A high content screen in macrophages identifies small molecule\nmodulators of STING-IRF3 and NFkB signaling
Summary We screened a library of bioactive small molecules for activators and inhibitors of innate immune signaling through IRF3 and NFkB pathways with the goals of advancing pathway understanding and discovering probes for immunology research. We used high content screening to measure the translocation from the cytoplasm to nucleus of IRF3 and NFkB in primary human macrophages; these transcription factors play a critical role in the activation of STING and other pro-inflammatory pathways. Our pathway activator screen yielded a diverse set of hits that promoted nuclear translocation of IRF3 and/or NFkB, but the majority of these compounds did not cause activation of downstream pathways. Screening for antagonists of the STING pathway yielded multiple kinase inhibitors, some of which inhibit kinases not previously known to regulate the activity of this pathway. Structure-activity relationships (SAR) and subsequent chemical proteomics experiments suggested that MAPKAPK5 (PRAK) is a kinase that regulates IRF3 translocation in human macrophages. Our work establishes a high content screening approach for measuring pro-inflammatory pathways in human macrophages and identifies novel ways to inhibit such pathways; among the targets of the screen are several molecules that may merit further development as anti-inflammatory drugs.
Introduction<!>Validation and summary of the activator screen<!>IRF3 nuclear translocation is necessary but not sufficient for downstream\npathway activity<!>IRF3 Pathway action by zinc chelation<!>Validation and summary of the inhibitor screen<!>Most pathway inhibitors block downstream STING signaling<!>Kinase inference from primary screen data<!>Diverse MAPKAPK2/5 inhibitors block STING pathway activation<!>SAR against MAPKAPK2/5 support their involvement in the STING pathway<!>Discussion<!>Primary Screening Assay<!>High content analysis<!><!>Activator screen<!>Inhibitor screen<!>Secondary Screening Assay<!>Additional follow-up<!>Sources and isolation of macrophages<!>Cherry-picked compounds for follow up<!>Kinase inference computations<!>Chemoproteomics methods
<p>The innate immune system has evolved to include many signaling pathways that
detect pathogens (1,2,3,4). Mutations in these pathways controlling the innate
immune system cause several diseases: hyperactivity has been linked to inflammatory
and autoimmune diseases (2,3,4), and
abnormally low activity has been linked to susceptibility to infectious disease, and
possibly even cancer (5,6). Several inhibitory drugs are already approved or in
development for inflammatory diseases (7),
while interest in pathway agonists has grown with the recent successes of
immuno-oncology checkpoint inhibitors (8).</p><p>An innate immune response is typically triggered by binding of a pathogenic
associated molecular pattern (PAMP) to a receptor. Different pathways are tailored
to recognize specific PAMPs. For example, lipopolysaccharide (LPS), a component of
bacterial cell membrane, is recognized by Toll-like-receptor 4 (TLR4), which leads
to NFkB translocation into the nucleus (1,2). Cytosolic dsRNA, a feature
of viral infection, is recognized by the receptors, MDA5 and RIG-I, which leads to
translocation of the transcription factor IRF3 into the nucleus (1,2). Of particular
interest is the recently discovered cGAS-STING pathway, which detects cytosolic DNA
(3,9) (fig. 1a). Cytosolic dsDNA, which is
another feature of viral infection, binds to cGAS (MB21D1) which cyclizes
intracellular GTP and ATP to form the second messenger 2′3′-cGAMP.
2′3′-cGAMP binds to a receptor, STING (Stimulator of Interferon
Genes). 2′3′-cGAMP-liganded STING activates the kinase TBK1, which
phosphorylates the transcription factor IRF3. STING activation has been reported to
activate the NFkB pathway as well (3), though
this aspect of STING biology has not been thoroughly explored. In the nucleus, both
IRF3 and NFkB are capable of activating the expression of many anti-viral and
pro-inflammatory genes. Multiple research groups have described the identification
of additional regulatory molecules (3,10), but at this point, the
cGAS-STING-TBK1-IRF3 axis appears to be the most validated and prominent.</p><p>The STING-IRF3 pathway is of considerable therapeutic interest. Direct STING
agonists are currently in clinical trial in cancer, based on the hypothesis that
activation of the STING pathway will trigger anti-tumor innate immune responses
(11,12,13,14). Inappropriate activation of the STING pathway has
been implicated in sterile inflammatory disease, notably the inherited condition
"STING-associated vasculopathy with onset in Infancy (SAVI)" (4). STING activation has also been proposed as a
contributing mechanism in a variety of chronic inflammatory diseases such as lupus
and arthritis (15). Thus, inhibitors of the
STING pathway may be of value in treating inflammatory disease. Known pathway
components druggable by small molecules include cGAS, STING, and TBK1, and it would
be useful to identify additional targets.</p><p>Phenotypic assays have a track record of success in discovering novel
signaling molecules, even in well-characterized signaling pathways (16,17). Previous
phenotypic screens on innate immune pathways have traditionally relied on
immortalized cell culture models, often coupled with artificial reporters (18,19).
