Metabolic landscape of the tumor microenvironment at single cell resolution

Identification of a Four-Gene Metabolic Signature to Evaluate the Prognosis of Colon Adenocarcinoma Patients

Background

Colon adenocarcinoma (COAD) is a highly heterogeneous disease, thus making prognostic predictions uniquely challenging. Metabolic reprogramming is emerging as a novel cancer hallmark that may serve as the basis for more effective prognosis strategies.

Methods

The mRNA expression profiles and relevant clinical information of COAD patients were downloaded from public resources. The least absolute shrinkage and selection operator (LASSO) Cox regression model was exploited to establish a prognostic model, which was performed to gain risk scores for multiple genes in The Cancer Genome Atlas (TCGA) COAD patients and validated in GSE39582 cohort. A forest plot and nomogram were constructed to visualize the data. The clinical nomogram was calibrated using a calibration curve coupled with decision curve analysis (DCA). The association between the model genes' expression and six types of infiltrating immunocytes was evaluated. Apoptosis, cell cycle assays and cell transfection experiments were performed.

Results

Univariate Cox regression analysis results indicated that ten differentially expressed genes (DEGs) were related with disease-free survival (DFS) (P-value< 0.01). A four-gene signature was developed to classify patients into high- and low-risk groups. And patients with high-risk exhibited obviously lower DFS in the training and validation cohorts (P < 0.05). The risk score was an independent parameter of the multivariate Cox regression analyses of DFS in the training cohort (HR > 1, P-value< 0.001). The same findings for overall survival (OS) were obtained GO enrichment analysis revealed several metabolic pathways with significant DEGs enrichment, G1/S transition of mitotic cell cycle, CD8+ T-cells and B-cells may be significantly associated with COAD in DFS and OS. These findings demonstrate that si-FUT1 inhibited cell migration and facilitated apoptosis in COAD.

Conclusion

This research reveals that a novel metabolic gene signature could be used to evaluate the prognosis of COAD, and targeting metabolic pathways may serve as a therapeutic alternative.

Prevention of Tumor Growth and Dissemination by In Situ Vaccination with Mitochondria-Targeted Atovaquone

Atovaquone, an FDA-approved drug for malaria, is known to inhibit mitochondrial electron transport. A recently synthesized mitochondria-targeted atovaquone increased mitochondrial accumulation and antitumor activity in vitro. Using an in situ vaccination approach, local injection of mitochondria-targeted atovaquone into primary tumors triggered potent T cell immune responses locally and in distant tumor sites. Mitochondria-targeted atovaquone treatment led to significant reductions of both granulocytic myeloid-derived suppressor cells and regulatory T cells in the tumor microenvironment. Mitochondria-targeted atovaquone treatment blocks the expression of genes involved in oxidative phosphorylation and glycolysis in granulocytic-myeloid-derived suppressor cells and regulatory T cells, which may lead to death of granulocytic-myeloid-derived suppressor cells and regulatory T cells. Mitochondria-targeted atovaquone inhibits expression of genes for mitochondrial complex components, oxidative phosphorylation, and glycolysis in both granulocytic-myeloid-derived suppressor cells and regulatory T cells. The resulting decreases in intratumoral granulocytic-myeloid-derived suppressor cells and regulatory T cells could facilitate the observed increase in tumor-infiltrating CD4+ T cells. Mitochondria-targeted atovaquone also improves the anti-tumor activity of PD-1 blockade immunotherapy. The results implicate granulocytic-myeloid-derived suppressor cells and regulatory T cells as novel targets of mitochondria-targeted atovaquone that facilitate its antitumor efficacy.

The MicroRNA miR-277 Controls Physiology and Pathology of the Adult Drosophila Midgut by Regulating the Expression of Fatty Acid β-Oxidation-Related Genes in Intestinal Stem Cells

Cell division, growth, and differentiation are energetically costly and dependent processes. In adult stem cell-based epithelia, cellular identity seems to be coupled with a cell's metabolic profile and vice versa. It is thus tempting to speculate that resident stem cells have a distinct metabolism, different from more committed progenitors and differentiated cells. Although investigated for many stem cell types in vitro, in vivo data of niche-residing stem cell metabolism is scarce. In adult epithelial tissues, stem cells, progenitor cells, and their progeny have very distinct functions and characteristics. In our study, we hypothesized and tested whether stem and progenitor cell types might have a distinctive metabolic profile in the intestinal lineage. Here, taking advantage of the genetically accessible adult Drosophila melanogaster intestine and the availability of ex vivo single cell sequencing data, we tested that hypothesis and investigated the metabolism of the intestinal lineage from stem cell (ISC) to differentiated epithelial cell in their native context under homeostatic conditions. Our initial in silico analysis of single cell RNAseq data and functional experiments identify the microRNA miR-277 as a posttranscriptional regulator of fatty acid β-oxidation (FAO) in the intestinal lineage. Low levels of miR-277 are detected in ISC and progressively rising miR-277 levels are found in progenitors during their growth and differentiation. Supporting this, miR-277-regulated fatty acid β-oxidation enzymes progressively declined from ISC towards more differentiated cells in our pseudotime single-cell RNAseq analysis and in functional assays on RNA and protein level. In addition, in silico clustering of single-cell RNAseq data based on metabolic genes validates that stem cells and progenitors belong to two independent clusters with well-defined metabolic characteristics. Furthermore, studying FAO genes in silico indicates that two populations of ISC exist that can be categorized in mitotically active and quiescent ISC, of which the latter relies on FAO genes. In line with an FAO dependency of ISC, forced expression of miR-277 phenocopies RNAi knockdown of FAO genes by reducing ISC size and subsequently resulting in stem cell death. We also investigated miR-277 effects on ISC in a benign and our newly developed CRISPR-Cas9-based colorectal cancer model and found effects on ISC survival, which as a consequence affects tumor growth, further underlining the importance of FAO in a pathological context. Taken together, our study provides new insights into the basal metabolic requirements of intestinal stem cell on β-oxidation of fatty acids evolutionarily implemented by a sole microRNA. Gaining knowledge about the metabolic differences and dependencies affecting the survival of two central and cancer-relevant cell populations in the fly and human intestine might reveal starting points for targeted combinatorial therapy in the hope for better treatment of colorectal cancer in the future.

