Disease allele-dependent small-molecule sensitivities in blood cells from monogenic diabetes.

Abstract

Even as genetic studies identify alleles that influence human disease susceptibility, it remains challenging to understand their functional significance and how they contribute to disease phenotypes. Here, we describe an approach to translate discoveries from human genetics into functional and therapeutic hypotheses by relating human genetic variation to small-molecule sensitivities. We use small-molecule probes modulating a breadth of targets and processes to reveal disease allele-dependent sensitivities, using cells from multiple individuals with an extreme form of diabetes (maturity onset diabetes of the young type 1, caused by mutation in the orphan nuclear receptor HNF4α). This approach enabled the discovery of small molecules that show mechanistically revealing and therapeutically relevant interactions with HNF4α in both lymphoblasts and pancreatic β-cells, including compounds that physically interact with HNF4α. Compounds including US Food and Drug Administration-approved drugs were identified that favorably modulate a critical disease phenotype, insulin secretion from β-cells. This method may suggest therapeutic hypotheses for other nonblood disorders.

Full text

Page 1

Disease allele-dependent small-molecule sensitivities
in blood cells from monogenic diabetes
Stanley Y. Shawa,b,c,1, David M. Blodgetta, Maggie S. Maa, Elizabeth C. Westlyc, Paul A. Clemonsc,
Aravind Subramanianc, and Stuart L. Schreiberc,d,1
aCenter for Systems Biology, Massachusetts General Hospital, Simches Research Center, 185 Cambridge Street, Boston, MA 02114;
Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115;
MA 02142; and
Cambridge, MA 02138
bDepartment of
cBroad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge,
dHoward Hughes Medical Institute and Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street,
Contributed by Stuart L Schreiber, November 10, 2010 (sent for review September 19, 2010)
Even as genetic studies identify alleles that influence human dis-
ease susceptibility, it remains challenging to understand their func-
tional significance and how they contribute to disease phenotypes.
Here, we describean approach to translatediscoveriesfrom human
genetics into functional and therapeutic hypotheses by relating
human genetic variation to small-molecule sensitivities. We use
small-molecule probes modulating a breadth of targets and pro-
cesses to reveal disease allele-dependent sensitivities, using cells
from multiple individuals with an extreme form of diabetes (matur-
ity onset diabetes of the young type 1, caused by mutation in the
orphan nuclear receptor HNF4α). This approach enabled the discov-
ery of small molecules that show mechanistically revealing and
therapeutically relevant interactions with HNF4α in both lympho-
blasts and pancreatic β-cells, including compounds that physically
interact with HNF4α. Compounds including US Food and Drug
Administration–approved drugs were identified that favorably
modulateacriticaldiseasephenotype,insulinsecretionfromβ-cells.
This method may suggest therapeutic hypotheses for other non-
blood disorders.
synthetic interactions ∣ chemical screen
G
genetic lesions in cellular or animal models for functional studies
can be challenging, particularly when the causal variants are not
known orfall outside coding regions. Furthermore, for genetically
complex diseases such as type-2 diabetes, multiple genes influ-
ence an individual’s risk of disease. The use of patient-derived
cells allows disease alleles to be studied in their native genetic
background but is often hampered by limited access to traditional
disease-related cell types (such as pancreatic β-cells for diabetes).
In genetically tractable model organisms, genetic interaction
screens provide a powerful, systematic method to reveal genes
and pathways that interact with a given mutation (1–3); similarly,
in genetically defined cell lines, the concept of synthetic lethality
has been used to identify compounds that selectively kill cells
expressing an oncogene (4).
Here, we apply the logic of genetic interaction screens to study
human disease alleles in cells from individual patients, using
small-molecule probes as a source of systematic perturbation
(particularly marketed drugs and bioactives with characterized
activity).Todiscover small-molecule–gene interactions, wescreen
for small molecules that induce distinct cellular phenotypes
depending on the presence or absence of a disease mutation
(Fig. 1). This implies that the small molecules (or their protein
targets) functionally interact with the disease mutation and iden-
tifies nodes where cellular pathways intersect with a disease gene.
Both directions of small-molecule–induced effects could poten-
tiallybeinformative (i.e.,small-molecule–inducedassaymeasure-
ments greater in either mutant or wild-type cells). Small-
molecule–gene interactions can potentially be identified using
cell-based measurements that reflect specific biological pathways
enetic variants that influence susceptibility to disease have
been identified for many diseases. Reproducing human
or broader measures of cell function or viability; different mea-
surements would likely identify partially overlapping subsets of
interactions.
