Phenotype-Guided Natural Products Discovery Using Cytological Profiling

Article abstract of DOI:10.1021/acs.jnatprod.5b00455

Phenotype-guided natural products discovery is emerging as a useful new discovery tool that addresses challenges in early, unbiased natural product biological annotation. These high-content approaches yield screening results that report directly on the im

Full text of DOI:10.1021/acs.jnatprod.5b00455

Article
pubs.acs.org/jnp
Phenotype-Guided Natural Products Discovery Using Cytological
Profiling
Jessica L. Ochoa, Walter M. Bray, R. Scott Lokey, and Roger G. Linington*,§
Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High Street, Santa Cruz, California 95064,
United States
S
* Supporting Information
ABSTRACT: Phenotype-guided natural products discovery is
emerging as a useful new discovery tool that addresses challenges
in early, unbiased natural product biological annotation. These
high-content approaches yield screening results that report
directly on the impact of test compounds on cellular processes in
target organisms and can be used to predict the modes of action
of bioactive constituents from primary screening data. In this
study we explored the use of our recently implemented
cytological profiling platform for the isolation of compounds
with a specific, predefined mode of action, namely, induction of
mitotic arrest. Screening of a microbially derived extract library
revealed six extracts whose cytological profiles clustered closely
with those of known antimitotic agents from the pure compound
training set. Subsequent examination of one of these extracts revealed the presence of two separate bioactive constituents, each of
which possessed a unique cytological profile. The first, diketopiperazine XR334 (3), recapitulated the observed antimitotic
phenotype of the original extract, demonstrating that cytological profiling can be used for the targeted isolation of compounds
with specific modes of action. The second, nocapyrone L (6), possessed a cytological profile that clustered with known calcium
channel modulators, in line with previous published activities for this compound class, indicating that cytological profiling is a
flexible and powerful platform for the de novo characterization of compound modes of action.
lthough natural products have proven value in the area of
cancer chemotherapeutic development, the process from
multiparametric cellular responses of drug-treated cells.4,5
These data can be viewed as phenotypic “fingerprints” that
have been successfully used to differentiate classes of drugs with
discrete modes of action (MOAs).6 This screening approach
takes a global view of biological attributes of bioactive
compounds by determining the overall effect of small molecules
on cell morphology, rather than examining specific molecular
targets or pathways, as is common in target-based screening.
While high-content screening (HCS) is an extremely powerful
tool, it is predominately used for pure compound analysis;
therefore the time between sample collections and MOA
annotation still exists as a bottleneck in drug discovery.7
Development of untargeted HCS of natural product extracts
is providing a mechanism to circumvent this bottleneck by
obtaining detailed mechanistic information for large natural
product libraries in a high-throughput fashion. Elimination of
lengthy purification and workup protocols for nonbioactive or
broadly cytotoxic compounds rapidly prioritizes compounds
with desired bioactivities. Since the emergence of automated
image analysis, the development of algorithms that process and
categorize images to predict MOAs has provided new
opportunities for the analysis of compounds without published
A
the original collection to the biological annotation of natural
products is labor-intensive and time-consuming; for example,
the microtubule-stabilizing activity of the natural product
dictyostatin 1 was not established until 10 years after it was
originally isolated.1 In addition, collection of the plant material
containing paclitaxel occurred in the 1960s, but its microtubule-
stabilizing activity was not published until 1979.2 Traditionally,
targeted anticancer discovery for natural products is performed
by screening crude extracts in high-throughput screens using
reporter assays such as enzyme-linked immunosorbent assays
(ELISAs) or reporter gene assays (RGAs). While these assays
are high-throughput, they typically report on only a single
molecular target or pathway, meaning that discovery is limited
to a specific set of biological targets.3 These population
measurements miss variations in individual cell dynamics and
morphology, and hits in these screens are therefore unable to
account for off-target affects and are prone to false positives
when broadly cytotoxic compounds are encountered. For
example, screening campaigns for a selected target such as a
specific enzyme can yield hits that are indistinguishable from
compounds that are also active against a wider set of targets and
whose development potential is therefore typically low.
