Analysis Of Metabolomics Data Research Articles

Pulmonary Mycobacterium tuberculosisinfection alters the hepatic immunometabolic landscape and compromises insulin responsiveness early after infection

Abstract Immunity against Mycobacterium tuberculosis(Mtb) is associated with both regional and systemic inflammation as well as with metabolic disruption in tuberculosis (TB) patients. The metabolic and immunological responses are intertwined but the specific impact of localized disease on systemic metabolism is not well defined. We investigated the impact of low dose aerosol infection of the lung with Mtbon the systemic metabolic regulatory organ – the liver. Using an unbiased approach we defined the mRNA and protein dysregulation occurring in the early stage of infection. We found that infection leads to immune pathway activation and reduced metabolic activity in liver. Comparison of the gene expression patterns between of the lung, liver and blood revealed organ specific metabolic gene dysregulation in the context of non-organ specific activation of interferon response pathways. We have also found that infection leads to suppression of carbohydrate and lipid metabolism, without alteration of fasting or random blood glucose levels. Infected mice show reduced sensitivity to exogenous insulin but the glucose tolerance remains intact indicating insulin resistance. Analysis of metabolomics data from public repository reveals a metabolic signature of insulin resistance correlates with TB progression. Taken together, our finding shows that aerosol Mtbinfection influences hepatic metabolism very soon after infection and may contribute to early metabolic disease. Defining the early interplay between localised infection, inflammation and metabolic regulation is key to understanding the impact of latent and subclinical TB on the large proportion of the World’s population who do not exhibit TB symptoms but who are infected with Mtb. Supported by Grants from Royal Society Wolfson Merit Award (AMC) Newton Fellowship (MKD), Wellcome Trust Institutional Support Fund Fellowship (MKD), MRC award MR/P011136/1 (AMC).

Gut immunopathological signatures in Rag1 hypomorphic mice

Inflammatory bowel disease (IBD) has been reported in ~15% of patients with partial RAG deficiency. To investigate the pathophysiology of intestinal disease, we studied the interplay of T-cell immune dysregulation and microbiome abnormalities in a mouse model (Rag1w/w) homozygous fora hypomorphic Rag1 mutation (R972W) whose orthologue variant (R975W) has been reported in patients. Analysis of gut tissue in Rag1w/w mice revealed severe spontaneous colitis with lymphocytic infiltrates, which become histologically evident after weaning. To assess the relative contribution of genetic and environmental factors on the pathogenesis of gut inflammation, we performed flow cytometry and single-cell RNA-seq of lamina propria (LP) T cells and microbiome studies of fecal specimens in Rag1w/w and Rag1+/+ mice. Rag1w/w mice showed skewing towards a Th1/Th17 phenotype in CD4+ cells infiltrating the LP. Analysis of the fecal microbiota composition revealed a severe restriction of microbial diversity in Rag1w/w mice, with progressive depletion of three genera of firmicutes (Lachnospiraceae, Oscillibacter, Intestinimonas) producing butyrate. Principal component analysis of metabolomics data of fecal specimens clustered Rag1w/w separately from Rag1+/+ mice, with a severe defect in the amount of butyrate in the former. Consistent with an important role of butyrate in facilitating priming of IL-10 producing Tr1 cells, Il10 expression was markedly reduced in CD4+ LP T-cells from Rag1w/w compared to Rag1+/+ mice. Furthermore, scRNAseq of LP CD4+ T-cells revealed reduced presence of Tr1 cells in Rag1w/w mice, indicating that microbiome abnormalities contribute to the intestinal immune dysregulation. Treatment of Rag1w/w mice with vedolizumab (aiming at blocking gut homing of pathogenic T cells) had no effect on intestinal inflammation and on restriction of microbiome diversity. By contrast, both use of broad-spectrum antibiotics and bone marrow transplantation (BMT) significantly improved the inflammation and normalized the number of LP CD4+ cells. However, the LP CD4+ T cells of antibiotic-treated mice retained a strong Th1/Th17 signature, whereas only BMT rescued the phenotype, and induced normalization of microbiome diversity and restoration in the relative abundance of firmicutes. These data provide mechanistic insights into the pathophysiology of IBD associated with partial RAG deficiency, and confirm a curative role of bone marrow transplantation.

