Lipid Metabolism in Cancer: The Role of Acylglycerolphosphate Acyltransferases (AGPATs)

Effects of glyphosate-based herbicide on gut microbes and hepatopancreatic metabolism in Pomacea canaliculata

Roundup®, a prominent glyphosate-based herbicide (GBH), holds a significant position in the global market. However, studies of its effects on aquatic invertebrates, including molluscs are limited. Pomacea canaliculata, a large freshwater snail naturally thrives in agricultural environments where GBH is extensively employed. Our investigation involved assessing the impact of two concentrations of GBH (at concentrations of 19.98 mg/L and 59.94 mg/L, corresponding to 6 mg/L and 18 mg/L glyphosate) during a 96 h exposure experiment on the intestinal bacterial composition and metabolites of P. canaliculata. Analysis of the 16 S rRNA gene demonstrated a notable reduction in the alpha diversity of intestinal bacteria due to GBH exposure. Higher GBH concentration caused a significant shift in the relative abundance of dominant bacteria, such as Bacteroides and Paludibacter. We employed widely-targeted metabolomics analysis to analyze alterations in the hepatopancreatic metabolic profile as a consequence of GBH exposure. The shifts in metabolites primarily affected lipid, amino acid, and glucose metabolism, resulting in compromised immune and adaptive capacities in P. canaliculata. These results suggested that exposure to varying GBH concentrations perpetuates adverse effects on intestinal and hepatopancreatic health of P. canaliculata. This study provides an understanding of the negative effects of GBH on P. canaliculata and may sheds light on its potential implications for other molluscs.

Random walk with restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BACKGROUND

Biological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank.

RESULTS

We first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics.

CONCLUSION

Overall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

Targeting Hypoxia-Inducible Factor-1 (HIF-1) in Cancer: Emerging Therapeutic Strategies and Pathway Regulation

Hypoxia-inducible factor-1 (HIF-1) is a key regulator for balancing oxygen in the cells. It is a transcription factor that regulates the expression of target genes involved in oxygen homeostasis in response to hypoxia. Recently, research has demonstrated the multiple roles of HIF-1 in the pathophysiology of various diseases, including cancer. It is a crucial mediator of the hypoxic response and regulator of oxygen metabolism, thus contributing to tumor development and progression. Studies showed that the expression of the HIF-1α subunit is significantly upregulated in cancer cells and promotes tumor survival by multiple mechanisms. In addition, HIF-1 has potential contributing roles in cancer progression, including cell division, survival, proliferation, angiogenesis, and metastasis. Moreover, HIF-1 has a role in regulating cellular metabolic pathways, particularly the anaerobic metabolism of glucose. Given its significant and potential roles in cancer development and progression, it has been an intriguing therapeutic target for cancer research. Several compounds targeting HIF-1-associated processes are now being used to treat different types of cancer. This review outlines emerging therapeutic strategies that target HIF-1 as well as the relevance and regulation of the HIF-1 pathways in cancer. Moreover, it addresses the employment of nanotechnology in developing these promising strategies.

Lysophosphatidic acid triggers inflammation in the liver and white adipose tissue in rat models of 1-acyl-sn-glycerol-3-phosphate acyltransferase 2 deficiency and overnutrition

AGPAT2 (1-acyl-sn-glycerol-3-phosphate-acyltransferase-2) converts lysophosphatidic acid (LPA) into phosphatidic acid (PA), and mutations of the AGPAT2 gene cause the most common form of congenital generalized lipodystrophy which leads to steatohepatitis. The underlying mechanism by which AGPAT2 deficiency leads to lipodystrophy and steatohepatitis has not been elucidated. We addressed this question using an antisense oligonucleotide (ASO) to knockdown expression of Agpat2 in the liver and white adipose tissue (WAT) of adult male Sprague-Dawley rats. Agpat2 ASO treatment induced lipodystrophy and inflammation in WAT and the liver, which was associated with increased LPA content in both tissues, whereas PA content was unchanged. We found that a controlled-release mitochondrial protonophore (CRMP) prevented LPA accumulation and inflammation in WAT whereas an ASO against glycerol-3-phosphate acyltransferase, mitochondrial (Gpam) prevented LPA content and inflammation in the liver in Agpat2 ASO-treated rats. In addition, we show that overnutrition, due to high sucrose feeding, resulted in increased hepatic LPA content and increased activated macrophage content which were both abrogated with Gpam ASO treatment. Taken together, these data identify LPA as a key mediator of liver and WAT inflammation and lipodystrophy due to AGPAT2 deficiency as well as liver inflammation due to overnutrition and identify LPA as a potential therapeutic target to ameliorate these conditions.