Programmed Cell Death Tunes Tumor Immunity

The demise of cells in various ways enables the body to clear unwanted cells. Studies over the years revealed distinctive molecular mechanisms and functional consequences of several key cell death pathways. Currently, the most intensively investigated programmed cell death (PCD) includes apoptosis, necroptosis, pyroptosis, ferroptosis, PANoptosis, and autophagy, which has been discovered to play crucial roles in modulating the immunosuppressive tumor microenvironment (TME) and determining clinical outcomes of the cancer therapeutic approaches. PCD can play dual roles, either pro-tumor or anti-tumor, partly depending on the intracellular contents released during the process. PCD also regulates the enrichment of effector or regulatory immune cells, thus participating in fine-tuning the anti-tumor immunity in the TME. In this review, we focused primarily on apoptosis, necroptosis, pyroptosis, ferroptosis, PANoptosis, and autophagy, discussed the released molecular messengers participating in regulating their intricate crosstalk with the immune response in the TME, and explored the immunological consequence of PCD and its implications in future cancer therapy developments.

Single-cell transcriptomic analysis suggests two molecularly subtypes of intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by performing single-cell RNA sequencing on 144,878 cells from 14 pairs of iCCA tumors and non-tumor liver tissues, we find that S100P and SPP1 are two markers for iCCA perihilar large duct type (iCCA^phl) and peripheral small duct type (iCCA^pps). S100P + SPP1− iCCA^phl has significantly reduced levels of infiltrating CD4⁺ T cells, CD56⁺ NK cells, and increased CCL18⁺ macrophages and PD1⁺CD8⁺ T cells compared to S100P-SPP1 + iCCA^pps. The transcription factor CREB3L1 is identified to regulate the S100P expression and promote tumor cell invasion. S100P-SPP1 + iCCA^pps has significantly more SPP1⁺ macrophage infiltration, less aggressiveness and better survival than S100P + SPP1− iCCA^phl. Moreover, S100P-SPP1 + iCCA^pps harbors tumor cells at different status of differentiation, such as ALB + hepatocyte differentiation and ID3+ stemness. Our study extends the understanding of the diversity of tumor cells in iCCA.

The molecular classification and tumour microenvironment in intrahepatic cholangiocarcinoma (iCCA) need further characterisation. Here, the authors perform single cell RNA-sequencing from 14 pairs of iCCA tumours and non-tumour liver tissues and propose S100P and SPP1 as markers for patient classification.

Rewiring mitochondrial metabolism to counteract exhaustion of CAR-T cells

Short persistence and early exhaustion of T cells are major limits to the efficacy and broad application of immunotherapy. Exhausted T and chimeric antigen receptor (CAR)-T cells upregulate expression of genes associated with terminated T cell differentiation, aerobic glycolysis and apoptosis. Among cell exhaustion characteristics, impaired mitochondrial function and dynamics are considered hallmarks. Here, we review the mitochondrial characteristics of exhausted T cells and particularly discuss different aspects of mitochondrial metabolism and plasticity. Furthermore, we propose a novel strategy of rewiring mitochondrial metabolism to emancipate T cells from exhaustion and of targeting mitochondrial plasticity to boost CAR-T cell therapy efficacy.

Single-Cell Transcriptome Comparison of Bladder Cancer Reveals Its Ecosystem

Bladder carcinoma (BLCA) is a highly heterogeneous disease, and the underlying biological behavior is still poorly understood. Here, single-cell RNA sequencing was performed on four clinical samples of different grades from three patients, and 26,792 cell transcriptomes were obtained revealing different tumor ecosystems. We found that N-glycan biosynthesis pathway was activated in high-grade tumor, but TNF-related pathway was activated in cystitis glandularis. The tumor microenvironment (TME) of different samples showed great heterogeneity. Notably, cystitis glandularis was dominated by T cells, low-grade and high-grade tumors by macrophages, while TME in patient with high-grade relapse by stromal cells. Our research provides single-cell transcriptome profiles of cystitis glandularis and BLCA in different clinical states, and the biological program revealed by single-cell data can be used as biomarkers related to clinical prognosis in independent cohorts.

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.

An Optimized Workflow for the Analysis of Metabolic Fluxes in Cancer Spheroids Using Seahorse Technology

Three-dimensional cancer models, such as spheroids, are increasingly being used to study cancer metabolism because they can better recapitulate the molecular and physiological aspects of the tumor architecture than conventional monolayer cultures. Although Agilent Seahorse XFe96 (Agilent Technologies, Santa Clara, CA, United States) is a valuable technology for studying metabolic alterations occurring in cancer cells, its application to three-dimensional cultures is still poorly optimized. We present a reliable and reproducible workflow for the Seahorse metabolic analysis of three-dimensional cultures. An optimized protocol enables the formation of spheroids highly regular in shape and homogenous in size, reducing variability in metabolic parameters among the experimental replicates, both under basal and drug treatment conditions. High-resolution imaging allows the calculation of the number of viable cells in each spheroid, the normalization of metabolic parameters on a per-cell basis, and grouping of the spheroids as a function of their size. Multivariate statistical tests on metabolic parameters determined by the Mito Stress test on two breast cancer cell lines show that metabolic differences among the studied spheroids are mostly related to the cell line rather than to the size of the spheroid. The optimized workflow allows high-resolution metabolic characterization of three-dimensional cultures, their comparison with monolayer cultures, and may aid in the design and interpretation of (multi)drug protocols.

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.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Thyroid Hormone Receptor Agonist Promotes Hair Growth in Mice

Background

Thyroxine is important to maintain the normal operation of the body. Both clinical and experimental results show thyroxine is closely related to hair growth, the mechanism of which is not fully understood.

Purpose

Investigate the effect of thyroxine receptor agonist, TDM10842, for dorsal hair growth in C3H mice and explore its underlying mechanism.

