Anti‑fibrosis activity of combination therapy with epigallocatechin gallate, taurine and genistein by regulating glycolysis, gluconeogenesis, and ribosomal and lysosomal signaling pathways in HSC‑T6 cells

Li, Yan; Zhu, Min; Huo, Yani; Zhang, Xuerong; Liao, Ming

doi:10.3892/etm.2018.6743

Cited by 9 publications

(6 citation statements)

References 76 publications

(69 reference statements)

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“…A principal component analysis (PCA) was used to confirm that perturbation profiles of Mta1, Akap7 and Zranb1 differ most from that of Cftr , while the perturbation profiles of Krcc1 and Rps8 are relatively more similar to that of Cftr ( Supplementary Figure S4 ). The two genes closer to Cftr, Krcc1 and Rps8 have been reported to play a role in regulating cellular plasticity and fibrotic process [54, 55]. These results suggest that scTenifoldKnk specifically reveals gene function using the KO gene’s perturbation profile rather than the KO gene’s expression profile.…”

Section: Resultsmentioning

confidence: 99%

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scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2021

Preprint

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Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

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Section: Resultsmentioning

confidence: 99%

“…Both Krcc1 and Rps8 have been reported to play a role in regulating cellular plasticity and fibrotic process. 35,36 Duchenne muscular dystrophy…”

Section: Cystic Fibrosismentioning

confidence: 99%

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2021

Preprint

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“…Both Krcc1 and Rps8 have been reported to play a role in regulating cellular plasticity and fibrotic process. 34 , 35 …”

Section: Resultsmentioning

confidence: 99%

“…Both Krcc1 and Rps8 have been reported to play a role in regulating cellular plasticity and fibrotic process. 34,35 Duchenne muscular dystrophy Duchenne muscular dystrophy (DMD) arises as a result of mutations in the open reading frame of DMD. 36,37 The DMD gene encodes dystrophin, a large cytoskeletal structural protein, mostly absent in DMD patients.…”

Section: Articlementioning

confidence: 99%

scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2022

Patterns

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“…Despite its promise at present, significant expense and effort are required to validate efficacy of potential anti-fibrotics at different doses and in several rodent fibrosis models. In addition, noninvasive biomarkers is needed for the quantification of fibrogenesis, and liver function 143 . One of the difficulties on the way of the therapies is insufficient drug accumulation at the target site because of reduced hepatic blood flow 144 .…”

Section: Combination Therapymentioning

confidence: 99%

Efficient drug and gene delivery to liver fibrosis: rationale, recent advances, and perspectives

Mahdinloo

Kiaie

Amiri

et al. 2020

Acta Pharmaceutica Sinica B

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Liver fibrosis results from chronic damages together with an accumulation of extracellular matrix, and no specific medical therapy is approved for that until now. Due to liver metabolic capacity for drugs, the fragility of drugs, and the presence of insurmountable physiological obstacles in the way of targeting, the development of efficient drug delivery systems for anti-fibrotics seems vital. We have explored articles with a different perspective on liver fibrosis over the two decades, then collected and summarized the information by providing corresponding in vitro and in vivo cases. We have discussed the mechanism of hepatic fibrogenesis with different ways of fibrosis induction in animals. Furthermore, the critical chemical and herbal anti-fibrotics, biological molecules such as micro-RNAs, siRNAs, and growth factors, which can affect cell division and differentiation, are mentioned. Likewise, drug and gene delivery and therapeutic systems on in vitro and in vivo models are summarized in the data tables. This review article enlightens recent advances in emerging drugs and nanocarriers and represents perspectives on targeting strategies employed in liver fibrosis treatment.

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Anti‑fibrosis activity of combination therapy with epigallocatechin gallate, taurine and genistein by regulating glycolysis, gluconeogenesis, and ribosomal and lysosomal signaling pathways in HSC‑T6 cells

Cited by 9 publications

References 76 publications

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Efficient drug and gene delivery to liver fibrosis: rationale, recent advances, and perspectives

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