Tremendous progress has been made in recent years to
develop high-content imaging systems capable of quantifying
Received: May 20, 2015
© XXXX American Chemical Society and
American Society of Pharmacognosy
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Figure 1. (A) Fluorescence images of drug-treated wells all showing deviations in tubulin (green) and mitotic cells (cyan) from the control. The
corresponding phenotypic fingerprint generated from the image analysis is shown above each well image.27 (B) Cytological profiling fingerprint of
original natural products extracts and microtubule poisons. Positive deviations from DMSO-treated wells are displayed in yellow; negative deviations
are displayed in blue. Features highlighted for tubulin and mitotic cells display an increase in mitotic cells and variable tubulin staining.
MOAs. In our effort to expedite hit-to-lead development from
natural product libraries, we have recently reported the
implementation of a high-content cytological profiling platform
that uses image-based screening to directly visualize the
phenotypic effects of natural product extracts on HeLa cell
development.8,9 These reports analyzed the major compound
clusters found from screening a pilot library of 312 extracts in
order to validate this screening tool and identified both known
and novel natural products. In this study we applied this
technology to our entire 5304-member extract library and used
these data to test the hypothesis that cytological profiling (CP)
could be used to discover compounds with specific and
predefined modes of action.
To test the hypothesis that high-content screening can be
used to discover bioactive compounds with prespecified modes
of action, we explored our library for extracts displaying an
antimitotic phenotype and have demonstrated that this
bioactivity profiling approach can be used to map a single
specific constituent within this extract to this predefined
biological activity. This proof of concept provides evidence that
untargeted image-based screening can be used effectively for
the discovery of compounds with specific mechanisms of action
and opens the door for the use of this technology to discover
compounds that target other high-priority pathways and
molecular targets from crude natural products in a high-
throughput manner.
A number of approaches have been employed to derive
multiparametric annotation for natural product extracts.
Predominately applied to pure compound libraries, untargeted
profiling utilizes bioactivity profile matching where compounds
with similar phenotypic responses are inferred to have similar
mechanisms of action. Among these, gene expression profiling
and yeast gene deletion libraries have been used successfully to
annotate natural product MOAs in an untargeted manner.10,11
Zebrafish imaging has also been utilized to characterize whole
animal response of natural products and is one of the few whole
organism approaches to untargeted natural products screen-
ing.12 A number of computational tools exist to aid in the
interpretation of image-based screening data; however, to date,
there have been few examples of the application of these
methods to unbiased natural products discovery.
Induction of mitotic arrest is a validated anticancer
mechanism, with tubulin being one of the most studied cancer
targets and the target of a number of chemotherapeutic
agents.13 However, many mitosis-targeting treatments currently
struggle with incomplete neoplasm eradication, resistance, and
toxicity issues.14 Current antimitotic drug discovery has focused
on analyzing specific aspects of mitosis, often accomplished by
screening compounds in tubulin binding assays or in whole cell
assays where the amount of mitotic arrest with respect to
vehicle-treated cells is analyzed. As an example, a high-
throughput ELISA assay has been used to discover new
paclitaxel analogues with similar activity to the parent
compound.15 In addition, biphenabulin, an antimitotic agent
with nanomolar activity, was recently discovered utilizing whole
cell imaging of cells stained with phosphorylated histone H3
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(pHH3), a mitotic marker, to screen libraries of synthetic
compounds created by diversity orientated synthesis of
macrocycles with natural-product-like geometry.16 Our new
study focused on determining whether the unbiased CP screen
could be utilized to identify compounds that affect tubulin
dynamics from complex natural product extracts, while still
providing detailed yet untargeted biological annotations for all
bioactive constituents.
Our standard library preparation protocol involves organic
extraction of microbial culture broths, followed by initial
prefractionation on C18 SPE cartridges using a MeOH/H2O
step gradient to generate seven prefractions for screening.8 An
aliquot of prefraction RLUS1665D was first examined using our
standard peak library separation protocol.8 In brief, extracts are
analyzed by RP-HPLC using an analytical fraction collector to
separate the eluent into deep-well 96-well plates in 1 min
intervals (Phenomenex Synergi Fusion-RP 10 μm, 80 Å, 250 ×
4.6 mm, 25:75 MeCN/H2O + 0.02% formic acid to 90:10
MeCN/H2O + 0.02% formic acid; 2 mL min−1 flow rate). A
portion of this eluent (∼5%) is diverted from the fraction
collector to an ESI-single quadrupole mass spectrometer
performing fast polarity switching to simultaneously acquire
mass spectra in both positive and negative ionization modes.
