Bidirectional regulation between AP-1 and SUMOylation pathway genes modulates inflammatory signaling during Salmonella infection.

Post-translational modifications (PTMs), such as SUMOylation, are known to modulate fundamental processes of a cell. Infectious agents such as Salmonella Typhimurium (STm), which causes gastroenteritis, utilize the PTM mechanism SUMOylation to hijack the host cell. STm suppresses host SUMO pathway genes UBC9 (also known as UBE2I) and PIAS1 to perturb SUMOylation for an efficient infection. In the present study, the regulation of SUMO pathway genes during STm infection was investigated. A direct binding of c-Fos (encoded by FOS), a component of activator protein-1 (AP-1), to promoters of both UBC9 and PIAS1 was observed. Experimental perturbation of c-Fos led to changes in the expression of both UBC9 and PIAS1. STm infection of fibroblasts with SUMOylation-deficient c-Fos (c-FOS-KOSUMO-def-FOS) resulted in uncontrolled activation of target genes, leading to massive immune activation. Infection of c-FOS-KOSUMO-def-FOS cells favored STm replication, indicating misdirected immune mechanisms. Finally, chromatin immunoprecipitation assays confirmed a context-dependent differential binding and release of AP-1 to and from target genes due to its phosphorylation and SUMOylation, respectively. Overall, our data point towards the existence of a bidirectional cross-talk between c-Fos and the SUMO pathway and highlight their importance in AP-1 function in STm infection and beyond. This article has an associated First Person interview with the first author of the paper.

konnect2prot: a web application to explore the protein properties in a functional protein-protein interaction network.

MOTIVATION: The regulation of proteins governs the biological processes and functions and, therefore, the organisms' phenotype. So there is an unmet need for a systematic tool for identifying the proteins that play a crucial role in information processing in a protein-protein interaction (PPI) network. However, the current protein databases and web servers still lag behind to provide an end-to-end pipeline that can leverage the topological understanding of a context-specific PPI network to identify the influential spreaders. Addressing this, we developed a web application, 'konnect2prot' (k2p), which can generate context-specific directional PPI network from the input proteins and detect their biological and topological importance in the network. RESULTS: We pooled together a large amount of ontological knowledge, parsed it down into a functional network, and gained insight into the molecular underpinnings of the disease development by creating a one-stop junction for PPI data. k2p contains both local and global information about a protein, such as protein class, disease mutations, ligands and PDB structure, enriched processes and pathways, multi-disease interactome and hubs and bottlenecks in the directional network. It also identifies spreaders in the network and maps them to disease hallmarks to determine whether they can affect the disease state or not. AVAILABILITY AND IMPLEMENTATION: konnect2prot is freely accessible using the link https://konnect2prot.thsti.in. The code repository is https://github.com/samrat-lab/k2p_bioinfo-2022.

Tracing the footsteps of autophagy in computational biology.

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids' inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities.

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

Bistability regulates TNFR2-mediated survival and death of T-regulatory cells.

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.

Bistability in cell signalling and its significance in identifying potential drug-targets.

MOTIVATION: Bistability is one of the salient dynamical features in various all-or-none kinds of decision-making processes. The presence of bistability in a cell signalling network plays a key role in input-output (I/O) relation. Our study is aiming to capture and emphasize the role of motif structure influencing the I/O relation between two nodes in the context of bistability. Here, a model-based analysis is made to investigate the critical conditions responsible for the emergence of different bistable protein-protein interaction (PPI) motifs and their possible applications to find the potential drug-targets. RESULTS: The global sensitivity analysis is used to identify sensitive parameters and their role in maintaining the bistability. Additionally, the bistable switching through hysteresis is explored to develop an understanding of the underlying mechanisms involved in the cell signalling processes, when significant motifs exhibiting bistability have emerged. Further, we elaborate the application of the results by the implication of the emerged PPI motifs to identify potential drug-targets in three cancer networks, which is validated with existing databases. The influence of stochastic perturbations that could hinder desired functionality of any signalling networks is also described here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Understanding noise in cell signalling in the prospect of drug-targets.

The introduction of noise to signals can alter central regulatory switches of cellular processes leading to diseases. Noise is inherently present in the cellular signalling system and plays a decisive role in the input-output (I/O) relation. The current study aims to understand the noise tolerance of motif structures in the cell signalling processes. The vulnerability of a node to noise could be a significant factor in causing signalling error and need to be controlled. We developed stochastic differential equation (SDE) based mathematical models for different network motifs with two nodes and studied the association between motif structure and signal-noise relation. A two-dimensional parameter space analysis on motif sensitivity with noise and input signal variation was performed to classify and rank the motifs. Identifying sensitive motifs and their high druggability infers their significance in screening potential drug-target candidates. Finally, we proposed a theoretical framework to identify nodes from a network as potential drug targets. We applied this mathematical formalism to three cancer networks to identify drug-targets and validated them with existing databases.

