metabolomic signatures in lipid-loaded hepargs reveal pathways involved in steatotic progression

Metabolomic signatures in lipid-loaded HepaRGs reveal pathways involved in steatotic progression

Abbreviated Title: Metabolic signatures of lipid-loaded HepaRGs

MV Brown1*, SA Compton

2*, MV Milburn

1, KA Lawton

1, B Cheatham

2

1Metabolon, Inc. 617 Davis Drive, Suite 400, Durham, NC, 27713, USA

2ZenBio 3200 East Highway 54, Suite 100, Research Triangle Park, NC, 27709 USA

*These authors contributed equally to the work

Corresponding author: Bentley Cheatham, ZenBio 3200 East Highway 54, Suite 100, Research Triangle

Park, NC, 27709 USA. Telephone: (919) 547-0692. Fax: (919) 547-0693. e-mail: [email protected]

Reprint requests: Bentley Cheatham, ZenBio 3200 East Highway 54, Suite 100, Research Triangle

Park, NC, 27709 USA

Keywords: Steatosis, metabolomics, lipid, liver, hepatocyte, NAFLD, NASH

Grants/Fellowships: This work was supported by a grant from the National Institute of Health

DK088430. Support for MB and SC was received from the North Carolina Biotechnology Center

Industrial Fellowship Program.

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This article has been accepted for publication and undergone full peer review but has not beenthrough the copyediting, typesetting, pagination and proofreading process which may lead todifferences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/oby.20440

2

Abstract

Objectives; Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of disorders including

simple steatosis, non-alcoholic steatohepatitis, fibrosis, and cirrhosis. With the increased prevalence of

obesity, and consequently NAFLD, there is a need for novel therapeutics in this area. To facilitate this

effort, we developed a cellular model of hepatic steatosis using HepaRG cells and determined the

resulting biochemical alterations.

Design and methods; Using global metabolomic profiling, by means of a novel metabolite extraction

procedure, we examined the metabolic profiles in response to the saturated fatty acid palmitate, and a

mixture of saturated and unsaturated fatty acids, palmitate and oleate (1:2).

Results; We observed elevated levels of the branched chain amino acids, TCA cycle intermediates,

sphingosine and acylcarnitines, and reduced levels of carnitine in the steatotic HepaRG model with both

palmitate and palmitate:oleate treatments. In addition, palmitate-induced steatotic cells selectively

displayed elevated levels of diacylglycerols and monoacylglycerols, as well as altered bile acid

metabolism.

Conclusion; This global metabolomics approach reveals biochemical changes in pathways important in

the transition to hepatic steatosis including insulin resistance, altered mitochondrial metabolism, and

oxidative stress. Moreover, our data demonstrate the utility of this in vitro model for investigating

mechanisms of steatotic progression, insulin resistance and lipotoxicity in NAFLD.

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Introduction

In parallel with the increasing occurrence of obesity and type 2 diabetes, non-alcoholic fatty liver

disease (NAFLD) may be affecting up to 25% of the general population, including a significant portion of

pediatric cases (1, 2). Current predictions indicate that 40% of the population will be obese by 2025 with

NAFLD becoming the leading cause of liver disease and need for liver transplantation (3). While the

exact mechanisms are not completely understood, excess lipid accumulation in hepatocytes is a primary

event in the development of NAFLD.

Hepatic free fatty acids (FFAs) are derived from multiple sources including de novo synthesis,

adipose tissue, and dietary triglycerides (4). In the liver, FFAs are converted to fatty acyl CoAs which are

directed to specific metabolic pathways depending on energy demand and fatty acid composition. The

predominant pathway involves conversion of fatty acyl CoAs into triglycerides and glycerophospholipids

and results in the generation of lipid intermediates including diacylglycerols (DAGs). Alternatively, fatty

acids can be directed towards the generation of sphingolipids or converted to acyl-carnitines for transport

into the mitochondria for β-oxidation.

Hepatic steatosis occurs when excess FFAs are transported to the liver and/or when increased de

novo synthesis of triglycerides exceeds the ability of the liver to metabolize or secrete FFAs as very low

density lipoprotein. The accumulation of excess FFA in hepatocytes may directly cause lipotoxicity by

promoting oxidative stress and activation of inflammatory pathways or may sensitize hepatocytes to

mitochondrial dysfunction, oxidative stress, and inflammatory cytokines and adipokines (5). These events

will likely lead to simple steatosis and eventually progress to NASH.

NAFLD is frequently observed in patients with type 2 diabetes mellitus and is highly associated

with insulin resistance. While the contributing mechanisms involved in the relationship between NAFLD

and insulin resistance are unknown, there are several proposed models. One prominent hypothesis

suggests that underlying mitochondrial deficiency results in the accumulation of toxic lipid metabolites

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that can interfere with insulin signaling (6, 7). An alternative hypothesis suggests that increased lipids

cause mitochondrial overload, leading to the accumulation of lipid metabolites and reactive oxygen

species (ROS) that subsequently cause mitochondrial dysfunction and an insulin resistant state (7, 8).

Moreover, elevated ROS and associated reactive derivatives can deplete ATP, NAD and glutathione

levels, causing oxidative stress, DNA and protein damage and cell death (9). Studies using serum or liver

biopsies from patients with NAFLD have explored metabolic characteristics of NAFLD, and observed

altered glutathione, bile acids, carnitine, branched chain amino acids (BCAA) and γ-glutamyl peptides

and mouse models of diet-induced obesity show similar overlap in serum and liver-specific metabolites

(8, 10-14).

Metabolomics has been used to identify biochemical alterations in cellular systems (15-17). The

aim in the current study was to establish a cell-based model of steatosis and apply an unbiased

metabolomics approach to determine biochemical signatures associated with steatotic progression and

insulin resistance. We achieved this goal by developing a novel, high throughput method for extracting

metabolites from adherent cells. With this method, termed Intact Sample Extraction (ISE), metabolites

are extracted and processed for metabolomic analysis, leaving cells attached to the culture plate,

preserving the cells for additional analysis and streamlining metabolite extraction. We first examined

steatotic transition using the saturated fatty acid palmitate, which is associated with lipid-induced cell

toxicity (18, 19). In a separate experiment, we sought to understand the differences between steatosis

induced by palmitate and a more physiologically relevant mixture of saturated and unsaturated fatty acids,

palmitate and oleate (1:2). Using the ISE method, metabolites analyzed from the lipid-loaded HepaRG

cells revealed key metabolic pathways associated with the progression of human NAFLD including

insulin resistance, altered mitochondrial metabolism, and oxidative stress.

Methods and Procedures

Cell culture

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For the method comparison study, HEK293 cells were seeded at 4x104 cells per well in 6-well

plates and grown to confluence in EMEM (ATCC, Manassas, VA) supplemented with 10% FBS, 1%

Pen/Strep. For method scale-down studies, HepG2 cells were seeded in 6-, 12-, 24- and 96-well formats

and expanded to confluence over 48 hours in DMEM supplemented with 10% FBS, glutaMAX I (Life

Technologies, Grand Island, NY) and antibiotics. For each experiment, cells were seeded simultaneously

using an appropriate number of wells for metabolomic studies and protein analysis. HepaRG cells

(Biopredic International, Rennes, France) were seeded at 2.9x104 cells per cm

2 in 12-well plates and

differentiated as previously described (20).

Metabolite extraction and sample preparation

For method validation, four replicates of HEK293 cells were prepared using the conventional cell

scraping and homogenization extraction method. Briefly, cells were rinsed once in PBS, dislodged using

a cell scraper and pelleted. Cell pellets were extracted as previously described (15).

Metabolites were extracted from cells at confluence for the method development (4

replicates/condition), method scale down (5 replicates/condition), palmitate timecourse (4 replicates/

condition) and palmitate:oleate (3 replicates/condition) experiments. Except where indicated, metabolites

were extracted using the ISE method, media was removed, and cells were rinsed once in PBS. Room

temperature 80% methanol (20% ultrapure water) was immediately added to the cells, and metabolites

were extracted for five minutes at room temperature. The methanol extract was removed and stored at -

80°C until processing. Cells for the lipid timecourse experiment were rinsed briefly with PBS and fixed

for staining as indicated below. Methanol extracts were evaporated to dryness under a stream of nitrogen

gas at 40˚ C in a Turbovap LV evaporator (Zymark, Hopkinton, MA). The dried extracts were

reconstituted in 550 µl methanol:water (80:20) containing recovery standards (D,L-2-

fluorophenylglycine, D,L-4-chlorophenylalanine, tridecanoic acid, D6 cholesterol).

Metabolomic profiling

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Global metabolomic profiling was carried out on three independent instrument platforms, one gas

chromatography/mass spectrometry (GC/MS) and two ultra-high performance liquid

chromatography/tandem mass spectrometry (UHPLC/MS/MS2) platforms optimized for either basic or

acidic species as previously described (21, 22). Metabolites were identified by automated comparison of

the ion features in the experimental samples to a reference library of chemical standard entries that

included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as

their associated MS/MS2 spectra. This library allowed the rapid identification of metabolites in the

sample with high confidence.

Preparation of fatty acids and lipid loading

Stock solutions of palmitate (200 mM) or oleate (100 mM) (Sigma, St. Louis, MO) were prepared

in ethanol or 1.6 mM NaOH solution respectively. Palmitate or a mixture of palmitate:oleate at a ratio of

1:2 were conjugated to albumin in 30% essentially fatty acid free (FAF) BSA solution (Invitrogen, Grand

Island, NY) and diluted to 10mM in Williams E (Invitrogen). Differentiated cells were transferred to

media containing 0.5mM lipid or BSA alone for the indicated times, with media replaced daily.

Triglyceride Accumulation

HepaRG were loaded with 0.1-0.5mM BSA-conjugated palmitate or palmitate:oleate (1:2) for 48

hours. Samples were rinsed in PBS followed by determination of triglyceride content using the

triglyceride assay kit (ZenBio Inc, Research Triangle Park, NC). The optical density was read at 540nm

on a Spectramax (Molecular Devices, Sunnyvale, CA) plate reader, and the concentration of glycerol was

determined using the following equation: 1M triglyceride = 1M glycerol + fatty acids. The mean

concentration of triglyceride and standard deviation were calculated for each sample. A two-tailed t-test

was used to calculate significant (p-value ≤0.05) differences between BSA and lipid-loaded samples at

each concentration.

Oil Red O staining

Following metabolite extraction, the cells were rinsed briefly in PBS and stained with Oil Red O.

Cells were fixed for at least 1 hour in 4% paraformaldehyde at 4° C and rinsed twice with water. Cells

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were then stained with 0.5% Oil Red O solution for 20 minutes at RT, washed 2-3 times in water and

images were captured on a Ziess Axiovert microscope equipped with a digital camera and supporting IC

capture software (The Imaging Source, Charlotte, NC).

