Formal modeling and analysis of the hexosamine biosynthetic pathway: role of O-linked N-acetylglucosamine transferase in oncogenesis and cancer progression

Dynamics simulations of hypoxia inducible factor-1 regulatory network in cancer using formal verification techniques

Hypoxia-inducible factor-1 (HIF-1) regulates cell growth, protein translation, metabolic pathways and therefore, has been advocated as a promising biological target for the therapeutic interventions against cancer. In general, hyperactivation of HIF-1 in cancer has been associated with increases in the expression of glucose transporter type-1 (GLUT-1) thus, enhancing glucose consumption and hyperactivating metabolic pathways. The collective behavior of GLUT-1 along with previously known key players AKT, OGT, and VEGF is not fully characterized and lacks clarity of how glucose uptake through this pathway (HIF-1) probes the cancer progression. This study uses a Rene Thomas qualitative modeling framework to comprehend the signaling dynamics of HIF-1 and its interlinked proteins, including VEGF, ERK, AKT, GLUT-1, β-catenin, C-MYC, OGT, and p53 to elucidate the regulatory mechanistic of HIF-1 in cancer. Our dynamic model reveals that continuous activation of p53, β-catenin, and AKT in cyclic conditions, leads to oscillations representing homeostasis or a stable recovery state. Any deviation from this cycle results in a cancerous or pathogenic state. The model shows that overexpression of VEGF activates ERK and GLUT-1, leads to more aggressive tumor growth in a cancerous state. Moreover, it is observed that collective modulation of VEGF, ERK, and β-catenin is required for therapeutic intervention because these genes enhance the expression of GLUT-1 and play a significant role in cancer progression and angiogenesis. Additionally, SimBiology simulation unveils dynamic molecular interactions, emphasizing the need for targeted therapeutics to effectively regulate VEGF and ERK concentrations to modulate cancer cell proliferation.

An in-silico analysis of OGT gene association with diabetes mellitus

O-GlcNAcylation is a nutrient-sensing post-translational modification process. This cycling process involves two primary proteins: the O-linked N-acetylglucosamine transferase (OGT) catalysing the addition, and the glycoside hydrolase OGA (O-GlcNAcase) catalysing the removal of the O-GlCNAc moiety on nucleocytoplasmic proteins. This process is necessary for various critical cellular functions. The O-linked N-acetylglucosamine transferase (OGT) gene produces the OGT protein. Several studies have shown the overexpression of this protein to have biological implications in metabolic diseases like cancer and diabetes mellitus (DM). This study retrieved 159 SNPs with clinical significance from the SNPs database. We probed the functional effects, stability profile, and evolutionary conservation of these to determine their fit for this research. We then identified 7 SNPs (G103R, N196K, Y228H, R250C, G341V, L367F, and C845S) with predicted deleterious effects across the four tools used (PhD-SNPs, SNPs&Go, PROVEAN, and PolyPhen2). Proceeding with this, we used ROBETTA, a homology modelling tool, to model the proteins with these point mutations and carried out a structural bioinformatics method- molecular docking- using the Glide model of the Schrodinger Maestro suite. We used a previously reported inhibitor of OGT, OSMI-1, as the ligand for these mutated protein models. As a result, very good binding affinities and interactions were observed between this ligand and the active site residues within 4Å of OGT. We conclude that these mutation points may be used for further downstream analysis as drug targets for treating diabetes mellitus.

Modelling and analysis of the complement system signalling pathways: roles of C3, C5a and pro-inflammatory cytokines in SARS-CoV-2 infection

The complement system is an essential part of innate immunity. It is activated by invading pathogens causing inflammation, opsonization, and lysis via complement anaphylatoxins, complement opsonin's and membrane attack complex (MAC), respectively. However, in SARS-CoV-2 infection overactivation of complement system is causing cytokine storm leading to multiple organs damage. In this study, the René Thomas kinetic logic approach was used for the development of biological regulatory network (BRN) to model SARS-CoV-2 mediated complement system signalling pathways. Betweenness centrality analysis in cytoscape was adopted for the selection of the most biologically plausible states in state graph. Among the model results, in strongly connected components (SCCs) pro-inflammatory cytokines (PICyts) oscillatory behaviour between recurrent generation and downregulation was found as the main feature of SARS-CoV-2 infection. Diversion of trajectories from the SCCs leading toward hyper-inflammatory response was found in agreement with in vivo studies that overactive innate immunity response caused PICyts storm during SARS-CoV-2 infection. The complex of negative regulators FI, CR1 and DAF in the inhibition of complement peptide (C5a) and PICyts was found desirable to increase immune responses. In modelling role of MAC and PICyts in lowering of SARS-CoV-2 titre was found coherent with experimental studies. Intervention in upregulation of C5a and PICyts by C3 was found helpful in back-and-forth variation of signalling pattern linked with the levels of PICyts. Moreover, intervention in upregulation of PICyts by C5a was found productive in downregulation of all activating factors in the normal SCCs. However, the computational model predictions require experimental studies to be validated by exploring the activation role of C3 and C5a which could change levels of PICyts at various phases of SARS-CoV-2 infection.

