Fig 1: Western blot results for the LAMP2 and CTSL1 proteins in each group after rno-miR-352 intervention(A) Electrophoresis. (B) Chart of statistics. *P < 0.05.
Fig 2: Prediction dataset validation with a cell-free CTSL activity detection system. A, Among the available molecules, the top 50 molecules from the prediction dataset were chosen for verifying the inhibition effect against CTSL in a cell-free system at a single dose of 100 μM. Twelve of the 50 predicted molecules displayed over 50% inhibition against CTSL, and the top 5 were Mg-132, Z-FA-FMK, leupeptin hemisulfate, Mg-101 and calpeptin, with inhibition efficiencies greater than 90%. The data are expressed as the mean of three individual trials. B-F, Five predicted CTSL inhibitors, Mg-132(B), Z-FA-FMK(C), leupeptin hemisulfate(D), Mg-101(E) and calpeptin(F), with inhibition efficiencies greater than 90% at 100 μM were further tested for determination of the half maximal inhibitory concentration (IC50) in the cell-free system. Corresponding molecular structure was drawn by Chemdraw. Non-linear fit to a variable response curve from one representative experiment with three replicates is shown (black lines). The data are expressed as the mean ± s.e.m.
Fig 3: Initial model training and predicting potential CTSL inhibitors from bioactive compound libraries. A, Receiver operating characteristic-area under the curve (ROC-AUC) plot evaluating model performance after training. Blue is the mean of twenty folds (grey). B, Rank-ordered prediction scores of initial prediction dataset that were not present in the training dataset. C, Visualization of all molecules from the initial training dataset (green) and the initial prediction dataset(orange) using t-distributed stochastic neighbor embedding (t-SNE), revealing chemical relationships between these libraries. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig 4: The second model training and the identification of daptomycin. For drug repurposing screening for COVID-19 from the FDA-approved drug library, we trained the second model by adding the experimentally validated molecules from bioactive compounds aforementioned to the initial training dataset. A, The ROC-AUC plot evaluating the second model performance after training. Blue is the mean of twenty folds (grey). B, Rank-ordered prediction scores of the FDA-approved drug library that were not present in the training dataset. C, Visualization of all molecules from the second training dataset (green) and the second prediction dataset(orange) using t-SNE, revealing chemical relationships between these libraries. D, Among the available drugs, the top 50 drugs from the FDA-approved drug library were chosen for verifying the inhibition effect against CTSL in the cell-free system at a single dose of 100 μM. Four of the 50 predicted drugs displayed over 50% inhibition against CTSL, and the top 2 were daptomycin and beta-lapachone, with inhibition efficiencies greater than 90%. The data are expressed as the mean of three individual trials. E-F, Daptomycin(E) and beta-lapachone(F) were further tested for determination of IC50 in the cell-free system. These 2 drugs were used at a concentration ranging from 8 nM and 80 nM to 100 μM, respectively. The IC50 value of this graph is indicated. n = 3. The data are expressed as the mean ± s.e.m. G-H, Inhibition of pseudovirus infection by different doses of Daptomycin (G), and beta-lapachone (H) and viability of Huh7 cells treated with different doses of the drugs as indicated. Non-linear fit to a variable response curve from one representative experiment with four replicates is shown (red lines). Cytotoxic effect on Huh7 cells exposed to increasing concentrations of drugs in the absence of virus is also shown (blue lines). The CC50, EC50, and SI values of this graph are indicated. n = 4. The data are expressed as the mean ± s.e.m. I, Inhibition of pseudovirus infection by different doses of Daptomycin, and viability of A549 cells treated with different doses of the drugs as indicated. Non-linear fit to a variable response curve from one representative experiment with four replicates is shown (red lines). Cytotoxic effect on A549 cells exposed to increasing concentrations of drugs in the absence of virus is also shown (green lines). The CC50, EC50, and SI values of this graph are indicated. n = 5. The data are expressed as the mean ± s.e.m. J, Inhibition of SARS-CoV-2 B.1.351(Bata) variant pseudovirus infection by different doses of Daptomycin, and viability of Huh7 cells treated with different doses of the drugs as indicated. Non-linear fit to a variable response curve from one representative experiment with four replicates is shown (purple lines). Cytotoxic effect on Huh7 cells exposed to increasing concentrations of drugs in the absence of virus is also shown (blue lines). The CC50, EC50, and SI values of this graph are indicated. n = 5. The data are expressed as the mean ± s.e.m. K, Inhibition of SARS-CoV-2 B.1.351(Bata) variant pseudovirus infection by different doses of Daptomycin, and viability of Huh7 cells with TMPRSS2 overexpression. Non-linear fit to a variable response curve from one representative experiment with four replicates is shown (purple lines). Cytotoxic effect on Huh7 cells exposed to increasing concentrations of drugs as indicated is also shown (blue lines). The EC50 values of this graph are indicated. n = 5. The data are expressed as the mean ± s.e.m. L, Inhibition of SARS-CoV-2 B.1.351(Bata) variant pseudovirus infection by different doses of Daptomycin, and viability of A549 cells with TMPRSS2 overexpression. Non-linear fit to a variable response curve from one representative experiment with four replicates is shown (purple lines). Cytotoxic effect on A549 cells exposed to increasing concentrations of drugs as indicated is also shown (green lines). The EC50 values of this graph are indicated. n = 5. The data are expressed as the mean ± s.e.m. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig 5: The predicted CTSL inhibitors from bioactive compounds prevent live SARS-CoV-2 infection in Huh7 cells in vitro. A, Schematic of the predicted CTSL inhibitor assay setup. Huh7 cells were pretreated with different drugs 1 h before infection with live SARS-CoV-2 at the same dose (0.5 moi), followed by changing to fresh medium with the indicated concentrations of drugs 1 h later. The detection of infected cells was performed 48 h later by using an immunofluorescence assay. B-D, Inhibition of live SARS-CoV-2 infection by different doses of Mg-132 (B), Z-FA-FMK (C), and Leupeptin Hemisulfate (D). Non-linear fit to a variable response curve from one representative experiment with three replicates is shown (blue lines). The EC50 value of this graph is indicated. n = 3. The data are expressed as the mean ± s.e.m. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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