The translation of lipid profiles to nutritional biomarkers in the study of infant metabolism

A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data

Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.

Metabolomics can provide new insights into perinatal nutrition

Perinatal nutrition is a key factor related to the Developmental Origin of Health and Disease hypothesis, which states that each and every event that happens during the periconceptional period and pregnancy can affect the health status of an individual. Metabolomics can be a very useful tool for gathering information about the effect of perinatal nutrition on both mothers and newborn infants. This non-systematic review focuses on the main metabolites detected by this technique, with regard to gestational diabetes, intrauterine growth restriction and breast milk. Conclusion. Nutrition, metabolome and microbiome interactions are gaining interest in the scientific community.

Lipid profiling analyses from mouse models and human infants

This protocol outlines a translational lipidomic approach to discover lipid biomarkers that could predict morphometric body and histological organ measurements (e.g., weight and adiposity gains) during specific stages of life (e.g., early life). We describe procedures ranging from animal experimentation and histological analyses to downstream analytical steps through lipid profiling, both in mice and humans. This protocol represents a reliable and versatile approach to translate and validate candidate lipid biomarkers from animal models to a human cohort. For complete details on the use and execution of this protocol, please refer to Olga et al. (2021).

Multi-omics analysis from archival neonatal dried blood spots: limitations and opportunities

Newborn screening (NBS) programs operate in many countries, processing millions of dried bloodspot (DBS) samples annually. In addition to early identification of various adverse health outcomes, these samples have considerable potential as a resource for population-based research that could address key questions related to child health. The feasibility of archival DBS samples for emerging targeted and untargeted multi-omics analysis has not been previously explored in the literature. This review aims to critically evaluate the latest advances to identify opportunities and challenges of applying omics analyses to NBS cards in a research setting. Medline, Embase and PubMed databases were searched to identify studies utilizing DBS for genomic, proteomic and metabolomic assays. A total of 800 records were identified after removing duplicates, of which 23 records were included in this review. These papers consisted of one combined genomic/metabolomic, four genomic, three epigenomic, four proteomic and 11 metabolomic studies. Together they demonstrate that the increasing sensitivity of multi-omic analytical techniques makes the broad use of NBS samples achievable for large cohort studies. Maintaining the pre-analytical integrity of the DBS sample through storage at temperatures below -20 °C will enable this important resource to be fully realized in a research capacity.

Impact of Extensively Hydrolyzed Infant Formula on Circulating Lipids During Early Life

Background

Current evidence suggests that the composition of infant formula (IF) affects the gut microbiome, intestinal function, and immune responses during infancy. However, the impact of IF on circulating lipid profiles in infants is still poorly understood. The objectives of this study were to (1) investigate how extensively hydrolyzed IF impacts serum lipidome compared to conventional formula and (2) to associate changes in circulatory lipids with gastrointestinal biomarkers including intestinal permeability.

Methods

In a randomized, double-blind controlled nutritional intervention study (n = 73), we applied mass spectrometry-based lipidomics to analyze serum lipids in infants who were fed extensively hydrolyzed formula (HF) or conventional, regular formula (RF). Serum samples were collected at 3, 9, and 12 months of age. Child’s growth (weight and length) and intestinal functional markers, including lactulose mannitol (LM) ratio, fecal calprotectin, and fecal beta-defensin, were also measured at given time points. At 3 months of age, stool samples were analyzed by shotgun metagenomics.

Results

Concentrations of sphingomyelins were higher in the HF group as compared to the RF group. Triacylglycerols (TGs) containing saturated and monounsaturated fatty acyl chains were found in higher levels in the HF group at 3 months, but downregulated at 9 and 12 months of age. LM ratio was lower in the HF group at 9 months of age. In the RF group, the LM ratio was positively associated with ether-linked lipids. Such an association was, however, not observed in the HF group.

Conclusion

Our study suggests that HF intervention changes the circulating lipidome, including those lipids previously found to be associated with progression to islet autoimmunity or overt T1D.

Clinical Trial Registration

[Clinicaltrials.gov], identifier [NCT01735123].

Metabolomics in early life and the association with body composition at age 2 years

BACKGROUND AND OBJECTIVES

Early life is a critical window for adiposity programming. Metabolic-profile in early life may reflect this programming and correlate with later life adiposity. We investigated if metabolic-profile at 3 months of age is predictive for body composition at 2 years and if there are differences between boys and girls and between infant feeding types.

METHODS

In 318 healthy term-born infants, we determined body composition with skinfold measurements and abdominal ultrasound at 3 months and 2 years of age. High-throughput-metabolic-profiling was performed on 3-month-blood-samples. Using random-forest-machine-learning-models, we studied if the metabolic-profile at 3 months can predict body composition outcomes at 2 years of age.

RESULTS

Plasma metabolite-profile at 3 months was found to predict body composition at 2 years, based on truncal: peripheral-fat-skinfold-ratio (T:P-ratio), with a predictive value of 75.8%, sensitivity of 100% and specificity of 50%. Predictive value was higher in boys (Q²  = 0.322) than girls (Q²  = 0.117). Of the 15 metabolite variables most strongly associated with T:P-ratio, 11 were also associated with visceral fat at 2 years of age.

CONCLUSION

Several plasma metabolites (LysoPC(22:2), dimethylarginine and others) at 3 months associate with body composition outcome at 2 years. These results highlight the importance of the first months of life for adiposity programming.

