Tyrosine phosphorylation of CARM1 promotes its enzymatic activity and alters its target specificity.

An important epigenetic component of tyrosine kinase signaling is the phosphorylation of histones, and epigenetic readers, writers, and erasers. Phosphorylation of protein arginine methyltransferases (PRMTs), have been shown to enhance and impair their enzymatic activity. In this study, we show that the hyperactivation of Janus kinase 2 (JAK2) by the V617F mutation phosphorylates tyrosine residues (Y149 and Y334) in coactivator-associated arginine methyltransferase 1 (CARM1), an important target in hematologic malignancies, increasing its methyltransferase activity and altering its target specificity. While non-phosphorylatable CARM1 methylates some established substrates (e.g. BAF155 and PABP1), only phospho-CARM1 methylates the RUNX1 transcription factor, on R223 and R319. Furthermore, cells expressing non-phosphorylatable CARM1 have impaired cell-cycle progression and increased apoptosis, compared to cells expressing phosphorylatable, wild-type CARM1, with reduced expression of genes associated with G2/M cell cycle progression and anti-apoptosis. The presence of the JAK2-V617F mutant kinase renders acute myeloid leukemia (AML) cells less sensitive to CARM1 inhibition, and we show that the dual targeting of JAK2 and CARM1 is more effective than monotherapy in AML cells expressing phospho-CARM1. Thus, the phosphorylation of CARM1 by hyperactivated JAK2 regulates its methyltransferase activity, helps select its substrates, and is required for the maximal proliferation of malignant myeloid cells.

Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.

Best practices for managing and disseminating resources and outreach and evaluating the impact of the IDG Consortium.

The Illuminating the Druggable Genome (IDG) consortium generated reagents, biological model systems, data, informatic databases, and computational tools. The Resource Dissemination and Outreach Center (RDOC) played a central administrative role, organized internal meetings, fostered collaboration, and coordinated consortium-wide efforts. The RDOC developed and deployed a Resource Management System (RMS) to enable efficient workflows for collecting, accessing, validating, registering, and publishing resource metadata. IDG policies for repositories and standardized representations of resources were established, adopting the FAIR (findable, accessible, interoperable, reusable) principles. The RDOC also developed metrics of IDG impact. Outreach initiatives included digital content, the Protein Illumination Timeline (representing milestones in generating data and reagents), the Target Watch publication series, the e-IDG Symposium series, and leveraging social media platforms.

Wastewater based surveillance can be used to reduce clinical testing intensity on a university campus.

Clinical testing has been a vital part of the response to and suppression of the COVID-19 pandemic; however, testing imposes significant burdens on a population. College students had to contend with clinical testing while simultaneously dealing with health risks and the academic pressures brought on by quarantines, changes to virtual platforms, and other disruptions to daily life. The objective of this study was to analyze whether wastewater surveillance can be used to decrease the intensity of clinical testing while maintaining reliable measurements of diseases incidence on campus. Twelve months of human health and wastewater surveillance data for eight residential buildings on a university campus were analyzed to establish how SARS-CoV-2 levels in the wastewater can be used to minimize clinical testing burden on students. Wastewater SARS-CoV-2 levels were used to create multiple scenarios, each with differing levels of testing intensity, which were compared to the actual testing volumes implemented by the university. We found that scenarios in which testing intensity fluctuations matched rise and falls in SARS-CoV-2 wastewater levels had stronger correlations between SARS-CoV-2 levels and recorded clinical positives. In addition to stronger correlations, most scenarios resulted in overall fewer weekly clinical tests performed. We suggest the use of wastewater surveillance to guide COVID-19 testing as it can significantly increase the efficacy of COVID-19 surveillance while reducing the burden placed on college students during a pandemic. Future efforts should be made to integrate wastewater surveillance into clinical testing strategies implemented on college campuses.

Targeted degrader technologies as prospective SARS-CoV-2 therapies.

COVID-19 remains a severe public health threat despite the WHO declaring an end to the public health emergency in May 2023. Continual development of SARS-CoV-2 variants with resistance to vaccine-induced or natural immunity necessitates constant vigilance as well as new vaccines and therapeutics. Targeted protein degradation (TPD) remains relatively untapped in antiviral drug discovery and holds the promise of attenuating viral resistance development. From a unique structural design perspective, this review covers antiviral degrader merits and challenges by highlighting key coronavirus protein targets and their co-crystal structures, specifically illustrating how TPD strategies can refine existing SARS-CoV-2 3CL protease inhibitors to potentially produce superior protease-degrading agents.

Correction: AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.

[This corrects the article DOI: 10.1371/journal.pcbi.1010263.].

