Background:The ribosome is a highly charged complex comprising RNAs and proteins. Results: Ribosomal proteins exhibit low hydrophobicity and a significant degree of intramolecular charge segregation. Conclusion: The majority of ribosomal proteins from all organisms, particularly halophiles, use intramolecular charge segregation to minimize electrostatic repulsion with rRNA. Significance: The electrostatic properties of ribosomal proteins are important for ribosome stability, assembly, and interaction with translation factors and nascent proteins.
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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.
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’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.
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