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Jiao, Q.

Publications and source records attributed to Jiao, Q..

3 recordsLinked to original sources

Arabidopsis SUFB and CLP protease regulate SUFBC2D to manipulate iron-sulfur cluster biosynthesis in chloroplast

Iron-sulfur (Fe-S) clusters are essential cofactors for Fe-S proteins. SUFBC2D complex is the scaffold responsible for Fe-S cluster assembly in chloroplasts. However, the regulatory mechanism on SUFBC2D remains elusive. In this study, we report that the transcription of SUFB responds rapidly to leaf senescence, whereas the transcription of SUFC and SUFD does not. Intriguingly, their protein contents remain stable during leaf senescence. We further found that leaf death was occurred only when SUFB RNAi was induced, and SUFB and SUFC contents decreased much faster in the SUFB-RNAi lines than in the SUFC-RNAi lines, indicating that SUFB had a faster turnover rate than SUFC. Moreover, overexpressing SUFB increased the contents of SUFC and SUFD, and SUFBC2D, whereas overexpressing SUFC did not increase SUFB and SUFD. Our findings reveal that SUFB stabilizes SUFC and SUFD via forming SUFBC2D, whereas SUFC lacks this function. Furthermore, SUFB expression was sharply downregulated when the plants were subjected to iron deficiency, whereas SUFC and SUFD expression was not. Interestingly, the contents of all three SUF members decreased, indicating that plants degrade SUFBC2D in response to iron deficiency by downregulating SUFB transcription. We subsequently studied the degradation mechanism of SUFBC2D. Our results indicated that all SUFs are substrates of the caseinolytic protease (CLP) because they all accumulated in the CLP impaired mutant, and they physically interact with CLPS1, the substrate recognition adaptor of CLP. Collectively, our findings provide novel insights into how plants regulate SUFBC2D complex via SUFB to adapt to leaf senescence and iron deficiency.

plant biology↗

Unveiling Fine-scale Spatial Structures and Amplifying Gene Expression Signals in Ultra-Large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large ST data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conque framework specially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential oversmoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulation, HERGAST consistently outperformed other methods across all settings with more than 10% average gaining. In real-world data, HERGASTs high-precision spatial clustering enabled finding SPP1+ macrophages intermingled in tumors in colorectal cancer, while the enhanced gene expression signal enabled discovering unique spatial expression pattern of key genes in breast cancer.

bioinformatics↗

Ontology-Aware Deep Learning Enables Novel Antibiotic Resistance Gene Discovery Towards Comprehensive Profiling of ARGs

Antibiotic resistance genes (ARGs) have emerged in pathogens and arousing a worldwide concern, which is estimated to cause millions of deaths each year globally. Accurately identifying and classifying ARGs is a formidable challenge in studying the generation and spread of antibiotic resistance. Current methods could identify close homologous ARGs, have limited utility for discovery of novel ARGs, thus rendering the profiling of ARGs incomprehensive. Here, an ontology-aware neural network (ONN) approach, ONN4ARG, is proposed for comprehensive ARG discovery. Systematic evaluation shows ONN4ARG is advanced than previous methods such as DeepARG in efficiency, accuracy, and comprehensiveness. Experiments using 200 million candidate microbial genes collected from 815 microbial community samples from diverse environments or hosts have resulted in 120,726 candidate ARGs, out of which more than 20% are not yet present in public databases. These comprehensive set of ARGs have clarified the environment-specific and host-specific patterns. The wet-experimental functional validation, together with structural investigation of docking sites, have also validated a novel streptomycin resistance gene from oral microbiome samples, confirming ONN4ARGs ability for novel ARGs identification. In summary, ONN4ARG is superior to existing methods in efficiency, accuracy, and comprehensiveness. It enables comprehensive ARG discovery, which is helpful towards a grand view of ARGs worldwide. ONN4ARG is available at https://github.com/HUST-NingKang-Lab/ONN4ARG, and online web service is available at http://onn4arg.xfcui.com/.

bioinformatics↗