ROS Assays

Differentiated HepaRG cells were treated with BSA or palmitate (0.3 or 0.5 mM) for 72 hours in

triplicate. ROS production was analyzed using the Image-IT live reactive oxygen detection kit

(Invitrogen) using manufacturer’s protocol. The mean and standard deviation were calculated and

expressed as relative fluorescent units (RFU). Significance was determined by two-tailed t-test, with

p≤0.05 considered significant.

Insulin-stimulated phosphorylation of AKT

Lipid-loaded HepaRG cells were placed in serum free media for 8 hours, followed by stimulation

with recombinant human insulin (Roche Diagnostics, Indianapolis, IN) at 0.1, 1.0, 10 or 100nM for 10

minutes. Samples were rinsed in PBS and lysed in RIPA buffer (150mM NaCl, 50mM Tris, 1% NP-40,

0.5% sodium deoxycholate, 0.1% SDS, 1mM EDTA) supplemented with complete protease inhibitor

cocktail (Roche Diagnostics) and HALT phosphatase inhibitor cocktail (Thermo Scientific, Waltham,

MA). Protein concentration was determined in the lysates using BCA protein assay reagent (Thermo

Scientific). 50µg of protein was separated on a 4-12% NUPAGE BIS-Tris gel (Life Technologies),

transferred to nitrocellulose and blocked in Tris-buffered saline, pH 7.5, 0.1% Tween-20 and 3% FAF

BSA for 1 hour. Membranes were rocked at 4° C overnight with phospho or total AKT primary antibody

(Cell Signaling Technology, Danvers, MA) at 1:1000 dilution. The membranes were washed three times

in TBST for 5 minutes followed by incubation with secondary antibody 1:3000 in 5% milk in TBST (Cell

Signaling Technology). After 3 TBST washes, membranes were incubated with Supersignal West Pico

chemiluminescence detection reagent (Thermo Scientific), and luminescent signals were captured using a

BioRad Gel Documenation system (BioRad laboratories, Hercules, CA).

Statistical Analysis

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Missing values for a given metabolite were imputed with the observed minimum detection value

based on the assumption that they were below the limits of instrument detection sensitivity. All

comparisons were performed using log-transformed data. All samples were normalized to protein as

determined by BCA assay (described above). Welch’s two sample t-tests or two-way ANOVA with

contrasts (palmitate timecourse) were used for comparisons. Multiple comparisons were accounted for

with the false discovery rate (FDR) method, and each FDR was estimated using q-values. Biochemicals

with p≤0.05 and q≤0.1 were considered statistically significant. For convenience of data visualization,

raw area counts for each biochemical were re-scaled by dividing the value for a specific biochemical in

each sample by the median value for that specific biochemical. Comparisons are represented as fold of

change values. Principal Component Analysis (PCA) was performed to characterize the metabolic

differences between the palmitate and control groups at each timepoint. For the PCA, each principal

component is a linear combination of all metabolites. The coefficients for the first component are

determined by those that maximize the variance. The coefficients for the second component are chosen to

maximize the variance with the constraint that it is orthogonal to the first component. All statistical

analyses were generated using Array Studio software. Array Studio, Array Viewer and Array Server and

all other Omicsoft products or service names are registered trademarks or trademarks of Omicsoft

Corporation (Research Triangle Park, NC).

Results

Method development and scale down

The ISE method reported here was first compared to a traditional homogenization extraction

method of harvested pellets of HEK293 cells. We identified 182 metabolites using the ISE method and

163 using the harvested cell pellet extraction method; 161 metabolites were common to both methods

(Supplemental Figure 1A). The metabolites identified using the ISE method represent a broad range of

biochemical classes (Supplemental Figure 1B).

The ability to detect metabolites in multi-well formats was first determined using HepG2 cells

plated in various well formats and analyzed by global metabolomics. The well sizes, volume of solvent

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used for extraction, and the number of cells seeded per well are displayed in Supplemental Table 1.

Using the ISE method and non-targeted biochemical profiling, 221 distinct metabolites, matching named

structures in our chemical reference library, were identified from a confluent 6-well plate of HepG2 cells

(Supplemental Table 1). Not surprising, the average levels of metabolites decreased as the size of the

well and total cell number decreased (Supplemental Table 1); likely due to some metabolites to falling

below the level of detection by the instrument. Nonetheless, 189 out of 221 metabolites (86%) were

detected in the 96-well format when using this extraction method. Furthermore, the cells remained fixed

to the plate, allowing them to be utilized in additional cell staining procedures. With the ISE method, it is

possible to perform global metabolomics analysis using higher throughput plate formats.

Induction of steatosis in HepaRG cells

To establish conditions that induce steatosis, HepaRG cells were cultured in growth media

supplemented with 0.1, 0.3 or 0.5 mM BSA-conjugated palmitate or palmitate:oleate (1:2), or with BSA

alone for 48 hours, and triglyceride levels were measured. Increased total triglyceride levels were

observed in both lipid treatment conditions. These increases were significant for treatment with 0.3 and

0.5 mM lipid compared to BSA alone (Figure 1A). Using 0.5 mM fatty acid, triglyceride levels were

similarly increased for both fatty acid treatments (2.9- and 3.1-fold increases compared to BSA control

for palmitate and palmitate:oleate treatments, respectively) (Figure 1A). For subsequent experiments,

0.5mM fatty acid was used to induce steatosis.

Metabolite profiles of steatotic HepaRG cells

To capture the metabolic transition to steatosis, HepaRG cells were treated with BSA-conjugated

palmitate or BSA alone over a 48 hour timecourse. Lipid accumulation was visible in the palmitate-

loaded samples within 8 hours and continued to accumulate through the 48 hour timepoint as determined

by Oil Red O staining (Figure 1B). Following metabolite extraction, global metabolomic profiling of the

palmitate-loaded samples at 8, 16, 24 and 48 hours identified a total of 169 biochemicals (Supplemental

Table 2). Principal component analysis (PCA) was used as a way to visualize the samples based on their

metabolic profiles. Information from the complete data set was reduced to two principal components. As

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seen in Figure 1C, palmitate-treated samples separated from BSA samples at all timepoints with the

exception of time 0. These data imply that a clear metabolic signature of palmitate-induced steatosis can

be obtained in HepaRG cells. In fact, we observed distinct metabolic profiles of palmitate-treated

HepaRG cells over 48 hours in the transition to steatosis (Figures 1-5).

However, given the lipotoxic effects of prolonged exposure to saturated fatty acids, including

palmitate, we performed an independent follow-up study using palmitate alone or a physiological mixture

of saturated and unsaturated fatty acids (palmitate and oleate, 1:2) (Table 1). Based on the results of the

palmitate timecourse, we selected 8 and 48 hour treatment timepoints for the follow-up study. While

distinct metabolic profiles were observed for each steatotic treatment, both palmitate and palmitate:oleate

treated HepaRG cells demonstrated changes in metabolites that were indicative of altered lipid and energy

metabolism and insulin resistance. The results from both studies are presented in parallel below.

As expected from fatty acid loading, lipid metabolism was significantly altered in both

metabolomic studies, particularly evident through observed changes in carnitine metabolism. Treatment

with palmitate or palmitate:oleate led to reduced levels of carnitine (Figure 2A and Table 1) while

acylcarnitines including butyrylcarnitine, propionlycarnitine and oleoylcarnitine were significantly

elevated (Figure 2B and Table 1). Acetyl CoA was also significantly elevated with both fatty acid

treatments at 8 hours (Table 1). However, reduced levels of coenzyme A were observed only with

palmitate treatment at the 48 h time point (Figure 2C, Table 1).

Significant changes in energy metabolism were consistently observed between palmitate-treated

and BSA control samples. Carbohydrate metabolism contributions to the TCA cycle during the 48 h of

palmitate treatment are shown in Figure 3. The product of anaerobic glycolysis, lactate, and the TCA

cycle intermediates, cis-aconitate, fumarate and malate were significantly higher in palmitate-loaded

samples for all timepoints. Citrate was significantly elevated at 8, 16 and 24h, and alpha-ketoglutarate

was elevated at 16 and 48 h with palmitate treatment. Elevated lactate and TCA cycle intermediates were

also observed in palmitate:oleate-treated cells at the 48 hour timepoint (Table 1). The BCAAs,

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isoleucine, leucine, and valine, were significantly elevated in palmitate- and in palmitate:oleate-treated

cells at 48 hours (Figure 4A and Table 1), indicating possible anaplerotic contributions to the TCA cycle.

In palmitate-treated samples, DAG and monoacylglycerols (MAG) were significantly elevated

(Figure 4B, Supplemental Table 2 and Table 1). The DAGs, 1,2-dipalmitoylglycerol and 1,3-

dipalmitoylglycerol were elevated within 8 hours of palmitate treatment (Figure 4B and Table 1) whereas

increased MAG levels were observed at 48 hours (Supplemental Table 2, Table 1). In contrast, DAG and

MAG levels were not significantly altered in palmitate:oleate-treated cells (Table 1). Levels of the

ceramide precursor, sphingosine, were elevated in both palmitate- (Figure 4C and Table 1) and

palmitate:oleate-treated cells (Table 1).

Bile acids (BA) are involved in glucose homeostasis through interaction with BA receptors FXR

(Farnesoid X receptor) and TGR5 (23, 24). The primary bile acid taurocholate was significantly reduced

compared to BSA controls within 8 hours of palmitate treatment in both studies (Figure 1D and Table 1).

In contrast, taurocholate was unaltered by palmitate:oleate treatment (Table 1).

Biochemicals involved in glutathione metabolism, including cystathionine and S-

adenosylhomocysteine (SAH), were altered with palmitate treatment in both studies (Figure 5A, Table 1,

Supplemental Table 2). Several other biochemicals of glutathione metabolism were significant in the

palmitate timecourse study. Beginning at 24 hours, glutathione (GSH) levels in palmitate-treated samples

were reduced compared to BSA and were significant at 48 hours (Figure 5B). Gamma-glutamylthreonine

was transiently elevated by palmitate at 8-24h timepoints, with gamma-glutamylleucine elevated later at

16h and 24h (Figure 5B). Overall, glutathione metabolism was not significantly altered in HepaRG cells

treated with palmitate:oleate.

ROS Assays

To determine the extent of ROS generation in palmitate-treated cells, we labeled cells with

carboxy-H2DCFDA fluorescein. This cell-permeable fluorogenic marker is readily oxidized by ROS into

a fluorescent product. Cells were treated with BSA, 0.3 or 0.5mM palmitate; tert buytl hydroperoxide

(100µM, 1 hour) was used as a positive control. Compared to BSA control samples, ROS were elevated

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1.5 fold (p<0.036) and 1.7 fold (p<0.025) in the presence of 0.3 mM or 0.5 mM palmitate, respectively

(Figure 5C). These data support the metabolic indications of oxidative stress in the palmitate induced

steatotic model as predicted by the metabolic signatures.