These systems are convenient surrogates for dissecting signaling biology but fall
well short of fully recapitulating STING activity as observed human disease. To
maximize physiological relevance, we screened in primary human macrophages, and used
multiple donors in follow-up experiments. We measured localization of transcription
factors, IRF3 and NFkB in cells exposed to a collection of approximately 2700
bioactive small molecules comprising a "Mechanism of
Action" library (MOA) (fig.
1b). This library was carefully curated using bio- and chem-informatic
criteria, and consists of biochemically well-characterized molecules that interact
with protein targets having diverse biological functions (20). All compounds were screened at 8 doses, ranging from
100 uM to 31.6 nM. Screening in dose helps us more confidently discriminate true
hits from false positives. Our primary goal was to identify STING pathway
regulators, but changes in IRF3 and NFkB nuclear localization can result from other
innate immune pathways. As such, any probes and/or regulatory nodes identified in
our screen might be relevant to other immune pathways.</p><p>We report the results of both an activator and inhibitor screen (fig. 1). In the activator screen, we treated
macrophages with compound alone, and measured IRF3 and NFkB nuclear localization
after 4 hours of treatment. This screen should identify activators of the STING
pathway and other innate immune pathways, such as TLR4 and RIG-I. In the inhibitor
screen, we pre-treated macrophages with the MOA library compounds for 4 hours, and
then added the naturally occurring STING agonist, 2′3′-cGAMP, for
another 4 hours. This screen is more STING-centric, as we identify molecules that
modulate the cellular response to 2′3′ cGAMP. For both screens, we
then further profiled the hits in a secondary screen by measuring their effects on
secretion of pro-inflammatory cytokines, using sandwich ELISAs.</p><!><p>High content screens were executed in 1536 well plates using adherent
macrophages made by differentiation of human CD14+ monocytes with M-CSF.
We imaged IRF3 and NFkB in the same wells by immunofluorescence, and stained
nuclei to provide a mask for quantification of nuclear translocation. Validation
of the activator screen is shown in fig. 2,
where each dot represents the average of approximately 1250 cells in one well.
To assess how robustly this assay could distinguish IRF3 translocation from NFkB
translocation, cells were treated with either the STING specific pathway agonist
2′3′-cGAMP, 3′3′-cGAMP (a linkage isomer, with
weaker activity on STING), a TLR 7/8 agonist CL075, or LPS, a TLR4 agonist,
which activates both pathways. Treatment groups were well separated, with a
robust Z-factor near 0.5 for both IRF3 and NFkB channels, and replicates were
well-correlated (fig. 2a,b). Most plates
had outlier control wells, but as we included several replicates per control (at
least 16 wells), their effect on screen quality was minimal. Not shown in figure 2 is 3′3′-cGAMP, which
only minimally increased IRF3 nuclear fractions. 3′3′-cGAMP is a
non-endogenous cyclic dinucleotide, with much weaker binding affinity for STING.
We suspect its failure to more robustly increase IRF3 nuclear levels, at an
early time point, could be due to weaker binding and/or or cell permeability.
NFkB was translocated into the nucleus slightly above baseline following
2′3′-cGAMP treatment, suggesting that 2′3′-cGAMP
weakly activates NFkB at an early time point. This finding contrasts with
pathway diagrams that draw IRF3 and NFkB as equal outputs from activated STING
(3). Our data suggests that in human
primary human macrophages, the magnitude or kinetics of IRF3 and NFkB activation
by STING vary.</p><p>Our computational pipeline easily enables examination of phenotypes at a
single cell level (fig. 3). In the case of
2′3′-cGAMP stimulation, vehicle control wells exhibited IRF3
nuclear fractions that were centered around 0.2-0.3. Some
2′3′-cGAMP treated wells displayed distributions that were
sharply peaked around 0.6-0.7. Other wells had distributions that were nearly
flat, reflecting heterogeneity in the cellular population.</p><p>The hit rate for IRF3 translocators was low (fig. 2c). Only 6 hits scored higher than 2 standard
deviations above the H2O control (0.22 %), and 24 hits, about
1% of the library, scored above 1 standard deviation above the control.
The NFkB hit rate was higher, with 28 molecules scoring above 2 standard
deviations above the control, and 71 molecules scoring 1 standard deviation
above the control, perhaps reflecting more diverse inputs into this pathway</p><p>We chose to follow up on IRF3 activators, as there is potential clinical
value in immuno-oncology in small molecule inducers of IRF3-dependent cytokines
such as IFNb and CXCL10. To test the extent to which our hits identified by
changes in IRF3 translocation were truly
"2′3′-cGAMP-like" in their effects, we exploited
the high content nature of the screen to extract 36 image-based features of hit
and control compounds. Features included nuclear and cytoplasmic intensities,
nuclear and cytoplasmic texture features, and morphology parameters, for both
IRF3 and NFkB stains (see methods). These features were used to train a
quadratic support-vector machine (SVM) that classified compound-treated wells as
either "H2O-like",
"2′3′-cGAMP-like",
"3′3′-cGAMP-like", "LPS-like",
"CL075-like". This analysis is described in more detail in the
methods and supplement (s.
fig. 1) and helped to characterize the translocation phenotype; it
yielded 30, "2′3′-cGAMP-like" compounds of
interest, to which we added 6 additional IRF3 activators out of biological
interest (table 1). No particular
biological mechanisms seemed enriched, but multiple nuclear export inhibitors
scored, suggesting that our screen did identify valid hits.</p><!><p>Our secondary assay for pathway activators was secretion of the
pro-inflammatory, IRF3-dependent chemokine IP-10 (CXCL10) (s. fig. 2). As we aimed
to identify both new signaling proteins and potential therapeutics, we decided
to directly screen hits in the secondary assay, as the number of hits we had was
reasonably small, and the effect on gene expression needed to be assessed. To
our surprise, most primary screen hits did not reliably induce secretion of
IP-10. This shows that nuclear import of IRF3 is not sufficient to activate gene
expression. The diverse MOAs of compounds scoring positive in the high content
assay and negative for gene expression suggests that multiple mechanism can
promote IFR3 translocation without downstream gene expression. One obvious
mechanism is inhibition of nuclear export by compounds 10,11 (Table 1); these compounds inhibit XPO1, which has
been previously implicated as the exporter for IRF3 (21). How other compounds in table 1 act is less clear. Two compounds, annotated
as ubiquitin protease inhibitors (compounds 33,36), should promote
ubiquitination, which is often an activating process of innate immune pathways
(22,23). They did not promote secretion of IP-10, although in two out of
three donors, they did induce TNFa secretion in a dose dependent manner (s. fig. 3). This finding
may be coincidental though, as TNFa is driven by NFkB, not IRF3. Nonetheless, it
opens the possibility that deubiquitinase inhibitors may have utility in
immuno-oncology.</p><!><p>The Zn2+ chelator TPEN scored positive in duplicate,
and it induced secretion of IP-10 to levels comparable to those of the positive
controls 2′3′-cGAMP and LPS. As such, it was the highest
confidence true positive from the activator screen. To further investigate how
TPEN might function, we tested its effects in multiple cell types and assays.
TPEN induced the activity of a stably transfected luciferase reporter sensitive
to IRF3 activation in PMA-primed THP1 cells, a cell line model of macrophages,
as well as a murine macrophage line, RAW264.7 (fig. 4a). To test whether this effect was due to chelation of
Zn2+, we pre-complexed TPEN with ZnCl2 and
observed that the TPEN-Zn complex did not cause activation of the same reporter
in RAW cells (fig. 4a). This implies
pathway activation by removal of Zn2+ from intracellular
stores, or perhaps of another ion that is similar to Zn2+ in
binding tightly to TPEN. Our data support a published hypothesis that
intracellular zinc represses IRF3 signaling (24). At higher concentrations TPEN was cytotoxic (as measured by
Cell-Titer-Glo; fig. 4b), and thus, its
window of activity as an inducer of IRF3 dependent-genes is narrow. Upon further
testing in primary human cells from additional donors, TPEN did not score by
cytokine secretion assays. Given its narrow range of non-toxic activity and
donor-specific effects, TPEN is not likely to have therapeutic value. These
toxicity findings also open the possibility that some of the small molecule hits
in table 1 may activate IRF3, but poison
downstream gene expression.</p><!><p>Validation of the pathway inhibitor screen is described in fig. 5. Control groups are distinct as shown
in fig. 5a,b. The robust Z-factor for the
inhibitor screen was also around 0.5, although in this calculation, we ignored a
plate of cells that displayed image analysis segmentation errors in a few wells.
Control groups were well separated and replicates were correlated (fig. 5b). Two known TBK1 kinase inhibitors,
BX795 and MRT67307, blocked IRF3 nuclear translocation. Both compounds inhibit
multiple kinases, and the stronger effect of BX795 might be due to its broader
target spectrum (25). Interestingly, LPS,
which was simply used as a control for activation of NFkB, appeared to repress
IRF3 signaling in combination with 2′3′-cGAMP (fig. 5b).</p><p>The hit rate for the inhibitor screen was much higher than for the
activator screen, with 14% of the library blocking IRF3 activation to a
level 3 standard deviations below the 2′3′-cGAMP control. A
total of 24 hits (~ 1%) block IRF3 activation to a level below
the BX795 control and 48 hits (~ 2%) block IRF3 activation to a
level below the MRT67307 control (fig. 5c).