Decoding the Role of Epigenetics in Breast Cancer Using Formal Modeling and Machine-Learning Methods

Breast carcinogenesis is known to be instigated by genetic and epigenetic modifications impacting multiple cellular signaling cascades, thus making its prevention and treatments a challenging endeavor. However, epigenetic modification, particularly DNA methylation-mediated silencing of key TSGs, is a hallmark of cancer progression. One such tumor suppressor gene (TSG) RUNX3 (Runt-related transcription factor 3) has been a new insight in breast cancer known to be suppressed due to local promoter hypermethylation mediated by DNA methyltransferase 1 (DNMT1). However, the precise mechanism of epigenetic-influenced silencing of the RUNX3 signaling resulting in cancer invasion and metastasis remains inadequately characterized. In this study, a biological regulatory network (BRN) has been designed to model the dynamics of the DNMT1–RUNX3 network augmented by other regulators such as p21, c-myc, and p53. For this purpose, the René Thomas qualitative modeling was applied to compute the unknown parameters and the subsequent trajectories signified important behaviors of the DNMT1–RUNX3 network (i.e., recovery cycle, homeostasis, and bifurcation state). As a result, the biological system was observed to invade cancer metastasis due to persistent activation of oncogene c-myc accompanied by consistent downregulation of TSG RUNX3. Conversely, homeostasis was achieved in the absence of c-myc and activated TSG RUNX3. Furthermore, DNMT1 was endorsed as a potential epigenetic drug target to be subjected to the implementation of machine-learning techniques for the classification of the active and inactive DNMT1 modulators. The best-performing ML model successfully classified the active and least-active DNMT1 inhibitors exhibiting 97% classification accuracy. Collectively, this study reveals the underlined epigenetic events responsible for RUNX3-implicated breast cancer metastasis along with the classification of DNMT1 modulators that can potentially drive the perception of epigenetic-based tumor therapy.

O-GlcNAc: Regulator of Signaling and Epigenetics Linked to X-linked Intellectual Disability

Cellular identity in multicellular organisms is maintained by characteristic transcriptional networks, nutrient consumption, energy production and metabolite utilization. Integrating these cell-specific programs are epigenetic modifiers, whose activity is often dependent on nutrients and their metabolites to function as substrates and co-factors. Emerging data has highlighted the role of the nutrient-sensing enzyme O-GlcNAc transferase (OGT) as an epigenetic modifier essential in coordinating cellular transcriptional programs and metabolic homeostasis. OGT utilizes the end-product of the hexosamine biosynthetic pathway to modify proteins with O-linked β-D-N-acetylglucosamine (O-GlcNAc). The levels of the modification are held in check by the O-GlcNAcase (OGA). Studies from model organisms and human disease underscore the conserved function these two enzymes of O-GlcNAc cycling play in transcriptional regulation, cellular plasticity and mitochondrial reprogramming. Here, we review these findings and present an integrated view of how O-GlcNAc cycling may contribute to cellular memory and transgenerational inheritance of responses to parental stress. We focus on a rare human genetic disorder where mutant forms of OGT are inherited or acquired de novo. Ongoing analysis of this disorder, OGT- X-linked intellectual disability (OGT-XLID), provides a window into how epigenetic factors linked to O-GlcNAc cycling may influence neurodevelopment.

Deciphering the expression dynamics of ANGPTL8 associated regulatory network in insulin resistance using formal modelling approaches

ANGPTL8 is a recently identified novel hormone which regulates both glucose and lipid metabolism. The increase in ANGPTL8 during compensatory insulin resistance has been recently reported to improve glucose tolerance and a part of cytoprotective metabolic circuit. However, the exact signalling entities and dynamics involved in this process have remained elusive. Therefore, the current study was conducted with a specific aim to model the regulation of ANGPTL8 with emphasis on its role in improving glucose tolerance during insulin resistance. The main contribution of this study is the construction of a discrete model (based on kinetic logic of René Thomas) and its equivalent Stochastic Petri Net model of ANGPTL8 associated Biological Regulatory Network (BRN) which can predict its dynamic behaviours. The predicted results of these models are in‐line with the previous experimental observations and provide comprehensive insights into the signalling dynamics of ANGPTL8 associated BRN. The authors’ results support the hypothesis that ANGPTL8 plays an important role in supplementing the insulin signalling pathway during insulin resistance and its loss can aggravate the pathogenic process by quickly leading towards Diabetes Mellitus. The results of this study have potential therapeutic implications for treatment of Diabetes Mellitus and are suggestive of its potential as a glucose‐lowering agent.