Type-2 diabetes is a genetically heterogenous disease; mono-
genic forms comprise approximately 2–5% of cases but may shed
light on the pathogenesis or treatment of the common form of
type-2 diabetes. Maturity onset diabetes of the young type 1
(MODY1) is an extreme form of diabetes characterized by early
onset diabetes (often before age 25) due to impaired pancreatic
insulin secretion (5–7). The causal mutation is in the orphan nu-
clear hormone receptor HNF4α, a transcription factor that plays
wide-ranging roles in embryonic development, and the function
of liver, pancreas, and other tissues. HNF4α binds to upstream
sequences of hundreds of genes involved in glucose, lipid, choles-
terol, amino acid, and drug metabolism (8–10). Two different
conditional knockouts of HNF4α in β-cells yielded conflicting
phenotypes: although both knockout models showed impaired
insulin secretion in response to glucose challenge (11, 12), one
model also unexpectedly showed elevated resting insulin levels
with associated hypoglycemia (12), highlighting our incomplete
understanding of the physiologic effects of HNF4α mutation.
In this study, we develop an approach to identify small-
molecule–gene interactions in MODY1 diabetes, using patient-
derived lymphoblast cell lines (LCLs, derived from B-lympho-
cytes) as a surrogate cell model and a luminescence-based assay
for cellular ATP as the cell measurement. LCLs (and other cells
derived from the peripheral circulation) are easily accessible
from patient cohorts, but their utility as cell models for functional
studies of nonblood disorders is not widely established. Gene
expression data from LCLs have been used to study DNA var-
iants that influence gene expression (13)as well as the mechanism
Fig. 1.
derived cells. Small molecules are identified that induce distinct cellular
ATP measurements in the presence vs. absence of a disease mutation.
Overview of synthetic genetic interaction approach in patient-
Author contributions: S.Y.S. and S.L.S. designed research; S.Y.S., D.M.B., M.S.M., and E.C.W.
performed research; S.Y.S., D.M.B., P.A.C., and A.S. analyzed data; and S.Y.S., D.M.B., and
S.L.S. wrote the paper.
The authors declare no conflict of interest.
Freely available online through the PNAS open access option.
1To whom correspondence may be addressed. E-mail: shaw.stanley@mgh.harvard.edu or
stuart_schreiber@harvard.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/
doi:10.1073/pnas.1016789108/-/DCSupplemental.
492–497 ∣ PNAS ∣ January 11, 2011 ∣ vol. 108 ∣ no. 2 www.pnas.org/cgi/doi/10.1073/pnas.1016789108

Page 2

of genetic variants implicated in asthma (14) and systemic lupus
erythematosus (15). We identify multiple small molecules that
functionally interact with HNF4α in LCLs, including fatty acids
that physically interact with HNF4α. Several of these small-
molecule–gene interactions are preserved in murine pancreatic
β-cells, a canonical cell model for diabetes studies. Furthermore,
our approach identifies compounds that modulate a key disease
phenotype, insulin secretion from β-cells. These results suggest
that systematic identification of synthetic interactions between
small molecules and disease alleles can provide a functional
context for disease-causing alleles and suggest testable therapeu-
tic hypotheses.
Results
Small-Molecule–Disease Allele Interaction Screen in Patient LCLs. We
studied LCLs from 18 members of a MODY1 family (10 with
diabetes and 8 without; Fig. 2) in which affected members possess
a Q268X nonsense mutation (16). This mutation truncates
the ligand-binding domain and leads to impaired DNA binding,
abnormal cellular trafficking, and loss of transcriptional activity
(8, 17). Sequencing and RT-PCR confirmed the presence of a
heterozygous C-to-T substitution in codon 268 exclusively in the
diabetic subjects, and low levels of HNF4α expression. LCLs were
treated with 3,973 clinically used drugs and small molecules
whose activities have been previously characterized. The effects
of small-molecule treatments were measured using a lumines-
cence assay for cellular ATP content, chosen as a viability assay,
and also for reasons related to diabetic metabolism: Type-2
diabetes has been associated with impairments in oxidative phos-
phorylation (18), and ATP concentrations in pancreatic islets
are a key sensor during insulin secretion (19). ATP assay values
were converted to a Z score (multiples of the standard deviation
of the distribution of control wells in the same cell line) (20). The
assay was highly reproducible with a coefficient of variation
routinely <10% (Table S1). Each small-molecule treatment was
performed in duplicate at a single concentration.