Lipid-induced decrease in insulin-stimulated phosphorylation of AKT

Elevated mitochondrial β-oxidation and increased oxidative and anaplerotic contributions to the

TCA cycle are reported to generate lipid-derived metabolites and reactive oxygen species (ROS) in

human NAFLD and hepatocyte cultures, decreasing the ability of insulin to stimulate proximal signaling

events (25). To test the hypothesis of lipid-induced alterations in insulin signaling, BSA or palmitate-

loaded HepaRG cells were treated with increasing concentrations of insulin (0.1-100 nM) for 10 minutes

followed by analysis of total and phosphorylated AKT by immunoblotting. In BSA control samples,

insulin stimulation resulted in the expected concentration-dependent increase in AKT phosphorylation at

serine 473. In contrast, insulin-stimulated phosphorylation of AKT was reduced in palmitate-loaded cells

by ~30% at the highest concentrations (data not shown). Total AKT levels remained constant for all

treatment conditions. This finding supports our metabolomic data indicating an insulin resistant

phenotype in palmitate-loaded HepaRG cells.

Discussion

In this study, we characterized a cell-based model of lipid-induced hepatic steatosis using

HepaRG cells treated with palmitate alone or a combination of palmitate and oleate. We established a

protocol to extract metabolites from intact cells in a multi-well format, identified metabolic signatures of

steatotic progression induced by palmitate and palmitate:oleate, and validated several of these metabolic

profiles with biological assays. Our data from two independent metabolomic studies revealed changes in

metabolites related to energy metabolism, insulin resistance and oxidative stress, which are key

biochemical processes known to be involved in steatosis and NASH. Importantly, the distinct metabolic

signatures identified using this in vitro cell model, including elevated levels of lipid metabolites, TCA

cycle intermediates and BCAAs and reduced levels of carnitine and glutathione, recapitulates biochemical

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alterations observed in serum and liver tissue from human NAFLD patients and in animal models of

NAFLD (10-12, 14, 26-30).

Elevated DAG levels in liver biopsies from NAFLD patients and animal models of NAFLD are

associated with hepatic lipotoxicity (31). Elevated DAG has also been implicated in the development of

hepatic insulin resistance through activation of protein kinase C ε (32). We observed a significant

accumulation of DAG and MAG metabolites in palmitate-treated HepaRG cells, consistent with studies

describing insulin resistant and lipotoxic phenotypes in response to saturated fatty acids (18, 19, 33).

Metabolic indications of oxidative stress in palmitate-treated HepaRG cells were supported by ROS

assays. In contrast, oleate protects against palmitate-induced lipotoxicity by targeting palmitate for

triglyceride synthesis (33, 34). In our palmitate:oleate-treated steatotic model, DAG and MAG levels

were not significantly altered, suggesting that oleate is protective against accumulation of these lipid

metabolites. However, elevated levels of sphingolipid suggest that the ceramide signaling pathway may

be altered in palmitate:oleate-treated HepaRG cells. Ceramide levels are increased in insulin-resistant

humans, and this has been attributed to ceramide impairment of insulin-stimulated phosphorylation of Akt

(35). These data demonstrate that different fatty acids or combinations of fatty acids may induce insulin

resistance through distinct mechanisms.

Elevated acetyl CoA and concomitant decrease in carnitine levels in the steatotic HepaRG cells,

suggest that processing of fatty acids through β-oxidation is occurring at early timepoints but may be

limited with prolonged lipid treatment due to eventual depletion of free carnitine. Carnitine

supplementation has been explored for its ability to promote β-oxidation of fatty acids and reduce

steatosis in rodent models (36). Without carnitine supplementation, fatty acids may be preferentially

partitioned towards alternative metabolic pathways that could contribute to the generation of toxic lipid

metabolites (8). Interestingly, palmitoylcarnitine was elevated in palmitate-treated cells, whereas both

palmitoylcarnitine and oleoylcarnitine were elevated in palmitate:oleate conditions. These incompletely

oxidized fatty acids further suggest altered β-oxidation. In a mouse model of NAFLD, metabolomic

analysis revealed depletion of carnitine in liver tissue but not in serum (14). In a separate metabolomic

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study, elevated levels of circulating carnitine species including free carnitine, butyrylcarnitine and

methylbutyrylcarnitine, were observed in plasma from NAFLD patients (10). The results in liver tissue

are consistent with our findings in the two steatotic cell treatment conditions, and emphasize that

plasma/serum metabolites reflect the net result of all organs/tissues rather than organ/tissue-specific

alterations.

A recent study analyzing mitochondrial oxidative metabolism in the liver of individuals with

NAFLD demonstrates that elevated hepatic triglyceride is associated with higher flux in the TCA cycle

and elevated anaplerotic TCA cycle contributions from pyruvate cycling and gluconeogenesis (26). In the

present study, we observed an increase in lactate and TCA cycle intermediates in response to palmitate,

suggesting increased TCA cycle activity in this condition. This effect may be less severe or delayed in

palmitate:oleate conditions as TCA cycle intermediates were not elevated until 48 h, and lactate was not

significantly altered.

High levels of BCAAs, which include the essential amino acids valine, leucine, isoleucine and

their metabolites, are associated with insulin resistance (28, 37). Elevated BCAAs have been observed in

patients with obesity, type 2 diabetes and NAFLD (10, 28, 37, 38) and are strong candidates in predicting

insulin sensitivity (38) and monitoring disease progression and response to therapeutic intervention (28,

37). Consistent with these clinical observations and with the altered TCA cycle activity in this model, we

observed elevated levels of BCAAs in both fatty acid-loaded conditions, suggesting anaplerotic

contributions to the TCA cycle. The BCAA levels in palmitate:oleate-treated cells increased at 48 h,

which is consistent with the delayed TCA cycle effect in that condition. When taken in concert, the

metabolic indications of impaired β-oxidation and increased TCA cycle activity through anaplerotic

contributions, suggest clear mitochondrial abnormalities in both fatty acid-loaded conditions.

In addition to their role in dietary lipid absorption and cholesterol homeostasis, BAs are signaling

molecules that play a role in glucose and lipid metabolism (24). Unlike DAGs and ceramide, BA

signaling has been shown to improve insulin sensitivity by modulating triglyceride, cholesterol and

glucose homeostasis (39). Lower levels of the BA taurocholate are associated with early stage insulin

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resistance in mice (40). Our observation of reduced taurocholate levels in palmitate-treated HepaRG cells

may indicate the onset of an insulin-resistant phenotype. In contrast to our findings, increased levels of

bile acids were reported in steatotic liver tissues (12). These opposite effects may be due to sample type

differences (HepaRG cells used in our studies compared to human liver tissue), although additional

studies are needed to clarify the role of BA signaling in this steatotic HepaRG cell model. Regardless, the

data suggest that dysregulation of BA homeostasis is associated with insulin resistance and steatosis and

may contribute to the pathogenesis of NAFLD,

In this study, we used a novel method to extract metabolites from intact cells and performed an

unbiased metabolomics approach to examine global changes in hepatocyte-like cells during steatotic

progression induced by fatty acids. With this approach, we identified key biochemical signatures related

to steatosis and NAFLD, including altered mitochondrial metabolism, insulin resistance and oxidative

stress. These profiles support many of the reported biochemical alterations observed in animal models of

NAFLD and importantly are consistent with metabolomic signatures obtained with clinically relevant

human liver samples and serum from patients with varying degrees of fatty liver disease. Our results

demonstrate the suitability and utility of this steatotic cell model to further investigate steatosis and

insulin resistance and represent a novel platform for drug development and preclinical toxicity studies.

Disclosure summary: MB is employed by Metabolon and has equity interests in Metabolon. SC is

employed by ZenBio. MM is employed by Metabolon and has equity interests in Metabolon. KL is

employed by Metabolon and has equity interests in Metabolon. BC is employed by ZenBio.

Acknowledgments

The authors thank the Metabolon platform team and chemical spectra analysts for contributing to data

acquisition. The assistance of Jacob Wulff in statistical analysis of the data is gratefully acknowledged.

This work was supported by a grant from the National Institute of Health DK088430 (BC). Support for

MB and SC was received from the North Carolina Biotechnology Center Industrial Fellowship Program.

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Supplementary information description

Supplemental Figure 1 compares the number of metabolites detected when using the cell scraping and

homogenization or ISE method. Supplemental Table 1summarizes the conditions and results of

metabolomic analysis when scaling down the number of cells. Supplemental Table 2 displays the

metabolomic results for palmitate timecourse treatment of HepaRG cells. Supplementary Material is

available at www.nature.com/obesity.

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References

1. Erickson SK. Nonalcoholic fatty liver disease. J Lipid Res. 2009 Apr;50 Suppl:S412-6.

2. Dunn W, Schwimmer JB. The obesity epidemic and nonalcoholic fatty liver disease in children. Curr

Gastroenterol Rep. 2008 Feb;10(1):67-72.

3. Angulo P. Nonalcoholic fatty liver disease. N Engl J Med. 2002 Apr 18;346(16):1221-31.

4. Wei Y, Rector RS, Thyfault JP, Ibdah JA. Nonalcoholic fatty liver disease and mitochondrial dysfunction.

World J Gastroenterol. 2008 Jan 14;14(2):193-9.

5. Dowman JK, Tomlinson JW, Newsome PN. Pathogenesis of non-alcoholic fatty liver disease. QJM. 2010

Feb;103(2):71-83.

6. Smith BW, Adams LA. Nonalcoholic fatty liver disease and diabetes mellitus: pathogenesis and treatment.

Nat Rev Endocrinol. 2011 Aug;7(8):456-65.

7. Savage DB, Petersen KF, Shulman GI. Disordered lipid metabolism and the pathogenesis of insulin

resistance. Physiol Rev. 2007 Apr;87(2):507-20.

8. Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O, et al. Mitochondrial overload and

incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab. 2008 Jan;7(1):45-56.

9. Rolo AP, Teodoro JS, Palmeira CM. Role of oxidative stress in the pathogenesis of nonalcoholic

steatohepatitis. Free Radic Biol Med. 2012 Jan 1;52(1):59-69.

10. Kalhan SC, Guo L, Edmison J, Dasarathy S, McCullough AJ, Hanson RW, et al. Plasma metabolomic

profile in nonalcoholic fatty liver disease. Metabolism. 2011 Mar;60(3):404-13.

11. Pilvi TK, Seppanen-Laakso T, Simolin H, Finckenberg P, Huotari A, Herzig KH, et al. Metabolomic

changes in fatty liver can be modified by dietary protein and calcium during energy restriction. World J

Gastroenterol. 2008 Jul 28;14(28):4462-72.

12. Garcia-Canaveras JC, Donato MT, Castell JV, Lahoz A. A comprehensive untargeted metabonomic

analysis of human steatotic liver tissue by RP and HILIC chromatography coupled to mass spectrometry reveals

important metabolic alterations. J Proteome Res. 2011 Oct 7;10(10):4825-34.

