PARP14 and PARP9/DTX3L regulate interferon-induced ADP-ribosylation.

PARP-catalysed ADP-ribosylation (ADPr) is important in regulating various cellular pathways. Until recently, PARP-dependent mono-ADP-ribosylation has been poorly understood due to the lack of sensitive detection methods. Here, we utilised an improved antibody to detect mono-ADP-ribosylation. We visualised endogenous interferon (IFN)-induced ADP-ribosylation and show that PARP14 is a major enzyme responsible for this modification. Fittingly, this signalling is reversed by the macrodomain from SARS-CoV-2 (Mac1), providing a possible mechanism by which Mac1 counteracts the activity of antiviral PARPs. Our data also elucidate a major role of PARP9 and its binding partner, the E3 ubiquitin ligase DTX3L, in regulating PARP14 activity through protein-protein interactions and by the hydrolytic activity of PARP9 macrodomain 1. Finally, we also present the first visualisation of ADPr-dependent ubiquitylation in the IFN response. These approaches should further advance our understanding of IFN-induced ADPr and ubiquitin signalling processes and could shed light on how different pathogens avoid such defence pathways.

The nuclear pore complex: bridging nuclear transport and gene regulation.

Although the nuclear pore complex (NPC) is best known for its primary function as the key regulator of molecular traffic between the cytoplasm and the nucleus, a growing body of experimental evidence suggests that this structure participates in a considerably broader range of cellular activities on both sides of the nuclear envelope. Indeed, the NPC is emerging as an important regulator of gene expression through its influence on the internal architectural organization of the nucleus and its apparently extensive involvement in coordinating the seamless delivery of genetic information to the cytoplasmic protein synthesis machinery.

Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule.

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types, influencing pro-tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN-γ and IL-4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono-ADP-ribose and poly-ADP-ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL-4 driven pro-tumor gene expression in macrophages, however it is not clear what roles the non-enzymatic biomolecular recognition motifs play in PARP14-driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD+ -binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.

Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs.

Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity, while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative small molecule drug-response metrics that are insensitive to division number. These are based on estimation of the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of small molecule drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using small molecules and biologics and to facilitate the discovery of drug-response biomarkers and the identification of drugs effective against specific patient-derived tumor cells.

Classic and contemporary approaches to modeling biochemical reactions.

Recent interest in modeling biochemical networks raises questions about the relationship between often complex mathematical models and familiar arithmetic concepts from classical enzymology, and also about connections between modeling and experimental data. This review addresses both topics by familiarizing readers with key concepts (and terminology) in the construction, validation, and application of deterministic biochemical models, with particular emphasis on a simple enzyme-catalyzed reaction. Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation. We illustrate this point by showing how the familiar Briggs-Haldane formulation of Michaelis-Menten kinetics derives from the outer (or quasi-steady-state) solution of a dynamical system of ODEs describing a simple reaction under special conditions. We discuss how parameters in the Michaelis-Menten approximation and in the underlying ODE network can be estimated from experimental data, with a special emphasis on the origins of uncertainty. Finally, we extrapolate from a simple reaction to complex models of multiprotein biochemical networks. The concepts described in this review, hitherto of interest primarily to practitioners, are likely to become important for a much broader community of cellular and molecular biologists attempting to understand the promise and challenges of "systems biology" as applied to biochemical mechanisms.

GRcalculator: an online tool for calculating and mining dose-response data.

BACKGROUND: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS: We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.

Systematic analysis of BRAF(V600E) melanomas reveals a role for JNK/c-Jun pathway in adaptive resistance to drug-induced apoptosis.

Drugs that inhibit RAF/MEK signaling, such as vemurafenib, elicit profound but often temporary anti-tumor responses in patients with BRAF(V) (600E) melanoma. Adaptive responses to RAF/MEK inhibition occur on a timescale of hours to days, involve homeostatic responses that reactivate MAP kinase signaling and compensatory mitogenic pathways, and attenuate the anti-tumor effects of RAF/MEK inhibitors. We profile adaptive responses across a panel of melanoma cell lines using multiplex biochemical measurement, single-cell assays, and statistical modeling and show that adaptation involves at least six signaling cascades that act to reduce drug potency (IC50) and maximal effect (i.e., Emax â‰ª 1). Among these cascades, we identify a role for JNK/c-Jun signaling in vemurafenib adaptation and show that RAF and JNK inhibitors synergize in cell killing. This arises because JNK inhibition prevents a subset of cells in a cycling population from becoming quiescent upon vemurafenib treatment, thereby reducing drug Emax. Our findings demonstrate the breadth and diversity of adaptive responses to RAF/MEK inhibition and a means to identify which steps in a signaling cascade are most predictive of phenotypic response.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Analysis of growth factor signaling in genetically diverse breast cancer lines.

BACKGROUND: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. RESULTS: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. CONCLUSIONS: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an "indirect negative regulation" by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.

