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YANG, M.

Publications and source records attributed to YANG, M..

2 recordsLinked to original sources

ATG5- and ATG7-mediated autophagy regulates pollen tube guidance and male fertility in Arabidopsis thaliana

Autophagy functions as a crucial cellular scavenger by targeting cytoplasmic cargo to specific lysosome/vacuole for degradation. Autophagy-related (ATG) core proteins including ATG5 and ATG7 are evolutionarily conserved factors that are spatiotemporally orchestrated to regulate multiple processes of autophagy in yeast, mammalian and plant cells. However, autophagy is believed to be functionally dispensable in Arabidopsis thaliana since severe defects during plant growth, development and reproduction have not been observed in most of the ATG loss-of-function mutants, including atg5 and atg7, under standard cultivation conditions. In this study, we report that autophagy does in fact play roles in regulating pollen tube growth guidance and male fertility in Arabidopsis thaliana. A detailed re-assessment of atg5 and atg7 mutants revealed greatly reduced autophagy activity in germinated pollens and the seed formation within siliques is partially abolished. Next, we demonstrated that both the pollen germination ratio and pollen tube length of the mutants decreased at the beginning of germination by time-lapse tracking of pollen germination in vitro and in vivo. Additionally, we observed occurrences of pollen tube twisting and stacking during their growth towards the ovules. Finally, we found abnormal pollen grains containing only a single sperm cell or an undivided generative nucleus. Collectively, these results indicate that ATG5- and ATG7-mediated autophagy is functionally involved in regulating pollen germination, tube growth guidance and sperm cell development in Arabidopsis thaliana.

plant biology↗

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