13. de Wit NJ, Afman LA, Mensink M, Muller M. Phenotyping the effect of diet on non-alcoholic fatty liver

disease. Journal of hepatology. 2012 Dec;57(6):1370-3.

14. Kim HJ, Kim JH, Noh S, Hur HJ, Sung MJ, Hwang JT, et al. Metabolomic analysis of livers and serum

from high-fat diet induced obese mice. J Proteome Res. 2011 Feb 4;10(2):722-31.

15. Vulimiri SV, Misra M, Hamm JT, Mitchell M, Berger A. Effects of mainstream cigarette smoke on the

global metabolome of human lung epithelial cells. Chem Res Toxicol. 2009 Mar 16;22(3):492-503.

16. Watson M, Roulston A, Belec L, Billot X, Marcellus R, Bedard D, et al. The small molecule GMX1778 is

a potent inhibitor of NAD+ biosynthesis: strategy for enhanced therapy in nicotinic acid phosphoribosyltransferase

1-deficient tumors. Mol Cell Biol. 2009 Nov;29(21):5872-88.

17. Wetmore DR, Joseloff E, Pilewski J, Lee DP, Lawton KA, Mitchell MW, et al. Metabolomic profiling

reveals biochemical pathways and biomarkers associated with pathogenesis in cystic fibrosis cells. J Biol Chem.

2010 Oct 1;285(40):30516-22.

18. Malhi H, Bronk SF, Werneburg NW, Gores GJ. Free fatty acids induce JNK-dependent hepatocyte

lipoapoptosis. J Biol Chem. 2006 Apr 28;281(17):12093-101.

19. Nakamura S, Takamura T, Matsuzawa-Nagata N, Takayama H, Misu H, Noda H, et al. Palmitate induces

insulin resistance in H4IIEC3 hepatocytes through reactive oxygen species produced by mitochondria. J Biol Chem.

2009 May 29;284(22):14809-18.

20. Kanebratt KP, Andersson TB. Evaluation of HepaRG cells as an in vitro model for human drug metabolism

studies. Drug Metab Dispos. 2008 Jul;36(7):1444-52.

21. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E. Integrated, nontargeted ultrahigh performance

liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative

quantification of the small-molecule complement of biological systems. Anal Chem. 2009 Aug 15;81(16):6656-67.

22. Brown MV, McDunn JE, Gunst PR, Smith EM, Milburn MV, Troyer DA, et al. Cancer detection and

biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies. Genome Med.

2012;4(4):33.

23. Zhang Y, Lee FY, Barrera G, Lee H, Vales C, Gonzalez FJ, et al. Activation of the nuclear receptor FXR

improves hyperglycemia and hyperlipidemia in diabetic mice. Proc Natl Acad Sci U S A. 2006 Jan 24;103(4):1006-

11.

24. Hylemon PB, Zhou H, Pandak WM, Ren S, Gil G, Dent P. Bile acids as regulatory molecules. J Lipid Res.

2009 Aug;50(8):1509-20.

of 31


Obesity

18

25. Chavez JA, Summers SA. A ceramide-centric view of insulin resistance. Cell Metab. 2012 May

2;15(5):585-94.

26. Sunny NE, Parks EJ, Browning JD, Burgess SC. Excessive hepatic mitochondrial TCA cycle and

gluconeogenesis in humans with nonalcoholic fatty liver disease. Cell Metab. 2011 Dec 7;14(6):804-10.

27. Sunny Nishanth E, Parks Elizabeth J, Browning Jeffrey D, Burgess Shawn C. Excessive Hepatic

Mitochondrial TCA Cycle and Gluconeogenesis in Humans with Nonalcoholic Fatty Liver Disease. Cell

metabolism. 2011;14(6):804-10.

28. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, et al. A branched-chain amino acid-

related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell

Metab. 2009 Apr;9(4):311-26.

29. Gorden DL, Ivanova PT, Myers DS, McIntyre JO, VanSaun MN, Wright JK, et al. Increased

diacylglycerols characterize hepatic lipid changes in progression of human nonalcoholic fatty liver disease;

comparison to a murine model. PLoS One. 2011;6(8):e22775.

30. Yetukuri L, Katajamaa M, Medina-Gomez G, Seppanen-Laakso T, Vidal-Puig A, Oresic M. Bioinformatics

strategies for lipidomics analysis: characterization of obesity related hepatic steatosis. BMC Syst Biol. 2007;1:12.

31. Neuschwander-Tetri BA. Hepatic lipotoxicity and the pathogenesis of nonalcoholic steatohepatitis: the

central role of nontriglyceride fatty acid metabolites. Hepatology. 2010 Aug;52(2):774-88.

32. Jornayvaz FR, Shulman GI. Diacylglycerol activation of protein kinase Cepsilon and hepatic insulin

resistance. Cell Metab. 2012 May 2;15(5):574-84.

33. Montell E, Turini M, Marotta M, Roberts M, Noe V, Ciudad CJ, et al. DAG accumulation from saturated

fatty acids desensitizes insulin stimulation of glucose uptake in muscle cells. Am J Physiol Endocrinol Metab. 2001

Feb;280(2):E229-37.

34. Wei Y, Wang D, Pagliassotti MJ. Saturated fatty acid-mediated endoplasmic reticulum stress and apoptosis

are augmented by trans-10, cis-12-conjugated linoleic acid in liver cells. Mol Cell Biochem. 2007 Sep;303(1-2):105-

13.

35. Adams JM, 2nd, Pratipanawatr T, Berria R, Wang E, DeFronzo RA, Sullards MC, et al. Ceramide content

is increased in skeletal muscle from obese insulin-resistant humans. Diabetes. 2004 Jan;53(1):25-31.

36. Xia Y, Li Q, Zhong W, Dong J, Wang Z, Wang C. L-carnitine ameliorated fatty liver in high-calorie

diet/STZ-induced type 2 diabetic mice by improving mitochondrial function. Diabetol Metab Syndr. 2011;3:31.

37. Newgard CB. Interplay between Lipids and Branched-Chain Amino Acids in Development of Insulin

Resistance. Cell Metab. 2012 May 2;15(5):606-14.

38. McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, et al. Circulating branched-

chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents.

Pediatr Obes. 2012 Sep 7.

39. Wei J, Qiu de K, Ma X. Bile acids and insulin resistance: implications for treating nonalcoholic fatty liver

disease. J Dig Dis. 2009 May;10(2):85-90.

40. Li LO, Hu YF, Wang L, Mitchell M, Berger A, Coleman RA. Early hepatic insulin resistance in mice: a

metabolomics analysis. Mol Endocrinol. 2010 Mar;24(3):657-66.

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Figure Legends

Figure 1. Characterization of fatty acid-loaded HepaRG cells. A) Differentiated HepaRG cells were

treated with BSA alone or with the indicated concentrations of palmitate or palmitate:oleate (1:2) for 48

hours prior to assaying triglyceride content. Asterisks indicate a significant (p≤0.05) difference compared

to BSA alone. B) Differentiated HepaRG cells were incubated with BSA (top panels) or BSA-conjugated

palmitate (bottom panels) for 8 or 48 hours. Cells were briefly exposed to methanol to extract metabolites

followed by fixation and staining with 0.5% Oil Red O. Representative images revealing the

accumulation of lipid droplets (red staining) were captured on a Ziess Axiovert microscope equipped with

a digital camera and supporting IC capture software. C) Principal component analysis based on the

metabolic profiles of BSA (open shapes) and palmitate-treated (solid shapes) samples over time. Two

principal components were used (Component 1, x-axis; Component 2, y-axis), and the percent variance

for each component is shown. Each time is represented by a different shape (0, circle; 8 h, square; 16 h,

upward pointing triangle; 24 h, downward pointing triangle; 48 h, left pointing triangle). The inclusion of

the samples in each group (BSA or palmitate treatment) is indicated by the large ovals.

Figure 2. Lipid metabolism. A) Line plot view of carnitine. For the line plot, the mean relative

metabolite level is shown for BSA (gray) and palmitate-treated (black) samples at the indicated time

points. The x-axis represents the treatment time. The y-axis represents the relative level of the

metabolite. Asterisks indicate a significant difference (p≤0.05, q≤0.1) between palmitate treatment and

BSA control at the given time. Error bars indicate one standard error (SE). B) Line plot view of

butyrylcarnitine. C) Line plot view of coenzyme A.

Figure 3. Effect of palmitate on glycolytic contribution to the TCA cycle. Metabolites involved in

glycolysis and the TCA cycle are indicated by line plots. The mean relative metabolite level is shown for

BSA (gray) and palmitate-treated (black) samples at each time point. The x-axis represents the treatment

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time. The y-axis represents the relative level of the metabolite. Asterisks indicate a significant difference

(p≤0.05, q≤0.1) between palmitate treatment and BSA control at the given time. Error bars indicate one

SE.

Figure 4. Metabolic indications of insulin resistance. A) Line plot view of the branched chain amino

acids isoleucine, leucine and valine. The mean relative metabolite level is shown for BSA (gray) and

palmitate-treated (black) samples at each time point. The x-axis represents the treatment time. The y-axis

represents the relative level of the metabolite. Asterisks indicate a significant difference (p≤0.05, q≤0.1)

between palmitate treatment and BSA control at the given time. Error bars indicate one SE. B) Line plot

view of the diacylglycerols, 1,2-dipalmitoylglycerol and 1,3-dipalmitoylglycerol. C) Line plot view of the

ceramide precursor, sphingosine. D) Line plot view of the bile acid, taurocholate.

Figure 5. Glutathione metabolism and oxidative stress. A) Line plot view of cystathionine. The mean

relative metabolite level is shown for BSA (gray) and palmitate-treated (black) samples at each time

point. The x-axis represents the treatment time. The y-axis represents the relative level of the metabolite.

Asterisks indicate a significant difference (p≤0.05, q≤0.1) between palmitate treatment and BSA control

at the given time. Error bars indicate one SE. B) Line plot views of reduced glutathione (GSH), gamma-

glutamylthreonine and gamma-glutamylleucine. C) HepaRG cells were untreated (UNT), loaded with

BSA or BSA-conjugated palmitate (PA) at 0.3 or 0.5mM for 72 hours, or exposed to 100µM TBHP

(positive control). The mean level of reactive oxygen species (ROS) detected using carboxy-H2DCFDA

fluorescein is shown (n=3) and expressed as relative fluorescent units (RFU). Error bars represent

standard deviation. Statistically significant changes are indicted with their calculated p values.

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Figure 1

8 hour

BSA

Pa

lmit

ate

48 hour B.