Fetal programming of human energy homeostasis brain networks: Issues and considerations

In this paper, we present a transdisciplinary framework and testable hypotheses regarding the process of fetal programming of energy homeostasis brain circuitry. Our model proposes that key aspects of energy homeostasis brain circuitry already are functional by the time of birth (with substantial interindividual variation); that this phenotypic variation at birth is an important determinant of subsequent susceptibility for energy imbalance and childhood obesity risk; and that this brain circuitry exhibits developmental plasticity, in that it is influenced by conditions during intrauterine life, particularly maternal-placental-fetal endocrine, immune/inflammatory, and metabolic processes and their upstream determinants. We review evidence that supports the scientific premise for each element of this formulation, identify future research directions, particularly recent advances that may facilitate a better quantification of the ontogeny of energy homeostasis brain networks, highlight animal and in vitro-based approaches that may better address the determinants of interindividual variation in energy homeostasis brain networks, and discuss the implications of this formulation for the development of strategies targeted towards the primary prevention of childhood obesity.

Lipid Profiles from Dried Blood Spots Reveal Lipidomic Signatures of Newborns Undergoing Mild Therapeutic Hypothermia after Hypoxic-Ischemic Encephalopathy

Hypoxic-ischemic encephalopathy (HIE) is associated with perinatal brain injury, which may lead to disability or death. As the brain is a lipid-rich organ, various lipid species can be significantly impacted by HIE and these correlate with specific changes to the lipidomic profile in the circulation. Objective: To investigate the peripheral blood lipidomic signature in dried blood spots (DBS) from newborns with HIE. Using univariate analysis, multivariate analysis and sPLS-DA modelling, we show that newborns with moderate-severe HIE (n = 46) who underwent therapeutic hypothermia (TH) displayed a robust peripheral blood lipidomic signature comprising 29 lipid species in four lipid classes; namely phosphatidylcholine (PC), lysophosphatidylcholine (LPC), triglyceride (TG) and sphingomyelin (SM) when compared with newborns with mild HIE (n = 18). In sPLS-DA modelling, the three most discriminant lipid species were TG 50:3, TG 54:5, and PC 36:5. We report a reduction in plasma TG and SM and an increase in plasma PC and LPC species during the course of TH in newborns with moderate-severe HIE, compared to a single specimen from newborns with mild HIE. These findings may guide the research in nutrition-based intervention strategies after HIE in synergy with TH to enhance neuroprotection.

Human milk sphingomyelins and metabolomics: an enigma to be discovered

Sphingomyelins, the most abundant sphingolipids in most mammalian cells, appear to be among the most represented polar lipids in breast milk. Despite the variability of the data reported in the literature, human milk sphingomyelins are qualitatively unique and their quantities are five times higher than in most formula milk. The structural and functional role within the milk fat globule membranes, the involvement in neonatal neurological maturation both in neuro-typical development and in some pathological circumstances, together with the possible contribution in the intestinal development of newborns, are certainly among the main characteristics that have fueled the curiosity of the scientific world. Metabolomics studies, providing a unique metabolic fingerprint, allow an in-depth analysis of the role of these molecules in the extreme variability and uniqueness of breast milk. In the perspective of preventive medicine, at the base of which there is certainly personalized nutrition, it is possible, in the presence of particular conditions, such as neonatal growth retardation or in preterm infants, to consider supplementation of some target nutrients, such as certain sphingomyelins. Nevertheless, further studies are needed to more accurately assess whether and how the type and quantity of sphingomyelins present in breast milk could affect the metabolic health of newborns.HIGHLIGHTSBreast milk is the golden standard for infants' nutritionSphingomyelins are the most represented polar lipids in breast milkThese molecules are involved in both intestinal and neural developments of newbornsMetabolomics is a very useful tool to investigate their precise roleFurther studies are needed to provide eventual nutritional treatment.

Neonatal heel prick mass spectrometry identifies metabolic predictors of AML latency

Ongoing research efforts that consider cancer as a disease of dramatically altered cellular metabolism have accelerated interest in snapshot metabolomics in various human tissues. In this issue of Leukemia Research, Petrick et al performed metabolomic analysis on newborn blood spots and found a number of unexpected ceramide and sphingolipid compounds that may play a role in the development and latency of pediatric acute myeloid leukemia (AML). The chemical complexity and range of cellular metabolites massively exceeds the relatively limited building blocks of the transcriptome or the proteome and has high potential to find novel leukemia-specific macromolecular synthesis pathways, metabolic vulnerabilities and biomarkers.

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Random Forest (RF), machine learning (ML) algorithms, individually and collectively through an ensemble method based on bagging strategy (E-B). The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. Furthermore, for the first time in this study, we allied the E-B with the genetic algorithm (GA) to model the optimum values of yield components in an ideotype genotype in which the yield is maximized. The results revealed a better understanding of the relationships between soybean yield and its components, which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential.

Nutri-epigenetics: the effect of maternal diet and early nutrition on the pathogenesis of autoimmune diseases

Autoimmune diseases comprise a wide group of diseases involving a self-response of the immune system against the host. The etiopathogenesis is very complex involving disease-specific factors but also environmental factors, among which the diet. Maternal diet during pregnancy as well as early nutrition recently attracted the interest of the scientists as contributing to the immune programming. In this paper, we reviewed the most recent literature on the effect of maternal diet and early nutrition in modulating the immune system in a selected subset of autoimmune diseases: type 1 diabetes, celiac disease, inflammatory bowel disease, juvenile idiopathic arthritis and rheumatoid arthritis. Particularly, we focused our narrative on the role of maternal and perinatal nutrition in the epigenetic mechanisms underlying the auto-immune response. Maternal diet during pregnancy as well as breastfeeding and early nutrition play a big role in many epigenetic mechanisms. Most of the nutrients consumed by the mother and the infant are known exerting epigenetic functions, such as folate, methionine, zinc, vitamins B12 and D, fibers, casein and gliadin, and they were linked to gene expression changes in the immune pathways. Despite the common role of maternal diet, breastfeeding and early nutrition in almost all the autoimmune diseases, each disease seems to have specific diet-driver epigenetic mechanisms that require further investigations. The research in this field is opening new routes to establishing a precision nutrition approach to the auto-immune diseases.