Methods

Depilated mice were applied with the TDM10842, vehicle of this drug and without any materials on dorsal skin. RNA-sequencing (RNA-seq) was employed to identify the change in gene expression of skin tissues. Quantitative real-time PCR (rt-PCR) and immunoblotting were conducted to validate key differentially expressed genes (DEGs) between different groups.

Results

The TDM group showed early induction of anagen. 857, 782, and 276 differentially expressed genes were identified between 3 groups. As a critical DEG in group TDM, Pclaf was positively related to the motivation of Wnt/beta-catenin and Hedgehog signaling pathways, with a high expression of Ki67 and cyclinD1.

Conclusion

TDM10842 accelerates the anagen entrance and the potential mechanism might be the activation of Wnt/beta-catenin and Hedgehog pathways. Pclaf serves as a critical molecule involved in pathway activation, and cyclinD1 is an important effector protein downstream of the pathways.

Metabolism Characteristics of Th17 and Regulatory T Cells in Autoimmune Diseases

The abnormal number and functional deficiency of immune cells are the pathological basis of various diseases. Recent years, the imbalance of Th17/regulatory T (Treg) cell underlies the occurrence and development of inflammation in autoimmune diseases (AID). Currently, studies have shown that material and energy metabolism is essential for maintaining cell survival and normal functions and the altered metabolic state of immune cells exists in a variety of AID. This review summarizes the biology and functions of Th17 and Treg cells in AID, with emphasis on the advances of the roles and regulatory mechanisms of energy metabolism in activation, differentiation and physiological function of Th17 and Treg cells, which will facilitate to provide targets for the treatment of immune-mediated diseases.

Improving Cancer Immunotherapy: Exploring and Targeting Metabolism in Hypoxia Microenvironment

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into "understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME)," which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy.

Light-switchable diphtherin transgene system combined with losartan for triple negtative breast cancer therapy based on nano drug delivery system

Breast cancer is a common malignancy in women. The abnormally dense collagen network in breast cancer forms a therapeutic barrier that hinders the penetration and anti-tumor effect of drugs. To overcome this hurdle, we adopted a therapeutic strategy to treat breast cancer which combined a light-switchable transgene system and losartan. The light-switchable transgene system could regulate expression of the diphtheria toxin A fragment (DTA) gene with a high on/off ratio under blue light and had great potential for spatiotemporally controllable gene expression. We developed a nanoparticle drug delivery system to achieve tumor microenvironment-responsive and targeted delivery of DTA-encoded plasmids (pDTA) to tumor sites via dual targeting to cluster of differentiation-44 and α_vβ₃ receptors. In vivo studies indicated that the combination of pDTA and losartan reduce the concentration of collagen type I from 5.9 to 1.9 µg/g and decreased the level of active transforming growth factor-β by 75.0% in tumor tissues. Moreover, deeper tumor penetration was achieved, tumor growth was inhibited, and the survival rate was increased. Our combination strategy provides a novel and practical method for clinical treatment of breast cancer.

New Immunometabolic Strategy Based on Cell Type-Specific Metabolic Reprogramming in the Tumor Immune Microenvironment

Immunometabolism is an emerging discipline in cancer immunotherapy. Tumor tissues are heterogeneous and influenced by metabolic reprogramming of the tumor immune microenvironment (TIME). In the TIME, multiple cell types interact, and the tumor and immune cells compete for limited nutrients, resulting in altered anticancer immunity. Therefore, metabolic reprogramming of individual cell types may influence the outcomes of immunotherapy. Understanding the metabolic competition for access to limited nutrients between tumor cells and immune cells could reveal the breadth and complexity of the TIME and aid in developing novel therapeutic approaches for cancer. In this review, we highlight that, when cells compete for nutrients, the prevailing cell type gains certain advantages over other cell types; for instance, if tumor cells prevail against immune cells for nutrients, the former gains immune resistance. Thus, a strategy is needed to selectively suppress such resistant tumor cells. Although challenging, the concept of cell type-specific metabolic pathway inhibition is a potent new strategy in anticancer immunotherapy.

Single-Cell Transcriptome Reveals the Metabolic and Clinical Features of a Highly Malignant Cell Subpopulation in Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high mortality rate. PDAC exhibits significant heterogeneity as well as alterations in metabolic pathways that are associated with its malignant progression. In this study, we explored the metabolic and clinical features of a highly malignant subgroup of PDAC based on single-cell transcriptome technology.

Methods: A highly malignant cell subpopulation was identified at single-cell resolution based on the expression of malignant genes. The metabolic landscape of different cell types was analyzed based on metabolic pathway gene sets. In vitro experiments to verify the biological functions of the marker genes were performed. PDAC patient subgroups with highly malignant cell subpopulations were distinguished according to five glycolytic marker genes. Five glycolytic highly malignant-related gene signatures were used to construct the glycolytic highly malignant-related genes signature (GHS) scores.

Results: This study identified a highly malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. The analysis of the metabolic pathway revealed that highly malignant cells had an abnormally active metabolism, and enhanced glycolysis was a major metabolic feature. Five glycolytic marker genes that accounted for the highly malignant cell subpopulations were identified, namely, EN O 1, LDHA, PKM, PGK1, and PGM1. An in vitro cell experiment showed that proliferation rates of PANC-1 and CFPAC-1 cell lines decreased after knockdown of these five genes. Patients with metabolic profiles of highly malignant cell subpopulations exhibit clinical features of higher mortality, higher mutational burden, and immune deserts. The GHS score evaluated using the five marker genes was an independent prognostic factor for patients with PDAC.

Conclusion: We revealed a subpopulation of highly malignant cells in PDAC with enhanced glycolysis as the main metabolic feature. We obtained five glycolytic marker gene signatures, which could be used to identify PDAC patient subgroups with highly malignant cell subpopulations, and proposed a GHS prognostic score.

Determination of IC50 values of anticancer drugs on cells by D2O - single cell Raman spectroscopy

A simple, sensitive and repeatable D₂O-single cell Raman spectroscopy method is developed to quantify the inhibitory activity of anticancer drugs on cancer cell metabolism. The IC₅₀ values obtained from A549 cells incubated with cisplatin and taxol are comparable with results of CCK-8 and ATP luminescent cell viability assays.

