Untargeted Lipidomic Analysis to Broadly Characterize the Effects of Pathogenic and Non-Pathogenic Staphylococci on Mammalian Lipids

Modification of the host lipidome via secreted enzymes is an integral, but often overlooked aspect of bacterial pathogenesis. In the current era of prevalent antibiotic resistance, knowledge regarding critical host pathogen lipid interactions has the potential for use in developing novel antibacterial agents. While most studies to date on this matter have focused on specific lipids, or select lipid classes, this provides an incomplete picture. Modern methods of untargeted lipidomics have the capacity to overcome these gaps in knowledge and provide a comprehensive understanding of the role of lipid metabolism in the pathogenesis of infections. In an attempt to determine the role of lipid modifying enzymes produced by staphylococci, we exposed bovine heart lipids, a standardized model for the mammalian lipidome, to spent medium from staphylococcal cultures, and analyzed lipid molecular changes by MS/MSALLshotgun lipidomics. We elucidate distinct effects of different staphylococcal isolates, including 4 clinical isolates of the pathogenic species Staphylococcus aureus, a clinical isolate of the normally commensal species S. epidermidis, and the non-pathogenic species S. carnosus. Two highly virulent strains of S. aureus had a more profound effect on mammalian lipids and modified more lipid classes than the other staphylococcal strains. Our studies demonstrate the utility of the applied untargeted lipidomics methodology to profile lipid changes induced by different bacterial secretomes. Finally, we demonstrate the promise of this lipidomics approach in assessing the specificity of bacterial enzymes for mammalian lipid classes. Our data suggests that there may be a correlation between the bacterial expression of lipid-modifying enzymes and virulence, and could facilitate the guided discovery of lipid pathways required for bacterial infections caused by S. aureus and thereby provide insights into the generation of novel antibacterial agents

A Preliminary Investigation towards the Risk Stratification of Allogeneic Stem Cell Recipients with Respect to the Potential for Development of GVHD via Their Pre-Transplant Plasma Lipid and Metabolic Signature

The clinical outcome of allogeneic hematopoietic stem cell transplantation (SCT) may be influenced by the metabolic status of the recipient following conditioning, which in turn may enable risk stratification with respect to the development of transplant-associated complications such as graft vs. host disease (GVHD). To better understand the impact of the metabolic profile of transplant recipients on post-transplant alloreactivity, we investigated the metabolic signature of 14 patients undergoing myeloablative conditioning followed by either human leukocyte antigen (HLA)-matched related or unrelated donor SCT, or autologous SCT. Blood samples were taken following conditioning and prior to transplant on day 0 and the plasma was comprehensively characterized with respect to its lipidome and metabolome via liquid chromatography/mass spectrometry (LCMS) and gas chromatography/mass spectrometry (GCMS). A pro-inflammatory metabolic profile was observed in patients who eventually developed GVHD. Five potential pre-transplant biomarkers, 2-aminobutyric acid, 1-monopalmitin, diacylglycerols (DG 38:5, DG 38:6), and fatty acid FA 20:1 demonstrated high sensitivity and specificity towards predicting post-transplant GVHD. The resulting predictive model demonstrated an estimated predictive accuracy of risk stratification of 100%, with area under the curve of the ROC of 0.995. The likelihood ratio of 1-monopalmitin (infinity), DG 38:5 (6.0), and DG 38:6 (6.0) also demonstrated that a patient with a positive test result for these biomarkers following conditioning and prior to transplant will be at risk of developing GVHD. Collectively, the data suggest the possibility that pre-transplant metabolic signature may be used for risk stratification of SCT recipients with respect to development of alloreactivity

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

Effect of alloreactivity operator on T cell vector .

<p>Successive iterations (<i>t</i>, <i>t+1 etc</i>.) modify the vector to . In this simplified model, <i>mHA</i>_<i>x</i><i>-HLA</i> only binds <i>TC</i>_<i>x</i> and so on. Matrix below represents a single iteration <i>t</i>.</p

Exome sequencing and peptide results by donor type (n = 77).

<p>Exome sequencing and peptide results by donor type (n = 77).</p

Cox proportional hazards model for cumulative GVHD (N = 47 of 73 evaluable) association with simulated organ specific T cell counts.

<p>Cox proportional hazards model for cumulative GVHD (N = 47 of 73 evaluable) association with simulated organ specific T cell counts.</p

Cox proportional hazards model for cumulative grade 2–4 acute and moderate to severe chronic GVHD (N = 39 of 73 evaluable) association with simulated organ specific T cell counts (expressed in 1000’s).

<p>Cox proportional hazards model for cumulative grade 2–4 acute and moderate to severe chronic GVHD (N = 39 of 73 evaluable) association with simulated organ specific T cell counts (expressed in 1000’s).</p

Matrix illustrating the relative effect of antigen binding affinity on the T cell clonal interaction between different clones.

<p>Successive cells in each row of the matrix calculate the effect of <i>TC</i>_<i>i</i> on T cell being studied, <i>TC</i>_<i>x</i>. This generates a weighting factor, α, which modulates the impact of population of <i>TC</i>_<i>i</i> on the growth of <i>TC</i>_<i>x</i>.</p