Development of an R script for simple lipidomic and metabolomic data analysis

Background: Metabolomic and lipidomic studies generate vast quantities of data that are often analysed in a closed software environment with little to no access to the underlying algorithms. As a result, data processed via different software pipelines yield different results thus leading to a widespread problem of low reproducibility within these fields. To address this problem, we are developing LipidAnalyst; an R based lipidomics software pipeline. As a part of this project, we are creating a simple statistical analysis and graphing module in R to generate accurate, reproducible, high-resolution figures. Methods: R scripts were developed under version 3.5.3 with the capability to undertake statistical analyses (e.g. ANOVA) and post-hoc tests (e.g. Tukey). Additional code plotted resultant information as high resolution violin and box plots that depicted statistical significance. Thereafter, lipidomic and metabolomic data were analysed by this code and compared against commercial software and Metaboanalyst, a primary software used in metabolomic and lipidomic research. Results: Code generated in house demonstrated the same results as those generated using commercial software (e.g. JMP 14.0 Pro) but were different from results obtained by using the MetaboAnalyst pipeline. Conclusions: This study demonstrated the prevalent danger of using closed-source software pipelines for the analysis of lipidomic and metabolomic data without validating the analysis outcomes via open-source software. Open source software such as LipidAnalyst, that has also been independently validated using multiple data sets, can then be published with the results to enable transparency of data analysis and improve the replicability of results across different labs.https://scholarscompass.vcu.edu/gradposters/1092/thumbnail.jp

A Pre-transplant Blood-based Lipid Signature for Prediction of Antibody-mediated Rejection in Kidney Transplant Patients

Purpose. The aim of this study is to demonstrate the potential of the pre-transplant lipidome to predict post-transplant antibody-mediated rejection (AMR) in kidney transplant patients. Methods. Patients were selected from a prospective observational cohort of a single-center adult kidney transplant center in the United States. The study included 16 kidney transplant patients who develop AMR within 2 years post-transplant and 29 stable control (SC) kidney transplant patients who did not develop AMR at any time within the post-transplant follow up. Selection of group differences on the day of transplant was determined by t-test analysis. Stepwise forward method was used to create Linear Discrimination Analysis with regularized correction (RLDA). Changes over time were estimated using sparse partial least square method which is validated by permutation testing. T-test was performed to compare two time points for the same group and groups at matched time points. JMP Pro 13 and MetaboAnalyst were used in the analysis of the Data. Results. A comparison of lipids classes on the day of transplant revealed PLs relative concentration differences between SC and AMR. Concentration of phosphatidylcholine (PC) was significantly diminished in AMR, while there was a trend for increased concentration of lysophosphatidylcholine (LPC). AMR group also showed significantly lower concentration of phosphatidylethanolamine (PE), lysophosphatidylethanolamine (LPE), plasmanylethanolamine (PE-O), and plasmenylethanolamine (PE-P). Our data demonstrated that there are significant differences in the lipidome between SC and AMR on the day of transplant. The analysis identified 7 distinct lipids that discriminated between AMR and SC (AUC) =0.95 (95%CI=0.84- 0.98), R2=0.63 (95%CI=0.4-0.8). A sPLSDA analysis of the data revealed a statistically significant alteration in the lipid profile at 6 months post-transplant compared to the day of transplant. The analysis revealed a panel of 13 lipids that were found to differentiate the two groups at 6 month post-transplant . Further data analysis confirms the presence of a sustained lipid metabolic difference between SC and AMR over time that distinguish between the patients with favorable and non-favorable transplant outcomes. Conclusion. This study demonstrates the potential of the pre-transplant lipidome towards determining AMR in kidney transplant patients, raising the possibility of using this information in risk stratification of patients about to undergo transplant.https://scholarscompass.vcu.edu/gradposters/1086/thumbnail.jp

Metabolic Modulation Predicts Heart Failure Tests Performance

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction \u3c 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio

Molecular Predictors of Anakinra Treatment Success in Heart Failure Patients with Reduced Ejection Fraction

Background. Kineret (Anakinra) is an interleukin-1 antagonist that is under investigation for its novel clinical application treating patients that have heart failure with reduced (\u3c50%) ejection fraction (HFrEF). A prior study from our group indicated that Anakinra may restore heart function by addressing dysregulations in HFrEF metabolic pathways. Herein, we attempt to elicit Anakinra’s effects on both metabolome and lipidome. Methods. Lipids and metabolites that had previously been quantified by mass spectrometry (MS) from patients (n=49) who had ≥2 mg/L of high-sensitivity C-reactive protein (hs-CRP) were mTIC normalized and transformed. We conducted a stepwise Linear Discriminant Analysis (r- LDA) to test Anakinra (2 and 12 weeks) vs placebo for separation from combined baseline. Metabolic pathway analysis was performed with Fisher’s exact test algorithm for detection of over-represented and enriched analytes. Univariate analysis (one tailed t-test p\u3c0.05) compared placebo and Anakinra after 12-weeks for effect(s). Metaboanalyst 4.0, JMP Pro 14.0, and a proprietary package in R (version 3.4.4) were the software for all analyses and data wrangling. Results. Analytes such as acylcarnitines C10:0 and C16:0 and hsCRP showed significant improvements after 12 weeks of Anakinra, leading to improved mitochondrial function, reduced inflammation, and overall better health outcomes. Statistically significant (p\u3c0.05) pathways including the citrate cycle, cysteine and methionine metabolism, galactose metabolism among others were associated with treatment. Conclusions. We were able to determine significant alterations to metabolomic and lipidomic concentrations after 12 weeks of Anakinra therapy. Our biochemical analyses verifies that Anakinra did improve heart function within our HFrEF pilot cohort.https://scholarscompass.vcu.edu/gradposters/1081/thumbnail.jp