Identification of a Novel Immune-Related lncRNA CTD-2288O8.1 Regulating Cisplatin Resistance in Ovarian Cancer Based on Integrated Analysis

Ovarian cancer (OC) is the most lethal gynecological malignancy, in which chemoresistance is a crucial factor leading to the poor prognosis. Recently, immunotherapy has brought new light for the treatment of solid tumors. Hence, as a kind of immunologically active cancer, it is reasonably necessary to explore the potential mechanism between immune characteristics and cisplatin resistance in OC. Our study focused on the important role of cisplatin resistance-related lncRNAs on mediating the OC tumor immune microenvironment (TIME) using an integrative analysis based on the Cancer Genome Atlas (TCGA) database. First, the cisplatin resistance-related differentially expressed lncRNAs (DELs) and mRNAs (DEMs) were preliminarily screened to construct a DEL–DEM co-expression network. Next, the protein–protein interaction (PPI) network and pivot analysis were performed to reveal the relevance of these lncRNAs with tumor immune response. Second, the novel lncRNA CTD-2288O8.1 was identified as a key gene for the OC cisplatin resistance formation by qRT-PCR and survival analysis. Gain- and loss-of-function assays (Cell Counting Kit-8 (CCK-8) assay, wound-healing scratch assay, transwell assay, and colony formation assay) further verified the activity of CTD-2288O8.1 in OC progression as well. Third, gene set enrichment analysis (GSEA) was applied along with the correlation analyses of CTD-2288O8.1 with ImmuneScore, tumor-infiltrating immune cells (TICs), and immune inhibitory checkpoint molecules, illustrating that CTD-2288O8.1 was strongly associated with the TIME and has the potential to predict the effect of OC immunotherapy. In addition, basic experiments demonstrated that the expression of CTD-2288O8.1 impacted the EGFR/AKT signal pathway activity of OC tumor cells. Of greater significance, it promoted the M2 polarization of macrophage, which is a type of the most important components of the TIME in solid tumor. Taking together, our study revealed cisplatin resistance-related lncRNAs closely linked with tumor immunity in OC, underscoring the potential mechanism of the TIME in conferring cisplatin resistance, which provided the research basis for further clinical treatment. CTD-2288O8.1 was identified to mediate cisplatin resistance and affect the response of immunotherapy, which could serve as a promising biomarker for guiding clinical treatment and improving prognosis in OC.

Nutritional Regulation of Mammary Tumor Microenvironment

The mammary gland is a heterogeneous organ comprising of immune cells, surrounding adipose stromal cells, vascular cells, mammary epithelial, and cancer stem cells. In response to nutritional stimuli, dynamic interactions amongst these cell populations can be modulated, consequently leading to an alteration of the glandular function, physiology, and ultimately disease pathogenesis. For example, obesity, a chronic over-nutritional condition, is known to disrupt homeostasis within the mammary gland and increase risk of breast cancer development. In contrast, emerging evidence has demonstrated that fasting or caloric restriction can negatively impact mammary tumorigenesis. However, how fasting induces phenotypic and functional population differences in the mammary microenvironment is not well understood. In this review, we will provide a detailed overview on the effect of nutritional conditions (i.e., overnutrition or fasting) on the mammary gland microenvironment and its impact on mammary tumor progression.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

It’s Getting Complicated—A Fresh Look at p53-MDM2-ARF Triangle in Tumorigenesis and Cancer Therapy

Anti-tumorigenic mechanisms mediated by the tumor suppressor p53, upon oncogenic stresses, are our bodies’ greatest weapons to battle against cancer onset and development. Consequently, factors that possess significant p53-regulating activities have been subjects of serious interest from the cancer research community. Among them, MDM2 and ARF are considered the most influential p53 regulators due to their abilities to inhibit and activate p53 functions, respectively. MDM2 inhibits p53 by promoting ubiquitination and proteasome-mediated degradation of p53, while ARF activates p53 by physically interacting with MDM2 to block its access to p53. This conventional understanding of p53-MDM2-ARF functional triangle have guided the direction of p53 research, as well as the development of p53-based therapeutic strategies for the last 30 years. Our increasing knowledge of this triangle during this time, especially through identification of p53-independent functions of MDM2 and ARF, have uncovered many under-appreciated molecular mechanisms connecting these three proteins. Through recognizing both antagonizing and synergizing relationships among them, our consideration for harnessing these relationships to develop effective cancer therapies needs an update accordingly. In this review, we will re-visit the conventional wisdom regarding p53-MDM2-ARF tumor-regulating mechanisms, highlight impactful studies contributing to the modern look of their relationships, and summarize ongoing efforts to target this pathway for effective cancer treatments. A refreshed appreciation of p53-MDM2-ARF network can bring innovative approaches to develop new generations of genetically-informed and clinically-effective cancer therapies.

Anti-miR-135/SPOCK1 axis antagonizes the influence of metabolism on drug response in intestinal/colon tumour organoids

Little is known about the role of microRNAs (miRNAs) in rewiring the metabolism within tumours and adjacent non-tumour bearing normal tissue and their potential in cancer therapy. This study aimed to investigate the relationship between deregulated miRNAs and metabolic components in murine duodenal polyps and non-polyp-derived organoids (mPOs and mNPOs) from a double-mutant Apc^MinFbxw7^∆G mouse model of intestinal/colorectal cancer (CRC). We analysed the expression of 373 miRNAs and 12 deregulated metabolic genes in mPOs and mNPOs. Our findings revealed miR-135b might target Spock1. Upregulation of SPOCK1 correlated with advanced stages of CRCs. Knockdown of miR-135b decreased the expression level of SPOCK1, glucose consumption and lactic secretion in CRC patient-derived tumours organoids (CRC tPDOs). Increased SPOCK1 induced by miR-135b overexpression promoted the Warburg effect and consequently antitumour effect of 5-fluorouracil. Thus, combination with miR-135b antisense nucleotides may represent a novel strategy to sensitise CRC to the chemo-reagent based treatment.

