A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data

Single-Nucleus Transcriptomic Atlas of Human Pericoronary Epicardial Adipose Tissue in Normal and Pathological Conditions

BACKGROUND

Pericoronary epicardial adipose tissue (EAT) is a unique visceral fat depot that surrounds the adventitia of the coronary arteries without any anatomic barrier. Clinical studies have demonstrated the association between EAT volume and increased risks for coronary artery disease (CAD). However, the cellular and molecular mechanisms underlying the association remain elusive.

METHODS

We performed single-nucleus RNA sequencing on pericoronary EAT samples collected from 3 groups of subjects: patients undergoing coronary bypass surgery for severe CAD (n=8), patients with CAD with concomitant type 2 diabetes (n=8), and patients with valvular diseases but without concomitant CAD and type 2 diabetes as the control group (n=8). Comparative analyses were performed among groups, including cellular compositional analysis, cell type-resolved transcriptomic changes, gene coexpression network analysis, and intercellular communication analysis. Immunofluorescence staining was performed to confirm the presence of CAD-associated subclusters.

RESULTS

Unsupervised clustering of 73 386 nuclei identified 15 clusters, encompassing all known cell types in the adipose tissue. Distinct subpopulations were identified within primary cell types, including adipocytes, adipose stem and progenitor cells, and macrophages. CD83high macrophages and FOSBhigh adipocytes were significantly expanded in CAD. In comparison to normal controls, both disease groups exhibited dysregulated pathways and altered secretome in the primary cell types. Nevertheless, minimal differences were noted between the disease groups in terms of cellular composition and transcriptome. In addition, our data highlight a potential interplay between dysregulated circadian clock and altered physiological functions in adipocytes of pericoronary EAT. ANXA1 (annexin A1) and SEMA3B (semaphorin 3B) were identified as important adipokines potentially involved in functional changes of pericoronary EAT and CAD pathogenesis.

CONCLUSIONS

We built a complete single-nucleus transcriptomic atlas of human pericoronary EAT in normal and diseased conditions of CAD. Our study lays the foundation for developing novel therapeutic strategies for treating CAD by targeting and modifying pericoronary EAT functions.

Phosphoglycerate mutase regulates Treg differentiation through control of serine synthesis and one-carbon metabolism

The differentiation and suppressive functions of regulatory CD4 T cells (Tregs) are supported by a broad array of metabolic changes, providing potential therapeutic targets for immune modulation. In this study, we focused on the regulatory role of glycolytic enzymes in Tregs and identified phosphoglycerate mutase (PGAM) as being differentially overexpressed in Tregs and associated with a highly suppressive phenotype. Pharmacologic or genetic inhibition of PGAM reduced Treg differentiation and suppressive function while reciprocally inducing markers of a pro-inflammatory, T helper 17 (Th17)-like state. The regulatory role of PGAM was dependent on the contribution of 3-phosphoglycerate (3PG), the PGAM substrate, to de novo serine synthesis. Blocking de novo serine synthesis from 3PG reversed the effect of PGAM inhibition on Treg polarization, while exogenous serine directly inhibited Treg polarization. Additionally, altering serine levels in vivo with a serine/glycine-free diet increased peripheral Tregs and attenuated autoimmunity in a murine model of multiple sclerosis. Mechanistically, we found that serine limits Treg polarization by contributing to one-carbon metabolism and methylation of Treg-associated genes. Inhibiting one-carbon metabolism increased Treg polarization and suppressive function both in vitro and in vivo in a murine model of autoimmune colitis. Our study identifies a novel physiologic role for PGAM and highlights the metabolic interconnectivity between glycolysis, serine synthesis, one-carbon metabolism, and epigenetic regulation of Treg differentiation and suppressive function.

