Abstract
Objective
NRF2, a transcription factor that regulates cellular redox and
metabolic homeostasis, plays a dual role in human disease. While it is
well known that canonical intermittent NRF2 activation protects against
diabetes-induced tissue damage, little is known regarding the effects
of prolonged non-canonical NRF2 activation in diabetes. The goal of
this study was to determine the role and mechanisms of prolonged NRF2
activation in arsenic diabetogenicity.
Methods
To test this, we utilized an integrated transcriptomic and metabolomic
approach to assess diabetogenic changes in the livers of wild type,
Nrf2^−/−, p62^−/−, or Nrf2^−/−; p62^−/− mice exposed to arsenic in the
drinking water for 20 weeks.
Results
In contrast to canonical oxidative/electrophilic activation, prolonged
non-canonical NRF2 activation via p62-mediated sequestration of KEAP1
increases carbohydrate flux through the polyol pathway, resulting in a
pro-diabetic shift in glucose homeostasis. This p62- and NRF2-dependent
increase in liver fructose metabolism and gluconeogenesis occurs
through the upregulation of four novel NRF2 target genes,
ketohexokinase (Khk), sorbitol dehydrogenase (Sord), triokinase/FMN
cyclase (Tkfc), and hepatocyte nuclear factor 4 (Hnf4A).
Conclusion
We demonstrate that NRF2 and p62 are essential for arsenic-mediated
insulin resistance and glucose intolerance, revealing a pro-diabetic
role for prolonged NRF2 activation in arsenic diabetogenesis.
Keywords: Diabetes, Polyol pathway, Liver carbohydrate metabolism, NRF2
Highlights
* •
The role of non-canonical activation of the Nrf2 signaling pathway
in type II diabetes has not been determined.
* •
Chronic activation of Nrf2 promotes a pro-diabetic shift in the
liver polyol pathway that increases blood glucose levels.
* •
Four newly identified Nrf2 target genes are responsible for the
diabetogenic shift in liver carbohydrate metabolism.
1. Introduction
NRF2 is a transcription factor that regulates cellular redox and
metabolic homeostasis and plays an important role in human disease.
NRF2 regulates the expression of genes bearing an antioxidant response
element (ARE) in their regulatory regions. NRF2 target genes have been
shown to regulate nearly every facet of cellular function, including
redox homeostasis, energy metabolism, protein quality control, and
ultimately cell survival [[49]1,[50]2]. Despite being ubiquitously
expressed, the basal level of NRF2 is normally kept low by
KEAP1-CUL3-RBX1-mediated ubiquitylation and subsequent 26S proteasomal
degradation. However, during stress, NRF2 is primarily activated via
one of two mechanisms: (1) canonical KEAP1 cysteine-dependent
activation or (2) non-canonical p62-dependent activation. During
canonical NRF2 activation, KEAP1-Cys151 is modified, preventing NRF2
ubiquitylation and increasing the level of NRF2 and its downstream
genes to protect cells from xenobiotic insults [[51]3,[52]4]. Once
cellular homeostasis is restored, NRF2 returns to a low basal level,
ensuring transient NRF2 upregulation [[53]5]. In contrast, we also
discovered that NRF2 can be activated in a SQSTM1/p62-KEAP1-dependent
but Cys151-independent manner during autophagy dysregulation. Blockage
of autophagosome-lysosome fusion leads to p62-mediated sequestration of
KEAP1 into autophagosomes, preventing NRF2 ubiquitylation and
degradation [[54]6]. This non-canonical mode of activation leads to
prolonged upregulation of NRF2 and ARE-containing target genes,
conferring a cellular survival advantage and promoting metabolic
reprogramming. Interestingly, we discovered that arsenic, a globally
relevant environmental toxicant, carcinogen, and diabetogen, induces
prolonged NRF2 activation through this non-canonical mechanism
[[55]7,[56]8].
