Screening of focused compound library targeting liver X receptors in pancreatic cancer identified ligands with inverse agonist and degrader activity

Article abstract of DOI:10.1021/acschembio.0c00546

Pancreatic ductal adenocarcinoma (PDAC) is the predominant form of pancreatic cancer. PDACs harbor oncogenic mutations in the KRAS gene, and ongoing efforts to directly target its mutant protein product to inhibit tumor growth are a priority not only in pancreatic cancer but in other malignancies such as lung and colorectal cancers where KRAS is also commonly mutated. An alternative strategy to directly targeting KRAS is to identify and target druggable receptors involved in dysregulated cancer hallmarks downstream of KRAS dysregulation. Liver X receptors (LXRs) are members of the nuclear receptor family of ligand-modulated transcription factors and are involved in the regulation of genes which function in key cancer-related processes, including cholesterol transport, lipid and glucose metabolism, and inflammatory and immune responses. Modulation of LXRs via small molecule ligands has emerged as a promising approach for directly targeting tumor cells or the stromal and immune cells within the tumor microenvironment. We have previously shown that only one of the two LXR subtypes (LXRβ) is expressed in pancreatic cancer cells, and targeting LXR with available synthetic ligands blocked the proliferation of PDAC cells and tumor formation. In a screen of a focused library of drug-like small molecules predicted to dock in the ligand-binding pocket of LXRβ, we identified two novel LXR ligands with more potent antitumor activity than current LXR agonists used in our published studies. Characterization of the two lead compounds (GAC0001E5 and GAC0003A4) indicates that they function as LXR inverse agonists which inhibit their transcriptional activity. Prolonged treatments with novel ligands further revealed their function as LXR “degraders” which significantly reduced LXR protein levels in all three PDAC cell lines tested. These findings support the utility of these novel inhibitors in basic research on ligand design, allosteric mechanisms, and LXR functions and their potential application as treatments for advanced pancreatic cancer and other recalcitrant malignancies.

Full text of DOI:10.1021/acschembio.0c00546

pubs.acs.org/acschemicalbiology
Articles
Screening of Focused Compound Library Targeting Liver X
Receptors in Pancreatic Cancer Identified Ligands with Inverse
Agonist and Degrader Activity
Husna Karaboga,# Wentao Huang,# Shivangi Srivastava,# Scott Widmann, Sridevi Addanki,
Kasuni Thawalama Gamage, Zahra Mazhar, Jerry O. Ebalunode, James M. Briggs, Jan-Åke Gustafsson,
Carly S. Filgueira, Scott R. Gilbertson,* and Chin-Yo Lin*
Cite This: https://dx.doi.org/10.1021/acschembio.0c00546
Read Online
sı
*
Metrics & More
Article Recommendations
Supporting Information
ACCESS
ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is the
predominant form of pancreatic cancer. PDACs harbor oncogenic
mutations in the KRAS gene, and ongoing efforts to directly target
its mutant protein product to inhibit tumor growth are a priority
not only in pancreatic cancer but in other malignancies such as
lung and colorectal cancers where KRAS is also commonly
mutated. An alternative strategy to directly targeting KRAS is to
identify and target druggable receptors involved in dysregulated
cancer hallmarks downstream of KRAS dysregulation. Liver X
receptors (LXRs) are members of the nuclear receptor family of
ligand-modulated transcription factors and are involved in the regulation of genes which function in key cancer-related processes,
including cholesterol transport, lipid and glucose metabolism, and inflammatory and immune responses. Modulation of LXRs via
small molecule ligands has emerged as a promising approach for directly targeting tumor cells or the stromal and immune cells
within the tumor microenvironment. We have previously shown that only one of the two LXR subtypes (LXRβ) is expressed in
pancreatic cancer cells, and targeting LXR with available synthetic ligands blocked the proliferation of PDAC cells and tumor
formation. In a screen of a focused library of drug-like small molecules predicted to dock in the ligand-binding pocket of LXRβ, we
identified two novel LXR ligands with more potent antitumor activity than current LXR agonists used in our published studies.
Characterization of the two lead compounds (GAC0001E5 and GAC0003A4) indicates that they function as LXR inverse agonists
which inhibit their transcriptional activity. Prolonged treatments with novel ligands further revealed their function as LXR
“degraders” which significantly reduced LXR protein levels in all three PDAC cell lines tested. These findings support the utility of
these novel inhibitors in basic research on ligand design, allosteric mechanisms, and LXR functions and their potential application as
treatments for advanced pancreatic cancer and other recalcitrant malignancies.
involved in the so-called cancer hallmarks, such as growth
dysregulation and metabolic reprogramming, driven by
oncogene activation.3,11−16
Nuclear receptors (NRs) are a family of related ligand-
dependent transcription factors which control gene expression
through transcriptional and epigenetic regulatory mechanisms.
NRs function in normal developmental and physiological
processes and in human diseases, including those involved in
cancer hallmarks.17 Clinically, NRs have been successfully
targeted directly and indirectly in the treatment of breast and
INTRODUCTION
■
Pancreatic ductal adenocarcinoma (PDAC) is the predominant
form of pancreatic cancer and is a highly invasive and metastatic
disease. Pancreatic cancer is the third leading cause of cancer
deaths in the U.S., accounting for over 47,000 deaths annually
(Cancer Facts and Figures, ACS, 2020). Pancreatic cancer has
the worst prognosis of the major malignancies due to difficulties
in early detection and a paucity of effective targeted treatment
options for advanced diseases. The vast majority of PDACs
(∼95%) harbor oncogenic mutations in KRAS, a proto-
oncogene also commonly mutated in lung and colorectal
cancers.1−3 Efforts to target the oncogenic KRAS GTPase
protein have been challenging since it lacks small molecule
binding pockets, although more recent breakthroughs have
yielded promising candidates which covalently modify specific
amino acid substitutions.4−10 An alternative approach to directly
targeting mutant KRAS is to identify more druggable targets
Received: July 7, 2020
Accepted: October 8, 2020
https://dx.doi.org/10.1021/acschembio.0c00546
© XXXX American Chemical Society
ACS Chem. Biol. XXXX, XXX, XXX−XXX
A

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
prostate cancers.18−20 The canonical NR structure is composed
of an amino-terminal activation-function 1 (AF-1) domain, a
DNA binding domain, and a carboxyl-terminal ligand-binding
domain (LBD) containing activation function.21 The LBDs
typically contain 10−12 helices, along with 1−2 β turns. The
helices form a hydrophobic ligand-binding pocket (LBP).
Ligands exert their effects by altering the conformation of AF-
2 (helix 12), which can result in the recruitment and release of
coregulator proteins including those which function as
corepressors, such as the nuclear receptor corepressor
(NCOR) and silent mediator of retinoic acid receptor and
thyroid receptor (SMRT), or as coactivators, such as nuclear
receptor coactivator 1 (NCOA1).22,23
screening was carried out on three PDAC cell lines with different
mutation profiles using the MTT tetrazolium salt reduction
assay (Figures 1A, S1, S2). KRAS active mutations are found in
Liver X receptors (LXRs) are NRs which function in the
regulation of genes that are involved in cholesterol, glucose, and
lipid metabolism and inflammatory responses.24 LXR activity
can be modulated by a variety of endogenous ligands,
phytochemicals, and synthetic compounds, a number of which
have been developed for the treatment of atherosclerosis and
metabolic diseases and have undergone extensive functional,
pharmacological, and toxicological characterization and clinical
trials. LXRs have also emerged as promising targets in cancer
therapeutics. Studies of LXR ligands in cancer cell lines revealed
their antiproliferative effects in a variety of cancer types,
including our previously published study on pancreatic cancer,
and suggest their potential application in cancer therapeu-
tics.25−27 LXR ligands can target both tumor and stromal cells
and have been shown to regulate recruitment of immune
modulatory cells.28,29 Our group has shown that LXRβ, but not
LXRα, is expressed in PDAC tissue and is abnormally localized
in both the cytoplasm and nuclei of a PDAC tissue sample, and
treatments with synthetic LXR agonists GW3965 and T0901317
disrupted cell proliferation and cell-cycle progression in PDAC
cells.25 LXR activation has also been shown to reduce
immunosuppressive myeloid-derived suppressor cell (MDSC)
levels and enhance cytotoxic T lymphocytes (CTLs) in cancer.29
Accumulating evidence suggests that inhibiting LXR activity also
has antineoplastic effects. An LXR inverse agonist SR9243 has
been shown to reduce cancer growth by disrupting aerobic
glycolysis and lipogenesis.30 Relatedly, knockdown of LXRβ in
PDAC cells additionally disrupted cancer cell proliferation.25
Treatments with SR2943 were recently demonstrated to
enhance immune-mediated tumor cell killing by CD8+ T-cells
in triple negative breast cancer models, further supporting key
roles for LXRs in tumors and the tumor microenvironment.31
Synthetic ligands used in basic and translational research of
LXR functions in cancer were originally identified and
developed for the treatment of heart disease and metabolic
syndromes. To identify LXR ligands specifically for cancer
research and therapeutics, we conducted a functional screen in
PDAC cells for novel LXR ligands which have potential
antitumor activity. A focused library of putative LXR ligands
identified by molecular docking simulations of drug-like small
molecules were screened for antiproliferative effects in multiple
PDAC cell lines and reduced or no effect in nontransformed
pancreatic epithelial cells. Herein, we describe the identification
of two hits (GAC0001E5 and GAC0003A4) and the initial
characterization of their mechanisms of action.
