Aryl hydrocarbon receptor agonist indigo protects against obesity-related insulin resistance through modulation of intestinal and metabolic tissue immunity

Background/objectives Low-grade chronic inflammation in visceral adipose tissue and the intestines are important drivers of obesity associated insulin resistance. Bioactive compounds derived from plants are an important source of potential novel therapies for the treatment of chronic diseases. In search for new immune based treatments of obesity associated insulin resistance, we screened for tissue relevant anti-inflammatory properties in 20 plant-based extracts. Methods We screened 20 plant-based extracts to assess for preferential production of IL-10 compared to TNFα, specifically targetting metabolic tissues, including the visceral adipose tissue. We assessed the therapeutic potential of the strongest anti-inflammatory compound, indigo, in the C57BL/6J diet-induced obesity mouse model with supplementation for up to 16 weeks by measuring changes in body weight, glucose and insulin tolerance, and gut barrier function. We also utilized flow cytometry, quantitative PCR, enzyme-linked immunosorbent assay (ELISA), and histology to measure changes to immune cells populations and cytokine profiles in the intestine, visceral adipose tissue (VAT), and liver. 16SrRNA sequencing was performed to examine gut microbial differences induced by indigo supplementation. Results We identifed indigo, an aryl hydrocarbon receptor (AhR) ligand agonist, as a potent inducer of IL-10 and IL-22, which protects against high-fat diet (HFD)-induced insulin resistance and fatty liver disease in the diet-induced obesity model. Therapeutic actions were mechanistically linked to decreased inflammatory immune cell tone in the intestine, VAT and liver. Specifically, indigo increased Lactobacillus bacteria and elicited IL-22 production in the gut, which improved intestinal barrier permeability and reduced endotoxemia. These changes were associated with increased IL-10 production by immune cells residing in liver and VAT. Conclusions Indigo is a naturally occurring AhR ligand with anti-inflammatory properties that effectively protects against HFD-induced glucose dysregulation. Compounds derived from indigo or those with similar properties could represent novel therapies for diseases associated with obesity-related metabolic tissue inflammation.


Metabolic Cage Studies
We placed mice in automated metabolic cages (Oxymax Systems, Columbus Instruments) for 48 h with airflow held constant at 0.5 L/min and monitored for food and water intake after 14 weeks HFD or HFD-Indigo. We measured metabolic activity using indirect calorimetry, recording maximal O2 consumption (VO2), CO2 production (VCO2) normalized to body weight.
Respiratory exchange ratio (RER) was derived from VCO2/VO2. Energy expenditure (kcal) was calculated as calorific value (CV) xVO2. CV is 3.815 + 1.232 x RER. Food and water intake are calculated for light and dark measurements as an average over 24 hours by combining light and dark measurements. Ambulatory activity was measured by the breaking of infrared laser beams in the XYZ axis.

Histology
We enumerated CLS in the VAT by counting the number of adipocytes surrounded by immune cells identified on H&E staining per 100X low power field. We used the straight-line tool in the Leica Application Suite software to measure fat cell diameter. Analysis of histochemical stains was performed in a blinded fashion by a certified pathologist.

Isolation of Stromal Vascular Cells from Visceral Adipose Tissue
Epididymal VAT pads were dissected, mashed and digested in collagenase (0.2 mg/ml, DMEM/60min/37°C, manual shaking every15min, Sigma). Cells were washed, pelleted, filtered through a 70 μm filter and then followed by red blood cell lysis and washed to obtain SVC.

Isolation of Bowel-Associated Immune Cells
For isolation of small intestine lamina propria immune cells, we used the protocol previously described (1) and processed 10 cm from the distal end of the small bowel (jejunum and ileum). Finally, cells were washed and resuspended in ice-cold FACS buffer containing PBS supplemented with 2% FBS and analyzed by flow cytometry.

In vitro IL-22-producing CD4+ T cells Differentiation
We harvested mesenteric lymph nodes from mice and mashed lymph nodes through a sterile 70µm nylon cell strainer to achieve single-cell suspensions in RPMI 1640 containing 10% FCS.

