Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations

Iyer, Gayatri; Wigginton, Janis E.; Duren, William L.; LaBarre, Jennifer L; Brandenburg,; Burant, Charles F.; Michailidis, George; Karnovsky, Alla

doi:10.3390/metabo10120479

Cited by 7 publications

(6 citation statements)

References 90 publications

(111 reference statements)

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“…DNEA methodology has been described previously. 23 , 33 Briefly, DNEA computes a partial correlation network using metabolomics data from two conditions (i.e. ALS and control) jointly.…”

Section: Methodsmentioning

confidence: 99%

“…Due to an imbalance in the number of samples in ALS versus control groups, we employed a subsampling procedure coupled with partial correlation network estimation to obtain robust network edges, as described in Iyer et al . 23 The network was then clustered into densely-connected metabolite subnetworks. Next, we performed an enrichment analysis using the NetGSA method, 34 which takes into account differential metabolite abundances as well as the differences in network structure between cases and controls.…”

Section: Methodsmentioning

confidence: 99%

“…We identified shared and important pathways in the original and replication cross-sectional ALS cohorts using knowledge-based enrichment analysis, which centred on fatty acid and sphingomyelin metabolism as well as creatine and xanthine metabolism. We also combined the two datasets and implemented differential network enrichment analysis (DNEA), 23 a data-driven approach, which does not rely on known pathway annotation, allowing the method to uncover new potential metabolite correlations. Lastly, we generated prediction models leveraging metabolic data from the original cohort, which we employed to predict cases in the replication cohort.…”

Section: Introductionmentioning

confidence: 99%

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Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Goutman

Guo

Savelieff

et al. 2022

Brain

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Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression, and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex, and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected by the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than controls (64.0 vs. 62.0 years, p = 0.009). Cohort 2 controls were over-represented in females (68%, p < 0.001). The most concordant cohort 1 and 2 pathways centered heavily on lipid sub-pathways, including complex and signaling lipid species and metabolic intermediates. There were differences in sub-pathways that enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA on the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centered on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

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“…DNEA methodology has been described previously. 23 , 33 Briefly, DNEA computes a partial correlation network using metabolomics data from two conditions (i.e. ALS and control) jointly.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Goutman

Guo

Savelieff

et al. 2022

Brain

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“…A bioinformatic limitation in the study was the inability to map individual significant metabolites to biological pathways using MSEA, due to many metabolites with the pathways not being profiled in the untargeted metabolomics platform and the lack of HMDBs for metabolites that were significant. Future directions will incorporate a partial correlation-based approach [46] to assess alterations in the relationship of metabolites at the fasted random-fed visit and if subnetworks of metabolites are associated with insulin resistance cross-sectionally and longitudinally.…”

Section: Conclustions and Future Directionsmentioning

confidence: 99%

Comparing the Fasting and Random-Fed Metabolome Response to an Oral Glucose Tolerance Test in Children and Adolescents: Implications of Sex, Obesity, and Insulin Resistance

et al. 2021

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As the incidence of obesity and type 2 diabetes (T2D) is occurring at a younger age, studying adolescent nutrient metabolism can provide insights on the development of T2D. Metabolic challenges, including an oral glucose tolerance test (OGTT) can assess the effects of perturbations in nutrient metabolism. Here, we present alterations in the global metabolome in response to an OGTT, classifying the influence of obesity and insulin resistance (IR) in adolescents that arrived at the clinic fasted and in a random-fed state. Participants were recruited as lean (n = 55, aged 8–17 years, BMI percentile 5–85%) and overweight and obese (OVOB, n = 228, aged 8–17 years, BMI percentile ≥ 85%). Untargeted metabolomics profiled 246 annotated metabolites in plasma at t0 and t60 min during the OGTT. Our results suggest that obesity and IR influence the switch from fatty acid (FA) to glucose oxidation in response to the OGTT. Obesity was associated with a blunted decline of acylcarnitines and fatty acid oxidation intermediates. In females, metabolites from the Fasted and Random-Fed OGTT were associated with HOMA-IR, including diacylglycerols, leucine/isoleucine, acylcarnitines, and phosphocholines. Our results indicate that at an early age, obesity and IR may influence the metabolome dynamics in response to a glucose challenge.

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“…Here, we describe a protocol for merging disparately acquired untargeted LC-MS metabolomics data from the Michigan Mother–Infant Pairs (MMIP) cohort. Our group has previously used a lipidomics platform to identify maternal lipids associated with the cord blood lipidome and infant birth weight in this cohort. , Untargeted LC-MS metabolomics was performed on the first trimester maternal plasma (M1), delivery maternal plasma (M3), and umbilical cord blood (CB) for a total of 106 mother-infant dyads. A subset of these samples was analyzed in 2016, whereas the remaining data were acquired by the same laboratory in 2019, but with a different chromatography system, mass spectrometer, and experimental protocol.…”

Section: Introductionmentioning

confidence: 99%

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

et al. 2022

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Untargeted liquid chromatography−mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

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Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations

Cited by 7 publications

References 90 publications

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Comparing the Fasting and Random-Fed Metabolome Response to an Oral Glucose Tolerance Test in Children and Adolescents: Implications of Sex, Obesity, and Insulin Resistance

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

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