Figure 1. Screening of a focused library identified candidate LXR
ligands with inhibitory activity in pancreatic cancer cells. (A) A focused
480-compound library, based on molecular docking simulations of
drug-like small molecules, was screened using 96-well format
tetrazolium salt reduction assays for their effects on growth and
survival of PDAC cells. (B) Chemical structures of the two top hits,
GAC0001E5 (1E5) and GAC0003A4 (3A4), which exhibit inhibitory
activity in all three PDAC lines tested are shown. (C) Molecular
docking of 1E5 in the ligand-binding pocket of the LXRβ ligand-
binding domain (LBD) indicates that hydrogen bonds are formed with
the amide linker and imidizole ring of GAC0001E5 and Thr-316 and
Met-312 of helix 5 and with Ser-278 of helix 4. Interactions with His-
435 (helix 11) involved in receptor activation are absent. ΔG = −10.5.
(D) Predicted structure of LXRβ LBD bound with 3A4 suggests
potential hydrogen bonding between the oxygen of the aromatic ester
group and Ser-278 of helix 4. Hydrophobic interactions between His-
435 (helix 11) and Trp-457 (helix 12) are also predicted to occur. ΔG =
−9.1. Ligands and interacting amino acid residues are represented in
stick form, with interacting amino acid residues labeled. LXRβ
structural data are from Protein Data Bank entry 1UPV. The PDB
files of docked compounds are included as part of the Supporting
Information.
around 95% of PDAC patients along with mutations in TP53
and SMAD4.1 To emulate the mutational profiles of PDAC
patients, we screened the small molecules in three PDAC cell
lines. BxPC-3 cells have wildtype KRAS, and MIA PaCa-2 and
PANC-1 cells have mutant KRAS genes (G12C and G212D,
respectively). Two compounds, GAC0001E5 (1E5) and
GAC0003A4 (3A4), were selected based on their ability to
reduce cell growth across all three cell lines. The structures of
1E5 (6-phenyl-2-(3-trifluoromethyl-phenylamino)-3H-pyrimi-
din-4-one) and 3A4 ((3,5-dimethoxy-phenyl)-4-o-tolyl-piper-
azin-1-yl)-methanone) were confirmed by independent syn-
RESULTS AND DISCUSSION
■
To identify cancer specific novel ligands of LXRβ, we screened a
focused library of 486 putative LXRβ ligands for their
antiproliferative effects against pancreatic cancer cells. The
1
thesis (see detailed description in Methods) and H and 13C
NMR (Figures S3 and S4). Briefly, 1E5 was synthesized starting
B
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Figure 2. Molecular dynamics simulations of LXRβ-LBD with novel ligands reveal key amino acid interactions. (A−D) Representative superimposed
complex structures obtained for the 150 ns trajectory of LXRβ complexed with the T0901317, GW3965, 1E5, and 3A4 ligands, respectively. The green
dotted lines represent hydrogen bonding between atoms, whereas the spiked red arcs represent protein residues making nonbonded interactions with
the ligand. Red and gray circles represent the equivalent 3D positions of the residues for the superimposed LXRβ−ligand complex. The LXRβ/
T0901317 complex is the control as it was part of the original 1UPV crystal structure.
with the cyclization of guanidine carbonate and ethyl
benzoylacetate to give the pyrimidinone at 87%. The primary
amine was then arylated with 3-trifluoroaniline by a palladium
catalyzed Buchwald/Hartwig procedure to provide the desired
product at 41%. 3A4 was synthesized in three steps from
monoprotected piperazine. Amidation of the unprotected amine
with 3,5-dimethoxybenzoyl chloride proceeded at 93% yield.
This was followed by removal of the carbamate protecting group
with TFA in 87% yield. The secondary amine was then reacted
with 2-bromotoluene by a palladium catalyzed Buchwald/
Hartwig procedure to give the desired product. In general, both
compounds possess reasonable drug-like properties with 3.8
cLogP for the 1E5 pyrimidinone and 3.4 cLogP for the 3A4
piperazine. Both molecules also have molecular weights below
500.
Both novel ligands were predicted to occupy the ligand-
binding domain (LBD) of LXRβ. Binding of 1E5 is predicted to
have a strong free energy binding score of −10.5 kcal mol−1. In
addition to hydrophobic interactions, hydrogen bonding was
predicted to occur between residues Thr316, Met312, and
Ser278 and the amide linker and nitrogen atoms of the
pyrimidone group (Figure 1C). The interactions of 1E5 and
LXRβ are similar to other reported LXR antagonists and inverse
agonists.32,33 Modeling of 3A4 binding predicted a free energy
change of −9.1 kcal mol−1. Many hydrophobic interactions,
including with His-435, were predicted to occur, as well as one
hydrogen bond with Ser278 (Figure 1D). To compare the novel
ligands’ affinity for LXRβ ligand-binding domain to other
synthetic ligands, docking simulations for two well-characterized
agonists were also performed. Agonists GW3965 and T0901317
produced scores of −10.4 and −10.8 kcal mol−1, respectively,
C
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Figure 3. Novel ligands induce conformational change in helices H3, H11, and H12. (A) RMSD was calculated across 150 ns trajectories for ligands
LXRβ/T0901317 (yellow), LXRβ/GW3965 (green), and LXRβ/1E5 (purple). (B) RMSD of LXRβ/T0901317 (yellow), LXRβ/GW3965 (green),
and LXRβ/3A4 (purple). (C, D) Histograms of distances (Å) from Arg261(Cα in H3) to Gln445 (Cα in H11) across the 150 ns trajectories for
LXRβ/T0901317 with LXRβ/1E5 and LXRβ/3A4, respectively. The median value of each curve is shown with the dotted line. (E, F) Superimposition
of LXRβ/T0901317 with LXRβ/1E5 and LXRβ/3A4, respectively (brown ribbon for T0901317, blue ribbon for 1E5, and pink ribbon for 3A4).
Dotted yellow lines are distances between residues.
which are comparable in magnitude to the novel ligands. Thus,
1E5 and 3A4 are predicted to bind the ligand-binding pocket of
LXRβ.
with Leu442, Phe271, Phe340, Leu313, and Trp457. Notably,
the geometry of His435 interaction was altered in LXRβ/1E5 in
comparison to LXRβ/T0901317 along with geometry of the
cavity. Furthermore, the N−H−N type hydrogen bond formed
in 1E5 differs from the N−H−F hydrogen bond between
His435 and the T0901317 agonist CF3 group (Figure 2C).23
The LXRβ/3A4 complex formed greater hydrophobic inter-
actions than 1E5. These interactions included amino acids
known to interact strongly with agonists such as His435, Arg319,
Phe271, and Trp547 and various low interaction energy amino
acids, including Gln438, Ser278, Phe268, and Leu442 (Figure
2D). Collectively, interaction maps of novel LXR ligands
confirm that 1E5 and 3A4 bind to the LXRβ-LBD and interact
with key amino acid residues in the ligand binding domain. Next,
we measured root-mean-square displacements (RMSD) to
characterize the conformational changes upon binding of novel
ligands. LXRβ/1E5 RMSD of the MD trajectories (150 ns)
revealed a different receptor behavior from agonists LXRβ/
In order to investigate possible molecular determinants
involved in the interaction of 1E5 and 3A4 with the LXRβ
ligand binding pocket, we carried out 150 ns molecular
dynamics (MD) simulations of the LXRβ/1E5 and LXRβ/
3A4 docked complexes using LXRβ-LBD crystallized with
T0901317 (PDB: 1UPV). Consistent with previous studies,
T0901317 formed hydrogen bonding with His435 and Thr316
residues, and hydrophobic interactions with Thr316, Phe340,
Ile353, Met312, Ile309, Phe349, Leu345, Thr272, and Ala275.
These residues have been shown to interact with agonists
favorably (Figure 2A).23,34 LXRβ/GW3965 formed hydro-
phobic interactions with His435 and Trp457 and had other
hydrophobic interactions (Figure 2B).34 Interestingly, LXRβ/
1E5 formed hydrogen bonds with His435 through both
nitrogens. Additional hydrophobic interactions include those
D
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Figure 4. Inhibitory effects of novel LXR ligands on pancreatic cancer cells are validated by multiple growth and survival assays. (A) Bx-PC3 PDAC cell
lines were treated with the indicated concentrations of GW3965 (LXR agonist), GAC0001E5, and GAC0003A4 for 72 h. Cell viability was assessed
using tetrazodium salt-reduction MTT assays. (B) Mia-PaCa2 and (C) PANC1 PDAC cells were similarly treated, and their viability assayed. (D)
HPNE immortalized normal pancreatic ductal cells were likewise treated with LXR ligands for 72 h. (E−G) Bx-PC3, Mia-PaCa-2, and PANC-1 cells
were treated with the indicated concentrations of GW3965, 1E5, and 3A4 for 72 h, and cell viability was assessed using trypan blue exclusion and cell
counting. (H−J) PDAC cells were treated with the indicated concentrations of GW3965, 1E5, and 3A4 for 14 days. Colonies were stained with 0.5%
crystal violet. All values are the mean SEM of three independent experiments. Asterisks indicate statistically significant changes (*p < 0.05). Images
of stained colonies are representative of three independent experiments for each cell line and ligand treatment.
