GATA-4 Regulates Bcl-2 Expression in Ovarian Granulosa Cell Tumors

Kyrönlahti, Antti; Rämö, M.Pauliina; Tamminen, Maija; Unkila‐Kallio, Leila; Bützow, Ralf; Leminen, Arto; Nemer, Mona; Rahman, Nafis; Huhtaniemi, Ilpo; Heikinheimo, Markku; Anttonen, Mikko

doi:10.1210/en.2008-0148

Cited by 62 publications

(77 citation statements)

References 37 publications

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“…7,29,37,38 The latter were verified to possess the c.402C-G (p.C134W) mutation in FOXL2 5 and the mutation frequency of 95% corresponds to other published GCT series. [2][3][4] All histological diagnoses were carefully reevaluated, 29 patient charts were reviewed to obtain clinical data and recurrences were updated as of September 2009.…”

Section: Materials and Methods Human Tissue Samplesmentioning

confidence: 57%

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Anti-Müllerian hormone inhibits growth of AMH type II receptor-positive human ovarian granulosa cell tumor cells by activating apoptosis

Anttonen

Färkkilä

Tauriala

et al. 2011

Laboratory Investigation

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Ovarian granulosa cell tumors (GCTs) are sex cord stromal tumors that constitute 3-5% of all ovarian cancers. GCTs usually present with an indolent course but there is a high risk of recurrence, which associates with increased mortality, and targeted treatments would be desirable. Anti-Mü llerian hormone (AMH), a key factor regulating sexual differentiation of the reproductive organs, has been implicated as a growth inhibitor in ovarian cancer. GCTs and normal granulosa cells produce AMH, but its expression in large GCTs is usually downregulated. Further, as the lack of specific AMHsignaling pathway components leads to GCT development in mice, we hypothesized that AMH inhibits growth of GCTs. Utilizing a large panel of human GCT tissue samples, we found that AMH type I receptors (ALK2, ALK3 and ALK6) and type II receptor (AMHRII), as well as their downstream effectors Smad1/5, are expressed and active in GCTs. AMHRII expression was detected in the vast majority (96%) of GCTs and correlated with AMH mRNA and protein expression. AMH mRNA level was low in large GCTs, confirming previous findings on low-AMH protein expression in large human as well as mouse GCTs. To study the functional role of AMH in this peculiar ovarian cancer, we utilized a human GCT cell line (KGN) and 10 primary GCT cell cultures. We found that the AMH-Smad1/5-signaling pathway was active in these cells, and that exogenous AMH further activated Smad1/5 in KGN cells. Furthermore, AMH treatment reduced the number of KGN cells and primary GCT cells, with increasing amounts of AMH leading to augmented activation of caspase-3 and subsequent apoptosis. All in all, these data support the premise that AMH is a growth inhibitor of GCTs.

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Section: Materials and Methods Human Tissue Samplesmentioning

confidence: 57%

“…Primers listed in Table 1 were designed with IDT SciTools software or as previously described. 43,44 Western blotting of the protein samples was performed as described 38 with antibodies and dilutions listed in Table 1.…”

Section: Materials and Methods Human Tissue Samplesmentioning

confidence: 99%

Anti-Müllerian hormone inhibits growth of AMH type II receptor-positive human ovarian granulosa cell tumor cells by activating apoptosis

Anttonen

Färkkilä

Tauriala

et al. 2011

Laboratory Investigation

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“…u5SS, 3SS, 5SS and d3SS: intronic regions close to upstream exon's 5′ splice site, target exon's 3′ and 5′ splice sites, and downstream exon's 3′ splice site, respectively. npg A n A ly s i s granulosa cell tumors 38 . We analyzed all COSMIC 69 (ref.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…Given that GATA4 is an upstream regulator of BCL2 and BCLX L in some other cell types including cardiomyocytes, ovarian granulosa cell tumor cells, and lung vascular smooth muscle cells, 22,27 we hypothesized that the alterations in their expression in HUH6 cells during Dox treatment are due to a reduction in GATA4 expression. To this end, we first studied whether Dox treatment affects GATA4 levels in the human HUH6 cells.…”

Section: Gata4 Silencing Alters Expression Of Bcl2 Family Members In mentioning

confidence: 99%

“…25 Along with myocardium, GATA4 regulates BCL2 and BCLX L expression in other normal and neoplastic tissues including lung vascular smooth muscle 26 and ovarian granulosa cell tumors. 22,27 In contrast to most human cell types, normal postnatal hepatocytes lack expression of anti-apoptotic BCL2, presumably due to the slow regeneration of liver tissue. 28 It has been shown that BCL2 is, however, expressed in a subset of HBs.…”

Section: Introductionmentioning

confidence: 99%

Downregulation of transcription factor GATA4 sensitizes human hepatoblastoma cells to doxorubicin-induced apoptosis

et al. 2017

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Hepatoblastoma, the most common type of pediatric liver cancer, is treated with a combination of surgery and chemotherapy. An essential drug in the treatment of hepatoblastoma is doxorubicin, which in high doses is cardiotoxic. This adverse effect is due to downregulation of cardiac expression of transcription factor GATA4, leading in turn to diminished levels of anti-apoptotic BCL2 (B-cell lymphoma 2) protein family members. GATA4 is also expressed in early fetal liver, but absent from normal postnatal hepatocytes. However, GATA4 is highly expressed in hepatoblastoma tissue. In this study, we assessed the role of GATA4 in doxorubicin-induced apoptosis of hepatoblastoma cells. Herein, we demonstrate that doxorubicin decreases GATA4 expression and alters the expression pattern of BCL2 family members, most profoundly that of BCL2 and BAK, in the HUH6 hepatoblastoma cell line. Silencing of GATA4 by siRNA prior to doxorubicin treatment sensitizes HUH6 cells to the apoptotic effect of this drug by further shifting the balance of BCL2 family members to the pro-apoptotic direction. Specifically, expression levels of anti-apoptotic BCL2 were decreased and pro-apoptotic BID were increased after GATA4 silencing. On the whole, our results indicate that since high endogenous levels of transcription factor GATA4 likely protect hepatoblastoma cells from doxorubicin-induced apoptosis, these cells can be rendered more sensitive to the drug by downregulation of GATA4.

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GATA-4 Regulates Bcl-2 Expression in Ovarian Granulosa Cell Tumors

Cited by 62 publications

References 37 publications

Anti-Müllerian hormone inhibits growth of AMH type II receptor-positive human ovarian granulosa cell tumor cells by activating apoptosis

Anti-Müllerian hormone inhibits growth of AMH type II receptor-positive human ovarian granulosa cell tumor cells by activating apoptosis

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Downregulation of transcription factor GATA4 sensitizes human hepatoblastoma cells to doxorubicin-induced apoptosis

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