Single-cell transcriptomics reveals CD8+ T cell structure and developmental trajectories in idiopathic pulmonary fibrosis

Immune cells in the human lung are associated with idiopathic pulmonary fibrosis. However, the contribution of different immune cell subpopulations to the pathogenesis of pulmonary fibrosis remains unclear. We used single-cell RNA sequencing data to investigate the transcriptional profiles of immune cells in the lungs of 5 IPF patients and 3 subjects with non-fibrotic lungs. In an identifiable population of immune cells, we found increased percentage of CD8+ T cells in the T cell subpopulation in IPF. Monocle analyzed the dynamic immune status and cell transformation of CD8+ T cells, as well as the cytotoxicity and exhausted status of CD8+ T cell subpopulations at different stages. Among CD8+ T cells, we found differences in metabolic pathways in IPF and Ctrl, including lipid, amino acid and carbohydrate metabolic. By analyzing the metabolites of CD8+ T cells, we found that different populations of CD8+ T cells in IPF have unique metabolic characteristics, but they also have multiple identical up-regulated or down-regulated metabolites. In IPF, signaling pathways associated with fibrosis were enriched in CD8+ T cells, suggesting that CD8+ T cells may have an important contribution to fibrosis. Finally, we analyzed the interactions between CD8+ T cells and other cells. Together, these studies highlight key features of CD8+ T cells in the pathogenesis of IPF and help to develop effective therapeutic targets.

A pan-cancer single-cell transcriptional analysis of antigen-presenting cancer-associated fibroblasts in the tumor microenvironment

Background

Cancer-associated fibroblasts (CAFs) are the primary stromal cells found in tumor microenvironment, and display high plasticity and heterogeneity. By using single-cell RNA-seq technology, researchers have identified various subpopulations of CAFs, particularly highlighting a recently identified subpopulation termed antigen-presenting CAFs (apCAFs), which are largely unknown.

Methods

We collected datasets from public databases for 9 different solid tumor types to analyze the role of apCAFs in the tumor microenvironment.

Results

Our data revealed that apCAFs, likely originating mainly from normal fibroblast, are commonly found in different solid tumor types and generally are associated with anti-tumor effects. apCAFs may be associated with the activation of CD4+ effector T cells and potentially promote the survival of CD4+ effector T cells through the expression of C1Q molecules. Moreover, apCAFs exhibited highly enrichment of transcription factors RUNX3 and IKZF1, along with increased glycolytic metabolism.

Conclusions

Taken together, these findings offer novel insights into a deeper understanding of apCAFs and the potential therapeutic implications for apCAFs targeted immunotherapy in cancer.

Metabolic rewiring and communication in cancer immunity

The immune system shapes tumor development and progression. Although immunotherapy has transformed cancer treatment, its overall efficacy remains limited, underscoring the need to uncover mechanisms to improve therapeutic effects. Metabolism-associated processes, including intracellular metabolic reprogramming and intercellular metabolic crosstalk, are emerging as instructive signals for anti-tumor immunity. Here, we first summarize the roles of intracellular metabolic pathways in controlling immune cell function in the tumor microenvironment. How intercellular metabolic communication regulates anti-tumor immunity, and the impact of metabolites or nutrients on signaling events, are also discussed. We then describe how targeting metabolic pathways in tumor cells or intratumoral immune cells or via nutrient-based interventions may boost cancer immunotherapies. Finally, we conclude with discussions on profiling and functional perturbation methods of metabolic activity in intratumoral immune cells, and perspectives on future directions. Uncovering the mechanisms for metabolic rewiring and communication in the tumor microenvironment may enable development of novel cancer immunotherapies.

The novel molecular mechanism of pulmonary fibrosis: insight into lipid metabolism from reanalysis of single-cell RNA-seq databases