Canonical or non-canonical activation of NRF2 dictates its dual role in
disease [[57]9]. Pioneering research in the field previously
demonstrated that canonical and intermittent NRF2 activation by
chemopreventive compounds can prevent cancer initiation [[58]10];
however, since our proposal of a “dark side” role for NRF2 in cancer in
2008 [[59]11], our team and others have provided convincing evidence
that prolonged/uncontrolled NRF2 activation is a driver of cancer
progression, metastasis, and resistance to therapy. NRF2 is highly
expressed in many cancer types, and elevated NRF2 levels strongly
correlate with tumor resistance to chemotherapy, increased recurrence,
and a poorer prognosis [[60]2,[61]12]. While the “dark side” of NRF2 in
cancer continues to emerge, very little is known regarding chronic
non-canonical activation of NRF2 in the pathogenesis of other diseases,
including diabetes. The increased prevalence of type II diabetes
continues to represent a global health crisis. Similar to other
metabolic diseases, genetics, sedentary lifestyle, age, diet, and
environmental toxicant exposure represent the main risk factors
associated with developing type II diabetes [[62]13,[63]14]. In
particular, chronic exposure to a wide array of environmental
diabetogens, such as arsenic, has been shown to affect insulin
production/sensitivity, blood sugar levels, and lipid profiles, all of
which are common features of diabetes onset and progression
[[64]13,[65]15]. As such, determining the tissue-specific metabolic
profiles associated with diabetic outcomes as well as the molecular
changes that drive these pathogenic metabolic shifts represents a
critical need in the field.
In this study, we hypothesized that prolonged non-canonical activation
of the NRF2 signaling cascade could be a key factor in driving the
onset of key diabetic phenotypes. To test this, we used a chronic
arsenic-induced type II diabetes mouse model as (1) human
epidemiological studies have associated populations living in high
arsenic-contaminated areas with a higher prevalence of type II diabetes
[[66]15,[67]16] and (2) we found that arsenic induces NRF2 activation
via the non-canonical p62-dependent mechanism [[68]7]. Using arsenic
treated WT, Nrf2^−/−, p62^−/−, and Nrf2^−/−;p62^−/− mice, we
demonstrate that p62-dependent non-canonical activation of NRF2 is
essential for promoting insulin resistance and glucose intolerance in a
chronic model of diabetes. Furthermore, detailed transcriptomic and
metabolomic analyses reveal that prolonged NRF2 activation regulates
liver fructose metabolism and gluconeogenesis, which could represent a
key driver of changes in systemic blood glucose. Thus, our findings
indicate a pro-diabetogenic role of non-canonical activation of the
NRF2 pathway.
2. Results
2.1. A non-canonical NRF2 model of diabetogenesis
To determine the pro-diabetogenic effects of p62-dependent
non-canonical NRF2 activation, a chronic arsenic exposure model was
used. Specifically, wild-type (WT), Nrf2^−/−, p62^−/−, and
Nrf2^−/−;p62^−/− mice were exposed to 0 or 25 ppm sodium arsenite in
their drinking water for 20 weeks ([69]Figure 1A). The arsenic-exposed
WT mice did not exhibit any obvious change in total body mass, although
the p62^−/− and Nrf2^−/−;p62^−/− mice weighed ∼5 g more than the WT and
Nrf2^−/− mice at 20 weeks of age, consistent with the reported
observation of p62^−/− mice being obese starting at three months of age
[[70]17] ([71]Figure 1B). Similarly, arsenic had no effect on liver
weight or food intake but reduced water consumption in all of the mice
genotypes ([72]Figure S1A-C). Interestingly, arsenic exposure caused
glucose intolerance and decreased insulin sensitivity in the WT but not
Nrf2^−/−, p62^−/−, or Nrf2^−/−;p62^−/− mice, whereas arsenic had no
effects on serum insulin levels across all of the groups
([73]Figure 1C–F), suggesting that loss of p62 and/or NRF2 counteracted
arsenic-induced effects on insulin sensitivity and glucose tolerance.
Figure 1.
[74]Figure 1
[75]Open in a new tab
A non-canonical model of NRF2 diabetogenesis. (A) Schematic of the
treatment regime and experimental workflow. (B–D) Body weight (g),
glucose tolerance test, and insulin tolerance test in the 20-week-old
WT, Nrf2^−/−, p62^−/−, and Nrf2^−/−;p62^−/− arsenic-treated mice
compared to controls. (E) Area under the curve in panel C. (F) Serum
insulin levels of the indicated genotypes following 20 weeks of arsenic
treatment. Data = mean
[MATH: ± :MATH]
SD (n = 5). ∗p < 0.05 compared to controls.
