Agenda

In utero exposures during fetal development lead to developmental programming (DP) of offspring with increased risk of cardiometabolic disease. Maternal overnutrition (MON) during gestation is a common adverse exposure that programs offspring metabolically, and disproportionally affects individuals from disadvantaged communities. The long-term impact of MON on offspring are important to public health, but difficult to determine in humans due to confounding by socioeconomic factors such as access to health care and healthy foods. Skeletal muscle (SKM) comprises ~40% of total body mass, is a major metabolic tissue, and important for quality of life. Most prior studies to identify mechanisms underlying DP by MON in adult offspring have been conducted on rodents which limits our understanding of relevant processes and potential mechanisms in humans. Nonhuman primates (NHP) complement rodent studies for translation to understand human aging-related diseases. In this study we compared young adult MON baboon offspring born to mothers fed an obesogenic diet prior to and during pregnancy and lactation with age-matched controls (CON) to identify early SKM molecular and metabolic changes. We generated untargeted integrated-omics data (transcriptomic, proteomic, metabolomic) from SKM biopsies, imaging data from SKM in vivo magnetic resonance spectroscopy (MRS), and clinical blood measures of metabolic status, and used unbiased clustering analyses to identify modules of molecules that correlate with in vivo and clinical metabolic measures that differ between MON and CON.  We found that circulating lipoproteins positively correlated with SKM metabolism in CON, but were negatively correlated in MON young adults. These results indicate that standard clinical measures of metabolic status differ in adult offspring of suboptimal pregnancies compared with healthy pregnancies, and highlight the need to develop relevant clinical measures for offspring of suboptimal in utero environments. Our results also show the power of integrating multi-omics with in vivo imaging to better understand metabolic health/disease.

Toll-like receptor (TLR) signaling in macrophages is essential for generating effective innate immune responses. Quantitative differences dependent on the dose and timing of the stimulus critically affect cell function and often involve proteins that are not components of widely shared transduction pathways. Mathematical modeling is an important approach to better understand how these signaling networks function in time and space. Wehave successfully modeled the S1P signaling pathway in macrophages using selected reaction monitoring (SRM) to measure the absolute abundance of the pathway proteins. The resulting values became parameters in a computational pathway model. To model the TLR signaling networks, we developed assays for the canonical TLR signaling pathway and related proteins and phosphoproteins and used parallel reaction monitoring (PRM) with heavy-labeled internal peptide standards to quantify protein and phosphorylated protein moleculenumbers per cell in untreated and LPS-stimulated macrophages. The absolute protein abundance values were entered into a model of the TLR pathway developed using Simmune, the rule-based modeling tool with a visual interface. To reach beyond basal level quantification, the TLR signaling network model is tested further and combined with global proteomic approaches to discover biologically important new proteins, protein complexes and PTMs involved in this innate immune pathway, of which some examples will be given. The protein and PTM levels are quantified in macrophages under diverse, but well-defined conditions (different TLR ligands, whole pathogens, and cells with mutations in specific signaling molecules). These data will allow to parameterize and test the TLR network model under a variety of conditions. Together, the interconnected projects will lead to the better understanding how the immune signaling pathways are regulated and activated during an infection. This research was supported by the Intramural Research Program of NIAID, NIH.