Pulmonary fibrosis (PF) is a severe pulmonary disease with limited available therapeutic choices. Recent evidence increasingly points to abnormal lipid metabolism as a critical factor in PF pathogenesis. Our latest research identifies the dysregulation of low-density lipoprotein (LDL) is a new risk factor for PF, contributing to alveolar epithelial and endothelial cell damage, and fibroblast activation. In this study, we first integrative summarize the published literature about lipid metabolite changes found in PF, including phospholipids, glycolipids, steroids, fatty acids, triglycerides, and lipoproteins. We then reanalyze two single-cell RNA-sequencing (scRNA-seq) datasets of PF, and the corresponding lipid metabolomic genes responsible for these lipids' biosynthesis, catabolism, transport, and modification processes are uncovered. Intriguingly, we found that macrophage is the most active cell type in lipid metabolism, with almost all lipid metabolic genes being altered in macrophages of PF. In type 2 alveolar epithelial cells, lipid metabolic differentially expressed genes (DEGs) are primarily associated with the cytidine diphosphate diacylglycerol pathway, cholesterol metabolism, and triglyceride synthesis. Endothelial cells are partly responsible for sphingomyelin, phosphatidylcholine, and phosphatidylethanolamines reprogramming as their metabolic genes are dysregulated in PF. Fibroblasts may contribute to abnormal cholesterol, phosphatidylcholine, and phosphatidylethanolamine metabolism in PF. Therefore, the reprogrammed lipid profiles in PF may be attributed to the aberrant expression of lipid metabolic genes in different cell types. Taken together, these insights underscore the potential of targeting lipid metabolism in developing innovative therapeutic strategies, potentially leading to extended overall survival in individuals affected by PF.

Single-cell omics analysis with genome-scale metabolic modeling

Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.

Comprehensive scRNA-seq Model Reveals Artery Endothelial Cell Heterogeneity and Metabolic Preference in Human Vascular Disease

Vascular disease is one of the major causes of death worldwide. Endothelial cells are important components of the vascular structure. A better understanding of the endothelial cell changes in the development of vascular disease may provide new targets for clinical treatment strategies. Single-cell RNA sequencing can serve as a powerful tool to explore transcription patterns, as well as cell type identity. Our current study is based on comprehensive scRNA-seq data of several types of human vascular disease datasets with deep-learning-based algorithm. A gene set scoring system, created based on cell clustering, may help to identify the relative stage of the development of vascular disease. Metabolic preference patterns were estimated using a graphic neural network model. Overall, our study may provide potential treatment targets for retaining normal endothelial function under pathological situations.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.

Acid-base Homeostasis and Implications to the Phenotypic Behaviors of Cancer

Acid-base homeostasis is a fundamental property of living cells, and its persistent disruption in human cells can lead to a wide range of diseases. In this study, we conducted a computational modeling analysis of transcriptomic data of 4750 human tissue samples of 9 cancer types in The Cancer Genome Atlas (TCGA) database. Built on our previous study, we quantitatively estimated the average production rate of OH- by cytosolic Fenton reactions, which continuously disrupt the intracellular pH (pHi) homeostasis. Our predictions indicate that all or at least a subset of 43 reprogrammed metabolisms (RMs) are induced to produce net protons (H+) at comparable rates of Fenton reactions to keep the pHi stable. We then discovered that a number of well-known phenotypes of cancers, including increased growth rate, metastasis rate, and local immune cell composition, can be naturally explained in terms of the Fenton reaction level and the induced RMs. This study strongly suggests the possibility to have a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors. In addition, strong evidence is provided to demonstrate that a popular view that Na+/H+ exchangers along with lactic acid exporters and carbonic anhydrases are responsible for the intracellular alkalization and extracellular acidification in cancer may not be justified.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis

BACKGROUND AND AIMS

The recurrence and metastasis of hepatocellular carcinoma (HCC) are mainly caused by microvascular invasion (MVI). Our study aimed to uncover the cellular atlas of MVI+ HCC and investigate the underlying immune infiltration patterns with radiomics features.

METHODS

Three MVI positive HCC and three MVI negative HCC samples were collected for single-cell RNA-seq analysis. 26 MVI positive HCC and 30 MVI negative HCC tissues were underwent bulk RNA-seq analysis. For radiomics analysis, radiomics features score (Radscore) were built using preoperative contrast MRI for MVI prediction and overall survival prediction. We deciphered the metabolism profiles of MVI+ HCC using scMetabolism and scFEA. The correlation of Radscore with the level of APOE+ macrophages and iCAFs was identified. Whole Exome Sequencing (WES) was applied to distinguish intrahepatic metastasis (IM) and multicentric occurrence (MO). Transcriptome profiles were compared between IM and MO.