2.2. Loss of NRF2 and/or p62 diminished arsenic-induced transcriptomic
changes
To ascertain NRF2- and/or p62-dependent transcriptomic changes that
might drive this pro-diabetic metabolic shift in the arsenic-treated WT
mice, transcriptome sequencing was performed on liver tissues from all
four of the mice genotypes. A heatmap of genes differentially regulated
in the arsenic-exposed WT mice compared to the control WT mice
indicated that transcriptomic changes induced by arsenic in a wild-type
setting were lost in the Nrf2^−/−, p62^−/−, or Nrf2^−/−;p62^−/− mice
([76]Figure 2A). KEGG pathway enrichment using the upregulated portion
of the geneset indicated that numerous aspects of amino acid, fatty
acid, carbohydrate, lipid, and drug/xenobiotic metabolism were all
significantly enhanced by arsenic ([77]Figure 2B). Furthermore,
hierarchical clustering and sign test analysis of genes differentially
expressed across the different treatment and genotype combinations
showed that the arsenic-exposed Nrf2^−/− mice had a transcriptomic
profile that most closely resembled that of the non-arsenic exposed WT
controls, followed by the arsenic-exposed-p62^−/− mice
([78]Figure S2A-B). These data indicated that non-canonical NRF2
activation was a critical driver of the significant transcriptomic
changes that occurred during arsenic exposure.
Figure 2.
[79]Figure 2
[80]Open in a new tab
Non-canonical activation of NRF2 drove arsenic-induced transcriptomic
changes. Liver tissue from the control or arsenic-exposed WT, Nrf2^−/−,
p62^−/−, and Nrf2^−/−;p62^−/− mice was assessed for changes in the
transcriptome via RNAseq. (A) Heatmap indicating log2 fold change of
significantly altered transcripts across the indicated genotypes
compared to controls. (B) KEGG pathway analysis of metabolic pathways
enhanced by arsenic exposure. (C) Heatmap indicating transcripts were
significantly increased by arsenic in a p62-NRF2-dependent manner. Gene
ontology (GO) enrichment analysis of biological processes enhanced by
arsenic exposure in the WT mice. (D) Heatmap indicating transcripts
were significantly decreased by arsenic in a p62-NRF2-dependent manner.
Gene ontology (GO) enrichment analysis of biological processes
inhibited by arsenic exposure in the WT mice. n = 5 mice per group.
A more stringent in-depth analysis of the transcriptome sequencing
results revealed that a total of 48 genes were significantly
upregulated and 86 were downregulated in the livers of the
arsenic-exposed WT mice compared to the controls ([81]Supplementary
Tables 1 and 2). Gene ontology (GO) enrichment analysis of the affected
biological processes indicated that arsenic significantly upregulated
genes involved in catabolic or reductive processes, including turnover
of metabolic intermediates, oxidation/reduction reactions, and fatty
acid/lipid metabolism, whereas genes involved in inflammation and the
response to certain stressors/xenobiotics were downregulated
([82]Figure 2C–D). Importantly, almost all of the observed
arsenic-dependent gene expression changes in the WT mice were
diminished in the arsenic-exposed Nrf2^−/−, p62^−/−, and
Nrf2^−/−;p62^−/− mice compared to their untreated controls
([83]Figure 2C–D). Notably, 21 of the 48 genes upregulated by arsenic,
including Gclc, Gsta3, Gstm1, Abcc3, Ces1d, and Cyp2a5, were
established NRF2 target genes ([84]Supplementary Table 1), none of
which were upregulated in the arsenic-exposed Nrf2^−/−, p62^−/−, or
Nrf2^−/−;p62^−/− mice ([85]Figure S3). These data, coupled with the
known involvement of NRF2 in mediating the catabolic and xenobiotic
stress response pathways during arsenic exposure, highlight the
importance of non-canonical NRF2 activation in mediating this metabolic
shift.