While toxicity can contribute to a higher inhibitor hit rate, we do not think it
is significant enough at the time point to fully account for the higher value.
We think that the higher rate likely reflects more avenues to block signal
transduction than to activate it. For example, as innate immune pathways utilize
kinases to mediate signaling, there are several proteins one can target with
multiple drugs, potentially offering more ways to inhibit the pathway.</p><p>In selecting pathway inhibitors, we utilized the high content
information provided by the screen, though our approach was simpler, involving
selecting compounds close to the H2O control in the multidimensional principal
component space (see methods, s. fig. 4). We selected 55 compounds from the primary inhibitor
screen (table 2) and prioritized 40 for
re-testing, based on availability and likely biological interest.</p><p>We observed clear enrichment of kinase inhibitors in the hit list, and
they represented approximately 65% of pathway inhibitor hits (fig 5d). The pan-kinase inhibitor,
staurosporine, which is an inhibitor of TBK1, was one of the top scoring hits.
Apart from kinase inhibitors, there also was enrichment of natural product
antibiotics. Brefeldin A, a known STING pathway antagonist, also scored
strongly. Upon activation, STING, which is an ER membrane protein, traffics to
the perinuclear region when it is activated (4) and brefeldin A is thought to block this transport. Multiple
ATP6V1A inhibitors also scored very potently. Due to availability, we
substituted bafilomycin A1 and concanomycin A, known ATP6V1A inhibitors, as tool
compounds in future assays.</p><p>While our focus was on finding pathway inhibitors, it is worth noting
that the screen had the capability to find pathway potentiators as well. (fig. 5c). For example, both MG132 and
bortezomib, proteasome inhibitors which block NFkB activation, enhance IRF3
activation. This result also has been previously reported (26). It is unlikely these compounds promote gene
expression at later time points, given data from the activator screen showing
that nuclear location of IRF3 is not sufficient for downstream gene
activation.</p><!><p>We again used IP-10 secretion as a secondary assay for compounds scoring
in the translocation assay. We re-tested 40 compounds, based on interest and
availability, and found that most primary screen hits blocked IP-10 secretion by
ELISA assays (s. fig.
5). To test if the effect was due to pathway inhibition or to more
general cellular toxicity, we also measured percent of living cells by
Cell-Titer-Glo (CTG). The majority of compounds had little or no toxicity at the
relevant time point (0-30% reduction in ATP levels by CTG assay). Only
four compounds (compounds 1, 9, 24, and 32) exhibited greater than 50%
cell killing at one more doses. It is worth noting that toxicity does not mean
that these compounds are false positive from the primary screen, but does
suggest that such compounds are likely to have limited therapeutic value. In
supplemental fig.
5, we normalize the amount of the pathway inhibition to the amount of
remaining cells on the plate. Most compounds show inhibition of the STING
pathway, even accounting for toxicity.</p><p>Annotated kinase inhibitors comprised the majority of compounds that
could be confirmed using the secondary (IP-10 secretion) assay, further
enforcing our view that kinase inhibitors are among the most promising tools to
inhibit STING-dependent signaling. Of the non-kinase inhibitors, we were
intrigued by both bafilomycin and concanomycin, which blocked pathway activation
at low doses, but unexpectedly potentiated pathway activity at higher doses. We
did not delve into this further, but note that a recent report (27) implicated a negative regulatory role of ATP6V1A,
consistent with our finding.</p><p>Compounds that did not block IP-10 secretion may have been false
positives from the primary screen, although it is also possible that
donor-variability is a confounder. For example, two EHMT inhibitors (compounds
23 and 49) scored in the primary screen, but compound 23 did not score in the
secondary screen.</p><!><p>Preliminary analysis of kinase inhibitor specificity suggested that TBK1
is not the only kinase required for STING-IRF3 signaling in human macrophages.
We therefore used informatic approaches to identify other kinases required for
STING-IRF3 signaling based on the known specificity of the library compounds.