Wound Healing Activity of Alcoholic Extract of Tamarix Aphylla L. on Animal Models

To evaluate the wound healing activity of ethanolic extract of Tamarix aphylla L. on animal model. Wound creation like circular excision and linear incision method were considered for this study. The various parameters were studied like DNA estimation, total protein estimation, estimation of Hexosamine and Uronic acid, estimation of lipid peroxides and antioxidant activity, Tensile Strength of tissues from incision wounds, Antioxidant activity, Antimicrobial activity, Period of epithelialization and finally TNF-a concentration in the wound tissue homogenate were estimated. The treatment groups with the extract showed significant antimicrobial activity with compare to the standard drug. Significantly, 93. 86% increase in the collagen content and significant 52% up regulation in tensile strength was observed in the treated group. 40% reduction was observed in epithelialization period of the treated wounds. The results of the current study confirm that the ethanolic extract of T. aphylla has potent wound healing capacity.

Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks

Modeling and simulation of molecular systems helps in understanding the behavioral mechanism of biological regulation. Time delays in production and degradation of expressions are important parameters in biological regulation. Constraints on time delays provide insight into the dynamical behavior of a Biological Regulatory Network (BRN). A recently introduced Process Hitting (PH) Framework has been found efficient in static analysis of large BRNs, however, it lacks the inference of time delays and thus determination of their constraints associated with the evolution of the expression levels of biological entities of BRN is not possible. In this paper we propose a Hybrid Process Hitting scheme for introducing time delays in Process Hitting Framework for dynamical modeling and analysis of Large Biological Regulatory Networks. It provides valuable insights into the time delays corresponding to the changes in the expression levels of biological entities thus possibly helping in identification of therapeutic targets. The proposed framework is applied to a well-known BRNs of Bacteriophage λ and ERBB Receptor-regulated G1/S transition involved in the breast cancer to demonstrate the viability of our approach. Using the proposed approach, we are able to perform goal-oriented reduction of the BRN and also determine the constraints on time delays characterizing the evolution (dynamics) of the reduced BRN.

Computational modeling and analysis of the impacts of sleep deprivation on glucose stimulated insulin secretion

Circadian clock is an exquisite internal biological clock functioning in all living organisms. Lifestyle changes such as shift work or frequent travelling might result in malfunctioning of the central and consequently the peripheral clocks leading to different metabolic disorders. Disruptions in β cell clock have been found to be a potential reason behind β cell failure that makes a person prone towards developing type 2 diabetes (T2DM). In this study, a Petri net model for β cell circadian clock has been developed, followed by analysis of the negative impacts of sleep deprivation conditions on the process of glucose stimulated insulin secretion (GSIS) through misalignment of circadian clock. The analysis of structural properties of the Petri net model reveals robustness of the circadian system. The simulation results predict that sleep loss negatively affects the expression of circadian genes which eventually leads to impaired GSIS and β cell failure. These results suggest that sleep/wake cycle is a vital contributor for the entrainment of the circadian clock and normal functioning of β cell.

Parameter estimation of qualitative biological regulatory networks on high performance computing hardware

BACKGROUND

Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware.

RESULTS

The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster.

CONCLUSIONS

Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .

Model-based in silico analysis of the PI3K/Akt pathway: the elucidation of cross-talk between diabetes and breast cancer

BACKGROUND

A positive association between diabetes and breast cancer has been identified by various epidemiological and clinical studies. However, the possible molecular interactions between the two heterogeneous diseases have not been fully determined yet. There are several underlying mechanisms which may increase the risk of breast cancer in diabetic patients.

INTRODUCTION

In this study, we focused on the role of O-GlcNAc transferase (OGT) enzyme in the regulation of phosphatidylinositol-3 kinase (PI3K) pathway through activation/deactivation of Akt protein. The efficiency of insulin signaling in adipocytes is reduced as a result of OGT overexpression which further attenuates Akt signaling; as a result, the efficiency of insulin signaling is reduced by downregulation of insulin-responsive genes. On the other hand, increased expression of OGT results in Akt activation in breast cancer cells, leading to enhanced cell proliferation and inhibition of the apoptosis. However, the interplay amongst these signaling pathways is still under investigation.

