Phenotypic drug screening in a human fibrosis model identified a novel class of antifibrotic therapeutics

Gerckens, Michael; Schorpp, Kenji; Pelizza, Francesco; Wögrath, M.; Reichau, Kora; Ma, Haiying; Dworsky, A.-M.; Sengupta, Arunima; Stoleriu, Mircea Gabriel; Heinzelmann, Katharina; Merl-Pham, Juliane; Irmler, Martin; Alsafadi, Hani N.; Trenkenschuh, Eduard; Sarnová, Lenka; Jiroušková, Markéta; Frieß, Wolfgang; Hauck, Stefanie M.; Beckers, Johannes; Kneidinger, Nikolaus; Behr, Jürgen; Hilgendorff, Anne; Hadian, Kamyar; Lindner, Michael; Königshoff, Mélanie; Eickelberg, Oliver; Gregor, Martin; Plettenburg, Oliver; Yildirim, Ali Önder; Burgstaller, Gerald

doi:10.1126/sciadv.abb3673

Cited by 19 publications

(13 citation statements)

References 74 publications

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“…1A). To analyze antifibrotic drug effects, we cotreated FC-stimulated hPCLS with the clinically approved antifibrotic drug nintedanib ( 3 ), as well as an N -(2-butoxyphenyl)-3-(phenyl)acrylamide (N23P) derivative of tranilast (CMP4), which we recently identified as an antifibrotic compound ( 24 ). After treatment, we performed scRNA-seq using the 10x Genomics Chromium platform (Fig.…”

Section: Resultsmentioning

confidence: 99%

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Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Lang,

Gote-Schniering,

Porras-Gonzalez

et al. 2023

Sci. Transl. Med.

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Pulmonary fibrosis develops as a consequence of failed regeneration after injury. Analyzing mechanisms of regeneration and fibrogenesis directly in human tissue has been hampered by the lack of organotypic models and analytical techniques. In this work, we coupled ex vivo cytokine and drug perturbations of human precision-cut lung slices (hPCLS) with single-cell RNA sequencing and induced a multilineage circuit of fibrogenic cell states in hPCLS. We showed that these cell states were highly similar to the in vivo cell circuit in a multicohort lung cell atlas from patients with pulmonary fibrosis. Using micro-CT–staged patient tissues, we characterized the appearance and interaction of myofibroblasts, an ectopic endothelial cell state, and basaloid epithelial cells in the thickened alveolar septum of early-stage lung fibrosis. Induction of these states in the hPCLS model provided evidence that the basaloid cell state was derived from alveolar type 2 cells, whereas the ectopic endothelial cell state emerged from capillary cell plasticity. Cell-cell communication routes in patients were largely conserved in hPCLS, and antifibrotic drug treatments showed highly cell type–specific effects. Our work provides an experimental framework for perturbational single-cell genomics directly in human lung tissue that enables analysis of tissue homeostasis, regeneration, and pathology. We further demonstrate that hPCLS offer an avenue for scalable, high-resolution drug testing to accelerate antifibrotic drug development and translation.

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Section: Resultsmentioning

confidence: 99%

“…8). As a proof of concept, we analyzed the effects of the clinically approved antifibrotic drug nintedanib ( 3 ) and the tranilast-derivative CMP4, which we recently have identified as an antifibrotic drug candidate ( 24 ).…”

Section: Resultsmentioning

confidence: 99%

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Lang,

Gote-Schniering,

Porras-Gonzalez

et al. 2023

Sci. Transl. Med.

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“…We determined drug concentrations via a literature search, prioritizing concentrations that yielded significant effects in vitro in fibroblasts or similar cell types. The drugs with their respective concentrations are as follows: [0.25,1,2] µg/ml of anakinra (Kineret, SOBI Inc.), [1,5,10] µM valsartan (Sigma-Aldrich, SML0142-10MG), [0.2,1,2] µM BNP (Sigma-Aldrich, B5900-.5MG), [1,5,10]µM valsartan combos respectively with [0.2,1,2] µM BNP, [10,30,60]mM glutathione (Sigma-Aldrich, G4251-1G), [1,3,5] µM CW-HM12 (Cayman Chemical Company, 19480), [10,20,50] µM salbutamol (Sigma-Aldrich, S8260-25MG), [5,10,25] µM marimistat (Sigma-Aldrich, M2699-5Mg), [1,5,10] µM galunisertib (Selleck Chemicals, S2230), [12.5,25,50] µM fasudil (Sigma-Aldrich, CDS021620-10MG), [10,25,50 ]µM SB203580 (Sigma-Aldrich, S8307-1MG), [1,5,10] mg/mL pirfenidone (Sigma-Aldrich, P2116-10MG), [5,10,20] µM defactinib (MedChem Express, HY-12289A), [5,10,20] µM WH-4-023 (Sigma-Aldrich, SML1334-5MG), and 20 µM LY294002 (Selleck Chemicals, S1105). Cells were grown in these conditions for 72 hours.…”