Adaptive informatics for multifactorial and high-content biological data.

Whereas genomic data are universally machine-readable, data from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically typed data hypercubes (SDCubes) that combine hierarchical data format 5 (HDF5) and extensible markup language (XML) file types. We demonstrate the application of SDCube-based storage using ImageRail, a software package for high-throughput microscopy. Experimental design and its day-to-day evolution, not rigid standards, determine how ImageRail data are organized in SDCubes. We applied ImageRail to collect and analyze drug dose-response landscapes in human cell lines at single-cell resolution.

Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE).

MOTIVATION: Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations. RESULTS: Application of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets. AVAILABILITY: Software is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie. CONTACT: jstuart@ucsc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kinome-wide selectivity profiling of ATP-competitive mammalian target of rapamycin (mTOR) inhibitors and characterization of their binding kinetics.

An intensive recent effort to develop ATP-competitive mTOR inhibitors has resulted in several potent and selective molecules such as Torin1, PP242, KU63794, and WYE354. These inhibitors are being widely used as pharmacological probes of mTOR-dependent biology. To determine the potency and specificity of these agents, we have undertaken a systematic kinome-wide effort to profile their selectivity and potency using chemical proteomics and assays for enzymatic activity, protein binding, and disruption of cellular signaling. Enzymatic and cellular assays revealed that all four compounds are potent inhibitors of mTORC1 and mTORC2, with Torin1 exhibiting ∼20-fold greater potency for inhibition of Thr-389 phosphorylation on S6 kinases (EC(50) = 2 nM) relative to other inhibitors. In vitro biochemical profiling at 10 µM revealed binding of PP242 to numerous kinases, although WYE354 and KU63794 bound only to p38 kinases and PI3K isoforms and Torin1 to ataxia telangiectasia mutated, ATM and Rad3-related protein, and DNA-PK. Analysis of these protein targets in cellular assays did not reveal any off-target activities for Torin1, WYE354, and KU63794 at concentrations below 1 µM but did show that PP242 efficiently inhibited the RET receptor (EC(50), 42 nM) and JAK1/2/3 kinases (EC(50), 780 nM). In addition, Torin1 displayed unusually slow kinetics for inhibition of the mTORC1/2 complex, a property likely to contribute to the pharmacology of this inhibitor. Our results demonstrated that, with the exception of PP242, available ATP-competitive compounds are highly selective mTOR inhibitors when applied to cells at concentrations below 1 µM and that the compounds may represent a starting point for medicinal chemistry efforts aimed at developing inhibitors of other PI3K kinase-related kinases.

PARP14 inhibition restores PD-1 immune checkpoint inhibitor response following IFNγ-driven acquired resistance in preclinical cancer models.

Resistance mechanisms to immune checkpoint blockade therapy (ICBT) limit its response duration and magnitude. Paradoxically, Interferon γ (IFNγ), a key cytokine for cellular immunity, can promote ICBT resistance. Using syngeneic mouse tumour models, we confirm that chronic IFNγ exposure confers resistance to immunotherapy targeting PD-1 (α-PD-1) in immunocompetent female mice. We observe upregulation of poly-ADP ribosyl polymerase 14 (PARP14) in chronic IFNγ-treated cancer cell models, in patient melanoma with elevated IFNG expression, and in melanoma cell cultures from ICBT-progressing lesions characterised by elevated IFNγ signalling. Effector T cell infiltration is enhanced in tumours derived from cells pre-treated with IFNγ in immunocompetent female mice when PARP14 is pharmacologically inhibited or knocked down, while the presence of regulatory T cells is decreased, leading to restoration of α-PD-1 sensitivity. Finally, we determine that tumours which spontaneously relapse in immunocompetent female mice following α-PD-1 therapy upregulate IFNγ signalling and can also be re-sensitised upon receiving PARP14 inhibitor treatment, establishing PARP14 as an actionable target to reverse IFNγ-driven ICBT resistance.

Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop.

High-throughput measurement of cells perturbed using libraries of small molecules, gene knockouts, or different microenvironmental factors is a key step in functional genomics and pre-clinical drug discovery. However, it remains difficult to perform accurate single-cell assays in 384-well plates, limiting many studies to well-average measurements (e.g., CellTiter-Glo®). Here we describe a public domain Dye Drop method that uses sequential density displacement and microscopy to perform multi-step assays on living cells. We use Dye Drop cell viability and DNA replication assays followed by immunofluorescence imaging to collect single-cell dose-response data for 67 investigational and clinical-grade small molecules in 58 breast cancer cell lines. By separating the cytostatic and cytotoxic effects of drugs computationally, we uncover unexpected relationships between the two. Dye Drop is rapid, reproducible, customizable, and compatible with manual or automated laboratory equipment. Dye Drop improves the tradeoff between data content and cost, enabling the collection of information-rich perturbagen-response datasets.