Automation, live-cell imaging, and endpoint cell viability for prostate cancer drug screens.

Androgen deprivation therapy (ADT) is the standard of care for high risk and advanced prostate cancer; however, disease progression from androgen-dependent prostate cancer (ADPC) to lethal and incurable castration-resistant prostate cancer (CRPC) and (in a substantial minority of cases) neuroendocrine prostate cancer (NEPC) is common. Identifying effective targeted therapies is challenging because of acquired resistance to established treatments and the vast heterogeneity of advanced prostate cancer (PC). To streamline the identification of potentially active prostate cancer therapeutics, we have developed an adaptable semi-automated protocol which optimizes cell growth and leverages automation to enhance robustness, reproducibility, and throughput while integrating live-cell imaging and endpoint viability assays to assess drug efficacy in vitro. In this study, culture conditions for 72-hr drug screens in 96-well plates were established for a large, representative panel of human prostate cell lines including: BPH-1 and RWPE-1 (non-tumorigenic), LNCaP and VCaP (ADPC), C4-2B and 22Rv1 (CRPC), DU 145 and PC3 (androgen receptor-null CRPC), and NCI-H660 (NEPC). The cell growth and 72-hr confluence for each cell line was optimized for real-time imaging and endpoint viability assays prior to screening for novel or repurposed drugs as proof of protocol validity. We demonstrated effectiveness and reliability of this pipeline through validation of the established finding that the first-in-class BET and CBP/p300 dual inhibitor EP-31670 is an effective compound in reducing ADPC and CRPC cell growth. In addition, we found that insulin-like growth factor-1 receptor (IGF-1R) inhibitor linsitinib is a potential pharmacological agent against highly lethal and drug-resistant NEPC NCI-H660 cells. This protocol can be employed across other cancer types and represents an adaptable strategy to optimize assay-specific cell growth conditions and simultaneously assess drug efficacy across multiple cell lines.

Smoking induces WEE1 expression to promote docetaxel resistance in esophageal adenocarcinoma.

Esophageal adenocarcinoma (EAC) patients have poor clinical outcomes, with an overall 5-year survival rate of 20%. Smoking is a significant risk factor for EAC. The role of WEE1, a nuclear kinase that negatively regulates the cell cycle in normal conditions, in EAC tumorigenesis and drug resistance is not fully understood. Immunohistochemistry staining shows significant WEE1 overexpression in human EAC tissues. Nicotine, nicotine-derived nitrosamine ketone, or 2% cigarette smoke extract treatment induces WEE1 protein expression in EAC, detected by western blot and immunofluorescence staining. qRT-PCR and reporter assay indicates that smoking induces WEE1 expression through miR-195-5p downregulation in EAC. ATP-Glo cell viability and clonogenic assay confirmed that WEE1 inhibition sensitizes EAC cells to docetaxel treatment in vitro. A TE-10 smoking machine with EAC patient-derived xenograft mouse model demonstrated that smoking induces WEE1 protein expression and resistance to docetaxel in vivo. MK-1775 and docetaxel combined treatment improves EAC patient-derived xenograft mouse survival in vivo. Our findings demonstrate, for the first time, that smoking-induced WEE1 overexpression through miRNA dysregulation in EAC plays an essential role in EAC drug resistance. WEE1 inhibition is a promising therapeutic method to overcome drug resistance and target treatment refractory cancer cells.

Correction: The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways.

[This corrects the article DOI: 10.1158/2767-9764.CRC-21-0022.][This corrects the article DOI: 10.1158/2767-9764.CRC-21-0022.].

An extension of the BioAssay Ontology to include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows.

With the capacity to produce and record data electronically, Scientific research and the data associated with it have grown at an unprecedented rate. However, despite a decent amount of data now existing in an electronic form, it is still common for scientific research to be recorded in an unstructured text format with inconsistent context (vocabularies) which vastly reduces the potential for direct intelligent analysis. Research has demonstrated that the use of semantic technologies such as ontologies to structure and enrich scientific data can greatly improve this potential. However, whilst there are many ontologies that can be used for this purpose, there is still a vast quantity of scientific terminology that does not have adequate semantic representation. A key area for expansion identified by the authors was the pharmacokinetic/pharmacodynamic (PK/PD) domain due to its high usage across many areas of Pharma. As such we have produced a set of these terms and other bioassay related terms to be incorporated into the BioAssay Ontology (BAO), which was identified as the most relevant ontology for this work. A number of use cases developed by experts in the field were used to demonstrate how these new ontology terms can be used, and to set the scene for the continuation of this work with a look to expanding this work out into further relevant domains. The work done in this paper was part of Phase 1 of the SEED project (Semantically Enriching electronic laboratory notebook (eLN) Data).