Integrated analysis of plasma and single immune cells uncovers metabolic changes in individuals with COVID-19

A better understanding of the metabolic alterations in immune cells during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may elucidate the wide diversity of clinical symptoms experienced by individuals with coronavirus disease 2019 (COVID-19). Here, we report the metabolic changes associated with the peripheral immune response of 198 individuals with COVID-19 through an integrated analysis of plasma metabolite and protein levels as well as single-cell multiomics analyses from serial blood draws collected during the first week after clinical diagnosis. We document the emergence of rare but metabolically dominant T cell subpopulations and find that increasing disease severity correlates with a bifurcation of monocytes into two metabolically distinct subsets. This integrated analysis reveals a robust interplay between plasma metabolites and cell-type-specific metabolic reprogramming networks that is associated with disease severity and could predict survival.

Systematic Investigation of Biocompatible Cationic Polymeric Nucleic Acid Carriers for Immunotherapy of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third-largest cause of cancer death worldwide, while immunotherapy is rapidly being developed to fight HCC with great potential. Nucleic acid drugs are the most important modulators in HCC immunotherapy. To boost the efficacy of therapeutics and amplify the efficiency of genetic materials, biocompatible polymers are commonly used. However, under the strong need of a summary for current developments of biocompatible polymeric nucleic acid carriers for immunotherapy of HCC, there is rare review article specific to this topic to our best knowledge. In this article, we will discuss the current progress of immunotherapy for HCC, biocompatible cationic polymers (BCPs) as nucleic acid carriers used (or potential) to fight HCC, the roles of biocompatible polymeric carriers for nucleic acid delivery, and nucleic acid delivery by biocompatible polymers for immunotherapy. At the end, we will conclude the review and discuss future perspectives. This article discusses biocompatible polymeric nucleic acid carriers for immunotherapy of HCC from multidiscipline perspectives and provides a new insight in this domain. We believe this review will be interesting to polymer chemists, pharmacists, clinic doctors, and PhD students in related disciplines.

Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.

Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines

There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers (e.g. NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors.

Integrated proteogenomic analysis revealed the metabolic heterogeneity in noncancerous liver tissues of patients with hepatocellular carcinoma

Understanding the adjacent liver microenvironment of hepatocellular carcinoma (HCC) with possible metastasis tendency might provide a strategy for risk classification of patients and potential therapies by converting the unique metastasis-inclined microenvironment to a metastasis-averse one. In this study, we performed an integrated proteogenomic analysis to have a comprehensive view on the heterogeneity of hepatic microenvironment contributing to HCC metastasis. Pairing mRNA-protein analysis revealed consistent and discordant mRNA-protein expressions in metabolism regulations and cancer-related pathways, respectively. Proteomic profiling identified three subgroups associated with the recurrence-free survival of patients. These proteomic subgroups demonstrated distinct features in metabolic reprogramming, which was potentially modified by epigenetic alterations. This study raises the point of metabolic heterogeneity in HCC noncancerous tissues and may offer a new perspective on HCC treatment.

Immune Regulatory Processes of the Tumor Microenvironment under Malignant Conditions

The tumor microenvironment (TME) is a critical regulator of tumor growth, progression, and metastasis. Since immune cells represent a large fraction of the TME, they play a key role in mediating pro- and anti-tumor immune responses. Immune escape, which suppresses anti-tumor immunity, enables tumor cells to maintain their proliferation and growth. Numerous mechanisms, which have been intensively studied in recent years, are involved in this process and based on these findings, novel immunotherapies have been successfully developed. Here, we review the composition of the TME and the mechanisms by which immune evasive processes are regulated. In detail, we describe membrane-bound and soluble factors, their regulation, and their impact on immune cell activation in the TME. Furthermore, we give an overview of the tumor/antigen presentation and how it is influenced under malignant conditions. Finally, we summarize novel TME-targeting agents, which are already in clinical trials for different tumor entities.

Cellular export of sugars and amino acids: role in feeding other cells and organisms

Sucrose, hexoses, and raffinose play key roles in the plant metabolism. Sucrose and raffinose, produced by photosynthesis, are translocated from leaves to flowers, developing seeds and roots. Translocation occurs in the sieve elements or sieve tubes of angiosperms. But how is sucrose loaded into and unloaded from the sieve elements? There seem to be two principal routes: one through plasmodesmata and one via the apoplasm. The best-studied transporters are the H+/SUCROSE TRANSPORTERs (SUTs) in the sieve element-companion cell complex. Sucrose is delivered to SUTs by SWEET sugar uniporters that release these key metabolites into the apoplasmic space. The H+/amino acid permeases and the UmamiT amino acid transporters are hypothesized to play analogous roles as the SUT-SWEET pair to transport amino acids. SWEETs and UmamiTs also act in many other important processes-for example, seed filling, nectar secretion, and pollen nutrition. We present information on cell type-specific enrichment of SWEET and UmamiT family members and propose several members to play redundant roles in the efflux of sucrose and amino acids across different cell types in the leaf. Pathogens hijack SWEETs and thus represent a major susceptibility of the plant. Here, we provide an update on the status of research on intercellular and long-distance translocation of key metabolites such as sucrose and amino acids, communication of the plants with the root microbiota via root exudates, discuss the existence of transporters for other important metabolites and provide potential perspectives that may direct future research activities.

Single-cell analysis of diverse immune phenotypes in malignant pleural effusion

The complex interactions among different immune cells have important functions in the development of malignant pleural effusion (MPE). Here we perform single-cell RNA sequencing on 62,382 cells from MPE patients induced by non-small cell lung cancer to describe the composition, lineage, and functional states of infiltrating immune cells in MPE. Immune cells in MPE display a number of transcriptional signatures enriched for regulatory T cells, B cells, macrophages, and dendritic cells compared to corresponding counterparts in blood. Helper T, cytotoxic T, regulatory T, and T follicular helper cells express multiple immune checkpoints or costimulatory molecules. Cell-cell interaction analysis identifies regulatory B cells with more interactions with CD4⁺ T cells compared to CD8⁺ T cells. Macrophages are transcriptionally heterogeneous and conform to M2 polarization characteristics. In addition, immune cells in MPE show the general up-regulation of glycolytic pathways associated with the hypoxic microenvironment. These findings show a detailed atlas of immune cells in human MPE and enhance the understanding of potential diagnostic and therapeutic targets in advanced non-small cell lung cancer.

