Biology subjects

Nguyen, L. K.

Publications and source records attributed to Nguyen, L. K..

11 recordsLinked to original sources

bioRxiv2026

Network topology dictates sequential drug efficacy through bistability-mediated state switching

Sequential drug combinations can significantly enhance therapeutic efficacy, yet the general principles governing when and why sequential administration outperforms concurrent treatment remain poorly understood. While empirical evidence demonstrates that the order and timing of drug exposure can be critical, a mechanistic framework to predict which regulatory architectures are primed for sequential benefit is currently lacking. Here, we systematically enumerated and dynamically analysed 59,040 four-node network topologies to identify the structural design principles that dictate sequential efficacy. Our analysis reveals that only a small fraction of network architectures robustly confer a sequential advantage and identifies a minimal structural requirement for this benefit: a positive feedback loop between the primary drug target and its downstream oncogenic output, coupled with antagonistic crosstalk from a secondary drug target. We demonstrate that this architecture enables bistability, allowing the first drug to reconfigure the network into a suppressed attractor state that is inaccessible through concurrent administration. The treatment schedule determines which of two coexisting stable states the system ultimately occupies, with the gap time between doses defining a critical therapeutic window. Only when the first drug is given sufficient time to displace the system past a threshold does the sequential regimen achieve superior suppression. Our findings establish bistability-enabling network motifs as predictive determinants of sequential drug efficacy and provide a topology-based framework for the rational design of time-dependent combination therapies.

systems biology↗

bioRxiv2025

HCMV promotes viral reactivation through the coordinated regulation of Notch signaling by UL8 and miR-UL36

Human cytomegalovirus (HCMV) establishes latency in CD34+ hematopoietic progenitor cells (HPCs), where reactivation is intimately linked to cellular differentiation. We demonstrate that the Notch signaling pathway, a key regulator of stem cell maintenance and differentiation, functions as a barrier to HCMV reactivation. Two viral gene products, UL8 and miR-UL36, modulate this pathway during reactivation. UL8 promotes degradation of the Notch3 receptor via the endosomal/lysosomal pathway, dependent on two tyrosine-based motifs (Y305/314) in its cytoplasmic tail. A UL8 mutant lacking these motifs fails to degrade Notch3, resulting in sustained Notch signaling and impaired reactivation in vitro and in humanized mice. Similarly, miR-UL36 reduces expression of Notch3 and the Notch transcription factor Recombination Signal Binding Protein For Immunoglobulin Kappa J Region (RBPJ), suppressing Notch signaling. Deletion of miR-UL36 inhibits reactivation, but this defect, like that of the UL8 mutant, can be rescued by pharmacologic Notch inhibition. Thus, HCMV employs multiple gene products to suppress Notch signaling and promote conditions conducive to reactivation. These findings reveal how HCMV manipulates host differentiation pathways to control latency and suggest therapeutic strategies to prevent viral recurrence in immunocompromised patients. ImportanceHuman cytomegalovirus (HCMV) establishes lifelong latency, posing significant risks to transplant recipients and other immunocompromised individuals. Reactivation depends on progenitor cell differentiation, yet the viral mechanisms governing this process remain unclear. We identify Notch signaling as a major inhibitory pathway to reactivation and show that HCMV uses UL8 and miR-UL36 to suppress this pathway. UL8 degrades Notch3, while miR-UL36 downregulates Notch3 and RBPJ, together reducing Notch signaling and enabling reactivation. Mutant viruses lacking these regulators fail to reactivate efficiently, but this can be reversed by pharmacological inhibition of Notch. These findings establish Notch pathway suppression as a critical viral strategy for reactivation and highlight potential therapeutic targets for preventing HCMV disease.

microbiology↗

bioRxiv2025

Human Cytomegalovirus UL78 is a Nuclear-Localized GPCR Necessary for Efficient Reactivation from Latent Infection in CD34+ Hematopoietic Progenitor Cells

