The mTORC1/4E-BP1 axis represents a critical signaling node during fibrogenesis

Myofibroblasts are the key effector cells responsible for excessive extracellular matrix deposition in multiple fibrotic conditions, including idiopathic pulmonary fibrosis (IPF). The PI3K/Akt/mTOR axis has been implicated in fibrosis, with pan-PI3K/mTOR inhibition currently under clinical evaluation in IPF. Here we demonstrate that rapamycin-insensitive mTORC1 signaling via 4E-BP1 is a critical pathway for TGF-β1 stimulated collagen synthesis in human lung fibroblasts, whereas canonical PI3K/Akt signaling is not required. The importance of mTORC1 signaling was confirmed by CRISPR-Cas9 gene editing in normal and IPF fibroblasts, as well as in lung cancer-associated fibroblasts, dermal fibroblasts and hepatic stellate cells. The inhibitory effect of ATP-competitive mTOR inhibition extended to other matrisome proteins implicated in the development of fibrosis and human disease relevance was demonstrated in live precision-cut IPF lung slices. Our data demonstrate that the mTORC1/4E-BP1 axis represents a critical signaling node during fibrogenesis with potential implications for the development of novel anti-fibrotic strategies.


LC-MS/MS analysis
Samples were dried in vacuum and resuspended in 0.05 % trifluoroacetic acid in water.
Of the sample, 50% was injected into an Ultimate3000 nanoRLSC HPLC (Dionex) coupled to a Q-Exactive Mass Spectrometer (ThermoFisher Scientific). Peptides were trapped on a 5 mm x 300 μm C18 column (Pepmap100, 5 μm, 300 Å, Thermo Fisher Scientific) in water with 0.05 % TFA at 60 °C. Separation was performed on custom 50 cm × 100 μm (ID) reversed-phase columns (Reprosil) at 55°C. Gradient elution was performed from 2% acetonitrile to 40% acetonitrile in 0.1% formic acid and 3.5 % DMSO over 2 hours. Samples were online injected into Q-Exactive plus mass spectrometers operating with a data-dependent top 10 method. MS spectra were acquired by using 70.000 resolution and an ion target of 3x10^6. Higher energy collisional dissociation (HCD) scans were performed with 33% NCE at 35.000 resolution (at m/z 200), and the ion target settings was set to 2x10^5 so as to avoid coalescence 4 .
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD010164 5 .

Protein identification and quantification:
Mascot 2.4 (Matrix Science) was used for protein identification by using a 10 p.p.m.
mass tolerance for peptide precursors and 20 mD (HCD) mass tolerance for fragment ions. Carbamidomethylation of cysteine residues and TMT modification of lysine residues were set as fixed modifications and methionine oxidation, and N-terminal acetylation of proteins and TMT modification of peptide N-termini were set as variable modifications. The search database consisted of a customized version of the International Protein Index protein sequence database combined with a decoy version of this database created by using a script supplied by Matrix Science. Unless stated otherwise, we accepted protein identifications as described 4 . Reporter ion intensities were read from raw data and multiplied with ion accumulation times (the unit is milliseconds) so as to yield a measure proportional to the number of ions; this measure is referred to as ion area 4 . Spectra matching to peptides were filtered according to the following criteria: mascot ion score >15, signal-to-background of the precursor ion > 4, and signal-to-interference > 0.5 6 . Fold changes were corrected for isotope purity as described and adjusted for interference caused by co-eluting nearly isobaric peaks as estimated by the signal-to-interference measure 7 . Protein quantification was derived from individual spectra matching to distinct peptides by using a sum-based bootstrap algorithm; 95% confidence intervals were calculated for all protein fold changes that were quantified with more than three spectra 6 . Protein fold changes were only reported for proteins with at least 2 quantified unique peptide (QUP) matches.

Statistical analysis of MS data
Proteins quantified with at least 2 unique peptide matches were divided into bins. The bins are constructed according to the number of quantified spectrum sequence matches. Each bin consists of at least 300 proteins. This data quality-dependent binning strategy is analogous to the procedure described previously 8 . For each protein fold change (FC) a p-value is calculated using a Z-test with a robust estimation of the mean and standard deviation (using the 15.87, 50, and 84.13 percentiles). The standard deviation is calculated, per bin, from a distribution of proteins log2 transformed fold changes. Subsequently, an adjustment for multiple hypothesis testing was performed on the full data set by using Benjamini-Hochberg (BH) correction 9 . Proteins are considered significantly regulated when p-value <0.05 and ≥30 % change in relative abundance with same direction in both replicates.

MS data representation
Treatment effects were summarised as ratio to DMSO or TGF-β1 for each condition.

Supplementary Figure 1. Structures of pharmacological inhibitors used in fibroblast culture and tissue slice studies.
Supplementary    Supplementary Figure 5. Effect of doxycycline treatment on untransduced control pHLFs. pHLFs expressing 4E-BP1-4A dominant negative phosphomutant and untransduced pHLFs were treated with doxycycline 1μg/ml for 24 hours prior to cell lysis and analysis of 4E-BP1 expression using Western blot (a). Additionally, untransduced pHLFs were treated with doxycycline for 24 hours prior to TGF-β1 stimulation for 72 hours with collagen deposition assessed by macromolecular crowding assay. Data are expressed as collagen I signal (n=4 fields of view imaged per well). Data are presented as mean +/-SEM (n=12). Differences between groups were evaluated with two-way ANOVA with Tukey multiple comparison testing. ****=p<0.0001. [CZ415]