Unveiling the MUFA-Cancer Connection: Insights from Endogenous and Exogenous Perspectives

Monounsaturated fatty acids (MUFAs) have been the subject of extensive research in the field of cancer due to their potential role in its prevention and treatment. MUFAs can be consumed through the diet or endogenously biosynthesized. Stearoyl-CoA desaturases (SCDs) are key enzymes involved in the endogenous synthesis of MUFAs, and their expression and activity have been found to be increased in various types of cancer. In addition, diets rich in MUFAs have been associated with cancer risk in epidemiological studies for certain types of carcinomas. This review provides an overview of the state-of-the-art literature on the associations between MUFA metabolism and cancer development and progression from human, animal, and cellular studies. We discuss the impact of MUFAs on cancer development, including their effects on cancer cell growth, migration, survival, and cell signaling pathways, to provide new insights on the role of MUFAs in cancer biology.

Stearoyl-CoA Desaturases1 Accelerates Non-Small Cell Lung Cancer Metastasis by Promoting Aromatase Expression to Improve Estrogen Synthesis

Metastases contribute to the low survival rate of non-small cell lung cancer (NSCLC) patients. Targeting lipid metabolism for anticancer therapies is attractive. Accumulative evidence shows that stearoyl-CoA desaturases1 (SCD1), a key enzyme in lipid metabolism, enables tumor metastasis and the underlying mechanism remains unknown. In this study, immunohistochemical staining of 96 clinical specimens showed that the expression of SCD1 was increased in tumor tissues (p < 0.001). SCD1 knockdown reduced the migration and invasion of HCC827 and PC9 cells in transwell and wound healing assays. Aromatase (CYP19A1) knockdown eliminated cell migration and invasion caused by SCD1 overexpression. Western blotting assays demonstrated that CYP19A1, along with β-catenin protein levels, was reduced in SCD1 knocked-down cells, and estrogen concentration was reduced (p < 0.05) in cell culture medium measured by enzyme-linked immunosorbent assay. SCD1 overexpression preserving β-catenin protein stability was evaluated by coimmunoprecipitation and Western blotting. The SCD1 inhibitor A939572, and a potential SCD1 inhibitor, grape seed extract (GSE), significantly inhibited cell migration and invasion by blocking SCD1 and its downstream β-catenin, CYP19A1 expression, and estrogen concentration. In vivo tumor formation assay and a tail vein metastasis model indicated that knockdown of SCD1 blocked tumor growth and metastasis. In conclusion, SCD1 could accelerate metastasis by maintaining the protein stability of β-catenin and then promoting CYP19A1 transcription to improve estrogen synthesis. SCD1 is expected to be a promised therapeutic target, and its novel inhibitor, GSE, has great therapeutic potential in NSCLC.

Saturated fatty acid- and/or monounsaturated fatty acid-containing phosphatidic acids selectively interact with heat shock protein 27

Diacylglycerol kinase (DGK) α, which is a key enzyme in the progression of cancer and, in contrast, in T-cell activity attenuation, preferentially produces saturated fatty acid (SFA)- and/or monounsaturated fatty acid (MUFA)-containing phosphatidic acids (PAs), such as 16:0/16:0-, 16:0/18:0-, and 16:1/16:1-PA, in melanoma cells. In the present study, we searched for the target proteins of 16:0/16:0-PA in melanoma cells and identified heat shock protein (HSP) 27, which acts as a molecular chaperone and contributes to cancer progression. HSP27 more strongly interacted with PA than other phospholipids, including phosphatidylcholine, phosphatidylserine, phosphatidylglycerol, cardiolipin, phosphatidylinositol, phosphatidylinositol 4-monophosphate, and phosphatidylinositol 4,5-bisphosphate. Moreover, HSP27 is more preferentially bound to SFA- and/or MUFA-containing PAs, including 16:0/16:0- and 16:0/18:1-PAs, than PUFA-containing PAs, including 18:0/20:4- and 18:0/22:6-PA. Furthermore, HSP27 and constitutively active DGKα expressed in COS-7 cells colocalized in a DGK activity-dependent manner. Notably, 16:0/16:0-PA, but not phosphatidylcholine or 16:0/16:0-phosphatidylserine, induced oligomer dissociation of HSP27, which enhances its chaperone activity. Intriguingly, HSP27 protein was barely detectable in Jurkat T cells, while the protein band was intensely detected in AKI melanoma cells. Taken together, these results strongly suggest that SFA- and/or MUFA-containing PAs produced by DGKα selectively target HSP27 and regulate its cancer-progressive function in melanoma cells but not in T cells.