Evidence from Neonatal Piglets Shows How Infant Formula and Other Mammalian Milk Shape Lipid Metabolism

We tested the hypothesis that the consumption of different milk lipids is one of the factors affecting metabolic response to lipid in the early life of infants. Neonatal piglets, as animal models, were stratified by the feeding mode (formula-fed, bovine-, caprine-, and human milk-fed). Lipidomic profiles of plasma and liver samples were detected using liquid chromatography-mass spectrometry (LC-MS). The results indicate that 31, 54, and 28 differential lipid species could be used as potential biomarkers for bovine milk, caprine milk, and infant formula-fed samples, respectively, and the main lipid classes screened in plasma were SM, PC, and PE, including PC(14:1/P-20:0) as the isoform of PC(34:1), which regulates the lipid metabolism gene peroxisome proliferator-activated receptor α, PPAR-α. SM(d15:1/22:0) was the common potential biomarker screened from all of the groups. The amounts of biomarkers screened from the caprine milk-fed liver samples were the highest, which had a significant effect on the distribution of SM, PI, and PA. Infant formula, bovine-, and caprine milk-fed samples had an obvious effect on the metabolism of glycerophospholipid and glycerol ester, especially TG (16:0/18:0/18:2).

Human Milk-Fed Piglets Have a Distinct Small Intestine and Circulatory Metabolome Profile Relative to That of Milk Formula-Fed Piglets

Exclusive HM feeding for newborns is recommended at least for the first 6 months of life. However, when breastfeeding is not possible, MF is recommended as a substitute.

The impact of human milk (HM) feeding compared with cow’s milk formula (MF) feeding on small intestinal and circulatory metabolome patterns has not been fully investigated. Therefore, 2-day-old male piglets were fed HM or MF (n = 26/group) from postnatal day 2 (PND 2) through 21 and were weaned to a solid diet until PND 51. The small intestine (gastrointestinal [GI]) contents, serum, and urine were collected from subsets of piglets at PND 21 and PND 51. Samples were subjected to primary metabolomics analyses at the West Coast Metabolomics Center, UC Davis. The metabolome data assessment and the statistical analyses were performed with MetaboAnalyst software. Compared with MF feeding, at PND 21, HM feeding resulted in a higher abundance of fucose in the jejunum and urine and a greater concentration of myo-inositol in serum. In HM-fed piglets, 1,5-anhydroglucitol was higher in the duodenum, serum, and urine at PND 21. Additionally, the HM group had higher levels of urinary kynurenic acid at PND 21. Correlations between bacterial genera and altered metabolites in ileum revealed that Turicibacter sp. and Campylobacter sp. were positively correlated with maltotriose and panose at PND 21, while ileal Campylobacter sp. was negatively correlated with fumaric acid. At PND 51, no significant metabolites were identified between HM and MF diet groups. The metabolites associated with the neonatal diets may serve as the substrates and signals that contribute to the physiological effects in HM and MF during infancy, with a subset reflecting diet-associated differences in microbial metabolism and ecology.

IMPORTANCE Exclusive HM feeding for newborns is recommended at least for the first 6 months of life. However, when breastfeeding is not possible, MF is recommended as a substitute. Due to the challenges associated with sample collection from infants fed HM or MF, their gut metabolism is poorly understood. Thus, an established piglet model from our team was used to determine the metabolite profile in relation to host, diet, and microbiota. The current study is the first to provide novel insights across the small intestine metabolism and its association with circulatory metabolites in the HM group relative to the MF group at the weaning and postweaning period. Data also demonstrate that during the neonatal period, diet, host, and microbial metabolism contribute to the lumen and circulatory metabolite profile. Furthermore, small intestinal lumen metabolome can be tracked in the urine as a biomarker of dietary differences, which would be a useful tool for clinical interventions.

Lipid ratios representing SCD1, FADS1, and FADS2 activities as candidate biomarkers of early growth and adiposity

BACKGROUND

Altered lipid metabolism in early life has been associated with subsequent weight gain and predicting this could aid in obesity prevention and risk management. Here, a lipidomic approach was used to identify circulating markers for future obesity risk in translational murine models and validate in a human infant cohort.

METHODS

Lipidomics was performed on the plasma of APOE*3 Leiden, Ldlr-/-.Leiden, and the wild-type C57BL/6J mice to capture candidate biomarkers predicting subsequent obesity parameters after exposure to high-fat diet. The identified candidate biomarkers were mapped onto corresponding lipid metabolism pathways and were investigated in the Cambridge Baby Growth Study. Infants' growth and adiposity were measured at 0-24 months. Capillary dried blood spots were sampled at 3 months for lipid profiling analysis.

FINDINGS

From the mouse models, cholesteryl esters were correlated with subsequent weight gain and other obesity parameters after HFD period (Spearman's r≥0.5, FDR p values <0.05) among APOE*3 Leiden and Ldlr-/-.Leiden mice, but not among the wild-type C57BL/6J. Pathway analysis showed that those identified cholesteryl esters were educts or products of desaturases activities: stearoyl-CoA desaturase-1 (SCD1) and fatty acid desaturase (FADS) 1 and 2. In the human cohort, lipid ratios affected by SCD1 at 3 months was inversely associated with 3-12 months weight gain (B±SE=-0.31±0.14, p=0.027), but positively with 12-24 months weight and adiposity gains (0.17±0.07, p=0.02 and 0.17±0.07, 0.53±0.26, p=0.04, respectively). Lipid ratios affected by SCD1 and FADS2 were inversely associated with adiposity gain but positively with height gain between 3-12 months.