Acute Fish Oil supplementation and Aspirin treatment modulates lipid profile in Platelet Rich Plasma: a randomized pilot trial

Aims: Platelet rich plasma (PRP) has been used in tissue repair to treat numerous inflammatory pathophysiologies. Recent studies have elucidated that the bioactive lipid fraction of PRP significantly contributes towards the resolution of inflammation. There has been great interest in how therapeutics could modulate the PRP lipidome to formulate a more pro-resolving matrix. Many of the pathways used to produce either pro-resolving or pro-inflammatory lipids are shared between ω-3 and ω-6 polyunsaturated fatty acids (PUFA). Here, we explored the separate and interacting effects acute exogenous ω-3 PUFA supplementation and aspirin had on the lipidome of PRP within 6 hours. Methods:PRP from 45 patients was obtained at baseline and after 6-hours from either control or those receiving one 1400 mg fish oil tablet, Bayer low-dose aspirin (81mg), or combinational therapy. Lipids were acquired by liquid chromatography mass spectrometry. Spearman rank correlation analysis visually assessed what effects treatments had on the relative abundance of PUFA derivatives. The control group was referenced for lipid selection across groups; lipids were selected on the basis that they significantly (p Results: Fish oil ω-3 PUFA supplementation and aspirin had separate and interacting effects on oxylipin and neutral lipid correlations. Strongly correlated (rho \u3e 0.65) ω-6 PUFA metabolites were reversed or reduced in magnitude following either treatment. A total of 24 lipid species were significantly modulated in the fish oil treatment group, with notable (p4), and lipoxin A4 (LXA4). Conclusion: We can confirm that fish oil supplementation and aspirin do exert modulatory effects on the lipid fraction of PRP within a short period of time (6-hours). The PUFAs composing fish oil impacted a wide range of the lipidome – possibly though a mechanism of ω-3/ω-6 enzymatic competition. Our results support that ω-3 PUFA supplementation may improve the efficacy of PRP for short-term use

SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway

Vaccines based on the spike protein of SARS-CoV-2 are a cornerstone of the public health response to COVID-19. The emergence of hypermutated, increasingly transmissible variants of concern (VOCs) threaten this strategy. Omicron (B.1.1.529), the fifth VOC to be described, harbours multiple amino acid mutations in spike, half of which lie within the receptor-binding domain. Here we demonstrate substantial evasion of neutralization by Omicron BA.1 and BA.2 variants in vitro using sera from individuals vaccinated with ChAdOx1, BNT162b2 and mRNA-1273. These data were mirrored by a substantial reduction in real-world vaccine effectiveness that was partially restored by booster vaccination. The Omicron variants BA.1 and BA.2 did not induce cell syncytia in vitro and favoured a TMPRSS2-independent endosomal entry pathway, these phenotypes mapping to distinct regions of the spike protein. Impaired cell fusion was determined by the receptor-binding domain, while endosomal entry mapped to the S2 domain. Such marked changes in antigenicity and replicative biology may underlie the rapid global spread and altered pathogenicity of the Omicron variant

Investigating the Metabolic Alterations in Response to IL-1 Antagonism via Anakinra among Patients with HFrEF

Heart failure with reduced ejection fraction (HFrEF) is a highly prevalent clinical syndrome with a mortality rate worse than most cancers, despite the availability of numerous pharmacotherapies and medical devices for management. Imperfect clinical biomarkers have been suggested as a reason for suboptimal therapy, and biomarker research has revealed an overactive innate immune response and inflammation, driven by overexpression of interleukin-1 (IL-1), as an undermedicated pathophysiological pathway. The metabolome offers a source for pharmacodynamic biomarkers that can capture the residual risks missed by conventional markers. However, current methodologies for identifying metabolomic biomarkers are limited, as the annotation of compounds from untargeted mass spectrometry assays typically relies on incomplete spectral libraries. Therefore, quantitative structure retention relationship (QSRR) models, which derive a relationship between retention time (RT) with the physicochemical properties of known compounds, can help annotate metabolomics data. In this dissertation, a natural language processing tool, LRN, is introduced to expedite systematic literature reviews. Central to this dissertation are two studies: in the first study, 20 pharmacodynamic biomarkers are developed for an IL-1 receptor antagonist, anakinra, with ribonic acid determined as a metabolomic therapeutic target for anakinra. Additionally, 3 anakinra-specific phenotypic metabolic pathways elucidated mechanisms underlying HFrEF. The second study employed an artificial intelligence workflow, MetaboLyte, that deployed 6 QSRR machine learning algorithms to identify 27 previously unknown lipids from metabolomics studies. MetaboLyte also distinctly enhanced QSRR modeling for carboxylic acids by predicting RTs at deeper taxonomic levels. The results presented herein aid in the advancement of precision medicine for HFrEF

Metabolic Modulation Predicts Heart Failure Tests Performance

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors’ coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio

Metabolic modulation predicts heart failure tests performance.

The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors' coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio