Structural Analysis and Development of Notum Fragment Screening Hits

The Wnt signaling suppressor Notum is a promising target for osteoporosis, Alzheimer's disease, and colorectal cancers. To develop novel Notum inhibitors, we used an X-ray crystallographic fragment screen with the Diamond-SGC Poised Library (DSPL) and identified 59 fragment hits from the analysis of 768 data sets. Fifty-eight of the hits were found bound at the enzyme catalytic pocket with potencies ranging from 0.5 to >1000 μM. Analysis of the fragments' diverse binding modes, enzymatic inhibitory activities, and chemical properties led to the selection of six hits for optimization, and five of these resulted in improved Notum inhibitory potencies. One hit, 1-phenyl-1,2,3-triazole 7, and its related cluster members, have shown promising lead-like properties. These became the focus of our fragment development activities, resulting in compound 7d with IC50 0.0067 μM. The large number of Notum fragment structures and their initial optimization provided an important basis for further Notum inhibitor development

High quality linked data for stroke obtained using non-government clinical registry and routinely collected hospital and death data

Introduction Recent advances in data linkage infrastructure in Australia mean that data can be linked based on various identifiers across datasets. In a first for Australia, we tested the feasibility of linking data between a clinical quality disease registry with Australian and state government health data across multiple jurisdictions. Objectives and Approach To determine whether high quality linked data for stroke can be obtained using a non-government managed registry (Australian Stroke Clinical Registry, AuSCR), national death registry data (Australian government), and hospital admission and emergency presentation data (state governments) to assess the accuracy of consistent variables across the different datasets. We used a cohort design with probabilistic data linkage to merge patient-level records. Descriptive statistics presented for matching concordance and Cohen’s kappa for concordance across demographic variables. The sensitivity and specificity of in-hospital deaths collected in the AuSCR was assessed against national death registrations. Results There were 16,214 registrants in the study cohort. Their identifiers in the AuSCR from 2009-2013 were linked with death, emergency department and hospital discharge data from April 2004 to December 2016. In total, 99% of the AuSCR registrants were linked to one or more datasets; 98\% were linked with emergency presentation (80%) and/or admission (95%) data. Linkage to national death registrations identified 4,183 death; 1440 of these were identified as in-hospital deaths in both data sets demonstrating that in-hospital death classification in AuSCR had a 98.7% sensitivity and 99.6% specificity. Concordance between common demographic variables was excellent (kappa 0.84 for aboriginal status and kappa 0.99 for sex). Conclusion/Implications The majority of AuSCR registrants were accurately linked to the Australian and state government datasets. Linkage quality was excellent and there was high concordance between common variables. The ability to reliably merge the datasets assures future comprehensive analyses of stroke care, ongoing health care resource utilisation and patient outcomes

Design of a Potent, Selective, and Brain-Penetrant Inhibitor of Wnt-Deactivating Enzyme Notum by Optimization of a Crystallographic Fragment Hit

Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human diseases such as colorectal cancer and Alzheimer's disease, supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we have described the discovery and profile of 8l (ARUK3001185) as a potent, selective, and brain-penetrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identified 6a and 6b as a pair of outstanding hits. Fragment development of 6 delivered 8l that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in pharmacology screens showed 8l to be selective against serine hydrolases, kinases, and drug targets

CLICK-enabled analogues reveal pregnenolone interactomes in cancer and immune cells.

Pregnenolone (P5) promotes prostate cancer cell growth, and de novo synthesis of intratumoural P5 is a potential cause of development of castration resistance. Immune cells can also synthesize P5 de novo. Despite its biological importance, little is known about P5's mode of actions, which appears to be context dependent and pleiotropic. A comprehensive proteome-wide spectrum of P5-binding proteins that are involved in its trafficking and functionality remains unknown. Here, we describe an approach that integrates chemical biology for probe synthesis with chemoproteomics to map P5-protein interactions in live prostate cancer cells and murine CD8+ T cells. We subsequently identified P5-binding proteins potentially involved in P5-trafficking and in P5's non-genomic action that may drive the promotion of castrate-resistance prostate cancer and regulate CD8+ T cell function. We envisage that this methodology could be employed for other steroids to map their interactomes directly in a broad range of living cells, tissues, and organisms.The ERC consolidator grant (ThDEFINE, Project ID: 646794) supported this study. SR was supported by an EIPOD fellowship and Ashoka University Individual Research grant for research visits. BM was supported by a CRUK Cancer Immunology grant (Ref. 20193)