Collectively these analyses provide retention time, UV
absorbance, and MS data for all constituents from each extract.
When combined with screening data from the eluent plate,
these data can be used to connect individual constituents with
observed bioactivities from primary screening results.
In the case of RLUS1665D the peak library revealed the
presence of two major families of compounds, both of which
showed weak phenotypes in the cytological profiling screen of
the peak library. One limitation of peak library screening is that
if an extract contains families of compounds with similar
bioactivities but different retention times, separation into
individual wells can decrease the titer in each well sufficiently
to reduce the strength of the observed phenotype. In this
instance, refermentation of the producing organism followed by
a combination of normal phase silica gel and reversed phase
HPLC steps provided fractions containing these two
compound families, each of which displayed strong independ-
ent cytological profiles when reevaluated in the cytological
profiling screen.
Examination of the screening results from the second round
of fractionation indicated that one of these compound families
recapitulated the original antimitotic phenotype, while the
other family was strongly cytotoxic at the tested concentration,
affording test wells with few cells (Figure S9, Supporting
Information). This result highlights the second challenge with
phenotypic screening, whereby testing at too high a
concentration leads to a situation where few cells remain for
image analysis. This “death phenotype” affords cytological
profiles where the majority of size and shape features are scored
as strongly negative (due to the low cell count), which in turn
precludes accurate MOA predictions. This limitation is resolved
by screening test materials as fine-scale dilution series, typically
16 2-fold dilutions with a concentration range of 100 μM to 3
nM. Appropriate concentrations for clustering are selected by
identifying the first dilution with a cell count within three
standard deviations of the mean cell count for untreated control
wells. In this instance, final purification by sequential stages of
RP-HPLC led to the isolation of representatives from both of
these compound classes. Screening of these pure compounds as
dilutions series in the CP assay (Figure S10, Supporting
Information) confirmed that one of these compounds displayed
an antimitotic cytological profile, while the other clustered
tightly with the known calcium channel modulators benzamil,
BAY-K-8644, and A-23187 (Figure 3C). This result highlights
how cytological profiling can be used to focus on the discovery
of compounds within a specific mechanistic class (antimitotic
agents) while simultaneously annotating compounds with
unknown MOAs (calcium channel modulators).
RESULTS AND DISCUSSION
■
In the original report describing the development of cytological
profiling for natural products discovery we evaluated a pilot
library of 312 extracts and characterized the bioactive
constituents from a subset of these bioactive fractions.8 We
have now extended this analysis to our full natural products
library, consisting of 5304 prefractionated extracts from marine-
derived Actinobacteria. Cytological profiling of this entire
library, followed by hierarchical clustering with the training set
of 480 compounds from the Harvard Institute of Chemistry
and Cell Biology collection (ICCB) possessing known MOAs,
revealed 41 discrete clusters, including 27 clusters that
contained both bioactive extracts and reference compounds
from the training set. Among these there are a number of
expanded versions of clusters originally identified and
annotated in our initial cytological profiling paper, including
DNA synthesis inhibitors, vacuolar ATPase inhibitors,
potassium channel inhibitors, and kinase inhibitors. In addition,
this expanded cytological profiling set contains multiple clusters
not observed in the original training set, many of which contain
training set compounds with related MOAs. Finally, there are
numerous smaller clusters that do not cluster well with
compounds from the training set, suggesting either that these
extracts contain compounds with MOAs not represented by
members of the training set or that these extracts contain
multiple bioactive constituents that cause “mixed-mode”
phenotypes that are not representative of any of the individual
components, an inherent limitation of cytological profiling with
complex mixtures.