0

5

10

15

20

25

30

BSA Palmitate Palmitate:Oleate

µM

Tri

glyc

eri

de

0.1mM

0.3mM

0.5mM

*

*

*

*

Component 1 [18.10%]

Com

po

nen

t 2

[1

7.5

1%

]

Time

Treatment

BSA Palmitate (PA)

BSA

PA

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*

Figure 2

B.

* * *

*

* *

BSA Palmitate

Treatment

Rel

ativ

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etab

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* * * *

Rel

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olit

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* *

Rel

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pyruvate

acetyl-CoA

citrate

cis-aconitate

isocitrate

α-ketoglutarate

succinyl-CoA succinate

fumarate

malate

oxaloacetate

lactate

glucose

* * *

Figure 3

*

Rel

ativ

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etab

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vel

* * *

Rel

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etab

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* * * *

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* * * *

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BSA Palmitate

Treatment

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* *

A.

* * * *

* * * *

B.

* * *

C.

*

Figure 4

BSA Palmitate

Treatment

Rel

ativ

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etab

olit

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vel

Rel

ativ

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etab

olit

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Rel

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* * * *

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D.

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Figure 5

BSA Palmitate

Treatment

Rel

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etab

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*

cystathionine

*

Rel

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glutathione, reduced (GSH)

* *

gamma-glutamylleucine

Rel

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* * *

gamma-glutamylthreonine

Rel

ativ

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0

2

4

6

8

10

12

BSA 0.3mMPA

0.5mMPA

UNT TBHP

RO

S (R

FU)

p=0.025

p=0.036

p=0.020

p=0.018

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Table 1. Selected metabolites altered in palmitate- and palmitate:oleate-treated HepaRG cells at 8 and 48 hours. Metabolite

values are expressed as fold of change for the indicated condition relative to BSA control. Bold values are significant with p≤0.05 and

q≤0.1

Fold Change Relative to BSA Control

Palmitate Palmitate:Oleate

Classification Biochemical Name 8 Hours 48 Hours 8 Hours 48 Hours

Carnitine metabolism

carnitine 0.58 0.35 0.27 0.56

3-dehydrocarnitine 0.83 0.67 0.87 1.46

acetylcarnitine 1.23 0.64 1.44 1.16

palmitoylcarnitine 2.88 2.7 1.86 1.38

oleoylcarnitine 1.01 0.86 2.37 2.83

propionylcarnitine 1.38 1.23 1.21 1.39

butyrylcarnitine 2.18 3.43 2.41 2.48

Pantothenate and CoA metabolism coenzyme A 0.93 0.63 0.92 1.02

acetyl CoA 1.99 0.94 1.78 0.78

Glycolysis, gluconeogenesis, pyruvate

metabolism

glucose-6-phosphate (G6P) 0.86 1.15 0.95 1.69

glucose 0.58 0.93 0.81 1.3

lactate 1.37 1.86 1.11 1.54

TCA cycle

citrate 1.45 1.38 1.39 1.98

alpha-ketoglutarate 1.25 1.4 0.91 1.45

succinate 0.67 1.36 0.77 1.46

fumarate 1.3 1.38 1.05 1.78

malate 1.52 1.27 1.13 1.73

Valine, leucine and isoleucine

metabolism

isoleucine 0.81 1.86 0.91 1.65

leucine 0.99 2.1 0.98 1.82

valine 0.83 1.45 0.87 1.4

2-methylbutyrylcarnitine (C5) 1.13 1.34 1.01 1.25

isovalerylcarnitine 1.27 2.42 1.25 1.96

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Monoacylglycerol

1-pentadecanoylglycerol (1-monopentadecanoin) 1.07 1.37 1.02 1.4

1-heptadecanoylglycerol (1-monoheptadecanoin) 0.9 1.37 0.85 1.17

1-palmitoylglycerol (1-monopalmitin) 0.86 1.39 0.84 1.14

2-palmitoylglycerol (2-monopalmitin) 0.91 1.58 0.89 1.07

1-stearoylglycerol (1-monostearin) 0.89 1.41 0.83 1.14

2-stearoylglycerol (2-monostearin) 0.85 1.64 0.81 1.11

1-oleoylglycerol (1-monoolein) 0.89 1.19 0.89 1.3

1-behenoylglycerol (1-monobehenin) 0.95 1.43 0.91 1.08

Diacylglycerol 1,2-dipalmitoylglycerol 9.76 13.28 1.29 1.44

1,3-dipalmitoylglycerol 2.59 5.93 0.84 1.59

Sphingolipid sphingosine 1.84 0.98 1.36 1.73

palmitoyl sphingomyelin 0.99 0.87 1.05 1.46

Bile acid metabolism taurocholate 0.56 0.28 0.64 0.8

taurochenodeoxycholate 0.57 0.26 0.83 1.04

Glutathione metabolism

cysteine 0.7 1 0.8 1

cystathionine 0.8 0.6 0.9 1.1

S-adenosylhomocysteine (SAH) 1.1 0.5 1.3 1.5

glutathione, reduced (GSH) 0.97 0.92 1.03 1.05

S-methylglutathione 0.85 0.95 0.5 1.19

5-oxoproline 1 0.81 1.65 1.42

glutathione, oxidized (GSSG) 0.79 0.7 0.97 1.29

S-lactoylglutathione 2.42 1.59 0.89 1.27

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2 161 21

ISE

182

Cell Scraping

163

Intact Sample Extraction Cell Scraping B.

Supplemental Figure 1

Biochemical Class Total

Metabolites Detected

Metabolites Detected by Extraction Method

Cell Scraping Intact Sample

Extraction

Amino Acid 65 62 65

Peptide 5 4 5

Carbohydrate 15 15 14

Energy 5 5 5

Lipid 60 54 59

Nucleotide 15 9 15

Cofactors and vitamins 12 9 12

Xenobiotics 7 5 7

TOTAL 184 163 182

(A) The overlap in the metabolites detected using each extraction method is shown in the Venn diagram, and the number of metabolites detected is shown in the boxes below. (B) The number of metabolites detected with each extraction method were grouped by biochemical class.

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Table S1. Summary of cell scale-down method results

Well size

MeOH

volume

Cells per well

at seeding

Metabolite

Number

Mean

metabolite %

decreasea

6-well 2ml 8.6 M 221 N/A

12-well 1ml 2.3 M 218 46%

24-well 500μl 1.3 M 214 69%

48-well 200μl .68 M 211 70%

96-well 100μl .33 M 189 82%

a The percent change in the level of each metabolite compared to that of the 6-well condition was calculated. The

mean percent decrease of all metabolites for the given condition is reported.

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0 8 16 24 48 P-Value Q-Value P-Value Q-Value P-Value Q-Value P-Value Q-Value