2.3. Arsenic-induced metabolic changes were p62-and/or NRF2-dependent
Based on the transcriptomic data indicating that the non-canonical
activation of NRF2 promoted a shift in primary metabolic pathways, we
next evaluated the global metabolome. Similar to the observed
transcriptomic alterations, hierarchical clustering and sign test
analyses revealed that the Nrf2^−/− mice exposed to arsenic exhibited a
metabolic profile similar to the WT control animals ([86]Figure S4A-B).
Furthermore, loss of p62 and/or NRF2 shifted the subset of metabolites
that were altered by arsenic exposure back to near the WT control
levels, verifying an integral role of p62 and NRF2 in mediating the
transcriptomic and subsequent metabolomic responses associated with
pre-diabetic phenotypes ([87]Figure 3A). As arsenic caused a modest but
significant increase in fasting blood glucose levels, significant
changes in key liver carbohydrate intermediates were assessed.
Interestingly, the observed increase in carbohydrates that resulted
from arsenic exposure in the WT mice was lost in the Nrf2^−/−, p62^−/−,
or Nrf2^−/−;p62^−/− arsenic-exposed mice ([88]Figure 3B). Enrichment
analysis of the carbohydrate-metabolizing pathways altered by arsenic
revealed significant changes to the glycolysis, gluconeogenesis,
pentose phosphate pathway, and fructose/mannose degradation
([89]Figure 3C). These results, similar to the observed changes in the
transcriptome, indicated that non-canonical NRF2 activation was
essential for the metabolic reprogramming caused by prolonged arsenic
exposure.
Figure 3.
[90]Figure 3
[91]Open in a new tab
Arsenic-induced metabolic changes were p62-and/or NRF2-dependent. (A)
Heatmap indicating log2 fold change of significantly altered
metabolites across the indicated genotypes compared to controls. (B)
Heatmap indicating carbohydrates were significantly increased by
arsenic in a p62-and NRF2-dependent manner. n = 5 mice per group. (C)
Pathway enrichment analysis of carbohydrate-metabolizing pathways
significantly affected by arsenic exposure. Sizes of dots correspond to
the enrichment ratio, which was computed by observed hits/expected hits
using the MetaboAnalyst metabolite set enrichment analysis. Dot color
indicates p value.
2.4. NRF2-dependent regulation of hepatic carbohydrate metabolism controlled
glucose homeostasis
As both the transcriptomic and metabolomic data identified numerous
metabolic pathways that were clearly affected by arsenic exposure, the
next step was to determine which pathways were enriched in both
datasets to ascertain the metabolic cascade most affected by
non-canonical activation of NRF2. Consistent with the pathogenesis of
diabetes, we observed an enrichment of four pathways only in the
arsenic-treated WT mice: choline metabolism, fructose metabolism,
valine/leucine/isoleucine degradation, and bile acid metabolism
([92]Figure 4A). Since all of these pathways have been linked to liver
glucose homeostasis, the transcriptional control of these pathways by
NRF2 was investigated. Interestingly, WT but not Nrf2^−/− liver slices
treated with arsenic resulted in increased mRNA levels of the key
fructose metabolism pathway enzymes ketohexokinase (Khk), sorbitol
dehydrogenase (Sord), and triokinase/FMN cyclase (Tkfc) as well as a
key mediator of liver gluconeogenesis hepatocyte nuclear factor 4
(Hnf4A) ([93]Figure 4B). Intriguingly, assessment of other key
gluconeogenic genes revealed that only Hnf4A was altered by arsenic in
an NRF2-dependent manner, as glucose-6-phosphatase catalytic subunit 1
(G6pc) and phosphoenolpyruvate carboxykinase 1 (Pck1) levels increased,
whereas forkhead box O1 (Foxo1) and PPARG coactivator 1 alpha
(Ppargc1a) expression was unchanged in the WT and Nrf2^−/− liver slices
([94]Figure S5). An in silico analysis revealed a number of putative
AREs in the promoter regions of Khk, Sord, Tkfc, and Hnf4a; thus,
ChIP-PCR was performed on liver slices. Interestingly, NRF2-ARE binding
was confirmed in all four targets, revealing fructose metabolism and
gluconeogenesis as previously undiscovered branches of NRF2 regulation
([95]Figure 4C). These data were confirmed in the arsenic-exposed WT
mice, with liver tissue from these mice showing significant elevations
in the transcript levels of these four enzymes that were reduced in the
Nrf2^−/− mice exposed to arsenic ([96]Figure 4D). The notion that
altered liver fructose metabolism and gluconeogenesis are major drivers
of arsenic impairment of glucose homeostasis was further validated in
an ex vivo liver tissue model using stable isotope labeling. Tissues
were cultured in ^13C-fructose and the formation of ^13C-glucose and
^13C-sorbitol was quantified in each cohort. As shown in [97]Figure 4E,
^13C-glucose was elevated in the media of the WT but not Nrf2^−/− or
p62^−/− arsenic-exposed liver slices. Furthermore, the majority of
fructose was metabolized into glucose and released but not converted
into sorbitol via the polyol pathway, as the level of ^13C-sorbitol in
the media was very low (less than 1% of the amount of ^13C-glucose) and
was similar in untreated or arsenic-treated liver tissues
([98]Figure 4E). Taken together, these results suggested that
non-canonical activation of NRF2 by arsenic enhanced glucose production
and release from the liver into the bloodstream via hepatic
upregulation of key regulators of fructose metabolism and
gluconeogenesis ([99]Figure 4F).
