Biology subjects

Ghomlaghi, M.

Publications and source records attributed to Ghomlaghi, M..

3 recordsLinked to original sources

bioRxiv2022

Systems modelling of TGF-β/Hippo signalling crosstalk uncovers molecular switches that coordinate YAP transcriptional complexes

The Hippo pathway is an evolutionarily conserved signaling network that integrates diverse cues to regulate cell fate and organ homeostasis. The central downstream pathway protein is the transcriptional co-activator Yes-associated protein (YAP). Although capable of inducing gene transcription, YAP cannot bind DNA directly. Instead, it mediates transcriptional activity through interaction with distinct DNA-binding transcriptional factors (TFs), including TEAD, SMAD, and p73, to form active and functionally opposing transcriptional complexes. Growing evidence in mammals demonstrates that YAP has a dual role and can either promote cell proliferation or apoptosis, which underpin its ability to function as both an oncogene or a tumour suppressor depending on the specific context. However, the mechanisms by which YAP coordinates its distinct transcriptional complexes and mediates context-dependent function remain poorly defined. This is in part due to the lack of systems-level studies that can decrypt the complexities of upstream signalling pathways and their crosstalk, which together dictate the transcriptional regulation at the YAP level. Here, we undertake an integrative systems-based approach combining computational network modelling and experimental studies to interrogate the dynamic formation of and transition between the YAP-SMAD and YAP-p73 transcriptional complexes, which control proliferative and apoptotic gene expression, respectively. We developed a new experimentally-validated mathematical model of the TGF-{beta}/Hippo signalling crosstalk and used this model to elucidate dynamic network behaviour. Our integrative studies uncovered previously unknown molecular switches that control the YAP-SMAD/p73 complexes in an on/off, switch-like manner. RASSF1A and ITCH were identified as major regulators of the switches, whereby a graded increase in ITCH expression can trigger YAP to abruptly switch from binding p73 to SMAD, swiftly promoting proliferative gene expression. Further, adjusting the model to reflect cell type-specific protein expression profiles using both in-house and publicly available experimental data enabled us to study the YAP switches under diverse and varied cellular contexts. Overall, our studies provide a new quantitative and systems-level understanding of the dynamic regulation of functionally opposing YAP transcriptional complexes in mammalian cells.

cell biology↗

bioRxiv2021

Integrative Modelling of Signalling Network Dynamics Identifies Cell Type-selective 1 Therapeutic Strategies for FGFR4-driven Cancers

Oncogenic FGFR4 signalling represents a potential therapeutic target in various cancer types, including triple negative breast cancer (TNBC) and hepatocellular carcinoma (HCC). However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signalling and provide network-level insights into resistance mechanisms driven by signalling dynamics. Our integrated approach, combining computational network modelling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in the TNBC cell line MDA-MB-453. By systematically simulating the model to analyse the effects of co-targeting specific network nodes, we were able to predict, and subsequently confirm through experimental validation, the strong synergy of co-targeting FGFR4 and AKT or specific ErbB kinases, but not PI3K. Incorporating protein expression data from hundreds of cancer cell lines, we then adapted our model to diverse cellular contexts. This revealed that while AKT rebound is common, it is not a general phenomenon. ERK reactivation, for example, occurs in certain cell types, including the FGFR4-driven HCC cell line Hep3B, where there is a synergistic effect of co-targeting FGFR4 and MEK, but not AKT. In summary, our study offers key insights into drug-induced network remodelling and the role of protein expression heterogeneity in targeted therapy responses. We underscore the utility of computational network modelling for designing cell type-selective combination therapies and enhancing precision cancer treatment. SignificanceThis study underscores the potential of computational predictive modelling in deciphering mechanisms of cancer cell resistance to targeted therapies and in designing more effective, cancer type-specific combination treatments.

systems biology↗

bioRxiv2020

Dynamic modelling of the PI3K/mTOR signalling network uncovers biphasic dependence of mTORC1 activation on the mTORC2 subunit Sin1

The PI3K/mTOR signalling network critically regulates a broad array of important biological processes, including cell growth, metabolism and autophagy. Dysregulation of PI3K/mTOR signalling is associated with a variety of human diseases, including cancer and metabolic disorders. The mechanistic target of rapamycin (mTOR) is a kinase that functions as a core catalytic subunit in two physically and functionally distinct complexes termed mTOR complex 1 (mTORC1) and mTORC2, which also share other common components such as mLTS8 (also known as G{beta}L) and DEPTOR. Despite being the subject of intensive research, a full picture of how mTORC1/2 assembly and activity are coordinated, and how they are functionally connected remain to be fully characterised. This is due primarily to the complex network wiring, featuring a growing number of intricate feedback loops and post-translational modifications, which require quantitative systems-level approaches to decipher. Here, we integrate predictive computational modelling, in vitro experiments and -omics data analysis to elucidate the dynamic and emergent features of the PI3K/mTOR network behavior. We construct new mechanistic models of the network that encapsulate novel critical mechanistic details, including mTORC1/2 coordination by mLTS8 (de)ubiquitination, and Akt-to-mTORC2 positive feedback loop. Model simulations subsequently confirmed by experimental validation revealed a previously unknown biphasic, threshold-gated dependence of mTORC1 activity on the key mTORC2 subunit Sin1, which is robust against cell-to-cell variation in protein expression. Furthermore, our results support the essential role of mLST8 in both mTORC1 and 2 activity, and suggest mLST8 could serve as a viable therapeutic target in breast cancer. Overall, our integrated analyses provide fresh systems-level insights into the dynamic behavior of PI3K/mTOR signalling and shed new light on the complexity of this important network. AUTHOR SUMMARYSignalling networks are the key information-processing machineries that underpin the ability of living cells to respond proportionately to extra- (and intra-) cellular cues. The PI3K/mTOR signalling network is one of the most important signalling networks in human cells that regulates cellular response to hormones such as insulin, yet our understanding of the network behaviour remains far from complete. Here, we employed a highly integrative approach that combines predictive mathematical modelling, biological experimentation, and data analysis to gain novel systems-level insights into PI3K/mTOR signalling. We constructed new mathematical models of this complex network incorporating important regulatory mechanisms. In contrary to commonly held views that mTORC2 lies upstream and is a positive regulator of mTORC1, we found that their relationship is highly nonlinear and dose dependent. This finding has major implications for mTORC2-directed anti-cancer strategies as depending on the cellular contexts, blocking mTORC2 may reduce or even enhance mTORC1 activation, the latter could inadvertently blunt the effect of mTORC2 blockade. Furthermore, our results demonstrate that mLST8 is required for the assembly and activity of both mTOR complexes, and suggest mLST8 is a viable therapeutic target in breast cancer, notably breast cancer.

systems biology↗