Microbes, metabolites and muscle: Metagenomic and metabolomic analyses in a mouse model of muscular dystrophy

Skeletal muscle is the largest metabolic organ. The metabolic and endocrine nature of skeletal muscle allows for long-range signaling and influence over the enteric environment and the resident microbiota. Commensal microbial signaling can influence the composition and development of skeletal muscle, forming a bidirectional gut-muscle axis.Duchenne muscular dystrophy (DMD) is a fatal neuromuscular disease characterised by skeletal muscle atrophy, fibrosis, and ultimately cardiorespiratory failure. The pathways involved in muscle atrophy can be influenced via gut microbial signaling; thus we sought to characterize metabolic signaling between the gut and the diaphragm in a mouse model of DMD ( mdx) to look for potential therapeutic targets.The study was divided into two arms and conducted following institutional ethical approval. The first arm investigated metabolomic signaling. 6-month-old male BL10 wildtype (WT; n = 12) and mdx (n = 12) mice were anaesthetized (induction by 5% isoflurane) and decapitated. Following collection of trunk blood, the diaphragm was excised, and colonic/cecal contents were collected. The samples were then processed by mass-spectrometry to isolate the metabolomic profiles of semi-polar, secondary bile acid, and short chain fatty acid (SCFA) targets in the gut, plasma and diaphragm of WT and mdx mice. The second arm of the study explores the gut microbial profile and diaphragm gene expression, structure and function. 6-month-old male WT (n = 15) and mdx (n = 15) were anesthetized (induction by 5% isoflurane) followed by cervical dislocation. The diaphragm was excised, and longitudinal strips were isolated for structural analysis (i.e., histological staining and immunohistochemistry for fibre type and size), PCR gene expression, and isometric force assessment ex vivo. Additionally, fecal pellets were collected and analyzed via shotgun metagenomic sequencing to assess microbial populations.Diaphragm twitch and tetanic force are profoundly decreased in mdx mice compared to WT. Our preliminary analysis suggests a substantially altered metabolic profile within skeletal muscle affecting glycolysis and oxidative metabolism. Interestingly, mdx diaphragms have lowered expression of the Olfr78 receptor, found within the vasculature of skeletal muscle, which transduces short-chain fatty acid signaling, primarily acetate and propionate. Our analysis of faecal metabolites showed a significant decrease in acetate (which also functions as a substrate in oxidative metabolism) in mdx mice. Activation of Olfr78 receptors contributes to vasodilation which suggests that blood supply to the mdx diaphragm may be compromised. Full analysis of metabolomic data and faecal metagenomic data is currently underway.A comprehensive characterisation of the gut-muscle axis in mdx mice may pave the way for novel microbiota-directed therapeutic strategies to ameliorate gut and muscle dysfunction in DMD. Funded by Science Foundation Ireland SFI/19/6628 INSPIRE DMD. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Integrative Analysis of Metabolomic and Transcriptomic Data Reveals the Mechanism of Color Formation in Corms of Pinellia ternata.