Amino acid Glycine, serine and threonine metabolism glycine 1.02 0.89 0.88 0.66 0.97 0.8127 1.0000 0.1622 0.4726 0.1441 0.1635 < 0.001 0.0007Amino acid Glycine, serine and threonine metabolism serine 1.01 0.93 0.91 0.65 0.80 0.9590 1.0000 0.5432 0.8948 0.4514 0.3522 0.0014 0.0104Amino acid Glycine, serine and threonine metabolism 3-phosphoserine 1.24 0.82 0.77 0.37 0.56 0.3241 1.0000 0.4013 0.8323 0.2152 0.2143 < 0.001 0.0005Amino acid Glycine, serine and threonine metabolism threonine 1.01 1.07 1.31 1.22 1.32 0.8715 1.0000 0.3607 0.7848 < 0.001 0.0019 0.0068 0.0381Amino acid Alanine and aspartate metabolism aspartate 1.09 1.34 1.19 1.07 1.00 0.2973 1.0000 0.0013 0.0110 0.0423 0.0671 0.4204 0.6965Amino acid Alanine and aspartate metabolism asparagine 0.98 1.26 1.43 1.01 1.26 0.8863 1.0000 0.0198 0.1045 < 0.001 0.0036 0.8231 0.9882Amino acid Alanine and aspartate metabolism beta-alanine 1.35 1.36 1.07 1.00 0.73 0.1341 1.0000 0.0635 0.2491 0.8505 0.5072 0.9687 1.0000Amino acid Alanine and aspartate metabolism alanine 1.05 1.42 1.57 1.22 1.49 0.4533 1.0000 < 0.001 0.0000 < 0.001 0.0000 0.0013 0.0104Amino acid Glutamate metabolism glutamate 1.04 1.22 1.22 1.21 1.02 0.5734 1.0000 0.0161 0.0907 0.0181 0.0339 0.0183 0.0789Amino acid Glutamate metabolism glutamate, gamma-methyl ester 1.10 0.96 0.83 0.75 1.06 0.4968 1.0000 0.7144 0.9583 0.1971 0.2006 0.0604 0.1792Amino acid Glutamate metabolism glutamine 0.98 1.06 1.09 1.02 0.87 0.7851 1.0000 0.4444 0.8445 0.2395 0.2288 0.7890 0.9877Amino acid Glutamate metabolism N-acetylglutamate 1.25 0.98 0.77 0.93 1.16 0.4568 1.0000 0.9465 1.0000 0.2627 0.2394 0.8728 1.0000Amino acid Histidine metabolism histidine 1.10 1.14 0.93 1.32 1.13 0.7600 1.0000 0.6620 0.9481 0.7817 0.4816 0.3022 0.5625Amino acid Lysine metabolism lysine 1.13 1.38 1.03 0.84 1.16 0.2811 1.0000 0.0137 0.0796 0.8301 0.4982 0.1411 0.3359Amino acid Phenylalanine & tyrosine metabolism phenylalanine 1.11 1.04 1.31 0.92 1.24 0.2043 1.0000 0.6109 0.9136 0.0040 0.0109 0.3549 0.6248Amino acid Phenylalanine & tyrosine metabolism tyrosine 1.12 1.01 1.28 0.94 1.27 0.1538 1.0000 0.8930 1.0000 0.0081 0.0181 0.5068 0.7781Amino acid Phenylalanine & tyrosine metabolism 3-(4-hydroxyphenyl)lactate 1.00 1.00 0.80 1.00 1.00 1.0000 1.0000 1.0000 1.0000 0.0329 0.0547 1.0000 1.0000Amino acid Phenylalanine & tyrosine metabolism phenylacetylglutamine 1.31 1.96 150.76 0.68 0.06 0.7784 1.0000 0.4438 0.8445 0.0785 0.1081 0.5626 0.8058Amino acid Phenylalanine & tyrosine metabolism p-toluic acid 1.48 1.36 1.02 0.77 1.23 0.0215 1.0000 0.0530 0.2249 0.9102 0.5394 0.1363 0.3339Amino acid Tryptophan metabolism kynurenine 1.03 2.94 1.52 1.32 0.74 0.7246 1.0000 < 0.001 0.0006 0.0753 0.1052 0.2718 0.5405Amino acid Tryptophan metabolism tryptophan 1.08 1.01 1.23 0.95 1.77 0.4558 1.0000 0.9602 1.0000 0.1790 0.1905 0.5897 0.8236Amino acid Tryptophan metabolism C-glycosyltryptophan 1.11 0.70 2.22 0.51 0.48 0.7502 1.0000 0.3242 0.7473 0.4667 0.3611 0.0722 0.2069Amino acid Valine, leucine and isoleucine metabolism 3-methyl-2-oxovalerate 1.00 1.08 1.32 1.00 1.15 1.0000 1.0000 0.4461 0.8445 0.0124 0.0253 1.0000 1.0000Amino acid Valine, leucine and isoleucine metabolism isoleucine 1.05 1.18 1.31 1.04 1.50 0.4391 1.0000 0.0532 0.2249 0.0030 0.0086 0.6458 0.8662Amino acid Valine, leucine and isoleucine metabolism leucine 1.02 1.07 1.40 1.08 1.58 0.6761 1.0000 0.5652 0.9118 0.0053 0.0137 0.4360 0.7066Amino acid Valine, leucine and isoleucine metabolism valine 1.07 1.36 1.22 0.93 1.62 0.5293 1.0000 0.0098 0.0637 0.0848 0.1151 0.4432 0.7066Amino acid Valine, leucine and isoleucine metabolism 4-methyl-2-oxopentanoate 1.00 1.04 1.16 1.12 1.49 1.0000 1.0000 0.8124 0.9622 0.3385 0.2805 0.4761 0.7450Amino acid Valine, leucine and isoleucine metabolism isobutyrylcarnitine 0.82 0.91 3.15 0.81 0.40 0.6846 1.0000 0.7526 0.9622 0.7298 0.4649 0.5616 0.8058Amino acid Valine, leucine and isoleucine metabolism 2-methylbutyroylcarnitine 1.13 0.99 1.22 0.63 0.49 0.6680 1.0000 0.9722 1.0000 0.7883 0.4819 0.0449 0.1460Amino acid Valine, leucine and isoleucine metabolism isovalerylcarnitine 1.00 1.15 1.17 0.73 0.69 0.9957 1.0000 0.3272 0.7473 0.3365 0.2805 0.0786 0.2214Amino acid Valine, leucine and isoleucine metabolism hydroxyisovaleroyl carnitine 1.12 0.92 1.16 0.61 0.44 0.4607 1.0000 0.6368 0.9199 0.7568 0.4750 0.0107 0.0545Amino acid Valine, leucine and isoleucine metabolism tiglyl carnitine 0.98 0.80 1.91 0.58 0.78 0.9476 1.0000 0.2309 0.5912 0.0018 0.0059 0.0084 0.0442Amino acid Cysteine, methionine, SAM, taurine metabolism cysteine 0.95 1.23 1.10 0.96 1.01 0.8345 1.0000 0.4038 0.8323 0.4326 0.3433 0.8151 0.9882Amino acid Cysteine, methionine, SAM, taurine metabolism cystathionine 1.15 0.98 0.97 0.73 0.61 0.2789 1.0000 0.9967 1.0000 0.7925 0.4819 0.0287 0.1009Amino acid Cysteine, methionine, SAM, taurine metabolism methionine sulfoxide 1.03 1.27 1.46 1.59 1.86 0.7729 1.0000 0.0612 0.2461 0.0057 0.0137 < 0.001 0.0054Amino acid Cysteine, methionine, SAM, taurine metabolism S-adenosylhomocysteine (SAH) 1.32 1.37 1.32 1.02 0.66 0.2391 1.0000 0.1582 0.4697 0.2378 0.2288 0.5406 0.8014Amino acid Cysteine, methionine, SAM, taurine metabolism methionine 1.11 0.97 1.29 1.09 1.32 0.1844 1.0000 0.5756 0.9136 0.0039 0.0109 0.3396 0.6106Amino acid Urea cycle; arginine-, proline-, metabolism arginine 0.84 0.81 1.35 0.83 1.72 0.5860 1.0000 0.3919 0.8278 0.2343 0.2288 0.6044 0.8304Amino acid Urea cycle; arginine-, proline-, metabolism ornithine 1.01 1.28 1.31 0.91 1.06 0.9167 1.0000 0.0446 0.2091 0.0233 0.0419 0.3029 0.5625Amino acid Urea cycle; arginine-, proline-, metabolism proline 1.04 1.51 1.67 1.41 1.94 0.5612 1.0000 < 0.001 0.0001 < 0.001 0.0000 < 0.001 0.0007Amino acid Urea cycle; arginine-, proline-, metabolism N-acetylornithine 0.87 1.09 0.87 0.94 1.37 0.5072 1.0000 0.7071 0.9583 0.2678 0.2411 0.8902 1.0000Amino acid Urea cycle; arginine-, proline-, metabolism trans-4-hydroxyproline 0.65 1.33 1.23 0.94 0.98 0.0893 1.0000 0.2279 0.5912 0.4747 0.3613 0.9247 1.0000Amino acid Creatine metabolism creatine 1.00 1.04 0.98 0.87 0.64 0.9474 1.0000 0.7279 0.9622 0.7456 0.4717 0.2048 0.4327Amino acid Polyamine metabolism 5-methylthioadenosine (MTA) 1.06 1.07 1.08 1.14 1.25 0.5612 1.0000 0.6004 0.9136 0.5156 0.3772 0.1411 0.3359Amino acid Polyamine metabolism putrescine 1.06 0.87 1.45 0.75 0.76 0.5500 1.0000 0.5986 0.9136 0.0890 0.1177 0.1668 0.3746Amino acid Polyamine metabolism spermidine 0.78 0.73 0.94 0.65 0.81 0.1719 1.0000 0.0759 0.2789 0.6607 0.4387 0.0335 0.1157Amino acid Guanidino and acetamido metabolism 4-guanidinobutanoate 1.42 0.72 1.59 0.53 0.64 0.3836 1.0000 0.2832 0.6936 0.3917 0.3162 0.0486 0.1551Amino acid Guanidino and acetamido metabolism 4-acetamidobutanoate 0.91 1.07 1.94 0.94 0.95 0.8293 1.0000 0.8143 0.9622 0.1315 0.1578 0.8303 0.9882Amino acid Glutathione metabolism glutathione, reduced (GSH) 1.03 1.08 0.98 0.84 0.69 0.7100 1.0000 0.4648 0.8445 0.7983 0.4822 0.0570 0.1720Amino acid Glutathione metabolism S-methylglutathione 1.06 1.01 1.19 0.72 0.52 0.7738 1.0000 0.9111 1.0000 0.4175 0.3342 0.2604 0.5366Amino acid Glutathione metabolism 5-oxoproline 1.08 1.22 1.36 0.96 1.04 0.5781 1.0000 0.1133 0.3831 0.0325 0.0547 0.8555 1.0000Amino acid Glutathione metabolism glutathione, oxidized (GSSG) 1.03 0.96 1.05 0.78 0.93 0.8301 1.0000 0.6365 0.9199 0.7122 0.4631 0.1170 0.2952Amino acid Glutathione metabolism ophthalmate 0.98 1.41 1.67 1.03 0.79 0.9043 1.0000 0.0475 0.2170 0.0029 0.0086 0.5750 0.8099Amino acid Glutathione metabolism S-lactoylglutathione 1.36 0.75 1.00 1.04 0.99 0.0458 1.0000 0.0648 0.2491 0.9499 0.5540 0.7617 0.9679Peptide Dipeptide alanylglutamine 1.00 1.13 1.00 1.00 1.00 1.0000 1.0000 0.5835 0.9136 1.0000 0.5540 1.0000 1.0000Peptide Dipeptide pro-hydroxy-pro 0.97 1.28 5.17 0.81 0.71 0.9473 1.0000 0.6843 0.9558 0.0897 0.1177 0.5115 0.7781Peptide Dipeptide cysteinylglycine 0.95 1.45 1.16 0.88 0.76 0.7608 1.0000 0.0094 0.0637 0.2905 0.2450 0.3773 0.6441Peptide gamma-glutamyl gamma-glutamylleucine 0.75 1.02 2.30 1.94 1.36 0.4324 1.0000 0.7376 0.9622 0.0092 0.0196 0.0245 0.0919Peptide gamma-glutamyl gamma-glutamylthreonine 1.08 2.36 2.70 1.63 1.32 0.6885 1.0000 < 0.001 0.0001 < 0.001 0.0000 0.0041 0.0255Carbohydrate Aminosugars metabolism erythronate 0.79 1.42 0.99 0.99 1.16 0.1738 1.0000 0.0587 0.2419 0.6977 0.4568 0.9100 1.0000Carbohydrate Fructose, mannose, galactose, starch, and sucrose

metabolismfructose 1.17 6.23 1.17 0.98 2.15 0.6814 1.0000 0.0501 0.2230 0.6562 0.4387 0.9812 1.0000

Carbohydrate Fructose, mannose, galactose, starch, and sucrose metabolism

mannitol 0.93 0.62 1.18 0.00 1.49 0.9991 1.0000 0.7030 0.9583 0.9263 0.5455 0.1658 0.3746Carbohydrate Fructose, mannose, galactose, starch, and sucrose

metabolismsorbitol 1.14 1.80 1.14 0.55 2.09 0.6328 1.0000 0.1855 0.5313 0.6170 0.4248 0.1017 0.2643

Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism glucose-6-phosphate (G6P) 0.92 1.20 1.15 0.86 0.97 0.7501 1.0000 0.4637 0.8445 0.5682 0.4061 0.8140 0.9882Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism glucose 1.09 1.02 0.86 0.64 1.15 0.3565 1.0000 0.9901 1.0000 0.1434 0.1635 < 0.001 0.0008Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism fructose 1-phosphate 0.78 1.43 0.81 0.64 0.81 0.2951 1.0000 0.0926 0.3261 0.3745 0.3049 0.1874 0.4010Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism Isobar: fructose 1,6-diphosphate, glucose 1,6-

diphosphate, myo-inositol 1,4 or 1,3-diphosphate0.99 2.92 1.67 1.62 1.69 0.9634 1.0000 < 0.001 0.0001 0.0163 0.0311 0.0199 0.0802

Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism 3-phosphoglycerate (isobar with 2-phosphoglycerate) 1.04 1.06 0.87 0.96 1.38 0.7083 1.0000 0.8216 0.9622 0.5019 0.3729 0.8952 1.0000Carbohydrate Glycolysis, gluconeogenesis, pyruvate metabolism lactate 1.03 1.60 2.00 1.41 2.13 0.8781 1.0000 < 0.001 0.0010 < 0.001 0.0000 0.0025 0.0166Carbohydrate Nucleotide sugars, pentose metabolism UDP-glucuronate 0.98 1.19 0.95 0.79 0.87 0.7875 1.0000 0.1030 0.3551 0.6105 0.4234 0.0192 0.0792Energy Krebs cycle citrate 1.09 1.70 1.65 1.21 1.05 0.3188 1.0000 < 0.001 0.0000 < 0.001 0.0000 0.0213 0.0838Energy Krebs cycle cis-aconitate 0.99 1.28 1.52 1.25 1.16 0.8442 1.0000 < 0.001 0.0066 < 0.001 0.0000 0.0018 0.0135Energy Krebs cycle alpha-ketoglutarate 1.29 1.63 1.79 1.23 2.11 0.1208 1.0000 0.0219 0.1124 0.0122 0.0253 0.3304 0.6004Energy Krebs cycle fumarate 0.91 1.68 1.58 1.37 1.36 0.5190 1.0000 < 0.001 0.0027 < 0.001 0.0043 0.0110 0.0545Energy Krebs cycle malate 1.10 2.22 1.73 1.39 1.36 0.4273 1.0000 < 0.001 0.0000 < 0.001 0.0002 0.0064 0.0375Energy Oxidative phosphorylation phosphate 1.05 0.88 1.13 0.75 1.16 0.5460 1.0000 0.1584 0.4697 0.1449 0.1635 0.0013 0.0104Energy Oxidative phosphorylation pyrophosphate (PPi) 1.43 1.78 1.03 0.85 0.56 0.1345 1.0000 0.0167 0.0911 0.9812 0.5540 0.5586 0.8058Lipid Essential fatty acid docosahexaenoate (DHA; 22:6n3) 0.85 0.59 0.61 0.85 1.35 0.6151 1.0000 0.3622 0.7848 0.2833 0.2450 0.7322 0.9519Lipid Medium chain fatty acid caproate (6:0) 1.00 1.27 1.00 0.85 1.20 1.0000 1.0000 0.1390 0.4271 1.0000 0.5540 0.2864 0.5564Lipid Medium chain fatty acid caprylate (8:0) 0.85 1.00 1.00 1.06 1.42 0.3702 1.0000 1.0000 1.0000 1.0000 0.5540 0.7596 0.9679Lipid Long chain fatty acid palmitate (16:0) 0.88 1.12 1.17 1.28 1.49 0.2764 1.0000 0.4545 0.8445 0.2491 0.2332 0.0805 0.2231Lipid Long chain fatty acid arachidonate (20:4n6) 1.02 0.47 0.73 0.87 0.50 0.8713 1.0000 0.1310 0.4179 0.4842 0.3627 0.9286 1.0000Lipid Fatty acid, amide erucamide 0.51 3.20 3.47 2.38 1.32 0.2591 1.0000 0.5861 0.9136 0.1725 0.1878 0.4383 0.7066Lipid Fatty acid metabolism (also BCAA metabolism) propionylcarnitine 1.34 0.90 0.93 0.67 0.63 0.0914 1.0000 0.5665 0.9118 0.6254 0.4274 0.0252 0.0925Lipid Fatty acid metabolism (also BCAA metabolism) butyrylcarnitine 1.01 2.02 1.46 0.90 0.76 0.8522 1.0000 < 0.001 0.0000 < 0.001 0.0017 0.3123 0.5737Lipid Carnitine metabolism deoxycarnitine 1.00 0.90 0.70 0.49 0.43 0.9581 1.0000 0.5415 0.8948 0.0269 0.0475 < 0.001 0.0008Lipid Carnitine metabolism carnitine 0.96 0.96 0.68 0.38 0.15 0.7880 1.0000 0.7963 0.9622 0.0054 0.0137 < 0.001 0.0001Lipid Carnitine metabolism 3-dehydrocarnitine 1.06 0.95 1.02 0.41 0.50 0.7341 1.0000 0.7892 0.9622 0.6710 0.4424 < 0.001 0.0007Lipid Carnitine metabolism acetylcarnitine 1.05 0.85 0.71 0.51 0.46 0.6105 1.0000 0.0699 0.2625 < 0.001 0.0022 < 0.001 0.0000Lipid Carnitine metabolism stearoylcarnitine 1.75 0.78 2.27 0.80 1.23 0.6180 1.0000 0.7948 0.9622 0.1296 0.1578 0.5617 0.8058Lipid Carnitine metabolism oleoylcarnitine 1.74 0.58 1.14 0.34 0.43 0.1427 1.0000 0.2267 0.5912 0.4706 0.3612 0.0161 0.0717Lipid Bile acid metabolism cholate 1.00 1.00 1.00 1.00 1.00 1.0000 1.0000 1.0000 1.0000 1.0000 0.5540 1.0000 1.0000Lipid Bile acid metabolism taurocholate 0.86 0.57 0.49 0.53 0.58 0.4621 1.0000 0.0047 0.0361 0.0016 0.0059 0.0019 0.0136Lipid Bile acid metabolism taurochenodeoxycholate 0.89 0.69 0.83 0.76 0.90 0.4382 1.0000 0.0236 0.1173 0.2769 0.2423 0.0965 0.2588Lipid Bile acid metabolism deoxycholate 1.44 1.50 1.69 0.01 2.30 0.8014 1.0000 0.9790 1.0000 0.6511 0.4387 0.3880 0.6492Lipid Glycerolipid metabolism choline phosphate 1.01 0.49 0.13 0.14 0.59 0.8839 1.0000 < 0.001 0.0000 < 0.001 0.0000 < 0.001 0.0000Lipid Glycerolipid metabolism phosphoethanolamine 1.00 0.32 0.41 0.30 0.33 0.9258 1.0000 0.0125 0.0755 0.0549 0.0829 0.0187 0.0789Lipid Glycerolipid metabolism choline 1.06 0.42 0.50 0.42 0.65 0.7065 1.0000 < 0.001 0.0000 < 0.001 0.0001 < 0.001 0.0000Lipid Glycerolipid metabolism glycerol 3-phosphate (G3P) 1.05 1.07 0.94 0.69 0.57 0.6304 1.0000 0.6015 0.9136 0.5345 0.3879 0.0029 0.0191Lipid Glycerolipid metabolism glycerophosphorylcholine (GPC) 1.08 0.95 0.96 0.84 0.60 0.4264 1.0000 0.6104 0.9136 0.5918 0.4166 0.1079 0.2762Lipid Inositol metabolism myo-inositol 1.04 0.59 0.13 0.16 0.22 0.8591 1.0000 0.0119 0.0743 < 0.001 0.0000 < 0.001 0.0000Lipid Inositol metabolism inositol 1-phosphate (I1P) 0.87 1.44 1.34 1.33 1.49 0.5514 1.0000 0.3013 0.7163 0.2752 0.2423 0.1729 0.3746Lipid Inositol metabolism scyllo-inositol 1.01 1.02 0.62 0.35 0.24 0.9335 1.0000 0.7698 0.9622 0.0013 0.0053 < 0.001 0.0000Lipid Lysolipid 1-palmitoylglycerophosphoethanolamine 1.39 2.14 4.36 1.64 2.11 0.3533 1.0000 0.1980 0.5486 0.0012 0.0050 0.6778 0.8950Lipid Lysolipid 2-palmitoylglycerophosphoethanolamine 0.62 2.03 3.32 4.23 3.99 0.3193 1.0000 0.0091 0.0637 0.0056 0.0137 < 0.001 0.0081Lipid Lysolipid 1-stearoylglycerophosphoethanolamine 1.23 1.76 4.79 1.70 2.02 0.3410 1.0000 0.4550 0.8445 0.0018 0.0059 0.8297 0.9882Lipid Lysolipid 1-oleoylglycerophosphoethanolamine 0.60 1.92 1.35 1.33 1.19 0.3909 1.0000 0.3601 0.7848 0.4369 0.3438 0.3874 0.6492Lipid Lysolipid 2-oleoylglycerophosphoethanolamine 1.97 0.82 1.63 0.63 0.70 0.1460 1.0000 0.3852 0.8240 0.2053 0.2067 0.2107 0.4396Lipid Lysolipid 2-arachidonoylglycerophosphoethanolamine 1.65 0.73 1.61 0.48 0.90 0.2828 1.0000 0.3052 0.7163 0.1956 0.2006 0.0519 0.1596Lipid Lysolipid 1-palmitoylglycerophosphocholine 1.65 2.21 5.22 1.52 2.19 0.2275 1.0000 0.2613 0.6495 0.0018 0.0059 0.9243 1.0000Lipid Lysolipid 2-palmitoylglycerophosphocholine 1.42 2.89 5.15 1.65 2.19 0.3335 1.0000 0.0337 0.1625 < 0.001 0.0013 0.5712 0.8099Lipid Lysolipid 1-palmitoleoylglycerophosphocholine 1.63 1.19 2.60 1.01 1.89 0.1142 1.0000 0.7493 0.9622 0.0088 0.0191 0.5156 0.7781Lipid Lysolipid 2-palmitoleoylglycerophosphocholine 2.03 1.23 3.69 0.64 1.76 0.1197 1.0000 0.8312 0.9622 0.0131 0.0257 0.1601 0.3707Lipid Lysolipid 1-stearoylglycerophosphocholine 1.56 2.14 5.15 1.61 2.01 0.2773 1.0000 0.2916 0.7040 0.0021 0.0065 0.9072 1.0000Lipid Lysolipid 2-stearoylglycerophosphocholine 1.30 1.46 1.69 1.15 1.39 0.4656 1.0000 0.5243 0.8948 0.2588 0.2394 0.9022 1.0000Lipid Lysolipid 1-oleoylglycerophosphocholine 2.02 1.34 2.60 0.77 0.95 0.1584 1.0000 0.8275 0.9622 0.0333 0.0547 0.3456 0.6148Lipid Lysolipid 2-oleoylglycerophosphocholine 1.96 0.81 2.40 0.62 0.81 0.1870 1.0000 0.5453 0.8948 0.0566 0.0841 0.1713 0.3746Lipid Lysolipid 1-linoleoylglycerophosphocholine 1.00 0.75 1.61 1.00 1.03 1.0000 1.0000 0.6694 0.9499 0.2877 0.2450 1.0000 1.0000Lipid Lysolipid 1-palmitoylglycerophosphoinositol 1.00 2.49 6.62 3.47 4.71 1.0000 1.0000 < 0.001 0.0005 < 0.001 0.0000 < 0.001 0.0000Lipid Lysolipid 1-stearoylglycerophosphoinositol 1.05 3.21 3.46 2.40 3.65 0.9245 1.0000 0.0013 0.0110 < 0.001 0.0017 0.0076 0.0416Lipid Lysolipid 1-oleoylglycerophosphoinositol 0.64 2.50 2.58 2.45 2.11 0.0398 1.0000 < 0.001 0.0023 < 0.001 0.0002 < 0.001 0.0013Lipid Monoacylglycerol 1-myristoylglycerol (1-monomyristin) 1.06 1.38 1.20 1.19 1.40 0.6442 1.0000 0.0095 0.0637 0.1030 0.1321 0.1250 0.3106Lipid Monoacylglycerol 1-pentadecanoylglycerol (1-monopentadecanoin) 1.09 1.03 1.14 0.88 1.13 0.4232 1.0000 0.8150 0.9622 0.1970 0.2006 0.2710 0.5405Lipid Monoacylglycerol 1-heptadecanoylglycerol (1-monoheptadecanoin) 1.07 1.08 1.21 0.87 1.36 0.5278 1.0000 0.5021 0.8839 0.0905 0.1177 0.1706 0.3746Lipid Monoacylglycerol 1-palmitoylglycerol (1-monopalmitin) 1.08 1.00 1.13 0.94 1.47 0.1834 1.0000 0.8819 1.0000 0.0491 0.0754 0.2693 0.5405Lipid Monoacylglycerol 2-palmitoylglycerol (2-monopalmitin) 1.11 1.02 1.14 0.87 1.36 0.1910 1.0000 0.7545 0.9622 0.1404 0.1635 0.1001 0.2643Lipid Monoacylglycerol 1-stearoylglycerol (1-monostearin) 1.05 1.02 1.12 0.98 1.46 0.5185 1.0000 0.7653 0.9622 0.1759 0.1893 0.7971 0.9882Lipid Monoacylglycerol 2-stearoylglycerol (2-monostearin) 1.02 1.03 1.14 0.86 1.30 0.7727 1.0000 0.6269 0.9199 0.0705 0.1015 0.0369 0.1223

