Human alveolar macrophages are critical components of the innate immune system. Cigarette smoking-induced changes in alveolar macrophage gene expression are linked to reduced resistance to pulmonary ...infections and to the development of emphysema/COPD. We hypothesized that microRNAs (miRNAs) could control, in part, the unique messenger RNA (mRNA) expression profiles found in alveolar macrophages of cigarette smokers. Activation of macrophages with different stimuli in vitro leads to a diverse range of M1 (inflammatory) and M2 (anti-inflammatory) polarized phenotypes that are thought to mimic activated macrophages in distinct tissue environments. Microarray mRNA data indicated that smoking promoted an "inverse" M1 mRNA expression program, defined by decreased expression of M1-induced transcripts and increased expression of M1-repressed transcripts with few changes in M2-regulated transcripts. RT-PCR arrays identified altered expression of many miRNAs in alveolar macrophages of smokers and a decrease in global miRNA abundance. Stratification of human subjects suggested that the magnitude of the global decrease in miRNA abundance was associated with smoking history. We found that many of the miRNAs with reduced expression in alveolar macrophages of smokers were predicted to target mRNAs upregulated in alveolar macrophages of smokers. For example, miR-452 is predicted to target the transcript encoding MMP12, an important effector of smoking-related diseases. Experimental antagonism of miR-452 in differentiated monocytic cells resulted in increased expression of MMP12. The comprehensive mRNA and miRNA expression profiles described here provide insight into gene expression regulation that may underlie the adverse effects cigarette smoking has on alveolar macrophages.
Many genetic studies that aim to identify genetic variants associated with complex phenotypes are subject to unobserved confounding factors arising from environmental heterogeneity. This poses a ...challenge to detecting associations of interest and is known to induce spurious associations when left unaccounted for. Penalized linear mixed models (LMMs) are an attractive method to correct for unobserved confounding. These methods correct for varying levels of relatedness and population structure by modeling it as a random effect with a covariance structure estimated from observed genetic data. Despite an extensive literature on penalized regression and LMMs separately, the two are rarely discussed together. The aim of this review is to do so while examining the statistical properties of penalized LMMs in the genetic association setting. Specifically, the ability of penalized LMMs to accurately estimate genetic effects in the presence of environmental confounding has not been well studied. To clarify the important yet subtle distinction between population structure and environmental heterogeneity, we present a detailed review of relevant concepts and methods. In addition, we evaluate the performance of penalized LMMs and competing methods in terms of estimation and selection accuracy in the presence of a number of confounding structures.
We used targeted metabolomics to determine associations of maternal BMI and glucose levels with cord blood metabolites and associations of cord blood metabolites with newborn birth weight and ...adiposity in mother-offspring dyads.
Targeted metabolomic assays were performed on cord blood serum samples from European ancestry, Afro-Caribbean, Thai, and Mexican American newborns (400 from each ancestry group) whose mothers participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and who had anthropometric measurements at birth.
Meta-analysis across the four cohorts demonstrated significant correlation of all cord blood metabolites analyzed with maternal fasting levels of the same metabolites at ∼28 weeks' gestation except for triglycerides, asparagine/aspartate, arginine, and the acylcarnitine C14-OH/C12-DC. Meta-analyses also demonstrated that maternal BMI with or without adjustment for maternal glucose was associated with cord blood metabolites including the branched-chain amino acids and their metabolites as well as phenylalanine. One-hour but not fasting glucose was associated with cord blood 3-hydroxybutyrate and its carnitine ester, a medium-chain acylcarnitine, and glycerol. A number of cord blood metabolites were associated with newborn birth weight and sum of skinfolds, including a negative association of triglycerides and positive association of 3-hydroxybutyrate, its carnitine ester, and serine with both newborn outcomes.
Maternal BMI and glycemia are associated with different components of the newborn metabolome, consistent with their independent effects on newborn size at birth. Maternal BMI is associated with a newborn metabolic signature characteristic of insulin resistance and risk of type 2 diabetes in adults.