T0901317 and LXRβ/GW3965. 1E5 caused large alterations
with a higher RMSD (Cα) of 3 Å in the first 50 ns of the
simulation. After 50 ns, 1E5’s RMSD value dropped and became
almost constant around 1.5 Å. In contrast, agonists T0901317
and GW3965 showed an opposite trend with increasing RMSDs
at 100 ns (Figure 3A). LXRβ/3A4 had an almost constant
RMSD(Cα) around 1.5 Å up to 125 ns and then increased at 150
ns. LXRβ/3A4 RMSD values varied from agonists at 100 ns
(Figure 3B). These data suggest that 1E5 caused large alteration
in the receptor as seen by the initial increment and then a dip at
50 ns after which the system stabilized. 3A4 on the other hand
induced conformational changes after 100 ns.
We next examined the distance between Arg261 (Cα in H3)
and Gln445 (Cα in H11) in LXRβ complexed with T0901317
and 1E5/3A4. This distance indicates the range of motion for
H3 and H11, which is an important determinant of agonistic and
inverse agonistic states of LXRβ. On interacting with T0901317,
the H3−H11 distance was around 9 Å, and the distance
increased dramatically to 14.5 Å with 1E5 (Figure 3C). A similar
trend was observed in 3A4 trajectories with an increase in
distance to 15 Å. (Figure 3D). The greater range of motion
observed in the presence of novel ligands 1E5 and 3A4 indicates
movement of H11 away from H3 that results in a bigger
hydrophobic groove that can accommodate corepressors.
E
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Figure 5. LXR ligands inhibit PDAC cell growth in a dosage-dependent manner. (A−C) BxPC-3, MIA PaCa-2 and PANC-1 cells treated with
increasing doses of GAC0003A4 for 72 h. Cell viability was assessed with MTT assays. (D−F) PDAC cells were similarly treated with GAC0001E5.
Values shown are the average percent viability (as compared to vehicle-treated control) SEM of three independent experiments. IC50 values were
calculated using the available function in GraphPad PRISM.
To further assess the conformational changes, we super-
imposed LXRβ/1E5 and LXRβ/3A4 with LXRβ/T0901317.
Consistent with the aforementioned distance analysis, 1E5
pushes away H3 and causes a conformational change in H12
(Figure 3E). 3A4 also shifts H3 and moves H11 (Figure 3F).
H12 loops are noticeably shifted, suggesting conformational
changes are occurring with both ligands. The observed
movements in H3, H11, and H12 induced with novel ligands
are in line with a recent study showing that LXR inverse agonism
results in the conformational change in H3 which destabilizes
H12.35 Taken together, molecular dynamics simulations showed
that LXR novel ligands 1E5 and 3A4 induce conformational
changes that are distinct from known agonists, suggestive of their
potential functional differences in modulating LXR activity.
To validate the results from the screening and assess the
efficacy of the ligands, we performed a cell proliferation assay
using three different concentrations of the novel ligands (1 μM,
5 μM, and 10 μM). PDAC cells BxPC-3, MIA PaCa-2, and
PANC-1 were treated with GW3965, a synthetic LXR agonist
that has previously been shown to have an antiproliferative effect
on PDAC cells and novel ligands 1E5 and 3A4. After 72 h,
proliferation of BxPC-3 cells was significantly reduced by 3A4 at
the three concentrations tested.1E5 inhibited BxPC-3 cell
proliferation at 10 μM concentration (Figure 4A). In the MIA
PaCa-2 cell line, GW3965 significantly decreased cell pro-
liferation at 10 μm, whereas 1E5 significantly inhibited cell
proliferation at 5 μM and 10 μM concentrations. 3A4 inhibited
cell proliferation at all three concentrations. However, 1E5 had a
more potent effect at 10 μM than 3A4 (Figure 4B). In PANC-1
cells, GW3965 did not show any significant effect on cell growth.
Novel ligand 1E5 at 10 μM concentration reduced the cell
growth by 40%, whereas 3A4 inhibited cell proliferation to 60%
at 5 and 10 μM (Figure 4C). Treatments with 3A4 did not have
any effect on the growth of nontransformed pancreatic ductal
cells (HPNE), whereas 1E5 decreased the cell growth at 10 μM
but not at 1 and 5 μM concentrations (Figure 4D). In summary,
3A4 elicited antiproliferative effects at lower concentrations in
BxPC-3 and MIA PaCa-2, whereas in PANC1 cells, it had an
F
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
effect at the higher concentrations. 1E5, however, potently
disrupted growth at 10 μM in BxPC-3 and PANC-1 cells,
whereas in MIA PaCa-2 cells, it showed an effect at a lower
concentration as well. These findings point to cell line- and
ligand-specific effects of the novel ligands.
After establishing the growth inhibitory effects of the ligands,
we next determined their effects on the survival and growth of
PDAC cells using trypan blue exclusion and colony formation
assays. After 72 h of treatment, in BxPC-3 cells, GW3965
significantly reduced cell viability at 10 μM concentration.1E5
failed to significantly reduce the cell viability, whereas 3A4
showed a significant effect at 5 and 10 μM concentrations
(Figure 4E). In MIA, PaCa-2 cells and PANC-1, LXR agonist,
and GW3965 significantly inhibited cell viability at 10 μM but
not at 1 and 5 μM concentrations. 1E5 and 3A4 inhibited cell
survivability in a dose-dependent manner (*p value < 0.05;
Figure 4F,G). Treatments with increasing concentrations of 1E5
and 3A4 significantly reduced colony formation in BxPC-3 cells,
whereas GW3965 significantly inhibited colony formation at 5
and 10 μM concentrations. Interestingly, 1E5 completely
abrogated colony formation of BxPC-3 cells at a 10 μM
concentration (Figure 4H), which contrasts with what we
observed in the short term survival assays (Figure 4A). In MIA
PaCa-2 cells, GW3965 exhibited dosage-dependent effects on
colony formation. Novel ligand 1E5 did not significantly reduce
colonies at 1 μM, but no colonies formed at higher
concentrations, suggesting a higher minimal threshold, as
compared to 3A4 in activating its growth inhibitory mecha-
nisms. In comparison, 3A4 significantly inhibited colony
formation at all the concentrations tested (Figure 4I). Similar
effects were observed in PANC-1 cells where 3A4 showed a
more potent effect in inhibiting colony formation than 1E5
(Figure 4J). Collectively, these results demonstrate that novel
LXR ligands 1E5 and 3A4 impede the growth of PDAC cells.
To further characterize the inhibitory effects of novel ligands
and determine their half maximal inhibitory concentrations
(IC50), we conducted tetrazolium salt reduction assays at
multiple concentrations. Both ligands showed dose-dependent
effects across all three cell lines. 3A4 exhibited greater potency
with a lower IC50 (0.86 μM, 1.2 μM, and 3.5 μM) than 1E5 (7.2
μM, 3.7 μM, and 7.0 μM) across the three PDAC cell lines (see
Figure 5). Both novel ligands have significantly greater potency
than GW3965 as determined in a previously published study.25
Once the antiproliferative effects of 1E5 and 3A4 were
established in PDAC cells, we next examined their potential
mechanisms of action. Since LXRs function as a ligand-
dependent transcription factor, we first determined the effects
of novel ligands on the expression levels of known LXR target
genes by quantitative PCR. As expected, with treatment with the
LXR synthetic agonist, GW3965 significantly increased the
expression of the LXR target genes ABCA1, SREBP1c, SCD1,
and FASN in all three cell lines (see Figure 6). These results
validated the presence and functionality of LXRβ in the PDAC
cell lines. Interestingly, both 1E5 and 3A4 decreased the
expression of LXRβ target genes below the basal level in all cell
lines, suggesting opposite effects to those of the agonist
GW3965. Treatments with 1E5 showed greater effects on
reducing LXRβ target gene expression in MIA PaCa-2 and
PANC-1 cells (Figure 6B and C) than BxPC-3 cells, whereas
3A4 had a more pronounced effect in BxPC-3 cells (Figure 6A).
The differential regulation of genes by 1E5 and 3A4 is consistent
with variation in antiproliferative effects of the ligands. These
Figure 6. Novel ligands act as LXR inverse agonists which
downregulate basal expression of known target genes. (A) BxPC-3,
(B) MIA PaCa-2, and (C) PANC-1 PDAC cells were treated with the
vehicle (DMSO), GW3965 (10uM), 1E5 (10uM), or 3A4 (10uM) for
48 h. Total RNA was extracted and gene expression analyzed using
qRT-PCR. Data were normalized to house-keeping gene expression
levels and are displayed as average fold-change (treatment/vehicle)
SEM of three independent experiments.
results indicate that the novel ligands function as LXR inverse
agonists.