Pinellia ternata (Thunb.) Breit. (P. ternata) is a very important plant that is commonly used in traditional Chinese medicine. Its corms can be used as medicine and function to alleviate cough, headache, and phlegm. The epidermis of P. ternata corms is often light yellow to yellow in color; however, within the range of P. ternata found in JingZhou City in Hubei Province, China, there is a form of P. ternata in which the epidermis of the corm is red. We found that the total flavonoid content of red P. ternata corms is significantly higher than that of yellow P. ternata corms. The objective of this study was to understand the molecular mechanisms behind the difference in epidermal color between the two forms of P. ternata. The results showed that a high content of anthocyanidin was responsible for the red epidermal color in P. ternata, and 15 metabolites, including cyanidin-3-O-rutinoside-5-O-glucoside, cyanidin-3-O-glucoside, and cyanidin-3-O-rutinoside, were screened as potential color markers in P. ternata through metabolomic analysis. Based on an analysis of the transcriptome, seven genes, including PtCHS1, PtCHS2, PtCHI1, PtDFR5, PtANS, PtUPD-GT2, and PtUPD-GT3, were found to have important effects on the biosynthesis of anthocyanins in the P. ternata corm epidermis. Furthermore, two transcription factors (TFs), bHLH1 and bHLH2, may have regulatory functions in the biosynthesis of anthocyanins in red P. ternata corms. Using an integrative analysis of the metabolomic and transcriptomic data, we identified five genes, PtCHI, PtDFR2, PtUPD-GT1, PtUPD-GT2, and PtUPD-GT3, that may play important roles in the presence of the red epidermis color in P. ternata corms.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations

BackgroundInherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype–phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.MethodsOur framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.ResultsThe proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.ConclusionThe presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

The mechanism of palmatine-mediated intestinal flora and host metabolism intervention in OA-OP comorbidity rats.

ErXian decoction is a Chinese herbal compound that can prevent and control the course of osteoarthritis (OA) and osteoporosis (OP). OP and OA are two age-related diseases that often coexist in elderly individuals, and both are associated with dysregulation of the gut microbiome. In the initial study, Palmatine (PAL) was obtained by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and network pharmacological screening techniques, followed by 16S rRNA sequencing and serum metabolomics of intestinal contents, to explore the mechanism of PAL in the treatment of OA and OP. The rats selected for this study were randomly divided into three groups: a sham group, an OA-OP group and a PAL group. The sham group was intragastrically administered normal saline solution, and the PLA group was treated with PAL for 56 days. Through microcomputed tomography (micro-CT), ELISA, 16S rRNA gene sequencing and non-targeted metabonomics research, we explored the potential mechanism of intestinal microbiota and serum metabolites in PAL treatment of OA-OP rats. Palmatine significantly repair bone microarchitecture of rat femur in OA-OP rats and improved cartilage damage. The analysis of intestinal microflora showed that PAL could also improve the intestinal microflora disorder of OA-OP rats. For example, the abundance of Firmicutes, Bacteroidota, Actinobacteria, Lactobacillus, unclassified_f_Lachnospiraceae, norank_f_Muribaculaceae, Lactobacillaceae, Lachnospiraceae and Muribaculaceae increased after PAL intervention. In addition, the results of metabolomics data analysis showed that PAL also change the metabolic status of OA-OP rats. After PAL intervention, metabolites such as 5-methoxytryptophol, 2-methoxy acetaminophen sulfate, beta-tyrosine, indole-3-carboxylic acid-O-sulfate and cyclodopa glucoside increased. Association analysis of metabolomics and gut microbiota (GM) showed that the communication of multiple flora and different metabolites played an important role in OP and OA. Palmatine can improve cartilage degeneration and bone loss in OA-OP rats. The evidence we provided supports the idea that PAL improves OA-OP by altering GM and serum metabolites. In addition, the application of GM and serum metabolomics correlation analysis provides a new strategy for uncovering the mechanism of herbal treatment for bone diseases.

Analysis of the main antioxidant enzymes in the roots of Tamarix ramosissima under NaCl stress by applying exogenous potassium (K+).

