Non-Essential Amino Acid Availability Influences Proteostasis and Breast Cancer Cell Survival During Proteotoxic Stress.

Protein homeostasis (proteostasis) regulates tumor growth and proliferation when cells are exposed to proteotoxic stress, such as during treatment with certain chemotherapeutics. Consequently, cancer cells depend to a greater extent on stress signaling, and require the integrated stress response (ISR), amino acid metabolism, and efficient protein folding and degradation pathways to survive. To define how these interconnected pathways are wired when cancer cells are challenged with proteotoxic stress, we investigated how amino acid abundance influences cell survival when Hsp70, a master proteostasis regulator, is inhibited. We previously demonstrated that cancer cells exposed to a specific Hsp70 inhibitor induce the ISR via the action of two sensors, GCN2 and PERK, in stress-resistant and sensitive cells, respectively. In resistant cells, the induction of GCN2 and autophagy supported resistant cell survival, yet the mechanism by which these events were induced remained unclear. We now report that amino acid availability reconfigures the proteostasis network. Amino acid supplementation, and in particular arginine addition, triggered cancer cell death by blocking autophagy. Consistent with the importance of amino acid availability, which when limited activates GCN2, resistant cancer cells succumbed when challenged with a potentiator for another amino acid sensor, mTORC1, in conjunction with Hsp70 inhibition. IMPLICATIONS: These data position amino acid abundance, GCN2, mTORC1, and autophagy as integrated therapeutic targets whose coordinated modulation regulates the survival of proteotoxic-resistant breast cancer cells.

Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel.

The renal outer medullary potassium (ROMK) channel is essential for potassium transport in the kidney, and its dysfunction is associated with a salt-wasting disorder known as Bartter syndrome. Despite its physiological significance, we lack a mechanistic understanding of the molecular defects in ROMK underlying most Bartter syndrome-associated mutations. To this end, we employed a ROMK-dependent yeast growth assay and tested single amino acid variants selected by a series of computational tools representative of different approaches to predict each variants' pathogenicity. In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all possible single amino acid substitutions at all sequence positions to estimate their impact on function, and then employed a new machine learning classifier known as Rhapsody. We also used two additional tools, EVmutation and Polyphen-2, which permitted us to make consensus predictions on the pathogenicity of single amino acid variants in ROMK. Experimental tests performed for selected mutants in different classes validated the vast majority of our predictions and provided insights into variants implicated in ROMK dysfunction. On a broader scope, our analysis suggests that consolidation of data from complementary computational approaches provides an improved and facile method to predict the severity of an amino acid substitution and may help accelerate the identification of disease-causing mutations in any protein.

Predicting protein targets for drug-like compounds using transcriptomics.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Identification of an allosteric small-molecule inhibitor selective for the inducible form of heat shock protein 70.

Inducible Hsp70 (Hsp70i) is overexpressed in a wide spectrum of human tumors, and its expression correlates with metastasis, poor outcomes, and resistance to chemotherapy in patients. Identification of small-molecule inhibitors selective for Hsp70i could provide new therapeutic tools for cancer treatment. In this work, we used fluorescence-linked enzyme chemoproteomic strategy (FLECS) to identify HS-72, an allosteric inhibitor selective for Hsp70i. HS-72 displays the hallmarks of Hsp70 inhibition in cells, promoting substrate protein degradation and growth inhibition. Importantly, HS-72 is selective for Hsp70i over the closely related constitutively active Hsc70. Studies with purified protein show HS-72 acts as an allosteric inhibitor, reducing ATP affinity. In vivo HS-72 is well-tolerated, showing bioavailability and efficacy, inhibiting tumor growth and promoting survival in a HER2+ model of breast cancer. The HS-72 scaffold is amenable to resynthesis and iteration, suggesting an ideal starting point for a new generation of anticancer therapeutics targeting Hsp70i.

Synthesis and initial evaluation of YM-08, a blood-brain barrier permeable derivative of the heat shock protein 70 (Hsp70) inhibitor MKT-077, which reduces tau levels.

The molecular chaperone, heat shock protein 70 (Hsp70), is an emerging drug target for treating neurodegenerative tauopathies. We recently found that one promising Hsp70 inhibitor, MKT-077, reduces tau levels in cellular models. However, MKT-077 does not penetrate the blood-brain barrier (BBB), limiting its use as either a clinical candidate or probe for exploring Hsp70 as a drug target in the central nervous system (CNS). We hypothesized that replacing the cationic pyridinium moiety in MKT-077 with a neutral pyridine might improve its clogP and enhance its BBB penetrance. To test this idea, we designed and synthesized YM-08, a neutral analogue of MKT-077. Like the parent compound, YM-08 bound to Hsp70 in vitro and reduced phosphorylated tau levels in cultured brain slices. Pharmacokinetic evaluation in CD1 mice showed that YM-08 crossed the BBB and maintained a brain/plasma (B/P) value of ∼0.25 for at least 18 h. Together, these studies suggest that YM-08 is a promising scaffold for the development of Hsp70 inhibitors suitable for use in the CNS.