ZDHHC_metabolomics_chemgen_2023.zip

MetabolomicsAcyl- and probe-Coenzyme A analysis.HEK293T cells were seeded in 6-well plates, grown to 70% confluency in media containing 0.5% FBS and treated with 30 mM YnPal or 18-Bz for 2 h. Cells were dislodged into their growth media by pipetting and pelleted by centrifugation (500 x g, 5 min). The cell pellet was washed twice by resuspending in ice-cold PBS and pelleting by centrifugation.Sample extractionTo each sample, 400 µL chloroform was added and vortexed for ~1 min, followed by addition of 200 µL methanol and a repeated vortex. Samples were incubated in a water bath sonicator (4°C, 1 h), with 3 × 8 min sonication pulses, followed by centrifugation (4°C, 10 min, 17,000 x g). The supernatant was transferred to a new Eppendorf 1.5 mL tube (E1). The pellet was re-extracted with 450 µL methanol:water (2:1 v:v, containing internal standard, 13C3-Malonyl-CoA), sonicated (8 min, 4°C) and centrifuged, as above. The supernatant was added to the first extract (E1). Combined extracts were dried using a speedvac concentrator, re-suspended in 350 µL chloroform:methanol:water (1:3:3, v/v), and centrifuged, as above. The upper, aqueous phase containing the polar metabolites (including probe, probe-CoA, and acyl-CoA molecules) was dried using the speedvac concentrator and resuspended in 100 µL acetonitrile/ammonium carbonate 20 mM (7:3, v/v) for LC-MS injection.Liquid chromatography-mass spectrometry (LC-MS)Chromatography conditions:Chromatography prior to all mass spectrometry was performed using an adaptation of a method previously described61. Samples were injected into a Dionex UltiMate 3000 LC system (Thermo Fisher) with a Phenomenex Luna C18(2) 100 Å (100 x 2 mm, 3 μm) column coupled with a SecurityGuard C18 guard column (4 x 2 mm). Analytes were separated using 20 mM ammonium carbonate in water (Optima HPLC grade, Sigma Aldrich) as solvent A and acetonitrile (Optima HPLC grade, Sigma Aldrich) as solvent B at 0.3 mL/min flow rate. Elution began at 5% Solvent B, maintained for 3 min, increased to 100% B over 12 min, followed by a 3 min wash of 100% B and subsequent 3 min re-equilibration to 5% B. Other parameters were as follows: column temperature, 30°C; injection volume, 10 μL; needle wash, 50% methanol; autosampler temperature, 4°C.High resolution mass spectrometryPost-chromatography, high resolution (HR) MS was performed with positive and negative polarity switching using a Q-Exactive Orbitrap (Thermo Fisher) with a HESI-II (Heated electrospray ionization) probe. MS parameters were as follows: spray voltage, 3.5 kV and 3.2 kV (for positive and negative modes, respectively); probe temperature, 320°C; sheath and auxiliary gases, 30 and 5 arbitrary units (au), respectively; full scan range: 100 to 1300 m/z with settings of AGC target and resolution as Balanced and High (3 × 106 and 70,000), respectively. Data were recorded using Xcalibur 3.0.63 software (Thermo Fisher). Mass calibration was performed for both ESI polarities before analysis using the standard Thermo Fisher Calmix solution. Qualitative analysis was performed using Xcalibur FreeStyle 1.8 SP1 and Tracefinder 5.1 software (Thermo Fisher) according to the manufacturer’s workflows. Masses, retention times, and fragmentation of all relevant sample-derived molecules were compared to authentic chemical standards.MS/MS MS parameters were optimized by direct infusion of 16 μM acyl-CoAs dissolved in 10 mM MeOH/ammonium acetate at 5 μL/min into an TSQ Quantiva triple quadrupole MS (Thermo Fisher). The heated electrospray was set in positive mode with the following parameters: capillary voltage, 3472 V; sheath gas, 60 au; aux gas, 10 au; sweep gas, 1 au; ion transfer tube temp, 325°C; vaporizer temp, 275°C. A selected reaction monitoring (SRM) function was applied for the simultaneous detection of acyl-CoA and probe-CoA molecules with RF lens and collision energies as shown in the Supplementary Table 7. Data were recorded using the Xcalibur 4.0.27.10 software and analysed using QuanBrowser 4.5.445.18 (Thermo Fisher).</p