INTERPRETATION

From murine models to human setting, the ratios of circulating lipid species indicating key desaturase activities in lipid metabolism were associated with subsequent body size increase, providing a potential tool to predict early life weight gain.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

A random forest based biomarker discovery and power analysis framework for diagnostics research

Background

Biomarker identification is one of the major and important goal of functional genomics and translational medicine studies. Large scale –omics data are increasingly being accumulated and can provide vital means for the identification of biomarkers for the early diagnosis of complex disease and/or for advanced patient/diseases stratification. These tasks are clearly interlinked, and it is essential that an unbiased and stable methodology is applied in order to address them. Although, recently, many, primarily machine learning based, biomarker identification approaches have been developed, the exploration of potential associations between biomarker identification and the design of future experiments remains a challenge.

Methods

In this study, using both simulated and published experimentally derived datasets, we assessed the performance of several state-of-the-art Random Forest (RF) based decision approaches, namely the Boruta method, the permutation based feature selection without correction method, the permutation based feature selection with correction method, and the backward elimination based feature selection method. Moreover, we conducted a power analysis to estimate the number of samples required for potential future studies.

Results

We present a number of different RF based stable feature selection methods and compare their performances using simulated, as well as published, experimentally derived, datasets. Across all of the scenarios considered, we found the Boruta method to be the most stable methodology, whilst the Permutation (Raw) approach offered the largest number of relevant features, when allowed to stabilise over a number of iterations. Finally, we developed and made available a web interface (https://joelarkman.shinyapps.io/PowerTools/) to streamline power calculations thereby aiding the design of potential future studies within a translational medicine context.

Conclusions

We developed a RF-based biomarker discovery framework and provide a web interface for our framework, termed PowerTools, that caters the design of appropriate and cost-effective subsequent future omics study.

Translational biomarkers in the era of precision medicine

In this chapter we discuss the past, present and future of clinical biomarker development. We explore the advent of new technologies, paving the way in which health, medicine and disease is understood. This review includes the identification of physicochemical assays, current regulations, the development and reproducibility of clinical trials, as well as, the revolution of omics technologies and state-of-the-art integration and analysis approaches.

Lipid extraction from dried blood spots and dried milk spots for untargeted high throughput lipidomics

Dried blood spots (DBS) and dried milk spots (DMS) represent convenient matrices for collecting and storing human samples. However, the use of these sample types for researching lipid metabolism remains relatively poorly explored, and especially unclear is the efficiency of lipid extraction in the context of high throughput, untargeted lipidomics. A visual inspection of punched DBSs after standard extraction suggests that the samples remain largely intact. DMSs comprise a dense aggregate of milk fat globules on one side of the card, suggesting that part of the lipid fraction may be physically inaccessible. This led us to the hypothesis that decoagulating may facilitate lipid extraction from both DBSs and DMSs. We tested this hypothesis using a mixture of strong chaeotropes (guanidine and thiourea) in both DBS and DMS in the context of high throughput lipidomics (96/384w plate). Extraction of lipids from DMSs was tested with established extractions and one novel solvent mixture in a high throughput format. We found that exposure of DBSs to chaeotropes facilitated collection of the lipid fraction but was ineffective for DMSs. The lipid fraction of DMSs was best isolated without water, using a mixture of xylene/methanol/isopropanol (1 : 2 : 4). We conclude that decoagulation is essential for efficient extraction of lipids from DBSs and that a non-aqueous procedure using a spectrum of solvents is the best procedure for extracting lipids from DMSs. These methods represent convenient steps that are compatible with the sample structure and type, and with high throughput lipidomics.

Milk fat globule membrane: the role of its various components in infant health and development

Breastfeeding confers many benefits to the breast-fed infant which are reflected by better short-term and long-term outcomes as compared to formula-fed infants. Many components of breast milk are likely to contribute to these favorable outcomes, and there has recently been focus on the milk fat globule membrane (MFGM). This fraction is a heterogenous mixture of proteins (many of them glycosylated), phospholipids, sphingolipids, gangliosides, choline, sialic acid and cholesterol which is lacking in infant formula as milk fat (which is also low in these components) is replaced by vegetable oils. Many of these components have been shown to have biological effects, and there is considerable evidence from preclinical studies and clinical trials that providing bovine MFGM results in improved outcomes, in particular with regard to infections and neurodevelopment. Since bovine MFGM is commercially available, it is possible to add it to infant formula. There are, however, considerable variations in composition among commercial sources of bovine MFGM, and as it is not known which of the individual components provide the various bioactivities, it becomes important to critically review studies to date and to delineate the mechanisms behind the activities observed. In this review, we critically examine the preclinical and clinical studies on MFGM and its components in relation to resistance to infections, cognitive development, establishment of gut microbiota and infant metabolism, and discuss possible mechanisms of action.

Microencapsulated algal feeds as a sustainable replacement diet for broodstock in commercial bivalve aquaculture

The global bivalve shellfish industry makes up 25% of aquaculture, is worth USD $17.2 billion year⁻¹, and relies upon a supply of juvenile bivalves produced by adult broodstock in hatcheries. Today large quantities of live algae are grown to feed broodstock at $220 kg⁻¹, driving highly unsustainable energy and resource use. New advances in algal and microencapsulation technology provide solutions. We developed microencapsulated Schizochytrium algae diets, which can be produced sustainably at < $2 kg⁻¹ from organic side-streams, and are shelf-stable to minimise waste. Physiological, histological, and cutting-edge metabolomic analyses demonstrate that in commercial settings sustainable microencapsulated diets facilitate improved sexual development and 12 × greater omega-3 levels in oysters relative to conventional live algal diets. Every tonne bivalve protein produced instead of fish spares 9 ha, 67 tonnes CO₂, and 40,000 L freshwater. Further research into microencapsulated diets could support bivalve industry expansion, and contribute towards a step-change in sustainable global food production through improved aquaculture practices.

Dietary Intake Influences Metabolites in Healthy Infants: A Scoping Review

Metabolites are generated from exogenous sources such as diet. This scoping review will summarize nascent metabolite literature and discriminating metabolites for formula vs. human- milk-fed infants. Using the PICOS framework (P—Patient, Problem or Population; I—Intervention; C—Comparison; O—Outcome; S—Study Design) and PRISMA item-reporting protocols, infants less than 12 months old, full-term, and previously healthy were included. Protocol was registered with Open Science Framework (OSF). Publications from 1 January 2009–2019 were selected, for various biofluids, study designs, and techniques (such as high-performance liquid chromatography (HPLC)). From 711 articles, blinded screening of 214 articles using Abstrackr^® software, resulted in 24 for final review. Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines were adopted, which included a 24-point checklist. Articles were stratified according to biofluid. Of articles reporting discriminating metabolites between formula- and human milk-fed infants, 62.5% (5/8) of plasma/serum/dried blood spot, 88% (7/8) of urine and 100% (6/6) of feces related articles reported such discriminating metabolites. Overall, no differences were found between analytical approach used (targeted (n = 9) vs. un-targeted (n = 10)). Current articles are limited by small sample sizes and differing methodological approaches. Of the metabolites reviewed herein, fecal metabolites provided the greatest distinction between diets, which may be indicative of usefulness for future diet metabolite-focused work.

Metabolomic Insights into the Effects of Breast Milk Versus Formula Milk Feeding in Infants

PURPOSE OF REVIEW

This review summarizes the latest scientific evidence for the presence of metabolomic differences between infants fed breast milk (I-BM) and infants fed formula milk (I-FM).

RECENT FINDINGS

Across the studies included in this review, a total of 261 metabolites were analyzed, of which 151 metabolites were reported as significantly associated with infant feeding modality (BM versus FM). However, taken as a whole, the relevant literature was notable both for methodological limitations, such as small sample sizes, and heterogeneity between the studies. This may be why many associations between infant metabolite profile and feeding modality have not replicated across studies. To our knowledge, this is the first review to integrate the available literature on metabolomic differences between I-BM versus I-FM. This narrative review synthesized the data across studies and identified those metabolites which show the most robust associations with infant feeding modality. Methodological limitations of the current studies are identified, followed by recommendations for how to address these in future studies.

Metabolomics and adductomics of newborn bloodspots to retrospectively assess the early-life exposome

PURPOSE OF REVIEW

Exposomics studies can measure health-relevant chemical exposures during a lifetime and estimate the 'internal' environment. However, sampling limitations make these features difficult to capture directly during the critical neonatal time period.

RECENT FINDINGS

We review the use of newborn dried bloodspots (DBS) archived from newborn screening programs for exposomic analysis in epidemiological children's health studies. Emerging 'omics technologies such as adductomics and metabolomics have been adapted for DBS analysis, and these technologies can now provide valuable etiological information on the complex interplay between exposures, biological response, and population phenotypes.

SUMMARY

Adductomics and metabolomics of DBS can provide robust measurements for retrospective epidemiological investigations. With extensive bioarchiving programs in the United States and other countries, DBS are poised to substantially aid epidemiological studies, particularly for rare and low-frequency childhood diseases and disorders.

A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues

Lipidomics is of increasing interest in studies of biological systems. However, high-throughput data collection and processing remains non-trivial, making assessment of phenotypes difficult. We describe a platform for surveying the lipid fraction for a range of tissues. These techniques are demonstrated on a set of seven different tissues (serum, brain, heart, kidney, adipose, liver, and vastus lateralis muscle) from post-weaning mouse dams that were either obese (> 12 g fat mass) or lean (<5 g fat mass). This showed that the lipid metabolism in some tissues is affected more by obesity than others. Analysis of human serum (healthy non-pregnant women and pregnant women at 28 weeks' gestation) showed that the abundance of several phospholipids differed between groups. Human placenta from mothers with high and low BMI showed that lean placentae contain less polyunsaturated lipid. This platform offers a way to map lipid metabolism with immediate application in metabolic research and elsewhere. Graphical abstract.

Clinical insights gained through metabolomic analysis of human breast milk

Introduction: Among the OMICS technologies, that have emerged in recent years, metabolomics has allowed relevant step forwards in clinical research. Several improvements in disease diagnosis and clinical management have been permitted, even in neonatology. Among potentially evaluable biofluids, breast milk (BM) results are highly interesting, representing a fluid of conjunction between mothers newborns, describing their interaction.Areas covered: in this review, updating a previous review article, we discuss research articles and reviews on BM metabolomics and found in MEDLINE using metabolomics, breast milk, neonatal nutrition, breastfeeding, human milk composition, and preterm neonates as keywords.Expert opinion: Our research group has a profound interest in metabolomics research. In 2012, we published the first metabolomic analysis on BM samples, reporting interesting data on its composition and relevant differences with formula milk (FM), useful to improve FM composition. As confirmed by successive studies, such technology can detect the specific BM composition and its dependence on several variables, including lactation stage, gestational age, maternal or environmental conditions. Moreover, since BM contaminants or drug levels can be detected, metabolomics also results useful to determine BM safety. These are only a few practical applications of BM analysis, which will be reviewed in this paper.