Salinization affects more than 25% of the world's arable land, and Tamarix ramosissima Ledeb (T. ramosissima), the representative of Tamarix plants, is widely grown in salinized soil. In contrast, less is known about the mechanism of potassium's antioxidative enzyme activity in preventing NaCl stress damage to plants. This study examined changes in root growth for T. ramosissima at 0h, 48h, and 168h, performed antioxidant enzyme activity assays, transcriptome sequencing, and non-targeted metabolite analysis to understand changes in their roots as well as changes in the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Quantitative real-time PCR (qRT-PCR) was used to identify differentially expressed genes (DEGs) and differential metabolites associated with antioxidant enzyme activities. As the time increased, the results showed that compared with the 200 Mm NaCl group, the root growth of the 200 mM NaCl + 10 mM KCl group increased, the activities of SOD, POD and CAT increased the most, but the contents of hydrogen peroxide (H2O2) and Malondialdehyde (MDA) increased less. Meanwhile, 58 DEGs related to SOD, POD and CAT activities were changed during the application of exogenous K+ for 48h and 168h in T. ramosissima. Based on association analysis of transcriptomic and metabolomic data, we found coniferyl alcohol, which can act as a substrate to label catalytic POD. It is worth noting that Unigene0013825 and Unigene0014843, as POD-related genes, have positively regulated the downstream of coniferyl alcohol, and they have a significant correlation with coniferyl alcohol. In summary, 48h and 168h of exogenous K+ applied to the roots of T. ramosissima under NaCl stress can resist NaCl stress by scavenging the reactive oxygen species (ROS) generated by high salt stress by enhancing the mechanism of antioxidant enzyme activity, relieving NaCl toxicity and maintaining growth. This study provides genetic resources and a scientific theoretical basis for further breeding of salt-tolerant Tamarix plants and the molecular mechanism of K+ alleviating NaCl toxicity.

Interaction Metabolomics to Discover Synergists in Natural Product Mixtures.

Mass spectrometry metabolomics has become increasingly popular as an integral aspect of studies to identify active compounds from natural product mixtures. Classical metabolomics data analysis approaches do not consider the possibility that interactions (such as synergy) could occur between mixture components. With this study, we developed "interaction metabolomics" to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms (CITs), which are calculated as the product of the intensities of each pair of features (detected ions) in the data matrix. Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high-resolution mass spectrometry. When the data set was processed without CITs (classical metabolomics), statistical analysis yielded a pattern of false positives. However, interaction metabolomics correctly identified berberine and piperine as the compounds responsible for the synergistic activity. To further validate the interaction metabolomics approach, we prepared mixtures from extracts of goldenseal (Hydrastis canadensis) and habañero pepper (Capsicum chinense) and correctly correlated synergistic activity of these mixtures to the combined action of berberine and several capsaicinoids. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for applications in natural products research and other research areas that require comprehensive mixture analysis.

Untargeted metabolomics and lipidomics to assess plasma metabolite changes in dairy goats with subclinical hyperketonemia

Subclinical hyperketonemia (SCHK) is the major metabolic disease observed during the transition period in dairy goats, and is characterized by high plasma levels of nonesterified fatty acids (NEFA) and β-hydroxybutyrate (BHB). However, no prior study has comprehensively assessed metabolomic profiles of dairy goats with SCHK. Plasma samples were collected within 1 h after kidding from SCHK goats (BHB concentration >0.8 mM, n = 7) and clinically healthy goats (BHB concentration <0.8 mM, n = 7) with similar body condition score (2.75 ± 0.15, mean ± standard error of the mean) and parity (primiparous). A combination of targeted and untargeted mass spectrometric approaches was employed for analyzing the various changes in the plasma lipidome and metabolome. Statistical analyses were performed using the GraphPad Prism 8.0, SIMCA-P software (version 14.1), and R packages (version 4.1.3). Plasma aminotransferase, nonesterified fatty acids, and BHB concentrations were greater in the SCHK group, but plasma glucose concentrations were lower. A total of 156 metabolites and 466 lipids were identified. The analysis of untargeted metabolomics data by principal component analysis and orthogonal partial least squares discriminant analysis revealed a separation between SCHK and clinically healthy goats. According to the screening criteria (unpaired t-test, P < 0.05), 30 differentially altered metabolites and 115 differentially altered lipids were detected. Pathway enrichment analysis identified citrate cycle, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, and phenylalanine metabolism as significantly altered pathways. A greater concentration of plasma isocitric acid and cis-aconitic acid levels was observed in SCHK goats. In addition, AA such as lysine and isoleucine were greater, whereas alanine and phenylacetylglycine were lower in SCHK dairy goats. Dairy goats with SCHK also exhibited greater oleic acid, acylcarnitine, and phosphatidylcholine and lower choline and sphingomyelins. Acylcarnitines, oleic acid, and tridecanoic acid displayed positive correlations with several lipid species. Alanine, hippuric acid, and histidinyl-phenylalanine were negatively correlated with several lipids. Overall, altered metabolites in SCHK dairy goats indicated a more severe degree of negative energy balance. Data also indicated an imbalance in the tricarboxylic acid (TCA) cycle, lipid metabolism, and AA metabolism. The findings provide a more comprehensive understanding of the pathogenesis of SCHK in dairy goats.