RESULTS

Elevated levels of APOE+ macrophages and iCAFs were detected in MVI+ HCC. There was a strong correlation between the infiltration of APOE+ macrophages and iCAFs, as confirmed by immunofluorescent staining. MVI positive tumors exhibited increased lipid metabolism, which was attributed to the increased presence of APOE+ macrophages. APOE+ macrophages and iCAFs were also found in high levels in IM, as opposed to MO. The difference of infiltration level and Radscore between two nodules in IM was relatively small. Furthermore, we developed Radscore for predicting MVI and HCC prognostication that were also able to predict the level of infiltration of APOE+ macrophages and iCAFs.

CONCLUSION

This study demonstrated the interactions of cell subpopulations and distinct metabolism profiles in MVI+ HCC. Besides, MVI prediction Radscore and MVI prognostic Radscore were highly correlated with the infiltration of APOE+ macrophages and iCAFs, which helped to understand the biological significance of radiomics and optimize treatment strategy for MVI+ HCC.

Targeting neoadjuvant chemotherapy-induced metabolic reprogramming in pancreatic cancer promotes anti-tumor immunity and chemo-response

The molecular dynamics of pancreatic ductal adenocarcinoma (PDAC) under chemotherapy remain incompletely understood. The widespread use of neoadjuvant chemotherapy (NAC) provides a unique opportunity to investigate PDAC samples post-chemotherapy. Leveraging a cohort from Fudan University Shanghai Cancer Center, encompassing PDAC samples with and without exposure to neoadjuvant albumin-bound paclitaxel and gemcitabine (AG), we have compiled data from single-cell and spatial transcriptomes, proteomes, bulk transcriptomes, and metabolomes, deepening our comprehension of the molecular changes in PDACs in response to chemotherapy. Metabolic flux analysis reveals that NAC induces a reprogramming of PDAC metabolic patterns and enhances immunogenicity. Notably, NAC leads to the downregulation of glycolysis and the upregulation of CD36. Tissue microarray analysis demonstrates that high CD36 expression is linked to poorer survival in patients receiving postoperative AG. Targeting CD36 synergistically improves the PDAC response to AG both in vitro and in vivo, including patient-derived preclinical models.

Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches

The accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository[1].

Shared metabolic shifts in endothelial cells in stroke and Alzheimer's disease revealed by integrated analysis

Since metabolic dysregulation is a hallmark of both stroke and Alzheimer's disease (AD), mining shared metabolic patterns in these diseases will help to identify their possible pathogenic mechanisms and potential intervention targets. However, a systematic integration analysis of the metabolic networks of the these diseases is still lacking. In this study, we integrated single-cell RNA sequencing datasets of ischemic stroke (IS), hemorrhagic stroke (HS) and AD models to construct metabolic flux profiles at the single-cell level. We discovered that the three disorders cause shared metabolic shifts in endothelial cells. These altered metabolic modules were mainly enriched in the transporter-related pathways and were predicted to potentially lead to a decrease in metabolites such as pyruvate and fumarate. We further found that Lef1, Elk3 and Fosl1 may be upstream transcriptional regulators causing metabolic shifts and may be possible targets for interventions that halt the course of neurodegeneration.

Single-cell transcriptome sequencing reveals tumor heterogeneity in family neuroblastoma

Neuroblastoma(NB) is the most common extracranial solid tumor in childhood, and it is now believed that some patients with NB have an underlying genetic susceptibility, which may be one of the reasons for the multiplicity of NB patients within a family line. Even within the same family, the samples show great variation and can present as ganglioneuroblastoma or even benign ganglioneuroma. The genomics of NB is still unclear and more in-depth studies are needed to reveal its key components. We first performed single-cell RNA sequencing(sc-RNAseq) analysis on clinical specimens of two family neuroblastoma(FNB) and four sporadic NB cases. A complete transcriptional profile of FNB was constructed from 18,394 cells from FNB, and we found that SDHD may be genetically associated with FNB and identified a prognostic related CAF subtype in FNB: Fib-4. Single-cell flux estimation analysis (scFEA) results showed that malignant cells were associated with arginine spermine, oxaloacetate and hypoxanthine, and that malignant cells metabolize lactate at lower levels than T cells. Our study provides new resources and ideas for the development of the genomics of family NB, and the mechanisms of cell-to-cell interactions and communication and the metabolic landscape will provide new therapeutic targets.