Altered triglyceride and phospholipid metabolism predates the diagnosis of gestational diabetes in obese pregnancy

Gestational diabetes (GDM), a common pregnancy complication associated with obesity and long-term health risks, is usually diagnosed at approximately 28 weeks of gestation. An understanding of lipid metabolism in women at risk of GDM could contribute to earlier diagnosis and treatment. We tested the hypothesis that altered lipid metabolism at the beginning of the second trimester in obese pregnant women is associated with a diagnosis of GDM. Plasma samples from 831 participants (16-45 years, 15-18 weeks gestation, BMI ≥ 30) from the UPBEAT study of obese pregnant women were used. The lipid, sterol and glyceride fraction was isolated and analysed in a semi-quantitative fashion using direct infusion mass spectrometry. A combination of uni-, multi-variate and multi-variable statistical analyses was used to identify candidate biomarkers in plasma associated with a diagnosis of GDM (early third trimester; IADPSG criteria). Multivariable adjusted analyses showed that participants who later developed GDM had a greater abundance of several triglycerides (48:0, 50:1, 50:2, 51:5, 53:4) and phosphatidylcholine (38:5). In contrast sphingomyelins (32:1, 41:2, 42:3), lyso-phosphatidylcholine (16:0, 18:1), phosphatidylcholines (35:2, 40:7, 40:10), two polyunsaturated triglycerides (46:5, 48:6) and several oxidised triglycerides (48:6, 54:4, 56:4, 58:6) were less abundant. We concluded that both lipid and triglyceride metabolism were altered at least 10 weeks before diagnosis of GDM. Further investigation is required to determine the functional consequences of these differences and the mechanisms by which they arise.

Investigation of the 12-Month Stability of Dried Blood and Urine Spots Applying Untargeted UHPLC-MS Metabolomic Assays

The use of dried blood spot (DBS) and dried urine spot (DUS) samples represents an attractive opportunity for researchers in biomedical metabolomics to collect whole blood and urine samples in the absence of a processing laboratory and so to allow collection in remote areas or in longitudinal studies away from the clinic. The 12-month stability of the thousands of metabolites present in these biofluids and the applicability of DBS and DUS samples for untargeted metabolomics applications has not previously been investigated in detail and compared to blood and urine samples. Here, the 12-month stability of DBS and DUS at different storage temperatures (-20, +4, and +21 °C) have been compared to plasma and urine biofluids stored at the same storage temperatures and time. Samples were analyzed applying complementary HILIC and C₁₈ reversed-phase UHPLC-MS untargeted metabolomic assays. Results show that metabolites demonstrate increased stability in DBS and DUS compared to whole blood and urine at all storage temperatures and times. DBS and DUS stored at +21 °C are stable for up to 4 weeks but are not stable over a 1 year period. DBS and DUS showed good stability when stored at -20 °C for 1 year. We recommend that DBS and DUS samples are collected and transported within 28 days at room temperature and are stored for longer periods of time at -20 or -80 °C. The metabolomes of DUS samples and urine were very similar but the metabolome of DBS included additional metabolites not detected in plasma and therefore proposed to be released from cells in whole blood.

Evidence from 3-month-old infants shows that a combination of postnatal feeding and exposures in utero shape lipid metabolism

We tested the hypothesis that both postnatal feeding and conditions in utero affect lipid metabolism in infants. Infants who experienced restrictive growth conditions in utero and others exposed to maternal hyperglycaemia were compared to a control group with respect to feeding mode. Dried blood spots were collected from a pilot subset of infant participants of the Cambridge Baby Growth Study at 3mo. Groups: (a) a normal gestation (control, n = 40), (b) small for gestational age (SGA, n = 34) and (c) whose mothers developed hyperglycaemia (n = 59). These groups were further stratified by feeding mode; breastfed, formula-fed or received a mixed intake. Their phospholipid, glyceride and sterol fractions were profiled using direct infusion mass spectrometry. Statistical tests were used to identify molecular species that indicated differences in lipid metabolism. The abundance of several phospholipids identified by multivariate analysis, PC(34:1), PC(34:2) and PC-O(34:1), was 30-100% higher across all experimental groups. SM(39:1) was around half as abundant in in utero groups among breastfed infants only. The evidence from this pilot study shows that phospholipid metabolism is modulated by both conditions in utero and postnatal feeding in a cohort of 133 Caucasian infants, three months post partum.

A Phospholipid Profile at 4 Months Predicts the Onset of Celiac Disease in at-Risk Infants

Celiac disease (CeD) is a multifactorial disease influenced by both genetic and environmental risk factors. CeD genetic components are mainly due to HLA class II genes, which account for approximately 40% of the disease heritability. The environmental factor is linked to gliadin ingestion. Despite genetic and epigenetic studies, the pathological molecular mechanism remains unclarified. The strong genetic component does not explain more than half of the hereditability; we identified several epigenetic features that contribute to the understanding of the missing hereditability. The lipid profile of infants has been proposed as a potential biomarker of CeD metabolism that can be measured before they exhibit developmental disorders and clinical symptoms. We suggest that the state of the host is a main factor for the abnormal immune response to gluten. Long before any exposure to the offending agent or any production of specific antibodies, several molecular mechanisms are differentially expressed in infants who will develop CeD compared to their peers matched for the same genetic profile. The present study explored the serum phospholipid profile of a group of infants at risk for celiac disease, followed up to 8 years to monitor the onset of CeD. We compared 30 patients who developed the disease with 20 age- and sex-matched peers with similar genetic profiles who did not develop the disease within 8 years. Serum phospholipids were analysed at 4 months, before exposure to gluten, and at 12 months of age, when none showed any marker of disease. In the 30 CeD patients, we also analysed the serum at the time of diagnosis (>24 months). The serum phospholipid profile was fairly constant across 4 and 12 months of age and, in CeD, up to 24–36 months. The phospholipid signature was dramatically different in infants who developed CeD when compared to that of control NY-CeD (Not Yet developing Celiac Disease) peers. We identified a specific serum phospholipid signature that predicts the onset of celiac disease in HLA at-risk infants years before the appearance of antibodies specific for CeD in the serum and before any clinical symptoms, even before gluten introduction into the diet at 4 months. Specifically, lysophosphatidylcholine, phosphatidylcholine, alkylacyl-phosphatidylcholine, phosphoethanolamines, phosphatidylserines, phosphatidylglycerol and phosphatidylinositol were found to be differentially represented in CeD versus NY-CeD. A set constituted by a limited number of alkylacyl-phosphatidylcholine and lyso-phosphatidylcholine, together with the duration of breast-feeding, allows the discrimination of infants who develop celiac disease before 8 years of age from those at a similar genetic risk who do not develop the disease. In addition to recent discovery, our paper unveiled a specifc phopholipid profile, able to discriminate infants who eventually develop celiac disease years before antibodies or clinical symptoms ensue.