Figure 4.
[100]Figure 4
[101]Open in a new tab
NRF2-dependent regulation of hepatic carbohydrate metabolism controlled
glucose homeostasis. (A) Network diagram showing pathways enriched
across different genotypes/treatment groups. Each black dot represents
an indicated genotype/treatment group. Each red dot represents an
enriched pathway. Pathways enriched in a particular genotype/treatment
group are indicated by a line joining the red dots to the black dots.
Pathway names are shown for those enriched in only one
genotype/treatment group. (B) Relative mRNA levels of Khk, Sord, Tkfc,
and Hnf4A from WT and Nrf2^−/− liver slices cultured in normal media or
media containing 1 or 2 μm of sodium arsenite for 16 h. (C) Putative
ARE sequences and verification of NRF2-ARE binding via ChIP-PCR
analysis. (D) RNAseq analysis of Khk, Sord, Tkfc, and Hnf4A transcript
levels from the WT and Nrf2^−/− mice exposed to 25 ppm of arsenic in
the drinking water for 20 weeks. (E) LC-MS/MS analysis of ^13C-glucose
and ^13C-sorbitol formation in WT, Nrf2^−/−, or p62^−/− liver slices
incubated in glucose-free medium containing ^13C-fructose for 16 h. The
relative amount of ^13C-glucose or ^13C-sorbitol in the medium
normalized to the total tissue protein levels are shown. (F) Schematic
illustration of how NRF2 regulates fructose metabolism and
gluconeogenesis following arsenic exposure.
3. Discussion
The increased prevalence of type II diabetes continues to represent a
global health crisis. It is estimated that ∼10% of the world's
population aged 20–79 will be diabetic by 2030, resulting in over 4
million deaths and an absolute economic cost upward of 2 trillion U.S.
dollars [[102]18]. As the risk of developing type II diabetes continues
to increase, so does the need to understand the basic molecular and
cellular processes that drive pathogenesis. Importantly, diabetes does
not represent a “one size fits all” disease, as a myriad of possible
underlying mechanisms have been proposed in a variety of contexts.
However, a common unifying thread is a shift from normal homeostatic
metabolism to a pro-pathogenic metabolic state. Herein, we show for the
first time a “dark side” role of NRF2 in type II diabetes: prolonged
non-canonical activation of NRF2, a critical regulator of cellular
redox and metabolic homeostasis, is a key driver of a pro-diabetic
shift in hepatic glucose metabolism. This diabetogenic shift in
carbohydrate metabolism is driven by an NRF2-dependent upregulation of
liver fructose metabolism and gluconeogenesis, as four enzymes that
regulate these metabolic pathways were identified as novel NRF2 target
genes. This finding not only implies that non-canonical NRF2 activation
promotes diabetic outcomes, but also highlights a previously unknown
facet of NRF2-regulated metabolism: increased hepatic glucose flux
through the fructose metabolic and gluconeogenic cascades. Thus, a
critical conclusion of this study is that non-canonical p62-dependent
activation of NRF2 could be a key underlying cause of decreased insulin
sensitivity and glucose intolerance observed in most diabetic patients,
which represents a significant step forward in developing
preventive/therapeutic strategies for type II diabetes.
