Protein kinases have emerged as attractive targets for treatment of several diseases prompting large-scale phosphoproteomics studies to elucidate their cellular actions and the design of novel inhibitory compounds. Current limitations include extensive reliance on consensus predictions to derive kinase-substrate relationships from phosphoproteomics data and incomplete experimental validation of inhibitors. To overcome these limitations in the case of protein kinase CK2, we employed functional proteomics and chemical genetics to enable identification of physiological CK2 substrates and validation of CK2 inhibitors including TBB and derivatives. By 2D electrophoresis and mass spectrometry, we identified the translational elongation factor EEF1D as a protein exhibiting CK2 inhibitor-dependent decreases in phosphorylation in (32)P-labeled HeLa cells. Direct phosphorylation of EEF1D by CK2 was shown by performing CK2 assays with EEF1D -FLAG from HeLa cells. Dramatic increases in EEF1D phosphorylation following λ-phosphatase treatment and phospho- EEF1D antibody recognizing EEF1D pS162 indicated phosphorylation at the CK2 site in cells. Furthermore, phosphorylation of EEF1D in the presence of TBB or TBBz is restored using CK2 inhibitor-resistant mutants. Collectively, our results demonstrate that EEF1D is a bona fide physiological CK2 substrate for CK2 phosphorylation. Furthermore, this validation strategy could be adaptable to other protein kinases and readily combined with other phosphoproteomic methods.

Most novel cancer therapeutics target kinases that are essential to tumor survival. Some of these kinase inhibitors are associated with cardiotoxicity, whereas others appear to be cardiosafe. The basis for this distinction is unclear, as are the molecular effects of kinase inhibitors in the heart.

Understanding the relative importance of signaling pathway components which regulate a specific cellular response is a major focus of current efforts in biology. This interest, along with the inherit complexity of these systems, is driving the development of approaches capable of providing both quantitative predictions as well as guiding the design of future experiments. Of particular interest is the establishment of methods for the analysis of cellular-level input-output signaling relationships that have been characterized over time.

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of "localized metabolomics" where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

Metastasis suppression by high-dose, multi-drug targeting is unsuccessful due to network heterogeneity and compensatory network activation. Here, we show that targeting driver network signaling capacity by limited inhibition of core pathways is a more effective anti-metastatic strategy. This principle underlies the action of a physiological metastasis suppressor, Raf Kinase Inhibitory Protein (RKIP), that moderately decreases stress-regulated MAP kinase network activity, reducing output to transcription factors such as pro-metastastic BACH1 and motility-related target genes. We developed a low-dose four-drug mimic that blocks metastatic colonization in mouse breast cancer models and increases survival. Experiments and network flow modeling show limited inhibition of multiple pathways is required to overcome variation in MAPK network topology and suppress signaling output across heterogeneous tumor cells. Restricting inhibition of individual kinases dissipates surplus signal, preventing threshold activation of compensatory kinase networks. This low-dose multi-drug approach to decrease signaling capacity of driver networks represents a transformative, clinically relevant strategy for anti-metastatic treatment.

Glycine levels are inversely associated with branched-chain amino acids (BCAAs) and cardiometabolic disease phenotypes, but biochemical mechanisms that explain these relationships remain uncharted. Metabolites and genes related to BCAA metabolism and nitrogen handling were strongly associated with glycine in correlation analyses. Stable isotope labeling in Zucker fatty rats (ZFRs) shows that glycine acts as a carbon donor for the pyruvate-alanine cycle in a BCAA-regulated manner. Inhibition of the BCAA transaminase (BCAT) enzymes depletes plasma pools of alanine and raises glycine levels. In high-fat-fed ZFRs, dietary glycine supplementation raises urinary acyl-glycine content and lowers circulating triglycerides but also results in accumulation of long-chain acyl-coenzyme As (acyl-CoAs), lower 5' adenosine monophosphate-activated protein kinase (AMPK) phosphorylation in muscle, and no improvement in glucose tolerance. Collectively, these studies frame a mechanism for explaining obesity-related glycine depletion and also provide insight into the impact of glycine supplementation on systemic glucose, lipid, and amino acid metabolism.

