GEM-Based Metabolic Profiling for Human Bone Osteosarcoma under Different Glucose and Glutamine Availability

Unraveling the glycosphingolipid metabolism by leveraging transcriptome-weighted network analysis on neuroblastic tumors

BACKGROUND

Glycosphingolipids (GSLs) are membrane lipids composed of a ceramide backbone linked to a glycan moiety. Ganglioside biosynthesis is a part of the GSL metabolism, which involves sequential reactions catalyzed by specific enzymes that in part have a poor substrate specificity. GSLs are deregulated in cancer, thus playing a role as potential biomarkers for personalized therapy or subtype classification. However, the analysis of GSL profiles is complex and requires dedicated technologies, that are currently not included in the commonly utilized high-throughput assays adopted in contexts such as molecular tumor boards.

METHODS

In this study, we developed a method to discriminate the enzyme activity among the four series of the ganglioside metabolism pathway by incorporating transcriptome data and topological information of the metabolic network. We introduced three adjustment options for reaction activity scores (RAS) and demonstrated their application in both exploratory and comparative analyses by applying the method on neuroblastic tumors (NTs), encompassing neuroblastoma (NB), ganglioneuroblastoma (GNB), and ganglioneuroma (GN). Furthermore, we interpreted the results in the context of earlier published GSL measurements in the same tumors.

RESULTS

By adjusting RAS values using a weighting scheme based on network topology and transition probabilities (TPs), the individual series of ganglioside metabolism can be differentiated, enabling a refined analysis of the GSL profile in NT entities. Notably, the adjustment method we propose reveals the differential engagement of the ganglioside series between NB and GNB. Moreover, MYCN gene expression, a well-known prognostic marker in NTs, appears to correlate with the expression of therapeutically relevant gangliosides, such as GD2. Using unsupervised learning, we identified subclusters within NB based on altered GSL metabolism.

CONCLUSION

Our study demonstrates the utility of adjusting RAS values in discriminating ganglioside metabolism subtypes, highlighting the potential for identifying novel cancer subgroups based on sphingolipid profiles. These findings contribute to a better understanding of ganglioside dysregulation in NT and may have implications for stratification and targeted therapeutic strategies in these tumors and other tumor entities with a deregulated GSL metabolism.

Functional Amino Acids in the Regulation of Bone and Its Diseases

Bone as a vigorous tissue is constantly undergoing bone remodeling. The homeostasis of bone remodeling requires combined efforts of multifarious bone cells. Amino acids (AA), known as essential components of life support, are closely related to the regulation of bone homeostasis. In recent years, the concept of functional amino acids (FAAs) has been proposed, which is defined as AA that regulate key metabolic pathways to improve health, survival, growth, development, lactation, and reproduction of organisms, to highlight their outstanding contributions in the body. In the hope of exploring new therapeutic strategies, this review focus on summarizing recent progress in the vital role of FAAs in bone homeostasis maintaining and potential implications of FAAs in bone-related diseases, and discussing related mechanisms. The results showed that FAAs are closely related to bone metabolism and therapeutic strategy targeting FAAs metabolism is one of the future trends for bone disorders, while the explorations about possible impact of FAAs-based diets are still limited.

Mitochondrial enzyme FAHD1 reduces ROS in osteosarcoma

This study investigated the impact of overexpressing the mitochondrial enzyme Fumarylacetoacetate hydrolase domain-containing protein 1 (FAHD1) in human osteosarcoma epithelial cells (U2OS) in vitro. While the downregulation or knockdown of FAHD1 has been extensively researched in various cell types, this study aimed to pioneer the exploration of how increased catalytic activity of human FAHD1 isoform 1 (hFAHD1.1) affects human cell metabolism. Our hypothesis posited that elevation in FAHD1 activity would lead to depletion of mitochondrial oxaloacetate levels. This depletion could potentially result in a decrease in the flux of the tricarboxylic acid (TCA) cycle, thereby accompanied by reduced ROS production. In addition to hFAHD1.1 overexpression, stable U2OS cell lines were established overexpressing a catalytically enhanced variant (T192S) and a loss-of-function variant (K123A) of hFAHD1. It is noteworthy that homologs of the T192S variant are present in animals exhibiting increased resistance to oxidative stress and cancer. Our findings demonstrate that heightened activity of the mitochondrial enzyme FAHD1 decreases cellular ROS levels in U2OS cells. However, these results also prompt a series of intriguing questions regarding the potential role of FAHD1 in mitochondrial metabolism and cellular development.

NAD+ regulates nucleotide metabolism and genomic DNA replication

The intricate orchestration of enzymatic activities involving nicotinamide adenine dinucleotide (NAD+) is essential for maintaining metabolic homeostasis and preserving genomic integrity. As a co-enzyme, NAD+ plays a key role in regulating metabolic pathways, such as glycolysis and Kreb's cycle. ADP-ribosyltransferases (PARPs) and sirtuins rely on NAD+ to mediate post-translational modifications of target proteins. The activation of PARP1 in response to DNA breaks leads to rapid depletion of cellular NAD+ compromising cell viability. Therefore, the levels of NAD+ must be tightly regulated. Here we show that exogenous NAD+, but not its precursors, has a direct effect on mitochondrial activity. Short-term incubation with NAD+ boosts Kreb's cycle and the electron transport chain and enhances pyrimidine biosynthesis. Extended incubation with NAD+ results in depletion of pyrimidines, accumulation of purines, activation of the replication stress response and cell cycle arrest. Moreover, a combination of NAD+ and 5-fluorouridine selectively kills cancer cells that rely on de novo pyrimidine synthesis. We propose an integrated model of how NAD+ regulates nucleotide metabolism, with relevance to healthspan, ageing and cancer therapy.

Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer

Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.

Circular RNA-Related CeRNA Network and Prognostic Signature for Patients with Osteosarcoma

Introduction

Osteosarcoma (OSA) is characterized by its relatively high morbidity in children and adolescents. Patients usually have advanced disease at the time of diagnosis, resulting in poor outcomes. This study focused on building a circular RNA-based ceRNA network to develop a reliable model for OSA risk prediction.

Methods

We used the Gene Expression Omnibus (GEO) datasets to explore the expression patterns of circRNA, miRNA, and mRNA in OSA. The prognostic value of circRNA host genes was assessed with data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database using Kaplan–Meier survival analysis. We established a circRNA-related ceRNA network and annotated its biological functions. Next, we developed a prognostic risk signature based on mRNAs extracted from the ceRNA network. We also developed a prognostic model and constructed a nomogram to enhance the prediction of OSA prognosis.

Results

We identified 166 DEcircRNAs, 233 DEmiRNAs, and 1317 DEmRNAs and used them to create a circRNA-related ceRNA network. We then established a prognostic risk model consisting of four genes (MLLT11, TNFRSF11B, SLC7A7, and PARVA). Moreover, we found that inhibition of MLLT11 and SLC7A7 blocked OSA cell proliferation and migration in in vitro experiments.

Conclusion

Our study identifies crucial prognostic genes and provides a circRNA-related ceRNA network for OSA, which will contribute to the elucidation of the molecular mechanisms underlying the oncogenesis and development of OSA.

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).