Changes in hemicellulose metabolism in banana peel during fruit development and ripening.

Hemicellulose is key in determining the fate of plant cell wall in almost all growth and developmental stages. Nevertheless, there is limited knowledge regarding its involvement in the development and ripening of banana fruit. This study investigated changes in the temporal-spatial distribution of various hemicellulose components, hemicellulose content, activities of the main hydrolysis enzymes, and transcription level of the main hemicellulose-related gene families in banana peels. Both hemicellulose and xylan contents were positively correlated to the fruit firmness observed in our previous study. On the contrary, the xylanase activity was negatively correlated to xylan content and the fruit firmness. The vascular bundle cells, phloem, and cortex of bananas are abundant in xyloglucan, xylan, and mannan contents. Interestingly, the changes in the signal intensity of the CCRC-M104 antibody recognizing non-XXXG type xyloglucan are positively correlated to hemicellulose content. According to RNA-Seq analysis, xyloglucan and xylan-related genes were highly active in the early stages of growth, and the expression of MaMANs and MaXYNs increased as the fruit ripened. The abundance of plant hormonal and growth-responsive cis-acting elements was detected in the 2 kb upstream region of hemicellulose-related gene families. Interaction between hemicellulose and cell wall-specific proteins and MaKCBP1/2, MaCKG1, and MaHKL1 was found. The findings shed light on cell wall hemicellulose's role in banana fruit development and ripening, which could improve nutrition, flavor, and reduce postharvest fruit losses.

Genome-wide identification of XTH gene family in Musa acuminata and response analyses of MaXTHs and xyloglucan to low temperature.

Banana (Musa spp.) production is seriously threatened by low temperature (LT) in tropical and subtropical regions. Xyloglucan endotransglycosylase/hydrolases (XTHs) are considered chief enzymes in cell wall remodelling and play a central role in stress responses. However, whether MaXTHs are involved in the low temperature stress tolerance in banana is not clear. Here, the identification and characterization of MaXTHs were carried out, followed by prediction of their cis-acting elements and protein-protein interactions. In addition, candidate MaXTHs involved in banana tolerance to LT were screened through a comparison of their responses to LT between tolerant and sensitive cultivars using RNA-Seq analysis. Moreover, immunofluorescence (IF) labelling was employed to compare changes in the temporal and spatial distribution of different types of xyloglucan components between these two cultivars upon stress. In total, 53 MaXTHs have been identified, and all were predicted to be located in the cell wall, 14 of them also in the cytoplasm. Only 11 MaXTHs have been found to interact with other proteins. Among 16 MaXTHs with LT responsiveness elements, MaXTH26/29/32/35/50 (Group I/II members) and MaXTH7/8 (Group IIIB members) might be involved in banana tolerance to LT stress. IF results suggested that the content of xyloglucan components recognized by CCRC-M87/103/104/106 antibodies might be negatively related to banana chilling tolerance. In conclusion, we have identified the MaXTH gene family and assessed cell wall re-modelling under LT stress. These results will be beneficial for banana breeding against stresses and enrich the cell wall-mediated resistance mechanism in plants to stresses.

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD.

BACKGROUND: Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment. AIM: To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD. METHODS: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts. RESULTS: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts. CONCLUSION: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.

Identification of Genes Involved in Low Temperature-Induced Senescence of Panicle Leaf in Litchi chinensis.

Warm winters and hot springs may promote panicle leaf growing and repress floral development. To identify genes potentially involved in litchi panicle leaf senescence, eight RNA-sequencing (RNA-Seq) libraries of the senescing panicle leaves under low temperature (LT) conditions and the developing panicle leaves under high temperature (HT) conditions were constructed. For each library, 4.78⁻8.99 × 106 clean reads were generated. Digital expression of the genes was compared between the senescing and developing panicle leaves. A total of 6477 upregulated differentially expressed genes (DEGs) (from developing leaves to senescing leaves), and 6318 downregulated DEGs were identified, 158 abscisic acid (ABA)-, 68 ethylene-, 107 indole-3-acetic acid (IAA)-, 27 gibberellic acid (GA)-, 68 cytokinin (CTK)-, 37 salicylic acid (SA)-, and 23 brassinolide (BR)-related DEGs. Confirmation of the RNA-Seq data by quantitative real-time PCR (qRT-PCR) analysis suggested that expression trends of the 10 candidate genes using qRT-PCR were similar to those revealed by RNA-Seq, and a significantly positive correlation between the obtained data from qRT-PCR and RNA-Seq were found, indicating the reliability of our RNA-Seq data. The present studies provided potential genes for the future molecular breeding of new cultivars that can induce panicle leaf senescence and reduce floral abortion under warm climates.