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DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae.

ABSTRACT: Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 ﬂuorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.Abbreviations: Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7138222 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRecombinant proteins fused with specific cleavage sequences are widely used as substrate for quantitatively analyzing the activity of proteases. Here we propose a new fusion platform for multiple proteases, by using diaminopropionate ammonia-lyase (DAL) as the fusion protein. It was based on the finding that a fused His6-tag could significantly decreases the activities of DAL from E. coli (eDAL) and Salmonella typhimurium (sDAL). Previously, we have shown that His6GST-tagged eDAL could be used to determine the activity of tobacco etch virus protease (TEVp) under different temperatures or in the denaturant at different concentrations. In this report, we will assay different tags and cleavage sequences on DAL for expressing yield in E. coli, stability of the fused proteins and performance of substrate of other common proteases.ResultsWe tested seven different protease cleavage sequences (rhinovirus 3C, TEV protease, factor Xa, Ssp DnaB intein, Sce VMA1 intein, thrombin and enterokinase), three different tags (His6, GST, CBD and MBP) and two different DALs (eDAL and sDAL), for their performance as substrate to the seven corresponding proteases. Among them, we found four active DAL-fusion substrates suitable for TEVp, factor Xa, thrombin and DnaB intein. Enterokinase cleaved eDAL at undesired positions and did not process sDAL. Substitution of GST with MBP increase the expression level of the fused eDAL and this fusion protein was suitable as a substrate for analyzing activity of rhinovirus 3C. We demonstrated that SUMO protease Ulp1 with a N-terminal His6-tag or MBP tag displayed different activity using the designed His6SUMO-eDAL as substrate. Finally, owing to the high level of the DAL-fusion protein in E. coli, these protein substrates can also be detected directly from the crude extract.ConclusionThe results show that our designed DAL-fusion proteins can be used to quantify the activities of both sequence- and conformational-specific proteases, with sufficient substrate specificity.

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DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae.

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