The primary challenge we faced is polypharmacology. Most of the hits were either
not potent and/or selective enough to assume that its efficacy target(s) is the
nominal (primary) ascribed target(s). We examined data from all kinase
inhibitors, both hits and non-hits, and calculated an enrichment score for each
kinase gene in the active compound list, using hypergeometric enrichment. We
classified compounds as active or inactive in cellular assays based on their
IRF3 inhibition scores, and classified them as active or inactive against a
kinase target by examining available biochemical data (fig. 6a). A major challenge was that not all
inhibitors have been comprehensively profiled and available biochemical data are
subject to unknown errors. To simplify the analysis, we made the assumption that
if biochemical data on a compound is missing, the molecule is inactive against
the target. With this limitation in mind, we computed an enrichment score for
each kinase gene (28), by calculating the
p-value based on 2×2 contingency table. For each kinase, we look at the
number of inhibitors that are either: (A) active biochemically and active in
cell-based assays (B) active biochemically but inactive in cell-based assays (C)
inactive biochemically and active in cell-based assays or (D) inactive
biochemically and inactive in cell-based assays. The number of inhibitors in
groups (A) and (D) enrich for the kinase of interest, while the numbers in (C)
and (D) de-enrich (fig 6a).</p><p>TBK1 was among the top enriched genes in this analysis (#8,
s. fig. 6) as were
ALK and AMPK (#7 and #17), two kinases that have been proposed
as STING pathway regulators (29,30,31). To focus on novel kinases, we removed compounds with known
activities against TBK1 and re-ran enrichment. MAPKAPK5 emerged as a
particularly promising kinase required for STING-pathway activity. First,
MAPKAPK5 passed the hypergeometric enrichment test (p-value < 0.005,
#18). A second, non-binary method described in the supplement, in which
partial correlations are computed between the IRF3 and biochemical inhibition
sore, also yielded a high score for MAPKAPK5. MAPKAPK5 was inhibited by 5 probes
in our screen, which was favorable from a practical perspective for subsequent
experiments. MAPKAPK5 is expressed in macrophages, appears to be an inflammatory
kinase, and has been reported to phosphorylate IRF3 in vitro
(32). MAPKAPK5 has been considered as
a therapeutic target for rheumatoid arthritis (33), yet its overall biological function has not been explored in
depth. A recent report also suggests that it may be involved in the inflammatory
pathology of Alzheimer's disease (34).</p><!><p>In table 2, hits 11, 12 and 17
all inhibit MAPKAPK5. However, our biochemical IC50 data also indicated that
they inhibit MAPKAPK2, a closely related kinase which has been implicated in
TLR4 signaling (35). While not enriched
in our initial analysis, MAPKAPK2 is a potent target of these compounds, and
given its previously described role in TLR signaling (35), we thought it was plausible that it could also
be an efficacy target of the compounds we identified. Indeed, it seemed possible
that dual inhibition of MAPKAPK2 and MAPKAPK5 could be required to block the
STING pathway. In subsequent assays, we used compound 11 and a more potent
tetracycle analog of compound 14 (fig. 6b,
INHIB1 and INHIB2) as probes to inhibit MAPKAPK2/5. We also included INHIB3
(fig. 6b), a tetracycle compound that
resembles INHIB2, but has much weaker affinity for MAPKAPK2/5.</p><p>We reconfirmed that INHIB1 and INHIB2 were active in blocking IRF3
nuclear translocation induced by 2′3′-cGAMP in primary human
macrophages. Cells from this particular donor did not respond strongly to
2′3′-cGAMP, but examination of IRF3 nuclear fraction at the
single cell level showed that INHIB1, INHIB2, and BX795 phenocopied untreated
cells, while INHIB3, the much weaker MAPKAPK5 inhibitor, phenocopied
2′3′-cGAMP treated cells (fig.
6c). Additionally, while BX795 treatment completely ablated IRF3
phosphorylation at Ser396, INHIB1 had a slight effect while INHIB2 had no effect
on phospho-IRF3-Ser396 levels, respectively (fig.
6d). Further replicates are needed to determine if the reduction by
INHIB1 is physiologically relevant; however, we performed a similar experiment
in THP1 cells and saw no effect on phospho-IRF3 levels (s. fig. 7). Taken
together, these data indicate that INHIB1 and INHIB2 are active in blocking IRF3
nuclear transaction but are unlikely to be active against the main STING pathway
kinase TBK1.</p><!><p>Next we tested a panel of 9 tetracycles, structurally similar to INHIB2
(s. fig. 8), for
their effect on the STING pathway, using ELISA to determine the extent of IP-10
induction. The 9 tetracycles had a range of inhibitory activities against STING
pathway, consistent with their respective MAPKAPK5 biochemical affinities (fig. 6e). As mentioned above, these compound
also inhibited MAPKAPK2, and the biological and chemical activities were also
weakly correlated (s. fig.
9). No biologically active compounds were inactive against
MAPKAPK2/5, so neither of these kinases can be ruled out as the relevant
efficacy target for the tetracycle-like compounds.</p><p>Toxicity is unlikely to account for the inhibitory activities of INHIB1
and INHIB2. We measured inhibition of the STING pathway by INHIB1 and INHIB2,
using IP-10 ELISA in a different donor, and also in parallel ran a
Cell-Titer-Glo (CTG) assay on the same plate (s. fig. 10a). Both INHIB1
and INHIB2 blocked IP-10 secretion in this donor. By Cell-Titer-Glo, INHIB1
showed no toxicity, and INHIB2 only showed toxicity at higher doses. Examination
of the dose response curves shows that INHIB2 reduces pathway activation around
1-5 uM, with little effect on the CTG count. Additionally, in the aforementioned
mini-SAR experiment, we also stained for nuclei after collecting the media for
ELISA (s. fig. 10b).