Abstract 2824: Metabolomics in radiotherapy-induced early adverse skin reactions in breast cancer patients

Abstract Background: Early adverse skin reactions (EASRs) are common side effects of postoperative adjuvant radiotherapy (RT) that significantly impact the quality of life (QOL) of breast cancer patients. This study used global metabolomics profiles of breast cancer populations to identify metabolic pathways and biomarkers that are significantly associated with RT-induced EASRs to identify potential targets for precision interventions. Methods: Using a frequency-matched study design, pre-RT urine samples from 60 female breast cancer patients receiving RT after breast-conserving surgery were metabolically profiled. Patients were recruited from the University of Miami Sylvester Comprehensive Cancer Center and Jackson Memorial Hospital. Using MetaboAnalyst 3.0, we performed metabolomic data analysis, visualization, and interpretation on 84 candidate metabolites from a dataset of 478 total compounds. Student’s t-tests, correlation analyses, pathway enrichment, and topology analyses were conducted sequentially to identify metabolite biomarkers and pathways associated with RT-induced EASRs. Results: The study population consisted of 24 African American (40%), 18 non-Hispanic white (30%), 14 Hispanic white (24%), and 4 “other” (7%) patients with frequency matched by race/ethnicity and body mass index (BMI) categories (i.e., normal, overweight, and obesity) to have an equal number of high (n=30) and low (n=30) EASRs. Seven metabolic pathways were significantly associated with RT-induced EASRs, including alanine, aspartate, and glutamate metabolism; caffeine metabolism; pentose and glucuronate interconversions; glycine, serine, and threonine metabolism; beta-alanine metabolism; pantothenate and CoA biosynthesis; and glutathione metabolism. The alanine, aspartate, and glutamate metabolism pathway had the lowest false discovery rate (FDR)-adjusted p-value (p=0.0028) and the highest pathway impact value (0.60) of all enriched metabolic pathways. Thirteen metabolite biomarkers were significantly associated with RT-induced EASRs, including the principal compound of interest glutamate. Conclusions: In our study of breast cancer patients receiving adjuvant RT, alanine, aspartate, and glutamate metabolism had the highest impact value and significant FDR-adjusted p-value in predicting RT-induced EASRs. Our findings suggest that glutaminase inhibitors may have broader clinical applications in preventing RT-induced EASRs in addition to their potential enhancement of chemotherapy by triggering metabolic crises in tumor cells. Keywords: breast cancer, metabolomics, radiation therapy, and early adverse skin reactions. Citation Format: Alexandra N. McMahon, Cristiane Takita, Jean L. Wright, Eunkyung Lee, Joshua J. Kleinman, Isildinha M. Reis, Jennifer J. Hu. Metabolomics in radiotherapy-induced early adverse skin reactions in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2824.

Integrated transcriptome and metabolome analysis revealed that flavonoids enhanced the resistance of Oryza sativa against Meloidogyne graminicola.