Flow Cytometry
We stained single-cell suspensions for 30 min on ice with commercial antibodies. We gated ILCs as described (2). Flow cytometry antibodies including CD45. from Biolegend. RORγt (AFKJS-9) was purchased from eBioscience. Intracellular staining was performed using a Foxp3 staining buffer kit (eBioscience). We acquired data from the Fortessa flow cytometer (BD Biosciences) and analyzed the data with FlowJo software (Tree Star).

RNA Isolation and Quantitative Real Time-PCR
We extracted total RNA from the small intestine, liver using the RNeasy Mini Kit (QIAGEN), and for VAT and BAT, we used the RNeasy Lipid Extraction kit (QIAGEN). We reversetranscribed the RNA by SensiFAST cDNA synthesis kit (Bioline). We performed qPCR with a QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems) using SYBR Green Master Mix reagent (Applied Biosystems). Each sample was checked in triplicate and normalized to housekeeping genes, Actb. We calculated relative fold changes in gene expression normalized to Actb by the DDCT method using the equation 2 -△△ CT . The results are shown as fold changes compared to the control group.

Gut Permeability Assays
Mice were fasted overnight and then orally gavaged with FD4 (0.4 mg/g body weight (BW) of a 100 mg/mL solution; Sigma-Aldrich). After 4 hours, 120 μL of leg vein blood was collected, and plasma fluorescence was measured at the excitation wavelengths of 485 nm and the emission wavelengths of 528 nm, compared with a standard curve using FD4 in normal plasma (3).

16S rRNA gene sequencing
The V4 hypervariable region of the 16S rRNA gene is amplified using a universal forward sequencing primer and a uniquely barcoded reverse sequencing primer to allow for multiplexing (4). Amplification reactions are performed using 12.5 uL of KAPA2G Robust HotStart ReadyMix (KAPA Biosystems), 1.5 uL of 10 uM forward and reverse primers, 7.5 uL of sterile water and 2 uL of DNA. The V4 region was amplified by cycling the reaction at 95°C for 3 minutes, 22x cycles of 95°C for 15 seconds, 50°C for 15 seconds and 72°C for 15 seconds, followed by a 5 minute 72°C extension. All amplification reactions were done in triplicate, checked on a 1% agarose TBE gel, and then pooled to reduce amplification bias. Pooled triplicates were quantified using PicoGreen and combined by even concentrations. The library was then purified using Ampure XP beads and loaded on to the Illumina MiSeq for sequencing, according to manufacturer instructions (Illumina, San Diego, CA). Sequencing is performed using the V2 (150bp x 2) chemistry. A single-species (Pseudomonas aeruginosa DNA), a mock community (Zymo Microbial Standard: https://www.zymoresearch.de/zymobiomics-communitystandard) and a template-free negative control were included in your sequencing run.

Analysis of the bacterial microbiome
The UNOISE pipeline, available through USEARCH USEARCH v10.0.240 and vsearch v2.5.0, was used for sequence analysis (5-7). The last base was removed from all sequences. Sequences were assembled and quality trimmed using -fastq_mergepairs and -fastq_filter, with a -fastq_maxee set at 1.0. Sequences shorter than 233 base pairs were discarded. The trimmed data was then processed following the UNOISE pipeline. Sequences were first de-replicated and sorted to remove singletons, then denoised and chimeras were removed using the unoise3 command. Assembled sequences were mapped back to the chimera-free denoised sequences at 97% identity OTUs. Taxonomy assignment was executed using SINTAX, available through USEARCH, and the UNOISE compatible Ribosomal Database Project (RDP) database version 16, with a minimum confidence cutoff of 0.8 (7). OTU sequences were aligned using PyNast accessed through QIIME (8). Sequences that did not align were removed from the dataset and a phylogenetic tree of the filtered aligned sequence data was made using FastTree (9). The bar chart was generated in R version 3.4.4 (10) using the packages funrar version 1.2.2 (11), dplyr version 0.7.6 (12), reshape2 version 1.4.3 (13), and ggplot2 version 2.3.0 (14). STAMP version 2.1.5 (15) was used to generate the box plots and the PCA plot and group mean comparison was performed using the White's non-parametric t-test (16).