Among these active clusters, one small subset clustered
tightly with the known microtubule poisons nocodazole and
paclitaxel, suggesting that these extracts contain compounds
that disrupted tubulin dynamics. In general, CP fingerprints of
known microtubule stabilizers and destabilizers cluster closely
together but can be differentiated by comparing tubulin staining
from DMSO controls. Tubulin stabilizers such as paclitaxel
show a positive deviation, while destabilizers such as colchicine
and nocodazole exhibit a negative deviation in tubulin staining
(Figure 1A). Analysis of the heatmap suggested that the
clustering was predominately driven by an increase in the
mitotic index. Inspection of the well images for extract
RLUS1665D (Figure 1A) showed a strong mitotic stall and
diminished tubulin staining consistent with the phenotypes
observed for neighboring reference compounds that inhibit
tubulin polymerization (Figure 1B). The precise order of
extracts and reference compounds in the hierarchical clustering
network is contingent upon both the number and concen-
tration of compounds in the original input set. Clustering these
active extracts with several different dilution series for the
training set of known bioactives identified extract RLUS1665D
as having the most reliable clustering with known antimitotic
agents. We therefore prioritized RLUS1665D for further
chemical evaluation.
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The identity of the antimitotic agent was determined through
a combination of 1D and 2D NMR analyses, mass
spectrometry, and total synthesis.17 Initial ESI-single-quadru-
pole LCMS analysis identified two mass spectrometric features
consistent with the [M + H]+ and [M − H]− adducts with m/z
values of 321.2 and 319.0, respectively. Subsequent HRESI-
TOFMS analysis revealed an [M + Na]+ adduct ion with m/z
343.1875, consistent with the molecular formula C19H16N2O3.
Examination of the UV-absorbance spectrum for this
compound revealed strong absorbances at 250 and 352 nm,
suggestive of a conjugated aromatic compound. A query of the
Antimarin database revealed one compound, diketopiperazine
XR334, which possessed a matching molecular formula, as well
as identical NMR and UV spectroscopic features.18,19 Because
this scaffold is synthetically accessible through a two-step
condensation reaction between diacetyl-2,5-piperazinedione,
benzaldehyde, and p-anisaldehyde, the structure of the isolated
antimitotic agent was confirmed by total synthesis (Scheme S1,
Supporting Information) and validated by NMR and HPLC-
MS comparison with the original reported data (Figure S2,
Supporting Information).19−21
In addition to the structural verification, the biological
activity for the isolated natural product was confirmed through
parallel CP screening for dilution series of both the natural and
synthetic materials (Figure 3A). These results demonstrated
that the synthetic and naturally occurring material possessed
the same cytological profiles and confirmed that the observed
activity was caused by XR334, rather than any potent but minor
constituents present as contaminants in the natural sample.
Clustering of the CP fingerprints from the synthetic and natural
samples of XR334 with the training set library revealed that
XR334 clustered closely with other microtubule poisons,
including nocodazole and vinblastine (Figure 3B).
Figure 2. Structures of known antimitotic agents and compounds
isolated from RLUS1665.
XR334 is structurally similar to plinabulin (2), a synthetic
analogue of the natural product phenylahistin (1). Plinabulin is
both a vasculature-disrupting and an antimicrotubule agent that
maintains efficacy in vivo and works synergistically with
docetaxel, a synthetic derivative of paclitaxel.17 Examination
of the high-content images generated in the CP screen showed
that XR334 displays moderate tubulin depolymerization
compared to DMSO controls or strong destabilizers such as
nocodazole. Therefore, while this clustering is predominately
driven by the increased mitotic index, closer inspection of its
effect on the microtubule cytoskeleton morphology indicates
that it is possible to further subdivide this large cluster of
microtubule poisons into several classes, based on their effect
on tubulin polymerization. This is consistent with MOA studies
performed on synthetic analogues of plinabulin.22−24
While screening approaches focused on antimitotic com-
pounds would have successfully identified XR334 as the
bioactive constituent, upon peak library generation they
would be unable to annotate compounds within the extract
with alternative MOAs. In addition to XR334, a second class of
bioactive compounds was also identified from this extract. This
second class of compounds shared similar UV profiles and
differed by sequential mass differences of 14 Da, consistent with
variation in the number of methylene units in a set of
structurally related analogues. Initial isolation of one of these
family members afforded a white solid that gave an
HRESITOFMS [M + H]+ ion at m/z 267.1957, consistent
with the molecular formula C16H26O3. 1H and 13C NMR
analyses revealed the presence of one carbonyl, one methoxy,
and four methyl groups. Further NMR analysis identified
Figure 3. (A) Cytological profiling analysis of isolated XR334
(XR334_NP) and synthesized XR334 (XR334_SYN). (B) Cytological
profiling analysis of nocapyrone L. (C) Chemical structures of
compounds that clustered with nocapyrone L.
