A general method for determining the contribution of split pathways in metabolite production in the yeast Saccharomyces cerevisiae

We describe a simple method using (13)C labeling and NMR spectroscopy to determine the flux contributions of alternative pathways in Saccharomyces cerevisiae that produce the same metabolite with identical labeling patterns. Cells were incubated with a (13)C-labeled precursor for one of the branches, and the absolute enrichment of the product and its metabolic precursor(s) was quantified. The ratio of the absolute enrichment of the product to that of its precursor reflects the contribution of the pathway. The method was applied to the biosynthesis of glycine in yeast, which can occur from threonine via threonine aldolase or from serine via serine hydroxymethyltransferase. [2-(13)C]Aspartate and [2-(13)C]serine were used as labeled precursors for the threonine aldolase and serine hydroxymethyltransferase pathways, respectively. The data show that in cells possessing both pathways, the serine hydroxymethyltransferase pathway contributes 65-75% of the total glycine production. In comparison with other approaches, this method provides an inexpensive, flexible alternative to determining the flux contributions of split pathways under controlled conditions and should have wide applicability in the metabolic engineering of microorganisms.     

Exploration of the molecular targets and mechanisms of suxiao xintong dropping pills for myocardial infarction by network pharmacology method

Background: Suxiao Xintong dropping pills (SXXTDP), a traditional Chinese medicine, is widely applied for treating myocardial infarction (MI). However, its therapy mechanisms are still unclear. Therefore, this research is designed to explore the molecular mechanisms of SXXTDP in treating MI.Methods: The active ingredients of SXXTDP and their corresponding genes of the active ingredients were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. MI-related genes were identified via analyzing the expression profiling data (accession number: {"type":"entrez-geo","attrs":{"text":"GSE97320","term_id":"97320"}}GSE97320). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to study the shared genes of drug and disease. Through protein–protein interaction (PPI) network and the Cytoscape plugin cytoHubba, the hub genes were screened out. The compounds and hub targets binding were simulated through molecular docking method.Results: We obtained 21 active compounds and 253 corresponding target genes from TCMSP database. 1833 MI-related genes were identified according to P<0.05 and |log₂FC| ≥ 0.5. 27 overlapping genes between drug and disease were acquired. GO analysis indicated that overlapping genes were mainly enriched in MAP kinase activity and antioxidant activity. KEGG analysis indicated that overlapping genes were mainly enriched in IL-17 signaling pathway and TNF signaling pathway. We obtained 10 hub genes via cytoHubba plugin. Six of the 10 hub genes, including PTGS2, MAPK14, MMP9, MAPK1, NFKBIA, and CASP8, were acted on molecular docking verification with their corresponding compounds of SXXTDP.Conclusion: SXXTDP may exert cardioprotection effect through regulating multiple targets and multiple pathways in MI.     

基于基因表达变异性的通路富集方法研究

当前的通路富集方法主要是基于基因的表达差异,很少有方法从通路变异性(方差)角度对其富集分析．我们注意到用合适的统计量描述通路的变异性时,在疾病表型下一些通路的变异性有明显的上升或者下降．因此本研究假设：通路变异性程度在不同表型中存在差异．本文设计了14种描述通路变异性的统计量与检验方法,检测不同表型下变异性有差异的通路即富集通路,并将富集结果与文献检索结果进行比较,同时,分析不同芯片预处理方法对数据和结果的影响．研究结果表明：5种预处理方法中,多阵列对数健壮算法(RMA)是数据预处理的最优方法;不同表型下通路的变异性程度存在差异;根据文献检索的通路结果,14种基于变异性的通路富集方法中,以通路中各基因欧氏距离的方差做统计量进行permutation检验(方法11)能有效识别显著通路,其富集结果优于基因集富集分析(GSEA)．综上所述,基于通路变异性的通路富集策略具有可行性,不仅对通路富集分析有一定的理论指导意义,而且为人类疾病研究提供新的视角．     

SEAS: a system for SEED-based pathway enrichment analysis

Pathway enrichment analysis represents a key technique for analyzing high-throughput omic data, and it can help to link individual genes or proteins found to be differentially expressed under specific conditions to well-understood biological pathways. We present here a computational tool, SEAS, for pathway enrichment analysis over a given set of genes in a specified organism against the pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes of a bacterium to pathway genes covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes. Our evaluation of SEAS indicates that the program provides highly reliable pathway mapping results and identifies more organism-specific pathways than similar existing programs. SEAS is publicly released under the GPL license agreement and freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/.     