To assess the extent to which each small molecule induced
different assay Z scores in mutant vs. wild-type LCLs, we calcu-
lated a signal-to-noise statistic (S2N): S2N ¼ ðμMutant− μWTÞ∕
ðσMutantþ σWTÞ (21). (μMutantand σMutantare the mean and stan-
dard deviation, respectively, of the ATP assay Z scores averaged
across all mutant cell lines; μWTand σWTare the corresponding
values across wild-type cell lines.) The magnitude of S2N is great-
est (either positive or negative in sign) for small molecules that
best discriminate between mutant vs. wild-type LCLs in our assay
(Fig. 3 A and B and Dataset S1); this suggests a small-molecule–
gene interaction, such as a physical association or functional
interaction between HNF4α and the small molecule (or its target
protein).
Compound Set Enrichment Analysis Identifies Small Molecules with
Disease Allele-Dependent Effects. Although many small molecules
with the most positive or most negative S2N had P values <0.05
(calculated by permutation of mutant and wild-type class labels;
Dataset S1), after correcting for multiple hypothesis testing,
individual compounds did not meet statistical significance. To
better identify patterns in these data, we tested whether sets
of related compounds were statistically enriched among the most
discriminating compounds, even if the discriminating effect of
individual compounds was subtle. We curated the screened com-
pounds into 141 compound sets (each containing from 3–60 com-
pounds) based on drug class or activity in a common biologic
pathway (Table S2). A weighted Kolmogorov–Smirnov-like statis-
tic [normalized enrichment score (NES)] was calculated for each
compound set that reflects the extent to which set members are
statistically enriched among compounds with the most positive or
negative S2N; P values were calculated by randomly permuting
mutant vs. wild-type class labels 1,000 times (Fig. 4 A and B). This
approach isanalytically analogous to gene setenrichment analysis
(18, 22) for gene expression data, and has been used to robustly
identify groups of genes (e.g., related by virtue of belonging to a
common biological process or pathway) that are differentially
expressed in two cell types.
Fig. 2.
in this study. Individuals whose lymphoblasts were screened have a number
below their symbol. (Numbers correspond to the cell line’s designation in the
Coriell Repository.) The above is a partial pedigree of the previouslydescribed
MODY1 family R–W (16).
Pedigree showing relations among MODY1 individuals screened
Fig. 3.
using cells derived from individual patients. (A) Heatmap of small molecules
that caused the most distinct effects on cellular ATP in mutant vs. wild-type
LCLs; i.e., with the most positive and most negative S2N from the entire
dataset of 3,973 screened small molecules. Each row represents a different
small molecule, and each column is an individual patient-derived LCL sample;
heatmap cells reflect the Z score of the ATP assay. (B) Distribution of S2N for
all 3,973 screened compounds.
Identification of disease allele-dependent chemical sensitivities,
Fig. 4.
cules that interact with an HNF4α mutation. (A) NES from compound set
enrichment analysis, plotted against −log10P (where P values are deter-
mined by randomly permuting wild-type vs. mutant class labels 1,000 times).
Each data point corresponds to a compound set; compound sets are listed in
Table S2. Dotted line corresponds to P ¼ 0.05. (B) Display of output of com-
pound set enrichment analysis. The “linoleic acid,” “class Ic antiarrhythmics,”
“Kþ-sparing diuretics,” ‘HMG CoA-reductase inhibitors,” and “glucocorti-
coids” compound sets are among those that showed an interaction with
HNF4α in LCLs. The verticle red-blue bar represents the ranked list of 3,973
screened compounds, ranked according to S2N (compounds with most
positive S2N at the top); horizontal lines indicate where each member of
the indicated compound set falls within this ranked list. Corresponding
NES and permutation P value for the indicated compound set are shown
below each bar.
Compound set enrichment analysis identifies classes of small mole-
Shaw et al.PNAS
∣
January 11, 2011
∣
vol. 108
∣
no. 2
∣
493
APPLIED BIOLOGICAL
SCIENCES

Page 3

The most highly ranked compound sets from this approach are
listed in Table 1 (compound set members are listed in Table S3).