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nocapyrone L (6) (Figure 2) as a candidate match, which was
subsequently confirmed by comparison of NMR and MS
literature data.25 The remaining pyrone analogues from this
series were identified as nocapyrones B (4) and H (5) through
readily observable by eye. Knowing which features are drivers of
clustering for a specific group of compounds provides an
additional tool for evaluating the biological significance of a
given cluster by highlighting specific size and shape cell
deviations that are positively correlated with the clustering of
any specific group of compounds.
This “GSEA-CP” analysis identified enrichment in nuclear
stain 5-ethynyl-2′-deoxyuridine (EdU) intensity features among
those that correlated with the cluster containing the
nocapyrones and calcium ion channel modulators (Figure 4).
1
comparison of methylation patterns of H and COSY NMR
spectra versus nocapyrone L and comparison with reported
literature data.25−27
γ-Pyrones have been identified in a number of screening
campaigns26,27 and have previously been reported to impact
intracellular calcium ion concentrations.25 In order to predict
the MOA of these compounds against HeLa cells, a dilution
series of the most active compound, nocapyrone L (6), was
evaluated in the CP assay. Nocapyrone L was previously
isolated from a venom duct of a sea snail, Conus rolani. It was
found to be active in a calcium ion imaging assay of dissociated
dorsal root ganglion neurons from mice and reported to
modulate calcium ion levels.25 In line with previous reports,
sublethal concentrations of compound 6 clustered closely with
compounds that affect calcium ion channels from the ICCB
library training set including Bay K-8644 and benzamil (Figure
3B). These results indicate that disruption of calcium ion
channel function is likely the predominant MOA for the
observed cytotoxicity of the nocapyrones in our assay system;
this result demonstrates that the CP platform can differentiate
between a wide array of biological mechanisms, even if these
mechanisms are not directly reported on by the structural and
cell cycle stains used in CP analysis.
In order to determine which cytological phenotypes underlie
the clustering of the nocapyrones with the calcium ion channel
modulators from the reference library, we used a modification
of gene sets enrichment analysis (GSEA) using software
publically available from the Broad Institute.10 GSEA was
originally developed to identify gene sets (i.e., sets of genes
grouped by function, biological process, co-regulation, etc.) that
are enriched in genes whose up- or down-regulation is
correlated with a particular phenotype (e.g., metastatic vs
nonmetastatic) among a set of samples (e.g., tissue samples
from different patients).28,29 For example, initial gene analysis
of type 2 diabetes mellitus (DM2) revealed that no single gene
could be correlated to biopsy samples taken from patients with
DM2. While the expression of individual genes can have large
variations within sample sets, if these genes are placed in gene
sets based on their connection to specific biological pathways, it
is then possible to identify consistent correlations between gene
set expression and specific phenotypes. Analysis of DM2 tissue
samples using GSEA revealed the correlation between the
expression of OXPHOS-CR genes and DM2.29
To apply this approach to the chemical genetic signatures
derived from the cytological profiling screen, we grouped the
CP features into 23 classes (analogous to the gene sets
discussed above) based on the broad phenotypic signatures on
which they report (nuclear shape, pHH3 intensity in large
areas, cell count, mitotic index, etc.). Applying GSEA to our
data set, we replaced genes with CP features, gene sets with
feature classes, phenotypes with annotated MOA classes, and
samples with compound-dose instances. This allowed us to ask,
for the calcium ion channel modulators and nocapyrones that
cluster with them, which CP feature classes correlate
significantly with the distinction between these compounds
and the rest of the library. The advantage of this analysis is that
it provides a quantifiable method for identifying key biological
features that drive associations between test compounds in the
cytological profiling clusters, even if these features are not
Figure 4. Gene sets enrichment analysis (GSEA) analysis of EdU
features for calcium channels and nocapyrone L. Seven EdU features
were identified to drive the clustering of this class as denoted by
asterisks; six of these enriched features showed significant positive
deviations for the cells treated with nocapyrone L as denoted by black
asterisks.
