Loss of Acta2 in cardiac fibroblasts does not prevent the myofibroblast differentiation or affect the cardiac repair after myocardial infarction

Li, Yuxia; Li, Chaoyang; Liu, Qianglin; Wang, Leshan; Bao, Adam X.; Jung, Jangwook P.; Dodlapati, Sanjeev; Sun, Jianfang; Gao, Peidong; Zhang, Xujia; Francis, Joseph; Molkentin, Jeffery D; Fu, Xing

doi:10.1016/j.yjmcc.2022.08.003

Cited by 17 publications

(10 citation statements)

References 45 publications

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“…Actin alpha 2, smooth muscle (ACTA2) encodes one of six different actin proteins ( 19 ). Collagen type I alpha 1 chain (COL1A1) encodes the pro-alpha1 chains of type I collagen whose triple helix comprises two alpha1 chains and one alpha2 chain ( 20 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

Liu,

Yang,

et al. 2024

Front. Med.

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BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal disease of unknown etiology with a poor prognosis, characterized by a lack of effective diagnostic and therapeutic interventions. The role of immunity in the pathogenesis of IPF is significant, yet remains inadequately understood. This study aimed to identify potential key genes in IPF and their relationship with immune cells by integrated bioinformatics analysis and verify by in vivo and in vitro experiments.MethodsGene microarray data were obtained from the Gene Expression Omnibus (GEO) for differential expression analysis. The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. Then their diagnostic significance and potential co-regulators were elucidated. We further analyzed the correlation between key genes and immune infiltrating cells via single-sample gene set enrichment analysis (ssGSEA). Subsequently, a single-cell RNA sequencing data (scRNA-seq) was used to explore which cell types expressed key genes in IPF samples. Finally, a series of in vivo and in vitro experiments were conducted to validate the expression of candidate genes by western blot (WB), quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) analysis.ResultsA total of 647 DEGs of IPF were identified based on two datasets, including 225 downregulated genes and 422 upregulated genes. They are closely related to biological functions such as cell migration, structural organization, immune cell chemotaxis, and extracellular matrix. CFH and FHL2 were identified as key genes with diagnostic accuracy for IPF by three machine learning algorithms. Analysis using ssGSEA revealed a significant association of both CFH and FHL2 with diverse immune cells, such as B cells and NK cells. Further scRNA-seq analysis indicated CFH and FHL2 were specifically upregulated in human IPF tissues, which was confirmed by in vitro and in vivo experiments.ConclusionIn this study, CFH and FHL2 have been identified as novel potential biomarkers for IPF, with potential diagnostic utility in future clinical applications. Subsequent investigations into the functions of these genes in IPF and their interactions with immune cells may enhance comprehension of the disease’s pathogenesis and facilitate the identification of therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

Liu,

Yang,

et al. 2024

Front. Med.

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“…The processes of αSMA protein expression and fiber assembly have different degrees of contribution to pathological fibrosis. For example, a recent study showed that fibroblasts can compensate for the loss of Acta2 transcription and form stress fibers using similar proteins, implying that stress fiber formation is more important than αSMA production for the fibrotic response 46 . Incorporating an expanded set of measurements in future fibrosis studies may provide greater resolution of the fibrotic phenotype in response to therapies and help evaluate changes in pathologically relevant features beyond protein expression.…”

Section: Discussionmentioning

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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“…Lenti-shGFP and Lenti-shDlk1 lentiviruses were produced in 293T cells (ATCC #CRL-3216) using pLKO.1 GFP shRNA (Addgene #30323) and pLKO.1 mouse Dlk1 shRNA (Sigma # TRCN0000095446), respectively. 54 …”

Section: Methodsmentioning

confidence: 99%

Loss of Acta2 in cardiac fibroblasts does not prevent the myofibroblast differentiation or affect the cardiac repair after myocardial infarction

Cited by 17 publications

References 45 publications

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis

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

Tcf21 marks visceral adipose mesenchymal progenitors and functions as a rate-limiting factor during visceral adipose tissue development

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