Decoding Aging Hallmarks at the Single-Cell Level

Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models

Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.

FLUXestimator: a webserver for predicting metabolic flux and variations using transcriptomics data

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.

Heterogeneity of cancer-associated fibroblasts in head and neck squamous cell carcinoma

Cancer-associated fibroblasts (CAFs) consist of heterogeneous cellular populations that contribute critical roles in head and neck squamous cell carcinoma (HNSCC). A series of computer-aided analyses were performed to determine various aspects of CAFs in HNSCC, including their cellular heterogeneity, prognostic value, relationship with immune suppression and immunotherapeutic response, intercellular communication, and metabolic activity. The prognostic significance of CKS2+ CAFs was verified using immunohistochemistry. Our findings revealed that fibroblasts group demonstrated prognostic significance, with the CKS2+ subset of inflammatory CAFs (iCAFs) exhibiting a significant correlation with unfavorable prognosis and being localized in close proximity to cancer cells. Patients with a high infiltration of CKS2+ CAFs had a poor overall survival rate. There is a negative correlation between CKS2+ iCAFs and cytotoxic CD8+ T cells and natural killer (NK) cells, while a positive correlation was found with exhausted CD8+ T cells. Additionally, patients in Cluster 3, characterized by a high proportion of CKS2+ iCAFs, and patients in Cluster 2, characterized by a high proportion of CKS2- iCAFs and CENPF-/MYLPF- myofibroblastic CAFs (myCAFs), did not exhibit significant immunotherapeutic responses. Moreover, close interactions was confirmed to exist between cancer cells and CKS2+ iCAFs/ CENPF+ myCAFs. Furthermore, CKS2+ iCAFs demonstrated the highest level of metabolic activity. In summary, our study enhances the understanding of the heterogeneity of CAFs and provided insights into improving the efficacy of immunotherapies and prognostic accuracy for HNSCC patients.

Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer

Introduction

Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue.

Method

In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data.

Result

Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression.

Discussion

This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat

Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

Single-nucleus transcriptional profiling uncovers the reprogrammed metabolism of astrocytes in Alzheimer's disease

Astrocytes play an important role in the pathogenesis of Alzheimer's disease (AD). It is widely involved in energy metabolism in the brain by providing nutritional and metabolic support to neurons; however, the alteration in the metabolism of astrocytes in AD remains unknown. Through integrative analysis of single-nucleus sequencing datasets, we revealed metabolic changes in various cell types in the prefrontal cortex of patients with AD. We found the depletion of some important metabolites (acetyl-coenzyme A, aspartate, pyruvate, 2-oxoglutarate, glutamine, and others), as well as the inhibition of some metabolic fluxes (glycolysis and tricarbocylic acid cycle, glutamate metabolism) in astrocytes of AD. The abnormality of glutamate metabolism in astrocytes is unique and important. Downregulation of GLUL (GS) and GLUD1 (GDH) may be the cause of glutamate alterations in astrocytes in AD. These results provide a basis for understanding the characteristic changes in astrocytes in AD and provide ideas for the study of AD pathogenesis.

Single-cell and spatial analyses reveal the association between gene expression of glutamine synthetase with the immunosuppressive phenotype of APOE+CTSZ+TAM in cancers

An immunosuppressive state is regulated by various factors in the tumor microenvironment (TME), including, but not limited to, metabolic plasticity of immunosuppressive cells and cytokines secreted by these cells. We used single-cell RNA-sequencing (scRNA-seq) data and applied single-cell flux estimation analysis to characterize the link between metabolism and cellular function within the hypoxic TME of colorectal (CRC) and lung cancer. In terms of metabolic heterogeneity, we found myeloid cells potentially inclined to accumulate glutamine but tumor cells inclined to accumulate glutamate. In particular, we uncovered a tumor-associated macrophage (TAM) subpopulation, APOE+CTSZ+TAM, that was present in high proportions in tumor samples and exhibited immunosuppressive characteristics through upregulating the expression of anti-inflammatory genes. The proportion of APOE+CTSZ+TAM and regulatory T cells (Treg) were positively correlated across CRC scRNA-seq samples. APOE+CTSZ+TAM potentially interacted with Treg via CXCL16-CCR6 signals, as seen by ligand-receptor interactions analysis. Notably, glutamate-to-glutamine metabolic flux score and glutamine synthetase (GLUL) expression were uniquely higher in APOE+CTSZ+TAM, compared with other cell types within the TME. GLUL expression in macrophages was positively correlated with anti-inflammatory score and was higher in high-grade and invasive tumor samples. Moreover, spatial transcriptome and multiplex immunofluorescence staining of samples showed that APOE+CTSZ+TAM and Treg potentially colocalized in the tissue sections from CRC clinical samples. These results highlight the specific role and metabolic characteristic of the APOE+CTSZ+TAM subpopulation and provide a new perspective for macrophage subcluster-targeted therapeutic interventions or metabolic checkpoint-based cancer therapies.