ZDHHC_lipidomics_2023.zip

Lipidomics Methods Lipid extraction: HEK293T cells were seeded in 6-well plates, grown to 70% confluency and treated with bumped fatty acid probes (15 µM) for 4 h. Cells were dislodged into their growth media by pipetting and pelleted by centrifugation (500 x g, 5 min). The cell pellet was washed 2X with ice-cold PBS and pelleting by centrifugation. Subsequently, the cells were resuspended in 500 µL of ice-cold 150 mM ammonium bicarbonate. An aliquot (10%) was kept aside for protein concentration determination and the remaining sample snap-frozen in liquid nitrogen and stored at -80°C until further processing. For protein concentration determination, cells were lysed in M-PER™ Mammalian Protein Extraction Reagent (Thermo Fisher, 78501) and protein content determined using the Pierce™ BCA Protein Assay Kit (Thermo Fisher, 23227) as per the manufacturer’s instructions. An aliquot equivalent to 100 µg protein per sample were used for lipid extraction. Lipid were extracted by the methyl-tert-butyl ether (MTBE) method with minor modifications62. Extractions were performed in glass vials fitted with Teflon-lined caps using MS-grade solvents and water. Glass pipettes were used to handle any MTBE-containing solutions or lipid extracts. Methanol (1.5 mL) was added and the protein sample vortexed. MTBE (5 mL) was added and the mixture was incubated for 1 h at RT on a shaker. Phase separation was induced by the addition of water (1.25 mL) followed by incubation for 10 min at room temperature. The sample was centrifuged (1,000 x g, 10 min) and the upper organic phase collected. The lower aqueous phase was re-extracted by addition of 1.67 mL of solvent mixture comprising MTBE/methanol (10:3, v/v) and 0.32 mL water. The samples were vortexed, incubated for 10 min and centrifuged (1000 x g, 10 min). The upper phase was recovered, and the combined organic phases were evaporated at 37°C under a stream of nitrogen and stored at -20°C. Lipid extracts were reconstituted in 100 µL loading buffer (isopropanol/water/acetonitrile, 2:1:1, v/v/v). Blank control extraction was performed on a 200 µL aliquot of 150 mM ammonium bicarbonate solution. Quality control (QC) samples were prepared by pooling a small aliquot of all experimental samples after resuspension in loading buffer. Ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS): Analysis was performed on a 1290 Infinity II UHPLC system coupled to a 6550 iFunnel quadrupole time-of-flight (QTOF) mass spectrometer (Agilent Technologies). The reversed-phase chromatography protocol was optimized with minor modifications from Cajka and Fiehn63. Extracted lipids were separated on an Acquity UPLC CSH C18 column (130 Å, 1.7 μm, 2.1 x 100 mm) fitted with an Acquity UPLC CSH C18 VanGuard pre-column (130 Å, 1.7 µm, 2.1 mm x 5 mm) (both Waters). The column was maintained at 65°C at a flowrate of 0.6 mL/min. The mobile phases used were 60:40 (v:v) acetonitrile/H2O (solvent A) and 10:90 (v:v) acetonitrile/isopropanol (solvent B). Solvent A and B were supplemented with 10 mM ammonium formate and 0.1% formic acid for ESI positive mode and with 10 mM ammonium acetate for ESI negative mode analysis. UHPLC gradient elution was carried out as follows: 0−2 min 15-30% B; 2-2.5 min 30-48% B; 2.5−11 min 48-82% B; 11-11.5 min 82-99% B; 11.5-14.50 min 99% B. The gradient was returned to initial conditions over 0.5 min and the column equilibrated for 3 min before subsequent runs. Between injections a 100% isopropanol needle wash was performed. For negative mode 5 µL (MS mode) or 10 µL (MS/MS mode) of sample and for positive mode 4 µL (MS mode) or 8 µL (MS/MS mode) of sample were injected. Samples were injected in randomized order, with QC sample injections added to the start, middle and end of each sample sequence to ensure consistency and reproducibility of all acquisition parameters. Samples were loaded in a random order by blinded selection from pooled anonymously labelled samples. Electrospray parameters were set as follows: gas and sheath gas temperature, 200°C; drying gas flow, 14 L/min; sheath gas flow, 11 L/min; sheath gas temperature, 350°C; nebulizer pressure, 35 psig; capillary voltage, 3,000 V; nozzle voltage, 1,000 V. MS-TOF fragmentor and Oct 1 RF Vpp radio voltage were set to 350 and 750 V respectively. The QTOF was calibrated and operated in the extended dynamic range mode (∼2 GHz) in the mass range 50 to 1700 m/z. Spectra were acquired in centroid mode with an acquisition rate of 2 spectra/s for MS mode acquisition. Data was acquired in MS mode for quantitative analysis of the natural lipidome, and MS/MS mode to obtain data for lipid structure assignment. MS/MS data was acquired in auto-MS/MS mode (data-dependent). Spectra