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.

Identification of critical autophagy-related proteins in diabetic retinopathy: A multi-dimensional computational study.

Diabetic retinopathy (DR) is a common consequence of diabetes mellitus and a primary cause of visual impairment in middle-aged and elderly individuals. DR is susceptible to cellular degradation facilitated by autophagy. In this study, we have employed a multi-layer relatedness (MLR) approach to uncover novel autophagy-related proteins involved in DR. The objective of MLR is to determine the relatedness of autophagic and DR proteins by incorporating both expression and prior-knowledge-based similarities. We constructed a prior knowledge-based network and identified the topologically significant novel disease-related candidate autophagic proteins (CAPs). Then, we evaluated their significance in a gene co-expression and a differentially-expressed gene (DEG) network. Finally, we investigated the proximity of CAPs to the known disease-related proteins. Leveraging this methodology, we identified three crucial autophagy-related proteins, TP53, HSAP90AA1, and PIK3R1, which can influence the DR interactome in various layers of heterogeneity of clinical manifestations. They are strongly related to multiple detrimental characteristics of DR, such as pericyte loss, angiogenesis, apoptosis, and endothelial cell migration, and hence may be used to prevent or delay the progression and development of DR. We evaluated one of the identified targets, TP53, in a cell-based model and found that its inhibition resulted in reduced angiogenesis in high glucose condition required to control DR.

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.

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 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.

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.

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.

Extracting proteins involved in disease progression using temporally connected networks.

BACKGROUND: Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease. RESULTS: In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap. CONCLUSIONS: The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.