Identification of metabolic pathways using pathfinding approaches: a systematic review.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

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.

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.

<p>Water treatment ecosystem provides important habitats for various bacterial communities, but the response mechanism of this ecosystem under ultraviolet rays is not yet clear. For this reason, 16S rRNA gene sequencing is used to study the bacterial community structure and metabolic pathways under ultraviolet irradiation. Studies have shown that the bacterial communities under different ultraviolet irradiation are different, which indicates that the time of ultraviolet irradiation has an impact on the structure of bacterial communities. Analysis of driving factors shows that COD, pH, TN and NH3-N also have an impact on the relative abundance of key species. Key species under ultraviolet irradiation are Bacteroidetes, Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, and Chlamydiae, accounting for 96.69%～98.30%, and ultraviolet irradiation has a significant inhibitory effect on the relative abundance of the key species. As the dominant bacterial phyla in Plateau environment, Chlamydiae is discovered for the first time. The network co-occurrence diagrams constructed under different ultraviolet radiation show that each sample is composed of three independent network diagrams. There are 6 common dominant phyla and 33 common dominant bacterial genera in each sample, which reveals that the structure of the ecosystem is composed of more similar microorganisms, rather than random phenomena. It also reflects the competitive relationship between species and the adaptability of bacteria to the environment. Through the analysis of metabolic pathways, it is found that the dominant metabolic pathways in Plateau environment have certain changes under ultraviolet radiation. Further analysis of metabolic pathways of carbon, nitrogen and phosphorus shows that the relative abundance of related metabolic pathways has a certain change, but the difference in metabolic diagram is small, that is, the effect of ultraviolet radiation is mainly reflected in the relative abundance of metabolic pathways. These findings indicate that ultraviolet radiation in Plateau environment as an important influencing factor has an impact on microbial structure and metabolic pathways. This research provides an important theoretical basis for further understanding of water treatment ecosystem in Plateau environment, and also provides a new perspective for research on sewage treatment systems.</p>

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.

BackgroundInfections caused by Salmonella enterica, a Gram-negative facultative anaerobic bacteria belonging to the family of Enterobacteriaceae, are major threats to the health of humans and animals. The recent availability of complete genome data of pathogenic strains of the S. enterica gives new avenues for the identification of drug targets and drug candidates. We have used the genomic and metabolic pathway data to identify pathways and proteins essential to the pathogen and absent from the host.MethodsWe took the whole proteome sequence data of 42 strains of S. enterica and Homo sapiens along with KEGG-annotated metabolic pathway data, clustered proteins sequences using CD-HIT, identified essential genes using DEG database and discarded S. enterica homologs of human proteins in unique metabolic pathways (UMPs) and characterized hypothetical proteins with SVM-prot and InterProScan. Through this core proteomic analysis we have identified enzymes essential to the pathogen.ResultsThe identification of 73 enzymes common in 42 strains of S. enterica is the real strength of the current study. We proposed all 73 unexplored enzymes as potential drug targets against the infections caused by the S. enterica. The study is comprehensive around S. enterica and simultaneously considered every possible pathogenic strain of S. enterica. This comprehensiveness turned the current study significant since, to the best of our knowledge it is the first subtractive core proteomic analysis of the unique metabolic pathways applied to any pathogen for the identification of drug targets. We applied extensive computational methods to shortlist few potential drug targets considering the druggability criteria e.g. Non-homologous to the human host, essential to the pathogen and playing significant role in essential metabolic pathways of the pathogen (i.e. S. enterica). In the current study, the subtractive proteomics through a novel approach was applied i.e. by considering only proteins of the unique metabolic pathways of the pathogens and mining the proteomic data of all completely sequenced strains of the pathogen, thus improving the quality and application of the results. We believe that the sharing of the knowledge from this study would eventually lead to bring about novel and unique therapeutic regimens against the infections caused by the S. enterica.

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

Flux analysis of microbial metabolic pathways using a visual programming environment

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.