Identification of key genes and pathways associated with sex difference in osteoarthritis based on bioinformatics analysis

Objectives:The present study aimed to identify different key genes and pathways between postmenopausal females and males by studying differentially expressed genes (DEGs).Methods:{"type":"entrez-geo","attrs":{"text":"GSE32317","term_id":"32317"}}GSE32317 and {"type":"entrez-geo","attrs":{"text":"GSE55457","term_id":"55457"}}GSE55457 gene expression data were downloaded from the GEO database, and DEGs were discovered using R software to obtain overlapping DEGs. The interaction between overlapping DEGs was further analyzed by establishing the protein-protein interaction (PPI) network. Finally, GO and KEGG were used for enrichment analysis.Results:924 overlapping DEGs between postmenopausal women and men with osteoarthritis (OA) were identified, including 674 up-regulated genes and 249 down-regulated ones. And 10 hub genes were identified in the PPI network, including BMP4, KDM6A, JMJD1C, NFATC1, PRKX, SRF, ZFX, LAMTOR5, UFD1L and AMBN. The findings of the functional enrichment analysis suggested that these genes were predominantly expressed in MAPK signaling pathway as well as the Thyroid hormone signaling pathway, indicating that those two pathways may be involved in onset and disease progression of OA in postmenopausal patients.Conclusion:BMP4, KDM6A, JMJD1C, PRKX, ZFX and LAMTOR5 are expected to play crucial roles in disease development in postmenopausal patients and may be ideal targets or prognostic markers for the treatment of OA.     

Chemical constituents and synergistic anti-gout studies on Eurycoma longifolia and potential mechanisms evaluation based on systemic analysis approach

37 compounds mainly including triterpenoids with the quassinoid skeleton and β-carboline alkaloids have been isolated from the roots of Eurycoma longifolia Jack (EL), which has been used as traditional medicine for a long history. It has been demonstrated that the total extracts from EL could significantly inhibit the joint swelling in MSU-induced acute gout arthritis rat model at middle and high doses (P < 0.05, P < 0.01), as meanwhile, better performance than that of positive control (P < 0.05, P < 0.01) has been observed at the dose of 10 g/kg. Aiming to search potential compounds and probable mechanisms, network pharmacology, molecular docking and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were adopted, leading to the hypothesis of 17 targets related to different pathogenesis of gout and 5 potential compounds (C36, C107, C124, C125 and C130) among 156 selected compounds, playing synergetic role with multiple targets. Instead of the guiding ideology of “a gene, a drug, a disease”, varieties of compounds but not a single one from EL display holistic performance through multiple pathways with multi-targets. It was noteworthy that Xanthine dehydrogenase/oxidase (XDH), Prostaglandin G/H synthase 2 (PTGS2), Fatty acid-binding protein, liver (FABP1), Purine nucleoside phosphorylase (PNP), and Peroxisome proliferator activated receptor alpha (PPARA) were the key targets with intensely interaction. Furthermore, the functional enrichment analysis indicated that EL probably produced the gout protection effects by synergistic regulation in multiple biological pathways, including Toll-like receptor signaling pathway, MAPK signaling pathway, and NOD-like receptor signaling pathway, etc.     

Degradation of iprodione by a soil Arthrobacter-like strain.

A bacterial strain able to transform iprodione was isolated from a fast iprodione-degrading soil by enrichment procedures. Transformation was detected through 3,5-dichloroaniline production as measured by a rapid colorimetric method. The strain, MA6, was tentatively identified as an Arthrobacter sp. When it was incubated with MA6 in a minimum mineral medium (pH 6.5), iprodione (8.8 mumol/liter) was transformed into two major metabolites that were identified by high-performance liquid chromatography analysis: 3,5-dichlorophenylcarboximide (metabolite 1) and (3,5-dichlorophenylurea) acetic acid (metabolite 2), which was produced after ring cleavage of the former product. These products were synthesized in the laboratory and compared with metabolites 1 and 2 which were formed during iprodione degradation. Small quantities of 3,5-dichloroaniline also appeared in the bacterial culture but did not substantially increase between the first and second days of incubation. In contrast, in the sterile control medium, iprodione was spontaneously transformed into hydantoic acid and an iprodione isomer. Chemical and biological transformations of iprodione seem to occur through two different pathways. One biological degradation pathway is proposed.     

