Biology subjects

Pan, M.

Publications and source records attributed to Pan, M..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Bone morphogenetic protein (BMP) signaling determines neuroblastoma cell fate and sensitivity to retinoic acid.

Retinoic acid (RA) is a standard-of-care neuroblastoma drug thought to be effective by inducing differentiation. Curiously, RA has little effect on primary human tumors during upfront treatment but can eliminate neuroblastoma cells from the bone marrow during post-chemo consolidation therapy--a discrepancy that has never been explained. To investigate this, we treated a large cohort of neuroblastoma cell lines with RA and observed that the most RA-sensitive cells predominantly undergo apoptosis or senescence, rather than differentiation. We conducted genome-wide CRISPR knockout screens under RA treatment, which identified BMP signaling as controlling the apoptosis/senescence vs differentiation cell fate decision and determining RAs overall potency. We then discovered that BMP signaling activity is markedly higher in neuroblastoma patient samples at bone marrow metastatic sites, providing a plausible explanation for RAs ability to clear neuroblastoma cells specifically from the bone marrow, seemingly mimicking interactions between BMP and RA during normal development.

cancer biology↗

bioRxiv2024

The segmented flavivirus Alongshan virus reduces mitochondrial mass via degrading STAT2 to suppress innate immune response

Alongshan virus (ALSV) is a newly discovered pathogen of the Flaviviridae family, characterized by a multi-segmented genome distantly related to the canonical flaviviruses. Understanding the pathogenic mechanism of the emerging segmented flavivirus is crucial for the development of intervention strategies. Here we show that ALSV can infect multiple mammalian cells and induces the expression of antiviral genes. Moreover, ALSV is sensitive to IFN-{beta} and possesses the ability to counteract type I IFN response. Mechanistically, ALSVs nonstructural protein NSP1 binds to and degrades human STAT2 through an autophagy pathway in a species-dependent manner, resulting in direct inhibition of the expression of interferon-stimulated genes (ISGs). Specifically, NSP1 methyltransferase domain binds to the key sites of F175/R176 located in coiled-coil domain of STAT2. Moreover, NSP1-mediated degradation of STAT2 results in a reduction in mitochondrial mass by disrupting mitochondrial dynamics to induce mitophagy and inhibiting mitochondrial biogenesis, thereby suppressing the innate immune response. Interestingly, inhibiting mitophagy using 3-Methyladenine and enhancing mitochondrial biogenesis using the PPAR{gamma} agonist pioglitazone can reverse NSP1-mediated inhibition of ISGs, suggesting that promoting mitochondrial mass presents an effective antiviral strategy. Our findings elucidate the intricate regulatory crosstalk between ALSV and the hosts innate immune response, providing valuable insights into the pathogenesis and intervention strategy of emerging segmented flavivirus.

microbiology↗

bioRxiv2024

Characterization of the SARS-CoV-2 BA.5 Variants in H11-K18-hACE2 Hamsters

This study aims to comprehensively characterize the SARS-CoV-2 BA.5 variants using K18 hACE2 transgenic mice and golden hamsters as model organisms. Previous research on SARS-CoV-2 has utilized both mouse and hamster models, leading to conflicting results concerning the viruss lethality. In our study, the finding suggests that H11-K18 hACE2 golden hamsters closely mimic the disease progression observed in human COVID-19 cases caused by BA.5 variants, demonstrating consistent severity and symptoms comparable to severe infections. Additionally, hamsters exhibit heightened respiratory viral replication, accurately reflecting the clinical viral kinetics observed in humans. The study emphasizes the critical importance of selecting an appropriate animal model for SARS-CoV-2 research, while also providing robust support for the hypothesis that BA.5 variants contribute to fatal outcomes in COVID-19 cases. These findings highlight the pivotal role of the golden hamster model in advancing our understanding of the pathogenic mechanisms underlying SARS-CoV-2 variants, as well as in the development of targeted therapeutic strategies. Significance StatementOur research work explores groundbreaking insights that could reshape our understanding of COVID-19 and pave the way for targeted therapies. We use golden hamster models to express the possibility of different animal models could contribute to human diseases. We hope this finding could clarify some conflicts existed, and help further development of medication for COVID.

immunology↗

bioRxiv2024

Gene Regulatory Network topology governs resistance and treatment escape in glioma stem-like cells

