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Correlation between body weight, fasting blood glucose, and major classes of measured metabolites and lipids. Heatmap is shown displaying hierarchical clustered Spearman correlations between animal characteristics (weight and fasting blood glucose) and major classes of measured metabolites and lipids.
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Non-obese diabetic (NOD) mice are a commonly-used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra high performance liquid chrom...
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... was conducted to calculate and visualize relationships between precursor and product metabolite reactant pairs and molecules sharing a high degree of structural similarity (Fig. 1). A heatmap based on hierarchical cluster analysis was used to summarize the relationships between classes of measured molecules, fasting blood glucose, and body weight (Fig. ...
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... plasma carbohydrate levels were increased by 169% in diabetic mice (Table 2) and were generally positively correlated with measured fasting blood glucose and negatively correlated with body weight (Fig. 2). Moreover, circulating levels of glucose, gluconic acid lactone, glucuronic acid, idonic acid, ribose, cellobiose, and 2-deoxytetronic acid, all of which were elevated in diabetic mice compared with nondiabetic mice, were selected as being within the top 10% discriminants of the diabetic phenotype (Supplemental Table S1). Elevations in ...
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... including a 180% increase in panthothenic acid and 140% increases in valine, isoleucine, and 5-hydroxyindole-3-acetic acid (Supplemental Table S1). Of the noted shifts, only methionine was found to be included as a top predictor of the T1D phenotype. Overall, amino acids were negatively correlated with body weight but not fasting blood glucose (Fig. ...
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... and SMs, many of the measured PEs and PIs were decreased in diabetic animals (Table 2). On a global scale, PCs, SMs, PIs, LPCs, and PEs were all positively correlated with body weight and negatively correlated with fasting blood glucose, whereas TGs were negatively correlated with body weight and positively correlated with fasting blood glucose (Fig. ...
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... free fatty acids were generally lower in diabetic mice, led by a 60% reduction in palmitoleic acid and 40% reductions in palmitic acid and arachidonic acid (Supplemental Table S1). The reductions in circulating free fatty acids were negatively correlated with fasting blood glucose (Fig. ...
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... were significantly reduced in diabetic mice, including LTB 4 , PGE 2 , PGD 2 , TXB 2 , 8-HETE, 12-HETE, 15-HETE, 12-OxoETE, and 15-OxoETE (Fig. 4). Moreover, global concentrations of circulating 20C-ketones, 20C-hydroxy acids, and prostacyclins were all positively correlated with body weight and negatively correlated with fasting blood glucose ( Fig. 2 and Supplemental Table S1). Only 12-oxo-ETE, 9-oxo-ODE, and PGF 2 were identified as being within the top 10% of discriminant metabolites (Supplemental Table S1). The T1D-associated decrease in 12-oxo-ETE paralleled the observed reductions in PC (38:2) and threonic acid (Fig. ...
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AIM
To investigate the effect of PNPLA3 polymorphisms on serum lipidomics and pathological characteristics in nonalcoholic fatty liver disease (NAFLD).
METHODS
Thirty-four biopsy-proven NAFLD patients from Northern, Central, and Southern China were subjected to stratification by genotyping their single nucleotide polymorphisms (SNPs) in PNPLA3. Ul...
Citations
... In Aedes, other blood components, such as glucose, have also been shown to modulate mosquito vector capacity through AKT/TOR [23]. However, artificial single-point alterations of blood composition rarely reflect the blood's physiological modulations where several components are found altered because of metabolic syndromes and altered physiological states [24,25]. In that sense, the impact of metabolic syndromes and nutritional imbalance, such as those derived from Western-type diets on mosquito biology, remains mostly unknown. ...
Background
The high prevalence of metabolic syndrome in low- and middle-income countries is linked to an increase in Western diet consumption, characterized by a high intake of processed foods, which impacts the levels of blood sugar and lipids, hormones, and cytokines. Hematophagous insect vectors, such as the yellow fever mosquito Aedes aegypti, rely on blood meals for reproduction and development and are therefore exposed to the components of blood plasma. However, the impact of the alteration of blood composition due to malnutrition and metabolic conditions on mosquito biology remains understudied.
