Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.

Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC–MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.

During their 6 month development, pear (Pyrus communis) fruits undergo drastic changes in their morphology and their chemical composition. To gain a better understanding of the metabolic pathways and transport processes active during fruit development, we performed a time-course analysis using mass spectrometry (MS)-based protein identification and quantification of fruit flesh tissues. After pre-fractionation of the samples, 2,841 proteins were identified. A principal component analysis (PCA) separated the samples from seven developmental stages into three distinct clusters representing the early, mid and late developmental phase. Over-representation analysis of proteins characteristic of each developmental phase revealed both expected and novel biological processes relevant at each phase. A high abundance of aquaporins was detected in samples from fruits in the cell expansion stage. We were able quantitatively to reconstruct basic metabolic pathways such as the tricarboxylic acid (TCA) cycle, which indicates sufficient coverage to reconstruct other metabolic pathways. Most of the enzymes that presumably contribute to sugar accumulation in pear fruits could be identified. Our data indicate that invertases do not play a major role in the sugar conversions in developing pear fruits. Rather, sucrose might be broken down by sucrose synthases. Further focusing on sugar transporters, we identified several putative sugar transporters from diverse families which showed developmental regulation. In conclusion, our data set comprehensively describes the proteome of developing pear fruits and provides novel insights about sugar accumulation as well as candidate genes for key reactions and transport steps.

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.

3111 – FUNCTIONAL REGULATION OF GENE EXPRESSION, CELL FATE AND DRUG RESPONSE BY HEME IN AML

Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development. Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development.

The in vitro metabolism of dronedarone and its major metabolites has been studied in human liver microsomes and cryopreserved hepatocytes in primary culture through the use of specific or total cytochrome P450 (CYP) and monoamine oxidase (MAO) inhibitors. The identification of the main metabolites and enzymes participating in their metabolism was also elucidated by using rhCYP, rhMAO, flavin monooxygenases (rhFMO) and UDP-glucuronosyltransferases (rhUGT) and liquid chromatography/tandem mass spectrometry (LC/MS-MS) analysis. Dronedarone was extensively metabolized in human hepatocytes with a metabolic clearance being almost completely inhibited (98 ± 2%) by 1-aminobenzotriazole. Ketoconazole also inhibited dronedarone metabolism by 89 ± 7%, demonstrating the crucial role of CYP3A in its metabolism. CYP3A isoforms mostly contributed to N-debutylation while hydroxylation on the butyl-benzofuran moiety was catalyzed by CYP2D6. However, hydroxylation on the dibutylamine moiety did not appear to be CYP-dependent. N-debutyl-dronedarone was less rapidly metabolized than dronedarone, the major metabolic pathway being catalyzed by MAO-A to form propanoic acid-dronedarone and phenol-dronedarone. Propanoic acid-dronedarone was metabolized at a similar rate to that of N-debutyl-dronedarone and was predominantly hydroxylated by CYP2C8 and CYP1A1. Phenol-dronedarone was extensively glucuronidated while C-dealkyl-dronedarone was metabolized at a slow rate. The evaluation of the systemic clearance of each metabolic process together with the identification of both the major metabolites and predominant enzyme systems and isoforms involved in the formation and subsequent metabolism of these metabolites has enhanced the overall understanding of metabolism of dronedarone in humans.

BackgroundPneumococcal infection, caused by Streptococcus pneumoniae, is a prevalent cause of community-acquired pneumonia and a major pathogen responsible for illnesses such as meningitis, sepsis, and pharyngitis. The complex etiology of S. pneumoniae infection poses significant challenges in elucidating the molecular mechanisms underlying its pathogenesis.MethodsIn this study, twenty serum samples from individuals infected with S. pneumoniae and fifteen serum samples from normal controls were analyzed using liquid chromatography/mass spectrometry (LC–MS) to identify metabolites. Multivariate statistical analyses, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were used to identify potential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to map the metabolic pathways associated with these metabolites.ResultsThrough comparative analysis of the metabolic profiles of infected individuals and normal controls, we identified 418 metabolites that significantly contributed to the differentiation of group samples. The identified metabolites were categorized into various groups, such as amino acids, fatty acids, and phosphatidylcholine, and were enriched in pathways including galactose metabolism, the hypoxia-inducible factor-1 (HIF-1) signaling pathway, the citrate cycle, the pentose phosphate pathway, and glycolysis/gluconeogenesis. S. pneumoniae infection induced significant variations in the serum metabolome, with activation of metabolic pathways implicated in the immune response.ConclusionOur study provides a comprehensive and real-time analysis of the metabolic network, elucidating the complex processes that occur following pathogen invasion in the human body. The identification of metabolic biomarkers and enriched pathways lays a foundational framework for research and offers visualizable targets for the diagnosis and treatment of pneumococcal infections.

