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Human cytochrome P450 17A1 structures with metabolites of prostate cancer drug abiraterone reveal substrate-binding plasticity and a second binding site
Get PDFAbiraterone acetate is a first-line therapy for castration-resistant prostate cancer. This prodrug is deacetylated in vivo to abiraterone, which is a potent and specific inhibitor of cytochrome P450 17A1 (CYP17A1). CYP17A1 performs two sequential steps that are required for the biosynthesis of androgens that drive prostate cancer proliferation, analogous to estrogens in breast cancer. Abiraterone can be further metabolized in vivo on the steroid A ring to multiple metabolites that also inhibit CYP17A1. Despite its design as an active-site–directed substrate analog, abiraterone and its metabolites demonstrate mixed competitive/noncompetitive inhibition. To understand their binding, we solved the X-ray structures of CYP17A1 with three primary abiraterone metabolites. Despite different conformations of the steroid A ring and substituents, all three bound in the CYP17A1 active site with the steroid core packed against the I helix and the A ring C3 keto or hydroxyl oxygen forming a hydrogen bond with N202 similar to abiraterone itself. The structure of CYP17A1 with 3-keto, 5α-abiraterone was solved to 2.0 Å, the highest resolution to date for a CYP17A1 complex. This structure had additional electron density near the F/G loop, which is likely a second molecule of the inhibitor and which may explain the noncompetitive inhibition. Mutation of the adjacent Asn52 to Tyr positions its side chain in this space, maintains enzyme activity, and prevents binding of the peripheral ligand. Collectively, our findings provide further insight into abiraterone metabolite binding and CYP17A1 function
EWSR1 prevents the induction of aneuploidy through direct regulation of Aurora B
Get PDFEWSR1 (Ewing sarcoma breakpoint region 1) was originally identified as a part of an aberrant EWSR1/FLI1 fusion gene in Ewing sarcoma, the second most common pediatric bone cancer. Due to formation of the EWSR1/FLI1 fusion gene in the tumor genome, the cell loses one wild type EWSR1 allele. Our previous study demonstrated that the loss of ewsr1a (homologue of human EWSR1) in zebrafish leads to the high incidence of mitotic dysfunction, of aneuploidy, and of tumorigenesis in the tp53 mutant background. To dissect the molecular function of EWSR1, we successfully established a stable DLD-1 cell line that enables a conditional knockdown of EWSR1 using an Auxin Inducible Degron (AID) system. When both EWSR1 genes of DLD-1 cell were tagged with mini-AID at its 5′-end using a CRISPR/Cas9 system, treatment of the (AID-EWSR1/AID-EWSR1) DLD-1 cells with a plant-based Auxin (AUX) led to the significant levels of degradation of AID-EWSR1 proteins. During anaphase, the EWSR1 knockdown (AUX+) cells displayed higher incidence of lagging chromosomes compared to the control (AUX-) cells. This defect was proceeded by a lower incidence of the localization of Aurora B at inner centromeres, and by a higher incidence of the protein at Kinetochore proximal centromere compared to the control cells during pro/metaphase. Despite these defects, the EWSR1 knockdown cells did not undergo mitotic arrest, suggesting that the cell lacks the error correction mechanism. Significantly, the EWSR1 knockdown (AUX+) cells induced higher incidence of aneuploidy compared to the control (AUX-) cells. Since our previous study demonstrated that EWSR1 interacts with the key mitotic kinase, Aurora B, we generated replacement lines of EWSR1-mCherry and EWSR1:R565A-mCherry (a mutant that has low affinity for Aurora B) in the (AID-EWSR1/AID-EWSR1) DLD-1 cells. The EWSR1-mCherry rescued the high incidence of aneuploidy of EWSR1 knockdown cells, whereas EWSR1-mCherry:R565A failed to rescue the phenotype. Together, we demonstrate that EWSR1 prevents the induction of lagging chromosomes, and of aneuploidy through the interaction with Aurora B
Towards a structurally resolved human protein interaction network
Get PDFCellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology
TOM-1/tomosyn acts with the UNC-6/netrin receptor UNC-5 to inhibit growth cone protrusion in Caenorhabditis elegans
Get PDFIn the polarity/protrusion model of growth cone repulsion from UNC-6/netrin, UNC-6 first polarizes the growth cone of the VD motor neuron axon via the UNC-5 receptor, and then regulates protrusion asymmetrically across the growth cone based on this polarity. UNC-6 stimulates protrusion dorsally through the UNC-40/DCC receptor, and inhibits protrusion ventrally through UNC-5, resulting in net dorsal growth. Previous studies showed that UNC-5 inhibits growth cone protrusion via the flavin monooxygenases and potential destabilization of F-actin, and via UNC-33/CRMP and restriction of microtubule plus-end entry into the growth cone. We show that UNC-5 inhibits protrusion through a third mechanism involving TOM-1/tomosyn. A short isoform of TOM-1 inhibited protrusion downstream of UNC-5, and a long isoform had a pro-protrusive role. TOM-1/tomosyn inhibits formation of the SNARE complex. We show that UNC-64/syntaxin is required for growth cone protrusion, consistent with a role of TOM-1 in inhibiting vesicle fusion. Our results are consistent with a model whereby UNC-5 utilizes TOM-1 to inhibit vesicle fusion, resulting in inhibited growth cone protrusion, possibly by preventing the growth cone plasma membrane addition required for protrusion
