Pemphigus vulgaris autoantibodies induce apoptosis in HaCaT keratinocytes

Pemphigus vulgaris (PV) is an autoimmune disease characterized by binding of IgG autoantibodies to epidermal keratinocyte desmosomes. IgG autoantibodies obtained from a patient with mucocutaneous PV reacted with plakoglobin (Plkg) in addition to desmoglein-3 (Dsg3) and Dsg1. Immunofluorescence analysis confirmed that IgG autoantibodies, unlike antibodies from a healthy volunteer, caused disruption of cell-cell contacts in HaCaT keratinocytes. Moreover, apoptosis was enhanced in cells treated with autoantibodies compared to those treated with normal antibodies. The apoptotic process induced by IgG autoantibodies was characterized by caspase-3 activation, Bcl-2 depletion and Bax expression. The present report demonstrates that PV IgG autoantibodies promote apoptosis in HaCaT keratinocytes

Revising endosomal trafficking under insulin receptor activation

The endocytosis of ligand-bound receptors and their eventual recycling to the plasma membrane (PM) are processes that have an influence on signalling activity and therefore on many cell functions, including migration and proliferation. Like other tyrosine kinase receptors (TKR), the insulin receptor (INSR) has been shown to be endocytosed by clathrin-dependent and -independent mechanisms. Once at the early endosome (EE), the sorting of the receptor, either to the late endosome (LE) for degradation or back to the PM through slow or fast recycling pathways, will determine the intensity and duration of insulin effects. Both the endocytic and the endosomic pathways are regulated by many proteins, the Arf and Rab families of small GTPases being some of the most relevant. Here, we argue for a specific role for the slow recycling route, whilst we review the main molecular mechanisms involved in INSR endocytosis, sorting and recycling, as well as their possible role in cell functions

AGAP2: modulating TGF beta 1-Signaling in the regulation of liver fibrosis

AGAP2 (Arf GAP with GTP-binding protein-like domain, Ankyrin repeat and PH domain 2) isoform 2 is a protein that belongs to the Arf GAP (GTPase activating protein) protein family. These proteins act as GTPase switches for Arfs, which are Ras superfamily members, being therefore involved in signaling regulation. Arf GAP proteins have been shown to participate in several cellular functions including membrane trafficking and actin cytoskeleton remodeling. AGAP2 is a multi-tasking Arf GAP that also presents GTPase activity and is involved in several signaling pathways related with apoptosis, cell survival, migration, and receptor trafficking. The increase of AGAP2 levels is associated with pathologies as cancer and fibrosis. Transforming growth factor beta-1 (TGF-beta 1) is the most potent pro-fibrotic cytokine identified to date, currently accepted as the principal mediator of the fibrotic response in liver, lung, and kidney. Recent literature has described that the expression of AGAP2 modulates some of the pro-fibrotic effects described for TGF-beta 1 in the liver. The present review is focused on the interrelated molecular effects between AGAP2 and TGF beta 1 expression, presenting AGAP2 as a new player in the signaling of this pro-fibrotic cytokine, thereby contributing to the progression of hepatic fibrosis

Pemphigus vulgaris autoantibodies induce apoptosis in HaCaT keratinocytes

Pemphigus vulgaris (PV) is an autoimmune disease characterized by binding of IgG autoantibodies to epidermal keratinocyte desmosomes. IgG autoantibodies obtained from a patient with mucocutaneous PV reacted with plakoglobin (Plkg) in addition to desmoglein-3 (Dsg3) and Dsg1. Immunofluorescence analysis confirmed that IgG autoantibodies, unlike antibodies from a healthy volunteer, caused disruption of cell-cell contacts in HaCaT keratinocytes. Moreover, apoptosis was enhanced in cells treated with autoantibodies compared to those treated with normal antibodies. The apoptotic process induced by IgG autoantibodies was characterized by caspase-3 activation, Bcl-2 depletion and Bax expression. The present report demonstrates that PV IgG autoantibodies promote apoptosis in HaCaT keratinocytes

Tumor necrosis factor α down-regulates expression of the α1(I) collagen gene in rat hepatic stellate cells through a p20C/EBPβ- and C/EBPδ-dependent mechanism

Tumor necrosis factor α (TNF-α) is one of the key cytokines of the acute phase response and of many inflammatory processes. This cytokine has several antifibrogenic actions and down-regulates the expression of the type I collagen genes and induces the expression of metalloproteinases. Because TNF-α directly antagonizes some fibrogenic actions of transforming growth factor β1 (TGF-β1), we considered it important to map the cis-acting regulatory element of the α1(I) collagen (col1a1) promoter involved in TNF- α responsiveness in hepatic stellate cells (HSC), to investigate the transcription factors that bind to it, and to establish possible mechanisms by which TNF-α downregulates its expression. In this article, we show the presence of a functional TNF-α-responsive element (TaRE) in the -378 to -345 region of the col1a1 promoter. This element colocalizes with a previously reported TGF-β1-responsive element. We further demonstrate that TNF-α induces nuclear translocation and binding of transcriptional complexes containing p20C/EBPβ, p35C/EBPβ, and C/EBPδ to this sequence of the promoter. Transient overexpression of C/EBPδ or p20C/EBPβ, the natural dominant negative form of C/EBPβ in HSC, down-regulated activity of a CAT reporter vector driven by -412 to +110 of the col1a1 promoter. Taken together, these data suggest that the -378 to -340 region of the col1a1 promoter is the site of convergence of different stimuli that ultimately modulate col1a1 gene transcription

Tumor necrosis factor ? down-regulates expression of the ?1(I) collagen gene in rat hepatic stellate cells through a p20C/EBP?- and C/EBP?-dependent mechanism

Tumor necrosis factor ? (TNF-?) is one of the key cytokines of the acute phase response and of many inflammatory processes. This cytokine has several antifibrogenic actions and down-regulates the expression of the type I collagen genes and induces the expression of metalloproteinases. Because TNF-? directly antagonizes some fibrogenic actions of transforming growth factor ?1 (TGF-?1), we considered it important to map the cis-acting regulatory element of the ?1(I) collagen (col1a1) promoter involved in TNF- ? responsiveness in hepatic stellate cells (HSC), to investigate the transcription factors that bind to it, and to establish possible mechanisms by which TNF-? downregulates its expression. In this article, we show the presence of a functional TNF-?-responsive element (TaRE) in the -378 to -345 region of the col1a1 promoter. This element colocalizes with a previously reported TGF-?1-responsive element. We further demonstrate that TNF-? induces nuclear translocation and binding of transcriptional complexes containing p20C/EBP?, p35C/EBP?, and C/EBP? to this sequence of the promoter. Transient overexpression of C/EBP? or p20C/EBP?, the natural dominant negative form of C/EBP? in HSC, down-regulated activity of a CAT reporter vector driven by -412 to +110 of the col1a1 promoter. Taken together, these data suggest that the -378 to -340 region of the col1a1 promoter is the site of convergence of different stimuli that ultimately modulate col1a1 gene transcription

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac