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Sepsis‐induced metabolic dysfunction contributes to organ failure and death. L‐carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor‐dependent septic shock demonstrated a non‐significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90‐day mortality benefit from L‐carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L‐carnitine dose, on 90‐day mortality was determined by logistic regression. A grid‐search analysis maximizing the Z‐statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L‐carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan‐Meier estimate. Accounting for L‐carnitine treatment and dose, 11 1H‐NMR metabolites and 12 acylcarnitines were independent predictors of 90‐day mortality. Based on the grid‐search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L‐carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L‐carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90‐day sepsis mortality. Our proof‐of‐concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.

INTRODUCTION: Oxidation-reduction (redox) reactions, and the redox potential (RP) that must be maintained for proper cell function, lie at the heart of physiologic processes in critical illness. Imbalance in RP reflects systemic oxidative stress, and whole blood RP measures have been shown to correlate with oxygen debt level over time in swine traumatic shock. We hypothesize that RP measures will reflect changing concentrations of metabolites involved in oxidative stress. To test this hypothesis, we compared blood and urine RP with concentrations of multiple metabolites in a swine traumatic shock model to identify meaningful RP-metabolite relationships. METHODS: Seven swine were subjected to traumatic shock. Mixed venous (MV) RP, urine RP, and concurrent MV and urine metabolite concentrations were assessed at baseline, max O2 Debt (80mL/kg), end resuscitation, and 2 hrs post resuscitation. RP was measured at collection via open circuit potential using nanoporous gold electrodes with Ag/AgCl reference and a ParstatMC potentiostat. Metabolite concentrations were measured by quantitative 1H-NMR spectroscopy. MV and urine RP were compared with time-matched metabolites across all swine. LASSO regression with leave-one-out cross validation was used to determine meaningful RP/metabolite relationships. Metabolites had to maintain magnitude and direction of coefficients across 6 or more swine to be considered as having a meaningful relationship. KEGG IDs of these metabolites were uploaded into Metscape for pathway identification and evaluation for physiologic function. RESULTS: Meaningful metabolite relationships (and mean coefficients) with MV RP include: Choline (−6.27), ATP (−4.39), Glycine (5.93), ADP (1.84), Glucose (15.96), Formate (−13.09), Pyruvate (6.18), and Taurine (−7.18). Relationships with urine RP were: cis-Aconitate (4.55)Taurine (7.40), Glycine (−4.88) Creatine (8.16), Betaine (5.11), Urea (6.30), 3-Hydroxyisobutyrate (−6.44), N-Phenylacetylglycine, PAG (−15.48), Hippurate (10.66), and Formate (−5.36). Pathway analysis revealed these metabolites scavenge extracellular peroxide (pyruvate), inhibit ROS and activate cellular antioxidant defense (taurine), act as indicators of antioxidant mobilization against oxidative stress (glycine + PAG), and reflect renal hydroxyl radical trapping (hippurate), among other activity. CONCLUSIONS: Real-time RP measures demonstrate significant relationships with metabolites attributable to metabolic pathways involved in systemic responses to oxidative stress, as well as those active in these processes. These data support RP measures as a feasible, biologically relevant marker of oxidative stress. As a direct measure of redox state, RP may be a useful biomarker and clinical tool in guiding diagnosis and therapy in states of increased oxidative stress, and may offer value as a marker for organ injury in these states as well.

The degree of hydrophobicity and net charge per residue are physical properties that enable the discrimination of folded from intrinsically disordered proteins (IDPs) solely on the basis of amino acid sequence. Here, we improve upon the existing classification of proteins and IDPs based on the parameters mentioned above by adopting the scale of nonpolar content of Rose et al. and by taking amino acid side-chain acidity and basicity into account. The resulting algorithm, denoted here as net charge nonpolar or NECNOP, enables the facile prediction of the folded and disordered status of proteins under physiologically relevant conditions with >95% accuracy, based on amino-acid sequence alone. The NECNOP approach displays a much-enhanced performance for proteins with >140 residues, suggesting that small proteins are more likely to have irregular charge and hydrophobicity features. NECNOP analysis of the entire Escherichia coli proteome identifies specific net charge and nonpolar regions peculiar to soluble, integral membrane, and non-integral membrane proteins. Surprisingly, protein net charge and hydrophobicity are found to converge to specific values as chain length increases, across the E. coli proteome. In addition, NECNOP plots enable the straightforward identification of protein sequences corresponding to prion proteins and promise to serve as a powerful predictive tool for the design of large proteins. In summary, NECNOP plots are a straightforward approach that improves our understanding of the relation between the amino acid sequence and three-dimensional structure of proteins as a function of molecular mass.

Sepsis-induced metabolic dysfunction is associated with mortality, but the signatures that differentiate variable clinical outcomes among survivors are unknown. Our aim was to determine the relationship between host metabolism and chronic critical illness (CCI) in patients with septic shock. We analyzed metabolomics data from mechanically ventilated patients with vasopressor-dependent septic shock from the placebo arm of a recently completed clinical trial. Baseline serum metabolites were measured by liquid chromatography-mass spectrometry and 1H-nuclear magnetic resonance. We conducted a time-to-event analysis censored at 28 days. Specifically, we determined the relationship between metabolites and time to extubation and freedom from vasopressors using a competing risk survival model, with death as a competing risk. We also compared metabolite concentrations between CCI patients, defined as intensive care unit level of care ≥ 14 days, and those with rapid recovery. Elevations in two acylcarnitines and four amino acids were related to the freedom from organ support (subdistributional hazard ratio < 1 and false discovery rate < 0.05). Proline, glycine, glutamine, and methionine were also elevated in patients who developed CCI. Our work highlights the need for further testing of metabolomics to identify patients at risk of CCI and to elucidate potential mechanisms that contribute to its etiology.

BackgroundSepsis-induced metabolic dysfunction contributes to organ failure and death. L-carnitine has shown promise for septic shock, but a recent study demonstrated a non-significant reduction in mortality.MethodsA pharmacometabolomics study of patients (n=250) in a Phase II trial of L-carnitine to identify metabolic profiles predictive of a 90-day mortality benefit from L-carnitine. The independent predictive value of each pre-treatment metabolite concentration, adjusted for L-carnitine dose, on 90-day mortality was determined by logistic regression. A grid-search analysis maximizing the Z-statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L-carnitine responsive patients. Threshold concentrations were further assessed by hazard-ratio and Kaplan-Meier estimate.FindingsAccounting for L-carnitine treatment and dose, 11 1H-NMR metabolites and 12 acylcarnitines were independent predictors of 90-day mortality. Based on the grid-search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L-carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with acetylcarnitine ≥35µM were less likely to die at 90 days if treated with L-carnitine (18 g) versus placebo (p=0.01 by log rank test).InterpretationMetabolomics identified independent predictors of 90-day sepsis mortality. Our proof-of-concept approach shows how pharmacometabolomics may be useful for tackling the heterogeneity of sepsis and informing clinical trial design. Also, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, since sepsis induces alterations in numerous metabolite concentrations.

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite&rsquo, s true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

Objective The objective of this review is to discuss the therapeutic use and differential treatment response to Levo-carnitine (l-carnitine) treatment in septic shock, and to demonstrate common lessons learned that are important to the advancement of precision medicine approaches to sepsis. We propose that significant interpatient variability in the metabolic response to l-carnitine and clinical outcomes can be used to elucidate the mechanistic underpinnings that contribute to sepsis heterogeneity. Methods A narrative review was conducted that focused on explaining interpatient variability in l-carnitine treatment response. Relevant biological and patient-level characteristics considered include genetic, metabolic, and morphomic phenotypes; potential drug interactions; and pharmacokinetics (PKs). Main results Despite promising results in a phase I study, a recent phase II clinical trial of l-carnitine treatment in septic shock showed a nonsignificant reduction in mortality. However, l-carnitine treatment induces significant interpatient variability in l-carnitine and acylcarnitine concentrations over time. In particular, administration of l-carnitine induces a broad, dynamic range of serum concentrations and measured peak concentrations are associated with mortality. Applied systems pharmacology may explain variability in drug responsiveness by using patient characteristics to identify pretreatment phenotypes most likely to derive benefit from l-carnitine. Moreover, provocation of sepsis metabolism with l-carnitine offers a unique opportunity to identify metabolic response signatures associated with patient outcomes. These approaches can unmask latent metabolic pathways deranged in the sepsis syndrome and offer insight into the pathophysiology, progression, and heterogeneity of the disease. Conclusions The compiled evidence suggests there are several potential explanations for the variability in carnitine concentrations and clinical response to l-carnitine in septic shock. These serve as important confounders that should be considered in interpretation of l-carnitine clinical studies and broadly holds lessons for future clinical trial design in sepsis. Consideration of these factors is needed if precision medicine in sepsis is to be achieved.

PURPOSE: Approximately 25% of patients receiving weekly paclitaxel for breast cancer require treatment disruptions to avoid severe, irreversible peripheral neuropathy (PN). Vitamin insufficiencies are PN risk factors in many diseases, but their relevance to chemotherapy-induced PN is unknown. METHODS: We investigated whether baseline insufficiency of vitamin D, vitamin B12, folate, or homocysteine increased PN in patients with breast cancer receiving weekly paclitaxel in a retrospective analysis of a prospective observational study. Patient-reported PN was collected at baseline and during treatment on the Quality of Life Questionnaire Chemotherapy-Induced Peripheral Neuropathy(CIPN20). The primary analysis tested associations between vitamin deficiency and the maximum increase from baseline in the CIPN20 sensory subscale (ΔCIPN8). Secondary analyses tested for association with PN-induced treatment disruptions and adjusted associations for treatment and clinical variables. RESULTS: 25-hydroxy-vitamin D was the only nutrient with sufficient deficiency (< 20 ng/mL) for analysis (15/37=41%). Vitamin D deficient patients had a greater mean PN increase than non-deficient patients (ΔCIPN8±SD, 36±23 vs. 16±16, p=0.003) and a non-significant, approximately 3-fold increase in risk of treatment disruption (OR = 2.98, 95% CI [0.72, 12.34], p=0.16). In multivariable models adjusted for clinical and treatment variables, baseline vitamin D level was inversely associated with PN (β = −0.04, p = 0.02). CONCLUSIONS: Pre-treatment vitamin D deficiency was associated with PN in women receiving weekly paclitaxel for breast cancer. Vitamin D deficiency may be an easily detected PN risk factor that could be resolved prior to treatment to prevent PN, avoid treatment disruptions, and improve treatment outcomes.

Background: Sepsis-induced alterations in mitochondrial function contribute to organ dysfunction and mortality. Measuring mitochondrial function in vital organs is neither feasible nor practical, highlighting the need for non-invasive approaches. Mitochondrial function may be reflected in the concentrations of metabolites found in platelets and whole blood (WB) samples. We proposed to use these as alternates to indirectly estimate platelet mitochondrial oxygen consumption rate (mOCR) in sepsis patients. Methods: We determined the relationships between platelet mOCR and metabolites in both platelets and WB, as measured by quantitative 1H-NMR metabolomics. The associations were identified by building multiple linear regression models with stepwise forward-backward variable selection. We considered the models to be significant with an ANOVA test (p-value &le, 0.05) and a positive predicted-R2. Results: The differences in adjusted-R2 and ANOVA p-values (platelet adj-R2: 0.836 (0.0003), 0.711 (0.0004) vs. WB adj-R2: 0.428 (0.0079)) from the significant models indicate the platelet models were more associated with platelet mOCR. Conclusions: Our data suggest there are groups of metabolites in WB (leucine, acetylcarnitine) and platelets (creatine, ADP, glucose, taurine) that are associated with platelet mOCR. Thus, WB and platelet metabolites could be used to estimate platelet mOCR.

Ribosomes are large and highly charged macromolecular complexes consisting of RNA and proteins. Here, we address the electrostatic and nonpolar properties of ribosomal proteins that are important for ribosome assembly and interaction with other cellular components and may influence protein folding on the ribosome. We examined 50 S ribosomal subunits from 10 species and found a clear distinction between the net charge of ribosomal proteins from halophilic and non-halophilic organisms. We found that ∼67% ribosomal proteins from halophiles are negatively charged, whereas only up to ∼15% of ribosomal proteins from non-halophiles share this property. Conversely, hydrophobicity tends to be lower for ribosomal proteins from halophiles than for the corresponding proteins from non-halophiles. Importantly, the surface electrostatic potential of ribosomal proteins from all organisms, especially halophiles, has distinct positive and negative regions across all the examined species. Positively and negatively charged residues of ribosomal proteins tend to be clustered in buried and solvent-exposed regions, respectively. Hence, the majority of ribosomal proteins is characterized by a significant degree of intramolecular charge segregation, regardless of the organism of origin. This key property enables the ribosome to accommodate proteins within its complex scaffold regardless of their overall net charge.

e12027 Background: Peripheral neuropathy (PN) is a severe, dose-limiting toxicity of paclitaxel that occurs in up to 25% of patients and can lead to permanent loss of balance and manual dexterity. Due to the lack of effective strategies for PN prevention or treatment, there is a critical need to identify predictive risk factors for paclitaxel-induced PN. Vitamin insufficiencies are known risk factors for PN in other disease states. However, the effect of vitamin insufficiency on paclitaxel-induced PN has not been adequately investigated. Methods: Baseline levels of vitamin D and other nutrients (vitamin B, homocysteine, folate) were measured, and PN was assessed weekly in an observational trial of patients receiving paclitaxel 80 mg/m2 for 12 weeks for non-metastatic breast cancer (NCT0233811). Nutrient levels were measured by Michigan Medicine and insufficiency defined by institutional standards (vitamin D insufficiency < 20 ng/mL). In the primary analysis, the maximum increase from baseline in the 8-item sensory subscale (ΔCIPN8) of the EORTC CIPN20, a validated patient-reported PN assessment tool, was compared in nutrient insufficient and sufficient patients. The effect of vitamin insufficiencies on PN-induced treatment disruptions (dose decrease, delay, or discontinuation) was conducted as a secondary analysis. Results: Only vitamin D insufficiency was identified in enough patients for analysis (15/37 = 41%). Vitamin D insufficient patients reported a greater mean (+/- SD) ΔCIPN8 (36.39 ±22.8) than vitamin D sufficient patients (16.29 ±16.3) (p = 0.003). However, the increase in treatment disruption for vitamin D insufficient patients was not significant (OR = 2.98, 95% CI [0.72, 12.34], p = 0.16). Conclusions: Paclitaxel-treated patients who were vitamin D insufficient at baseline had greater increases in patient-reported PN. If validated in larger studies, vitamin D insufficiency may be a clinically translatable, modifiable risk factor that can be used to prevent paclitaxel-induced PN in patients with non-metastatic breast cancer.

Proteins are biosynthesized from N to C terminus before they depart from the ribosome and reach their bioactive state in the cell. At present, very little is known about the evolution of conformation and the free energy of the nascent protein with chain elongation. These parameters critically affect the extent of folding during ribosome-assisted biosynthesis. Here, we address the impact of vectorial amino acid addition on the burial of nonpolar surface area and on the free energy of native-like structure formation in the absence of the ribosomal machinery. We focus on computational predictions on proteins bearing the globin fold, which is known to encompass the 3/3, 2/2, and archaeal subclasses. We find that the burial of nonpolar surface increases progressively with chain elongation, leading to native-like conformations upon addition of the last C-terminal residues, corresponding to incorporation of the last two helices. Additionally, the predicted folding entropy for generating native-like structures becomes less unfavorable at nearly complete chain lengths, suggesting a link between the late burial of nonpolar surface and water release. Finally, the predicted folding free energy takes a progressive favorable dip toward more negative values, as the chain gets longer. These results suggest that thermodynamic stabilization of the native structure of newly synthesized globins during translation in the cell is significantly enhanced as the chain elongates. This is especially true upon departure of the last C-terminal residues from the ribosomal tunnel, which hosts ca., 30-40 amino acids. Hence, we propose that release from the ribosome is a crucial step in the life of single-domain proteins in the cell.