Biology subjects

Ban, Z.

Publications and source records attributed to Ban, Z..

3 recordsLinked to original sources

bioRxiv2024

BPMs regulate Arabidopsis seedling development by promoting auxin-independent degradation of the Aux/IAA protein IAA10

After germination, seedlings undergo etiolated development (skotomorphogenesis), enabling them to grow towards the soil surface. In Arabidopsis, etiolated seedlings exhibit rapid hypocotyl elongation, apical hook formation and closed cotyledons to protect the meristem. In this study, we found that high-order mutants in the BPM gene family displayed defects in seedling development, characterized by a shorter hypocotyl, early apical hook opening, and opened cotyledons in the dark. BPM1, BPM2, BPM4, and BPM5 exhibit distinct expression patterns and subcellular localization in etiolated seedlings. In a hypocotyl segment assay the bpm mutants showed defects in auxin response indicating impaired auxin signaling in the hypocotyl. Expression of the auxin reporter DR5:GFP was also altered in the bpm1,4,5 mutant in various tissues compared to the wild type. Furthermore, we showed that BPM1 and IAA10 interact in yeast two-hybrid, BiFC, and Co-IP assays. Experiments in protoplasts indicated that BPM1 promotes ubiquitylation and degradation of IAA10, and the level of IAA10 protein is greater in the bpm1,4,5 mutant. In addition, IAA10 over-expression resulted in phenotypes similar to the bpm mutants. These results indicate that the BPMs target the Aux/IAA proteins for ubiquitylation and degradation. Overall, our findings shed light on the key roles of the BPMs in auxin signaling during seedling development.

plant biology↗

bioRxiv2024

Unravelling biomolecular and community grammars of RNA granules via machine learning

RNA granules are dynamic compartments within cells that play a crucial role in posttranscriptional regulation of gene expression. They are associated with a variety of human neurodegenerative diseases. While RNA granules play vital roles in cellular functions, the comprehension of their assembly has remained elusive. In this study, we employed robust machine learning models combining residue content and physicochemical features to accurately identify potential RNA granule (i.e., stress granule and P-body) proteome within the human proteome. Our models achieved good performance with high areas under the receiver operating characteristic curve of up to 0.88, outperforming previous liquid-liquid phase separation models. Intriguingly, the predicted RNA granule proteome reveals a significant enrichment in biological functions and domains associated with RNA granule-related processes, mirroring findings from observed high-confidence RNA granule protein datasets. Furthermore, our analysis unveils critical physicochemical attributes, notably hydrophobicity, influencing the formation of RNA granules. Using the constructed model, we uncovered the central roles of RNA granule proteins with high propensities within the comprehensive RNA granule protein-protein interaction (PPI) network and their commonality in diverse RNA granules. Furthermore, we identified prominent clusters with dense PPIs, significantly contributing to critical biological processes within diverse RNA granules, including translation, mRNA decay, rRNA processing, and mRNA splicing. This analysis proposes a hypothesis: dense PPI clusters are integral functional subunits, constituting relatively stable cores within diverse RNA granules. In conclusion, this study provides a comprehensive molecular and community-based foundation for understanding the importance of PPIs in the stability of RNA granule formation and functionality. This analysis contributes to a deeper and more comprehensive understanding of the intricate nature of RNA granules and opens avenues for future research and therapeutic interventions targeting RNA granule- related diseases.

cell biology↗

bioRxiv2019

Effect of different forage-to-concentrate ratios on the structure of rumen bacteria and its relationship with nutrition levels and real-time methane production in sheep

Emission from ruminants has become the largest source of anthropogenic emission of methane in China. The structure of the rumen flora has a significant effect on methane production. To establish a more accurate prediction model for methane production, the rumen flora should be one of the most important parameters. The objective of the present study was to investigate the relationship among changes in rumen flora, nutrient levels, and methane production in sheep fed with the diets of different forage-to-concentration ratios, as well as to screen for significantly different dominant genera. Nine rumen-cannulated hybrid sheep were separated into three groups and fed three diets with forage-to-concentration ratios of 50:50, 70:30, and 90:10. Three proportions of the diets were fed according to a 3 x 3 incomplete Latin square, design during three periods of 15 d each. The ruminal fluid was collected for real-time qPCR, high-throughput sequencing and in vitro rumen fermentation in a new real-time fermentation system wit. Twenty-two genera were screened, the abundance of which varied linearly with forage-to-concentration ratios and methane production. In addition, during the 12-hour in vitro fermentation, the appearance of peak concentration was delayed by 26-27 min with the different structure of rumen bacteria. The fiber-degrading bacteria were positively correlated with this phenomenon, but starch-degrading and protein-degrading bacteria were negative correlated. These results would facilitate macro-control of rumen microorganisms and better management of diets for improved nutrition in ruminants. In addition, our findings would help in screening bacterial genera that are highly correlated with methane production.

microbiology↗