Altered methionine-sulfone levels are associated with impaired growth in HIV-exposed-uninfected children.

OBJECTIVE: To determine immune-metabolic dysregulation in children born to women living with HIV. METHODS: Longitudinal immune-metabolomic analyses of plasma of 32 pregnant women with HIV (WHIV) and 12 uninfected women and their children up to 1.5 years of age were performed. RESULTS: Using liquid chromatography-mass spectrometry and a multiplex bead assay, 280 metabolites (57 amino acids, 116 positive lipids, 107 signalling lipids) and 24 immune mediators (e.g. cytokines) were quantified. combinational antiretroviral therapy (cART) exposure was categorized as cART initiation preconception (long), cART initiation postconception up to 4 weeks before birth (medium) and cART initiation within 3 weeks of birth (short). Plasma metabolite profiles differed between HIV-exposed-uninfected (HEU)-children with long cART exposure compared to HIV-unexposed-children (HUU). Specifically, higher levels of methionine-sulfone, which is associated with oxidative stress, were detected in HEU-children with long cART exposure compared to HUU-children. High infant methionine-sulfone levels were reflected by high prenatal plasma levels in the mother. Increased methionine-sulfone levels in the children were associated with decreased growth, including both weight and length. CONCLUSION: These findings based on longitudinal data demonstrate that dysregulation of metabolite networks associated with oxidative stress in children born to WHIV is associated with restricted infant growth.

Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data.

BACKGROUND: Collateral effects of antibiotic resistance occur when resistance to one antibiotic agent leads to increased resistance or increased sensitivity to a second agent, known respectively as collateral resistance (CR) and collateral sensitivity (CS). Collateral effects are relevant to limit impact of antibiotic resistance in design of antibiotic treatments. However, methods to detect antibiotic collateral effects in clinical population surveillance data of antibiotic resistance are lacking. OBJECTIVES: To develop a methodology to quantify collateral effect directionality and effect size from large-scale antimicrobial resistance population surveillance data. METHODS: We propose a methodology to quantify and test collateral effects in clinical surveillance data based on a conditional t-test. Our methodology was evaluated using MIC data for 419 Escherichia coli strains, containing MIC data for 20 antibiotics, which were obtained from the Pathosystems Resource Integration Center (PATRIC) database. RESULTS: We demonstrate that the proposed approach identifies several antibiotic combinations that show symmetrical or non-symmetrical CR and CS. For several of these combinations, collateral effects were previously confirmed in experimental studies. We furthermore provide insight into the power of our method for multiple collateral effect sizes and MIC distributions. CONCLUSIONS: Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling.

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.