These include several classes of drugs approved for conditions
other than diabetes, as well as a subtype of fatty acids (Fig. 4B).
Of note, these compound sets scored narrowly better than a
set comprised of the most widely used therapy for MODY1, the
insulin secretagogues belonging to the sulfonylurea class (such as
glimepiride and tolbutamide; NES ¼ 1.27). We verified that
previously reported confounders of small-molecule treatment in
LCLs such as LCL growth rate and EBV copy number (23) do not
differ in mutant vs. wild-type LCLs (P ¼ 0.59 and 0.26, respec-
tively) (Fig. S1). We also confirmed that the observed variation
in small-molecule–induced ATP measurements did not correlate
with the growth rate of individual LCLs (Fig. S2).
A Significant Fraction of Small-Molecule–Gene Interactions Are Pre-
served from LCLs to Pancreatic β-Cells. Representative members
of the compound sets in Table 1 were validated by two methods.
First, through dose-ranging experiments, we identified concentra-
tions at which representative members of a compound set induce
distinct ATP measurements in mutant vs. wild-type LCLs. Sec-
ond, we mimicked the HNF4α mutation in MIN6 cells (murine
insulinoma cells composed of pancreatic β-cells) by stably knock-
ing down HNF4α by lentiviral shRNA infection and tested
whether discriminating compounds also cause distinct ATP mea-
surements in HNF4α knockdown vs. control β-cells.
Representative individual compounds from compound sets in
Table 1 were confirmed to induce distinct responses in mutant vs.
wild-type LCLs (Fig. S3). In pancreatic β-cells, 44% (four out of
nine) of the representative compounds induced distinct ATP
measurements in HNF4α knockdown vs. control cells (Table 1;
Fig. 5 shows results for these four compounds, as well as for dex-
amethasone, a glucocorticoid that did not show an interaction
with HNF4α in β-cells). These include the ω-6 fatty acid linoleic
acid and several US Food and Drug Administration (FDA)-
approved drugs: amiloride (a potassium-sparing diuretic that
inhibits the epithelial sodium channel), propafenone (an antiar-
rhythmic drug that inhibits sodium channels as well as other ion
channels), and simvastatin [a hydroxymethylglutaryl (HMG)
CoA-reductase inhibitor used to treat hyperlipidemia] (Fig. 5).
These experiments demonstrate that a significant portion of
small-molecule–gene interactions that are elucidated in indivi-
dual patient LCLs are also preserved in pancreatic β-cells, a
canonical cell model for diabetes.
Small Molecules that Functionally Interact with HNF4α Modulate
Insulin Secretion. Small molecules that emerge from the screen or
their protein targets presumably participate in the same or
related biological processes as those involving HNF4α. We
hypothesized that some of these proteins (and their associated
pathways) may help mediate the cellular effects of disease muta-
tions in HNF4α and that modulation of these proteins may favor-
ably modify disease phenotypes. Because MODY1 patients
display impaired insulin secretion (24), we tested the small mole-
cules identified in our screen for their effects on insulin secretion
from pancreatic β-cells. Several compounds that showed an inter-
action with HNF4α status in β-cells also modulated insulin secre-
tion, and compounds from different classes exerted distinct
patterns of effect (Fig. 6 and Table S4). In β-cells wild-type for
HNF4α, linoleic acid, amiloride, propafenone, and simvastatin
all increased insulin secretion at basal glucose concentrations
(2.5 mM); linoleic acid and propafenone also increased insulin
secretion at elevated glucose (12.5 mM). Notably, in HNF4α
knockdown cells, linoleic acid, propafenone, and amiloride also
increased insulin secretion in the presence of low glucose, high
glucose, or both, respectively; at high glucose concentration,
amiloride and propafenone restored insulin secretion in HNF4α
knockdown cells to wild-type levels (Fig. 6). Simvastatin caused
Table 1. Compound sets that best discriminate between HNF4α mutant vs. wild-type LCLs, based on a
cellular ATP assay and compound set enrichment analysis
Compound setNES
P valueRepresentative compound Interaction in β-cells
Linoleic acid
ω-6 fatty acids
Oxicam antiinflammatories
Class Ic antiarrhythmics
Coxib antiinflammatories
Potassium-sparing diuretics
Acetic acid antiinflammatories
HMG CoA-reductase inhibitors
Imidazole/triazole antiinfectives
Glucocorticoids
−1.73
−1.61
−1.48
−1.34
1.44
1.52
1.53
1.53
1.55
1.69
6.1 × 10−3
0.03
0.026
0.12
0.035
0.015
0.026
7.9 × 10−3
0.056
0.028
linoleic acid
linoleic acid
meloxicam
propafenone
rofecoxib
amiloride
diclofenac
simvastatin
fluconazole
dexamethasone
+
+
−
+
−
+
−
+
−
−
Compound sets shown had the lowest permutation P values of all the compound sets tested (Table S2). Representative
compounds from each set were tested for their ability to also discriminate between control- and HNF4α-knockdown
β-cells (last column and Fig. 5)
Fig. 5.
