Multi-Omic Analyses of Dietary Fatty Acid-Microbe-Host Interactions Reveal Metaorganismal Lipid Metabolic Crosstalk Impacting Cardiometabolic Disease
Mouannes, N.; Burrows, A. C.; Horak, A. J.; Venkateshwari, V.; Dutta, S.; Mahen, K.; Banerjee, R.; Massey, W. J.; Ye, X.; Mrdjen, M.; Brown, A. L.; Awoniyi, M.; Kitao, K.; Sandhu, A.; Tomimoto, C.; Tsuji, K.; Yonejima, Y.; Ampong, I.; Zhang, R.; Qiu, Y.; Willard, B.; Hajjar, A. M.; Dwidar, M.; Sangwan, N.; Walker, M. E.; Spite, M.; Cheng, F.; Brown, J. M.
Show abstract
Following a meal, our gut microbiome and human cells collaborate via metaorganismal metabolic circuits to produce diverse nutrient metabolites that systemically circulate to influence health and disease. Although there are now several examples of bacterial fiber-, amino acid-, and micronutrient-derived metabolites impacting cardiometabolic disease, very little is known in regards to how diet-microbe-host interactions impact lipid homeostasis. Here we address this by defining dietary fatty acid substrate availability in germ-free versus conventionally-raised mice coupled to deep multi-omic metabolic phenotyping. Our data demonstrate that the effects of dietary saturated (SFA), monounsaturated (MUFA), and polyunsaturated fatty acids (PUFA) on the host lipidome, transcriptome, proteome and metabolome are uniquely impacted by resident microbiota. Also, the hepatic levels of both pro-inflammatory and pro-resolving lipid mediators are strongly influenced by dietary fatty acid-microbe interactions. This study presents a unique resource to the nutrition and metabolism research community to advance our understanding of metaorganismal lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/705588v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@4fc7e3org.highwire.dtl.DTLVardef@1cc1dc8org.highwire.dtl.DTLVardef@1b7648dorg.highwire.dtl.DTLVardef@12a8088_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG
Matching journals
The top 5 journals account for 50% of the predicted probability mass.