Previous studies have shown that LXR inverse agonists
enhance the recruitment of NR corepressors to the ligand
binding domain of LXR.30,36−38 In the presence of agonists,
coactivators are recruited to the LBD, whereas in the presence of
inverse agonists, coactivators are released and corepressors are
recruited. We conducted time-resolved fluorescence energy
transfer (TR-FRET) experiments using purified glutathione-S-
transferase (GST)-tagged LXRβ-LBD and coactivator and
corepressor peptides to determine whether modulation of
these interactions also mediates the actions of the novel LXR
ligands. We determined that GW3965 yielded an EC50 value of
G
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Figure 7. Treatments with novel LXR ligands enhance the recruitment of corepressor peptides to the LXRβ ligand-binding domain. (A) Time-resolved
fluorescence energy transfer assay of nuclear receptor-co-regulator interactions showed GW3965 enhanced the dosage-dependent binding of
coactivator peptide D22. (B) Treatments with GAC0001E5 had no effect on D22 binding. (C) Similarly, GAC0003A4 had no effect on coactivator
binding. (D) GW3965 treatment decreased the binding of SMRT corepressor peptides. (E) 1E5 and (F) 3A4 treatments increased the binding of
SMRT peptides. (G) GW3965 treatment decreased the binding of NCOR corepressor peptides. (H) 1E5 and (I) 3A4 treatments increased the
binding of NCOR peptides.
Figure 8. Novel ligands GAC0001E5 and GAC0003A4 function as LXRβ protein degraders. BxPC-3, MIA PaCa-2, and PANC-1 PDAC cells were
treated with DMSO, GW3965 (10uM), 1E5 (10uM), or 3A4 (10uM) for 48 h. Protein expression was determined by Western blotting using specific
antibodies against LXRβ. Western analysis using antibodies against β-actin was included as a loading control. The gel images shown are representative
of those from three independent experiments. LXRβ structural data used for molecular docking of novel ligands are from Protein Data Bank entry
1UPV.
55 nM, demonstrating agonist-induced recruitment of the
fluorescein D22 coactivator peptide (Figure 7A). Although this
value was slightly greater than that reported (13 nM) in the
technical support documents for this assay, we attribute any
differences to assay setup, conditions, and instrumentation. As
expected, both 1E5 and 3A4 were unable to generate fits with the
D22 coactivator peptide, providing evidence of their actions as
inverse agonists (Figure 7B,C). Further, GW3965 inhibited
recruitment of SMRT and NCOR corepressor peptides, yielding
IC50 values of 14 nM and 30 nM, respectively (Figure 7D,G).
Conversely, the novel ligands 1E5 and 3A4 yielded dosage
responsive curves of their recruitment. Since the ligand EC50
value is a composite of multiple equilibria, including both ligand
binding to the receptor and peptide binding to the complex, the
lower EC50 values for 3A4 for SMRT and NCOR, 0.10 μM for
both (Figure 7F,I), indicate that this ligand induced greater
affinity for both corepressor peptides than 1E5 with EC50 values
of 25 μM for SMRT and 7.1 μM for NCOR (Figure 7E,H).
These results further suggest that these novel LXR ligands may
function as inverse agonists by altering receptor conformation
and enhancing the recruitment of corepressors to the LXR
complex.
H
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
To further characterize the effects and potential mechanisms
of action of novel LXR ligands, we performed western blot
analysis to determine the effects of 1E5 and 3A4 on LXRβ
protein levels following 48-h treatments with ligands. Un-
expectedly, treatments with 1E5 and 3A4 decreased LXR
protein levels significantly following treatment (Figure 8) in all
three PDAC cell lines. Treatments with 1E5 were especially
effective in decreasing LXR expression in MIA PaCa-2 and
PANC-1 cells (Figures 8B,C). In Bx-PC3 cells, 3A4 treatments
had a greater effect on LXR protein levels than 1E5 (Figure 8A).
Synthetic agonist GW3965 slightly increased LXRβ levels in
MIA PaCa-2 and PANC-1 cells. Consistent with the growth
inhibitory effects of 1E5 and 3A4 and their effects on LXR
protein levels, knockdown of LXRβ in PDAC cells also blocked
their proliferation in our previously published study.25 These
findings not only reveal a possible mechanism of action but also
indicate that novel ligands 1E5 and 3A4 may function as the first
described LXR degraders. Among drugs targeting nuclear
receptors, small molecule degraders targeting steroid hormone
receptors are exploited as therapeutic options in breast and
prostate cancers.39,40 Of the other members of the nuclear
receptor superfamily, it has been reported that retinoid X
receptor (RXR) agonists LGD1069 and LGD1268 induce
ubiquitin-mediated degradation of RXR in kidney cells, although
this may be a normal mechanism of receptor turnover following
ligand activation.41 The novel LXR ligands described here,
therefore, appear to be new classes of small molecules which
affect receptor stability without activating its activity. Future
studies are warranted to determine the consequences of LXR
inhibition and degradation on downstream gene networks,
pathways, and cancer-related processes, and their impact on
cancer cell proliferation and survival, tumor growth, and
metastasis, in in vitro and in vivo models. Moreover, the
specificity of these small molecules for LXR subtypes, possible
interactions with other related NRs, and their pharmacological
profiles remain to be determined.
equiv), Xanphos (155 mg, 0.27 mmoL, 0.1 equiv), tBuONa (513 mg,
5.34 mmol, 2 equiv), and 11 mL of 1,4-dioxane. The mixture was stirred
at RT for 20 min. Then, the compound from the first step (500 mg, 2.67
mmol, 1 equiv) was added, and the reaction was heated to 100 °C and
run overnight. Upon completion, the crude mixture was filtered
through Celite, and solvent was removed under a vacuum. The crude
was extracted with ethyl acetate. The organic phases were combined
and washed with brine, dried over anhydrous MgSO4, filtered, and
concentrated to yield the crude product. The product was purified by
column chromatography on silica gel using 10% MeOH in CH2Cl2. A
total of 365 mg of white solid was obtained, 41% yield.
1H NMR (600 MHz, DMSO-d6): δ 11.10 (s, 1H), 9.37 (s, 1H), 8.50
(s, 1H), 8.09−8.00 (m, 2H), 7.81 (d, J = 8.2 Hz, 1H), 7.57 (t, J = 7.9 Hz,
1H), 7.53−7.44 (m, 3H), 7.36 (d, J = 7.7 Hz, 1H), 6.55 (s, 1H).
13C NMR (126 MHz, DMSO-d6): δ 164.95, 162.01, 153.48, 140.01,
136.63, 130.33, 129.66, 129.32 (q, JC‑F = 31.4 Hz), 128.51, 126.54,
124.17 (q, JC‑F = 272.3 Hz), 122.79, 118.27 (q, JC‑F = 4.5 Hz), 115.51
(q, JC‑F = 4.4 Hz), 99.03.
HRMS-ESI, m/z [M + H]+ calculated: 332.1005. Measured:
332.1003
GAC0003A4: To a stirred solution of 1-Boc-piperazine (1207 mg,
6.48 mmol, 1.3 equiv) in 12 mL of anhydrous CH2Cl2 was added
K2CO3 (689 mg, 4.99 mmol, 1 equiv). The mixture was stirred at 0 °C
for 10 min under nitrogen. Then, 3,5-dimethoxy benzoyl chloride (1.0
g, 4.99 mmol, 1 equiv) was added, and the mixture was stirred for 3 h at
0 °C. After completion, the mixture was concentrated by evaporation of
the solvent. Water was added, and the mixture was extracted with ethyl
acetate. The organic phases were combined and washed with brine,
dried over anhydrous MgSO4, filtered, and concentrated to yield the
crude product. The product was purified by column chromatography
on silica gel using 5% MeOH in CH2Cl2. A total of 1.60 g of white solid
was obtained, 93% yield.
CONCLUSION
■
In this study, we identified two novel LXR ligands which can
disrupt the growth of pancreatic cancer cells. They appear to
function as inhibitors of LXR by enhancing the recruitment of
corepressor proteins and by decreasing LXR protein levels over
time. Their ability to modulate LXR activity and expression
suggest their utility in basic structure−function research in the
design and synthesis of LXR and NR inhibitors and degraders for
medicinal chemistry and drug discovery, loss-of-function studies
in relevant experimental models, and potential development as
therapeutic agents in pancreatic cancers and other malignancies
and diseases where LXRs function in key pathogenic
mechanisms.
1H NMR (500 MHz, methanol-d4): δ 6.57 (t, J = 2.3 Hz, 1H), 6.54
(d, J = 2.3 Hz, 2H), 3.79 (s, 6H), 3.70 (s, 2H), 3.52 (s, 2H), 3.41 (s,
4H), 1.46 (s, 9H).
METHODS
■
13C NMR (126 MHz, acetone-d6): δ 169.23, 161.03, 154.23, 138.43,
104.89, 101.26, 79.26, 55.11, 47.19, 44.15, 43.28, 41.76, 27.86.
