Biology subjects

Zhen, C.

Publications and source records attributed to Zhen, C..

5 recordsLinked to original sources

bioRxiv2025

Body temperature drives azole tolerance in Candida albicans by hindering the autophagic degradation of Erg11

Human body temperature has been shown to limit the antifungal efficacy of azoles; however, the precise mechanisms underlying this limitation remain insufficiently understood. In this study, we observed that short-term exposure (24 h) to human body temperature (37{square}{degrees}C), in contrast to 30{square}{degrees}C, significantly enhances the tolerance of Candida albicans to azoles without inducing resistance. Our findings suggest that this increased tolerance is due to a reduction in the degradation of C. albicans Erg11 at 37{square}{degrees}C compared to 30{square}{degrees}C, thereby promoting azole tolerance. This phenomenon occurs because Erg11 degradation is mediated by autophagy, which is inhibited in C. albicans at 37{square}{degrees}C. The suppression of autophagy at this temperature may be associated with elevated mitochondrial production of reactive oxygen species, leading to mitochondrial dysfunction. In response to heat stress-induced mitochondrial dysfunction, autophagy-related proteins such as Atg8 accumulate around the mitochondria, while their levels in the endoplasmic reticulum decrease, consequently inhibiting autophagy in C. albicans. These findings suggest that promoting the degradation of Erg11 in C. albicans may serve as a viable strategy to augment the fungicidal efficacy of azoles in the human host.

microbiology↗

bioRxiv2025

Research on Crop Phenotype Prediction Methods Based on SNP-context and Whole-genome Features Embedding

Modern agriculture demands precise genomic prediction to accelerate elite crop breeding, yet traditional genomic prediction approaches, such as genomic best linear unbiased prediction (GBLUP) and Bayesian methods, focus primarily on the cumulative effect of individual SNPs, thus neglecting the concerted influence that the surrounding sequence context has on the phenotype. To overcome these limitations, we propose two novel feature embedding modes (SNP-context and whole-genome) based on DNABERT-2, a cross-species genomic foundation model that uses self-attention mechanisms and transfer learning to automatically identify conserved sequence features across diverse evolutionary lineages without prior biological assumptions. The whole-genome feature embedding aggregates genomic information at a global scale by pooling vectors from chunked sequences processed by DNABERT-2, whereas the context feature embedding captures local information by directly encoding variable-length (500--3000 bp) sequences centered on target SNPs. To reduce noise in the high-dimensional feature embeddings, we employed principal component analysis (PCA) and partial least squares (PLS) to project the features into a lower-dimensional space. We generated two kinds of feature embedding for three crop datasets (rice413, rice395, and maize301), investigated the impact of 500--3000 bp flanking SNP contexts on phenotypic prediction, and compared prediction accuracy variations across algorithms at 4--768 feature dimensions among the PCA, PLS, and no dimensionality reduction strategies. The results demonstrate that machine learning (ML) algorithms operating under the SNP-context embedding mode achieve greater accuracy and lower mean absolute errors (MAEs) than traditional SNP features do at specific context lengths, particularly for traits with low-to-moderate heritability (h2[isin](0.2, 0.7]). In contrast, using whole-genome embeddings as input for ML can further improve the prediction accuracy for highly heritable traits (h2[isin](0.7, 1.0]), even outperforming state-of-the-art deep learning models (such as DNNGP and ResGS) that rely on SNP markers. Our code is available on https://github.com/oliveSpring/Crop_DNA_Embedding.git

genetics↗

bioRxiv2025

A bibliometric analysis of research on the mitochondrial roles in prostate cancer and the virtual design of LONP1 - specific antibodies using the GeoBiologics platform

BackgroundProstate cancer remains one of the most prevalent malignancies among men globally, with its incidence showing an upward trend worldwide. Mitochondria, as central regulators of cellular energy metabolism, play crucial roles in prostate cancer initiation, progression, and drug resistance mechanisms. While mitochondria-targeted therapeutic strategies have emerged as a significant focus in cancer research in recent years, comprehensive bibliometric analyses mapping the evolving landscape of this field remain scarce. This study systematically investigates research trends in mitochondrial-prostate cancer interactions through bibliometric methods, identifying LONP1 as an emerging research focus in mitochondria-related prostate cancer therapy. Building on these findings, we employed artificial intelligence to virtually design a LONP1-specific antibody, proposing novel therapeutic targeting strategies for this field. MethodsUtilizing the Web of Science Core Collection database (2015-2023), we conducted visualization analyses through CiteSpace and VOSviewer to map network relationships among countries, institutions, journals, authors, and keywords. Building on this foundation, a humanized antibody targeting LONP1 was computationally designed and screened through the GeoBiologics platform. ResultsAnalysis of 452 included publications revealed the United States and China as leading contributors in this research domain. The field has progressively transitioned from fundamental mechanistic investigations to clinical applications, particularly focusing on drug resistance mechanisms, and combination therapy. LONP1 was identified as a critical mitochondrial regulator strongly associated with prostate cancer progression. Our AI-designed antibody (Antibody_82) demonstrated superior binding affinity and stability through effective targeting of LONP1s ATP-binding site. ConclusionThis bibliometric study delineates evolving research trends in mitochondrial involvement in prostate cancer. The developed LONP1-targeting antibody shows promising therapeutic potential for castration-resistant prostate cancer (CRPC) patients, potentially offering more effective treatment alternatives.