Relationship between the lipid composition of maternal plasma and infant plasma through breast milk

INTRODUCTION

This study was motivated by the report that infant development correlates with particular lipids in infant plasma.

OBJECTIVE

The hypothesis was that the abundance of these candidate biomarkers is influenced by the dietary intake of the infant.

METHODS

A cohort of 30 exclusively-breastfeeding mother-infant pairs from a small region of West Africa was used for this observational study. Plasma and milk from the mother and plasma from her infant were collected within 24 h, 3 months post partum. The lipid, sterol and glyceride composition was surveyed using direct infusion MS in positive and negative ion modes. Analysis employed a combination of univariate and multivariate tests.

RESULTS

The lipid profiles of mother and infant plasma samples are similar but distinguishable, and both are distinct from milk. Phosphatidylcholines (PC), cholesteryl esters (CEs) and cholesterol were more abundant in mothers with respect to their infants, e.g. PC(34:1) was 5.66% in mothers but 3.61% in infants (p = 3.60 × 10^-10), CE(18:2) was 8.05% in mothers but 5.18% in infants (p = 1.37 × 10^-11) whilst TGs were lower in mothers with respect to their infants, e.g. TG(52:2) was 2.74% in mothers and 4.23% in infants (p = 1.63 × 10^-05). A latent structure model showed that four lipids in infant plasma previously shown to be biomarkers clustered with cholesteryl esters in the maternal circulation.

CONCLUSION

This study found evidence that the abundance of individual lipid isoforms associated with infant development are associated with the abundance of individual molecular species in the mother's circulation.

Enzymatic Production and Characterization of Pectic Oligosaccharides Derived from Citrus and Apple Pectins: A GC-MS Study Using Random Forests and Association Rule Learning

Pectic oligosaccharides (POS) from citrus and apple pectin hydrolysis using ViscozymeL and Glucanex200G have been obtained. According to the results, maximum POS formation was achieved from citrus pectin after 30 min of hydrolysis with ViscozymeL, with a yield of 652 mg g^-1 and average molecular mass ( M_w) of 0.8-2.5 kDa, while with Glucanex200G, the yield was 518 mg g^-1 and M_w was 0.8-7.1 kDa. Digalacturonic and trigalacturonic acids were identified among other low M_w compounds as di- and tri-POS. In addition, differences in GC-MS spectra of all oligosaccharides found in the hydrolysates were studied by employing random forests and other algorithms to identify structural differences between the obtained POS, and high prediction rates were shown for new samples. Chemical structures were proposed for some influential m/ z ions, and 12 association rules that explain differences according to pectin and enzyme origin were built. This information could be used to establish structure-function relationships of POS.

The Lipid and Glyceride Profiles of Infant Formula Differ by Manufacturer, Region and Date Sold

We tested the hypothesis that the lipid composition of infant formula is consistent between manufacturers, countries and target demographic. We developed techniques to profile the lipid and glyceride fraction of milk and formula in a high throughput fashion. Formula from principal brands in the UK (2017-2019; bovine-, caprine-, soya-based), the Netherlands (2018; bovine-based) and South Africa (2018; bovine-based) were profiled along with fresh British animal and soya milk and skimmed milk powder. We found that the lipid and glyceride composition of infant formula differed by region, manufacturer and date of manufacture. The formulations within some brands, aimed at different target age ranges, differed considerably where others were similar across the range. Soya lecithin and milk lipids had characteristic phospholipid profiles. Particular sources of fat, such as coconut oil, were also easy to distinguish. Docosahexaenoic acid is typically found in triglycerides rather than phospholipids in formula. The variety by region, manufacturer, date of manufacture and sub-type for target demographics lead to an array of lipid profiles in formula. This makes it impossible to predict its molecular profile. Without detailed profile of the formula fed to infants, it is difficult to characterise the relationship between infant nutrition and their growth and development.

-Omics biomarker identification pipeline for translational medicine

BACKGROUND

Translational medicine (TM) is an emerging domain that aims to facilitate medical or biological advances efficiently from the scientist to the clinician. Central to the TM vision is to narrow the gap between basic science and applied science in terms of time, cost and early diagnosis of the disease state. Biomarker identification is one of the main challenges within TM. The identification of disease biomarkers from -omics data will not only help the stratification of diverse patient cohorts but will also provide early diagnostic information which could improve patient management and potentially prevent adverse outcomes. However, biomarker identification needs to be robust and reproducible. Hence a robust unbiased computational framework that can help clinicians identify those biomarkers is necessary.

METHODS

We developed a pipeline (workflow) that includes two different supervised classification techniques based on regularization methods to identify biomarkers from -omics or other high dimension clinical datasets. The pipeline includes several important steps such as quality control and stability of selected biomarkers. The process takes input files (outcome and independent variables or -omics data) and pre-processes (normalization, missing values) them. After a random division of samples into training and test sets, Least Absolute Shrinkage and Selection Operator and Elastic Net feature selection methods are applied to identify the most important features representing potential biomarker candidates. The penalization parameters are optimised using 10-fold cross validation and the process undergoes 100 iterations and a combinatorial analysis to select the best performing multivariate model. An empirical unbiased assessment of their quality as biomarkers for clinical use is performed through a Receiver Operating Characteristic curve and its Area Under the Curve analysis on both permuted and real data for 1000 different randomized training and test sets. We validated this pipeline against previously published biomarkers.