tative compound’s induced ATP measurement in β-cells is shown; dexamethasone, a representative compound from the glucocorticoids set, did not interact
with HNF4α in β-cells. White bars, ATP Z scores in β-cells subjected to control knockdown; gray bars, Z scores in cells subjected to HNF4α knockdown. Data are
plotted as mean ? standard error of the mean (SEM) and are representative results from 2–3 independent experiments.
Many small-molecule–disease allele interactions identified in LCLs are also preserved in β-cells. For each of the compound sets in Fig. 4B, a represen-
494
∣
www.pnas.org/cgi/doi/10.1073/pnas.1016789108 Shaw et al.

Page 4

a complex phenotype, stimulating insulin secretion at 2.5 mM
glucose but inhibiting insulin secretion at 12.5 mM glucose. The
distinct effects these compounds exert on insulin secretion (e.g.,
as a function of glucose concentration and mutation status;
Table S4) likely reflect both the complexity of insulin secretion
physiology and the different mechanisms of different compounds.
Overall, these data illustrate that small-molecule–disease allele
interactions can identify compounds that modulate disease-mod-
ifying pathways and exert therapeutically beneficial or adverse
effects on disease phenotypes.
Discussion
By studying blood-derived cells from individuals, we have identi-
fied functional connections between HNF4α, several drugs,
cellular ATP, and insulin secretion. These connections add a func-
tional framework to the existing vast catalog of HNF4α transcrip-
tional targets, and suggest several testable mechanistic and
therapeutic hypotheses. None of the drugs identified in our study
have been studied in association with MODY1, and none were
approved for diabetes-related indications.
One of the compounds identified in our screen, linoleic acid,
was the predominant species bound to HNF4α (based upon gas
chromatography/mass spectrometry) in livers from fed mice or
when rat HNF4α was expressed in human cells (25). Further-
more, crystal structures of HNF4α show a mixture of fatty acids
spontaneously bound to the ligand-binding pocket (26, 27). These
reports suggest that the emergence of linoleic acid and related
fatty acids from our screen likely arises from their physical asso-
ciation with HNF4α, which strongly supports the ability of our
method to identify physiologically relevant small-molecule–gene
interactions in cells from individual patients. A physical interac-
tion has also been reported between HNF4α and the glucocorti-
coid receptor (the target of dexamethasone); the glucocorticoid
receptor immunoprecipitates with HNF4α in HeLa cells and
human hepatocytes, and dexamethasone increases HNF4α-
mediated transcriptional activation (28).
Amiloride inhibits the epithelial sodium channel (SCNN1A/B)
and also shows activity against Naþ∕Hþexchangers (29). Deriva-
tives of amiloride have been reported to increase insulin secretion
fromβ-cellsinvitroandinvivo(30,31).Incontrast,noassociation
between propafenone and insulin secretion has been previously
reported. Propafenone inhibits several channels, especially the
voltage-gated sodium channel SCN5A and the voltage-gated
potassium channel KCNH2 (29). Propafenone also inhibits ATP-
sensitive potassium channel (KATP) currents in myocytes (32) and
vascular smooth muscle (33), and the delayed rectifier channel
Kv2.1 in Xenopus oocytes (34). Decreased activity of KATPor
Kv2.1 in islets increases insulin secretion (19), suggesting that
these channels may mediate propafenone’s effects on insulin
secretion. Although amiloride and propafenone have not been
previously studied in the context of HNF4α, SCNN1A (encoding
the target of amiloride) and KCNJ11 (encoding Kir6.2, a subunit
of KATP) are among the hundreds of reported transcriptional
targets of HNF4α (though data on the dependence of KCNJ11
expression on HNF4α in vivo are conflicting) (10–12).