The mechanisms underlying COVID-19-associated pulmonary mucormycosis (CAPM) remain unclear. We use a transcriptomic analysis of the innate immune cells to investigate the host immune and metabolic response pathways in patients with CAPM. We enrolled subjects with CAPM (n = 5), pulmonary mucormycosis (PM) without COVID-19 (n = 5), COVID-19 (without mucormycosis, n = 5), healthy controls (n = 5) without comorbid illness and negative for SARS-CoV-2. Peripheral blood samples from cases were collected before initiating antifungal therapy, and neutrophils and monocytes were isolated. RNA sequencing was performed using Illumina HiSeqX from monocytes and neutrophils. Raw reads were aligned with HISAT-2 pipeline and DESeq2 was used for differential gene expression. Gene ontology (GO) and metabolic pathway analysis were performed using Shiny GO application and R packages (ggplot2, Pathview). The derangement of core immune and metabolic responses in CAPM patients was noted. Pattern recognition receptors, dectin-2, MCL, FcRγ receptors and CLEC-2, were upregulated, but signalling pathways such as JAK-STAT, IL-17 and CARD-9 were downregulated; mTOR and MAP-kinase signalling were elevated in monocytes from CAPM patients. The complement receptors, NETosis, and pro-inflammatory responses, such as S100A8/A9, lipocalin and MMP9, were elevated. The major metabolic pathways of glucose metabolism-glycolysis/gluconeogenesis, pentose phosphate pathway, HIF signalling and iron metabolism-ferroptosis were also upregulated in CAPM. We identified significant alterations in the metabolic pathways possibly leading to cellular iron overload and a hyperglycaemic state. Immune responses revealed altered recognition, signalling, effector functions and a pro-inflammatory state in monocytes and neutrophils from CAPM patients.

Long non-coding RNAs (lncRNAs) have been consistently demonstrated to be involved in oral squamous cell carcinoma (OSCC) as either tumor oncogenes or tumor suppressors. However, the underlying mechanisms of OSCC tumorigenesis and development have not yet been fully elucidated. The expression profiles of mRNAs and lncRNAs in OSCC were analyzed by a microarray assay. To verify the results of the microarray, 10 differentially expressed lncRNAs were randomly selected and measured by quantitative RT-PCR (qRT-PCR). Gene Ontology (GO) and metabolic pathway analyses were performed to analyze gene function and identify enriched pathways. Subsequently, two independent algorithms were used to predict the target genes of the lncRNAs. We identified 2,294 lncRNAs and 1,938 mRNAs that were differentially expressed in all three OSCC tissues by a microarray assay. Through the construction of co-expression networks of differentially expressed genes, 4 critical lncRNAs nodes were identified as potential key factors in the pathogenesis of OSCC. Expression of the 4 critical lncRNA nodes was not associated with age, sex, smoking or tumor location (P>0.05) but was positively correlated with clinical stage, lymphatic metastasis, distant metastasis and survival status (P<0.05). Kaplan-Meier analysis demonstrated that low expression levels of these 4 critical lncRNA nodes contributed to poor median progression-free survival (PFS) and overall survival (OS) (P<0.05). GO and pathway analyses indicated that the functions and enriched pathways of many dysregulated genes are associated with cancer. Potential target genes of dysregulated lncRNAs were enriched in 43 metabolic pathways, with cancer pathways being the primary enrichment pathways. In summary, we analyzed the profile of lncRNAs in OSCC and identified the functions and enriched metabolic pathways of both dysregulated mRNAs and the target genes of dysregulated lncRNAs, providing new insights into molecular markers and therapeutic targets for OSCC.

BackgroundGenome‐wide association studies show that a broad polygenic architecture, estimated through polygenic risk scoring (PRS), can predict Alzheimer’s disease risk. A detailed analysis of the SNPs involved in a relevant PRS may reveal biological pathways to target for prevention or treatment of AD.MethodA generalized linear regression model (glm) was . The PRS was obtained using a threshold and pruning method, utilizing the IGAP summary statistic (Lambert et al., 2013). A protein‐protein interaction network and pathway analysis were conducted for the set of proteins encoded by the genes carrying the SNPs with the highest AUC.ResultWe fitted 14 models based on different thresholds, demographic information, and APOE genotype. The SNPs that have effect size at a p‐value threshold of 5 × 10−9 had the highest AUC when purely genetic effects were considered. The 62 SNPs involved in this PRS are located on 38 different genes, 13 of which are Long Intergenic Non‐Protein Coding RNAs (LINC). A protein‐protein interaction network of the protein‐coding genes showed a very high degree of interaction between among them (Figure 1). Pathway analysis shows the involvement of the genes in different metabolic pathways, including the immune system (Figure 2).ConclusionOur findings support the notion that a cumulative impact of large combinations of risk alleles is associated with AD. However, the diagnostic accuracy of the PRS was too low for practical diagnostic utility. The findings especially highlight the role of a few genes as contributors to AD risk. Previous studies link the genes like SORL1 and CLU to the regulation of beta‐amyloid formation and tau‐protein kinase activity. Our metabolic pathway analysis also emphasized the involvement of genes in different metabolic pathways. Further analysis of the functional connection between the immune system and the central nervous system, as well as analyses of LINC, may shed more light on druggable targets in AD.