Poor prognosis and drug resistance in glioblastoma (GBM) manifests from heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation to glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation is inherently resistant to temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and thereby drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how topology of transcription factor interactions drives distinct trajectories of cell state transitions of susceptible and resistant PD-GSCs in response to cytotoxic drug treatment. By experimentally testing TRN simulation-based predictions, we show that drug treatment drives surviving cells of a PD-GSC along a trajectory of intermediate states, akin to a bottleneck in gene expression space, exposing vulnerability to potentiated killing by sequential addition of siRNA or a second drug targeting transcriptional programs governing non-genetic plasticity of a PD-GSC. Thus, our findings demonstrate an approach to uncover and use TRN topology of a PD-GSC to rationally predict combinatorial and sequential treatments that block treatment escape and acquired resistance in GBM.

systems biology↗

bioRxiv2024

Capturing Snapshots of Nucleosomal H2A K13/K15 Ubiquitination Mediated by the Monomeric E3 Ligase RNF168

AbstractsThe DNA damage repair regulatory protein RNF168, a monomeric RING-type E3 ligase, plays a crucial role in regulating cell fate and DNA repair by specific and efficient ubiquitination of the adjacent Lys13 and Lys15 sites at the H2A N-terminal tail. However, understanding how RNF168 coordinates with its cognate E2 enzyme UbcH5c to ubiquitinate H2AK13/15 site-specifically has long been hampered by the lack of high-resolution structures of RNF168 and UbcH5c[~]Ub in complex with nucleosomes. Here, we developed mechanism-based chemical trapping strategies and determined the cryo-EM structures of the RNF168/UbcH5c-Ub/NCP complex captured in transient H2AK13/15 monoubiquitination and adjacent dual-monoubiquitination reactions. Our structural analysis revealed that RNF168 stably binds to the nucleosomal H2A-H2B acidic patch through a basic helix with multiple interactions, which positions the UbcH5c active centre directly over the H2A N-terminus, providing a "helix-anchoring" mode for monomeric E3 ligase RNF168 on nucleosome in contrast to the "compass-binding" mode of dimeric E3 ligases. Furthermore, our chemically synthesized ubiquitinated histones have enabled the elucidation of the efficiency of Ub installation and the interplay between the initial and subsequent Ub modifications on the adjacent H2A K13 and K15 sites. Overall, our work not only provides structural snapshots of H2A K13/K15 site-specific monoubiquitination and adjacent dual-monoubiquitination, but also offers a near-atomic resolution structural framework for understanding how pathogenic mutations or physiological modifications affect the molecular function of RNF168 in H2A K13/15 ubiquitination.

biochemistry↗

bioRxiv2023

Predicting the Trend of SARS-CoV-2 Mutation Frequencies Using Historical Data

As the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral variations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies in the coming months. In this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. To improve upon the accuracy of traditional time-series models, we transformed the prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models, in this supervised learning setting. Empowered by the novel transformation strategy and the high capacity of machine learning models, we witnessed low prediction error that is confined within 0.1% and 1% when making predictions for future 30 and 80 days respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by lower MAE, and MSE for predictions made within different time horizons. To further assess the methods effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. To enhance accessibility and utility, we built our methodology into an R-shiny app (https://swdatpredicts.shinyapps.io/rshiny_predict/), a tool with potential applicability in studying other infectious diseases, thus extending its relevance beyond the current pandemic.

bioinformatics↗

bioRxiv2023

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells. TeaserMitoTracer enables automatically and accurately discover informative mitochondrial mutations for lineage tracing.

bioinformatics↗

bioRxiv2023

Thermodynamically-consistent, reduced models of gene regulatory networks

Synthetic biology aims to engineer novel functionalities into biological systems. While the approach has to date been predominantly applied to single cells, a richer set of biological phenomena can be engineered by applying synthetic biology to cell populations. To rationally design cell populations, we require mathematical models that link between intracellular biochemistry and intercellular interactions. In this study, we develop a kinetic model of gene expression that is suitable for incorporation into agent-based models of cell populations. To be scalable to large cell populations, models of gene expression should be both computationally efficient and compliant with the laws of physics. We satisfy the first requirement by applying a model reduction scheme to translation, and the second requirement by formulating models using bond graphs. Our reduced model is significantly faster to simulate than the full model, and faithfully reproduces important behaviours of the full model. We couple separate models of gene expression to build models of the toggle switch and repressilator. With these models, we explore the effects of resource availability and cell-to-cell heterogeneity on circuit behaviour. The modelling approaches developed in this study are a bridge towards engineering collective cell behaviours such as synchronisation and division of labour.

