A large scale proteome analysis of the gefitinib primary resistance overcome by KDAC inhibition in KRAS mutated adenocarcinoma cells overexpressing amphiregulin

et al. 2021

IJMS

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Mutations in the X-linked MECP2 gene are responsible for Rett syndrome (RTT), a severe neurological disorder. MECP2 is a transcriptional modulator that finely regulates the expression of many genes, specifically in the central nervous system. Several studies have functionally linked the loss of MECP2 in astrocytes to the appearance and progression of the RTT phenotype in a non-cell autonomous manner and mechanisms are still unknown. Here, we used primary astroglial cells from Mecp2-deficient (KO) pups to identify deregulated secreted proteins. Using a differential quantitative proteomic analysis, twenty-nine proteins have been identified and four were confirmed by Western blotting with new samples as significantly deregulated. To further verify the functional relevance of these proteins in RTT, we tested their effects on the dendritic morphology of primary cortical neurons from Mecp2 KO mice that are known to display shorter dendritic processes. Using Sholl analysis, we found that incubation with Lcn2 or Lgals3 for 48 h was able to significantly increase the dendritic arborization of Mecp2 KO neurons. To our knowledge, this study, through secretomic analysis, is the first to identify astroglial secreted proteins involved in the neuronal RTT phenotype in vitro, which could open new therapeutic avenues for the treatment of Rett syndrome.

Section: Discussionmentioning

confidence: 99%

Analysis of Astroglial Secretomic Profile in the Mecp2-Deficient Male Mouse Model of Rett Syndrome

Ehinger

Matagne

et al. 2021

IJMS

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“…In order to study the molecular mechanisms by which nanoparticles can act, we carried out a proteomic study “Bottom-up” based on mass spectrometry and iTRAQ isotope labeling followed by OFFGEL fractionation as previously described by Lehmann et al 2017; 2019 and Askri et al 2019 [28,29,30]. The lung, liver and brain were chosen to study their respective proteomes following intranasal exposure to iron NPs for 7 consecutive days at a daily dose of 10 mg/kg.…”

Section: Resultsmentioning

confidence: 99%

“…In order to perform statistical analysis for the quantified proteins we used the R package isobar (version 1.14.0). The statistical approach used through isobar was described in detail in our previous paper [29]. The analysis was performed using a normal fit and proteins which ratio had a p -value ratio and a p -value sample < 0.05 were considered significantly differentially expressed.…”

Section: Methodsmentioning

confidence: 99%

Effects of Iron Oxide Nanoparticles (γ-Fe2O3) on Liver, Lung and Brain Proteomes following Sub-Acute Intranasal Exposure: A New Toxicological Assessment in Rat Model Using iTRAQ-Based Quantitative Proteomics

Askri

Ouni

et al. 2019

IJMS

Iron Oxide Nanoparticles (IONPs) present unique properties making them one of the most used NPs in the biomedical field. Nevertheless, for many years, growing production and use of IONPs are associated with risks that can affect human and the environment. Thus, it is essential to study the effects of these nanoparticles to better understand their mechanism of action and the molecular perturbations induced in the organism. In the present study, we investigated the toxicological effects of IONPs (γ-Fe2O3) on liver, lung and brain proteomes in Wistar rats. Exposed rats received IONP solution during 7 consecutive days by intranasal instillation at a dose of 10 mg/kg body weight. An iTRAQ-based quantitative proteomics was used to study proteomic variations at the level of the three organs. Using this proteomic approach, we identified 1565; 1135 and 1161 proteins respectively in the brain, liver and lung. Amon them, we quantified 1541; 1125 and 1128 proteins respectively in the brain, liver and lung. Several proteins were dysregulated comparing treated samples to controls, particularly, proteins involved in cytoskeleton remodeling, cellular metabolism, immune system stimulation, inflammation process, response to oxidative stress, angiogenesis, and neurodegenerative diseases.

“…The overall workflow of our quantitative proteomic approach for the study is illustrated in Figure 8. Briefly, our approach included a filter-aided sample preparation (FASP) step prior iTRAQ labeling of peptides, followed by a two-step fractionation of labeled peptides (OFFGEL IEF/RP-nano-LC), MALDI-MS/MS analysis, database search and quantitative iTRAQ analysis as already published [121,122].…”

Section: Proteomic Analysismentioning

confidence: 99%

“…step prior iTRAQ labeling of peptides, followed by a two-step fractionation of labeled peptides (OFFGEL IEF/RP-nano-LC), MALDI-MS/MS analysis, database search and quantitative iTRAQ analysis as already published [121,122]. under normoxia and hypoxia.…”

Section: Proteomic Analysismentioning

confidence: 99%

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

Martín-Bernabé

Tarragó-Celada

et al. 2021

IJMS

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Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.