Methods
In this study, we investigated the impact of whole-blood alterations resulting from a Western-type diet on the biology of Ae. aegypti. We kept C57Bl6/J mice on a high-fat, high-sucrose (HFHS) diet for 20 weeks and followed biological parameters, including plasma insulin and lipid levels, insulin tolerance, and weight gain, to validate the development of metabolic syndrome. We further allowed Ae. aegypti mosquitoes to feed on mice and tracked how altered host blood composition modulated parameters of vector capacity.
Results
Our findings identified that HFHS-fed mice resulted in reduced mosquito longevity and increased fecundity upon mosquito feeding, which correlated with alteration in the gene expression profile of nutrient sensing and physiological and metabolic markers as studied up to several days after blood ingestion.
Conclusions
Our study provides new insights into the overall effect of alterations of blood components on mosquito biology and its implications for the transmission of infectious diseases in conditions where the frequency of Western diet-induced metabolic syndromes is becoming more frequent. These findings highlight the importance of addressing metabolic health to further understand the spread of mosquito-borne illnesses in endemic areas.
Graphical Abstract
... Fatty acids derived from adipose tissue lipolysis are oxidized for providing energy primarily through mitochondrial β-oxidation. An increase in fatty acid oxidation induces excessive oxidative stress, and the observed increases in 2-hydroxybutyrate and uric acid reflected an imbalance between prooxidants and antioxidants (Ames et al. 1981;Gall et al. 2010;Fahrmann et al. 2015). Our previous study demonstrated a sustained state of oxidative stress in dairy goats during the peripartal period . ...
Dairy goats experience metabolic stress during the peripartal period, and their ability to navigate this stage of lactation is related to the occurrence and development of metabolic diseases. Unlike dairy cows, there is a lack of comprehensive analysis of changes in the plasma profiles of peripartal dairy goats, particularly using high-throughput techniques. A subset of 9 clinically-healthy dairy goats were used from a cohort of 96 primiparous Guanzhong dairy goats (BCS, 2.75 ± 0.15). Blood samples were collected at seven time points around parturition (d 21, 14, 7 before parturition, the day of kidding, and d 7, 14, 21 postpartum), were analyzed using untargeted metabolomics and targeted lipidomics. The orthogonal partial least squares discriminant analysis model revealed a total of 31 differential metabolites including p-cresol sulfate, pyruvic acid, cholic acid, and oxoglutaric acid. The pathway enrichment analysis identified phenylalanine metabolism, aminoacyl-tRNA biosynthesis, and citrate cycle as the top three significantly-altered pathways. The Limma package identified a total of 123 differentially expressed lipids. Phosphatidylserine (PS), free fatty acids (FFA), and acylcarnitines (ACs) were significantly increased on the day of kidding, while diacylglycerols (DAG) and triacylglycerols (TAG) decreased. Ceramides (Cer) and lyso-phosphatidylinositols (LPI) were significantly increased during postpartum period, while PS, FFA, and ACs decreased postpartum and gradually returned to antepartum levels. Individual species of FFA and phosphatidylcholines (PC) were segregated based on the differences in the saturation and length of the carbon chain. Overall, this work generated the largest repository of the plasma lipidome and metabolome in dairy goats across the peripartal period, which contributed to our understanding of the multifaceted adaptations of transition dairy goats.
... Furthermore, there is evidence that low methionine levels are associated with increased oxidative stress. Significant T1D-dependent increases in circulating oxidation products and decreases in methionine and cysteine levels were detected, indicating increased oxidative stress [50]. Methionine is a component of the transsulfuration process, which leads to glutathione production, an important intracellular antioxidant. ...
