Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization

Magi, Shigeyuki; Iwamoto, Kenji; Yumoto, Noriko; Hiroshima, Michio; Nagashima, Takeshi; Ohki, Rieko; García-Muñoz, Amaya; Volinsky, Natalia; Kriegsheim, Alex von; Sako, Yasushi; Takahashi, Koichi; Kimura, Shiushichi; Kholodenko, Boris N.; Okada‐Hatakeyama, Mariko

doi:10.1074/jbc.m117.778399

Cited by 11 publications

(13 citation statements)

References 51 publications

(42 reference statements)

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“…It has been implicated in the regulation of cell death and suppression of metastasis in cancers [33–35], and its expression is significantly reduced in human cancers [36, 37]. PHLDA1 overexpression significantly inhibits the phosphorylation and activation of Akt (also known as protein kinase B) [36, 38]. Akt1 may act to enhance HIV replication through promoting the survival of infected cells [39].…”

Section: Discussionmentioning

confidence: 99%

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

et al. 2019

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BackgroundIn the absence of antiretroviral treatments (ARTs), a small group of individuals infected with HIV, including long-term non-progressors (LTNPs) who maintain high levels of CD4+ T cells for more than 7–10 years in the absence of ART and in particular a subgroup of LTNPs, elite controllers (ECs), who have low levels of viremia, remain clinically and/or immunologically stable for years. However, the mechanism of stable disease progression in LTNPs and ECs needs to be elucidated to help those infected with HIV-1 remain healthy. In this study, to identify the characteristics of gene expression profiles and biomarkers in LTNPs, we performed a meta-analysis using multiple gene expression profiles among LTNPs, individuals infected with HIV-1 without ART, individuals infected with HIV-1 with ART, and healthy controls.MethodsThe gene expression profiles obtained from the Gene Expression Omnibus (GEO) microarray data repositories were classified into three groups: LTNPs versus healthy controls (first group, 3 studies), LTNPs versus patients infected with HIV-1 without ART (second group, 3 studies), and LTNPs versus patients infected with HIV-1 with ART (third group, 3 studies). In addition, we considered a fourth group, patients infected with HIV-1 without ART versus healthy controls (3 studies), to exclude genes associated with HIV-1 infection in the three groups. For each group, we performed a meta-analysis using the RankProd method to identify and compare the differentially expressed genes (DEGs) in the three groups.ResultsWe identified the 14 common DEGs in the three groups when comparing them with each other. Most belonged to immune responses, antigen processing and presentation, the interferon-gamma-mediated signaling pathway, and T cell co-stimulation. Of these DEGs, PHLDA1 was up-regulated and ACTB and ACTG1 were down-regulated in all three groups. However, the rest of the up- or down-regulated genes were discordant in the three groups. Additionally, ACTB and ACTG1 are known to inhibit viral assembly and production, and THBS1 is known to inhibit HIV-1 infection.ConclusionsThese results suggest that significant genes identified in a meta-analysis provide clues to the cause of delayed disease progression and give a deeper understanding of HIV pathogenesis in LTNPs.Electronic supplementary materialThe online version of this article (10.1186/s12920-018-0443-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

et al. 2019

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“…Gene silencing in MCF-7 cells performed by reverse transfection of 30 nM of SMARTpool ON-TARGETplus siRNA (Horizon Discovery, UK), targeting PML (L-006547-00) and Non-targeting Pool (D-001810-10) in 96well-plates, as described previously 51 .…”

Section: Gene Silencing With Sirnamentioning

confidence: 99%

A Combination Approach of Pseudotime Analysis And Mathematical Modeling For Understanding The Drug-Resistant Mechanisms

Magi

Ukai

et al. 2021

Preprint

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Cancer cells acquire drug resistance through the following nonresistant, pre-resistant, and resistant stages. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation. The other showed high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.

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“…However, during this process, we found that the integration of independently developed mathematical models is not simple. Because an integrated model becomes extremely large, the network contains multiple non-linear regulatory mechanisms such as feedback loops [ 21 , 22 , 23 , 24 , 25 ], and the available experimental data is limited. Therefore, it was difficult to obtain reproducible parameters (e.g., rate constants of enzymes and protein interactions) by minimizing an objective function, i.e., the distance between simulated values and experimental data [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway

Imoto

Zhang

Okada

2020

Cancers

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A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

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Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization

Cited by 11 publications

References 51 publications

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

A Combination Approach of Pseudotime Analysis And Mathematical Modeling For Understanding The Drug-Resistant Mechanisms

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway

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