The prognostic value of the GPAT/AGPAT gene family in hepatocellular carcinoma and its role in the tumor immune microenvironment

Background

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma accounts for an estimated 90% of all liver cancers. Many enzymes of the GPAT/AGPAT family are required for the synthesis of triacylglycerol. Expression of AGPAT isoenzymes has been reported to be associated with an increased risk of tumorigenesis or development of aggressive phenotypes in a variety of cancers. However, whether members of the GPAT/AGPAT gene family also influence the pathophysiology of HCC is unknown.

Methods

Hepatocellular carcinoma datasets were obtained from the TCGA and ICGC databases. Predictive models related to the GPAT/AGPAT gene family were constructed based on LASSO-Cox regression using the ICGC-LIRI dataset as an external validation cohort. Seven immune cell infiltration algorithms were used to analyze immune cell infiltration patterns in different risk groups. IHC, CCK-8, Transwell assay, and Western blotting were used for in vitro validation.

Results

Compared with low-risk patients, high-risk patients had shorter survival and higher risk scores. Multivariate Cox regression analysis showed that risk score was a significant independent predictor of overall survival (OS) after adjustment for confounding clinical factors (p < 0.001). The established nomogram combined risk score and TNM staging to accurately predict survival at 1, 3, and 5 years in patients with HCC with AUC values of 0.807, 0.806, and 0.795, respectively. This risk score improved the reliability of the nomogram and guided clinical decision-making. In addition, we comprehensively analyzed immune cell infiltration (using seven algorithms), response to immune checkpoint blockade, clinical relevance, survival, mutations, mRNA expression-based stemness index, signaling pathways, and interacting proteins related to the three core genes of the prognostic model (AGPAT5, LCLAT1, and LPCAT1). We also performed preliminary validation of the differential expression, oncological phenotype, and potential downstream pathways of the three core genes by IHC, CCK-8, Transwell assay, and Western blotting.

Conclusion

These results improve our understanding of the function of GPAT/AGPAT gene family members and provide a reference for prognostic biomarker research and individualized treatment of HCC.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Hallmarks of Metabolic Reprogramming and Their Role in Viral Pathogenesis

Metabolic reprogramming is a hallmark of cancer and has proven to be critical in viral infections. Metabolic reprogramming provides the cell with energy and biomass for large-scale biosynthesis. Based on studies of the cellular changes that contribute to metabolic reprogramming, seven main hallmarks can be identified: (1) increased glycolysis and lactic acid, (2) increased glutaminolysis, (3) increased pentose phosphate pathway, (4) mitochondrial changes, (5) increased lipid metabolism, (6) changes in amino acid metabolism, and (7) changes in other biosynthetic and bioenergetic pathways. Viruses depend on metabolic reprogramming to increase biomass to fuel viral genome replication and production of new virions. Viruses take advantage of the non-metabolic effects of metabolic reprogramming, creating an anti-apoptotic environment and evading the immune system. Other non-metabolic effects can negatively affect cellular function. Understanding the role metabolic reprogramming plays in viral pathogenesis may provide better therapeutic targets for antivirals.

Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma

Colon adenocarcinoma (COAD) is the primary factor responsible for cancer-related mortalities in western countries, and its development and progression are affected by altered sphingolipid metabolism. The current study aimed at investigating the effects of sphingolipid metabolism-related (SLP) genes on multiple human cancers, especially on COAD. We obtained 1287 SLP genes from the GeneCard and MsigDb databases along with the public transcriptome data and the related clinical information. The univariate Cox regression analysis suggested that 26 SLP genes were substantially related to the prognosis of COAD, and a majority of SLP genes served as the risk genes for the tumor, insinuating a potential pathogenic effect of SLP in COAD development. Pan-cancer characterization of SLP genes summarized their expression traits, mutation traits, and methylation levels. Subsequently, we focused on the thorough research of COAD. With the help of unsupervised clustering, 1008 COAD patients were successfully divided into two distinct subtypes (C1 and C2). C1 subtype is characterized by a poor prognosis, activation of SLP pathways, high expression of SLP genes, disordered carcinogenic pathways, and immune microenvironment. Based on the clusters of SLP, we developed and validated a novel prognostic model, consisting of ANO1, C2CD4A, EEF1A2, GRP, HEYL, IGF1, LAMA2, LSAMP, RBP1, and TCEAL2, to quantitatively evaluate the clinical outcomes of COAD. The Kaplain-Meier survival curves and ROC curves highlighted the accuracy of our SLP model in both internal and external cohorts. Compared to normal colon tissues, expression of C2CD4A was detected to be significantly higher in COAD; whereas, expression levels of EEF1A2, IGF1, and TCEAL2 were detected to be significantly lower in COAD. Overall, our research emphasized the pathogenic role of SLP in COAD and found that targeting SLP might help improve the clinical outcomes of COAD. The risk model based on SLP metabolism provided a new horizon for prognosis assessment and customized patient intervention.