Identifying and annotating the molecular composition of individual foods will improve scientific understanding of how foods impact human health and how much variation exists in the molecular composition of foods of the same species. The complexity of this task includes distinct varieties and variations in natural occurring pigments of foods. Lipidomics, a sub-field of metabolomics, has emerged as an effective tool to help decipher the molecular composition of foods. For this proof-of-principle research, we determined the lipidomic profiles of green, yellow and red bell peppers (Capsicum annuum) using liquid chromatography mass spectrometry and a novel tool for automated annotation of compounds following database searches. Among 23 samples analyzed from 6 peppers (2 green, 1 yellow, and 3 red), over 8000 lipid compounds were detected with 315 compounds (106 annotated) found in all three colors. Assessments of relationships between these compounds and pepper color, using linear mixed effects regression and false discovery rate (<0.05) statistical adjustment, revealed 11 compounds differing by color. The compound most strongly associated with color was the carotenoid, β-cryptoxanthin (p-value = 7.4 × 10−5; FDR adjusted p-value = 0.0080). These results support lipidomics as a viable analytical technique to identify molecular compounds that can be used for unique characterization of foods.

Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

No abstract

Objectives Maternal nutrition can alter the offspring epigenome at birth. We sought to examine epigenome-wide DNA methylation (DNAme) from a subset of Guatemalan mother-infant dyads from the Women First Preconception Maternal Nutrition Trial (WF). Women were randomized to either: Arm 1) women consumed a daily maternal nutrition supplement (MNS) ≥ 3 months prior to conception until delivery; Arm 2) women consumed the same MNS starting at 12 weeks gestation until delivery; or Arm 3) no MNS. We tested if infant DNAme from amnion tissue at birth (N = 99) was associated with: 1) timing of exposure to maternal MNS; 2) pre-pregnancy body mass index (ppBMI); and 3) the interaction of maternal MNS and ppBMI. Methods Bisulfite-converted DNAme libraries were constructed using Roche NimbleGen SeqCap Epi CpGiant probes and were sequenced via 2 × 150 paired end reads. We assessed the relationship between Arm, ppBMI, and Arm x ppBMI interaction on CpG methylation. All statistical models adjusted for multiple testing using false discovery rate (FDR) and controlled for maternal age, infant sex, exposure to smoke, infant genetics, and cellular heterogeneity. Gene set enrichment analyses were performed via Enrichr. Results We identified 480 CpGs associated with Arm, 4 CpGs associated with ppBMI, and 22 CpGs associated with the interaction of Arm x ppBMI (FDR &lt; 0.05). Further, we found that DNAme was changed between Arms (1 vs 2, 1 vs 3). There were 300 CpGs that were different between Arms 1 and 2 and 159 CpGs that were different between Arms 1 and 3 that annotated to genes and passed FDR &lt; 0.05. These results suggest preconception consumption of maternal MNS elicits different epigenetic responses as compared to MNS commencing during gestation or not at all. In addition, CpGs that annotated to genes were enriched in pathways associated with growth, development, and metabolism that included circadian rhythm, TCA cycle, Wnt signaling, and melatonin metabolism. Conclusions Our findings indicate that maternal MNS was robustly associated with amnion DNAme at birth. More specifically, preconception MNS resulted in DNAme changes that differed from the other Arms in biologically relevant pathways suggesting timing of maternal nutrition impacts the fetal epigenome. Future studies will examine DNAme associated with birth outcomes. Funding Sources Bill & Melinda Gates Foundation and NIH NICHD/ODS.