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

FGF10 mitigates doxorubicin-induced myocardial toxicity in mice via activation of FGFR2b/PHLDA1/AKT axis

Doxorubicin is a common chemotherapeutic agent in clinic, but myocardial toxicity limits its use. Fibroblast growth factor (FGF) 10, a multifunctional paracrine growth factor, plays diverse roles in embryonic and postnatal heart development as well as in cardiac regeneration and repair. In this study we investigated the role of FGF10 as a potential modulator of doxorubicin-induced cardiac cytotoxicity and the underlying molecular mechanisms. Fgf10+/- mice and an inducible dominant negative FGFR2b transgenic mouse model (Rosa26rtTA; tet(O)sFgfr2b) were used to determine the effect of Fgf10 hypomorph or blocking of endogenous FGFR2b ligands activity on doxorubicin-induced myocardial injury. Acute myocardial injury was induced by a single injection of doxorubicin (25 mg/kg, i.p.). Then cardiac function was evaluated using echocardiography, and DNA damage, oxidative stress and apoptosis in cardiac tissue were assessed. We showed that doxorubicin treatment markedly decreased the expression of FGFR2b ligands including FGF10 in cardiac tissue of wild type mice, whereas Fgf10+/- mice exhibited a greater degree of oxidative stress, DNA damage and apoptosis as compared with the Fgf10+/+ control. Pre-treatment with recombinant FGF10 protein significantly attenuated doxorubicin-induced oxidative stress, DNA damage and apoptosis both in doxorubicin-treated mice and in doxorubicin-treated HL-1 cells and NRCMs. We demonstrated that FGF10 protected against doxorubicin-induced myocardial toxicity via activation of FGFR2/Pleckstrin homology-like domain family A member 1 (PHLDA1)/Akt axis. Overall, our results unveil a potent protective effect of FGF10 against doxorubicin-induced myocardial injury and identify FGFR2b/PHLDA1/Akt axis as a potential therapeutic target for patients receiving doxorubicin treatment.

HDAC Inhibition Restores Response to HER2-Targeted Therapy in Breast Cancer via PHLDA1 Induction

The downregulation of Pleckstrin Homology-Like Domain family A member 1 (PHLDA1) expression mediates resistance to targeted therapies in receptor tyrosine kinase-driven cancers. The restoration and maintenance of PHLDA1 levels in cancer cells thus constitutes a potential strategy to circumvent resistance to inhibitors of receptor tyrosine kinases. Through a pharmacological approach, we identify the inhibition of MAPK signalling as a crucial step in PHLDA1 downregulation. Further ChIP-qPCR analysis revealed that MEK1/2 inhibition produces significant epigenetic changes at the PHLDA1 locus, specifically a decrease in the activatory marks H3Kme3 and H3K27ac. In line with this, we show that treatment with the clinically relevant class I histone deacetylase (HDAC) inhibitor 4SC-202 restores PHLDA1 expression in lapatinib-resistant human epidermal growth factor receptor-2 (HER2)+ breast cancer cells. Critically, we show that when given in combination, 4SC-202 and lapatinib exert synergistic effects on 2D cell proliferation and colony formation capacity. We therefore propose that co-treatment with 4SC-202 may prolong the clinical efficacy of lapatinib in HER2+ breast cancer patients.

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer.

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. 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 and another showing 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.

Prognostic and predictive value of Pleckstrin homology-like domain, family A family members in breast cancer

Aim: The PHLDA (pleckstrin homology like domain, family A) gene family encodes proteins capable of inhibiting AKT (serine/threonine kinase) signaling through phosphoinositol binding competition. Results & methodology: Using in silico analysis, we found that Luminal A and B patients' short relapse-free survival was associated with low PHLDA1 or PHLDA3 and high PHLDA2 expression. In a cohort of 393 patients with luminal breast cancer evaluated by immunohistochemistry on tissue microarrays, we found a direct association of PHLDA3 expression with hormonal therapy response (p = 0.013). Conclusion: Our findings provide new information on the role played by the PHLDA family members as prognostic markers in breast cancer, and more importantly, we provide evidence that they might also predict a response to endocrine therapy.

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

Simple Summary

Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.

Abstract

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.

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

Background

In 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.

Methods

The 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.

Results

We 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.

Conclusions

These 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 material

The online version of this article (10.1186/s12920-018-0443-x) contains supplementary material, which is available to authorized users.