Effect of delay in transportation of extracellular glucose into cardiomyocytes under diabetic condition: a study through mathematical model.

A four-dimensional model was built to mimic the cross-talk among plasma glucose, plasma insulin, intracellular glucose and cytoplasmic calcium of a cardiomyocyte. A time delay was considered to represent the time required for performing various cellular mechanisms between activation of insulin receptor and subsequent glucose entry from extracellular region into intracellular region of a cardiac cell. We analysed the delay-induced model and deciphered conditions for stability and bifurcation. Extensive numerical computations were performed to validate the analytical results and give further insights. Sensitivity study of the system parameters using LHS-PRCC method reveals that some rate parameters, which represent the input of plasma glucose, absorption of glucose by noncardiac cells and insulin production, are sensitive and may cause significant change in the system dynamics. It was observed that the time taken for transportation of extracellular glucose into the cell through GLUT4 plays an important role in maintaining physiological oscillations of the state variables. Parameter recalibration exercise showed that reduced input rate of glucose in the blood plasma or an alteration in transportation delay may be used for therapeutic targets in diabetic-like condition for maintaining normal cardiac function.

Restoration of cytosolic calcium inhibits Mycobacterium tuberculosis intracellular growth: Theoretical evidence and experimental observation.

Mycobacterium tuberculosis (Mtb) is a highly successful intracellular pathogen because of its ability to modulate host's anti-microbial pathways. Phagocytosis acts as the first line of defence against microbial infection. However, Mtb inhibits Phosphatidylinositol 3-phosphate (PI3P) oscillations which is required for phagolysosomal fusion. Here we attempted to understand the mechanisms by which Mtb eliminates phagosome-lysosome fusion. To address this, we built a four dimensional ordinary differential equation model and explored the contribution of PI3P during Mtb phagocytosis. Using this model, we identified some sensitive parameters that influence the dynamics of host-pathogen interactions. We observed that PI3P dynamics can be controlled by regulating the intracellular calcium oscillations. Some plausible methods to restore PI3P oscillations are ER flux rate, recruitment rate of proteins, like Rab GTPase, and cooperativity coefficient of calcium dependent consumption of calmodulin. Further, we investigated whether modulation of these pathways is a potential therapeutic intervention strategy. Here we showed that RyR2 agonist caffeine stimulated calcium influx and inhibited growth of intracellular Mtb in macrophages. Taken together, we demonstrate that modulation of host calcium level is a plausible strategy for killing of intracellular Mtb.

Bilateral Acute Anterior Uveitis and Conjunctivitis following Intravenous Zoledronic Acid.

The ocular side-effects of bisphosphonates have the potential to escalate with their widespread use. We report a patient of osteoporosis who was treated with zoledronic acid infusion. He developed ocular pain, redness, watering, photophobia and swelling of both the eyes. He was diagnosed with acute anterior uveitis and conjunctivitis and treated with topical 1% prednisolone acetate and 1% atropine sulphate. The signs of inflammation abated by one week and the steroids were tapered over the next six weeks. There were no further recurrences. Patients must be educated about the ocular side-effects of bisphosphonate therapy, monitored closely after intravenous infusion and advised to seek ophthalmic consultation promptly if any ocular symptoms or signs develop.

A new method of finding groups of coexpressed genes and conditions of coexpression.

BACKGROUND: To study a biological phenomenon such as finding mechanism of disease, common methodology is to generate the microarray data in different relevant conditions and find groups of genes co-expressed across conditions from such data. These groups might enable us to find biological processes involved in a disease condition. However, more detailed understanding can be made when information of a biological process associated with a particular condition is obtained from the data. Many algorithms are available which finds groups of co-expressed genes and associated conditions of co-expression that can help finding processes associated with particular condition. However, these algorithms depend on different input parameters for generating groups. For real datasets, it is difficult to use these algorithms due to unknown values of these parameters. RESULTS: We present here an algorithm, clustered groups, which finds groups of co-expressed genes and conditions of co-expression with minimal input from user. We used random datasets to derive a cutoff on the basis of which we filtered the resultant groups and showed that this can improve the relevance of obtained groups. We showed that the proposed algorithm performs better than other known algorithms on both real and synthetic datasets. We have also shown its application on a temporal microarray dataset by extracting biclusters and biological information hidden in those biclusters. CONCLUSIONS: Clustered groups is an algorithm which finds groups of co-expressed genes and conditions of co-expression using only a single parameter. We have shown that it works better than other existing algorithms. It can be used to find these groups in different data types such as microarray, proteomics, metabolomics etc.