A screen for modulators of large T antigen's ATPase activity uncovers novel inhibitors of Simian Virus 40 and BK virus replication.

New polyomaviruses are continually being identified, and it is likely that links between this virus family and disease will continue to emerge. Unfortunately, a specific treatment for polyomavirus-associated disease is lacking. Because polyomaviruses express large Tumor Antigen, TAg, we hypothesized that small molecule inhibitors of the essential ATPase activity of TAg would inhibit viral replication. Using a new screening platform, we identified inhibitors of TAg's ATPase activity. Lead compounds were moved into a secondary assay, and ultimately two FDA approved compounds, bithionol and hexachlorophene, were identified as the most potent TAg inhibitors known to date. Both compounds inhibited Simian Virus 40 replication as assessed by plaque assay and quantitative PCR. Moreover, these compounds inhibited BK virus, which causes BKV Associated Nephropathy. In neither case was host cell viability compromised at these concentrations. Our data indicate that directed screening for TAg inhibitors is a viable method to identify polyomavirus inhibitors, and that bithionol and hexachlorophene represent lead compounds that may be further modified and/or ultimately used to combat diseases associated with polyomavirus infection.

High-throughput screening identifies a bisphenol inhibitor of SV40 large T antigen ATPase activity.

The authors conducted a high-throughput screening campaign for inhibitors of SV40 large T antigen ATPase activity to identify candidate antivirals that target the replication of polyomaviruses. The primary assay was adapted to 1536-well microplates and used to screen the National Institutes of Health Molecular Libraries Probe Centers Network library of 306 015 compounds. The primary screen had an Z value of ~0.68, signal/background = 3, and a high (5%) DMSO tolerance. Two counterscreens and two secondary assays were used to prioritize hits by EC(50), cytotoxicity, target specificity, and off-target effects. Hits that inhibited ATPase activity by >44% in the primary screen were tested in dose-response efficacy and eukaryotic cytotoxicity assays. After evaluation of hit cytotoxicity, drug likeness, promiscuity, and target specificity, three compounds were chosen for chemical optimization. Chemical optimization identified a class of bisphenols as the most effective biochemical inhibitors. Bisphenol A inhibited SV40 large T antigen ATPase activity with an IC(50) of 41 µM in the primary assay and 6.2 µM in a cytoprotection assay. This compound class is suitable as probes for biochemical investigation of large T antigen ATPase activity, but because of their cytotoxicity, further optimization is necessary for their use in studying polyomavirus replication in vivo.

Characterization of inhibitors of glucocorticoid receptor nuclear translocation: a model of cytoplasmic dynein-mediated cargo transport.

Agonist-induced glucocorticoid receptor [GR] transport from the cytoplasm to the nucleus was used as a model to identify dynein-mediated cargo transport inhibitors. Cell-based screening of the library of pharmacologically active compound (LOPAC)-1280 collection identified several small molecules that stalled the agonist-induced transport of GR-green fluorescent protein (GFP) in a concentration-dependent manner. Fluorescent images of microtubule organization, nuclear DNA staining, expression of GR-GFP, and its subcellular distribution were inspected and quantified by image analysis to evaluate the impact of compounds on cell morphology, toxicity, and GR transport. Given the complexity of the multi-protein complex involved in dynein-mediated cargo transport and the variety of potential mechanisms for interruption of that process, we therefore developed and validated a panel of biochemical assays to investigate some of the more likely intracellular target(s) of the GR transport inhibitors. Although the apomorphine enantiomers exhibited the most potency toward the ATPase activities of cytoplasmic dynein, myosin, and the heat-shock proteins (HSPs), their apparent lack of specificity made them unattractive for further study in our quest. Other molecules appeared to be nonspecific inhibitors that targeted reactive cysteines of proteins. Ideally, specific retrograde transport inhibitors would either target dynein itself or one of the other important proteins associated with the transport process. Although the hits from the cell-based screen of the LOPAC-1280 collection did not exhibit this desired profile, this screening platform provided a promising phenotypic system for the discovery of dynein/HSP modulators.