Rice is a crucial food crop worldwide, but its yield and quality are significantly affected by Meloidogyne graminicola is a root knot nematode. No rice variety is entirely immune to this nematode disease in agricultural production. Thus, the fundamental strategy to combat this disease is to utilize rice resistance genes. In this study, we conducted transcriptome and metabolome analyses on two rice varieties, ZH11 and IR64. The results indicated that ZH11 showed stronger resistance than IR64. Transcriptome analysis revealed that the change in gene expression in ZH11 was more substantial than that in IR64 after M. graminicola infection. Moreover, GO and KEGG enrichment analysis of the upregulated genes in ZH11 showed that they were primarily associated with rice cell wall construction, carbohydrate metabolism, and secondary metabolism relating to disease resistance, which effectively enhanced the resistance of ZH11. However, in rice IR64, the number of genes enriched in disease resistance pathways was significantly lower than that in ZH11, which further explained susceptibility to IR64. Metabolome analysis revealed that the metabolites detected in ZH11 were enriched in flavonoid metabolism and the pentose phosphate pathway, compared to IR64, after M. graminicola infection. The comprehensive analysis of transcriptome and metabolome data indicated that flavonoid metabolism plays a crucial role in rice resistance to M. graminicola infection. The content of kaempferin, apigenin, and quercetin in ZH11 significantly increased after M. graminicola infection, and the expression of genes involved in the synthetic pathway of flavonoids also significantly increased in ZH11. Our study provides theoretical guidance for the precise analysis of rice resistance and disease resistance breeding in further research.

Novel biomarkers and emerging tools to identify causal molecular pathways in hypertension and associated cardiovascular diseases.

Hypertension (HT) is a modifiable risk factor for life-threatening cardiovascular diseases (CVDs) including coronary artery disease, heart failure, or stroke. Despite significant progress in understanding the pathophysiological mechanisms of the disease, the molecular pathways targeted by HT treatment remain largely unchanged. This warrants the need for finding novel biomarkers, which are causally related to persistent high blood pressure (BP) and may be pharmacologically targeted. Analytical output derived from large-scale biobanks, containing high-throughput genetic and biochemical data, such as OLINK and SomaScan-based proteomics or Nuclear Magnetic Resonance-based metabolomics, as well as novel analytical tools including the Mendelian randomization (MR) approach, enabling genetic causal inference, may create new treatment opportunities for HT and related CVDs. MR analysis may constitute additional evidence for observational studies and facilitate selection of drug targets for clinical testing and has been already used to nominate potentially causal biomarkers for HT and CVDs such as circulating glycine, branched-chain amino acids, lipoprotein(a), insulin-like growth factor 1, or fibronectin 1. Using the MR framework, genetic proxies for targets of already known drugs, such as statins, PCSK9, and ACE inhibitors, may additionally be informative about potential side effects and eventually contribute to more personalized medicine. Finally, genetic causal inference may disentangle independent direct effects of correlated traits such as lipid classes or markers of inflammation on cardiovascular clinical outcomes such as atherosclerosis and HT. While several novel HT-targeting drugs are currently under clinical investigation (e.g. brain renin-angiotensin-aldosterone system inhibitors or endothelin-1 receptor antagonists), analysis of high-throughput proteomic and metabolomic data from well-powered studies may deliver novel druggable molecular targets for HT and associated CVDs.

Evaluation of graphical models for multi-group metabolomics data.

Gaussian graphical model is a strong tool for identifying interactions from metabolomics data based on conditional correlation. However, data may be collected from different stages or subgroups of subjects with heterogeneity or hierarchical structure. There are different integrating strategies of graphical models for multi-group data proposed by data scientists. It is challenging to select the methods for metabolism data analysis. This study aimed to evaluate the performance of several different integrating graphical models for multi-group data and provide support for the choice of strategy for similar characteristic data. We compared the performance of seven methods in estimating graph structures through simulation study. We also applied all the methods in breast cancer metabolomics data grouped by stages to illustrate the real data application. The method of Shaddox etal. achieved the highest average area under the receiver operating characteristic curve and area under the precision-recall curve across most scenarios, and it was the only approach with all indicators ranked at the top. Nevertheless, it also cost the most time in all settings. Stochastic search structure learning tends to result in estimates that focus on the precision of identified edges, while BEAM, hierarchical Bayesian approach and birth-death Markov chain Monte Carlo may identify more potential edges. In the real metabolomics data analysis from three stages of breast cancer patients, results were in line with that in simulation study.