The EdU stain is a modified nucleoside analogue that reports
on cells actively undergoing DNA synthesis. For each of the
feature classes an enrichment score was generated. The
enrichment score reflects the significance of a feature class in
terms of how well its individual members are correlated with a
designated phenotype. Expansion of these features enriched in
this class for nocapyrone L (6) identified that six out of the
seven enriched features from the GSEA analysis show
significant positive deviation from the DMSO control (Figure
S8, Supporting Information). Visual inspection of the images
did not reveal this subtle phenotypic difference, highlighting the
utility of computational image processing in classifying
compounds by phenotype. Therefore, CP is a powerful tool
capable of deciphering predetermined mechanisms from
complex data sets, yet flexible enough to annotate MOAs of
compounds with orthogonal bioactivities within the same
extract.
In conclusion, this study demonstrates the utility of
untargeted morphology-based screening for the broad classi-
fication of natural product MOAs. By quantifying specific
morphological features of HeLa cell development under
sublethal drug pressure, the CP platform is capable of
distinguishing a large number of discrete phenotypes that in
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100% MeOH (E), and finally ethyl acetate (F)) to afford seven
fractions designated as prefractions A−F. The 10% MeOH fraction
was discarded, and the remaining six were dried in vacuo. The 80%
MeOH prefraction, RLUS1665D, was subjected to RP-HPLC
(Phenomenex Synergi Fusion-RP 10 μm, 80 Å, 250 × 4.6 mm,
65:35 MeOH/H2O + 0.02% formic acid isocratic run over 25 min, 2
mL min−1 flow rate) to afford compounds 3−6, which eluted at 5.75,
14.10, 15.70, and 21.95 min, respectively.
turn report on the pathways and mechanisms disrupted by drug
treatment. We have shown that targeted phenotype matching
from untargeted cytological profiling data can be used to
identify compounds with specific predefined MOAs. In this
case the antimitotic XR334 was directly targeted and identified
from our complex natural product library through a
combination of CP screening and analytical chemistry.
The parallel identification of nocapyrones B (4), H (5), and
L (6) as predicted calcium ion channel modulators
demonstrates that the CP platform can be used to discover
and characterize bioactive constituents whether these bioactive
compounds directly target processes reported on by the
staining set (e.g., XR334, which disrupts tubulin dynamics) or
not (e.g., nocapyrone L, which disrupts calcium channel
function). These results therefore suggest that unbiased
phenotypic profiling may be a valuable tool for the broad
characterization of bioactive constituents from natural product
libraries and that this approach can be employed to identify
compounds with specific biological MOAs by targeting relevant
clusters in the CP profiles. In this study we have demonstrated
the practicability of this approach by mining our natural
product libraries for compounds with specific mechanisms and
validating their predicted MOAs through a combination of NP
isolation, synthesis, and phenotypic image-based screening.
Cytological Profiling Screening. Cytological profiling was
performed at the UC Santa Cruz Chemical Screening Center using
our standard screening protocol.8 In general, prefractions were tested
at two concentrations (1:5 and 1:25 dilutions from master stock
solutions). For the stock solution preparation protocol see ref 8. Pure
compounds were screened as 2-fold dilution series (16 dilutions, 100
μM to 3 nM final testing concentrations). DMSO solutions (150 nL)
of test compounds or extracts were added to two separate black-walled
clear-bottomed 384-well plates seeded with HeLa cells and incubated
at 37 °C for 19 h. Each plate was fixed and stained with the
appropriate stain set (plate 1: Hoechst dye (DNA), antiphosphohi-
stone H3 antibody (mitotic marker), and EdU (S-phase); plate 2:
Hoechst dye (DNA), TMR-phalloidin (actin), antitubulin Ab
(tubulin); for staining protocol see ref 8) then imaged at four sites
per well using an ImageXpress Micro epifluorescent microscope
(Molecular Devices) with a 10× Nikon objective lens. Images were
subsequently journaled to extract cell-by-cell features using Molecular
Devices MetaXpress software, and these cell-by-cell values converted
to final cytological profiles using our in-house data analysis pipeline.8
Finally, cytological profiles were clustered using Cluster 3.030 and
visualized using Java Treeview.31
EXPERIMENTAL SECTION
■
Synthesis of XR344. XR344 was synthesized from diacetyl-2,5-
piperazinedione, benzaldehyde, and p-anisaldehyde following estab-
lished literature procedures (Scheme S1, Supporting Information).19
The structures of all synthetic products were confirmed by NMR and
MS data and comparison with literature values. Synthetic and naturally
occurring XR334 were compared by NMR spectroscopy and LCMS
co-injection (Supporting Information).