Structural Confirmation by Synthesis. GAC001E5: To a stirred
solution of guanidine carbonate (1.0 g, 5.55 mmol, 1 equiv) in 28 mL of
ethanol was added ethyl benzoylacetate (1.9 mL, 11.1 mmol, 2 equiv).
The mixture was refluxed overnight. After cooling to RT, the resultant
precipitate was washed with ethanol, water, and acetone. A total of 899
mg of white solid was obtained, 87% yield.
1H NMR (500 MHz, DMSO-d6): δ 10.80 (s, 1H), 7.94 (dd, J = 6.7,
3.0 Hz, 2H), 7.46−7.40 (m, 3H), 6.63 (s, 2H), 6.10 (s, 1H).
13C NMR (151 MHz, DMSO-d6): δ 163.68, 162.66, 155.88, 137.35,
130.01, 128.45, 126.70, 97.63.
An oven-dried flask was then charged with 3-bromobenzotrifluoride
(0.56 mL, 4.01 mmol, 1.5 equiv), Pd2(dba)3 (147 mg, 0.16 mmol, 0.06
I
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
To a stirred solution of the product from the previous step (1.6 g,
4.66 mmol, 1 equiv) in 10 mL of CH2Cl2 was added 14 mL of TFA at 0
°C. The resulting mixture was allowed to warm to RT over 2 h. After
completion, 6N NaOH was added dropwise until the pH of the mixture
reached ∼10, after which the same was extracted with CH2Cl2. The
organic phases were combined and washed with brine, dried over
anhydrous MgSO4, filtered, and concentrated to yield the crude
product. The product was purified by column chromatography on silica
gel using 20% MeOH in CH2Cl2. A total of 1.01 g of white solid was
obtained, 87% yield.
and allowed 24 h for attachment. Cells were treated with 10 μM of
library ligands for 72 h, after which cell viability was assessed with the
MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-
(4-sulfophenyl)-2H-tetrazolium) reagent (Promega G3582). Cells
were treated with 10 μL of MTS reagent and incubated for 2−3 h.
Absorbance was measured at 490 nm with a microplate reader
(SpectraMAX M5). Assays were performed in triplicate.
Docking Simulations. The crystal structure of the LXRβ ligand-
binding domain (Protein Data Bank entry code 1UPV) was used for
docking simulations. Bound ligand and water molecules were removed
from the crystal structure prior to simulations. The AutoDock suite,
including AutoDockTools and AutoDock Vina, were used to simulate
docking of ligands 1E5 and 3A4 to the LXRβ ligand-binding domain
with an exhaustiveness level of 24. Ligands contained rotatable bonds,
while the receptor had no flexible residues. Known agonists GW3965
and T0901317 were docked as positive controls, while caffeine was used
as a negative control (ΔG = −5.6). Ligand−receptor interactions were
analyzed with Discovery Studio Visualizer (v19.1.0.18287) and
visualizations rendered using AutoDockTools.42 The lowest free
energy values (ΔG) are reported for each ligand.
1H NMR (400 MHz, methanol-d4): δ 6.56 (t, J = 2.3 Hz, 1H), 6.52
(d, J = 2.3 Hz, 2H), 3.79 (s, 6H), 3.70 (s, 2H), 3.39 (s, 2H), 2.88 (s,
2H), 2.75 (s, 2H).
13C NMR (126 MHz, acetone-d6): δ 169.57, 161.51, 139.35, 105.30,
101.41, 55.58, 49.19, 46.71, 46.26, 43.44.
Molecular Dynamics Simulations. To compare the ligand-
binding interactions of our small molecules1E5, 3A4, GW3965 and
T0901317we performed 150 ns (ns) molecular dynamics (MD)
simulations for all LXR-β−ligand complexes. The ligand T0901317 was
selected as a control for this study as it was complexed with the LXR-β−
ligand binding domain in the original crystal structure. Protein−ligand
docking was performed to generate the LXR-β−ligand complexes. The
COACH-D web server which utilizes an algorithm to predict protein−
ligand binding sites and ligand-binding poses of the queried protein
structure and ligand43 was used for docking the ligands to the LXR-β
ligand-binding domain. The COACH algorithm first utilizes five
individual methods to predict protein−ligand binding pocket and
residues including TM-SITE,44 S-SITE,44 COFACTOR, FINDSITE,
and ConCavity.43 The queried ligand is then docked into the predicted
binding sites using the state-of-the-art docking algorithm AutoDock
Vina.43 The docked poses with the highest confidence interval from the
range of 0 to 1 with a value close to 1 were selected for all small
molecules to set up MD simulations. All simulations were performed
using the GROMACS 2018.845 package and the Charmm3646
(charmm36-mar2019.ff) force field. The protonation states of the
LXR-β−ligand binding domain was first verified using PROPKA3.147,48
and the ProteinPrepare web server.49 NMR data were used to select the
correct protonation states of the ligands. The missing LXR-β−ligand
binding domain residues were built using the CharmmGUI PDB
Reader tool,50,51 and the ligands were prepared using the SwissParam52
web server. The complexes were solvated with TIP-3P water in a
dodecahedral box with a 0.15 mM NaCl ion concentration with
dimensions of 1.6 nm (nm) with periodic boundary conditions turned
on for the simulation. All charges were neutralized with sodium and
chloride ions to enable the use of Particle-Mesh Ewald (SPME)
electrostatics (cutoff distance selected to be 1.2 nm) for long-range
electrostatic calculations. Positional restraints with a force constant of
1000 kJ/mol/rad2 were applied during energy minimization as well as
during simulation in the isothermal−isochoric (NVT) ensemble
(temperature set to 310 Ks). The steepest descent algorithm, with
emtol set to 1000 kJ mol−1 nm−1 and step size (emstep) set to 0.01, was
selected to allow the system to minimize properly with the added
solvent. The Verlet cutoff scheme was used for the van der Waals
interactions between atoms with a cutoff distance set to 1.2 nm. The
NVT equilibration was carried out for 1 ns with the pressure-coupling
turned off and positional restraints turned on. These restraints were
slowly removed in the isothermal−isobaric (NPT) ensemble (pressure
coupling set to 1 atm). The Berendsen pressure coupling method was
used for NPT equilibration as it allows for the use of positional
restraints. The Parrinello−Rahman pressure coupling method was used
for the full 150 ns production runs. The 150 ns trajectories were first
analyzed using TTClust,4 which performs hierarchical clustering on the
RMSD of all frames. Trajectory files with a stride size of 100 (1500 total
frames) were processed by TTClust. TTClust parameters included
alignment and RMSD on protein backbone atoms, the Ward
An oven-dried flask was charged with 2-bromotoluene (0.23 mL,
1.92 mmol, 1.2 equiv), Pd2(dba)3 (88 mg, 0.09 mmol, 0.06 equiv),
BINAP (100 mg, 0.16 mmol, 0.1 equiv), tBuOK (359 mg, 3.19 mmol, 2
equiv), and 6 mL of toluene. The mixture was stirred at RT for 20 min.
Then, the end product from the previous step (400 mg, 1.59 mmol, 1
equiv) was added and the reaction was heated to 100 °C and run
overnight. Upon completion, the crude mixture was filtered through
Celite and the solvent was removed under a vacuum. The crude mixture
was extracted with ethyl acetate. The organic phases were combined
and washed with brine, dried over anhydrous MgSO4, filtered, and
concentrated to yield the crude product. The product was purified by
column chromatography on silica gel using 40% EtOAc in hexane. A
total of 360 mg of a yellow oil was obtained, 66% yield.
1H NMR (500 MHz, CDCl3): δ 7.20−7.14 (m, 2H), 7.03−6.97 (m,
2H), 6.56 (d, J = 2.3 Hz, 2H), 6.50 (t, J = 2.3 Hz, 1H), 3.92 (s, 2H), 3.80
(s, 6H), 3.58 (s, 2H), 2.97 (s, 2H), 2.84 (s, 2H), 2.32 (s, 3H).
13C NMR (126 MHz, CDCl3): δ 170.05, 160.88, 150.71, 137.74,
132.66, 131.21, 126.70, 123.83, 119.20, 104.85, 101.60, 55.47, 52.26,
51.74, 48.18, 42.56, 17.81.
HRMS-ESI, m/z [M + Na]+ calculated: 363.1679. Measured
363.1673
1
The H and 13C NMR results of compounds GAC0001E5 and
GAC0003A4 can be found in the Supporting Information.
Cell Lines and Culture. Human pancreatic cancer cell lines BxPC-
3, MIA PaCa-2, and PANC-1 were purchased directly from the
American Type Culture Collection (ATCC). Human pancreatic nestin
expressing cells (HPNE) were obtained from Dr. Jennifer M. Bailey at
The University of Texas Health Science Center at Houston. We thank
Dr. David Tuveson (Cold Spring Harbor Laboratory) for generously
providing the KPC cell line. BxPC-3 cells were cultured in RPMI 1640
(Gibco 11875085). MiaPaCa-2, PANC1, and KPC cells were cultured
in DMEM (Gibco 12430047) containing high glucose and HEPES.