Pathogenicity of Mycobacterium tuberculosis is expressed by regulating metabolic thresholds of the host macrophage.

The success of Mycobacterium tuberculosis as a pathogen derives from its facile adaptation to the intracellular milieu of human macrophages. To explore this process, we asked whether adaptation also required interference with the metabolic machinery of the host cell. Temporal profiling of the metabolic flux, in cells infected with differently virulent mycobacterial strains, confirmed that this was indeed the case. Subsequent analysis identified the core subset of host reactions that were targeted. It also elucidated that the goal of regulation was to integrate pathways facilitating macrophage survival, with those promoting mycobacterial sustenance. Intriguingly, this synthesis then provided an axis where both host- and pathogen-derived factors converged to define determinants of pathogenicity. Consequently, whereas the requirement for macrophage survival sensitized TB susceptibility to the glycemic status of the individual, mediation by pathogen ensured that the virulence properties of the infecting strain also contributed towards the resulting pathology.

Understanding PGE2, LXA4 and LTB4 balance during Mycobacterium tuberculosis infection through mathematical model.

Infection of humans with Mycobacterium tuberculosis (Mtb) results in diverse outcomes that range from acute disease to establishment of persistence and to even clearance of the pathogen. These different outcomes represent the combined result of host heterogeneity on the one hand, and virulence properties of the infecting strain of pathogen on the other. From the standpoint of the host, the balance between PGE2, LXA4 and LTB4 represents at least one of the factors that dictates the eventual pathophysiology. We therefore built an ODE model to describe the host-pathogen interaction and studied the local stability properties of the system, to obtain the parametric conditions that lead to different disease outcomes. We then modulated levels of the pro- and anti-inflammatory lipid mediators to better understand the convergence between host phenotype and factors that relate to virulence properties of the pathogen. Global sensitivity analysis, using the variance-based method of extended Fourier Amplitude Sensitivity Test (eFAST), revealed that disease severity was indeed defined by combined effects of phenotypic variability at the level of both host and pathogen. Interestingly here, [PGE2] was found to act as a switch between bacterial clearance and acute disease. Our mathematical model suggests that development of more effective treatments for tuberculosis will be contingent upon a better understanding of how the intrinsic variability at the level of both host and pathogen contribute to influence the nature of interactions between these two entities.

Mathematical model of mycobacterium-host interaction describes physiology of persistence.

Despite extensive studies on the interactions between Mycobacterium tuberculosis (M.tb) and macrophages, the mechanism by which pathogen evades anti-microbial responses and establishes persistence within the host cell remains unknown. In this study, we developed a four-dimensional ODE model to describe the dynamics of host-pathogen interactions in the early phase of macrophage infection. The aim was to characterize the role of host cellular regulators such as iron and lipids, in addition to the bactericidal effector molecule Nitric Oxide. Conditions for existence and stability of the equilibrium point were analysed by examining the behaviour of the model through numerical simulations. These computational investigations revealed that it was the ability of pathogen to interfere with iron and lipid homeostatic pathways of the host cell, which ensured a shift in balance towards pathogen survival and persistence. Interestingly, small perturbations in this equilibrium triggered the cell's bactericidal response, thereby producing an oscillatory dynamic for disease progression.

Portfolio analysis of layered security measures.

Layered defenses are necessary for protecting the public from terrorist attacks. Designing a system of such defensive measures requires consideration of the interaction of these countermeasures. In this article, we present an analysis of a layered security system within the lower Manhattan area. It shows how portfolios of security measures can be evaluated through portfolio decision analysis. Consideration is given to the total benefits and costs of the system. Portfolio diagrams are created that help communicate alternatives among stakeholders who have differing views on the tradeoffs between security and economic activity.

A framework for analyzing the economic tradeoffs between urban commerce and security against terrorism.