4. Materials and methods
4.1. Animal experiments
All of the mice were handled according to the Guide for the Care and
Use of Laboratory Animals, and all of the protocols were approved by
the University of Arizona Institutional Animal Care and Use Committee.
The generation of Nrf2^−/− and p62^−/− mice was reported previously
[[103]19,[104]20]. In this study, four genotypes of mice were used,
Nrf2^+/+;p62^+/+ (WT), Nrf2^−/−;p62^+/+ (Nrf2^−/−), Nrf2^+/+;p62^−/−
(p62^−/−), and Nrf2^−/−;p62^−/− mice, which were generated by breeding
Nrf2^+/−;p62^+/− mice in a C57BL/6J background. For chronic arsenic
exposure, 8- to 10-week-old mice (25–27 g) were randomly allocated to
the control (Ctrl) group or sodium arsenite (iAs) group (n = 5 mice per
group). The mice in the Ctrl group received normal drinking water,
while the mice in the iAs group received drinking water containing
sodium arsenite (25 ppm) for 20 weeks. The amount of sodium arsenite
(25 ppm) was chosen as it reflects the amount commonly used in the
literature to obtain diabetic phenotypes [[105]21,[106]22]. Following
exposure, water intake, food intake, and urine amounts were measured
using specialty metabolic cages (PLEXX).
4.2. Intraperitoneal glucose tolerance test (GTT) and insulin tolerance test
(ITT)
Following 20 weeks of iAs exposure, the mice from each group were
fasted for 16 h. Glucose (2 g/kg, Sigma) and insulin (0.6 U/kg, Sigma)
were administrated intraperitoneally for the GTT and ITT, respectively.
A small volume of blood was removed from the tail vein, and blood
glucose measurements were taken at time 0, followed by 15, 30, 60, 90,
and 120 min post-injection using a OneTouch Blood Glucose Monitoring
System (LifeScan). In addition, serum insulin levels were measured
using a Mouse Insulin ELISA Kit (Thermo Fisher Scientific).
4.3. RNAseq analysis
To assess transcriptomic changes following iAs exposure, liver tissue
from the 20-week-old Ctrl and iAs-treated WT, Nrf2^−/−, p62^−/−, and
Nrf2^−/−;p62^−/− mice was collected and snap frozen in liquid nitrogen
(n = 5 mice per group X 8 groups = 40 samples). The samples were then
sent to Qiagen Genomic Services (Qiagen) for subsequent RNA isolation,
quantification, and quality control checks as well as cDNA preparation.
Next-generation sequencing was then performed by Qiagen using a Qiagen
UPX 3’ Transcriptome Kit and an Illumina sequencing platform.
Transcriptome sequencing data analyses were performed using the DESeq2
package to identify differentially expressed genes among the different
genotype/treatment groups from count per gene data [[107]23]. To
determine the most significant changes, a false-discovery rate (FDR) of
0.05 was used as a statistical cut-off. A gene ontology analysis for
enriched biological processes was then performed using the ShinyGo
v0.61 web platform [[108]24]. A sign test for consistency between two
genesets was performed using a binomial test.
4.4. Metabolomics
Similar to the transcriptomic analysis, liver tissue from the
20-week-old Ctrl and iAs-treated WT, Nrf2^−/−, p62^−/−, and
Nrf2^−/−;p62^−/− mice was collected and snap frozen in liquid nitrogen
(n = 5 mice per group X 8 groups = 40 samples). Samples were then sent
to Metabolon for metabolomic analysis. Briefly, samples were prepared
using the MicroLab STAR system (Hamilton). Proteins were removed via
methanol precipitation and centrifugation, and the final extract was
processed and analyzed using ultrahigh performance liquid
chromatography-tandem mass spectroscopy (UPLC-MS/MS). Following the
analysis, raw data were extracted and peaks were identified and
validated by Metabolon. Metabolomic analyses were performed on Scaled
Input Data provided by Metabolon. The data were first multiplied by a
factor of 10,000 and then log2 transformed [
[MATH: f(x)=log2<
/mn>(x×10000) :MATH]
] to achieve homoscedasticity. The transformed data were analyzed using
the Linear Model for Microarray Analysis (LIMMA) package to identify
differentially enriched metabolites [[109]25]. A metabolite set
enrichment analysis (MSEA) was performed using the MetaboAnalyst 4.0
web platform [[110]26]. A sign test for consistency between two
metabolite sets was performed using a binomial test.