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction

Background

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism’s genome as a database restricts metabolite identification to only those compounds that the organism can produce.

Results

To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment’s MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis.

Conclusions

Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.     

Integrating Constitutive Gene Expression and Chemoactivity: Mining the NCI60 Anticancer Screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/and http://www.ncbi.nlm.nih.gov/projects/geo ({"type":"entrez-geo","attrs":{"text":"GPL96","term_id":"96"}}GPL96, {"type":"entrez-geo","attrs":{"text":"GSE32474","term_id":"32474"}}GSE32474).     

Personalized pathway enrichment map of putative cancer genes from next generation sequencing data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems.     

单细胞测序分析人类胚胎干细胞神经分化的分子机制

目的 探讨人类胚胎干细胞(ESCs)分化为神经细胞的关键性靶基因及分子机制,为临床靶向治疗神经康复患者提供分子理论依据.方法 基于GEO数据平台芯片,采用单细胞测序方法(scRNA-seq),利用R语言从多分子维度(单细胞差异基因、蛋白互作网络和基因通路等)分析人类ESCs分化过程中的关键Marker基因并利用质控和数...     

基于蛋白组学的脓毒症补体凝血级联通路及标志物KLKB1研究

摘要 目的：通过蛋白质组学方法鉴定脓毒症关键通路及诊断标志物。方法：选取2019年1月至12月西南医科大学附属医院急诊科收治的56例脓毒症患者（脓毒症组）,另取同期50名健康体检志愿者（对照组）。采用随机抽样法分别选取两组中12名脓毒症患者和8名健康体检志愿者,利用非数据依赖模式（DIA）进行血清蛋白数据采集,将数据上传至iDEP在线平台分析脓毒症患者外周血中差异表达蛋白,进一步对这些差异蛋白进行生物信息学分析,包括主成分分析（PCA）、基因本体富集分析（GO）、通路富集分析和蛋白-蛋白相互作用网络（PPI）分析,进而筛选出脓毒症关键蛋白。采用酶联免疫吸附试验（ELISA）对脓毒症组、对照组进行关键蛋白表达验证分析。采用受试者工作特征（ROC）曲线分析关键蛋白对脓毒症的诊断效能。结果：蛋白质组学分析共鉴定出690个蛋白,筛选出171个差异表达蛋白（DEPs）,其中39个蛋白显著下调,132个蛋白显著上调。DEPs富集的核心通路为补体和凝血级联通路。该条通路中的血清激肽释放酶 1（KLKB1）在脓毒症组的表达水平为（121.80±55.63 ng/mL）,显著高于对照组的（68.30±57.11 ng/mL）,差异具有统计学意义（t=4.881,P=0.000）。根据ELISA结果进行脓毒症诊断ROC曲线分析得出,KLKB1蛋白诊断脓毒症的 AUC（95%CI）为0.759（0.594～0.923）。结论：补体和凝血级联通路为脓毒症免疫途径的重要通路,KLKB1具有较好的脓毒症诊断特性,可能是脓毒症潜在的诊断生物标志物。     

Lipid metabolism and peroxisome proliferator-activated receptor signaling pathways participate in late-phase liver regeneration

Liver regeneration (LR) is of great clinical significance in various liver-associated diseases. LR proceeds along a sequence of three distinct phases: priming/initiation, proliferation, and termination. Compared with the recognition of the first two phases, little is known about LR termination and structure/function reorganization. A combination of "omics" techniques, along with bioinformatics, may provide new insights into the molecular mechanism of the late-phase LR. Gene, protein, and metabolite profiles of the rat liver were determined by cDNA microarray, two-dimensional electrophoresis, and HPLC-MS analysis. Pathway enrichment analysis was performed to identify the pathways: 427 differentially expressed genes extracted from the microarray experiment revealed two expression patterns representing the early and late phase of LR. Functionally, the genes expressing at a higher level at the early phase than at the late phase were mainly involved in the response to stress, proliferation, and resistance to apoptosis, while those expressing at a lower level at the early phase than at the late phase were mainly engaged in lipid metabolism. Compared with the sham-operation control (SH) group, 5 proteins in the 70% partial hepatectomy (70%PHx) group were upregulated at the protein level, and 3 proteins were downregulated at 168 h after the 70%PHx. E-FABP, an upregulated fatty acid binding protein, was found to be involved in the peroxisome proliferator-activated receptor (PPAR) signaling pathway. The metabolomic data confirmed the enhancement of lipid metabolism by the detection of the intermediate and final metabolites. We've concluded that increased lipid metabolism and activated PPAR signaling pathways play important roles in late-phase LR.     

