases, but assignment accuracy is constrained not only by short marker length and database incompleteness, but also by fundamental limitations, including recent species radiations, incomplete lineage sorting, introgression, NUMTs, and the imperfect correspondence between genetic variation and species boundaries. Here, we present TRIDENT (Taxonomic Resolution and IDentification using Environmental dNa Traces), an automated and simple protocol designed to improve taxonomic assignments in eDNA metabarcoding. Initially developed for marine vertebrates, TRIDENT may be used with any barcode and integrates three complementary sources of evidence: molecular similarity (NCBI/GenBank and BOLD), curated taxonomic information (WoRMS), and ecological plausibility derived from biogeographic occurrence data (GBIF). The workflow sequentially constructs candidate taxon lists based on sequence similarity, expands them through taxonomic hierarchies, and filters them using spatial occurrence constraints. It further identifies possible taxa lacking reference barcodes and evaluates their plausibility through CO1-based similarity if data exist in BOLD. TRIDENT has been implemented as a source-available Python tool and tested using empirical eDNA datasets from marine vertebrates as well as simulated communities. Results demonstrate that the tool produces taxonomic assignments consistent with expert manual curation while substantially reducing processing time and attention errors caused by manual processing of large datasets. By combining molecular, taxonomic, and ecological criteria within a single framework, TRIDENT improves transparency and reproducibility and provides a robust and flexible solution strengthening confidence in taxonomic identifications in eDNA-based biodiversity assessments.

Molecular Biology↗

Identifying and Addressing Systematic Data Leakage in Protein-Ligand Affinity Benchmarks

Accurate prediction of protein-ligand binding affinity is a crucial goal in structure-based drug discovery, with the potential to significantly shorten development timelines. Recently, a new wave of machine learning models based on co-folding, such as Boltz-2 and IsoDDE, has demonstrated performance that matches or exceeds that of gold-standard physics-based methods like Free Energy Perturbation (FEP). This paper provides a critical assessment of these claims, revealing that current benchmarks are heavily influenced by data leakage, and proposes a new benchmark that explicitly controls for data leakage. We demonstrate that splitting by protein-sequence identity is inherently insufficient to prevent data leakage due to "target mirroring," in which homologous proteins with low overall sequence identity still exhibit highly correlated binding profiles. Our meta-analysis of documents in the ChEMBL 36 database identifies more than 6,000 such assay pairs and finds that leakage persists for sequence-identity thresholds as low as 0.2, well below the values commonly used in benchmarks today. Additionally, we show that a ligand-only baseline model, which lacks protein structural information, achieves surprisingly high performance on the FEP+ 4 and OpenFE benchmarks (r = 0.66 and r = 0.36, respectively). Our results indicate that current benchmarks tend to reward models for memorizing training data and exploiting localized leakage rather than truly learning biophysical principles. To address this issue, we propose the Novelty-Tiered Affinity Benchmark, in which the test data is partitioned into ligand novelty tiers. In the most challenging tier (Tanimoto similarity < 0.35), ligand-only models perform notably worse (r = 0.14), offering a clear baseline for evaluating genuine generalization. We argue that the field must move beyond sequence-based splits to ensure that AI-driven discovery translates into successful prospective laboratory research.

Molecular Biology↗