were acquired in centroid mode with an acquisition rate of 1 and 5 spectra/s for MS and MS/MS acquisition, respectively. Collision energy was adjusted to -35 eV and 30 eV for negative and positive modes, respectively. Mass range for precursor selection was 300-1650 m/z (negative) and 250-1680 m/z (positive). Fragmentation was triggered if the precursor reached 5000 (negative) or 2000 (positive) counts and maximum precursors per cycle was set to 5. MS/MS isolation width for precursors was selected as narrow (1.3 m/z). Active exclusion was enabled, set to exclude after 3 spectra and release after 0.1 min. To improve precursor selection, background ions were added to an exclusion list. For structure determination of probe-derived lipids, a list of preferred precursor ions was generated for each probe to improve MS/MS coverage of features originating from probe metabolism. MS/MS analysis of DMSO control samples were used to confirm assignment of natural lipids. Quantitative analysis of natural lipidome: Lipid annotations and quantifications were performed following the guidelines of the Lipidomics Standard Initiative (https://lipidomics-standards-initiative.org/). Feature extraction was carried out in Mass Hunter Profinder (v. 10.0, Agilent Technologies) using the “Batch Targeted Feature Extraction” option. Features were matched to an in-house library containing mass and retention time information of lipid species including glycerophospholipids, sphingolipids, fatty acids and glycolipids. All lipids in the database were previously assigned from MS/MS data using MS-DIAL64 followed by manual curation. H+, Na+ and NH4+ adducts were selected for positive mode and H−, C2H3O2− and CHO2− adducts were selected for negative mode data. Both mass and retention time were required for feature matching. Match tolerance was set to 5 ppm for mass and 0.15 min for retention time. The EIC extraction range was limited to +/- 0.3 min of the expected retention time. An overall score of >70 was required for feature matching, with the contribution to overall score set as follows: Mass score 100, Isotope abundance score 60, Isotope spacing score 50, Retention time score 100. Features over 20% of saturation limit were excluded from the dataset. Matched features were manually inspected and re-integrated where required and checked for correct adduct pattern for the relevant lipid class. Data were exported as .csv files containing the identity, peak area and the retention time of each lipid species. Further data analysis and data representation was performed in Excel and GraphPad Prism. The relative abundance of each lipid species within a class was calculated as a percentage of the summed peak areas of all species identified within the class. TG species were quantified from data acquired in positive mode while all other species were quantified from data acquired in negative mode. n=5 for each experimental condition. Assignment of probe-derived lipids: Feature extraction of data acquired in MS mode was carried out in Mass Hunter Profinder (v. 10.0, Agilent Technologies) using the “Batch Recursive Feature Extraction (small molecule/peptide)” option. Samples were grouped according to experimental condition. All parameters except those detailed below were used as pre-set by the program. Peak heights were set to a minimum of 3000 counts. H+, Na+ and NH4+ adducts were selected for positive mode and H−, C2H3O2− and CHO2− adducts were selected for negative mode. For compound binning and alignment, retention time tolerance was set to (+/- 0% + 0.15 min) and mass tolerance to (+/- 5 ppm + 2 mDa). A MFE score of at least 70 was required in at least 4 of 6 samples per group. For match tolerance, the mass was set to +/- 10 ppm and retention time to +/- 0.15 min. The EIC extraction range was limited to +/- 0.15 min of the expected retention time. An overall score of >75 was required for feature matching, with the contribution to overall score set as follows: Mass score 100, Isotope abundance score 60, Isotope spacing score 50, Retention time score 100. Features over 20% of saturation limit were excluded from the dataset. Post-processing filters were set to require a score (Tgt) of at least 50 in 4 out of 6 samples per experimental group. Manual filtering was performed to remove features present in the blank extraction samples. To create a list of features originating from probe metabolism, only features unique to each probe condition were selected. All features present in DMSO control samples were discarded. Features were manually inspected and re-integrated where required. The feature lists were used to create inclusion lists for MS/MS analysis and peak lists for lipid annotations as described below. </p