Glycemic variability (GV) in some patients with type 1 diabetes (T1D) remains heterogeneous despite comparable clinical indicators, and whether other factors are involved is yet unknown. Metabolites in the serum indicate a broad effect of GV on cellular metabolism and therefore are more likely to indicate metabolic dysregulation associated with T1D. To compare the metabolomic profiles between high GV (GV-H, coefficient of variation (CV) of glucose ≥ 36%) and low GV (GV-L, CV < 36%) groups and to identify potential GV biomarkers, metabolomics profiling was carried out on serum samples from 17 patients with high GV, 16 matched (for age, sex, body mass index (BMI), diabetes duration, insulin dose, glycated hemoglobin (HbA1c), fasting, and 2 h postprandial C-peptide) patients with low GV (exploratory set), and another 21 (GV-H/GV-L: 11/10) matched patients (validation set). Subsequently, 25 metabolites were significantly enriched in seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the GV-H and GV-L groups in the exploratory set. Only the differences in spermidine, L-methionine, and trehalose remained significant after validation. The area under the curve of these three metabolites combined in distinguishing GV-H from GV-L was 0.952 and 0.918 in the exploratory and validation sets, respectively. L-methionine was significantly inversely related to HbA1c and glucose CV, while spermidine was significantly positively associated with glucose CV. Differences in trehalose were not as reliable as those in spermidine and L-methionine because of the relatively low amounts of trehalose and the inconsistent fold change sizes in the exploratory and validation sets. Our findings suggest that metabolomic disturbances may impact the GV of T1D. Additional in vitro and in vivo mechanistic studies are required to elucidate the relationship between spermidine and L-methionine levels and GV in T1D patients with different geographical and nutritional backgrounds.
... We illustrate our tool using a metabolomics dataset for mice with 100 metabolites from [28]. The data contains the metabolic profiles of 41 non-diabetic and 30 diabetic mice and has been recently analyzed in [29]. ...
Background
Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.
Results
We present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html.
Conclusions
Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.
... For this data, 112 KEGG signaling and metabolic pathways were analyzed. The final dataset is much smaller and measured metabolic profiles among 41 non-diabetic and 30 diabetic mice for 100 selected metabolites [25]. ...
Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods’ capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.
... Each time point includes four biological replicates. Cell lysates were extracted as previously described (20). Remaining lipid standards were selected as these were not identified in human plasmas during method development. ...
Methionine is an essential amino acid and critical precursor to the cellular methyl donor S-adenosylmethionine. Unlike non-transformed cells, cancer cells have a unique metabolic requirement for methionine and are unable to proliferate in growth media where methionine is replaced with its metabolic precursor, homocysteine. This metabolic vulnerability is common among cancer cells regardless of tissue origin and is known as “methionine dependence”, “methionine stress sensitivity”, or the Hoffman effect. The response of lipids to methionine stress, however, is not well-understood. Using mass spectroscopy, label-free vibrational microscopy, and next-generation sequencing, we characterize the response of lipids to methionine stress in the triple-negative breast cancer cell line MDA-MB-468 and its methionine stress insensitive derivative, MDA-MB-468res-R8. Lipidome analysis identified an immediate, global decrease in lipid abundances with the exception of triglycerides and an increase in lipid droplets in response to methionine stress specifically in MDA-MB-468 cells. Furthermore, specific gene expression changes were observed as a secondary response to methionine stress in MDA-MB-468, resulting in a downregulation of fatty acid metabolic genes and an upregulation of genes in the unfolded protein response pathway. We conclude that the extensive changes in lipid abundance during methionine stress is a direct consequence of the modified metabolic profile previously described in methionine stress sensitive cells. The changes in lipid abundance likely results in changes in membrane composition inducing the unfolded protein response we observe.
... Vitamins C and D and α-tocopherol, nutrients transported by chylomicrons, are part of our predictive signature, with lower levels identified in cases in comparison to controls at 9-12 MBSC (Fig. 4a). Interestingly, downregulation of chylomicron constitutive elements was associated with absorption deficiency of vitamin C and α-tocopherol in NOD mice [21]. Notably, levels of threonate (Fig. 5), the major breakdown product of vitamin C, were similar between cases and controls, suggesting that low vitamin C is not caused by higher degradation rate, but rather, by restricted absorption. ...
Background
The Environmental Determinants of Diabetes in the Young (TEDDY) is a prospective birth cohort designed to study type 1 diabetes (T1D) by following children with high genetic risk. An integrative multi-omics approach was used to evaluate islet autoimmunity etiology, identify disease biomarkers, and understand progression over time.
