Weight Regain in Formerly Obese Mice Hastens Development of Hepatic Steatosis Due to Impaired Adipose Tissue Function

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155467/1/oby22788-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155467/2/oby22788_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155467/3/oby22788.pd

Obesity results in adipose tissue T cell exhaustion

Despite studies implicating adipose tissue T cells (ATT) in the initiation and persistence of adipose tissue inflammation, fundamental gaps in knowledge regarding ATT function impedes progress toward understanding how obesity influences adaptive immunity. We hypothesized that ATT activation and function would have tissue-resident–specific properties and that obesity would potentiate their inflammatory properties. We assessed ATT activation and inflammatory potential within mouse and human stromal vascular fraction (SVF). Surprisingly, murine and human ATTs from obese visceral white adipose tissue exhibited impaired inflammatory characteristics upon stimulation. Both environmental and cell-intrinsic factors are implicated in ATT dysfunction. Soluble factors from obese SVF inhibit ATT activation. Additionally, chronic signaling from macrophage major histocompatibility complex II (MHCII) is necessary for ATT impairment in obese adipose tissue but is independent of increased PD1 expression. To assess intracellular signaling mechanisms responsible for ATT inflammation impairments, single-cell RNA sequencing of ATTs was performed. ATTs in obese adipose tissue exhibit enrichment of genes characteristic of T cell exhaustion and increased expression of coinhibitory receptor Btla. In sum, this work suggests that obesity-induced ATTs have functional characteristics and gene expression resembling T cell exhaustion induced by local soluble factors and cell-to-cell interactions in adipose tissue

Free Fatty Acids Differentially Downregulate Chemokines in Liver Sinusoidal Endothelial Cells: Insights into Non-Alcoholic Fatty Liver Disease

<div><p>Non-alcoholic fatty liver disease is a prevalent problem throughout the western world. Liver sinusoidal endothelial cells (LSEC) have been shown to play important roles in liver injury and repair, but their role in the underlying pathogenetic mechanisms of non-alcoholic fatty liver disease remains undefined. Here, we evaluated the effects of steatosis on LSEC gene expression in a murine model of non-alcoholic fatty liver disease and an immortalized LSEC line. Using microarray we identified distinct gene expression profiles following exposure to free fatty acids. Gene pathway analysis showed a number of differentially expressed genes including those involved in lipid metabolism and signaling and inflammation. Interestingly, in contrast to hepatocytes, fatty acids led to decreased expression of pro-inflammatory chemokines including CCL2 (MCP-1), CXCL10 and CXCL16 in both primary and LSEC cell lines. Chemokine downregulation translated into a significant inhibition of monocyte migration and LSECs isolated from steatotic livers demonstrated a similar shift towards an anti-inflammatory phenotype. Overall, these pathways may represent a compensatory mechanism to reverse the liver damage associated with non-alcoholic fatty liver disease.</p></div

Downregulation of chemokines by LSEC in response to treatment with FFA.

<p>TSEC (A) and AML12 (B) were cultured with 0.33mM OA and/or 0.33mM PA for 16 hours. RNA was isolated from cells and levels of the indicated chemokine were determined by quantitative RT PCR. (C) TSEC were treated with 0.33mM of the indicated FFA for 16 hours and CCL2 expression was determined by quantitative RT PCR. (D) TSEC and AML12 were treated with 0.33mM OA and 0.33mM PA for 16 hours followed by 6 hour stimulation with 100ng/ml LPS. CCL2 gene expression (top) and protein production (bottom) were measured. (E) The human LSEC (TMNK) and hepatocyte (HepG2) cell lines were stimulated with FFA and LPS as in (D) and CCL2 gene expression was measured by quantitative PCR. Plots represent the mean +/- SE of three experiments. *p<0.05.</p

KEGG pathways in FFA treated TSEC.

<p>Relevant enriched pathways and the involved genes were determined by analysis of all genes with a p value <0.05 and a fold-change > 1.25 within the KEGG pathway database. </p

Phenotype of LSEC from normal and DIO mice.

<p>A) Primary LSEC (CD45^-CD146⁺) isolated from normal C57Bl6 lean mice were analyzed for CD146 and CD31 expression and uptake of DiI-labeled Ac-LDL by flow cytometry. (B) Primary LSEC isolated from C57Bl6 lean mice were cultured with the indicated FFA for 24hours. CCL2 levels in the supernatant were measured by ELISA. Graphs represent the mean +/- SE from 3 mice. Primary LSEC (CD45^-CD146⁺) (C) and LSEC depleted non-parenchymal cells (CD45⁺CD146^-) (D) were isolated from mice fed a low fat diet (NCD) and obese mice fed a high fat diet (HFD) for 12 weeks and gene expression for chemokines was evaluated by quantitative qPCR. Graphs represent the mean +/- SE from 5–7 mice. *p<0.05.</p

FFA inhibits the migration of monocytes in vitro.

<p>Murine monocytes were isolated from the bone marrow of C56Bl6/J mice and co-cultured for 2 hours in transwell plates with TSEC (A) or AML12 (B) either resting or pre-treated with FFA and LPS for 18 hours. Cells were stained with the monocyte/macrophage markers CD11b, F/480 and Ly6C and the chemotactic index was calculated (#migrated cells in treatment wells/# migrated cell in the control well). (C) Representative histograms showing Ly6C expression on migrated cells. (D) The chemotactic index of Ly6C high and Ly6C low cells towards resting or FFA-treated TSEC. Plots represent the mean +/- SE of three experiments. *p<0.05.</p

Gene expression analysis of TSEC treated with FFA.

<p>TSECs were treated with 0.33mM OA and 0.33mM PA for 16 hours. RNA was isolated from cells and microarray analysis of triplicate wells was performed using IPA (Qiagen). (A) Heat map of the top 20 up-regulated and down-regulated genes in the FFA treated LSEC compared to untreated cells. Individual samples are given on the rows, while genes are listed by column. Gene expression is normalized to the mean and color-scaled by standard deviations above (red) and below (green) the average expression in all six independent samples. (B) Upregulation of genes involved in lipid metabolism genes (red) and the predicted activated pathways (orange) in TSEC treated with FFA. (C) Predicted activation of PPAR<b>α</b> based on Ingenuity upstream regulator analysis. Upregulated genes downstream of PPAR<b>α</b> are shown (red).</p