Maximising data value and avoiding data waste:a validation study in stroke research

Objectives To determine the feasibility of linking data from the Australian Stroke Clinical Registry (AuSCR ), the National Death Index (NDI ), and state‐managed databases for hospital admissions and emergency presentations; to evaluate data completeness and concordance between datasets for common variables. Design, setting, participants Cohort design; probabilistic/deterministic data linkage of merged records for patients treated in hospital for stroke or transient ischaemic attack from New South Wales, Queensland, Victoria, and Western Australia. Main outcome measures Descriptive statistics for data matching success; concordance of demographic variables common to linked databases; sensitivity and specificity of AuSCR in‐hospital death data for predicting NDI registrations. Results Data for 16 214 patients registered in the AuSCR during 2009–2013 were linked with one or more state datasets: 15 482 matches (95%) with hospital admissions data, and 12 902 matches (80%) with emergency department presentations data were made. Concordance of AuSCR and hospital admissions data exceeded 99% for sex, age, in‐hospital death (each κ = 0.99), and Indigenous status (κ = 0.83). Of 1498 registrants identified in the AuSCR as dying in hospital, 1440 (96%) were also recorded by the NDI as dying in hospital. In‐hospital death in AuSCR data had 98.7% sensitivity and 99.6% specificity for predicting in‐hospital death in the NDI . Conclusion We report the first linkage of data from an Australian national clinical quality disease registry with routinely collected data from several national and state government health datasets. Data linkage enriches the clinical registry dataset and provides additional information beyond that for the acute care setting and quality of life at follow‐up, allowing clinical outcomes for people with stroke (mortality and hospital contacts) to be more comprehensively assessed

Design of a potent, selective and brain penetrant inhibitor of Wnt-deactivating enzyme Notum by optimization of a crystallographic fragment hit

Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human disease such as colorectal cancer and Alzheimer’s disease supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we describe the discovery and profile of 8l (ARUK3001185) as a potent, selective and brain pentrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identifed 6a and 6b as a pair of outstanding hits. Fragment development of 6 delivered 8l that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in pharmacology screens showed 8l to be selective against serine hydrolases, kinases and drug targets