An outlook on protein S-acylation in plants: what are the next steps?

Hemsley, Piers A.

doi:10.1093/jxb/erw497

Cited by 17 publications

(14 citation statements)

References 71 publications

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“…So far, some machine learning methods, including principal component analysis (PCA), clustering and autoencoder, have been tested to classify cancer types [9,10]. Also, machine learning methods, including support vector machine (SVM), Bayesian network, semi-supervised learning and decision tree, have been applied to cancer prognosis prediction and shown some good success [11][12][13][14][15][16].…”

Section: Current Development In Cancer Prognosis Predictionmentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

263

161

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Current Development In Cancer Prognosis Predictionmentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

263

161

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Section: Resultsmentioning

confidence: 99%

“…Many PM-localized soluble proteins are often modified by S -acylation for anchoring to the membrane 30 – 33 . Upon de-acylation, the proteins are released from the PM to cytoplasm and increased their recruitments to endosomes 30 , 33 , 34 . Thus, if BRAF was a S -acylated protein for PM localization, de-acylation of the PM-localized BRAFs would release from the PM into cytosol, serving as a pool for endosome recruitment, and thus increase the endosome-localized BRAF.…”

Section: Resultsmentioning

confidence: 99%

“…Multiple studies have shown that certain cytoplasmic proteins, including the mammalian Ras proteins 43 , the plant MfNACsa (a NAM/ATAF1/2/CUC2 transcription factor) 44 , and the plant LIP1 (Lost In Pollen Tube Guidance 1) 45 , are S -acylated for anchoring to PM, while the de-acylated proteins are released from the PM to cytosol and increased their recruitments to endomembrane compartments 33 , 43 , 44 , 46 . Based on the findings of this study, we propose a working model of BRAF function in plants (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, since the S -acylation of proteins could be triggered and regulated by certain environmental cues, e.g. drought stress 44 , it is highly speculated that the cells may utilize certain environmental or growth signals to regulate the distributions of BRAF proteins in the cytoplasm, the PM, and the MVB/PVCs by the S -acylation process 31 – 33 , which will be an interesting topic for future study.…”

Section: Discussionmentioning

confidence: 99%

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A plant Bro1 domain protein BRAF regulates multivesicular body biogenesis and membrane protein homeostasis

et al. 2018

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Plant development, defense, and many physiological processes rely on the endosomal sorting complex required for transport (ESCRT) machinery to control the homeostasis of membrane proteins by selective vacuolar degradation. Although ESCRT core components are conserved among higher eukaryotes, the regulators that control the function of the ESCRT machinery remain elusive. We recently identified a plant-specific ESCRT component, FREE1, that is essential for multivesicular body/prevacuolar compartment (MVB/PVC) biogenesis and vacuolar sorting of membrane proteins. Here we identify a plant-specific Bro1-domain protein BRAF, which regulates FREE1 recruitment to the MVB/PVC membrane by competitively binding to the ESCRT-I component Vps23. Altogether, we have successfully identified a role for BRAF, whose function as a unique evolutionary ESCRT regulator in orchestrating intraluminal vesicle formation in MVB/PVCs and the sorting of membrane proteins for degradation in plants makes it an important regulatory mechanism underlying the ESCRT machinery in higher eukaryotes.

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