Section: Methodsmentioning

confidence: 99%

“…While high expression of these markers provides an initial indication of myofibroblast activation, traditional marker expression is inconsistent and does not fully capture the fibrotic response 7 . Recent studies of fibroblast phenotype have shown that fibroblasts exhibit high phenotypic heterogeneity across many facets in response to injury, and that phenotypic changes are also sensitive to drug perturbations [8][9][10][11] . Identifying drugs that regulate fibroblast signaling may provide targeted control of fibrosis.…”

Section: Introductionmentioning

confidence: 99%

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Nelson

Christiansen

Naegle

et al. 2023

Preprint

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Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce F-actin assembly and F-actin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces F-actin fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.SignificanceCardiac fibrosis is a dysregulation of the normal wound healing response, resulting in excessive scarring and cardiac dysfunction. As cardiac fibroblasts primarily regulate this process, we explored how candidate anti-fibrotic drugs alter the fibroblast phenotype. We identify a set of 137 phenotypic features that change in response to drug treatments. Using a new computational modeling approach termed logic-based mechanistic machine learning, we predict how pirfenidone and Src inhibition affect the regulation of the phenotypic features F-actin assembly and F-actin stress fiber formation. We also show that inhibition of PI3K reduces F-actin fiber formation and procollagen I production in human cardiac fibroblasts, supporting a role for PI3K as a mechanism by which Src inhibition may suppress fibrosis.

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“…The ECM deposition assay was carried in 384-well CellCarrier plates (PerkinElmer, #6007550), and modified procedures were performed based on the previously described (Gerckens et al, 2021) . Briefly, The plate was coated with 10 μg/mL FN1 (Sigma, # F1141-1MG) for 1 hour at RT before seeding cells.…”

Section: Ecm Depositionmentioning

confidence: 99%

Phosphoproteomics of cellular mechanosensing reveals NFATC4 as a regulator of myofibroblast activity

Mattner

Zeng

Mayr

et al. 2023

Preprint

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Feedback connections between tissue stiffness and cellular contractile forces can instruct cell identity and activity via a process referred to as mechanosensing. Specific phosphoproteome changes during mechanosensing are poorly characterized. In this work, we chart the global phosphoproteome dynamics of primary human lung fibroblasts sensing the stiffness of injury relevant fibronectin coated Poly(dimethylsiloxane) substrates. We discovered a key signaling threshold at a Young's modulus of eight kPa stiffness, above which cells activated a large number of pathways including RhoA, CK2A1, PKA, AMPK, AKT1, and Hippo-YAP1/TAZ mediated signaling. Time-resolved phosphoproteomics of cell spreading on stiff substrates revealed the temporal dynamics of these stiffness-sensitive signaling pathways. ECM substrate stiffness above eight kPA induced fibroblast contractility, cytoskeletal rearrangements, ECM secretion, and a fibroblast to myofibroblast transition. Our data indicate that phosphorylation of the transcriptional regulator NFATC4 at S213/S217 enhances myofibroblast activity, which is the key hallmark of fibrotic diseases. NFATC4 knock down cells display reduced stiffness induced collagen secretion, cell contractility, nuclear deformation and invasion, suggesting NFATC4 as a novel target for antifibrotic therapy.

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Phenotypic drug screening in a human fibrosis model identified a novel class of antifibrotic therapeutics

Abstract: Innovative high-content screening platform identified N -(2-butoxyphenyl)-3-(phenyl)acrylamides as novel antifibrotics.

Cited by 19 publications

References 74 publications

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis

Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts

Phosphoproteomics of cellular mechanosensing reveals NFATC4 as a regulator of myofibroblast activity

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