Inhalation of nanoparticles has been implicated in respiratory morbidity and mortality. In particular, carbon black nanoparticles are found in many different environmental exposures. Macrophages take ...up inhaled nanoparticles and respond via release of inflammatory mediators and in some cases cell death. Based on new data, we propose that exposure of macrophages (both a macrophage cell line and primary human alveolar macrophages) to carbon black nanoparticles induces pyroptosis, an inflammasome-dependent form of cell death. Exposure of macrophages to carbon black nanoparticles resulted in inflammasome activation as defined by cleavage of caspase 1 to its active form and downstream IL-1β release. The cell death that occurred with carbon black nanoparticle exposure was identified as pyroptosis by the protective effect of a caspase 1 inhibitor and a pyroptosis inhibitor. These data demonstrate that carbon black nanoparticle exposure activates caspase 1, increases IL-1β release after LPS priming, and induces the proinflammatory cell death, pyroptosis. The identification of pyroptosis as a cellular response to carbon nanoparticle exposure is novel and relates to environmental and health impacts of carbon-based particulates.
Metabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in ...population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds.
To curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability, we propose mixture model normalization (mixnorm) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples. Mixnorm outperforms other approaches across many metrics, including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch. For some metrics, particularly when truncation is less frequent for a metabolite, mean centering and median scaling demonstrate comparable performance to mixnorm.
When quality control samples are systematically included in batches, mixnorm is uniquely suited to normalizing non-targeted GC/MS metabolomics data due to explicit accommodation of batch effects, run order and varying thresholds of detectability. Especially in large-scale studies, normalization is crucial for drawing accurate conclusions from non-targeted GC/MS metabolomics data.
Aims/hypothesis
Maternal obesity increases the risk for large-for-gestational-age birth and excess newborn adiposity, which are associated with adverse long-term metabolic outcomes in offspring, ...probably due to effects mediated through the intrauterine environment. We aimed to characterise the maternal metabolic milieu associated with maternal BMI and its relationship to newborn birthweight and adiposity.
Methods
Fasting and 1 h serum samples were collected from 400 European-ancestry mothers in the Hyperglycaemia and Adverse Pregnancy Outcome Study who underwent an OGTT at ∼28 weeks gestation and whose offspring had anthropometric measurements at birth. Metabolomics assays were performed using biochemical analyses of conventional clinical metabolites, targeted MS-based measurement of amino acids and acylcarnitines and non-targeted GC/MS.
Results
Per-metabolite analyses demonstrated broad associations with maternal BMI at fasting and 1 h for lipids, amino acids and their metabolites together with carbohydrates and organic acids. Similar metabolite classes were associated with insulin resistance with unique associations including branched-chain amino acids. Pathway analyses indicated overlapping and unique associations with maternal BMI and insulin resistance. Network analyses demonstrated collective associations of maternal metabolite subnetworks with maternal BMI and newborn size and adiposity, including communities of acylcarnitines, lipids and related metabolites, and carbohydrates and organic acids. Random forest analyses demonstrated contribution of lipids and lipid-related metabolites to the association of maternal BMI with newborn outcomes.
Conclusions/interpretation
Higher maternal BMI and insulin resistance are associated with broad-based changes in maternal metabolites, with lipids and lipid-related metabolites accounting, in part, for the association of maternal BMI with newborn size at birth.
Maternal metabolites and metabolic networks underlying associations between maternal glucose during pregnancy and newborn birth weight and adiposity demand fuller characterization. We performed ...targeted and nontargeted gas chromatography/mass spectrometry metabolomics on maternal serum collected at fasting and 1 h following glucose beverage consumption during an oral glucose tolerance test (OGTT) for 400 northern European mothers at ∼28 weeks' gestation in the Hyperglycemia and Adverse Pregnancy Outcome Study. Amino acids, fatty acids, acylcarnitines, and products of lipid metabolism decreased and triglycerides increased during the OGTT. Analyses of individual metabolites indicated limited maternal glucose associations at fasting, but broader associations, including amino acids, fatty acids, carbohydrates, and lipids, were found at 1 h. Network analyses modeling metabolite correlations provided context for individual metabolite associations and elucidated collective associations of multiple classes of metabolic fuels with newborn size and adiposity, including acylcarnitines, fatty acids, carbohydrates, and organic acids. Random forest analyses indicated an improved ability to predict newborn size outcomes by using maternal metabolomics data beyond traditional risk factors, including maternal glucose. Broad-scale association of fuel metabolites with maternal glucose is evident during pregnancy, with unique maternal metabolites potentially contributing specifically to newborn birth weight and adiposity.