Single cell cortical bone transcriptomics define novel osteolineage gene sets altered in chronic kidney disease

INTRODUCTION

Due to a lack of spatial-temporal resolution at the single cell level, the etiologies of the bone dysfunction caused by diseases such as normal aging, osteoporosis, and the metabolic bone disease associated with chronic kidney disease (CKD) remain largely unknown.

METHODS

To this end, flow cytometry and scRNAseq were performed on long bone cells from Sost-cre/Ai9⁺ mice, and pure osteolineage transcriptomes were identified, including novel osteocyte-specific gene sets.

RESULTS

Clustering analysis isolated osteoblast precursors that expressed Tnc, Mmp13, and Spp1, and a mature osteoblast population defined by Smpd3, Col1a1, and Col11a1. Osteocytes were demarcated by Cd109, Ptprz1, Ramp1, Bambi, Adamts14, Spns2, Bmp2, WasI, and Phex. We validated our in vivo scRNAseq using integrative in vitro promoter occupancy via ATACseq coupled with transcriptomic analyses of a conditional, temporally differentiated MSC cell line. Further, trajectory analyses predicted osteoblast-to-osteocyte transitions via defined pathways associated with a distinct metabolic shift as determined by single-cell flux estimation analysis (scFEA). Using the adenine mouse model of CKD, at a time point prior to major skeletal alterations, we found that gene expression within all stages of the osteolineage was disturbed.

CONCLUSION

In sum, distinct populations of osteoblasts/osteocytes were defined at the single cell level. Using this roadmap of gene assembly, we demonstrated unrealized molecular defects across multiple bone cell populations in a mouse model of CKD, and our collective results suggest a potentially earlier and more broad bone pathology in this disease than previously recognized.

Nutrient metabolism of the nucleus pulposus: A literature review

Cells take in, consume, and synthesize nutrients for numerous physiological functions. This includes not only energy production but also macromolecule biosynthesis, which will further influence cellular signaling, redox homeostasis, and cell fate commitment. Therefore, alteration in cellular nutrient metabolism is associated with pathological conditions. Intervertebral discs, particularly the nucleus pulposus (NP), are avascular and exhibit unique metabolic preferences. Clinical and preclinical studies have indicated a correlation between intervertebral degeneration (IDD) and systemic metabolic diseases such as diabetes, obesity, and dyslipidemia. However, a lack of understanding of the nutrient metabolism of NP cells is masking the underlying mechanism. Indeed, although previous studies indicated that glucose metabolism is essential for NP cells, the downstream metabolic pathways remain unknown, and the potential role of other nutrients, like amino acids and lipids, is understudied. In this literature review, we summarize the current understanding of nutrient metabolism in NP cells and discuss other potential metabolic pathways by referring to a human NP transcriptomic dataset deposited to the Gene Expression Omnibus, which can provide us hints for future studies of nutrient metabolism in NP cells and novel therapies for IDD.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

scFASTCORMICS: A Contextualization Algorithm to Reconstruct Metabolic Multi-Cell Population Models from Single-Cell RNAseq Data

Tumours are composed of various cancer cell populations with different mutation profiles, phenotypes and metabolism that cause them to react to drugs in diverse manners. Increasing the resolution of metabolic models based on single-cell expression data will provide deeper insight into such metabolic differences and improve the predictive power of the models. scFASTCORMICS is a network contextualization algorithm that builds multi-cell population genome-scale models from single-cell RNAseq data. The models contain a subnetwork for each cell population in a tumour, allowing to capture metabolic variations between these clusters. The subnetworks are connected by a union compartment that permits to simulate metabolite exchanges between cell populations in the microenvironment. scFASTCORMICS uses Pareto optimization to simultaneously maximise the compactness, completeness and specificity of the reconstructed metabolic models. scFASTCORMICS is implemented in MATLAB and requires the installation of the COBRA toolbox, rFASTCORMICS and the IBM CPLEX solver.