ETHYLENE-INSENSITIVE 3-LIKE 2 regulates β-carotene and ascorbic acid accumulation in tomatoes during ripening.

ETHYLENE-INSENSITIVE 3/ETHYLENE-INSENSITIVE 3-LIKEs (EIN3/EILs) are important ethylene response factors during fruit ripening. Here, we discovered that EIL2 controls carotenoid metabolism and ascorbic acid (AsA) biosynthesis in tomato (Solanum lycopersicum). In contrast to the red fruits presented in the wild type (WT) 45 d after pollination, the fruits of CRISPR/Cas9 eil2 mutants and SlEIL2 RNA interference lines (ERIs) showed yellow or orange fruits. Correlation analysis of transcriptome and metabolome data for the ERI and WT ripe fruits revealed that SlEIL2 is involved in β-carotene and AsA accumulation. ETHYLENE RESPONSE FACTORs (ERFs) are the typical components downstream of EIN3 in the ethylene response pathway. Through a comprehensive screening of ERF family members, we determined that SlEIL2 directly regulates the expression of 4 SlERFs. Two of these, SlERF.H30 and SlERF.G6, encode proteins that participate in the regulation of LYCOPENE-β-CYCLASE 2 (SlLCYB2), encoding an enzyme that mediates the conversion of lycopene to carotene in fruits. In addition, SlEIL2 transcriptionally repressed L-GALACTOSE 1-PHOSPHATE PHOSPHATASE 3 (SlGPP3) and MYO-INOSITOL OXYGENASE 1 (SlMIOX1) expression, which resulted in a 1.62-fold increase of AsA via both the L-galactose and myoinositol pathways. Overall, we demonstrated that SlEIL2 functions in controlling β-carotene and AsA levels, providing a potential strategy for genetic engineering to improve the nutritional value and quality of tomato fruit.

Integrative analysis of genomic and metabolomic data reveals key metabolic pathways involved in lipid and carotenoid biosynthesis in oleaginous red yeast Rhodosporidiobolus odoratus XQR

Rhodosporidiobolus odoratus, one of the oleaginous red yeasts, is gaining biotechnological importance for their ability to produce microbial lipids and carotenoids. However, to date, the genomic resource underling lipid and carotenoid biosynthesis in R. odoratus has not been reported. Here, we reported the first genome assembly of R. odoratus using a combination of PacBio and Illumina techniques. The final genome assembly is 23.74 Mb in size, containing 52 scaffolds with a N50 length of 1200,460 bp and a GC content of 56.90%. Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment showed that our assembly contains 94.23% of Basidiomycota universal single-copy orthologs. The genome was predicted to contain 4986 protein-coding genes, 4967 of which were functionally annotated. Metabolomic profiling identified 574 lipids, 3 carotenoids, and 208 volatile organic compounds synthesized by R. odoratus. Integrative analysis of genomics and metabolomics provides insights into the biosynthesis of lipid, carotenoid, and other bioactive compounds in R. odoratus. Collectively, the results presented herein greatly enhance our understanding of R. odoratus in lipids and carotenoids biosynthesis, and thus further accelerate its fundamental molecular investigations and biotechnological applications.

Spatial metabolomics reveals skeletal myofiber subtypes.