HPNE cells were cultured in DMEM containing low glucose
GlutaMAX (Gibco 10567014). All media were supplemented with
10% FBS (Gibco 26140079). All cell lines were cultured in a humidified
atmosphere of 5% CO2 at 37 °C. All cell lines were free from
mycoplasma contamination and authenticated using short tandem
repeat (STR) profiling (ATCC 135-XV). DMSO was purchased from
VWR (97063-136). GW3965 was purchased from Tocris Bioscience
(2474). 1E5 and 3A4 were synthesized by OTAVA Chemicals.
Library Screening. A focused LXRβ Library was purchased form
OTAVA Chemicals and screened in PDAC cells using MTS assay.
Totals of 5 × 103 BxPC-3 and HPNE, 2.5 × 103 MIA PaCa-2, and 4 ×
103 PANC-1 cells/well were seeded in a 96-well plate (100 μL/well)
J
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
hierarchical clustering method, and k-means clustering with the elbow
method for the number of clusters. The representative frame (lowest
RMSD within cluster) from the largest cluster was chosen for
interaction analysis for each ligand. Interactions were analyzed using
LigPlot+5 and PyMOL6 software. Trajectory analysis, including RMSD,
RMSF, and atomic distances, were analyzed using ProDy7,8 in Python
and plotted in R.53−55
Cell Viability Assays. For standard trypan blue exclusion assays, 1
× 105 BxPC-3, 6 × 104 MIA PaCa-2, and 8 × 104 PANC-1 cells/well
were seeded in a 12-well plate and allowed 24 h for attachment. Cells
were treated with LXR ligands and vehicle (DMSO) at 1, 5, and 10 μM
concentrations for 72 h. Cells were harvested, stained with trypan blue
(Invitrogen T10282), and counted with a Countess automated cell
counter (Invitrogen C10227). All experiments and measurements were
performed in triplicate. For colony formation assay, 500 cells were
seeded in six-well plates and incubated for 24 h for attachment. Cells
were then treated with GW3965, 1E5, 3A4, and vehicle (DMSO) at 1,
5, and 10 μM concentrations for 1−2 weeks. At the end of the treatment
period, cells were washed with 1 × PBS and fixed in 4%
paraformaldehyde solution (Sigma-Aldrich 158127). Colonies were
stained with 0.05% (w/v) crystal violet (Sigma-Aldrich C0775).
Colonies were air-dried and manually counted. Images are
representative of one of three biological replicates.
signals. 520/490 nm emission ratios were normalized to DMSO
controls.
Western Analysis. Cells were plated in six-well plates at densities of
2 × 105 for BxPC-3 and MIA PaCa-2 and 2.5 × 105 for PANC-1 and
treated with DMSO (vehicle), GW3965, 1E5, and 3A4 at 10 μM for 48
h. Samples were lysed in RIPA buffer (ThermoFischer 89900) on ice for
30 min followed by centrifugation at 13,000 rpm at 4 °C for 15 min.
Then, 2× laemmili buffer (Bio-Rad 161−0737) was added to the lysed
samples followed by denaturing at 99 °C for 10 min. Samples were
applied to 10% SDS-PAGE and transferred to a PVDF membrane
(Thermo Fisher Scientific 88518). Membranes were incubated with
primary antibodies: anti-LXRβ (Perseus Proteomics) and anti-β-actin
(GE) overnight at 4 °C. Signals were detected using BIO-RAD
chemiluminescence reagents, and images were captured on a GE
Amersham Imager 600. Blot images are representative of three
biological replicates.
Statistical Analyses. All statistical analyses were performed on
GraphPad Prism 6.0. For TR-FRET, curves were fit on GraphPad Prism
using nonlinear regression. Data represent mean
SEM unless
otherwise noted. Unpaired two-tailed Student t tests were used to
determine p values. Significance is shown as *p value < 0.05.
ASSOCIATED CONTENT
* Supporting Information
■
RT-qPCR. Cells were plated in six-well plates at densities of 2 × 105
for BxPC-3 and MIA PaCa-2 and 2.5 × 105 for PANC-1 and allowed 24
h for attachment. Treatments included DMSO (vehicle), GW3965,
1E5, and 3A4 at 10 μM for 48 h. Total RNA was extracted using RNeasy
Kit (Qiagen) following the manufacturer’s directions. A total of 1 μg of
RNA was used for reverse-transcription using iScript (Bio-Rad
1708890) following the manufacturer’s directions. qPCR was
performed with Power SYBR Green Master Mix (Applied Biosystems)
on an Applied Biosystem 7500 Fast PCR Machine. The 36B4
housekeeping gene was used as an internal control. A minimum of
three biological replicates were conducted for each cell line. Relative
fold change (to vehicle) was determined using the ΔΔCt method.
Primer sequences for LXRβ target genes:
sı
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/acschembio.0c00546.
Supplementary Figures S1−S4 (PDF)
AUTHOR INFORMATION
Corresponding Authors
■
Scott R. Gilbertson − Department of Chemistry, University of
Houston, Houston, Texas 77004, United States; orcid.org/
0000-0003-3665-5693; Email: srgilbe2@central.uh.edu
Chin-Yo Lin − Center for Nuclear Receptors and Cell Signaling
and Department of Biology and Biochemistry, University of
Houston, Houston, Texas 77004, United States; orcid.org/
0000-0002-4461-6216; Email: clin23@central.uh.edu
36B4-Forward: 5′-GTGTTCGACAATGGCAGCAT-3′
36B4-Reverse: 5′-GACACCCTCCAGGAAGCGA-3′
ABCA1-Forward: 5′-TGTGAGGCGGGAAAGACAGAG-3′
ABCA1-Reverse: 5′-AGCCCAAAGCACTCACCAGGA-3′
ABCG1-Forward: 5′-CGATGAGCCCACCAGCGGC-3′
Authors
ABCG1-Reverse: 5′-ACCCCCTTGAGCGAGCCCTT-3′
Husna Karaboga − Center for Nuclear Receptors and Cell
Signaling and Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Wentao Huang − College of Pharmacy, Guangxi Medical
University, Qingxiu District, Nanning, Guangxi, China
Shivangi Srivastava − Center for Nuclear Receptors and Cell
Signaling and Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Scott Widmann − Center for Nuclear Receptors and Cell
Signaling and Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Sridevi Addanki − Center for Nuclear Receptors and Cell
Signaling and Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Kasuni Thawalama Gamage − Department of Chemistry,
University of Houston, Houston, Texas 77004, United States
Zahra Mazhar − Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Jerry O. Ebalunode − Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
James M. Briggs − Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
Jan-Åke Gustafsson − Center for Nuclear Receptors and Cell
Signaling and Department of Biology and Biochemistry,
University of Houston, Houston, Texas 77004, United States
FASN-Forward: 5′-ACAGGGACAACCTGGAGTTCT-3′
FASN-Reverse: 5′-CTGTGGTCCCACTTGATGAGT-3′
SCD1-Forward: 5′-TTCAGAAACACATGCTGATC-
CTCATAATTCCC-3′
SCD1-Reverse: 5′-ATTAAGCACCACAGCATATC-
GCAAGAAAGT-3′
SREBP-1c-Forward: 5′-GGAGGGGTAGGGCCAACGGCCT-3′
SREBP-1c-Reverse: 5′-CATGTCTTCGAAAGTGCAATCC-3′
TR-FRET Assays. All assays were conducted in white 384-well low
flange polystyrene microplates (Corning 07201443) in quadruplicate.
LanthaScreen TR-FRET Liver X Receptor Beta Coactivator Assay,
rabbit (A15129) was purchased from Invitrogen and included purified
LXRβ-LBD (GST), terbium (Tb)-labeled anti-GST antibody,
fluorescein (FL)-D22 coactivator peptide (PV4386, lot #
1752162H), TR-FRET coregulatory buffer 1, and DTT. Fluorescein-
SMRT ID2 Corepressor peptide (PV4423, lot # 1920512C) and
Fluorescein-NCOR ID2 Peptide (PV4624, lot # 1693738G) were
additionally purchased from Invitrogen. Ligand concentrations under-
went a 12-point, 3-fold dilution in DMSO beginning at 100 μM. Final
concentrations for LXRβ-LBD (GST), FL-coregulators, and Tb-anti-
GST were 5 nM, 0.1 μM, and 10 nM, respectively. Controls included
DMSO, LXRβ-LBD (GST), and Tb-anti-GST. Plates were covered,
gently shaken, and incubated at RT for 4 h. Measurements were
performed on a PerkinElmer Victor X4 plate reader with a 340 nm
excitation filter, and with 490 and 520 nm emission filters for terbium
and fluorescein, respectively. A counting delay of 100 μs and counting
window of 200 μs were used for detection of time-resolved emission
K
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
Yan, H., Wang, W., Chen, S., Viale, A., Zheng, H., Paik, J. H., Lim, C.,
Guimaraes, A. R., Martin, E. S., Chang, J., Hezel, A. F., Perry, S. R., Hu,
J., Gan, B., Xiao, Y., Asara, J. M., Weissleder, R., Wang, Y. A., Chin, L.,
Cantley, L. C., and DePinho, R. A. (2012) Oncogenic Kras maintains
pancreatic tumors through regulation of anabolic glucose metabolism.