Integrin activation controls cell adhesion, migration, invasion, and extracellular matrix remodeling. RIAM (RAP1-GTP-interacting adaptor molecule) is recruited by activated RAP1 to the plasma membrane (PM) to mediate integrin activation via an inside-out signaling pathway. This process requires the association of the pleckstrin homology (PH) domain of RIAM with the membrane PIP2. We identify a conserved intermolecular interface that masks the PIP2-binding site in the PH domains of RIAM. Our data indicate that phosphorylation of RIAM by Src family kinases disrupts this PH-mediated interface, unmasks the membrane PIP2-binding site, and promotes integrin activation. We further demonstrate that this process requires phosphorylation of Tyr267 and Tyr427 in the RIAM PH domain by Src. Our data reveal an unorthodox regulatory mechanism of small GTPase effector proteins by phosphorylation-dependent PM association of the PH domain and provide new insights into the link between Src kinases and integrin signaling.

Most uveal melanoma cases harbor activating mutations in either GNAQ or GNA11. Despite activation of the mitogen-activated protein kinase (MAPK) signaling pathway downstream of Gαq/11, there are no effective targeted kinase therapies for metastatic uveal melanoma. The human genome encodes numerous understudied kinases, also called the "dark kinome". Identifying additional kinases regulated by Gαq/11 may uncover novel therapeutic targets for uveal melanoma. In this study, we treated GNAQ-mutant uveal melanoma cell lines with a Gαq/11 inhibitor, YM-254890, and conducted a kinase signaling proteomic screen using multiplexed-kinase inhibitors followed by mass spectrometry. We observed downregulated expression and/or activity of 22 kinases. A custom siRNA screen targeting these kinases demonstrated that knockdown of microtubule affinity regulating kinase 3 (MARK3) and serine/threonine kinase 10 (STK10) significantly reduced uveal melanoma cell growth and decreased expression of cell cycle proteins. Additionally, knockdown of MARK3 but not STK10 decreased ERK1/2 phosphorylation. Analysis of RNA-sequencing and proteomic data showed that Gαq signaling regulates STK10 expression and MARK3 activity. Our findings suggest an involvement of STK10 and MARK3 in the Gαq/11 oncogenic pathway and prompt further investigation into the specific roles and targeting potential of these kinases in uveal melanoma.

Pharmacological inhibition of protein kinases induces adaptive reprogramming of tumor cell regulatory networks by altering expression of genes that regulate signaling, including protein kinases. Adaptive responses are dependent on transcriptional changes resulting from remodeling of enhancer and promoter landscapes. Enhancer and promoter remodeling in response to targeted kinase inhibition is controlled by changes in open chromatin state and by activity of specific transcription factors, such as c-MYC. This review focuses on the dynamic plasticity of protein kinase expression of the tumor cell kinome and the resulting adaptive resistance to targeted kinase inhibition. Plasticity of the functional kinome has been shown in patient window trials where triple-negative and human epidermal growth factor receptor 2-positive breast cancer patient tumors were characterized by RNAseq after biopsies before and after 1 week of therapy. The expressed kinome changed dramatically during drug treatment, and these changes in kinase expression were shown in cell lines and xenografts in mice to be correlated with adaptive tumor cell drug resistance. The dynamic transcriptional nature of the kinome also differs for inhibitors targeting different kinase signaling pathways (e.g., BRAF-MEK-ERK versus PI3K-AKT) that are commonly activated in cancers. Heterogeneity arising from differences in gene regulation and mutations represents a challenge to therapeutic durability and prevention of clinical drug resistance with drug-tolerant tumor cell populations developing and persisting through treatment. We conclude that understanding the heterogeneity of kinase expression at baseline and in response to therapy is imperative for development of combinations and timing intervals of therapies making interventions durable.

Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to cause misclassifications by DL models. The purpose of the study is to investigate the robustness of DL models trained on diagnostic images using adversarial images and explore the utility of an iterative adversarial training approach to improve the robustness of DL models against adversarial images.

Cancer cachexia, a metabolic wasting syndrome, affects up to 80% of cancer patients and leads to the death in up to 20% of cancer patients. While research is growing in the field, there are still no clear diagnostic criteria and cancer cachexia remains an untreated condition. Aerobic exercise has been shown to positively impact cachexia by slowing its development and attenuating muscle loss. The most effective timing, duration, and intensity of exercise as a preventative and protective measure against cancer cachexia remains questionable. Therefore, the purpose of this study was to examine the effects of preconditioning exercise as a protective measure for tumor-mediated muscle wasting. Female LC3 Tg+ and wildtype mice were randomly separated into four groups, sedentary non-tumor bearing (SED + NT), sedentary tumor bearing (SED + T), treadmill exercise non-tumor bearing (TM + NT), and treadmill exercise tumor bearing (TM + T). Mice underwent an 8-week treadmill exercise training protocol (TM) or remained sedentary (SED). Next, mice were implanted with tumor cells (T group; 5 × 105 Lewis Lung Carcinoma cells in flank) or remained non-tumor (NT) for 4 weeks. Tumor bearing resulted in a significant decline in cardiac function. SED + T showed a significant decrease in fractional shortening (p < 0.05) when compared to the other groups. This coincided with an increase in beclin-1 and MyD88 protein expression and decrease in p-FOXO1 (inactivated) protein expression in SED + T mice. Interestingly, preconditioning exercise (exercise prior to tumor bearing) appeared to preserve cardiac function (TM + T not significantly different than SED + NT). Exercise-mediated cardioprotection also coincided with abolished beclin-1 and MyD88 signaling that was not significantly elevated in TM + T mice. Additionally, TM resulted in a 22-fold decrease in estimated tumor volume (p < 0.05) and a 45% decrease in tumor mass (p < 0.05) compared to SED tumors. The data indicate potential cardioprotective effects of preconditioning exercise on preserving cardiac structure and function, as well as regulating autophagic (beclin-1), inflammatory (TGF-β and MyD88), and atrophy (p-FOXO1) pathways during tumor bearing. Preconditioning exercise may be an effective and accessible treatment intervention for early-stage cancer survivors. This data is crucial in identifying the significance of exercise and the timing of exercise as a protective measure against the detrimental effects of cancer cachexia.

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

RAC1P29S is the third most prevalent hotspot mutation in sun-exposed melanoma. RAC1 alterations in cancer are correlated with poor prognosis, resistance to standard chemotherapy, and insensitivity to targeted inhibitors. Although RAC1P29S mutations in melanoma and RAC1 alterations in several other cancers are increasingly evident, the RAC1-driven biological mechanisms contributing to tumorigenesis remain unclear. Lack of rigorous signaling analysis has prevented identification of alternative therapeutic targets for RAC1P29S-harboring melanomas. To investigate the RAC1P29S-driven effect on downstream molecular signaling pathways, we generated an inducible RAC1P29S expression melanocytic cell line and performed RNA-sequencing (RNA-seq) coupled with multiplexed kinase inhibitor beads and mass spectrometry (MIBs/MS) to establish enriched pathways from the genomic to proteomic level. Our proteogenomic analysis identified CDK9 as a potential new and specific target in RAC1P29S-mutant melanoma cells. In vitro, CDK9 inhibition impeded the proliferation of in RAC1P29S-mutant melanoma cells and increased surface expression of PD-L1 and MHC Class I proteins. In vivo, combining CDK9 inhibition with anti-PD-1 immune checkpoint blockade significantly inhibited tumor growth only in melanomas that expressed the RAC1P29S mutation. Collectively, these results establish CDK9 as a novel target in RAC1-driven melanoma that can further sensitize the tumor to anti-PD-1 immunotherapy.