Interpretable machine learning methods for predictions in systems biology from omics data

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

Physioxia-induced downregulation of Tet2 in hematopoietic stem cells contributes to enhanced self-renewal

Hematopoietic stem cells (HSCs) manifest impaired recovery and self-renewal with a concomitant increase in differentiation when exposed to ambient air as opposed to physioxia. Mechanism(s) behind this distinction are poorly understood but have the potential to improve stem cell transplantation. Single-cell RNA sequencing of HSCs in physioxia revealed upregulation of HSC self-renewal genes and downregulation of genes involved in inflammatory pathways and HSC differentiation. HSCs under physioxia also exhibited downregulation of the epigenetic modifier Tet2. Tet2 is α-ketoglutarate, iron- and oxygen-dependent dioxygenase that converts 5-methylcytosine to 5-hydroxymethylcytosine, thereby promoting active transcription. We evaluated whether loss of Tet2 affects the number and function of HSCs and hematopoietic progenitor cells (HPCs) under physioxia and ambient air. In contrast to wild-type HSCs (WT HSCs), a complete nonresponsiveness of Tet2-/- HSCs and HPCs to changes in oxygen tension was observed. Unlike WT HSCs, Tet2-/- HSCs and HPCs exhibited similar numbers and function in either physioxia or ambient air. The lack of response to changes in oxygen tension in Tet2-/- HSCs was associated with similar changes in self-renewal and quiescence genes among WT HSC-physioxia, Tet2-/- HSC-physioxia and Tet2-/- HSC-air. We define a novel molecular program involving Tet2 in regulating HSCs under physioxia.

Single-Cell Metabolomics in Hematopoiesis and Hematological Malignancies

Aberrant metabolism contributes to tumor initiation, progression, metastasis, and drug resistance. Metabolic dysregulation has emerged as a hallmark of several hematologic malignancies. Decoding the molecular mechanism underlying metabolic rewiring in hematological malignancies would provide promising avenues for novel therapeutic interventions. Single-cell metabolic analysis can directly offer a meaningful readout of the cellular phenotype, allowing us to comprehensively dissect cellular states and access biological information unobtainable from bulk analysis. In this review, we first highlight the unique metabolic properties of hematologic malignancies and underscore potential metabolic vulnerabilities. We then emphasize the emerging single-cell metabolomics techniques, aiming to provide a guide to interrogating metabolism at single-cell resolution. Furthermore, we summarize recent studies demonstrating the power of single-cell metabolomics to uncover the roles of metabolic rewiring in tumor biology, cellular heterogeneity, immunometabolism, and therapeutic resistance. Meanwhile, we describe a practical view of the potential applications of single-cell metabolomics in hematopoiesis and hematological malignancies. Finally, we present the challenges and perspectives of single-cell metabolomics development.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

scGraph: a graph neural network-based approach to automatically identify cell types

MOTIVATION

Single cell technologies play a crucial role in revolutionizing biological research over the past decade, which strengthens our understanding in cell differentiation, development, and regulation from a single-cell level perspective. Single-cell RNA sequencing (scRNA-seq) is one of the most common single cell technologies, which enables probing transcriptional states in thousands of cells in one experiment. Identification of cell types from scRNA-seq measurements is a fundamental and crucial question to answer. Most previous studies directly take gene expression as input while ignoring the comprehensive gene-gene interactions.

RESULTS

We propose scGraph, an automatic cell identification algorithm leveraging gene interaction relationships to enhance the performance of the cell type identification. ScGraph is based on a graph neural network to aggregate the information of interacting genes. In a series of experiments, we demonstrate that scGraph is accurate and outperforms eight comparison methods in the task of cell type identification. Moreover, scGraph automatically learns the gene interaction relationships from biological data and the pathway enrichment analysis shows consistent findings with previous analysis, providing insights on the analysis of regulatory mechanism.