Skeletal muscle myofibers are heterogeneous in their metabolism. However, metabolomic profiling of single myofibers has remained difficult. Mass spectrometry imaging (MSI) is a powerful tool for imaging molecular distributions. In this work, we optimized the workflow of matrix-assisted laser desorption/ionization (MALDI)-based MSI from cryosectioning to metabolomics data analysis to perform high-spatial resolution metabolomic profiling of slow- and fast-twitch myofibers. Combining the advantages of MSI and liquid chromatography-MS (LC-MS), we produced spatial metabolomics results that were more reliable. After the combination of high-spatial resolution MSI and LC-MS metabolomic analysis, we also discovered a new subtype of superfast type 2B myofibers that were enriched for fatty acid oxidative metabolism. Our technological workflow could serve as an engine for metabolomics discoveries, and our approach has the potential to provide critical insights into the metabolic heterogeneity and pathways that underlie fundamental biological processes and disease states.

Metabolic individuality: Limitations, challenges, and potential for clinical utility

In Nature Medicine, Surendran and colleagues recently reported the analysis of human plasma metabolomic data for 913 metabolites in ∼20,000 individuals, identifying 2,599 metabolite-genetic variant associations and >400 metabolite signatures comprised of jointly regulated metabolites. This extensive atlas of variant-metabolite relationships reveals novel genomic mechanisms driving metabolic phenotypes.

Investigating phenotypic relationships in persimmon accessions through integrated proteomic and metabolomic analysis of corresponding fruits.

Together with phenological and genomic approaches, gel-based and label-free proteomic as well metabolomic procedures were separately applied to plants to highlight differences between ecotypes, to estimate genetic variability within/between organism populations, or to characterize specific mutants/genetically modified lines at metabolic level. To investigate the possible use of tandem mass tag (TMT)-based quantitative proteomics in the above-mentioned contexts and based on the absence of combined proteo-metabolomic studies on Diospyros kaki cultivars, we here applied integrated proteomic and metabolomic approaches to fruits from Italian persimmon ecotypes with the aim to characterize plant phenotypic diversity at molecular level. We identified 2255 proteins in fruits, assigning 102 differentially represented components between cultivars, including some related to pomological, nutritional and allergenic characteristics. Thirty-three polyphenols were also identified and quantified, which belong to hydroxybenzoic acid, flavanol, hydroxycinnamic acid, flavonol, flavanone and dihydrochalcone sub-classes. Heat-map representation of quantitative proteomic and metabolomic results highlighted compound representation differences in various accessions, whose elaboration through Euclidean distance functions and other linkage methods defined dendrograms establishing phenotypic relationships between cultivars. Principal component analysis of proteomic and metabolomic data provided clear information on phenotypic differences/similarities between persimmon accessions. Coherent cultivar association results were observed between proteomic and metabolomic data, emphasizing the utility of integrating combined omic approaches to identify and validate phenotypic relationships between ecotypes, and to estimate corresponding variability and distance. Accordingly, this study describes an original, combined approach to outline phenotypic signatures in persimmon cultivars, which may be used for a further characterization of other ecotypes of the same species and an improved description of nutritional characteristics of corresponding fruits.

Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data.

Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.

Application of metabolomics in urolithiasis: the discovery and usage of succinate

Urinary stone is conceptualized as a chronic metabolic disorder punctuated by symptomatic stone events. It has been shown that the occurrence of calcium oxalate monohydrate (COM) during stone formation is regulated by crystal growth modifiers. Although crystallization inhibitors have been recognized as a therapeutic modality for decades, limited progress has been made in the discovery of effective modifiers to intervene with stone disease. In this study, we have used metabolomics technologies, a powerful approach to identify biomarkers by screening the urine components of the dynamic progression in a bladder stone model. By in-depth mining and analysis of metabolomics data, we have screened five differential metabolites. Through density functional theory studies and bulk crystallization, we found that three of them (salicyluric, gentisic acid and succinate) could effectively inhibit nucleation in vitro. We thereby assessed the impact of the inhibitors with an EG-induced rat model for kidney stones. Notably, succinate, a key player in the tricarboxylic acid cycle, could decrease kidney calcium deposition and injury in the model. Transcriptomic analysis further showed that the protective effect of succinate was mainly through anti-inflammation, inhibition of cell adhesion and osteogenic differentiation. These findings indicated that succinate may provide a new therapeutic option for urinary stones.