Our data suggest that functional connections relevant to β-cell
physiology can be elucidated from small-molecule–gene interac-
tions in a collection of patient-derived LCLs; the observation that
some compounds showed an interaction with HNF4α in LCLs but
not β-cells may reflect tissue- or species-specific interactions.
LCLs have also been used as a model cell system for congenital
hyperinsulinism/hyperammonemia (35), pharmacogenomic stu-
dies(36–38),ionchanneldisorders(39),andHuntington’sdisease
(40). Our approach may be extended to other diseases and pa-
tient-derived cells, including primary circulating blood cells, or
even induced pluripotent stem cells; the choice of cell model
foreachdiseasestudywillreflectabalancebetween accesstocells
and disease-relevant physiology.
We have identified small-molecule sensitivities that are depen-
dentonthepresenceofdiseaseallelesinthenativecontextofcells
from individual patients. This approach can therefore be particu-
larly useful to study disease alleles that are not easily studied
by traditional genetic methods. By using small molecules as a
source of targeted perturbations in patient-derived cells, the ap-
proach brings the logic of model organism genetics to genetically
complex human diseases. Chemical–genetic interaction screens
in patient samples can help discover proteins that participate in
disease-relevant processes (thus placing the disease mutation in
a biological context), and equally important, identify small mole-
cules,FDA-approveddrugs,orgenesthatactasdiseasemodifiers.
Biological discovery and clinical translation may be accelerated
because a smallmolecule can beused both toelicit theinteraction
with the disease mutation and to ameliorate disease phenotypes.
Fig. 6.
control- (white bars) or HNF4α-knockdown (gray bars) β-cells in the presence of the indicated compounds; compound treatments were performed in the
presence of either 2.5 mM or 12.5 mM glucose. ? ¼ P < 0.05 for comparison of glucose-stimulated insulin secretion in control- vs. HNF4α-knockdown β-cells
(at 12.5 mM glucose) in absence of compound treatment. Data are plotted as mean ? SEM and are representative results from 2–3 independent experiments.
Several small molecules that interact with HNF4α also enhance insulin secretion. Glucose-stimulated insulin secretion assays were performed on either
Shaw et al. PNAS
∣
January 11, 2011
∣
vol. 108
∣
no. 2
∣
495
APPLIED BIOLOGICAL
SCIENCES

Page 5

Small-molecule–based genetic interaction screens using patient-
derivedblood cells areusefulfor exploringa highlyheritable form
ofdiabetesandmayproveusefulforfunctionalstudiesandclinical
translation of human disease susceptibility alleles discovered
through genome-wide association and other genetic studies.
Methods
LCL Culture, Small-Molecule Screen, and ATP Assay. LCLs were purchased from
Coriell Cell Repositories (Fig. 2), and cultured and maintained as previously
described (23). For screening, LCLs were thawed from frozen stocks and main-
tained by diluting them to 100;000–300;000 cells∕mL daily for ∼20 d prior to
screening. LCLs were plated in 40 μL media at a density of 300;000 cells∕mL;
100 nL was then pin transferred from compound libraries using a CyBi-Well
Vario robot (CyBio US), and plates were incubated at 37 °C and 5% CO2for
40 h. The CellTiter-Glo (Promega Corporation) assay for cellular ATP was
performed as described (23); luminescence data were collected on an Analyst
HT plate reader (LJL Biosystems, Molecular Devices). Each library member was
screened in each LCL line in duplicate. For the small-molecule screen, LCLs
from different individuals were screened in random order. When individual
compounds were subsequently retested at various doses, LCLs were incu-
bated with compounds for 20 h; each condition (compound dose and cell
line) was tested in at least six replicates.
Small-Molecule Libraries. Compounds screened included the following com-
mercially available collections: Prestwick Chemical library of marketed
drugs (Prestwick Chemical, 1120 compounds, 2 mg∕mL stock concentration);
Spectrum Collection of known bioactives, including drugs, tool compounds,
and natural products (MicroSource Discovery Systems, 2000 compounds,
10 mM stock concentration); Institute of Chemistry and Cell Biology Bioac-
tives collection (Enzo Life Sciences, 480 compounds, variable concentrations);
Biomol-NT (Neurotransmitter) collection of neurotransmitter drugs and
bioactives (Enzo Life Sciences, subset of 287 compounds, 10 mM stock con-
centration); and 86 discretes (various sources, 10 mM stock concentration).
