High-Throughput Fractionation Coupled to Mass Spectrometry for Improved Quantitation in Metabolomics.

Metabolomics is emerging as an important field in life sciences. However, a weakness of current mass spectrometry (MS) based metabolomics platforms is the time-consuming analysis and the occurrence of severe matrix effects in complex mixtures. To overcome this problem, we have developed an automated and fast fractionation module coupled online to MS. The fractionation is realized by the implementation of three consecutive high performance solid-phase extraction columns consisting of a reversed phase, mixed-mode anion exchange, and mixed-mode cation exchange sorbent chemistry. The different chemistries resulted in an efficient interaction with a wide range of metabolites based on polarity, charge, and allocation of important matrix interferences like salts and phospholipids. The use of short columns and direct solvent switches allowed for fast screening (3 min per polarity). In total, 50 commonly reported diagnostic or explorative biomarkers were validated with a limit of quantification that was comparable with conventional LC-MS(/MS). In comparison with a flow injection analysis without fractionation, ion suppression decreased from 89% to 25%, and the sensitivity was 21 times higher. The validated method was used to investigate the effects of circadian rhythm and food intake on several metabolite classes. The significant diurnal changes that were observed stress the importance of standardized sampling times and fasting states when metabolite biomarkers are used. Our method demonstrates a fast approach for global profiling of the metabolome. This brings metabolomics one step closer to implementation into the clinic.

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 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.

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.

KIR3DS1 directs NK cell-mediated protection against human adenovirus infections.

Human adenoviruses (HAdVs) are a major cause for disease in children, in particular after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Currently, effective therapies for HAdV infections in immunocompromised hosts are lacking. To decipher immune recognition of HAdV infection and determine new targets for immune-mediated control, we used an HAdV infection 3D organoid system, based on primary human intestinal epithelial cells. HLA-F, the functional ligand for the activating NK cell receptor KIR3DS1, was strongly up-regulated and enabled enhanced killing of HAdV5-infected cells in organoids by KIR3DS1+ NK cells. In contrast, HLA-A and HLA-B were significantly down-regulated in HAdV5-infected organoids in response to adenoviral E3/glycoprotein19K, consistent with evasion from CD8+ T cells. Immunogenetic analyses in a pediatric allo-HSCT cohort showed a reduced risk to develop severe HAdV disease and faster clearance of HAdV viremia in children receiving KIR3DS1+/HLA-Bw4+ donor cells compared with children receiving non­KIR3DS1+/HLA-Bw4+ cells. These findings identify the KIR3DS1/HLA-F axis as a new target for immunotherapeutic strategies against severe HAdV disease.

Variability in the Log Domain and Limitations to Its Approximation by the Normal Distribution.

Pharmacometric models using lognormal distributions have become commonplace in pharmacokinetic-pharmacodynamic investigations. The extent to which it can be interpreted by traditional description of variability through the normal distribution remains elusive. In this tutorial, the comparison is made using formal approximation methods. The quality of the resulting approximation was assessed by the similarity of prediction intervals (PIs) to true values, illustrated using 80% PIs. Approximated PIs were close to true values when lognormal standard deviation (omega) was smaller than about 0.25, depending mostly on the desired precision. With increasing omega values, the precision of approximation worsens and starts to deteriorate at omega values of about 1. With such high omega values, there is no resemblance between the lognormal and normal distribution anymore. To support dissemination and interpretation of these nonlinear properties, some additional statistics are discussed in the context of the three regions of behavior of the lognormal distribution.

Intersubject and Intrasubject Variability of Potential Plasma and Urine Metabolite and Protein Biomarkers in Healthy Human Volunteers.

A limited understanding of intersubject and intrasubject variability hampers effective biomarker translation from in vitro/in vivo studies to clinical trials and clinical decision support. Specifically, variability of biomolecule concentration can play an important role in interpretation, power analysis, and sampling time designation. In the present study, a wide range of 749 plasma metabolites, 62 urine biogenic amines, and 1,263 plasma proteins were analyzed in 10 healthy male volunteers measured repeatedly during 12 hours under tightly controlled conditions. Three variability components in relative concentration data are determined using linear mixed models: between (intersubject), time (intrasubject), and noise (intrasubject). Biomolecules such as 3-carboxy-4-methyl-5-propyl-2-furanpropanoate, platelet-derived growth factor C, and cathepsin D with low noise potentially detect changing conditions within a person. If also the between component is low, biomolecules can easier differentiate conditions between persons, for example cathepsin D, CD27 antigen, and prolylglycine. Variability over time does not necessarily inhibit translatability, but requires choosing sampling times carefully.

Fingerprint resampling: A generic method for efficient resampling.

In resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible. However, because the computational problems and data sets are so similar, the speed of the resampling method may be increased by taking advantage of these similarities in method and data. As a generic solution, we propose to learn the relation between the resampled data sets and their corresponding optima. Using this learned knowledge, we are then able to predict the optima associated with new resampled data sets. First, these predicted optima are used as starting values for the optimization process. Once the predictions become accurate enough, the optimization process may even be omitted completely, thereby greatly decreasing the computational burden. The suggested method is validated using two simple problems (where the results can be verified analytically) and two real-life problems (i.e., the bootstrap of a mixed model and a generalized extreme value distribution). The proposed method led on average to a tenfold increase in speed of the resampling method.