Cell 149 (3), 656−70.
Carly S. Filgueira − Department of Nanomedicine, Houston
Methodist Research Institute, Houston, Texas 77030, United
States; orcid.org/0000-0002-3246-303X
Complete contact information is available at:
https://pubs.acs.org/10.1021/acschembio.0c00546
(12) Son, J., Lyssiotis, C. A., Ying, H., Wang, X., Hua, S., Ligorio, M.,
Perera, R. M., Ferrone, C. R., Mullarky, E., Shyh-Chang, N., Kang, Y.,
Fleming, J. B., Bardeesy, N., Asara, J. M., Haigis, M. C., DePinho, R. A.,
Cantley, L. C., and Kimmelman, A. C. (2013) Glutamine supports
pancreatic cancer growth through a KRAS-regulated metabolic
pathway. Nature 496 (7443), 101−5.
(13) Biancur, D. E., Paulo, J. A., Malachowska, B., Quiles Del Rey, M.,
Sousa, C. M., Wang, X., Sohn, A. S. W., Chu, G. C., Gygi, S. P., Harper, J.
W., Fendler, W., Mancias, J. D., and Kimmelman, A. C. (2017)
Compensatory metabolic networks in pancreatic cancers upon
perturbation of glutamine metabolism. Nat. Commun. 8, 15965.
(14) Perera, R. M., and Bardeesy, N. (2015) Pancreatic Cancer
Metabolism: Breaking It Down to Build It Back Up. Cancer Discovery 5
(12), 1247−61.
(15) Shimizu, T., Tolcher, A. W., Papadopoulos, K. P., Beeram, M.,
Rasco, D. W., Smith, L. S., Gunn, S., Smetzer, L., Mays, T. A., Kaiser, B.,
Wick, M. J., Alvarez, C., Cavazos, A., Mangold, G. L., and Patnaik, A.
(2012) The clinical effect of the dual-targeting strategy involving PI3K/
AKT/mTOR and RAS/MEK/ERK pathways in patients with advanced
cancer. Clin. Cancer Res. 18 (8), 2316−25.
(16) Pylayeva-Gupta, Y., Grabocka, E., and Bar-Sagi, D. (2011) RAS
oncogenes: weaving a tumorigenic web. Nat. Rev. Cancer 11 (11), 761−
74.
(17) Gronemeyer, H., Gustafsson, J. A., and Laudet, V. (2004)
Principles for modulation of the nuclear receptor superfamily. Nat. Rev.
Drug Discovery 3 (11), 950−64.
(18) Jordan, V. C. (2004) Selective estrogen receptor modulation:
concept and consequences in cancer. Cancer Cell 5 (3), 207−13.
(19) Brueggemeier, R. W., Hackett, J. C., and Diaz-Cruz, E. S. (2005)
Aromatase inhibitors in the treatment of breast cancer. Endocr. Rev. 26
(3), 331−345.
(20) Dahlman, I., Nilsson, M., Gu, H. F., Lecoeur, C., Efendic, S.,
Ostenson, C. G., Brismar, K., Gustafsson, J. A., Froguel, P., Vaxillaire,
M., Dahlman-Wright, K., and Steffensen, K. R. (2009) Functional and
genetic analysis in type 2 diabetes of liver X receptor alleles–a cohort
study. BMC Med. Genet. 10, 27.
Author Contributions
#H.K., W.H., and S.S. contributed equally.
Funding
This study was supported by funding from Golfers Against
Cancer and William and Ella Owens Research Foundation
awarded to C.Y.L. and funding from the Robert A. Welch
Foundation to J.Å.G.
Notes
The authors declare the following competing financial
interest(s): C.Y.L. and S.R.G. are co-inventors on a pending
patent application on the use of 1E5, 3A4, and their derivatives
in cancer therapeutics.
ACKNOWLEDGMENTS
■
The authors acknowledge the use of the Sabine and Carya
Clusters and the advanced support from the Research
Computing Data Core at the University of Houston for the
molecular simulations.
REFERENCES
■
(1) Waters, A. M., and Der, C. J. (2018) KRAS: The Critical Driver
and Therapeutic Target for Pancreatic Cancer. Cold Spring Harbor
Perspect. Med. 8 (9), a031435.
(2) Lee, C. S., Lee, L. C., Yuan, T. L., Chakka, S., Fellmann, C., Lowe,
S. W., Caplen, N. J., McCormick, F., and Luo, J. (2019) MAP kinase and
autophagy pathways cooperate to maintain RAS mutant cancer cell
survival. Proc. Natl. Acad. Sci. U. S. A. 116 (10), 4508−4517.
(3) Bryant, K. L., Mancias, J. D., Kimmelman, A. C., and Der, C. J.
(2014) KRAS: feeding pancreatic cancer proliferation. Trends Biochem.
Sci. 39 (2), 91−100.
(4) Papke, B., and Der, C. J. (2017) Drugging RAS: Know the enemy.
Science 355 (6330), 1158−1163.
(5) Hunter, J. C., Gurbani, D., Ficarro, S. B., Carrasco, M. A., Lim, S.
M., Choi, H. G., Xie, T., Marto, J. A., Chen, Z., Gray, N. S., and
Westover, K. D. (2014) In situ selectivity profiling and crystal structure
of SML-8−73−1, an active site inhibitor of oncogenic K-Ras G12C.
Proc. Natl. Acad. Sci. U. S. A. 111 (24), 8895−900.
(6) Karnoub, A. E., and Weinberg, R. A. (2008) Ras oncogenes: split
personalities. Nat. Rev. Mol. Cell Biol. 9 (7), 517−31.
(7) Patricelli, M. P., Janes, M. R., Li, L. S., Hansen, R., Peters, U.,
Kessler, L. V., Chen, Y., Kucharski, J. M., Feng, J., Ely, T., Chen, J. H.,
Firdaus, S. J., Babbar, A., Ren, P., and Liu, Y. (2016) Selective Inhibition
of Oncogenic KRAS Output with Small Molecules Targeting the
Inactive State. Cancer Discovery 6 (3), 316−29.
(8) Maurer, T., Garrenton, L. S., Oh, A., Pitts, K., Anderson, D. J.,
Skelton, N. J., Fauber, B. P., Pan, B., Malek, S., Stokoe, D., Ludlam, M. J.,
Bowman, K. K., Wu, J., Giannetti, A. M., Starovasnik, M. A., Mellman, I.,
Jackson, P. K., Rudolph, J., Wang, W., and Fang, G. (2012) Small-
molecule ligands bind to a distinct pocket in Ras and inhibit SOS-
mediated nucleotide exchange activity. Proc. Natl. Acad. Sci. U. S. A. 109
(14), 5299−304.
(9) Lito, P., Solomon, M., Li, L. S., Hansen, R., and Rosen, N. (2016)
Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping
mechanism. Science 351 (6273), 604−8.
(10) Zeitouni, D., Pylayeva-Gupta, Y., Der, C. J., and Bryant, K. L.
(2016) KRAS Mutant Pancreatic Cancer: No Lone Path to an Effective
Treatment. Cancers 8 (4), 45.
(21) Brelivet, Y., Rochel, N., and Moras, D. (2012) Structural analysis
of nuclear receptors: from isolated domains to integral proteins. Mol.
Cell. Endocrinol. 348 (2), 466−73.
(22) Gallastegui, N., Mackinnon, J. A., Fletterick, R. J., and Estebanez-
Perpina, E. (2015) Advances in our structural understanding of orphan
nuclear receptors. Trends Biochem. Sci. 40 (1), 25−35.
(23) Komati, R., Spadoni, D., Zheng, S., Sridhar, J., Riley, K. E., and
Wang, G. (2017) Ligands of Therapeutic Utility for the Liver X
Receptors. Molecules 22 (1), 88.
(24) Jakobsson, T., Treuter, E., Gustafsson, J. A., and Steffensen, K. R.
(2012) Liver X receptor biology and pharmacology: new pathways,
challenges and opportunities. Trends Pharmacol. Sci. 33 (7), 394−404.
(25) Candelaria, N. R., Addanki, S., Zheng, J., Nguyen-Vu, T.,
Karaboga, H., Dey, P., Gabbi, C., Vedin, L. L., Liu, K., Wu, W., Jonsson,
P. K., Lin, J. Z., Su, F., Bollu, L. R., Hodges, S. E., McElhany, A. L.,
Issazadeh, M. A., Fisher, W. E., Ittmann, M. M., Steffensen, K. R.,
Gustafsson, J. A., and Lin, C. Y. (2014) Antiproliferative effects and
mechanisms of liver X receptor ligands in pancreatic ductal
adenocarcinoma cells. PLoS One 9 (9), No. e106289.
(26) Lin, C. Y., Vedin, L. L., and Steffensen, K. R. (2016) The
emerging roles of liver X receptors and their ligands in cancer. Expert
Opin. Ther. Targets 20 (1), 61−71.