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Geospatially-resolved public-health surveillance via wastewater sequencing.

Wastewater, which contains everything from pathogens to pollutants, is a geospatially-and temporally-linked microbial fingerprint of a given population. As a result, it can be leveraged for monitoring multiple dimensions of public health across locales and time. Here, we integrate targeted and bulk RNA sequencing (n=1,419 samples) to track the viral, bacterial, and functional content over geospatially distinct areas within Miami Dade County from 2020-2022. First, we used targeted amplicon sequencing (n=966) to track diverse SARS-CoV-2 variants across space and time, and we found a tight correspondence with clinical caseloads from University students (N = 1,503) and Miami-Dade County hospital patients (N = 3,939 patients), as well as an 8-day earlier detection of the Delta variant in wastewater vs. in patients. Additionally, in 453 metatranscriptomic samples, we demonstrate that different wastewater sampling locations have clinically and public-health-relevant microbiota that vary as a function of the size of the human population they represent. Through assembly, alignment-based, and phylogenetic approaches, we also detect multiple clinically important viruses (e.g., norovirus ) and describe geospatial and temporal variation in microbial functional genes that indicate the presence of pollutants. Moreover, we found distinct profiles of antimicrobial resistance (AMR) genes and virulence factors across campus buildings, dorms, and hospitals, with hospital wastewater containing a significant increase in AMR abundance. Overall, this effort lays the groundwork for systematic characterization of wastewater to improve public health decision making and a broad platform to detect emerging pathogens.

Detection of the clinically persistent, pathogenic yeast spp. Candida auris from hospital and municipal wastewater in Miami-Dade County, Florida.

The use of wastewater-based surveillance (WBS) for detecting pathogens within communities has been growing since the beginning of the COVID-19 pandemic with early efforts investigating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA in wastewater. Recent efforts have shed light on the utilization of WBS for alternative targets, such as fungal pathogens, like Candida auris, in efforts to expand the technology to assess non-viral targets. The objective of this study was to extend workflows developed for SARS-CoV-2 quantification to evaluate whether C. auris can be recovered from wastewater, inclusive of effluent from a wastewater treatment plant (WWTP) and from a hospital with known numbers of patients colonized with C. auris. Measurements of C. auris in wastewater focused on culture-based methods and quantitative PCR (qPCR). Results showed that C. auris can be cultured from wastewater and that levels detected by qPCR were higher in the hospital wastewater compared to the wastewater from the WWTP, suggesting either dilution or degradation of this pathogenic yeast at downstream collection points. The results from this study illustrate that WBS can extend beyond SARS-CoV-2 monitoring to evaluate additional non-viral pathogenic targets and demonstrates that C. auris isolated from wastewater is competent to replicate in vitro using fungal-specific culture media.

AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.

PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.

Monkeypox viral nucleic acids detected using both DNA and RNA extraction workflows.

Molecular methods have been used to detect human pathogens in wastewater with sampling typically performed at wastewater treatment plants (WWTP) and upstream locations within the sewer system. A wastewater-based surveillance (WBS) program was established at the University of Miami (UM) in 2020, which included measurements of SARS-CoV-2 levels in wastewater from its hospital and within the regional WWTP. In addition to the development of a SARS-CoV-2 quantitative PCR (qPCR) assay, qPCR assays to detect other human pathogens of interest were also developed at UM. Here we report on the use of a modified set of reagents published by the CDC to detect nucleic acids of Monkeypox virus (MPXV) which emerged during May of 2022 to become a concern worldwide. Samples collected from the University hospital and from the regional WWTP were processed through DNA and RNA workflows and analyzed by qPCR to detect a segment of the MPXV CrmB gene. Results show positive detections of MPXV nucleic acids in the hospital and wastewater treatment plant wastewater which coincided with clinical cases in the community and mirrored the overall trend of nationwide MPXV cases reported to the CDC. We recommend the expansion of current WBS programs' methods to detect a broader range of pathogens of concern in wastewater and present evidence that viral RNA in human cells infected by a DNA virus can be detected in wastewater.

Reversal of Lactate and PD-1-mediated Macrophage Immunosuppression Controls Growth of PTEN/p53-deficient Prostate Cancer.