RESULTS

We applied this pipeline to three different datasets with previously published biomarkers: lipidomics data by Acharjee et al. (Metabolomics 13:25, 2017) and transcriptomics data by Rajamani and Bhasin (Genome Med 8:38, 2016) and Mills et al. (Blood 114:1063-1072, 2009). Our results demonstrate that our method was able to identify both previously published biomarkers as well as new variables that add value to the published results.

CONCLUSIONS

We developed a robust pipeline to identify clinically relevant biomarkers that can be applied to different -omics datasets. Such identification reveals potentially novel drug targets and can be used as a part of a machine-learning based patient stratification framework in the translational medicine settings.

Serum, plasma and erythrocyte membrane lipidomes in infants fed formula supplemented with bovine milk fat globule membranes

BACKGROUND

Supplementation of formula with bovine milk fat globule membranes has been shown to narrow the gap in immunological and cognitive development between breast-fed and formula-fed infants.

METHOD

In a double-blinded randomized controlled trial 160 formula-fed infants received an experimental formula (EF), supplemented with bovine milk fat globule membranes, or standard formula until 6 months of age. A breast-fed reference group was recruited. Lipidomic analyses were performed on plasma and erythrocyte membranes at 6 months and on serum at 4 and 12 months of age.

RESULTS

At 6 months of age, we observed a significant separation in the plasma lipidome between the two formula groups, mostly due to differences in concentrations of sphingomyelins (SM), phosphatidylcholines (PC), and ceramides, and in the erythrocyte membrane lipidome, mostly due to SMs, PEs and PCs. Already at 4 months, a separation in the serum lipidome was evident where SMs and PCs contributed. The separation was not detected at 12 months.

CONCLUSIONS

The effect of MFGM supplementation on the lipidome is likely part of the mechanisms behind the positive cognitive and immunological effects of feeding the EF previously reported in the same study population.

The validation of biomarkers of metabolic efficacy in infant nutrition

Breastfeeding is regarded as the ideal way to nourish infants. However, feeding with formula milk is also common in much of the West. Despite this, the function of the molecular components of breast and formula milks are not fully understood, less still the relationship between the composition of the milk and the infant's metabolism and how this influences the infant's development. The Biotechnology and Biological Sciences Research Council-funded project 'The validation of biomarkers of metabolic efficacy in infant nutrition' aims to identify lipid biomarkers that can be used to study the effect of diet on growth and development of infants. In this work, we have been able to validate these markers. Here, we present an approach to biomarker discovery that has new depth and will inform research questions about how metabolism is governed, and which species can be used to identify situations where metabolism is becoming defective.

Human Breast Milk: Exploring the Linking Ring Among Emerging Components

Maternal breast milk (BM) is a complex and unique fluid that evolution adapted to satisfy neonatal needs; in addition to classical nutrients, it contains several bioactive components. BM characteristically shows inter-individual variability, modifying its composition during different phases of lactation. BM composition, determining important consequences on neonatal gut colonization, influences both short and long-term development. Maternal milk can also shape neonatal microbiota, through its glycobiome rich in Lactobacilli spp. and Bifidobacteria spp. Therefore, neonatal nourishment during the first months of life seems the most important determinant of individual's outcomes. Our manuscript aims to provide new evidence in the characterization of BM metabolome and microbiome, and its comparison to formula milk, allowing the evaluation of each nutrient's influence on neonatal metabolism. This result very interesting since potentially offers an innovative approach to investigate the complex relationship between BM components and infant's health, also providing the chance to intervene in a sartorial way on diet composition, according to the nutritional requests. Future research, integrating metabolomics, microbiomics and stem cells knowledge, could make significant steps forward in understanding BM extraordinary properties and functions.

Lipidomics Insights in Health and Nutritional Intervention Studies

Lipids are among the major components of food and constitute the principal structural biomolecules of human body together with proteins and carbohydrates. Lipidomics encompasses the investigation of the lipidome, defined as the entire spectrum of lipids in a biological system at a given time. Among metabolomics technologies, lipidomics has evolved due to the relevance of lipids in nutrition and their well-recognized roles in health. Mass spectrometry advances have greatly facilitated lipidomics, but owing to the complexity and diversity of the lipids, lipidome purification and analysis are still challenging. This review focuses on lipidomics strategies, applications, and achievements of studies related to nutrition and health research.

Integrated Metabolomics Assessment of Human Dried Blood Spots and Urine Strips

(1) Background: Interest in the application of metabolomics toward clinical diagnostics development and population health monitoring has grown significantly in recent years. In spite of several advances in analytical and computational tools, obtaining a sufficient number of samples from patients remains an obstacle. The dried blood spot (DBS) and dried urine strip (DUS) methodologies are a minimally invasive sample collection method allowing for the relative simplicity of sample collection and minimal cost. (2) Methods: In the current report, we compared results of targeted metabolomics analyses of four types of human blood sample collection methods (with and without DBS) and two types of urine sample collection (DUS and urine) across several parameters including the metabolite coverage of each matrix and the sample stability for DBS/DUS using commercially available Whatman 903TM paper. The DBS/DUS metabolomics protocols were further applied to examine the temporal metabolite level fluctuations within hours and days of sample collection. (3) Results: Several hundred polar metabolites were monitored using DBS/DUS. Temporal analysis of the polar metabolites at various times of the day and across days identified several species that fluctuate as a function of day and time. In addition, a subset of metabolites were identified to be significantly altered across hours within a day and within successive days of the week. (4) Conclusion: A comprehensive DBS/DUS metabolomics protocol was developed for human blood and urine analyses. The described methodology demonstrates the potential for enabling patients to contribute to the expanding bioanalytical demands of precision medicine and population health studies.