(27) Lin, C. Y., and Gustafsson, J. A. (2015) Targeting liver X
receptors in cancer therapeutics. Nat. Rev. Cancer 15 (4), 216−24.
(28) Pencheva, N., Buss, C. G., Posada, J., Merghoub, T., and
Tavazoie, S. F. (2014) Broad-spectrum therapeutic suppression of
(11) Ying, H., Kimmelman, A. C., Lyssiotis, C. A., Hua, S., Chu, G. C.,
Fletcher-Sananikone, E., Locasale, J. W., Son, J., Zhang, H., Coloff, J. L.,
L
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX

ACS Chemical Biology
pubs.acs.org/acschemicalbiology
Articles
metastatic melanoma through nuclear hormone receptor activation.
Cell 156 (5), 986−1001.
comparison and sequence profile alignment. Bioinformatics 29 (20),
2588−95.
(45) Abraham, M. J., van der Spoel, D., and Lindahl, B. H. GROMACS,
version 2018.8; GROMACS Development Team, 2018.
(46) Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot,
(29) Tavazoie, M. F., Pollack, I., Tanqueco, R., Ostendorf, B. N., Reis,
B. S., Gonsalves, F. C., Kurth, I., Andreu-Agullo, C., Derbyshire, M. L.,
Posada, J., Takeda, S., Tafreshian, K. N., Rowinsky, E., Szarek, M.,
Waltzman, R. J., McMillan, E. A., Zhao, C., Mita, M., Mita, A.,
Chmielowski, B., Postow, M. A., Ribas, A., Mucida, D., and Tavazoie, S.
F. (2018) LXR/ApoE Activation Restricts Innate Immune Suppression
in Cancer. Cell 172 (4), 825−840 e18.
B. L., Grubmuller, H., and MacKerell, A. D. (2017) CHARMM36m: an
̈
improved force field for folded and intrinsically disordered proteins.
Nat. Methods 14 (1), 71−73.
(47) Søndergaard, C. R., Olsson, M. H. M., Rostkowski, M., and
Jensen, J. H. (2011) Improved Treatment of Ligands and Coupling
Effects in Empirical Calculation and Rationalization of pKa Values. J.
Chem. Theory Comput. 7 (7), 2284−2295.
(48) Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M., and
Jensen, J. H. (2011) PROPKA3: Consistent Treatment of Internal and
Surface Residues in Empirical pKa Predictions. J. Chem. Theory Comput.
7 (2), 525−537.
(49) Martínez-Rosell, G., Giorgino, T., and De Fabritiis, G. (2017)
PlayMolecule ProteinPrepare: A Web Application for Protein
Preparation for Molecular Dynamics Simulations. J. Chem. Inf. Model.
57 (7), 1511−1516.
(50) Jo, S., Kim, T., Iyer, V. G., and Im, W. (2008) CHARMM-GUI: A
web-based graphical user interface for CHARMM. J. Comput. Chem. 29
(11), 1859−1865.
(51) Jo, S., Cheng, X., Islam, S. M., Huang, L., Rui, H., Zhu, A., Lee, H.
S., Qi, Y., Han, W., Vanommeslaeghe, K., MacKerell, A. D., Roux, B.,
and Im, W., Chapter Eight - CHARMM-GUI PDB Manipulator for
Advanced Modeling and Simulations of Proteins Containing Non-
standard Residues. In Advances in Protein Chemistry and Structural
Biology, Karabencheva-Christova, T., Ed. Academic Press: 2014; Vol.
96, pp 235−265.
(52) Zoete, V., Cuendet, M. A., Grosdidier, A., and Michielin, O.
(2011) SwissParam: a fast force field generation tool for small organic
molecules. J. Comput. Chem. 32 (11), 2359−68.
(30) Flaveny, C. A., Griffett, K., El-Gendy, B. E.-D. M., Kazantzis, M.,
Sengupta, M., Amelio, A. L., Chatterjee, A., Walker, J., Solt, L. A.,
Kamenecka, T. M., and Burris, T. P. (2015) Broad Anti-tumor Activity
of a Small Molecule that Selectively Targets the Warburg Effect and
Lipogenesis. Cancer Cell 28 (1), 42−56.
(31) Carpenter, K. J., Valfort, A. C., Steinauer, N., Chatterjee, A.,
Abuirqeba, S., Majidi, S., Sengupta, M., Di Paolo, R. J., Shornick, L. P.,
Zhang, J., and Flaveny, C. A. (2019) LXR-inverse agonism stimulates
immune-mediated tumor destruction by enhancing CD8 T-cell activity
in triple negative breast cancer. Sci. Rep. 9 (1), 19530.
(32) Zuercher, W. J., Buckholz, R. G., Campobasso, N., Collins, J. L.,
Galardi, C. M., Gampe, R. T., Hyatt, S. M., Merrihew, S. L., Moore, J. T.,
Oplinger, J. A., Reid, P. R., Spearing, P. K., Stanley, T. B., Stewart, E. L.,
and Willson, T. M. (2010) Discovery of tertiary sulfonamides as potent
liver X receptor antagonists. J. Med. Chem. 53 (8), 3412−6.
(33) El-Gendy, B. E. M., Goher, S. S., Hegazy, L. S., Arief, M. M. H.,
and Burris, T. P. (2018) Recent Advances in the Medicinal Chemistry
of Liver X Receptors. J. Med. Chem. 61 (24), 10935−10956.
(34) Farnegardh, M., Bonn, T., Sun, S., Ljunggren, J., Ahola, H.,
Wilhelmsson, A., Gustafsson, J. A., and Carlquist, M. (2003) The three-
dimensional structure of the liver X receptor beta reveals a flexible
ligand-binding pocket that can accommodate fundamentally different
ligands. J. Biol. Chem. 278 (40), 38821−8.
(35) Chen, Z., Chen, H., Zhang, Z., Ding, P., Yan, X., Li, Y., Zhang, S.,
Gu, Q., Zhou, H., and Xu, J. (2020) Discovery of novel liver X receptor
inverse agonists as lipogenesis inhibitors. Eur. J. Med. Chem. 206,
112793.
(53) Tubiana, T., Carvaillo, J.-C., Boulard, Y., and Bressanelli, S.
(2018) TTClust: A Versatile Molecular Simulation Trajectory
Clustering Program with Graphical Summaries. J. Chem. Inf. Model.
58 (11), 2178−2182.
(36) Griffett, K., Solt, L. A., El-Gendy, B. E.-D. M., Kamenecka, T. M.,
and Burris, T. P. (2013) A liver-selective LXR inverse agonist that
suppresses hepatic steatosis. ACS Chem. Biol. 8 (3), 559−67.
(37) Albers, M., Blume, B., Schlueter, T., Wright, M. B., Kober, I.,
Kremoser, C., Deuschle, U., and Koegl, M. (2006) A novel principle for
partial agonism of liver X receptor ligands. Competitive recruitment of
activators and repressors. J. Biol. Chem. 281 (8), 4920−30.
(38) Filgueira, C. S., Benod, C., Lou, X., Gunamalai, P. S., Villagomez,
R. A., Strom, A., Gustafsson, J. A., Berkenstam, A. L., and Webb, P.
(2014) A screening cascade to identify ERbeta ligands. Nucl. Recept.
Signaling 12, No. nrs.12003.
(54) Laskowski, R. A., and Swindells, M. B. (2011) LigPlot+: multiple
ligand-protein interaction diagrams for drug discovery. J. Chem. Inf.
Model. 51 (10), 2778−86.
(55) The PyMOL Molecular Graphics System, version 2.3.2;
̈
Schrodinger LLC.
(39) Salami, J., Alabi, S., Willard, R. R., Vitale, N. J., Wang, J., Dong, H.,
Jin, M., McDonnell, D. P., Crew, A. P., Neklesa, T. K., and Crews, C. M.
(2018) Androgen receptor degradation by the proteolysis-targeting
chimera ARCC-4 outperforms enzalutamide in cellular models of
prostate cancer drug resistance. Communications Biology 1 (1), 100.
(40) Flanagan, J. J., and Neklesa, T. K. (2019) Targeting Nuclear
Receptors with PROTAC degraders. Mol. Cell. Endocrinol. 493, 110452.
(41) Osburn, D. L., Shao, G., Seidel, H. M., and Schulman, I. G.
(2001) Ligand-dependent degradation of retinoid X receptors does not
require transcriptional activity or coactivator interactions. Mol. Cell.
Biol. 21 (15), 4909−4918.
(42) Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S.,
and Olson, A. J. (2016) Computational protein-ligand docking and
virtual drug screening with the AutoDock suite. Nat. Protoc. 11 (5),
905−19.
(43) Wu, Q., Peng, Z., Zhang, Y., and Yang, J. (2018) COACH-D:
improved protein-ligand binding sites prediction with refined ligand-
binding poses through molecular docking. Nucleic Acids Res. 46 (W1),
W438−W442.
(44) Yang, J., Roy, A., and Zhang, Y. (2013) Protein-ligand binding
site recognition using complementary binding-specific substructure
M
https://dx.doi.org/10.1021/acschembio.0c00546
ACS Chem. Biol. XXXX, XXX, XXX−XXX