Selective Pharmaceutical Inhibition of PARP14 Mitigates Allergen-Induced IgE and Mucus Overproduction in a Mouse Model of Pulmonary Allergic Response.

The type 2 cytokines IL-4 and IL-13, which share use of an IL-4 receptor α-chain and its nuclear induction of the transcription factor STAT6, are crucial in elicitation and maintenance of allergic conditions including asthma. STAT6 binds poly(ADP-ribose) polymerase (PARP)14, an ADP-ribosyl monotransferase. Elimination of PARP14 by gene targeting led to attenuation of OVA-specific allergic lung inflammation. However, PARP14 has multiple functional domains apart from the portion that catalyzes ADP-ribosylation, and it is not clear whether inhibition of the catalytic function has any biological consequence. Using BALB/c mice sensitized to the allergen Alternaria alternata, we show that peroral administration of RBN012759, a highly selective inhibitor of ADP-ribosylation by PARP14 with negligible impact on other members of the PARP gene family, achieved biologically active plasma concentrations and altered several responses to the Ag. Specifically, the pharmaceutical compound decreased mucus after allergen challenge, blunted the induced increases in circulating IgE, and prevented suppression of IgG2a. We conclude that PARP14 catalytic activity can contribute to pathogenesis in allergic or atopic processes and propose that other biological endpoints dependent on ADP-ribosylation by PARP14 can be targeted using selective inhibition.

ADP-ribosyltransferases, an update on function and nomenclature.

ADP-ribosylation, a modification of proteins, nucleic acids, and metabolites, confers broad functions, including roles in stress responses elicited, for example, by DNA damage and viral infection and is involved in intra- and extracellular signaling, chromatin and transcriptional regulation, protein biosynthesis, and cell death. ADP-ribosylation is catalyzed by ADP-ribosyltransferases (ARTs), which transfer ADP-ribose from NAD+ onto substrates. The modification, which occurs as mono- or poly-ADP-ribosylation, is reversible due to the action of different ADP-ribosylhydrolases. Importantly, inhibitors of ARTs are approved or are being developed for clinical use. Moreover, ADP-ribosylhydrolases are being assessed as therapeutic targets, foremost as antiviral drugs and for oncological indications. Due to the development of novel reagents and major technological advances that allow the study of ADP-ribosylation in unprecedented detail, an increasing number of cellular processes and pathways are being identified that are regulated by ADP-ribosylation. In addition, characterization of biochemical and structural aspects of the ARTs and their catalytic activities have expanded our understanding of this protein family. This increased knowledge requires that a common nomenclature be used to describe the relevant enzymes. Therefore, in this viewpoint, we propose an updated and broadly supported nomenclature for mammalian ARTs that will facilitate future discussions when addressing the biochemistry and biology of ADP-ribosylation. This is combined with a brief description of the main functions of mammalian ARTs to illustrate the increasing diversity of mono- and poly-ADP-ribose mediated cellular processes.

A potent and selective PARP14 inhibitor decreases protumor macrophage gene expression and elicits inflammatory responses in tumor explants.

PARP14 has been implicated by genetic knockout studies to promote protumor macrophage polarization and suppress the antitumor inflammatory response due to its role in modulating interleukin-4 (IL-4) and interferon-γ signaling pathways. Here, we describe structure-based design efforts leading to the discovery of a potent and highly selective PARP14 chemical probe. RBN012759 inhibits PARP14 with a biochemical half-maximal inhibitory concentration of 0.003 µM, exhibits >300-fold selectivity over all PARP family members, and its profile enables further study of PARP14 biology and disease association both in vitro and in vivo. Inhibition of PARP14 with RBN012759 reverses IL-4-driven protumor gene expression in macrophages and induces an inflammatory mRNA signature similar to that induced by immune checkpoint inhibitor therapy in primary human tumor explants. These data support an immune suppressive role of PARP14 in tumors and suggest potential utility of PARP14 inhibitors in the treatment of cancer.

PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition triggers antitumor immunity.

PARP7 is a monoPARP that catalyzes the transfer of single units of ADP-ribose onto substrates to change their function. Here, we identify PARP7 as a negative regulator of nucleic acid sensing in tumor cells. Inhibition of PARP7 restores type I interferon (IFN) signaling responses to nucleic acids in tumor models. Restored signaling can directly inhibit cell proliferation and activate the immune system, both of which contribute to tumor regression. Oral dosing of the PARP7 small-molecule inhibitor, RBN-2397, results in complete tumor regression in a lung cancer xenograft and induces tumor-specific adaptive immune memory in an immunocompetent mouse cancer model, dependent on inducing type I IFN signaling in tumor cells. PARP7 is a therapeutic target whose inhibition induces both cancer cell-autonomous and immune stimulatory effects via enhanced IFN signaling. These data support the targeting of a monoPARP in cancer and introduce a potent and selective PARP7 inhibitor to enter clinical development.

Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.

The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.