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Serum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

Circulating ApolipoproteinL1 (ApoL1) is a component of pre-β-high-density lipoprotein (HDL), however little is known about the relationship of ApoL1 with cardiometabolic factors. Considering previous studies reporting the correlation of ApoL1 to triglyceride, we have hypothesized that ApoL1 associates with insulin-related metabolism. The current study examined their associations in 126 non-diabetic subjects and 36 patients with type 2 diabetes (T2DM). Non-diabetic subjects demonstrated triglyceride (standardized coefficients [s.c.] = 0.204, p p p

Influenza remains a world-wide health concern, causing 290,000–600,000 deaths and up to 5 million cases of severe illnesses annually. Noticing the host factors that control biological responses, such as inflammatory cytokine secretion, to influenza virus infection is important for the development of novel drugs. Sphingosine-1-phosphate (S1P) is a bioactive sphingolipid metabolite and has essential biological functions in inflammation. However, the kinetic effects of influenza virus infection on physiological S1P levels and their signaling in multiple tissues remain unknown. In this study, we utilized a mouse model intranasally infected with 50 or 500 plaque forming units (PFU) of A/Puerto Rico/8/34 (H1N1; PR8) virus to investigate how S1P levels and expression of its regulating factors are affected by influenza virus infection by the liquid-chromatography/mass spectrometry and real-time PCR, respectively. The S1P level was significantly high in the plasma of mice infected with 500 PFU of the virus than that in control mice at 6 day-post-infection (dpi). Elevated gene expression of sphingosine kinase-1 (Sphk1), an S1P synthase, was observed in the liver, lung, white adipose tissue, heart, and aorta of infected mice. This could be responsible for the increased plasma S1P levels as well as the decrease in the hepatic S1P lyase (Sgpl1) gene in the infected mice. These results indicate modulation of S1P-signaling by influenza virus infection. Since S1P regulates inflammation and leukocyte migration, it must be worth trying to target this signaling to control influenza-associated symptoms.

Circulating ApolipoproteinL1 (ApoL1) is a component of pre-β-high-density lipoprotein (HDL), however little is known about the relationship of ApoL1 with cardiometabolic factors. Considering previous studies reporting the correlation of ApoL1 to triglyceride, we have hypothesized that ApoL1 associates with insulin-related metabolism. The current study examined their associations in 126 non-diabetic subjects and 36 patients with type 2 diabetes (T2DM). Non-diabetic subjects demonstrated triglyceride (standardized coefficients [s.c.] = 0.204, p 0.05), body mass index (s.c. =0.232, p 0.05) and HDL cholesterol (s.c. = -0.203, p 0.05) as independent determinant of ApoL1 levels, and the significant elevation of ApoL1 in metabolic syndrome. Lipoprotein fractionation analysis revealed the predominant distribution of ApoL1 in large HDL fraction, and the significant increase of ApoL1 in large LDL fraction in high ApoL1 samples with insulin resistance. In T2DM, ApoL1 was higher in T2DM with metabolic syndrome, however ApoL1 was lower with β cell dysfunction. Insulin significantly promotes ApoL1 synthesis and secretion in HepG2 cells. In conclusion, circulating ApoL1 may be associated with abnormal HDL metabolism in insulin resistant status. This may suggest a regulation of insulin signal on the ApoL1 level, leading to offer a novel insight to the ApoL1 biology.

Understanding the gut microbiota in metabolic disorders, including type 2 diabetes mellitus (T2DM), is now gaining importance due to its potential role in disease risk and progression. We previously established a zebrafish model of T2DM, which shows glucose intolerance with insulin resistance and responds to anti-diabetic drugs. In this study, we analysed the gut microbiota of T2DM zebrafish by deep sequencing the 16S rRNA V3-V4 hypervariable regions, and imputed a functional profile using predictive metagenomic tools. While control and T2DM zebrafish were fed with the same kind of feed, the gut microbiota in T2DM group was less diverse than that of the control. Predictive metagenomics profiling using PICRUSt revealed functional alternation of the KEGG pathways in T2DM zebrafish. Several amino acid metabolism pathways (arginine, proline, and phenylalanine) were downregulated in the T2DM group, similar to what has been previously reported in humans. In summary, we profiled the gut microbiome in T2DM zebrafish, which revealed functional similarities in gut bacterial environments between these zebrafish and T2DM affected humans. T2DM zebrafish can become an alternative model organism to study host-bacterial interactions in human obesity and related diseases.