This article presents a framework for economic consequence analysis of terrorism countermeasures. It specifies major categories of direct and indirect costs, benefits, spillover effects, and transfer payments that must be estimated in a comprehensive assessment. It develops a spreadsheet tool for data collection, storage, and refinement, as well as estimation of the various components of the necessary economic accounts. It also illustrates the usefulness of the framework in the first assessment of the tradeoffs between enhanced security and changes in commercial activity in an urban area, with explicit attention to the role of spillover effects. The article also contributes a practical user interface to the model for emergency managers.

A mathematical model to study low-dose metronomic scheduling for chemotherapy.

Metronomic chemotherapy refers to the frequent administration of chemotherapeutic agents at a lower dose and presents an attractive alternative to conventional chemotherapy with encouraging response rates. However, the schedule of the therapy, including the dosage of the drug, is usually based on empiricism. The confounding effects of tumor-endothelial-immune interactions during metronomic administration of drugs have not yet been explored in detail, resulting in an incomplete assessment of drug dose and frequency evaluations. The present study aimed to gain a mechanistic understanding of different actions of metronomic chemotherapy using a mathematical model. We have established an analytical condition for determining the dosage and frequency of the drug depending on its clearance rate for complete tumor elimination. The model also brings forward the immune-mediated clearance of the tumor during the metronomic administration of the chemotherapeutic agent. The results from the global sensitivity analysis showed an increase in the sensitivity of drug and immune-mediated killing factors toward the tumor population during metronomic scheduling. Our results emphasize metronomic scheduling over the maximum tolerated dose (MTD) and define a model-based approach for approximating the optimal schedule of drug administration to eliminate tumors while minimizing harm to the immune cells and the patient's body.

Exploration of functional relations among differentially co-expressed genes identifies regulators in glioblastoma.

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the disease framework. Such context-specificity can be explored using clinical data by evaluating the change in interaction between the biological entities in different conditions by investigating the differential co-expression relationships. We believe that the integration and analysis of differential co-expression and the functional relationships, primarily focusing on the source nodes, will open novel insights about disease progression as the source proteins could trigger signaling cascades, mostly because they are transcription factors, cell surface receptors, or enzymes that respond instantly to a particular stimulus. A thorough contextual investigation of these nodes could lead to a helpful beginning point for identifying potential causal linkages and guiding subsequent scientific investigations to uncover mechanisms underlying observed associations. Our methodology includes functional protein-protein Interaction (PPI) data and co-expression information and filters functional linkages through a series of critical steps, culminating in the identification of a robust set of regulators. Our analysis identified eleven key regulators-AKT1, BRCA1, CAMK2G, CUL1, FGFR3, KIF3A, NUP210, PRKACB, RAB8A, RPS6KA2 and TGFB3-in glioblastoma. These regulators play a pivotal role in disease classification, cell growth control, and patient survivability and exhibit associations with immune infiltrations and disease hallmarks. This underscores the importance of assessing correlation towards causality in unraveling complex biological insights.

Bloodborne viral infections: Seroprevalence and relevance of preoperative screening in Indian eye care system - A retrospective study.

PURPOSE: To report the seroprevalence of bloodborne viral infection (BBVI) in patients undergoing ophthalmic surgeries and assess the utility and feasibility of preoperative screening for BBVI in India's current eye care system. METHODS: This retrospective, hospital-based, descriptive study included data from patients undergoing preoperative screening for Hepatitis B virus (HBV), Hepatitis C virus (HCV), and human immunodeficiency virus (HIV) at a tertiary eye care institute from 2018 to 2022. Rapid diagnostic tests (RDTs) were performed on the blood samples after obtaining informed consent from the patients. Seroreactive patients underwent surgery with additional safety precautions. The demographic data and surgical details of these patients were collected and analyzed. ANOVA was used to carry out statistical analysis between groups. During the study period, the number of healthcare workers (HCWs) sustaining needle stick injury (NSI) and accidental sharp injury (ASI) in the operating theater (OT) and details of these injuries were recorded. RESULTS: Samples from 28,563 patients were included. The seroprevalence of BBVI was 1.87% (536/28563). Hepatitis B virus (322, 60.1%) was the most commonly detected infection, and HIV (59, 11%) was the least detected infection. The mean age of the seroreactive patients was 60.3 ± 30.8 years. The incidence of NSI was 0.49/1000 surgeries. Nurses (11) and technicians (4) in the OT sustained maximum NSI. None of the HCWs had seroconversion after NSI. CONCLUSION: The overall seroprevalence of BBVI in the current study is lower than that reported in previously published studies from eye care organizations. Currently, mandatory preoperative screening for BBVI to prevent transmission of these infections to HCWs working in the eye care sector in India appears to be less cost-effective.