4.5. Combined dataset pathway enrichment analysis
Combined transcriptomic and metabolomic analysis was performed by first
assigning each gene and metabolite to their respective BioSystems ID
(NCBI BioSystems Database). Pathway enrichment for gene expression was
determined using a hypergeometric test, and the resulting p values were
corrected for multiple testing according to the method described by
Benjamini and Hochberg [[111]27]. This correction is based on the
assumption that transcriptional reprogramming changes by differential
activation of specific transcription factors, leading to coordinated
over- and/or under-expression of a particular set of genes. Pathway
enrichment for differentially enriched metabolites is solely based on
annotation alone. This is based on the assumption that when metabolic
reprogramming occurs, the metabolic pathway involved will enter a
constant flux, resulting in only a few metabolites within the pathway
showing a significant change in concentration. The enriched pathway
BioSystems IDs from the gene expression analysis were then intersected
with those from the metabolite analyses to identify pathways altered in
each comparison pair.
4.6. Real-time qRT-PCR (qPCR) analysis
Fresh liver tissue was isolated from the WT or Nrf2^−/− mice and cut
into 150 μm thick sections using a vibratome tissue slicer
(Precisionary Instruments). The tissue slices were then left untreated
or treated with iAs (1 μM or 2 μM) for 16 h. Following treatment, total
mRNA was extracted using TRIzol (Invitrogen) according to the
manufacturer's instructions. The cDNA was synthesized using 2 μg of RNA
and a Transcriptor First-Strand cDNA Synthesis Kit (Promega). Mouse
Actb levels were used for qPCR normalization, and all of the
experiments were performed in triplicate. The primer sequences were as
follows:
Khk-F-ATGTGGTGGACAAATACCCAGA
Khk-R-CAAGCAAGGAAAGGACAGTGC
Sord-F-GCTAAGGGCGAGAACCTGTC
Sord-R-CATGCTCCCAGTAGTGAACATC
Tkfc-F-CCTTGCTGGGTTAGTAGCCTC
Tkfc-R-CTTTCCCGATAAAACCGGCAT
Hnf4a-F-CACGCGGAGGTCAAGCTAC
Hnf4a-R-CCCAGAGATGGGAGAGGTGAT
Actb-F-AAGGCCAACCGTGAAAAGAT
Actb-R-GTGGTACGACCAGAGGCATAC
4.7. Chromatin immunoprecipitation (ChIP)-qPCR
A ChIP assay was performed according to the manufacturer's instructions
(EZ-CHIPTM, Merck, Germany). Briefly, 150 μm thick liver sections were
treated with 1% formaldehyde in DMEM for 10 min to cross-link
DNA-protein complexes. The tissue sections then were lysed using SDS
lysis buffer containing 1 mM of phenylmethylsulfonyl fluoride (PMSF)
and 1% protease inhibitor cocktail (Sigma). Solubilized chromatin was
then incubated with anti-NRF2 antibody (Santa Cruz Biotechnology) or
normal rabbit IgG (Santa Cruz Biotechnology) for 16 h at 4 °C with
rotation, and DNA-protein complexes were pulled down using Protein
G-agarose beads (Sigma). DNA from the immunoprecipitated complexes and
total chromatin input were extracted via ethanol precipitation, and
1 μL of purified DNA was used for qPCR detection. The primer sequences
were as follows:
Khk-ARE-F-AGTTGGAGTAGGCAGAGACTG
Khk-ARE-R-TTTGGTCAGACTCTTCACCTG
Sord-ARE1-F-AAACTAATCAAGCCTTCGACTC
Sord-ARE1-R-TAAATGCCACCATGCCACCT
Sord-ARE2-F-TACCTCTGCCTCTGGCTTTA
Sord-ARE2-R-GAGATGGCTGGTATCCAATC
Tkfc-ARE-F-CAAGTCCTAACTCTCAGCAAC
Tkfc-ARE-R-GAATCATGTCCAGCTTAAAGC
Hnf4a-ARE1/2-F-GAAAGCAAGTGAACTGAGGAGC
Hnf4a-ARE1/2-R-CAGCTACTCTCTCTCAGTCTCT
4.8. Analysis of ^13C-glucose, ^13C-sorbitol, and ^13C-fructose by LC-MS/MS
The levels of ^13C-glucose and ^13C-sorbitol formed and ^13C-fructose
remaining in the culture media were measured. Approximately 20 μL of
the culture media was moved to glass tubes containing 10 μL of internal
standard (methyl α-d-glucopyranoside in water). After vortexing for
15 s, 300 μL of ice-cold acetonitrile were added to the samples,
vortexed for 1 min, and centrifuged at 3700 rpm for 15 min to
precipitate protein. The supernatant was transferred to a clean glass
tube and dried at room temperature under nitrogen. The residue was
dissolved with 500 μL of water and extracted by solid phase extraction.