synthetic biology↗

bioRxiv2023

Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer's disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimers disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above [~]50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.

neuroscience↗

bioRxiv2023

A Cryptic Basic Groove formed by Ubiquitin and Histone H3 Mediates the Selective Recognition of H2AK119Ub Nucleosomes by Synovial Sarcoma X Breakpoint 1 Protein

The cancer-specific fusion oncoprotein SS18-SSX1 disturbs chromatin accessibility by hijacking the BAF complex from the promoters and enhancers to the polycomb-repressed chromatin regions. This process relies on the selective recognition of H2AK119Ub nucleosomes by SSX1. However, the mechanism by which SSX1 selective recognition of H2AK119Ub nucleosomes in the absence of ubiquitin (Ub)-binding capacity remains unknown. Here we report the cryo-EM structure of SSX1 bound to H2AK119Ub nucleosomes at 3.1 [A] resolution. Combined in vitro biochemical and cellular assays revealed that the Ub recognition by SSX1 is unique and depends on a cryptic basic groove formed by H3 and the Ub motif on the H2AK119 site. Moreover, this unorthodox binding mode of SSX1 induces DNA unwrapping at the entry/exit sites. Together, our results describe a unique mode of site-specific ubiquitinated nucleosome recognition that underlies the specific hijacking of the BAF complex to polycomb regions by SS18-SSX1 in synovial sarcoma.

molecular biology↗

bioRxiv2023

Rapid Prototyping of Thermoplastic Microfluidic 3D Cell Culture Devices by Creating Regional Hydrophilicity Discrepancy

Microfluidic three-dimensional cell culture devices that enable the recapitulation of key aspects of organ structures and functions in vivo represent a promising preclinical platform to improve translational success during drug discovery. Essential to these engineered devices is the spatial patterning of cells from different tissue types within a confined microenvironment. Traditional fabrication strategies lack the scalability, cost-effectiveness, and rapid prototyping capabilities required for industrial applications, especially for processes involving thermoplastic materials. Here, we introduce an approach to pattern fluid guides inside microchannels by establishing differential hydrophilicity using pressure-sensitive adhesives as masks and a subsequent selective coating with a biocompatible polymer. We identified optimal coating conditions using polyvinylpyrrolidone, which resulted in rapid and consistent hydrogel flow in both the open-chip prototype and the fully bonded device containing additional features for medium perfusion. We tested the suitability of our device for dynamic 3D cell culture by growing human hepatocytes in the device under controlled fluid flow for a 14-day period. Additionally, we demonstrated the potential of using our device for pharmaceutical high-throughput screening applications, such as predicting drug-induced liver injury. Our approach offers a facile strategy of rapid prototyping thermoplastic microfluidic organ chips with varying geometries, microstructures, and substrate materials.

bioengineering↗

bioRxiv2023

Gender-based heterogeneity of FAHFAs in trained runners

Fatty acid esters of hydroxy fatty acid (FAHFA) are anti-diabetic and anti-inflammatory lipokines. Recently FAHFAs were also found to predict cardiorespiratory fitness in trained runners. Here we compared the association between circulating FAHFA baseline concentrations and body composition, determined by dual x-ray absorptiometry, in female runners who were lean (BMI < 25 kg/m2, n = 6), to those who were overweight (BMI [≥] 25 kg/m2, n = 7). We also compared circulating FAHFAs in lean male runners (n = 8) to the same trained lean female (n = 6) runner group. Circulating FAHFAs were increased in females in a manner that was modulated by specific adipose depot sizes, blood glucose, and lean body mass. As expected, circulating FAHFAs were diminished in the overweight group, but, strikingly, in both lean and overweight cohorts, increases in circulating FAHFAs were promoted by increased fat mass, relative to lean mass. These studies suggest multimodal regulation of circulating FAHFAs and raise hypotheses to test endogenous FAHFA dynamic sources and sinks in health and disease, which will be essential for therapeutic target development. Baseline circulating FAHFA concentrations could signal sub-clinical metabolic dysfunction in metabolically healthy obesity.

physiology↗

bioRxiv2023

Structural Visualization of HECT-E3 Ufd4 accepting and transferring Ubiquitin to Form K29/K48-branched Polyubiquitination on N-degron