Results
We identify a multi-omics signature that was predictive of islet autoimmunity (IA) as early as 1 year before seroconversion. At this time, abnormalities in lipid metabolism, decreased capacity for nutrient absorption, and intracellular ROS accumulation are detected in children progressing towards IA. Additionally, extracellular matrix remodeling, inflammation, cytotoxicity, angiogenesis, and increased activity of antigen-presenting cells are observed, which may contribute to beta cell destruction. Our results indicate that altered molecular homeostasis is present in IA-developing children months before the actual detection of islet autoantibodies, which opens an interesting window of opportunity for therapeutic intervention.
Conclusions
The approach employed herein for assessment of the TEDDY cohort showcases the utilization of multi-omics data for the modeling of complex, multifactorial diseases, like T1D.
... We utilized Filigree to perform DNEA analysis of the metabolomics data from NOD mice that either progressed or did not progress to overt T1D [24,25]. Plasma metabolites from T1D and nondiabetic NOD mice produced a PCN with stronger connectivity in the non-diabetic mice (Figure 2A). ...
... We utilized Filigree to perform DNEA analysis of the metabolomics data from NOD mice that either progressed or did not progress to overt T1D [24,25]. Plasma metabolites from T1D and non-diabetic NOD mice produced a PCN with stronger connectivity in the non-diabetic mice ( Figure 2A). ...
... These findings also support previously described disruptions in metabolism associated with T1D, including alterations in mitochondrial metabolism, increased oxidative stress, and changes in redox state [44]. Indeed, Fahrmann et al. [24] previously reported increased levels of sugar-related metabolites, branched chain amino acids, gluconic acid and nitric oxide-derived saccharic acid markers of oxidative stress in T1D mice. Our network-based approach confirms and extends the understanding of alteration in metabolism that occurs in T1D, including changes in the metabolism of nucleotides (S2-S5). ...
Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
... The plasma lipid profile of patients with type 1 diabetes and murine models was characterized by the increase in the long-chain polyunsaturated triglycerides and the decreases in the long-chain lysophospholipids and cholesterol esters [88]. The corroborated hypothesis was the increase in the remodeling of both circulating lipoprotein and pro-inflammatory status in type 1 diabetes [88][89][90][91][92]. Notably, the levels of AA and AA-derived eicosanoids-such as thromboxane A2 (TXA2), leukotriene B4 (LTB4), PGD2, PGE2, 11-HETE, 12-HETE, 15-HETE, and 12-oxo-ETE-also decreased in a non-obese diabetic mouse, highlighting a reduced state of systemic inflammation [37]. ...
Lipids are apolar small molecules known not only as components of cell membranes but also, in recent literature, as modulators of different biological functions. Herein, we focused on the bioactive lipids that can influence the immune responses and inflammatory processes regulating vascular hyperreactivity, pain, leukocyte trafficking, and clearance. In the case of excessive pro-inflammatory lipid activity, these lipids also contribute to the transition from acute to chronic inflammation. Based on their biochemical function, these lipids can be divided into different families, including eicosanoids, specialized pro-resolving mediators, lysoglycerophospholipids, sphingolipids, and endocannabinoids. These bioactive lipids are involved in all phases of the inflammatory process and the pathophysiology of different chronic autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, type-1 diabetes, and systemic lupus erythematosus.
... When present in sufficiently high levels, galactonic acid can act as an acidogen and a metabotoxin which has multiple adverse effects on many organ systems. Galactonic acid, was previously shown to be associated with diabetes in a mouse model, due to a proposed mechanism of oxidative stress 48 . On the other hand, maternal ethnicity has the largest impact among physiological factors (importance score =84%). ...
Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal pre-pregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using coupled untargeted and targeted metabolomics approach, from the newborn cord blood metabolomes associated with a matched maternal pre-pregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multi-ethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using elastic net regularization based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age and ethnicity, parity and gravidity). We validated the model results in a subsequent set of samples (N=30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese vs. norm-weight groups. Interestingly, Hydroxy-3-methylbutyric acid showed significnatly higher levels in cord blood from the NHPI group, compared to asian and caucasian groups. In summary, significant associations were observed between maternal pre-pregnant obesity and offspring metabolomics alternation at birth, revealing the inter-generational impact of maternal obesity.
