Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism

Metabolic regulation of neutrophil functions in homeostasis and diseases

Neutrophils are the most abundant leukocytes in humans and play a role in the innate immune response by being the first cells attracted to the site of infection. While early studies presented neutrophils as almost exclusively glycolytic cells, recent advances show that these cells use several metabolic pathways other than glycolysis, such as the pentose phosphate pathway, oxidative phosphorylation, fatty acid oxidation, and glutaminolysis, which they modulate to perform their functions. Metabolism shifts from fatty acid oxidation-mediated mitochondrial respiration in immature neutrophils to glycolysis in mature neutrophils. Tissue environments largely influence neutrophil metabolism according to nutrient sources, inflammatory mediators, and oxygen availability. Inhibition of metabolic pathways in neutrophils results in impairment of certain effector functions, such as NETosis, chemotaxis, degranulation, and reactive oxygen species generation. Alteration of these neutrophil functions is implicated in certain human diseases, such as antiphospholipid syndrome, coronavirus disease 2019, and bronchiectasis. Metabolic regulators such as AMPK, HIF-1α, mTOR, and Arf6 are linked to neutrophil metabolism and function and could potentially be targeted for the treatment of diseases associated with neutrophil dysfunction. This review details the effects of alterations in neutrophil metabolism on the effector functions of these cells.

Sdc4 deletion perturbs intervertebral disc matrix homeostasis and promotes early osteopenia in the aging mouse spine

Syndecan 4 (SDC4), a cell surface heparan sulfate proteoglycan, is known to regulate matrix catabolism by nucleus pulposus cells in an inflammatory milieu. However, the role of SDC4 in the aging spine has never been explored. Here we analyzed the spinal phenotype of Sdc4 global knockout (KO) mice as a function of age. Micro-computed tomography showed that Sdc4 deletion severely reduced vertebral trabecular and cortical bone mass, and biomechanical properties of vertebrae were significantly altered in Sdc4 KO mice. These changes in vertebral bone were likely due to elevated osteoclastic activity. The histological assessment showed subtle phenotypic changes in the intervertebral disc. Imaging-Fourier transform-infrared analyses showed a reduced relative ratio of mature collagen crosslinks in young adult nucleus pulposus (NP) and annulus fibrosus (AF) of KO compared to wildtype discs. Additionally, relative chondroitin sulfate levels increased in the NP compartment of the KO mice. Transcriptomic analysis of NP tissue using CompBio, an AI-based tool showed biological themes associated with prominent dysregulation of heparan sulfate GAG degradation, mitochondria metabolism, autophagy, endoplasmic reticulum (ER)-associated misfolded protein processes and ER to Golgi protein processing. Overall, this study highlights the important role of SDC4 in fine-tuning vertebral bone homeostasis and extracellular matrix homeostasis in the mouse intervertebral disc.

Biochemical Aspects That Lead to Abusive Use of Trimetazidine in Performance Athletes: A Mini-Review

Trimetazidine (TMZ), used for treating stable angina pectoris, has garnered attention in the realm of sports due to its potential performance-enhancing properties, and the World Anti-Doping Agency (WADA) has classified TMZ on the S4 list of prohibited substances since 2014. The purpose of this narrative mini-review is to emphasize the biochemical aspects underlying the abusive use of TMZ among athletes as a metabolic modulator of cardiac energy metabolism. The myocardium's ability to adapt its energy substrate utilization between glucose and fatty acids is crucial for maintaining cardiac function under various conditions, such as rest, moderate exercise, and intense effort. TMZ acts as a partial inhibitor of fatty acid oxidation by inhibiting 3-ketoacyl-CoA thiolase (KAT), shifting energy production from long-chain fatty acids to glucose, reducing oxygen consumption, improving cardiac function, and enhancing exercise capacity. Furthermore, TMZ modulates pyruvate dehydrogenase (PDH) activity, promoting glucose oxidation while lowering lactate production, and ultimately stabilizing myocardial function. TMZs role in reducing oxidative stress is notable, as it activates antioxidant enzymes like glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD). In conclusion, TMZs biochemical mechanisms make it an attractive but controversial option for athletes seeking a competitive edge.

Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation

Glucose plays a central role in tumor metabolism and development and is a target for novel therapeutics. To characterize the response of cancer cells to blockade of glucose uptake, we collected time-resolved microscopy data to track the growth of MDA-MB-231 breast cancer cells. We then developed a mechanism-based, mathematical model to predict how a glucose transporter (GLUT1) inhibitor (Cytochalasin B) influences the growth of the MDA-MB-231 cells by limiting access to glucose. The model includes a parameter describing dose dependent inhibition to quantify both the total glucose level in the system and the glucose level accessible to the tumor cells. Four common machine learning models were also used to predict tumor cell growth. Both the mechanism-based and machine learning models were trained and validated, and the prediction error was evaluated by the coefficient of determination (R²). The random forest model provided the highest accuracy predicting cell dynamics (R² = 0.92), followed by the decision tree (R² = 0.89), k-nearest-neighbor regression (R² = 0.84), mechanism-based (R² = 0.77), and linear regression model (R² = 0.69). Thus, the mechanism-based model has a predictive capability comparable to machine learning models with the added benefit of elucidating biological mechanisms.

Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .

Dihydrolipoamide dehydrogenase, pyruvate oxidation, and acetylation-dependent mechanisms intersecting drug iatrogenesis

In human metabolism, pyruvate dehydrogenase complex (PDC) is one of the most intricate and large multimeric protein systems representing a central hub for cellular homeostasis. The worldwide used antiepileptic drug valproic acid (VPA) may potentially induce teratogenicity or a mild to severe hepatic toxicity, where the underlying mechanisms are not completely understood. This work aims to clarify the mechanisms that intersect VPA-related iatrogenic effects to PDC-associated dihydrolipoamide dehydrogenase (DLD; E3) activity. DLD is also a key enzyme of α-ketoglutarate dehydrogenase, branched-chain α-keto acid dehydrogenase, α-ketoadipate dehydrogenase, and the glycine decarboxylase complexes. The molecular effects of VPA will be reviewed underlining the data that sustain a potential interaction with DLD. The drug-associated effects on lipoic acid-related complexes activity may induce alterations on the flux of metabolites through tricarboxylic acid cycle, branched-chain amino acid oxidation, glycine metabolism and other cellular acetyl-CoA-connected reactions. The biotransformation of VPA involves its complete β-oxidation in mitochondria causing an imbalance on energy homeostasis. The drug consequences as histone deacetylase inhibitor and thus gene expression modulator have also been recognized. The mitochondrial localization of PDC is unequivocal, but its presence and function in the nucleus were also demonstrated, generating acetyl-CoA, crucial for histone acetylation. Bridging metabolism and epigenetics, this review gathers the evidence of VPA-induced interference with DLD or PDC functions, mainly in animal and cellular models, and highlights the uncharted in human. The consequences of this interaction may have significant impact either in mitochondrial or in nuclear acetyl-CoA-dependent processes.

Allele-Specific MicroRNA-Mediated Regulation of a Glycolysis Gatekeeper PDK1 in Cancer Metabolism

BACKGROUND

Emerging evidence has revealed that genetic variations in microRNA (miRNA) binding sites called miRSNPs can alter miRNA binding in an allele-specific manner and impart prostate cancer (PCa) risk. Two miRSNPs, rs1530865 (G > C) and rs2357637 (C > A), in the 3' untranslated region of pyruvate dehydrogenase kinase 1 (PDK1) have been previously reported to be associated with PCa risk. However, these results have not been functionally validated.

METHODS

In silico analysis was used to predict miRNA-PDK1 interactions and was tested using PDK1 knockdown, miRNA overexpression and reporter gene assay.

RESULTS

PDK1 expression was found to be upregulated in PCa metastasis. Further, our results show that PDK1 suppression reduced the migration, invasion, and glycolysis of PCa cells. Computational predictions showed that miR-3916, miR-3125 and miR-3928 had a higher binding affinity for the C allele than the G allele for the rs1530865 miRSNP which was validated by reporter gene assays. Similarly, miR-2116 and miR-889 had a higher affinity for the A than C allele of the rs2357637 miRSNP. Overexpression of miR-3916 and miR-3125 decreased PDK1 protein levels in cells expressing the rs1530865 SNP C allele, and miR-2116 reduced in cells with the rs2357637 SNP A allele.