For INHIB2, the nuclei count is reduced by less than 10% at the highest
dose. Taken together, while INHIB2 shows some toxicity, we think that it is not
significant enough to fully account for its biological activity.</p><p>As mentioned before, a key challenge faced was that not all inhibitors
were comprehensively profiled across the kinome. Biochemical IC50 data for the
inhibitors against several kinases was missing. To provide a comprehensive
kinase inhibitor selectivity profile, we performed a lysate-based chemical
proteomics experiment that utilized pan-kinase inhibitors coupled to sepharose
beads to assay the expressed kinome of THP1 cells (36, 37).
Pre-incubation of INHIB1 or INHIB2 blocked the enrichment of MAPKAPK5, but not
TBK1 or any other kinase that has been definitively linked to the STING pathway.
However, while the compounds were relatively clean, they were not sufficiently
selective to decisively implicate MAPKAPK2/5 in STING signaling (s. fig. 11)</p><!><p>We developed high content assays for assessing activation of STING and other
innate immune pathways, by measuring IFR3 and NFkB nuclear translocation. The high
content screening assay we developed has many advantages, and may find other uses.
It can be performed in a high-throughput 1536-well format, requires a small number
of cells per well, and is compatible with any adherent cell type including primary
human cells. No external marker or reporter is required and use with difficult to
culture cells (i.e. dendritic cells) and other pathways (i.e. STAT1) seems feasible,
as so long as validated antibodies are available. A key challenge we faced in the
case primary macrophages was donor-to-donor variability, which is potentially an
issue in any screen using fresh primary cells. For future work, we recommend
carefully characterizing this variable in all subsequent experiments. This is
particularly important in development of any potential therapeutics. For controls,
outlier wells presented another challenge, and it is thus important to include
several replicates for controls, and also to screen in duplicate if possible.
Lastly, screening in dose was also very important. The diverse small molecules in
the library had a wide range of active concentrations, and a single dose would not
be able to account for all of them.</p><p>In the activator screen, we identified multiple compounds that cause IRF3
and NFkB nuclear localization, but found that most did not induce downstream gene
expression programs. This is a significant liability of the assay. The existence of
such hits reveals new ways to move IRF3 and NFkB into the nucleus without activating
gene expression, which may be of use for dissecting mechanisms of translocation in
greater detail. In a secondary screen, the Zn2+ chelator TPEN
induced secretion of IP-10 to levels comparable to those of
2′3′-cGAMP and LPS. The effect was donor-specific, possibly because
of a narrow concentration range between activating IP-10 production and killing the
cells. This reduced the value of TPEN as a tool compound and it seems highly
unlikely that TPEN has therapeutic value.</p><p>The antagonist screen identified numerous STING pathway antagonists with
reasonable concordance between the primary screen and follow-up assays. Kinase
inhibitors dominated the list of hits and our follow up analysis focused on a set of
MAPKAPK2/5 inhibitors, from which we tried to identify an efficacy target using SAR
and chemical proteomics. Chemical proteomic data confirmed that these compounds
selectively bound to MAPKAPK5, and further SAR,suggested that MAPKAPK5 is the likely
efficacy target of these inhibitors, though we cannot rule out MAPKAPK2.
Establishing whether the mechanism of the inhibitors is certainly due to MAPKAPK5 or
MAPKAPK2 or both would be difficult in primary macrophages. The ideal solution would
be to perform genetic knockouts, or transfection with inhibitor-resistant mutants,
but doing so in primary human immune cells is difficult. An alternative would be to
use cell lines, though many of the cell lines with active STING pathways, such as
THP1, are also quite difficult to manipulate genetically. Moreover, if a KO in a
cell line did not show any phenotype, this could be due to cell-type specificity,
which has already been reported in the context of the STING pathway (16). The tetracycle scaffold, which achieved potent
inhibition of MAPKAPK5 and IP-10 secretion in human macrophages, could potentially
be further developed as a novel anti-inflammatory drug candidate. Such compounds are
active against STING pathway without noticeably inhibiting TBK1. Many inflammatory
diseases have been characterized by overactive STING signaling, and our work
suggests that there are druggable targets, particularly kinases, to consider beyond
cGAS, STING and TBK1.</p><!><p>Primary human CD14+ monocytes (Lonza) were seeded into 1536 well
plates (Greiner Lo base) at a density of 1250 cells/well. Monocytes were
differentiated into macrophages by adding 100 ng/mL recombinant human M-CSF
(Pepro-Tech, Catalog # 300-25) into the cell media (RPMI, 10%
FBS, 1% P/S) for 1 week. Media was replaced after 3 days of
differentiation. After 1 week, media was washed out and was replaced with just
RPMI, 10% FBS, 1% P/S. In the activator screen, cells were
treated with compound for 4 hours. In the inhibitor screen, cells were
pre-treated with compound for 4 hours, and subsequently treated with 100 uM
2′3′ cGAMP for 4 hours, still in the presence of compound. In
both screens, cells were fixed with 4% PFA for 30 min, washed with PBS,
and then blocked, permeabilized and stained overnight at room temperature.