Post-translational modifications of proteins regulate diverse cellular functions, with mounting evidence suggesting that hierarchical cross-talk between distinct modifications may fine-tune cellular responses. For example, in apoptosis, caspases promote cell death via cleavage of key structural and enzymatic proteins that in some instances is inhibited by phosphorylation near the scissile bond. In this study, we systematically investigated how protein phosphorylation affects susceptibility to caspase cleavage using an N-terminomic strategy, namely, a modified terminal amino isotopic labeling of substrates (TAILS) workflow, to identify proteins for which caspase-catalyzed cleavage is modulated by phosphatase treatment. We validated the effects of phosphorylation on three of the identified proteins and found that Yap1 and Golgin-160 exhibit decreased cleavage when phosphorylated, whereas cleavage of MST3 was promoted by phosphorylation. Furthermore, using synthetic peptides we systematically examined the influence of phosphoserine throughout the entirety of caspase-3, -7, and -8 recognition motifs and observed a general inhibitory effect of phosphorylation even at residues considered outside the classical consensus motif. Overall, our work demonstrates a role for phosphorylation in controlling caspase-mediated cleavage and shows that N-terminomic strategies can be tailored to study cross-talk between phosphorylation and proteolysis.

A branched-chain amino acid (BCAA)-related metabolic signature is strongly associated with insulin resistance and predictive of incident diabetes and intervention outcomes. To better understand the role that this metabolite cluster plays in obesity-related metabolic dysfunction, we studied the impact of BCAA restriction in a rodent model of obesity in which BCAA metabolism is perturbed in ways that mirror the human condition.

Obesity is associated with an aggressive subtype of breast cancer called basal-like breast cancer (BBC). BBC has no targeted therapies, making the need for mechanistic insight urgent. Reducing adiposity in adulthood can lower incidence of BBC in humans. Thus, this study investigated whether a dietary intervention to reduce adiposity prior to tumor onset would reverse HFD-induced BBC.

To understand relationships between exercise training-mediated improvements in insulin sensitivity (S(I)) and changes in circulating concentrations of metabolic intermediates, hormones, and inflammatory mediators.

Kinase inhibitors have limited success in cancer treatment because tumors circumvent their action. Using a quantitative proteomics approach, we assessed kinome activity in response to MEK inhibition in triple-negative breast cancer (TNBC) cells and genetically engineered mice (GEMMs). MEK inhibition caused acute ERK activity loss, resulting in rapid c-Myc degradation that induced expression and activation of several receptor tyrosine kinases (RTKs). RNAi knockdown of ERK or c-Myc mimicked RTK induction by MEK inhibitors, and prevention of proteasomal c-Myc degradation blocked kinome reprogramming. MEK inhibitor-induced RTK stimulation overcame MEK2 inhibition, but not MEK1 inhibition, reactivating ERK and producing drug resistance. The C3Tag GEMM for TNBC similarly induced RTKs in response to MEK inhibition. The inhibitor-induced RTK profile suggested a kinase inhibitor combination therapy that produced GEMM tumor apoptosis and regression where single agents were ineffective. This approach defines mechanisms of drug resistance, allowing rational design of combination therapies for cancer.

The metabolic and physiologic responses to exercise are increasingly interesting, given that regular physical activity enhances antioxidant capacity, improves cardiac function, and protects against type 2 diabetes. The metabolic interactions between tissues and the heart illustrate a critical cross-talk we know little about.

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a practical application of method validation. Specifically, we sought to identify and modify steps in gas chromatography-mass spectrometry (GC-MS)-based, non-targeted metabolomic profiling of human plasma that could influence metabolite identification and quantification. Our experimental design included two studies: (1) a limiting-dilution study, which investigated the effects of dilution on analyte identification and quantification; and (2) a concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We confirmed that contaminants, concentration, repeatability and intermediate precision are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized to provide recommendations for experimental design of GC-MS-based non-targeted profiling of human plasma.