AVAILABILITY

scGraph is freely available at https://github.com/QijinYin/scGraph and https://figshare.com/articles/software/scGraph/17157743.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ScRNA-seq identified the metabolic reprogramming of human colonic immune cells in different locations and disease states

Different regions and states of the human colon are likely to have a distinct influence on immune cell functions. Here we studied the immunometabolic mechanisms for spatial immune specialization and dysregulated immune response during ulcerative colitis at single-cell resolution. We revealed that the macrophages and CD8⁺ T cells in the lamina propria of the human colon possessed an effector phenotype and were more activated, while their lipid metabolism was suppressed compared with those in the epithelial. Also, IgA⁺ plasma cells accumulated in lamina propria of the sigmoid colon were identified to be more metabolically activated versus those in the cecum and transverse colon, and the improved metabolic activity was correlated with the expression of CD27. In addition to the immunometabolic reprogramming caused by spatial localization colon, we found dysregulated cellular metabolism was related to the impaired immune functions of macrophages and dendritic cells in patients with ulcerative colitis. The cluster of OSM⁺ inflammatory monocytes was also identified to play its role in resistance to anti-TNF treatment, and we explored targeted metabolic reactions that could reprogram them to a normal state. Altogether, this study revealed a landscape of metabolic reprogramming of human colonic immune cells in different locations and disease states, and offered new insights into treating ulcerative colitis by immunometabolic modulation.

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.

Angiogenesis goes computational – The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

Ref-1 redox activity alters cancer cell metabolism in pancreatic cancer: exploiting this novel finding as a potential target

BACKGROUND

Pancreatic cancer is a complex disease with a desmoplastic stroma, extreme hypoxia, and inherent resistance to therapy. Understanding the signaling and adaptive response of such an aggressive cancer is key to making advances in therapeutic efficacy. Redox factor-1 (Ref-1), a redox signaling protein, regulates the conversion of several transcription factors (TFs), including HIF-1α, STAT3 and NFκB from an oxidized to reduced state leading to enhancement of their DNA binding. In our previously published work, knockdown of Ref-1 under normoxia resulted in altered gene expression patterns on pathways including EIF2, protein kinase A, and mTOR. In this study, single cell RNA sequencing (scRNA-seq) and proteomics were used to explore the effects of Ref-1 on metabolic pathways under hypoxia.

METHODS

scRNA-seq comparing pancreatic cancer cells expressing less than 20% of the Ref-1 protein was analyzed using left truncated mixture Gaussian model and validated using proteomics and qRT-PCR. The identified Ref-1's role in mitochondrial function was confirmed using mitochondrial function assays, qRT-PCR, western blotting and NADP assay. Further, the effect of Ref-1 redox function inhibition against pancreatic cancer metabolism was assayed using 3D co-culture in vitro and xenograft studies in vivo.

RESULTS

Distinct transcriptional variation in central metabolism, cell cycle, apoptosis, immune response, and genes downstream of a series of signaling pathways and transcriptional regulatory factors were identified in Ref-1 knockdown vs Scrambled control from the scRNA-seq data. Mitochondrial DEG subsets downregulated with Ref-1 knockdown were significantly reduced following Ref-1 redox inhibition and more dramatically in combination with Devimistat in vitro. Mitochondrial function assays demonstrated that Ref-1 knockdown and Ref-1 redox signaling inhibition decreased utilization of TCA cycle substrates and slowed the growth of pancreatic cancer co-culture spheroids. In Ref-1 knockdown cells, a higher flux rate of NADP + consuming reactions was observed suggesting the less availability of NADP + and a higher level of oxidative stress in these cells. In vivo xenograft studies demonstrated that tumor reduction was potent with Ref-1 redox inhibitor similar to Devimistat.

CONCLUSION

Ref-1 redox signaling inhibition conclusively alters cancer cell metabolism by causing TCA cycle dysfunction while also reducing the pancreatic tumor growth in vitro as well as in vivo.