Lipid metabolism in cancer progression and therapeutic strategies

Dysregulated lipid metabolism represents an important metabolic alteration in cancer. Fatty acids, cholesterol, and phospholipid are the three most prevalent lipids that act as energy producers, signaling molecules, and source material for the biogenesis of cell membranes. The enhanced synthesis, storage, and uptake of lipids contribute to cancer progression. The rewiring of lipid metabolism in cancer has been linked to the activation of oncogenic signaling pathways and cross talk with the tumor microenvironment. The resulting activity favors the survival and proliferation of tumor cells in the harsh conditions within the tumor. Lipid metabolism also plays a vital role in tumor immunogenicity via effects on the function of the noncancer cells within the tumor microenvironment, especially immune‐associated cells. Targeting altered lipid metabolism pathways has shown potential as a promising anticancer therapy. Here, we review recent evidence implicating the contribution of lipid metabolic reprogramming in cancer to cancer progression, and discuss the molecular mechanisms underlying lipid metabolism rewiring in cancer, and potential therapeutic strategies directed toward lipid metabolism in cancer. This review sheds new light to fully understanding of the role of lipid metabolic reprogramming in the context of cancer and provides valuable clues on therapeutic strategies targeting lipid metabolism in cancer.

Cross-tissue single-cell transcriptomic landscape reveals the key cell subtypes and their potential roles in the nutrient absorption and metabolism in dairy cattle

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Discover 55 cell types and their specific markers in the first single-cell atlas of cattle;

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Identify and verify 3 epithelial progenitor-like cell subtypes in the forestomach

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Reveal vital but nonimmune functions of neutrophils in the mammary gland;

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Uncover key cell subtypes with preferential nutrient uptake;

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Find Th17 cells regulate epithelial cells responding to nutrient transport in the forestomach.

Introduction

Dairy cattle are a vitally important ruminant in meeting the demands for high-quality animal protein production worldwide. The complicated biological process of converting human indigestible biomass into highly digestible and nutritious milk is orchestrated by various tissues. However, poorly understanding of the cellular composition and function of the key metabolic tissues hinders the improvement of health and performance of domestic ruminants.

Objectives

The cellular heterogeneity, metabolic features, interactions across ten tissue types of lactating dairy cattle were studied at single-cell resolution in the current study.

Methods

Unbiased single-cell RNA-sequencing and analysis were performed on the rumen, reticulum, omasum, abomasum, ileum, rectum, liver, salivary gland, mammary gland, and peripheral blood of lactating dairy cattle. Immunofluorescences and fluorescence in situ hybridization were performed to verify cell identity.

Results

In this study, we constructed a single-cell landscape covering 88,013 high-quality (500 < genes < 4,000, UMI < 50, 000, and mitochondrial gene ratio < 40% or 15%) single cells and identified 55 major cell types in lactating dairy cattle. Our systematic survey of the gene expression profiles and metabolic features of epithelial cells related to nutrient transport revealed cell subtypes that have preferential absorption of different nutrients. Importantly, we found that T helper type 17 (Th17) cells (highly expressing CD4 and IL17A) were specifically enriched in the forestomach tissues and predominantly interacted with the epithelial cell subtypes with high potential uptake capacities of short-chain fatty acids through IL-17 signaling. Furthermore, the comparison between IL17RA^highIL17RC^high cells (epithelial cells with IL17RA and IL17RC expression levels both greater than 0.25) and other cells explained the importance of Th17 cells in regulating the epithelial cellular transcriptional response to nutrient transport in the forestomach.

Conclusion

The findings enhance our understanding of the cellular biology of ruminants and open new avenues for improved animal production of dairy cattle.

Identification of an Immune-Related Prognostic Gene CLEC5A Based on Immune Microenvironment and Risk Modeling of Ovarian Cancer

Objective: To understand the immune characteristics of the ovarian cancer (OC) microenvironment and explore the differences of immune-related molecules and cells to establish an effective risk model and identify the molecules that significantly affected the immune response of OC, to help guide the diagnosis. Methods: First, we calculate the TMEscore which reflects the immune microenvironment, and then analyze the molecular differences between patients with different immune characteristics, and determine the prognostic genes. Then, the risk model was established by least absolute shrinkage and selection operator (LASSO) analysis and combined with clinical data into a nomogram for diagnosis and prediction. Subsequently, the potential gene CLEC5A influencing the immune response of OC was identified from the prognostic genes by integrative immune-stromal analysis. The genomic alteration was explored based on copy number variant (CNV) and somatic mutation data. Results: TMEscore was a prognostic indicator of OC. The prognosis of patients with high TMEscore was better. The risk model based on immune characteristics was a reliable index to predict the prognosis of patients, and the nomogram could comprehensively evaluate the prognosis of patients. Besides, CLEC5A was closely related to the abundance of immune cells, immune response, and the expression of immune checkpoints in the OC microenvironment. OC cells with high expression of CLEC5A increased the polarization of M2 macrophages. CLEC5A expression was significantly associated with TTN and CDK12 mutations and affected the copy number of tumor progression and immune-related genes. Conclusion: The study of immune characteristics in the OC microenvironment and the risk model can reveal the factors affecting the prognosis and guide the clinical hierarchical treatment. CLEC5A can be used as a potential key gene affecting the immune microenvironment remodeling of OC, which provides a new perspective for improving the effect of OC immunotherapy.

Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial–mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

Deconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue's complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

Multi-omic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism

The circulating metabolome provides unique insights into multiple sclerosis (MS) pathophysiology, but existing studies are relatively small or characterized limited metabolites. We test for differences in the metabolome between people with MS (PwMS; n = 637 samples) and healthy controls (HC; n = 317 samples) and assess the association between metabolomic profiles and disability in PwMS. We then assess whether metabolic differences correlate with changes in cellular gene expression using publicly available scRNA-seq data and whether identified metabolites affect human immune cell function. In PwMS, we identify striking abnormalities in aromatic amino acid (AAA) metabolites (p = 2.77E−18) that are also strongly associated with disability (p = 1.01E−4). Analysis of scRNA-seq data demonstrates altered AAA metabolism in CSF and blood-derived monocyte cell populations in PwMS. Treatment with AAA-derived metabolites in vitro alters monocytic endocytosis and pro-inflammatory cytokine production. We identify shifts in AAA metabolism resulting in the reduced production of immunomodulatory metabolites and increased production of metabotoxins in PwMS.

