Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling

Dissecting metabolic landscape of alveolar macrophage

The highly plastic nature of Alveolar Macrophage (AM) plays a crucial role in the defense against inhaled particulates and pathogens in the lungs. Depending on the signal, AM acquires either the classically activated M1 phenotype or the alternatively activated M2 phenotype. In this study, we investigate the metabolic shift in the activated phases of AM (M1 and M2 phases) by reconstructing context specific Genome-Scale Metabolic (GSM) models. Metabolic pathways such as pyruvate metabolism, arachidonic acid metabolism, chondroitin/heparan sulfate biosynthesis, and heparan sulfate degradation are found to be important driving forces in the development of the M1/M2 phenotypes. Additionally, we formulated a bilevel optimization framework named MetaShiftOptimizer to identify minimal modifications that shift one activated state (M1/M2) to the other. The identified reactions involve metabolites such as glycogenin, L-carnitine, 5-hydroperoxy eicosatetraenoic acid, and leukotriene B4, which show potential to be further investigated as significant factors for developing efficient therapy targets for severe respiratory disorders in the future. Overall, our study contributes to the understanding of the metabolic capabilities of the M1 and M2 phenotype of AM and identifies pathways and reactions that can be potential targets for polarization shift and also be used as therapeutic strategies against respiratory diseases.

Glutathione Peroxidases: An Emerging and Promising Therapeutic Target for Pancreatic Cancer Treatment

Glutathione peroxidases (GPxs) are a family of enzymes that play a critical role in cellular redox homeostasis through the reduction of lipid hydroperoxides to alcohols, using glutathione as a substrate. Among them, GPx4 is particularly of interest in the regulation of ferroptosis, a form of iron-dependent programmed cell death driven by the accumulation of lipid peroxides in the endoplasmic reticulum, mitochondria, and plasma membrane. Ferroptosis has emerged as a crucial pathway in the context of cancer, particularly pancreatic cancer, which is notoriously resistant to conventional therapies. GPx4 acts as a key inhibitor of ferroptosis by detoxifying lipid peroxides, thereby preventing cell death. However, this protective mechanism also enables cancer cells to survive under oxidative stress, which makes GPx4 a potential druggable target in cancer therapy. The inhibition of GPx4 can trigger ferroptosis selectively in cancer cells, especially in those that rely heavily on this pathway for survival, such as pancreatic cancer cells. Consequently, targeting GPx4 and other GPX family members offers a promising therapeutic strategy to sensitize pancreatic cancer cells to ferroptosis, potentially overcoming resistance to current treatments and improving patient outcomes. Current research is focusing on the development of small-molecule inhibitors of GPx4 as potential candidates for pancreatic cancer treatment.

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Protocol for genome-scale differential flux analysis to interrogate metabolic differences from gene expression data

Deciphering the functional differences between diseased and healthy cells requires understanding the alterations in biochemical flux patterns. We present a genome-scale differential flux analysis (GS-DFA) protocol to elucidate these metabolic disparities by integrating condition-specific gene expression data into the human genome-scale metabolic model (humanGEM). In this protocol, we describe the steps to normalize and integrate data into the humanGEM and analyze differential flux across the biochemical network between diseased and healthy cells. For complete details on the use and execution of this protocol, please refer to Nanda et al.1.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models

Background

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually.

Results

We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends.

Conclusions

MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.

Cancer-associated fibroblast-derived acetate promotes pancreatic cancer development by altering polyamine metabolism via the ACSS2-SP1-SAT1 axis

The ability of tumour cells to thrive in harsh microenvironments depends on the utilization of nutrients available in the milieu. Here we show that pancreatic cancer-associated fibroblasts (CAFs) regulate tumour cell metabolism through the secretion of acetate, which can be blocked by silencing ATP citrate lyase (ACLY) in CAFs. We further show that acetyl-CoA synthetase short-chain family member 2 (ACSS2) channels the exogenous acetate to regulate the dynamic cancer epigenome and transcriptome, thereby facilitating cancer cell survival in an acidic microenvironment. Comparative H3K27ac ChIP-seq and RNA-seq analyses revealed alterations in polyamine homeostasis through regulation of SAT1 gene expression and enrichment of the SP1-responsive signature. We identified acetate/ACSS2-mediated acetylation of SP1 at the lysine 19 residue that increased SP1 protein stability and transcriptional activity. Genetic or pharmacologic inhibition of the ACSS2-SP1-SAT1 axis diminished the tumour burden in mouse models. These results reveal that the metabolic flexibility imparted by the stroma-derived acetate enabled cancer cell survival under acidosis via the ACSS2-SP1-SAT1 axis.

Metabolic Pathways as a Novel Landscape in Pancreatic Ductal Adenocarcinoma

Metabolism plays a fundamental role in both human physiology and pathology, including pancreatic ductal adenocarcinoma (PDAC) and other tumors. Anabolic and catabolic processes do not only have energetic implications but are tightly associated with other cellular activities, such as DNA duplication, redox reactions, and cell homeostasis. PDAC displays a marked metabolic phenotype and the observed reduction in tumor growth induced by calorie restriction with in vivo models supports the crucial role of metabolism in this cancer type. The aggressiveness of PDAC might, therefore, be reduced by interventions on bioenergetic circuits. In this review, we describe the main metabolic mechanisms involved in PDAC growth and the biological features that may favor its onset and progression within an immunometabolic context. We also discuss the need to bridge the gap between basic research and clinical practice in order to offer alternative therapeutic approaches for PDAC patients in the more immediate future.