The K29/K48-linked ubiquitination generated by the cooperative catalysis of E3 ligase Ufd4 and Ubr1 is an enhanced protein degradation signal, in which Ufd4 is responsible for introducing K29-linked ubiquitination to K48-linked ubiquitin chains to augment polyubiquitination. How HECT-E3 ligase Ufd4 mediates the ubiquitination event remains unclear. Here, we biochemically determine that Ufd4 preferentially catalyses K29-linked ubiquitination on K48-linked ubiquitin chains to generate K29/K48-branched ubiquitin chains and capture structural snapshots of Ub transfer cascades for Ufd4-mediated ubiquitination. The N-terminal ARM region and HECT domain C-lobe of Ufd4 are identified and characterized as key structural elements that together recruit K48-linked diUb and orient Lys29 of its proximal Ub to the active cysteine of Ufd4 for K29-linked branched ubiquitination. These structures not only provide mechanistic insights into the architecture of the Ufd4 complex but also provide structural visualization of branched ubiquitin chain formation by a HECT-type E3 ligase.

biochemistry↗

bioRxiv2023

C-type natriuretic peptide improves maternally aged oocytes quality by inhibiting excessive PINK1/Parkin-mediated mitophagy

The overall oocyte quality declines with ageing, and this effect is strongly associated with a higher reactive oxygen species (ROS) level and the resultant oxidative damage. C-type natriuretic peptide (CNP) is a well-characterized physiological meiotic inhibitor that has been successfully used to improve immature oocyte quality during in vitro maturation (IVM). However, the underlying roles of CNP in maternally aged oocytes have not been reported. Here, we found that the age-related reduction in the serum CNP concentration was highly correlated with decreased oocyte quality. Treatment with exogenous CNP promoted follicle growth and ovulation in aged mice and enhanced meiotic competency and fertilization ability. Interestingly, the cytoplasmic maturation of aged oocytes was thoroughly improved by CNP treatment, as assessed by spindle/chromosome morphology and redistribution of organelles (mitochondria, the endoplasmic reticulum [ER], cortical granules [CGs], and the Golgi apparatus). CNP treatment also ameliorated DNA damage and apoptosis caused by ROS accumulation in aged oocytes. Importantly, oocyte RNA-seq revealed that the beneficial effect of CNP on aged oocytes was mediated by restoration of mitochondrial oxidative phosphorylation, eliminating excessive mitophagy. CNP reversed the defective phenotypes in aged oocytes by alleviating oxidative damage and suppressing excessive PINK1/Parkin-mediated mitophagy. Mechanistically, CNP functioned as a cAMP/PKA pathway modulator to decrease PINK1 stability and inhibit Parkin recruitment. In summary, our results demonstrated that CNP supplementation constitutes an alternative therapeutic approach for advanced maternal age-related oocyte deterioration and may improve the overall success rates of clinically assisted reproduction in older women.

developmental biology↗

bioRxiv2023

BondGraphs.jl: Composable energy-based modelling in systems biology

SummaryBondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multi-scale, multi-physics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. Availability and ImplementationBondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl. Contactpan.m@unimelb.edu.au, mstumpf@unimelb.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2023

A CRISPR-drug perturbational map for identifying new compounds to combine with commonly used chemotherapeutics.

Combination chemotherapy is crucial for achieving durable cancer cures, however, developing safe and effective drug combinations has been a significant challenge. To improve this process, we conducted large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritized neuroblastoma, the most common pediatric solid tumor, where 50% of high-risk patients do not survive. Our screen examined all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest drug combination screens ever reported. Remarkably, using dense drug-drug rescreening, we found that the top CRISPR-nominated drug combinations were far more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discovered that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using PDX models. Our findings provide a valuable resource for the development of improved chemotherapeutic strategies and demonstrate the feasibility of using targeted CRISPR knockout to discover new combinations with common chemotherapeutics, a methodology with application across all cancers.

cancer biology↗

bioRxiv2023

An unsupervised learning approach uncovers divergent mesenchymal-like gene expression programs across human neuroblastoma tumors, preclinical models, and chemotherapy-exposed tumors.

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ( automatic consensus nonnegative matrix factorization (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

cancer biology↗

bioRxiv2023

Sensitivity Analysis of Biochemical Systems Using Bond Graphs

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearised with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

systems biology↗