Fungal keratitis is a common blinding eye disease, and Fusarium is one of the main species that cause fungal keratitis. As is well known, oxidative stress plays an important role in Fusarium keratitis and it is also a significant initiating factor of ferroptosis. But the relationship between Fusarium keratitis and ferroptosis is currently unclear. This study aimed to speculate and validate potential ferroptosis-related genes in Fusarium keratitis using bioinformatics analysis, which provided ideas for further research on its specific mechanism and new targets for its treatment. The microarray expression profiling dataset (GSE58291) came from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were obtained by the limma package of the R software. The DEGs were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the DEGs were intersected with the genes in the ferroptosis database. The top 5 hub genes were obtained by the protein-protein interaction (PPI) network analysis and the cytoHubba plug-in of Cytoscape software. The hub genes were subjected to GSEA analysis. Then we analyzed the immune infiltration of the samples by CIBERSORT and ssGSEA algorithm. Finally, we validated the mRNA of hub genes by qPCR. A total of 1,368 DEGs were identified and 26 ferroptosis-related DEGs were obtained. At the same time, ferroptosis-related pathways were enriched by GO and KEGG using DEGs. HMOX1, CYBB, GPX2, ALOX5 and SRC were obtained by the PPI network analysis and the cytoHubba plug-in of Cytoscape software. The iron metabolism and immune response related pathways were enriched using GSEA. They included hematopoietic cell lineage, lysosome and FC gamma R mediated phagocytosis. T cells follicular helper, monocytes, macrophages and mast cells might play an important role in Fusarium keratitis using analysis of immune infiltration. Finally, qPCR confirmed that the expression of HMOX1, CYBB, ALOX5 mRNA in the DON group was significantly elevated, while the expression of GPX2 were significantly decreased. Ferroptosis may play an important role in Fusarium keratitis. HMOX1, CYBB, ALOX5 and GPX2 may be key ferroptosis-related genes in the pathogenesis of Fusarium keratitis.

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).

Introduction Immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN), and diabetic nephropathy (DN) are prominent contributors to chronic kidney disease burden. Our main objective was to contribute to understanding of metabolic profiles of these three major types of nephropathies and identify potential metabolic biomarkers. Material and methods Kidney samples of 20 sex- and age-matched patients with biopsy-proven IgAN, MN, DN, and controls without any kidney diseases were included. Ultra high performance liquid chromatography-mass spectrometry analysis was conducted. t-test was used to calculate statistical significance of the identified metabolites. Metabolic pathways were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Specificity, sensitivity and area under the curve (AUC) were calculated to evaluate the predictive performance of metabolites. Results Among 557 identified differential metabolites, only 118 were found in all three comparison groups. Differential metabolites of IgAN vs controls were significantly enriched in arachidonic acid metabolism, starch and sucrose metabolism, ferroptosis, and other pathways. In the DN group, metabolites were mainly enriched in phenylalanine, tyrosine and tryptophan biosynthesis, histidine metabolism, etc. MN-enriched pathways included steroid hormone biosynthesis, neuroactive ligand-receptor interaction, and bile secretion. In the positive mode, cumulative AUC values for comparison pairs IgAN vs controls, MN vs controls, and DN vs controls were 0.965, 0.972, and 0.573, respectively, whereas in the negative mode the AUC values of all three pairs were slightly above 0.65. Conclusions IgAN, MN and DN have similar but distinct metabolic profiles. Only positive node metabolites of IgAN and MN exhibited great predictive performance.