METHODS

In this study, we used Petri nets (PNs) to model and investigate the role of PI3K and OGT pathways, acting as key players in crosstalk between diabetes and breast cancer, resulting in progression of these chronic diseases. Moreover, in silico perturbation experiments were applied on the model to analyze the effects of anti-cancer agents (shRNA and BZX) and anti-diabetic drug (Metformin) on the system.

RESULTS

Our PN model reflects the alterations in protein expression and behavior and the correlation between breast cancer and diabetes. The analysis proposed two combination therapies to combat breast cancer progression in diabetic patients including combination of OGTmRNA silencing and OGT inhibitor (BZX) as first combination and BZX and Metformin as the second.

CONCLUSION

The PN model verified that alterations in O-GlcNAc signaling affect both insulin resistance and breast cancer. Moreover, the combination therapy for breast cancer patients consisting of anti-diabetic drugs such as Metformin along with OGT inhibitors, for example BZX, can produce better treatment regimens.

O-GlcNAc in cancer: An Oncometabolism-fueled vicious cycle

Cancer cells exhibit unregulated growth, altered metabolism, enhanced metastatic potential and altered cell surface glycans. Fueled by oncometabolism and elevated uptake of glucose and glutamine, the hexosamine biosynthetic pathway (HBP) sustains glycosylation in the endomembrane system. In addition, the elevated pools of UDP-GlcNAc drives the O-GlcNAc modification of key targets in the cytoplasm, nucleus and mitochondrion. These targets include transcription factors, kinases, key cytoplasmic enzymes of intermediary metabolism, and electron transport chain complexes. O-GlcNAcylation can thereby alter epigenetics, transcription, signaling, proteostasis, and bioenergetics, key 'hallmarks of cancer'. In this review, we summarize accumulating evidence that many cancer hallmarks are linked to dysregulation of O-GlcNAc cycling on cancer-relevant targets. We argue that onconutrient and oncometabolite-fueled elevation increases HBP flux and triggers O-GlcNAcylation of key regulatory enzymes in glycolysis, Kreb's cycle, pentose-phosphate pathway, and the HBP itself. The resulting rerouting of glucose metabolites leads to elevated O-GlcNAcylation of oncogenes and tumor suppressors further escalating elevation in HBP flux creating a 'vicious cycle'. Downstream, elevated O-GlcNAcylation alters DNA repair and cellular stress pathways which influence oncogenesis. The elevated steady-state levels of O-GlcNAcylated targets found in many cancers may also provide these cells with a selective advantage for sustained growth, enhanced metastatic potential, and immune evasion in the tumor microenvironment.

Formal Modeling of mTOR Associated Biological Regulatory Network Reveals Novel Therapeutic Strategy for the Treatment of Cancer

Cellular homeostasis is a continuous phenomenon that if compromised can lead to several disorders including cancer. There is a need to understand the dynamics of cellular proliferation to get deeper insights into the prevalence of cancer. Mechanistic Target of Rapamycin (mTOR) is implicated as the central regulator of the metabolic pathway involved in growth whereas its two distinct complexes mTORC1 and mTORC2 perform particular functions in cellular propagation. To date, mTORC1 is a well defined therapeutic target to inhibit uncontrolled cell division, while the role of mTORC2 is not well characterized. Therefore, the current study is designed to understand the signaling dynamics of mTOR and its partner proteins such as PI3K, PTEN, mTORC2, PKB (Akt), mTORC1, and FOXO. For this purpose, a qualitative model of mTOR-associated Biological Regulatory Network (BRN) is constructed to predict its regulatory behaviors which may not be predictable otherwise. The depleted expression of PTEN and FOXO along with the overexpression of PI3K, mTORC2, mTORC1 and Akt is predicted as a stable steady state which is in accordance with their observed expression levels in the progression of various cancers. The qualitative model also predicts the homeostasis of all the entities in the form of qualitative cycles. The significant qualitative (discrete) cycle is identified by analyzing betweenness centralities of the qualitative (discrete) states. This cycle is further refined as a linear hybrid automaton model with the production (activation) and degradation (inhibition) time delays in order to analyze the real-time constraints for its existence. The analysis of the hybrid model provides a formal proof that during homeostasis the inhibition time delay of Akt is less than the inhibition time delay of mTORC2. In conclusion, our observations characterize that in homeostasis Akt is degraded with a faster rate than mTORC2 which suggests that the inhibition of Akt along with the activation of mTORC2 may be a better therapeutic strategy for the treatment of cancer.