Tracing the footsteps of autophagy in computational biology

From homeostasis to defense: Exploring the role of selective autophagy in innate immunity and viral infections

The process of autophagy, a conservative evolutionary mechanism, is responsible for the removal of surplus and undesirable cytoplasmic components, thereby ensuring cellular homeostasis. Autophagy exhibits a remarkable level of selectivity by employing a multitude of cargo receptors that possess the ability to bind both ubiquitinated cargoes and autophagosomes. In the context of viral infections, selective autophagy plays a crucial role in regulating the innate immune system. Notably, numerous viruses have developed strategies to counteract, evade, or exploit the antiviral effects of selective autophagy. This review encompasses the latest research progress of selective autophagy in regulating innate immunity and virus infectious.

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.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Towards AI-driven longevity research: An overview

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

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.

Bidirectional regulation between AP-1 and SUMO genes modulates inflammatory signalling 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) that causes gastroenteritis, utilizes PTM mechanism SUMOylation to highjack host cell. STm suppresses host SUMO-pathway genes Ubc9 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, a component of AP-1 (Activator Protein-1), to promoters of both UBC9 and PIAS1 was observed. Experimental perturbation of c-Fos led to changes in 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, resulting in massive immune activation. Infection of c-FOS-KOSUMO-def-FOS cells favored STm replication, indicating misdirected immune mechanisms. Finally, chromatin Immuno-precipitation assays confirmed a context dependent differential binding and release of AP-1 to/from target genes due to its Phosphorylation and SUMOylation respectively. Overall, our data point towards existence of a bidirectional cross-talk between c-Fos and the SUMO pathway and highlighting its importance in AP-1 function relevant to STm infection and beyond.

Perturbation-Based Modeling Unveils the Autophagic Modulation of Chemosensitivity and Immunogenicity in Breast Cancer Cells

In the absence of new therapeutic strategies, chemotherapeutic drugs are the most widely used strategy against metastatic breast cancer, in spite of eliciting multiple adverse effects and having low responses with an average 5-year patient survival rate. Among the new therapeutic targets that are currently in clinical trials, here, we addressed the association between the regulation of the metabolic process of autophagy and the exposure of damage-associated molecular patterns associated (DAMPs) to immunogenic cell death (ICD), which has not been previously studied. After validating an mCHR-GFP tandem LC3 sensor capacity to report dynamic changes of the autophagic metabolic flux in response to external stimuli and demonstrating that both basal autophagy levels and response to diverse autophagy regulators fluctuate among different cell lines, we explored the interaction between autophagy modulators and chemotherapeutic agents in regards of cytotoxicity and ICD using three different breast cancer cell lines. Since these interactions are very complex and variable throughout different cell lines, we designed a perturbation-based model in which we propose specific modes of action of chemotherapeutic agents on the autophagic flux and the corresponding strategies of modulation to enhance the response to chemotherapy. Our results point towards a promising therapeutic potential of the metabolic regulation of autophagy to overcome chemotherapy resistance by eliciting ICD.

The interplay between DNA damage and autophagy in lung cancer: A mathematical study

The rising mortality in lung cancer, as well as the constraints of the existing drugs, have made it a major research topic. DNA damage marks the early onset of cancer as it often results from vulnerabilities due to UV rays, oxidative stress, ionizing radiation, and various types of genotoxic attacks. p53 plays an unequivocal role in the DNA repair process and has an abiding presence at the crossroads of the pathways linking DNA damage and cancer. p53 also regulates autophagy in a dual manner based on its cellular localization. The plexus of autophagy regulated by p53 includes AMPK and BCL2, which are positive and negative regulators of prime autophagy inducer beclin1, respectively. Although autophagy is a quintessential process, its levels need to be monitored as uncontrolled autophagy may lead to cell death. The association of p53 and autophagic cell death is very vital as the former acts whenever any threat comes to DNA while the latter may play a role in getting rid of the culprit cell. Therefore, in this paper, we have formulated a seven-dimensional mathematical model connecting p53, DNA damage, and autophagy in lung cancer. We performed both local and global sensitivity analysis along with parameter recalibration analysis to understand the system dynamics. We hypothesized that, by the modulation of beclin1 level, the regulation of AMPK and BCL2 could be a possible strategy to mitigate the progression of lung cancer.