Buffer for staining was adjusted for final well concentration of 2% BSA,
0.125% Triton-X-100 in PBS. IRF3 XP antibody (Cell Signaling, 11904 T)
and NFkB antibody (Santa Cruz, sc-8008) were adjusted to final dilution of
1:400.</p><p>After primary antibody staining, plates were washed in PBS, and then
stained for 2 hours at room temperature, with goat anti-rabbit Alexa Fluor 488
(Thermo Fisher, A-11034), donkey anti-mouse Alexa Fluor 647 (Thermo Fisher
A-31571), and DAPI. Final dilution of antibody was 1:400 and DAPI concentration
was 0.1 ug/mL. Buffer was same as before (2% BSA, 0.125%
Triton-X-100 in PBS). Plates were then washed and images on an InCell 3000. FITC
channel was used to capture Alexa 488 stain and Cy5 was used to capture Alexa
647 channel.</p><!><p>Image processing was performed on the Columbus HMS server. To identify
cells, a mask was first used to identify nuclei from the DAPI channel.
Cytoplasmic spaces were then identified by segmenting areas of Alexa-488 and
Alexa-647 staining (outside each nuclei) that scored above background. From the
image processing, 36 features were computed.</p><!><p>Nuclear IRF3 = (size_nucleus) ×
(mean_FITC_nuclear – mean_FITC_background)</p><p>Cytoplasmic IRF3 = (size_cytoplasm_FITC)
× (mean_FITC_cytoplasm –
mean_FITC_background)</p><p>Nuclear fraction = Nuclear IRF3 / (Nuclear
IRF3 + Cytoplasmic IRF3)</p><p>NFkB intensity and localization (3 features: Nuclear Intensity,
Cytoplasmic Intensity, fraction nuclear). Parameters calculated as
above.</p><p>DAPI intensity (1 feature)</p><p>IRF3 nuclear texture (4 Haralick features: Correlation,
Contrast, Sum Variance, Homogeneity)</p><p>IRF3 cytoplasmic texture (4 Haralick features)</p><p>NFkB nuclear texture (4 Haralick features)</p><p>NFkB cytoplasmic texture (4 Haralick features)</p><p>DAPI nuclear texture (4 Haralick features)</p><p>IRF3 morphology (3 features using cytoplasm channel –
Area, Roundness, Length to width ratio)</p><p>NFkB morphology (3 features using cytoplasm channel –
Area, Roundness, Length to width ratio)</p><p>DAPI morpohology (3 features in the nucleus – Area,
Roundness, Length to width ratio)</p><!><p>Using the above features, a quadratic-kernel support vector machine
was trained using MATLAB Machine Learning Toolbox. The supervised learning
approach was based on data from control wells: H2O, 2′3′
cGAMP, 3′3′ cGAMP, LPS, and CL075. Training was done with
10-fold cross validation. After training, the SVM was used to classify every
well in the screening assay. For every compound, we considered 12 wells (6
doses, from 10 uM to 31.6 nM at half-log titration, in duplicate). Any
compound that was classified as 2′3′ cGAMP at least twice in
the 12 wells was selected for follow up.</p><!><p>Data from 2′3′ cGAMP and H2O wells were aggregated,
and PCA was used to reduce dimensionality to 6 features (~
90% of variability). In this transformed space, a Mahalanobis
distance between the H2O control group and every compound treated well. A
distance threshold was set by the 90% quantile of the BX795
+ 2′3′ cGAMP control.</p><p>A compound was selected if either: 1) Out of 12 wells (6 doses from
10 uM to 31.6 nM, in duplicate), at least two wells were close enough to H2O
control group OR 2) Out of 14 wells (7 doses from 31.6 uM to 31.6 nM, in
duplicate), at least three wells were sufficiently close to the H2O control
group.</p><!><p>CD14 monocytes were seeded in a 96 well plate, at approximately 50,000
cells/well. Cells were differentiated as above. In activator secondary screen,
cells were treated with compound for 11 hours, at 6 doses in duplicate, and
IP-10 levels were measured using an ELISA kit (R&D DY266-05). In the
inhibitor secondary screen, cells were pre-treated with compound for 1.5-2
hours, and then treated with 40 uM 2′3′ cGAMP for approximately
17 hours. IP-10 levels were again measured with an ELISA kit (R&D
DY266-05). The TNFa data in the supplement was also obtained using an ELISA kit
(R&D DY210-05)</p><!><p>Any immunofluorescence or IP-10 ELISAs were done as above.