Human cytomegalovirus (HCMV) is a ubiquitous pathogen that persists throughout the lifetime of the host due in part to the establishment of latency in CD34+ hematopoietic progenitor cells (HPCs) and CD14+ monocytes. HCMV encodes four putative G protein-coupled receptors (GPCRs): US27, US28, UL33, and UL78. While the roles of most of these receptors have been investigated, a definitive role for UL78 in HCMV infection has yet to be elucidated. Utilizing an in vitro CD34+ HPC model, we demonstrate that a recombinant virus lacking UL78 protein expression fails to efficiently reactivate from latent infection. Furthermore, we show using a Lumit-based assay that UL78 preferentially couples to the Gi family of G proteins and that a recombinant HCMV containing mutations in the UL78 G protein-coupling DRL motif also fails to reactivate from latent infection. Together our findings indicate that Gi coupling is important for UL78 function during reactivation in latently infected CD34+ HPCs, however the protein is not required to establish or maintain latency. To better understand the role of UL78, we conducted proximity-dependent labeling analyses in HCMV-UL78-TurboID infected fibroblasts and CD34+ HPCs undergoing reactivation from latency. Congruent with our coupling data, we found Gi was the only heterotrimeric G protein in proximity to UL78. Pathway analysis of the UL78 interactome revealed proteins associated with membrane trafficking, signaling, and the nuclear pore complex as enriched in both cell types. In addition, the UL78 interactome contained viral proteins with nuclear localization including viral transcription and DNA replication machinery. Nuclear localization of UL78 was validated using cell fractionation, immunofluorescence microscopy, and proximity-dependent labelling of isolated nuclei. Together, our results provide novel insights into the localization and function of UL78, previously unknown to contribute to reactivation from latent infection. Author SummaryHuman cytomegalovirus (HCMV) remains one of the most widespread viral infections globally. Primary HCMV infection is typically asymptomatic and leads to the establishment of latency in myeloid lineage cells, where the virus persists for the hosts lifetime. Reactivation of latent HCMV can cause severe complications, particularly in immunocompromised individuals such as transplant recipients and people living with HIV. Several factors influence the transition from latent to lytic infection, including signal transduction through the viral G protein-coupled receptors: US27, US28, UL33, and UL78. Using an advanced in vitro model, we show that recombinant viruses lacking UL78 fail to efficiently reactivate from latent infection. Moreover, we show that UL78 preferentially couples to the Gi family of G proteins via a conserved DRL motif, and this coupling is required for efficient reactivation. These results were confirmed by proximity-dependent labeling experiments where we identified Gi and several other proteins involved in trafficking, signaling, transcription, and nuclear localization. Nuclear localization of UL78 was confirmed by cell fractionation, immunofluorescence microscopy, and proximity-dependent labeling in isolated nuclei. Collectively, our results uncover a novel role for UL78 in reactivation from latency and shed new light on its localization and function.

microbiology↗

bioRxiv2024

Ribosomal Biogenesis Hyperactivation and ErbB signalling Mediated Network Rewiring Causes Adaptive Resistance to FGFR2 Inhibition

Fibroblast Growth Factor Receptor 2 inhibition presents a promising therapeutic approach for restraining the growth and survival of cancer cells, particularly in breast tumours. However, the emergence of resistance to FGFR2 inhibitors like PD173074 highlights the importance of understanding the molecular mechanisms driving resistance and identifying effective therapeutic strategies. In this study, we employed temporal quantitative proteomics and phosphoproteomics, complemented by computational clustering, PTM-SEA analysis, and kinase activity prediction, to monitor the response of MFM223 triple negative breast cancer cells to FGFR2 inhibition. Strikingly, we observed a marked enrichment of ribosome biogenesis function modules immediately following treatment, a phenomenon not observed with other inhibitors. Additionally, we discovered that CX-5461, an RNA polymerase I inhibitor, synergistically enhanced the growth inhibition induced by PD173074, mechanistically attributed to its significant suppression of rDNA transcription stimulated by PD173074. Moreover, our phosphoproteomics dynamic profiling identified the clustering of kinases within MAPK and ErbB signalling pathways, indicative of their reactivation in response to FGFR2 inhibition. Experimentally validating this finding, we observed a notable rebound in phosphorylation levels of key kinases such as ERK1/2 and ErbB3, and demonstrated a substantial synergistic effect of PD173074 in combination with Trametinib, a MEK inhibitor, in suppressing cancer cell growth. Collectively, our findings provide critical insights into the network rewiring triggered by FGFR2 inhibition and offer a foundation for the rational design of combinatorial therapeutic strategies to overcome resistance mechanisms associated with FGFR2 inhibitors.