Isolute multimode mixed-mode solid-phase extraction columns (Biotage,
Charlotte, NC, USA) were used. The columns were conditioned with 1 mL
of acetonitrile and then equilibrated with 1 mL of water. The
water-dissolved residues were loaded onto the columns, which were then
washed with 500 μL of water. The flow-through was collected and
combined equaling a volume of approximately 1 mL. After centrifuging at
3700 rpm for 15 min, an equal volume of acetonitrile was added to each
supernatant and the samples were subjected to LC-MS/MS for analysis.
The amount of ^13C-fructose obtained from the liver slice-free controls
was compared to the amount of ^13C-glucose or ^13C-sorbitol and
expressed as the percentage of the liver slice-free control per mg of
protein (%/mg protein), respectively.
4.9. LC−MS/MS analyses
^13C-glucose, ^13C-sorbitol, and ^13C-fructose were analyzed on an
AB-SCIEX model Q-Trap 6500 mass spectrometer (AB-SCIEX, Framingham, MA,
USA) interfaced online with a 1290 Infinity Series ultra-performance
liquid chromatography system. Chromatographic separation was carried
out with an ACQUITY UPLC BEN Amide column (1.7 μm and 2.1 × 150 mm;
Waters) by gradient elution at a flow rate of 0.3 mL/min for 15 min.
The mobile phases were composed of 0.025% (v/v) ammonium hydroxide in
water (A) and 0.025% (v/v) ammonium hydroxide in acetonitrile (B). The
linear gradient was as follows: 10–26% A for 0–8.5 min, 26-10% A for
8.5–9 min, and 10% A for 9–15 min ^13C-glucose, ^13C-sorbitol, and
^13C-fructose were analyzed in the negative ion mode by multiple
reaction monitoring scanning. The transitions were m/z 185.2/91.9 for
^13C-glucose and ^13C-fructose, m/z 187.1/92.0 for ^13C-sorbitol, and
m/z 193.2/100.9 for the internal standard. All of the data were
analyzed using AB SCIEX Analyst 1.6.3 software (Applied Biosystems).
4.10. Computational and statistical analyses
All of the results are presented as mean ± SD, and a biological
statistical analysis was performed using GraphPad Prism 8. Student's t
tests were used to compare the means of two groups, and one-way ANOVA
with Bonferroni's correction was used to compare the means of three or
more groups. p < 0.05 was considered statistically significant. All of
the computational statistical analyses were performed in the R
statistical environment.
Author contributions
P.L., M.D., A.O., and D.Z. conceptualized the study. P.L., M.D., H.L.,
C.J.S., A.S., and Y.W. generated, analyzed, and arranged the data
included in the figures and tables. M.D., P.L., A.O., and D.D.Z. wrote
the manuscript. E.C., P.R.K., Q.Z., X.D., J.G.N.G., and E.W. edited the
manuscript and provided feedback. A.O. and D.D.Z. supervised the study.
A.O. performed the biostatistical analysis.
Acknowledgments