Towards elucidation of microbial community metabolic pathways: unravelling the network of carbon sharing in a pollutant-degrading bacterial consortium by immunocapture and isotopic ratio mass spectrometry

Although much information on metabolic pathways within individual organisms is available, little is known about the pathways operating in natural communities in which extensive sharing of nutritional resources is the rule. In order to analyse such a consortium pathway, we have investigated the flow of 4-chlorosalicylate as carbon substrate within a simple chemostat microbial community using ¹³C-labelled metabolites and isotopic ratio mass spectrometric analysis of label enrichment in immunocaptured member populations of the community. A complex pathway network of carbon sharing was thereby revealed, involving two different metabolic routes, one of which is completely novel and involves the toxic metabolite protoanemonin. The high stability of the community results, at least in part, from interdependencies based on carbon sharing and the rapid removal of toxic metabolites.     

Revealing synergistic mechanism of multiple components in Stauntonia brachyanthera Hand.-Mazz. for gout by virtual screening and system pharmacological approach

Stauntonia brachyanthera Hand.-Mazz. (SB), reported as a traditional Chinese medicine, displays a wide spectrum of interesting bioactivities, such as anti-inflammatory and analgesia. It is noteworthy that anti-gout effects of the components in SB have been reported. Hence, this study contributes to the prediction of promising active compounds and mechanisms for the treatment of gout. The active compounds with better oral bioavailability, and drug-likeness of SB were selected for further investigation by the approach of network pharmacology, molecular docking, gene ontology (GO) analysis, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, respectively. A total of 34 predicted targets and 98 compounds in SB were obtained. Sorted by structure types of compounds, phenylethanoid glycosides exhibited the best anti-gout activity, followed by phenolics and flavonoids. What’s more, it was shown in the network analysis that Serine/threonine-protein kinase mTOR (mTOR), Mitogen-activated protein kinase 12 (MAPK12), tumor necrosis factor (TNF-α), Integrin alpha-4 (ITGA4) and Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma (PIK3CG) were the key targets with intensely interaction, which should be attached more attention for further study. The functional enrichment analysis indicated that SB probably produced the anti-gout effects by synergistically regulating many biological pathways, such as MAPK signaling pathway, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway and NOD-like receptor signaling pathway, etc. In addition, C61, C67, C68 and C81 might be promising leading compounds with good molecular docking score. As a consequence, the active constituents and mechanisms based on data analysis were holistically illuminated, which was of vital importance to the development of new drugs for gout.     

Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways – particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile ‘plug and play’ set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.     

Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain

Metabolomics is becoming an increasingly important tool in plant genomics to decipher the function of genes controlling biochemical pathways responsible for trait variation. Although theoretical models can integrate genes and metabolites for trait variation, biological networks require validation using appropriate experimental genetic systems. In this study, we applied an untargeted metabolite analysis to mature grain of wheat homoeologous group 3 ditelosomic lines, selected compounds that showed significant variation between wheat lines Chinese Spring and at least one ditelosomic line, tracked the genes encoding enzymes of their biochemical pathway using the wheat genome survey sequence and determined the genetic components underlying metabolite variation. A total of 412 analytes were resolved in the wheat grain metabolome, and principal component analysis indicated significant differences in metabolite profiles between Chinese Spring and each ditelosomic lines. The grain metabolome identified 55 compounds positively matched against a mass spectral library where the majority showed significant differences between Chinese Spring and at least one ditelosomic line. Trehalose and branched‐chain amino acids were selected for detailed investigation, and it was expected that if genes encoding enzymes directly related to their biochemical pathways were located on homoeologous group 3 chromosomes, then corresponding ditelosomic lines would have a significant reduction in metabolites compared with Chinese Spring. Although a proportion showed a reduction, some lines showed significant increases in metabolites, indicating that genes directly and indirectly involved in biosynthetic pathways likely regulate the metabolome. Therefore, this study demonstrated that wheat aneuploid lines are suitable experimental genetic system to validate metabolomics–genomics networks.