cancer biology↗

bioRxiv2024

GeneSurv: Multi-Gene Cancer Survival Analysis Tool

SummaryTraditional cancer survival analysis tools often restrict their analysis to single genes, thereby limiting their analytical scope. GeneSurv, a novel Web-Based Multi-Gene Cancer Survival Analysis Tool, addresses this limitation by enabling multi-gene analyses that correlates the expression of multiple genes, without restriction on their number, in various combinations, with patient survival outcomes. GeneSurv offers dynamic functionalities and an intuitive interface for flexible and precise adjustments of gene expression thresholds. Among its demonstrated applications, GeneSurv identified a significant association between high expression of CCND1 and CDK4 with poor breast cancer prognosis, and related the combined expression of five genes to patient survival, showcasing its capacity to unravel the complexities of genetic interactions and their impact on patient outcomes. ConclusionAvailable with user-friendly tutorials at https://can-sat.erc.monash.edu/, GeneSurv is an invaluable addition to the existing suite of bioinformatic tools, significantly enhancing the investigation of cancer prognosis.

bioinformatics↗

bioRxiv2024

Stress pathway outputs are encoded by pH-dependent phase separation of its components.

Signal processing by intracellular kinases control near all biological processes but how precise functions of signal pathways evolve with changed cellular contexts is poorly understood. Functional specificity of c-Jun N-terminal Kinases (JNK) activated in response to a broad range of pathological and physiological stimuli are partly encoded by signal strength. Here we reveal that intracellular pH (pHi) is a significant component of the JNK regulatory network and defines JNK signal response to precise stimuli. We showed that nuanced fluctuations in physiological pHi regulates JNK activity in response to cell stress. Interestingly, the relationship between pHi and JNK activity was dependent on specific stimuli and upstream kinases involved in pathway activation. Cytosolic alkalinisation promoted phase transition of upstream ASK1 to augment JNK activation. While increased pHi similarly induced JNK2 to form condensates, this led to attenuated JNK activity. Mathematical modelling of feedback signalling incorporating pHi and differential contribution by JNK2 and ASK1 condensates was sufficient to delineate the strength of JNK signal response to specific stimuli. This new knowledge of pHi regulation with consideration of JNK2 and ASK1 contribution to signal transduction may delineate oncogenic versus tumour suppressive functions of the JNK pathway and cancer cell drug responses.

systems biology↗

bioRxiv2024

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically "actionable" signalling axis in triple negative breast cancer

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLC{gamma}1/Ca2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro. Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically actionable target downstream of PEAK1.

cancer biology↗

bioRxiv2023

SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Directed at Cell Signaling Networks

The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.

systems biology↗

bioRxiv2023

Systematic Analysis of Network-driven Adaptive Resistance to CDK4/6 and Estrogen Receptor Inhibition using Meta-Dynamic Network Modelling

Drug resistance inevitably emerges during the treatment of cancer by targeted therapy. Adaptive resistance is a major form of drug resistance, wherein the rewiring of protein signalling networks in response to drug perturbation allows drug-targeted protein activity to recover. This can occur in the continuous presence of the drug and enables cells to survive/grow. Simultaneously, molecular heterogeneity enables the selection of drug-resistant cancer clones that can survive an initial drug insult, proliferate, and eventually cause disease relapse. Despite their importance, the link between heterogeneity and adaptive resistance, specifically how heterogeneity influences protein signalling dynamics to drive adaptive resistance, remains poorly understood. Here, we have explored the relationship between heterogeneity, protein signalling dynamics and adaptive resistance through the development of a novel modelling technique coined Meta Dynamic Network (MDN) modelling. We use MDN modelling to characterise how heterogeneity influences the drug-response signalling dynamics of the proteins that regulate early cell cycle progression and demonstrate that heterogeneity can robustly facilitate adaptive resistance associated dynamics for key cell cycle regulators. We determined the influence of heterogeneity at the level of both reaction coefficients and protein abundance and show that reaction coefficients are a much stronger driver of adaptive resistance. Owing to the mechanistic nature of the underpinning ODE framework, we then identified a full spectrum of subnetworks capable of driving adaptive resistance dynamics in the key early cell cycle regulators. Finally, we show that single-cell dynamic data supports the validity of our MDN modelling technique and a comparison between our predicted resistance mechanisms and known CDK4/6 and Estrogen Receptor inhibitor resistance mechanisms suggests MDN can be deployed to robustly predict network-level resistance mechanisms for novel drugs and additional protein signalling networks.

molecular biology↗

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↗

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↗