Telomere has been recognized as a biomarker of accelerating aging, and telomere length (TL) shortening is closely related to diverse chronic illnesses. Human serum metabolites have demonstrated close correlations with TL maintenance or shortening in observational studies. Nevertheless, little is known about the underlying pathological mechanisms, and Mendelian randomization (MR) analysis of serum metabolites may provide a more comprehensive understanding of the potential biological process. We employed a two-sample MR analysis method to assess the causal links between 486 serum metabolites and TL. We applied the inverse-variance weighted (IVW) approach as our primary analysis, and to assure the stability and robustness of our results, additional analysis methods including the weighted median, MR-Egger, and weighted mode were conducted. MR-Egger intercept test was utilized to detect the pleiotropy. Cochran's Q test was implemented to quantify the extent of heterogeneity. Furthermore, the pathway analysis was conducted to identify potential metabolic pathways. We identified 11 known blood metabolites associated with TL. Among these metabolites, four were lipid (taurocholate, dodecanedioate, 5,8-tetradecadienoate, and 15-methylpalmitate), one amino acid (levulinate (4-oxovaleate)), one carbohydrate (lactate), one nucleotide (pseudouridine), one energy (phosphate), and three xenobiotics (2-hydroxyacetaminophen sulfate, paraxanthine, and ergothioneine). The known protective metabolites included levulinate (4-oxovaleate), dodecanedioate, 5,8-tetradecadienoate, lactate, phosphate, paraxanthine, and ergothioneine. Multiple metabolic pathways have been identified as being implicated in the maintenance of telomere length. Our MR analysis provided suggestive evidence supporting the causal relationships between 11 identified blood metabolites and TL, necessitating further exploration to clarify the mechanisms by which these serum metabolites and metabolic pathways may affect the progression of telomeres.

Background: In our day-to-day life, we are facing many dreadful diseases caused by many infectious pathogens. These pathogens invade the living organisms (host) and lethally damaging them. These dreadful pathogens were also be used as bioweapons. Among them, Clostridium perfringens is taken for the study. Clostridium perfringens is an anaerobic, rod shaped, gram positive bacteria capable of forming spores. It is prevalent in the environment and in the intestine of humans and other animals. It is the causative agent for a wide range of diseases including food borne diseases, gas gangrene and flesh eating disease called necrotizing fasciitis. C. perfringens is commonly found on raw meat and poultry that espouse to grow in conditions with very little or no oxygen, and under ideal conditions can multiply very rapidly. These conditions are occasionally lethal due to the substantial number of toxins such as alpha toxin, beta toxin, epsilon toxin and iota toxin produced by C. perfringens. It is significantly important to analyze the Drug targets of the pathogen in order to destroy them. Objective: The present work aims in identifying potential drug targets in C. perfringens through metabolic pathway analysis. Method: Primarily, the metabolic pathways of the host and pathogen are compared to identify unique pathways in the bacteria. Among the enzymes that catalyze unique metabolic pathways, the essential ones for the survival of the pathogen are identified. The druggability of the essential enzymes are predicted through identification of its sub cellular localization and other druggable parameters. Results: The comparative metabolic pathway analysis result shows that, among the 98 metabolic pathways of C.perfringens, 25 pathways were unique that they did not have a counterpart with Human. There were 113 enzymes involved in these unique pathways. The NCBI’s protein Blast search against human was done to identify the non-homologous proteins. There were 93 non-homologous proteins. Among the 93 non-homologous proteins, 47 proteins were found to be essential. Based on their sub-cellular localization, 32 proteins were identified as potential drug targets and 15 are probable vaccine candidates. Conclusion: The present work which started with 25 different pathways with more than a hundred different enzymes, resulted in the identification of 32 putative drug targets against C.perfringens infection. All these 32 identified targets did not have any human homolog and are highly essential for the survival of the organism. They were concluded as potential drug targets. Designing of compounds to inhibit these enzymes would be successful for treating the life threatening infections caused by this pathogen.

Insights into cellular metabolic pathways of the combined toxicity responses of Caco-2 cells exposed to deoxynivalenol, zearalenone and Aflatoxin B1