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Significant alterations in the circulating metabolome are noted in multiple sclerosis

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Aromatic amino acid (AAA) metabolite levels are linked to disease severity

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Expression of AAA metabolism genes is altered in MS blood and CSF immune cells

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AAA metabolites alter human monocyte cytokine production and endocytosis

Prognostic Impact of Metabolic Heterogeneity in Patients With Newly Diagnosed Multiple Myeloma Using 18F-FDG PET/CT

PURPOSE

This study aimed to investigate the prognostic impact of metabolic heterogeneity (MH) in patients with multiple myeloma (MM).

PATIENTS AND METHODS

We retrospectively analyzed MH with 18F-FDG PET/CT in 203 patients with newly diagnosed MM. Metabolic heterogeneity was estimated using the area under the curve of the cumulative SUV volume histogram. To evaluate MH, we selected 2 lesions: "MH-SUVmax," a lesion with SUVmax, and "MH-metabolic tumor volume (MTV)," a lesion with the largest MTV.

RESULTS

Metabolic heterogeneity from an MH-SUVmax lesion showed more prognostic relevance than that from a lesion with the largest MTV. The progression-free survival (PFS) and overall survival (OS) rates were significantly lower in the high-MH-SUVmax group than in the low-MH-SUVmax group (median PFS: 25.2 vs 33.9 months; median OS: 41.6 vs 112.0 months; P = 0.004 and 0.046, respectively), whereas high MH-SUVmax retained independent prognostic power on multivariate analysis. Even among patients with high whole-body MTV, those with high MH-SUVmax tended to show poorer prognosis than those without (median PFS, 23.8 vs 30.2 months; P = 0.085). Moreover, patients with high MH-SUVmax and high-risk cytogenetic abnormalities showed dismal outcomes even with standard treatment (median PFS and OS, 10.0 and 33.3 months, respectively).

CONCLUSIONS

Our results suggested that high MH-SUVmax based on pretreatment with 18F-FDG PET/CT is a novel prognostic factor for cases of MM.

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

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

From OCR and ECAR to energy: Perspectives on the design and interpretation of bioenergetics studies

Biological energy transduction underlies all physiological phenomena in cells. The metabolic systems that support energy transduction have been of great interest due to their association with numerous pathologies including diabetes, cancer, rare genetic diseases, and aberrant cell death. Commercially available bioenergetics technologies (e.g., extracellular flux analysis, high-resolution respirometry, fluorescent dye kits, etc.) have made practical assessment of metabolic parameters widely accessible. This has facilitated an explosion in the number of studies exploring, in particular, the biological implications of oxygen consumption rate (OCR) and substrate level phosphorylation via glycolysis (i.e., via extracellular acidification rate (ECAR)). Though these technologies have demonstrated substantial utility and broad applicability to cell biology research, they are also susceptible to historical assumptions, experimental limitations, and other caveats that have led to premature and/or erroneous interpretations. This review enumerates various important considerations for designing and interpreting cellular and mitochondrial bioenergetics experiments, some common challenges and pitfalls in data interpretation, and some potential "next steps" to be taken that can address these highlighted challenges.

Robust metabolic transcriptional components in 34,494 patient-derived cancer-related samples and cell lines

Background

Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes.

Methods

We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape.

Results

A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment.

Conclusions

To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00272-7.

Interplay between Epigenetics and Cellular Metabolism in Colorectal Cancer

Cellular metabolism alterations have been recognized as one of the most predominant hallmarks of colorectal cancers (CRCs). It is precisely regulated by many oncogenic signaling pathways in all kinds of regulatory levels, including transcriptional, post-transcriptional, translational and post-translational levels. Among these regulatory factors, epigenetics play an essential role in the modulation of cellular metabolism. On the one hand, epigenetics can regulate cellular metabolism via directly controlling the transcription of genes encoding metabolic enzymes of transporters. On the other hand, epigenetics can regulate major transcriptional factors and signaling pathways that control the transcription of genes encoding metabolic enzymes or transporters, or affecting the translation, activation, stabilization, or translocation of metabolic enzymes or transporters. Interestingly, epigenetics can also be controlled by cellular metabolism. Metabolites not only directly influence epigenetic processes, but also affect the activity of epigenetic enzymes. Actually, both cellular metabolism pathways and epigenetic processes are controlled by enzymes. They are highly intertwined and are essential for oncogenesis and tumor development of CRCs. Therefore, they are potential therapeutic targets for the treatment of CRCs. In recent years, both epigenetic and metabolism inhibitors are studied for clinical use to treat CRCs. In this review, we depict the interplay between epigenetics and cellular metabolism in CRCs and summarize the underlying molecular mechanisms and their potential applications for clinical therapy.

Pharmacologic approaches to amino acid depletion for cancer therapy

Cancer cells undergo metabolic reprogramming to support increased demands in bioenergetics and biosynthesis and to maintain reactive oxygen species at optimum levels. As metabolic alterations are broadly observed across many cancer types, metabolic reprogramming is considered a hallmark of cancer. A metabolic alteration commonly seen in cancer cells is an increased demand for certain amino acids. Amino acids are involved in a wide range of cellular functions, including proliferation, redox balance, bioenergetic and biosynthesis support, and homeostatic functions. Thus, targeting amino acid dependency in cancer is an attractive strategy for a number of cancers. In particular, pharmacologically mediated amino acid depletion has been evaluated as a cancer treatment option for several cancers. Amino acids that have been investigated for the feasibility of drug-induced depletion in preclinical and clinical studies for cancer treatment include arginine, asparagine, cysteine, glutamine, lysine, and methionine. In this review, we will summarize the status of current research on pharmacologically mediated amino acid depletion as a strategy for cancer treatment and potential chemotherapeutic combinations that synergize with amino acid depletion to further inhibit tumor growth and progression.