CONCLUSIONS

The present study is the first to report the regulation of the PDK1 gene by miRNAs in an allele-dependent manner and highlights the role of PDK1 in metabolic adaption associated with PCa progression.

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

Mitochondrial Dysfunction: Cause or Consequence of Vascular Calcification?

Mitochondria are crucial bioenergetics powerhouses and biosynthetic hubs within cells, which can generate and sequester toxic reactive oxygen species (ROS) in response to oxidative stress. Oxidative stress-stimulated ROS production results in ATP depletion and the opening of mitochondrial permeability transition pores, leading to mitochondria dysfunction and cellular apoptosis. Mitochondrial loss of function is also a key driver in the acquisition of a senescence-associated secretory phenotype that drives senescent cells into a pro-inflammatory state. Maintaining mitochondrial homeostasis is crucial for retaining the contractile phenotype of the vascular smooth muscle cells (VSMCs), the most prominent cells of the vasculature. Loss of this contractile phenotype is associated with the loss of mitochondrial function and a metabolic shift to glycolysis. Emerging evidence suggests that mitochondrial dysfunction may play a direct role in vascular calcification and the underlying pathologies including (1) impairment of mitochondrial function by mineral dysregulation i.e., calcium and phosphate overload in patients with end-stage renal disease and (2) presence of increased ROS in patients with calcific aortic valve disease, atherosclerosis, type-II diabetes and chronic kidney disease. In this review, we discuss the cause and consequence of mitochondrial dysfunction in vascular calcification and underlying pathologies; the role of autophagy and mitophagy pathways in preventing mitochondrial dysfunction during vascular calcification and finally we discuss mitochondrial ROS, DRP1, and HIF-1 as potential novel markers and therapeutic targets for maintaining mitochondrial homeostasis in vascular calcification.

Hypoxia-induced alternative splicing: the 11th Hallmark of Cancer

Hypoxia-induced alternative splicing is a potent driving force in tumour pathogenesis and progression. In this review, we update currents concepts of hypoxia-induced alternative splicing and how it influences tumour biology. Following brief descriptions of tumour-associated hypoxia and the pre-mRNA splicing process, we review the many ways hypoxia regulates alternative splicing and how hypoxia-induced alternative splicing impacts each individual hallmark of cancer. Hypoxia-induced alternative splicing integrates chemical and cellular tumour microenvironments, underpins continuous adaptation of the tumour cellular microenvironment responsible for metastatic progression and plays clear roles in oncogene activation and autonomous tumour growth, tumor suppressor inactivation, tumour cell immortalization, angiogenesis, tumour cell evasion of programmed cell death and the anti-tumour immune response, a tumour-promoting inflammatory response, adaptive metabolic re-programming, epithelial to mesenchymal transition, invasion and genetic instability, all of which combine to promote metastatic disease. The impressive number of hypoxia-induced alternative spliced protein isoforms that characterize tumour progression, classifies hypoxia-induced alternative splicing as the 11th hallmark of cancer, and offers a fertile source of potential diagnostic/prognostic markers and therapeutic targets.

Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues

Cancer cells are known to exhibit unusual metabolic activity, and yet few metabolic cancer driver genes are known. Genetic alterations and epigenetic modifications of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared with normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-specific cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top-ranked one-hit dysregulations and 17 of the top-ranked two-hit oncogenes with high similarity ratios to the templates. According to the literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design

BACKGROUND

The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates. Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes.

RESULTS

After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids.

CONCLUSIONS

The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.

The poly-omics of ageing through individual-based metabolic modelling

BACKGROUND

Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype.

RESULTS

We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor.

CONCLUSIONS

We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells.

In silico engineering of Pseudomonas metabolism reveals new biomarkers for increased biosurfactant production

BACKGROUND

Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.

METHODS

We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.

RESULTS

We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida.

CONCLUSIONS

We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.