Microplastics (MPs) are ubiquitous pollutants that have potentially harmful and toxic effects. MPs are frequently ingested by aquatic animals, as microplastics share a similar size and color to their food. Heavy metals are harmful and difficult to degrade, have a wide range of sources and an extended residual time from exposure to recovery. Although the effects of MPs and heavy metals on the performance of aquatic species have been extensively studied, the molecular mechanisms of MP and heavy metal (Pb, Cd and Cu) exposure on aquatic organisms remain unclear. Here, the effects of MPs and heavy metal accumulation on the line seahorse, Hippocampus erectus, were investigated at the molecular level using transcriptome analysis. Using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we found that immune, metabolic, and apoptotic pathways were affected in the heavy metal group, whereas the DNA damage repair and metabolism pathways were mainly involved in the MP group. Both types of stress caused significant changes in the genes related to the antioxidant pathway in H. erectus larvae. Transcriptome differences between the treatment groups were analyzed, and sensitive candidate genes (Hsp70, Hsp90, Sod, etc.) were screened. The response characteristics of seahorses to MP environmental stress were also investigated. Using seahorse as a biological model and candidate sensitive genes as a basis, our results provide a theoretical basis for detecting MPs and heavy metals pollution in coastal areas.

Background Emerging studies have reported the contribution of cholesterol to hepatocellular carcinoma (HCC) progression. However, the specific role and mechanism of cholesterol metabolism on spontaneous and progressive HCC development from the point of view of ferroptosis are still worth exploring. The present study aimed to reveal a novel mechanism of cholesterol metabolism-related ferroptosis in hepatocellular carcinoma cells Methods Two microarray datasets (GSE25097, GSE22058) related to HCC were downloaded from Gene Expression Omnibus (GEO) datasets. Metabolomics analysis was performed by ultra performance liquid chromatography - tandem mass spectrometer (UPLC-MS/MS). The cholesterol-related proteins were downloaded from HMBD. Ferroptosis-related genes were extracted from FerrDb database. Data sets were separated into two groups. GSE25097 was used to identify ferroptosis-related genes, and GSE22058 was used to verify results. During these processes, chemical–protein interaction (CPI), protein–protein interaction (PPI), the Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Results finally identified 8 differentially expressed ferroptosis-related genes (HAMP, PTGS2, IL1B, ALOX15B, CDKN2A, RRM2, NQO1 and KIF20A) and 4 differentially expressed cholesterol-related genes (LCAT, CH25H, CEL and CYP7A1). Further function analysis revealed that the 8 differentially expressed ferroptosis-related genes are enriched in metabolic pathways, arachidonic acid metabolism, NF-kappa B signaling pathway, TNF signaling pathway, p53 signaling pathway, IL-17 signaling pathway, C-type lectin receptor signaling pathway, serotonergic synapse and other related pathways, and the 4 differentially expressed cholesterol-related genes are enriched in primary bile acid biosynthesis, cholesterol metabolism and other related pathways. Furthermore, based on the predicted results with STITCH, we identified indomethacin and IL1B as the essential node for cholesterol-mediated ferroptosis in hepatocellular carcinoma cell. Conclusions Our findings shed new light on the association between cholesterol metabolism and ferroptosis in HCC, and suggest that IL1B is the necessary node for cholesterol to lead to ferroptosis process in HCC. Also, we identified the potential role of indomethacin in adjuvant therapy of HCC with complications of abnormal cholesterol metabolism.

To systematically analyze the gene function of Dalbergia odorifera, the seedlings of D. odorifera were treated with low-temperature stress for 6 h. Total RNA was extracted from a mixture of seedling roots, stems, and leaves, and a low-temperature-induced D. odorifera yeast cDNA expression library was constructed. The library volume was 1.032 × 108 CFU, and the PCR (Polymerase Chain Reaction) identification of the library bacterial fluid showed that the amplification was around 1000 bp, with a single randomly distributed band, indicating that the library had been recombinantly inserted into the pYES2 vector. The GO (Gene Ontology) analysis showed that the library genes were mainly involved in metabolic and stress signaling pathways. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis showed that the genes were primarily related to energy and metabolic pathways. Twenty-one genes were screened or obtained at -20°C for low-temperature tolerance. In addition, the organ expression profiles of the candidate genes were analyzed based on RNA-seq data, and the expression profiles of the candidate genes under low-temperature stress were also examined. The construction of the yeast library provides genetic resources for the analysis of the mechanism of low-temperature tolerance of D. odorifera, which is important for comprehending and utilizing the genetic resources of D. odorifera.