Analysis of Interaction Screen. Replicate raw luminescence values for each
compound library member in each cell line were converted to a Z score using
a standard analytic pipeline (20). Calculation of S2N for each compound
across all LCL lines, and rank-ordering of compounds for their ability to
discriminate LCLs that are mutant vs. wild-type at HNF4α, was performed
using the Comparative Marker Selection module of GenePattern (41, 42),
a data analysis platform for genomics and other systematically acquired
datasets (http://www.broadinstitute.org/cancer/software/genepattern/); we
used compound Z scores instead of gene expression values as inputs, and
otherwise we used standard settings. P values were determined by randomly
permuting mutant and wild-type class labels. For compound set enrichment
analysis, we manually curated our screened compound list into 141 com-
pound sets of at least 3 compounds (Table S2 for all 141 set names; individual
compounds making up the top scoring sets are listed in Table S3). We used
the Gene Set Enrichment Analysis module of GenePattern, which calculates a
weighted Kolmogorov–Smirnov-like statistic (NES) for each compound set;
this score reflects the extent to which set members cause statistically signifi-
cant, concordant differences in ATP Z scores between mutant vs. wild-type
(at HNF4α) LCLs (18, 22). (See SI Methods for more details.) P values for each
compound set’s NES were calculated by randomly permuting mutant vs. wild-
type HNF4α class assignments 1,000 times.
The majority of compound sets were curated based on the World
Health Organization Anatomical Therapeutic Chemical Classification System
(http://www.whocc.no/atc_ddd_index/), which classifies drugs according to
therapeutic use and chemical properties; additional sets were curated based
on membership in pathways based on the Kyoto Encyclopedia of Genes and
Genomes (http://www.genome.jp/kegg/) and Ingenuity Pathways Analysis
(Ingenuity Systems).
β-Cell ATP and Insulin Assays. MIN6 cells were cultured in DMEM contain-
ing: 25 mM glucose, supplemented with 15% FBS (ATCC), penicillin
(50 internationalunits∕mL), streptomycin (50 μg∕mL), and 27.5 μM β-mercap-
toethanol under humidified conditions of 5% CO2and 95% air at 37°C. Upon
stable infection with shRNA lentiviral infection particles, DMEM was supple-
mented with 1.25 μg∕mL puromycin.
All assays were performed in 384-well plates. For ATP assays, MIN6 cells
were added to 384-well plates (10,000 cells in 30 μL) in white, solid-bottom
384-well plates (Corning 3570) and cultured for 20 h. Compound dilutions
were pin transferred into cells in at least six replicates and incubated
with cells for 20 h at 37 °C and 5% CO2. CellTiter-Glo (Promega) was then
performed according to manufacturer instructions. Luminescent output was
measured using an EnVision plate reader (PerkinElmer). The Z scores [Z ¼
ðmeantreated− meanDMSOÞ∕standarddeviationDMSO) were calculated for each
compound concentration (in each cell line) using the mean and standard
deviation of the 42 control DMSO wells found on each plate.
For glucose-stimulated insulin secretion assays, MIN6 cells were seeded at
9;000 cells∕well in black, clear bottom plates (Corning 3712) and cultured in
DMEM for 24 h. DMEM was aspirated using an ELx405 microplate washer
(Biotek). All further solutions were prepared in Krebs–Ringer buffer (KRB)
containing 0.5 % BSA (128 mM NaCl, 5 mM KCl, 2.7 mM CaCl2, 1.2 mM
MgSO4, 1 mM Na2HPO4, 1.2 mM KH2PO4, 5 mM NaHCO3, buffered with
10 mM Hepes, pH 7.4); 30 μL of 2.5 mM D-glucose was added to each well
and incubated with cells for 30 min at 37°C. The 2.5 mM glucose was
aspirated, cells were washed once with KRB, and then 30 μL of either 2.5
or 12.5 mM D-glucose was added to each well. Small molecules were pin
transferred using the CyBi-Well Vario robot, and incubated with cells for
2 h at 37°C. At the end of the incubation period, cells were pelleted for
5 min at 4 °C, and 20 μL of the secretion medium was collected from each
well and frozen at −20°C until processing with an Insulin ELISA kit (Alpco
Diagnostics).