We used targeted metabolomics in pregnant mothers to compare maternal metabolite associations with maternal BMI, glycemia, and insulin sensitivity.
Targeted metabolomic assays of clinical ...metabolites, amino acids, and acylcarnitines were performed on fasting and 1-h postglucose serum samples from European ancestry, Afro-Caribbean, Thai, and Mexican American mothers (400 from each ancestry group) who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and underwent an oral glucose tolerance test at ∼28 weeks gestation.
K-means clustering, which identified patterns of metabolite levels across ancestry groups, demonstrated that, at both fasting and 1-h, levels of the majority of metabolites were similar across ancestry groups. Meta-analyses demonstrated association of a broad array of fasting and 1-h metabolites, including lipids and amino acids and their metabolites, with maternal BMI, glucose levels, and insulin sensitivity before and after adjustment for the different phenotypes. At fasting and 1 h, a mix of metabolites was identified that were common across phenotypes or associated with only one or two phenotypes. Partial correlation estimates, which allowed comparison of the strength of association of different metabolites with maternal phenotypes, demonstrated that metabolites most strongly associated with different phenotypes included some that were common across as well as unique to each phenotype.
Maternal BMI and glycemia have metabolic signatures that are both shared and unique to each phenotype. These signatures largely remain consistent across different ancestry groups and may contribute to the common and independent effects of these two phenotypes on adverse pregnancy outcomes.
During the course of pregnancy, dynamic remodeling of the gut microbiota occurs and contributes to maternal metabolic changes through an undefined mechanism. Because short chain fatty acids (SCFAs) ...are a major product of gut microbiome fermentation, we investigated whether serum SCFA levels during pregnancy are related to key metabolic parameters in mothers and newborns. In this prospective study, 20 pregnant women without gestational diabetes were evaluated at 36–38 weeks of gestation, and their newborns were assessed after parturition. In this cohort, which included normal (n = 10) and obese (n = 10) subjects based on prepregnancy body mass index, serum levels of SCFAs (acetate, propionate, and butyrate), maternal adipokines, maternal glucose, and C-peptide were measured at 36–38 weeks of gestation. Maternal weight gain and newborn anthropometrics were also determined. Data were analyzed using linear regression to test for associations, adjusting for prepregnancy obesity. In this cohort, serum acetate levels were associated with maternal weight gain and maternal adiponectin levels. In addition, serum propionate correlated negatively with maternal leptin levels, newborn length, and body weight. Taken together, this study observed that novel relationships exist among maternal SCFA levels and multiple interrelated maternal/newborn metabolic parameters.
Additional prognostic and therapeutic biomarkers effective across different histological types of sarcoma are needed. Herein we evaluate expression of TAZ and YAP, the p53-MDM2 axis, and RABL6A, a ...novel oncoprotein with potential ties to both pathways, in sarcomas of different histological types. Immunohistochemical staining of a tissue microarray including 163 sarcomas and correlation with clinical data showed that elevated YAP and TAZ independently predict worse overall and progression-free survival, respectively. In the absence of p53 expression, combined TAZ and YAP expression adversely affect overall, progression free, and metastasis free survival more than TAZ or YAP activation alone. RABL6A independently predicted shorter time to metastasis and was positively correlated with p53, MDM2 and YAP expression, supporting a possible functional relationship between the biomarkers. Network analysis further showed that TAZ is positively correlated with MDM2 expression. The data implicate all five proteins as clinically relevant downstream players in the Hippo pathway. Finally, a novel inhibitor of MDM2 (MA242), effectively suppressed the survival of sarcoma cell lines from different histological types regardless of p53 status. These findings suggest both independent and cooperative roles for all five biomarkers across different histological types of sarcoma in predicting patient outcomes and potentially guiding future therapeutic approaches.
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