0 8Lipid/BSA

Fold of Change Statistical Values (Lipid/BSA)

16 24

Heat map of biochemicals profiled in this study. Shaded cells indicate p≤0.05, q≤0.1 (red indicates that

the mean values are significantly higher for that comparison; green values significantly lower). Blue-bolded text

indicates 0.05<p<0.10.

Supplemental Table 2 Palmitate Timecourse

SUPER PATHWAY SUB PATHWAY BIOCHEMICAL NAME

of 31


Obesity

Lipid Monoacylglycerol 1-oleoylglycerol (1-monoolein) 1.17 1.16 0.76 0.64 1.14 0.5038 1.0000 0.4178 0.8445 0.1106 0.1380 0.0264 0.0949Lipid Monoacylglycerol 1-behenoylglycerol (1-monobehenin) 1.02 0.93 0.97 0.82 1.32 0.7296 1.0000 0.6197 0.9187 0.7609 0.4750 0.1601 0.3707Lipid Diacylglycerol 1,2-dipalmitoylglycerol 1.01 14.80 14.49 9.68 8.43 0.5259 1.0000 < 0.001 0.0000 < 0.001 0.0000 < 0.001 0.0000Lipid Diacylglycerol 1,3-dipalmitoylglycerol 2.22 6.04 11.97 6.70 2.19 0.1197 1.0000 < 0.001 0.0013 < 0.001 0.0001 < 0.001 0.0007Lipid Sphingolipid sphinganine 1.50 1.08 2.18 0.78 0.60 0.2673 1.0000 0.7089 0.9583 0.0715 0.1015 0.4430 0.7066Lipid Sphingolipid sphingosine 1.61 1.37 3.48 1.16 0.98 0.2011 1.0000 0.6954 0.9583 0.0045 0.0122 0.6371 0.8614Lipid Sphingolipid palmitoyl sphingomyelin 0.98 1.10 0.87 0.72 0.83 0.9355 1.0000 0.4538 0.8445 0.2633 0.2394 0.0129 0.0608Lipid Sphingolipid stearoyl sphingomyelin 1.26 0.83 1.07 0.72 1.10 0.4779 1.0000 0.4356 0.8445 0.7271 0.4649 0.2780 0.5462Lipid Sterol/Steroid cholesterol 1.04 1.03 1.11 0.95 1.37 0.4616 1.0000 0.6745 0.9499 0.1427 0.1635 0.4741 0.7450Lipid Sterol/Steroid cortisol 0.96 0.94 1.14 1.03 1.58 0.6249 1.0000 0.4220 0.8445 0.1512 0.1685 0.5284 0.7903Nucleotide Purine metabolism, (hypo)xanthine/inosine containing inosine 1.00 1.00 1.00 1.00 1.00 1.0000 1.0000 1.0000 1.0000 1.0000 0.5540 1.0000 1.0000Nucleotide Purine metabolism, adenine containing adenine 1.06 1.29 1.27 1.03 1.21 0.5651 1.0000 0.1238 0.4024 0.1308 0.1578 0.4930 0.7644Nucleotide Purine metabolism, adenine containing adenosine 1.06 0.72 1.80 1.59 1.32 0.6849 1.0000 0.1910 0.5380 0.0058 0.0137 0.0120 0.0577Nucleotide Purine metabolism, adenine containing adenosine 5'-monophosphate (AMP) 1.25 0.89 1.80 1.05 0.68 0.2170 1.0000 0.5175 0.8948 0.0066 0.0151 0.8164 0.9882Nucleotide Purine metabolism, adenine containing adenosine 5'-diphosphate (ADP) 0.92 0.97 1.07 1.11 1.01 0.4821 1.0000 0.8168 0.9622 0.6539 0.4387 0.3664 0.6384Nucleotide Purine metabolism, urate metabolism urate 1.28 1.70 1.31 1.02 1.13 0.5206 1.0000 0.1371 0.4271 0.2705 0.2412 0.8438 0.9973Nucleotide Purine metabolism, urate metabolism allantoin 1.26 0.79 1.72 0.92 1.55 0.3077 1.0000 0.2286 0.5912 0.0128 0.0255 0.6204 0.8455Nucleotide Pyrimidine metabolism, cytidine containing cytidine 5'-monophosphate (5'-CMP) 1.05 1.21 1.49 1.05 0.90 0.6819 1.0000 0.1195 0.3960 0.0014 0.0056 0.6778 0.8950Nucleotide Pyrimidine metabolism, uracil containing pseudouridine 0.99 1.00 13.97 1.30 0.33 0.9837 1.0000 1.0000 1.0000 0.0613 0.0897 0.6990 0.9157Nucleotide Pyrimidine metabolism, uracil containing uridine 5'-triphosphate (UTP) 0.92 1.17 1.19 0.90 1.14 0.4364 1.0000 0.2363 0.5959 0.1679 0.1850 0.3727 0.6428Cofactors and vitamins Hemoglobin and porphyrin metabolism biliverdin 1.11 0.75 1.58 0.75 0.92 0.8250 1.0000 0.4937 0.8783 0.2360 0.2288 0.2980 0.5625Cofactors and vitamins Nicotinate and nicotinamide metabolism nicotinamide adenine dinucleotide (NAD+) 0.98 0.99 1.17 0.90 0.86 0.8897 1.0000 0.7683 0.9622 0.1081 0.1368 0.2948 0.5625Cofactors and vitamins Nicotinate and nicotinamide metabolism nicotinamide adenine dinucleotide reduced (NADH) 1.08 1.02 0.78 0.63 0.38 0.7040 1.0000 0.9054 1.0000 0.1866 0.1963 0.0502 0.1570Cofactors and vitamins Pantothenate and CoA metabolism pantothenate 1.04 1.01 0.92 0.70 0.60 0.6886 1.0000 0.9291 1.0000 0.2901 0.2450 < 0.001 0.0030Cofactors and vitamins Pantothenate and CoA metabolism coenzyme A 0.96 1.29 1.14 0.78 0.65 0.7468 1.0000 0.0910 0.3261 0.2454 0.2321 0.0698 0.2032Cofactors and vitamins Riboflavin metabolism flavin adenine dinucleotide (FAD) 0.88 1.00 0.92 0.75 0.79 0.1517 1.0000 0.9883 1.0000 0.5068 0.3737 0.0221 0.0849Cofactors and vitamins Thiamine metabolism thiamin (Vitamin B1) 1.19 0.93 1.29 1.42 1.38 0.1412 1.0000 0.5368 0.8948 0.0367 0.0592 0.0050 0.0301Cofactors and vitamins Vitamin B6 metabolism pyridoxate 1.08 1.02 1.09 0.83 1.39 0.3874 1.0000 0.8756 1.0000 0.3452 0.2835 0.0933 0.2543Xenobiotics Benzoate metabolism hippurate 0.84 2.04 0.50 0.87 0.36 0.6933 1.0000 0.2194 0.5912 0.6015 0.4203 0.7724 0.9742Xenobiotics Benzoate metabolism 2-hydroxyhippurate (salicylurate) 1.00 1.00 0.50 1.00 0.81 1.0000 1.0000 1.0000 1.0000 0.0469 0.0733 1.0000 1.0000Xenobiotics Chemical glycerol 2-phosphate 0.89 0.89 1.14 0.09 0.67 0.7858 1.0000 0.7387 0.9622 0.7769 0.4816 0.0365 0.1223Xenobiotics Chemical trizma acetate 1.00 1.00 1.00 1.00 1.00 1.0000 1.0000 1.0000 1.0000 1.0000 0.5540 1.0000 1.0000Xenobiotics Chemical 2-ethylhexanoate (isobar with 2-propylpentanoate) 0.96 0.82 1.04 0.84 1.85 0.9938 1.0000 0.5355 0.8948 0.9671 0.5540 0.9048 1.0000Xenobiotics Chemical phenol red 0.97 1.03 1.03 1.00 2.27 0.8303 1.0000 0.9304 1.0000 0.7207 0.4649 0.9850 1.0000Xenobiotics Drug 4-acetaminophen sulfate 1.00 1.00 0.45 1.00 1.00 1.0000 1.0000 1.0000 1.0000 0.0329 0.0547 1.0000 1.0000Xenobiotics Drug p-acetamidophenylglucuronide 1.00 1.00 1.00 1.00 1.00 1.0000 1.0000 1.0000 1.0000 1.0000 0.5540 1.0000 1.0000Xenobiotics Drug penicillin G 1.07 1.08 1.10 1.00 2.10 0.7003 1.0000 0.8210 0.9622 0.5778 0.4099 0.8841 1.0000Xenobiotics Food component/Plant stachydrine 1.00 5.26 2.55 0.91 0.02 1.0000 1.0000 0.4702 0.8453 0.4813 0.3627 0.9036 1.0000Xenobiotics Food component/Plant homostachydrine 1.20 0.52 0.58 0.60 0.94 0.3930 1.0000 0.0023 0.0181 0.0220 0.0404 0.0153 0.0700Xenobiotics Sugar, sugar substitute, starch erythritol 1.11 1.13 0.93 0.95 1.16 0.3590 1.0000 0.3587 0.7848 0.5502 0.3963 0.7606 0.9679Xenobiotics Detoxification metabolism ethyl glucuronide 1.00 3.61 1.85 1.09 1.00 1.0000 1.0000 < 0.001 0.0000 0.0018 0.0059 0.5990 0.8297

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Obesity

metabolomic signatures in lipid-loaded hepargs reveal pathways involved in steatotic progression

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