General Experimental Procedures. Solvents for all chromatog-
raphy were HPLC grade and used without further purification. Optical
rotations were measured on a Jasco P-2000 polarimeter using a 10 or
100 mm path length cell at 589 nm. UV spectra were recorded on a
Shimadzu UV−visible spectrophotometer (UV-1800) with a path
length of 1 cm. NMR spectra were acquired on a Varian Inova 600
MHz spectrometer equipped with a 5 mm H/C/N triple resonance
cryoprobe and referenced to residual solvent proton and carbon
signals (δH 7.26, δC 77.1 for CDCl3 and δH 3.31, δC 49.0 for methanol-
d4). HRMS data were acquired using an Agilent 6230 electrospray
ionization (ESI) accurate-mass time-of-flight (TOF) liquid chromato-
graph−mass spectrometer.
ASSOCIATED CONTENT
* Supporting Information
■
S
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.jnat-
prod.5b00455.
Fermentation and Isolation. The producing organism, RL10-
282-NTS-A, was isolated from a marine sediment sample collected by
scuba near American Samoa. The strain was originally isolated on NTS
medium (20.0 g of agar, 50.0 mg of nalidixic acid, 50.0 mg of
cycloheximide, 20.0 g of starch, 0.5 g of NaCl, 0.01 g of FeSO4·7H2O,
0.5 g MgSO4·7H2O, 0.5 g of K2PO4, 1.0 g of KNO3, 750 mL of 0.2 μm
filtered seawater, 250 mL of Milli-Q H2O). Frozen stocks of
environmental isolates were streaked onto fresh Marine Broth plates
(37.4 g of Difco Marine Broth, 18 g of agar, 1 L of Milli-Q water) and
incubated at 25 °C until discrete colonies became visible. Selected
colonies were inoculated into 10 mL of modified saline SYP (mSYP)
media (10 g of starch, 4 g of peptone, 2 g of yeast extract, and 31.2 g of
Instant Ocean in 1 L of distilled H2O). The cultures were stepped up
in stages at 7-day intervals by first inoculating 1.5 mL of the 10 mL cell
cultures into 50 mL of mSYP (medium-scale), followed by inoculation
of 40 mL of these medium-scale cell cultures into 1 L of the same
broth also containing 20.0 g of Amberlite XAD-16 adsorbent resin in
2.8 L Fernbach flasks for 7 days. All cultures were incubated at 25 °C
containing glass beads for 10 mL cultures and stainless steal springs for
50 mL and 1 L cultures and shaken at 200 rpm.
The cells and resin were removed by vacuum filtration using
Whatman glass microfiber filters and washed with deionized water.
This cell/resin slurry was extracted with 250 mL of 1:1 MeOH/
CH2Cl2, and the organic extract was removed by vacuum filtration and
concentrated to dryness in vacuo. The crude organic extract, given
extract code RLUS1665, was subjected to solid-phase extraction (SPE)
using a Supelco-Discovery C18 cartridge (10 g) and eluted using a step
gradient of 80 mL of MeOH/H2O solvent mixtures (10% MeOH, 20%
MeOH (A), 40% MeOH (B), 60% MeOH (C), 80% MeOH (D),
Experimental procedures, screening data, and 1H and 13
C
NMR spectra of XR334 (3) and nocapyrone B (4), H
(5), and L (6) (PDF)
AUTHOR INFORMATION
Corresponding Author
*Tel: (778) 782-3517. E-mail: rliningt@sfu.ca (R. G.
Linington).
■
Present Address
§Department of Chemistry, Simon Fraser University, 8888
University Drive, Burnaby BC V5A 1S6, CANADA.
Notes
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
■
We thank P. Crews for sample collection and K. Kurita for
assistance with accurate mass measurements. This work was
supported by NIH grant TW006634, The Santa Cruz Cancer
Benefit Society, and a UCSC Cota-Robles predoctoral
fellowship to J.L.O.
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DOI: 10.1021/acs.jnatprod.5b00455
J. Nat. Prod. XXXX, XXX, XXX−XXX