Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

Metabolic reprogramming and signalling cross-talks in tumour-immune interaction: a system-level exploration

Tumour-immune microenvironment (TIME) is pivotal in tumour progression and immunoediting. Within TIME, immune cells undergo metabolic adjustments impacting nutrient supply and the anti-tumour immune response. Metabolic reprogramming emerges as a promising approach to revert the immune response towards a pro-inflammatory state and conquer tumour dominance. This study proposes immunomodulatory mechanisms based on metabolic reprogramming and employs the regulatory flux balance analysis modelling approach, which integrates signalling, metabolism and regulatory processes. For the first time, a comprehensive system-level model is constructed to capture signalling and metabolic cross-talks during tumour-immune interaction and regulatory constraints are incorporated by considering the time lag between them. The model analysis identifies novel features to enhance the immune response while suppressing tumour activity. Particularly, altering the exchange of succinate and oxaloacetate between glioma and macrophage enhances the pro-inflammatory response of immune cells. Inhibition of glutamate uptake in T-cells disrupts the antioxidant mechanism of glioma and reprograms metabolism. Metabolic reprogramming through adenosine monophosphate (AMP)-activated protein kinase (AMPK), coupled with glutamate uptake inhibition, was identified as the most impactful combination to restore T-cell function. A comprehensive understanding of metabolism and gene regulation represents a favourable approach to promote immune cell recovery from tumour dominance.

GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids

Mathematical models of non-small-cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with "omics" approaches will improve our understanding of the nature of non-small-cell lung cancer. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

The global integrative network: integration of signaling and metabolic pathways

The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel "meta-pathway" structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species-human, mouse, Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42994-022-00078-1.

Pathway integration and annotation: building a puzzle with non-matching pieces and no reference picture

Biological pathways are a broadly used formalism for representing and interpreting the cascade of biochemical reactions underlying cellular and biological mechanisms. Pathway representation provides an ontological link among biomolecules such as RNA, DNA, small molecules, proteins, protein complexes, hormones and genes. Frequently, pathway annotations are used to identify mechanisms linked to genes within affected biological contexts. This important role and the simplicity and elegance in representing complex interactions led to an explosion of pathway representations and databases. Unfortunately, the lack of overlap across databases results in inconsistent enrichment analysis results, unless databases are integrated. However, due to absence of consensus, guidelines or gold standards in pathway definition and representation, integration of data across pathway databases is not straightforward. Despite multiple attempts to provide consolidated pathways, highly related, redundant, poorly overlapping or ambiguous pathways continue to render pathways analysis inconsistent and hard to interpret. Ontology-based integration will promote unbiased, comprehensive yet streamlined analysis of experiments, and will reduce the number of enriched pathways when performing pathway enrichment analysis. Moreover, appropriate and consolidated pathways provide better training data for pathway prediction algorithms. In this manuscript, we describe the current methods for pathway consolidation, their strengths and pitfalls, and highlight directions for future improvements to this research area.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Analysis of Pan-omics Data in Human Interactome Network (APODHIN)

Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Patterns and Direct/Indirect Signaling Pathways in Cardiovascular System in the Condition of Transient Increase of NO

Aim

To study “patterns” and connections of signaling pathways derived from the rat arterial pulse waveform (APW) under the condition of transient NO increase.

Methods and Results

The right jugular vein of anesthetized Wistar rats was cannulated for administration of NO donor S-nitrosoglutathione. The left carotid artery was cannulated to detect APW. From rat APW, 35 hemodynamic parameters (HPs) and several their crossrelationships were evaluated. We introduced a new methodology to study “patterns” and connections of different signaling pathways, which are suggested from hysteresis and nonhysteresis crossrelationships of 35 rat HPs. Here, we show parallel time-dependent patterns of 35 HPs and some of their crossrelationships under the condition of transient increase of NO bioavailability by administration of S-nitrosoglutathione. Approximate nonhysteresis relationships were observed between systolic blood pressure and at least 11 HPs suggesting that these HPs, i.e., their signaling pathways, responding to NO concentration, are directly connected. Hysteresis relationships were observed between systolic blood pressure and at least 14 HPs suggesting that the signaling pathways of these HPs are indirectly connected. Totally, from the crossrelationships of 35 HPs, one can obtain 595 “patterns” and indication of direct or indirect connections between the signaling pathways.

Conclusion

We described the procedure leading virtually to 595 relationships, from which “patterns” for transient NO increase and direct or indirect connections of signaling pathways can be suggested. From a clinical perspective, this approach may be used in animal models and in humans to create a data bank of patterns of crossrelationships of HPs for different cardiovascular conditions. By comparison with unknown patterns of studied APW with the data bank patterns, it would be possible to determine cardiovascular conditions of the studied subject from the recorded arterial blood pressure. Additionally, it can help to find molecular mechanism of particular (patho-) physiological conditions or drug action and may have predictive or diagnostic value.

Metabolic cell communication within tumour microenvironment: models, methods and perspectives

Environmental cues are essential in defining tumour malignancy, by promoting tumour initiation, progression and metastatic spreading. Stromal cells may metabolically cooperate or compete with cancer cells, playing a mandatory role in defining cancer metabolic plasticity, potentially dictating the final tumour outcome. Assessing shared nutrients between different tumoural or stromal compartments is essential to understand the impact of environmental nutrients on the metabolic plasticity of tumours. Here, we review analytical and computational approaches for studying the tumour metabolic microenvironment, the destiny of nutrients shared among tumour and stromal populations, as well as the molecular modules of these metabolic relationships.

Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours

Imaging mass spectrometry (IMS) has been rarely used to examine specimens of human brain tumours. In the current study, high quality brain tumour samples were selected by tissue observation. Further, IMS analysis was combined with a new hierarchical cluster analysis (IMS-HCA) and region of interest analysis (IMS-ROI). IMS-HCA was successful in creating groups consisting of similar signal distribution images of glial fibrillary acidic protein (GFAP) and related multiple proteins in primary brain tumours. This clustering data suggested the relation of GFAP and these identified proteins in the brain tumorigenesis. Also, high levels of histone proteins, haemoglobin subunit α, tubulins, and GFAP were identified in a metastatic brain tumour using IMS-ROI. Our results show that IMS-HCA and IMS-ROI are promising techniques for identifying biomarkers using brain tumour samples.