Thp1-ISG-LUCIA, and RAW-ISG-LUCIA cell lines were obtained from Invivogen. LUCIA
assays were performed as described in manufacturer protocol. For Western blots,
phospho-IRF3 antibody was obtained from Cell Signaling (4947S) and tubulin
antibody was obtained from Sigma (T9026). We performed near-IR Western
blottoing, using Odyssey blocking buffer (LI-COR, P/N 927-40100) and following
LI-COR Odyssey near-IR Western blot protocol. Secondary antibodies were Goat
Anti-Rabbit 800 DyLight (Thermo, 35571) and Goat Anti-Mouse 680 DyLight (Thermo
35518), used at 1:15000 dilution.</p><p>For s. fig. 11,
THP1 cells were first differentiated using phorbol 12-myristate 13-acetate (PMA)
to make them more macrophage-like and adherent. Cells were treated with PMA (200
ng/mL) for approximately 18 hours, and media was subsequently washed out with
plain cell culture media.</p><p>Cell-titer-glo assay (s. fig. 10) was performed as per manufacturer protocol.</p><!><p>For the primary screen, CD14+ monocytes were ordered directly
from Lonza. For all secondary screening and follow up, peripheral blood
mononuclear cells (PBMCs) were first isolated from leukapheresis donors (Boston
Children's Hospital), using standard protocol (38). CD14+ monocytes were subsequently
isolated using a CD14+ antibody and magnetic sorting (MACS, Miltenyi
Biotec). Cells were not further characterized.</p><p>Different donors were used in all screens and assays (primary activator
and inhibitor screens, secondary screens, follow-up). For each assay, we strived
to use a single donor for each assay. However, in the primary screens, in which
we ordered cells directly from Lonza, we did not have enough cells from a single
donor to cover all assay plates. As a result, we had to use a mixture of cells
that came predominantly from one donor (~ 75%). Different donors
were used for the activator and inhibitor screens.</p><!><p>Our primary screen was conducted at Novartis Insitutes for Biomedical
Research (NIBR), while our secondary screening and follow up was conducated at
Harvard Medical School (HMS). All molecules plated in the initial screen were
verified for chemical purity using liquid chromatography-mass spectrometry
(LC-MS).</p><p>All compounds selected for follow-up at HMS were public compounds;
however, several were not available from reliable, commercial sources. As a
result, we requested about 2/3 of the hits from NIBR. The purity of these hits
was verified using LC-MS. For practical reasons, we ordered the remaining 1/3 of
hits from standard chemical sources. The purity of these hits was verified by
the vendor, but not at HMS.</p><!><p>Biochemical IC50 data for every compound was assembled into a matrix,
where each row represents a compound, and each column represents a kinase. Each
element of the matrix contains the measured biochemical IC50, taken from
internal data, for the specified inhibitor and kinase target. If a kinase was
not profiled for a given inhibitor, it was assumed inactive by giving it a
biochemical IC50 of 100 uM.</p><p>For hypergeometric enrichment, an inhibitor was deemed biochemically
active in a well if the dose is 10 times greater than the biochemical IC50. This
is a free parameter, and it is to account for the fact that inhibitors typically
must be used at doses greater than their biochemical profile, for reasons such
as cell permeability, transport, metabolism, etc. Biological activity was
determined by the IRF3 nuclear localization score. An inhibitor was deemed
biologically active if the IRF3 nuclear localization score was within two
standard deviations of the H2O control. P-values for all expressed kinases were
then computed using the contingency table in fig.
6a. We subsequently removed TBK1-active compounds (compound with
IC50s less than 1 uM), and re-ran enrichment.</p><p>We note that p-values in this test were used primarily to rank the
kinases, and one must be cautious in interpreting the actual value itself, due
to a false discovery rate, which we did not correct for. In particular, the
hypergeometric test can enrich several genes strongly, as our biochemical IC50
data is complete, and some kinases can have several data points in group D
(biologically inactive and biochemically inactive), which enriches for the gene.
Additionally, several genes may be enriched simultaneously, because of
transitive effects, e.g. kinases commonly inhibited by the same inhibitors.</p><p>We also calculated partial correlations between the IRF3 score and
biochemical inhibition score. This approach is described in s. fig. 6.</p><!><p>Chemoproteomics were performed as in ref. 35. Immobilized pan-kinase inhibitors (compound-2
from ref. 35 & VI16832 from ref.
36) were combined in a 1:1 ratio and
used to pull down kinases from THP1 cell lysate.</p>