cancer biology↗

bioRxiv2021

A novel framework for single-cell Hi-C clustering based on graph-convolution-based imputation and two-phase-based feature extraction

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Singlecell Hi-C technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. However, few methods are well designed for single-cell Hi-C clustering, because of high sparsity, noise and heterogeneity of single-cell Hi-C data. In this manuscript, we propose a novel framework, named ScHiC-Rep, for singlecell Hi-C data representation and clustering. ScHiC-Rep mainly contains two parts: data imputation and feature extraction. In the imputation part, a novel imputation workflow is proposed, including graph convolution-based, random walk with restart-based and genomic neighbor-based imputation. In the feature extraction part, a two-phase feature extraction method is proposed, including linear phase for chromosome level and non-linear phase for cell level feature extraction. The evaluation results show that the proposed framework outperforms existing state-of-the-art approaches on both human and mouse datasets.

bioinformatics↗

bioRxiv2020

All-trans Retinoic Acid Regulates miR-106a-5p Inhibition of Autophagic in Developing Cleft Palates

BackgroundAll-trans retinoic acid (atRA) results in cleft palate, but the cellular and molecular mechanisms underlying the teratogenic effects on palatal development have not been fully elucidated. Autophagy interruption has been reported to seriously affect embryonic-cell differentiation and development. This study aimed to verify whether atRA-induced cleft palate occurs because atRA blocks autophagy and stemness of embryonic palatal mesenchyme (MEPM) cells, which are maintained via the phosphatase and tensin homolog (PTEN)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) autophagic signaling pathway, and inhibits osteogenic-differentiation potential of MEPM cells, which could lead to the development of cleft palate. MethodsTo assess the stemness and pluripotency of MEPM cells, we analyzed their surfacemarkers using immunofluorescence (IF) and flow cytometry (FCM). Differentiation potentials, such as osteogenic, adipogenic, and chondrogenic differentiation, were induced. We also investigated the role of the PTEN/Akt/mTOR autophagic signaling pathway, which maintains the stemness and pluripotency of MEPM cells. Using transmission electron microscopy (TEM), Western blot analysis, quantitative reverse transcriptase polymerase chain reaction (RT-qPCR), messenger ribonucleic acid (mRNA) microarray, dual-luciferase reporter system, and exosomes, we found that atRA blocks autophagy and osteogenic differentiation of MEPM cells through micro-ribonucleic acid (miR)-106a-5p by targeting the PTEN/Akt/mTOR autophagic pathway. ResultsIn vitro purified MEPM cells expressed cell surface markers similar to those of mouse bone marrow stem cells. Additionally, in vitro MEPM cells were ectomesenchymal and expressed the neural-crest marker human natural killer-1 (HNK-1), the mesodermal marker vimentin, and the ectodermal marker nestin. They were also positive for in vitro MEPM markers, including platelet-derived growth factor alpha (PDGFR), ephrin B1 (Efnb1), odd-skipped related 2 (Osr2), and Meox2, as well as for stemness markers including POU class 5 homeobox 4 (Oct4), Nanog, and sex-determining region Y-related HMG box 2 (Sox2). MEPM cell pluripotency was retained through activation of the PTEN/Akt/mTOR autophagic signaling pathway. We found that atRA blocked MEPM cell pluripotency to inhibit osteogenic differentiation via miR-106a-5p targeting of PTEN mRNA and subsequent suppression of the PTEN/Akt/mTOR autophagic pathway. ConclusionsIn vitro cultured MEPM cells are ectomesenchymal stem cells that have strong osteogenic differentiation potential, and MEPM pluripotency is regulated by autophagy via the PTEN/AKT/mTOR signaling pathway. atRA disrupts MEPM cell pluripotency through PTEN/AKT/mTOR signaling inactivation where miR-106a-5p targets PTEN mRNA to reduce osteogenic differentiation of MEPM cells and results in the development of cleft palates. Our findings provide new insight into the mechanism underlying the development of cleft palate, and miR-106a-5p may act as a prenatal screening biomarker for cleft palate as well as a new diagnostic and therapeutic target.

developmental biology↗