PURPOSE: Phosphatase and tensin homolog (PTEN) loss of function occurs in approximately 50% of patients with metastatic castrate-resistant prostate cancer (mCRPC), and is associated with poor prognosis and responsiveness to standard-of-care therapies and immune checkpoint inhibitors. While PTEN loss of function hyperactivates PI3K signaling, combinatorial PI3K/AKT pathway and androgen deprivation therapy (ADT) has demonstrated limited anticancer efficacy in clinical trials. Here, we aimed to elucidate mechanism(s) of resistance to ADT/PI3K-AKT axis blockade, and to develop rational combinatorial strategies to effectively treat this molecular subset of mCRPC. EXPERIMENTAL DESIGN: Prostate-specific PTEN/p53-deficient genetically engineered mice (GEM) with established 150-200 mm3 tumors, as assessed by ultrasound, were treated with either ADT (degarelix), PI3K inhibitor (copanlisib), or anti-PD-1 antibody (aPD-1), as single agents or their combinations, and tumors were monitored by MRI and harvested for immune, transcriptomic, and proteomic profiling, or ex vivo co-culture studies. Single-cell RNA sequencing on human mCRPC samples was performed using 10X Genomics platform. RESULTS: Coclinical trials in PTEN/p53-deficient GEM revealed that recruitment of PD-1-expressing tumor-associated macrophages (TAM) thwarts ADT/PI3Ki combination-induced tumor control. The addition of aPD-1 to ADT/PI3Ki combination led to TAM-dependent approximately 3-fold increase in anticancer responses. Mechanistically, decreased lactate production from PI3Ki-treated tumor cells suppressed histone lactylation within TAM, resulting in their anticancer phagocytic activation, which was augmented by ADT/aPD-1 treatment and abrogated by feedback activation of Wnt/ß-catenin pathway. Single-cell RNA-sequencing analysis in mCRPC patient biopsy samples revealed a direct correlation between high glycolytic activity and TAM phagocytosis suppression. CONCLUSIONS: Immunometabolic strategies that reverse lactate and PD-1-mediated TAM immunosuppression, in combination with ADT, warrant further investigation in patients with PTEN-deficient mCRPC.

A novel chemical attack on Notch-mediated transcription by targeting the NACK ATPase.

Notch activation complex kinase (NACK) is a component of the Notch transcriptional machinery critical for the Notch-mediated tumorigenesis. However, the mechanism through which NACK regulates Notch-mediated transcription is not well understood. Here, we demonstrate that NACK binds and hydrolyzes ATP and that only ATP-bound NACK can bind to the Notch ternary complex (NTC). Considering this, we sought to identify inhibitors of this ATP-dependent function and, using computational pipelines, discovered the first small-molecule inhibitor of NACK, Z271-0326, that directly blocks the activity of Notch-mediated transcription and shows potent antineoplastic activity in PDX mouse models. In conclusion, we have discovered the first inhibitor that holds promise for the efficacious treatment of Notch-driven cancers by blocking the Notch activity downstream of the NTC.

Degradation rates influence the ability of composite samples to represent 24-hourly means of SARS-CoV-2 and other microbiological target measures in wastewater.

The utility of using severe-acute respiratory syndrome coronavirus-2 (SARS-CoV-2) RNA for assessing the prevalence of COVID-19 within communities begins with the design of the sample collection program. The objective of this study was to assess the utility of 24-hour composites as representative samples for measuring multiple microbiological targets in wastewater, and whether normalization of SARS-CoV-2 by endogenous targets can be used to decrease hour to hour variability at different watershed scales. Two sets of experiments were conducted, in tandem with the same wastewater, with samples collected at the building, cluster, and community sewershed scales. The first set of experiments focused on evaluating degradation of microbiological targets: SARS-CoV-2, Simian Immunodeficiency Virus (SIV) - a surrogate spiked into the wastewater, plus human waste indicators of Pepper Mild Mottle Virus (PMMoV), Beta-2 microglobulin (B2M), and fecal coliform bacteria (FC). The second focused on the variability of these targets from samples, collected each hour on the hour. Results show that SARS-CoV-2, PMMoV, and B2M were relatively stable, with minimal degradation over 24-h. SIV, which was spiked-in prior to analysis, degraded significantly and FC increased significantly over the course of 24 h, emphasizing the possibility for decay and growth within wastewater. Hour-to-hour variability of the source wastewater was large between each hour of sampling relative to the variability of the SARS-CoV-2 levels calculated between sewershed scales; thus, differences in SARS-CoV-2 hourly variability were not statistically significant between sewershed scales. Results further provided that the quantified representativeness of 24-h composite samples (i.e., statistical equivalency compared against hourly collected grabs) was dependent upon the molecular target measured. Overall, improvements made by normalization were minimal within this study. Degradation and multiplication for other targets should be evaluated when deciding upon whether to collect composite or grab samples in future studies.

Pharos 2023: an integrated resource for the understudied human proteome.

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.