Biology subjects

zhang, b.

Publications and source records attributed to zhang, b..

3 recordsLinked to original sources

bioRxiv2025

Analysis of Nutritional Components in Brown Sugar and Its Effects on Anti-fatigue, Alleviation of Qi Deficiency and Blood Stasis, and Antioxidant Effects

Brown sugar (BS), Red granulated sugar (RGS) and White sugar(WS) are three key sweeteners and food additives in China. Notably, BS possesses significant nutritional value and has been widely used in Chinese medicine for its supportive therapeutic properties.This study evaluated the anti-fatigue, the amelioration of qi deficiency and blood stasis, as well as the antioxidant activities of these sugars using a zebrafish model. BS and RGS demonstrated significantly greater efficacy than WS in terms of both anti-fatigue effects and amelioration of qi Deficiency and blood stasis in the zebrafish model. Antioxidant assays revealed that BS, RGS, and WS (500 g/mL) significantly enhanced superoxide dismutase (SOD) activity in zebrafish embryos compared with the control group. Furthermore, the mineral composition of sugar were determined. Nine mineral elements--potassium (K), calcium (Ca), sodium (Na), magnesium (Mg), iron (Fe), zinc (Zn), copper (Cu), phosphorus (P), and selenium (Se)--were analyzed. BS exhibited markedly higher concentrations of P, Ca, Fe, and Mg than RGS and WS, while selenium (detected at 50 mg/kg) was exclusively detected in BS. Intriguingly, RGS contained four-fold higher potassium levels than BS. Total polyphenol content, quantified by Folin-Ciocalteu assay, was 4.35 g/kg in BS and 1.53 g/kg in RGS, with no detectable polyphenols in WS.

biochemistry↗

bioRxiv2022

Chaperone-mediated Autophagy Deficiency Reprograms Cancer Metabolism Via TGFβ Signaling to drive Mesenchymal Tumor Growth

The role of chaperone-mediated autophagy (CMA) in cancer initiation and progression is not well understood due to the lack of a loss-of-function cancer models of LAMP2A, the key regulator of this process. Here, by generating an isoform-specific knockout of LAMP2A, we show that CMA deficiency promotes proliferation and tumor growth in human cancers of mesenchymal origin. Accordingly, we observed that LAMP2A diminishes in metastatic lesions compared to matched primary human tumors from the same patients. Loss of CMA enhanced TGF{beta} signaling in tumors, rewired the tumor metabolome to promote anabolic pathways and mitochondrial metabolism, meeting the metabolic requirements of rapid growth. Mechanistically, we show that TGF{beta}R2 enhances the enzymatic activity of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the pentose phosphate pathway (PPP), to promote the generation of nucleotides. Consequently, pharmacological inhibition of TGF{beta}-signaling in LAMP2A-KO cells suppresses G6PD activity, mitochondrial metabolism, and proliferation to WT levels. Conversely, pharmacological inhibition of mitochondrial metabolism suppressed LAMP2A-KO driven proliferation. Overall, our study provides a molecular mechanism on the CMAs tumor-suppressive function by connecting two important oncogenic pathways, the TGF{beta} signaling and PPP metabolism, to the loss-of-function LAMP2A in mesenchymal cancer types.

cancer biology↗

bioRxiv2021

Multi-task deep learning for concurrent prediction of protein structural properties

Protein structural properties are diverse and have the characteristics of spatial hierarchy, such as secondary structures, solvent accessibility and backbone angles. Protein tertiary structures are formed in close association with these features. Separate prediction of these structural properties has been improved with the increasing number of samples of protein structures and with advances in machine learning techniques, but concurrent prediction of these tightly related structural features is more useful to understand the overall protein structure and functions. We introduce a multi-task deep learning method for concurrent prediction of protein secondary structures, solvent accessibility and backbone angles ({phi}, {psi}). The new method has main two deep network modules: the first one is designed as a DenseNet architecture a using bidirectional simplified GRU (GRU2) network, and the second module is designed as an updated Google Inception network. The new method is named CRRNN2. CRRNN2 is trained on 14,100 protein sequences and its prediction performance is evaluated by testing on public benchmark datasets: CB513, CASP10, CASP11, CASP12 and TS1199. Compared with state-of-the-art methods, CRRNN2 achieves similar, or better performance on the prediction of 3- and 8-state secondary structures, solvent accessibility and backbone angles ({phi}, {psi}). Online CRRN-N2 applications, datasets and standalone software are available at http://qianglab.scst.suda.edu.cn/crrnn2/.

bioinformatics↗