Seeking Windows of Opportunity to Shape Lifelong Immune Health: A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation.

A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into prophylactic or therapeutic approaches, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. Six early life immune causal networks, each compromising a different time period in early life (the 1st, 2nd, 3rd trimester of gestations, birth, newborn, and infant period), were generated. Thereto information was extracted and structured from early life literature using the automated text mining and machine learning tool: Integrated Network and Dynamical Reasoning Assembler (INDRA). The tool identified relevant entities (e.g., genes/proteins/metabolites/processes/diseases), extracted causal relationships among these entities, and assembled them into early life-immune causal networks. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools (GO/GO-SLIM), inferred missing relationships and added expert knowledge to generate information-dense early life immune networks. Analysis of the six early life immune networks by PageRank, not only confirmed the central role of the "commonly used immune markers" (e.g., chemokines, interleukins, IFN, TNF, TGFB, and other immune activation regulators (e.g., CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious candidates (e.g., CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP, and IFNG), and also genes that were only described in certain early life period(s). Concluding, here we describe a network-based approach that provides a science-based and systematical method to explore the functional development of the early life immune system through time. This systems approach aids the generation of a testing strategy for the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development.

The Impact of Immune Interventions: A Systems Biology Strategy for Predicting Adverse and Beneficial Immune Effects.

Despite scientific advances it remains difficult to predict the risk and benefit balance of immune interventions. Since a few years, network models have been built based on comprehensive datasets at multiple molecular/cellular levels (genes, gene products, metabolic intermediates, macromolecules, cells) to illuminate functional and structural relationships. Here we used a systems biology approach to identify key immune pathways involved in immune health endpoints and rank crucial candidate biomarkers to predict adverse and beneficial effects of nutritional immune interventions. First, a literature search was performed to select the molecular and cellular dynamics involved in hypersensitivity, autoimmunity and resistance to infection and cancer. Thereafter, molecular interaction between molecules and immune health endpoints was defined by connecting their relations by using database information. MeSH terms related to the immune health endpoints were selected resulting in the following selection: hypersensitivity (D006967: 184 genes), autoimmunity (D001327: 564 genes), infection (parasitic, bacterial, fungal and viral: 357 genes), and cancer (D009369: 3173 genes). In addition, a sequence of key processes was determined using Gene Ontology which drives the development of immune health disturbances resulting in the following selection: hypersensitivity (164 processes), autoimmunity (203 processes), infection (187 processes), and cancer (309 processes). Finally, an evaluation of the genes for each of the immune health endpoints was performed, which indicated that many genes played a role in multiple immune health endpoints, but also unique genes were observed for each immune health endpoint. This approach helps to build a screening/prediction tool which indicates the interaction of chemicals or food substances with immune health endpoint-related genes and suggests candidate biomarkers to evaluate risks and benefits. Several anti-cancer drugs and omega 3 fatty acids were evaluated as in silico test cases. To conclude, here we provide a systems biology approach to identify genes/molecules and their interaction with immune related disorders. Our examples illustrate that the prediction with our systems biology approach is promising and can be used to find both negatively and positively correlated interactions. This enables identification of candidate biomarkers to monitor safety and efficacy of therapeutic immune interventions.

Content of n-3 LC-PUFA in Breast Milk Four Months Postpartum is Associated with Infancy Blood Pressure in Boys and Infancy Blood Lipid Profile in Girls.

Blood pressure (BP) and blood lipid profile (BLP) have been shown to track from childhood into adulthood, and n-3 long-chain polyunsaturated fatty acids (LC-PUFAs) in breast milk have been suggested as mediators of the beneficial long-term effect of breastfeeding on BP and BLP. We aimed to investigate associations between n-3 LC-PUFA content in breast milk at 4 months postpartum and offspring BP and BLP in early life. BP and BLP were measured at 4, 18, and 36 months. Statistical analyses were sex-stratified and adjusted for gestational age, maternal pre-pregnancy body mass index (BMI), and maternal educational level. Based on 336 mother-child dyads, high n-3 LC-PUFA in breast milk was inversely associated with systolic and diastolic BP in boys at 4 months (ß = -20.0 (95% CI = -33.4, -6.7), p = 0.004 and ß = -10.2 (95% CI = -19.8, -0.5), p = 0.039, respectively); inversely associated with HDL cholesterol, and directly associated with triglyceride in girls at 4 months (ß = -0.7 (95% CI = -1.1, -0.3), p = 0.001 and ß = 3.1 (95% CI = 1.0, 5.2), p = 0.005, respectively). Associations observed at the later time points were non-significant. Furthermore, we observed sex-specific changes over time in both size and direction of the associations. Our results indicate that early intake of n-3 LC-PUFA can affect early development in cardiometabolic factors such as BP and BLP in a sex-specific manner. Follow-up and further investigation in later childhood is planned.

Satiety Factors Oleoylethanolamide, Stearoylethanolamide, and Palmitoylethanolamide in Mother's Milk Are Strongly Associated with Infant Weight at Four Months of Age-Data from the Odense Child Cohort.

Regulation of appetite and food intake is partly regulated by N-acylethanolamine lipids oleoylethanolamide (OEA), stearoylethanolamide (SEA), and palmitoylethanolamide (PEA), which induce satiety through endogenous formation in the small intestine upon feeding, but also when orally or systemic administered. OEA, SEA, and PEA are present in human milk, and we hypothesized that the content of OEA, SEA, and PEA in mother's milk differed for infants being heavy (high weight-for-age Z-score (WAZ)) or light (low WAZ) at time of milk sample collection. Ultra-high performance liquid chromatography-mass spectrometry was used to determine the concentration of OEA, SEA, and PEA in milk samples collected four months postpartum from mothers to high (n = 50) or low (n = 50) WAZ infants. Associations between OEA, SEA, and PEA concentration and infant anthropometry at four months of age as well as growth from birth were investigated using linear and logistic regression analyses, adjusted for birth weight, early infant formula supplementation, and maternal pre-pregnancy body mass index. Mean OEA, SEA, and PEA concentrations were lower in the high compared to the low WAZ group (all p < 0.02), and a higher concentration of SEA was associated with lower anthropometric measures, e.g., triceps skinfold thickness (mm) (ß = -2.235, 95% CI = -4.04, -0.43, p = 0.016), and weight gain per day since birth (g) (ß = -8.169, 95% CI = -15.26, -1.08, p = 0.024). This raises the possibility, that the content of satiety factors OEA, SEA, and PEA in human milk may affect infant growth.