Statistical Analysis. When comparing the assay results of two different cell
populations (e.g., individual compound treatments of mutant vs. wild-type
LCLs, or of HNF4α- vs. control-knockdown β-cells), the two-sided, unpaired
Student’s t test was used. For compound set enrichment analysis, P values
for enrichment scores were calculated by randomly permuting wild-type
vs. mutant class labels for the LCLs 1,000 times (see the Analysis of Interaction
Screen section of Methods for details). Two-way ANOVAs were performed to
examine the effect of compound treatment and HNF4α mutation status on
insulin secretion assays.
Additional details on LCL characterization and shRNA knockdown of
HNF4α in MIN6 cells are available in SI Methods.
ACKNOWLEDGMENTS. This work was supported by the National Institutes of
Health (Grants K08 HL077186 to S.Y.S. and GM38627 to S.L.S.) and the de
Gunzburg Family Foundation at Massachusetts General Hospital (S.Y.S.).
S.L.S. is an Investigator at the Howard Hughes Medical Institute. The Chemical
Biology screening facility at the Broad Institute is supported by the National
Institutes of Health/National Cancer Institute under Contract N01-CO-12400.
1. Lehner B, Crombie C, Tischler J, Fortunato A, Fraser AG (2006) Systematic mapping of
genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse
signaling pathways. Nat Genet 38:896–903.
2. St Onge RP, et al. (2007) Systematic pathway analysis using high-resolution fitness
profiling of combinatorial gene deletions. Nat Genet 39:199–206.
3. DixonSJ, CostanzoM, BaryshnikovaA,AndrewsB, BooneC (2009) Systematicmapping
of genetic interaction networks. Annu Rev Genet 43:601–625.
4. Dixon SJ, Stockwell BR (2009) Identifying druggable disease-modifying gene products.
Curr Opin Chem Biol 13:549–555.
5. Byrne MM, et al. (1995) Altered insulin secretory responses to glucose in subjects with
a mutation in the MODY1 gene on chromosome 20. Diabetes 44:699–704.
6. Fajans SS (1990) Scope and heterogeneous nature of MODY. Diabetes Care 13:49–64.
7. Vaxillaire M, Froguel P (2008) Monogenic diabetes in the young, pharmacogenetics
and relevance to multifactorial forms of type 2 diabetes. Endocr Rev 29:254–264.
8. Stoffel M, Duncan SA (1997) The maturity-onset diabetes of the young (MODY1)
transcription factor HNF4α regulates expression of genes required for glucose
transport and metabolism. Proc Natl Acad Sci USA 94:13209–13214.
9. Odom DT, et al. (2004) Control of pancreas and liver gene expression by HNF transcrip-
tion factors. Science 303:1378–1381.
10. Bolotin E, et al. (2010) Integrated approach for the identification of human hepato-
cyte nuclear factor 4α target genes using protein binding microarrays. Hepatology
51:642–653.
11. Miura A, et al. (2006) Hepatocyte nuclear factor-4alpha is essential for glucose-stimu-
lated insulin secretion by pancreatic beta-cells. J Biol Chem 281:5246–5257.
12. Gupta RK, et al. (2005) The MODY1 gene HNF-4alpha regulates selected genes
involved in insulin secretion. J Clin Invest 115:1006–1015.
13. Cheung VG, et al. (2005) Mapping determinants of human gene expression by
regional and genome-wide association. Nature 437:1365–1369.
14. MoffattMF, et al. (2007) Genetic variants regulating ORMDL3 expression contribute to
the risk of childhood asthma. Nature 448:470–473.
15. Graham RR, et al. (2006) A common haplotype of interferon regulatory factor 5
(IRF5) regulates splicing and expression and is associated with increased risk of
systemic lupus erythematosus. Nat Genet 38:550–555.
16. Yamagata K, et al. (1996) Mutations in the hepatocyte nuclear factor-4alpha gene in
maturity-onset diabetes of the young (MODY1). Nature 384:458–460.
17. Sladek FM, Dallas-Yang Q, Nepomuceno L (1998) MODY1 mutation Q268X in hepa-
tocyte nuclear factor 4alpha allows for dimerization in solution but causes abnormal
subcellular localization. Diabetes 47:985–990.
496
∣
www.pnas.org/cgi/doi/10.1073/pnas.1016789108Shaw et al.