Extracellular and Intracellular Factors in Brain Cancer

The external and internal factors of the cell are critical to glioma initiation. Several factors and molecules have been reported to be implicated in the initiation and progression of brain cancer. However, the exact sequence of events responsible for glioma initiation is still unknown. Existing reports indicate that glioma stem cells are the cell of glioma origin. During cell division, chromosome breakage, DNA alteration increases the chance of cell genome modifications and oncogene overexpression. Although there is a high risk of gene alteration and oncogene overexpression, not everyone develops cancer. During embryogenesis, the same oncogenes that promote cancers have also been reported to be highly expressed, but this high expression which does not lead to carcinogenesis raises questions about the role of oncogenes in carcinogenesis. The resistance of cancer cells to drugs, apoptosis, and immune cells does not rely solely on oncogene overexpression but also on the defect in cell organelle machinery (mitochondria, endoplasmic reticulum, and cytoskeleton). This review discusses factors contributing to cancer; we report the dysfunction of the cell organelles and their contribution to carcinogenesis, while oncogene overexpression promotes tumorigenesis, maintenance, and progression through cell adhesion. All these factors together represent a fundamental requirement for cancer and its development.

Pulmonary Aerosol Delivery of Let-7b microRNA Confers a Striking Inhibitory Effect on Lung Carcinogenesis through Targeting the Tumor Immune Microenvironment

MicroRNAs are potential candidates for lung cancer prevention and therapy. A major limitation is the lack of an efficient delivery system to directly deliver miRNA to cancer cells while limiting systemic exposure. The delivery of miRNA via inhalation is a potential strategy for lung cancer prevention in high-risk individuals. In this study, the authors investigate the efficacy of aerosolized let-7b miRNA treatment in lung cancer prevention. Let-7b shows significant inhibition of B[a]P-induced lung adenoma with no detectable side effects. Single-cell RNA sequencing of tumor-infiltrating T cells from primary tumors reveals that Let-7b post-transcriptionally suppresses PD-L1 and PD-1 expression in the tumor microenvironment, suggesting that let-7b miRNAs may promote antitumor immunity in vivo. Let-7b treatment decreases the expression of PD-1 in CD8+ T cells and reduces PD-L1 expression in lung tumor cells. The results suggest that this aerosolized let-7b mimic is a promising approach for lung cancer prevention, and that the in vivo tumor inhibitory effects of let-7b are mediated, at least in part, by immune-promoting effects via downregulating PD-L1 in tumors and/or PD-1 on CD8+ T cells. These changes potentiate antitumor CD8+ T cell immune responses, and ultimately lead to tumor inhibition.

Spatiotemporal Immune Landscape of Colorectal Cancer Liver Metastasis at Single-Cell Level

Liver metastasis, the leading cause of colorectal cancer mortality, exhibits a highly heterogeneous and suppressive immune microenvironment. Here, we sequenced 97 matched samples by using single-cell RNA-seq and Spatial Transcriptomics. Strikingly, metastatic microenvironment underwent remarkable spatial reprogramming of immunosuppressive cells such as MRC1+ CCL18+ M2-like macrophages. We further developed scMetabolism, a computational pipeline for quantifying single-cell metabolism, and observed that those macrophages harbored enhanced metabolic activity. Interestingly, neoadjuvant chemotherapy could block this status and restore the antitumor immune balance in responsive patients, while the non-responsive patients deteriorated into a more suppressive one. Our work described the immune evolution of metastasis and uncovered the black box of how tumors respond to neoadjuvant chemotherapy.

Current Insights and Advancements in Head and Neck Cancer: Emerging Biomarkers and Therapeutics with Cues from Single Cell and 3D Model Omics Profiling

Head and neck cancer (HNC) is among the ten leading malignancies worldwide, with India solely contributing one-third of global oral cancer cases. The current focus of all cutting-edge strategies against this global malignancy are directed towards the heterogeneous tumor microenvironment that obstructs most treatment blueprints. Subsequent to the portrayal of established information, the review details the application of single cell technology, organoids and spheroid technology in relevance to head and neck cancer and the tumor microenvironment acknowledging the resistance pattern of the heterogeneous cell population in HNC. Bioinformatic tools are used for study of differentially expressed genes and further omics data analysis. However, these tools have several challenges and limitations when analyzing single-cell gene expression data that are discussed briefly. The review further examines the omics of HNC, through comprehensive analyses of genomics, transcriptomics, proteomics, metabolomics, and epigenomics profiles. Patterns of alterations vary between patients, thus heterogeneity and molecular alterations between patients have driven the clinical significance of molecular targeted therapies. The analyses of potential molecular targets in HNC are discussed with connotation to the alteration of key pathways in HNC followed by a comprehensive study of protein kinases as novel drug targets including its ATPase and additional binding pockets, non-catalytic domains and single residues. We herein review, the therapeutic agents targeting the potential biomarkers in light of new molecular targeted therapies. In the final analysis, this review suggests that the development of improved target-specific personalized therapies can combat HNC's global plight.

The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing

Background: The advent of single-cell RNA sequencing (scRNAseq) and additional single-cell omics technologies have provided scientists with unprecedented tools to explore biology at cellular resolution. However, reaching an appropriate number of good quality reads per cell and reasonable numbers of cells within each of the populations of interest are key to infer relevant conclusions about the underlying biology of the dataset. For these reasons, scRNAseq studies are constantly increasing the number of cells analysed and the granularity of the resultant transcriptomics analyses. Methods: We aimed to identify previously described fibroblast subpopulations in healthy adult human skin by using the largest dataset published to date (528,253 sequenced cells) and an unsupervised population-matching algorithm. Results: Our reanalysis of this landmark resource demonstrates that a substantial proportion of cell transcriptomic signatures may be biased by cellular stress and response to hypoxic conditions. Conclusions: We postulate that careful design of experimental conditions is needed to avoid long processing times of biological samples. Additionally, computation of large datasets might undermine the extent of the analysis, possibly due to long processing times.