Spectral Cohabitation and Interference Mitigation via Physical Radar Emissions
No full textAuctioning of frequency bands to support growing demand for high bandwidth 5G communications is driving research into spectral cohabitation strategies for next generation radar systems. The loss of radio frequency (RF) spectrum once designated for radar operation is forcing radar systems to either learn how to coexist in these frequency spectrum bands, without causing mutual interference, or move to other bands of the spectrum, the latter being the more undesirable choice. Two methods of spectral cohabitation are proposed and presented in this work, each taking advantage of recent developments in random frequency modulation (RFM) waveforms, which have the advantage of never repeating. RFM waveforms are optimized to have favorable radar waveform properties while also readily incorporating agile spectral notches. The first method of spectral cohabitation uses these spectral notches to avoid narrow-band RF interference (RFI) in the form of other spectrum users residing in the same band as the radar system, allowing both to operate while minimizing mutual interference. The second method of spectral cohabitation uses an optimization procedure to embed a communications signal into a dual-purpose radar/communications emission, thus allowing one waveform to serve both functions simultaneously. Both of these methods are presented and described in detail as well as being validated through simulation and physical open-air experimentation
Impact of Climate Variability and Weather Extremes on Terrestrial and Aquatic Systems
No full textInland freshwater ecosystems have been experiencing rapid and notable transformations in direct response to both climate shifts and human-induced stressors during recent decades. Land Surface Models (LSMs) play a vital role in providing information on various aspects of the Earth's surface, including hydrological processes, biophysical characteristics, and biogeochemical dynamics. Over time, LSMs have evolved from simplified depictions of land surface biophysics to incorporate a diverse range of interrelated processes, including modified vegetation dynamics, groundwater interactions, and hydrological processes. Given the profound impact of hydrological processes on a multitude of biophysical and biogeochemical mechanisms within the Earth system, the inclusion of lakes and human-made reservoirs in land surface models (LSMs) is currently in its nascent stages. To enhance our understanding of the impact of hydrometeorological changes on water quality and to address the limitations in representing inland water bodies within Land Surface Models (LSMs), three research studies were conducted. These studies employed the Noah land surface model (Noah-MP) with multiple parameterization options and the General Lake Model (GLM); models were used individually and in combination. In the first study, the influence of vegetation dynamics is thoroughly examined, with specific attention given to six different configurations of leaf area index (LAI) and vegetation fraction (FVEG) and the impact on streamflow. The main objective was to evaluate how these configurations impact the representation of eco-hydrological processes in a semi-arid region primarily characterized by grasslands. Additionally, the study aims to analyze the performance of streamflow simulation and the capability to predict drought conditions at scales beyond the site level. The results indicate that the incoming net radiation plays a crucial role in constraining the total evaporation process in energy-limited environments. It was observed that an overestimation of latent heat (LE) led to an underestimation of streamflow. Furthermore, the analysis indicated that all of the newer version (Noah-MP 4.0.1) vegetation physics demonstrated a higher degree of accuracy in reproducing spatial patterns of drought compared to the older version 3.6. These findings are then used to select the optimal Noah-MP model configuration which is used in the last chapter. The second study focused on quantifying the impact of atmospheric stilling on polymictic reservoirs, aiming to enhance predictions of the phytoplankton community composition. High-resolution temporal in-situ data from Marion Reservoir in Kansas were employed to identify the biotic and abiotic factors that influence the composition and dynamics of phytoplankton in shallow reservoirs. The study revealed that a combination of rising air temperatures, calm weather conditions, and light penetration depth emerged as the primary drivers responsible for triggering algal blooms. Additionally, the internal nutrient loading during anoxic conditions was found to have a direct impact on the intensification of harmful cyanobacterial blooms (CyanoHABs). The last chapter then focuses on the integration of the General Lake Model (GLM) with a Noah-MP to improve the prediction of lake thermodynamic patterns, particularly in shallow lakes and reservoirs. Remarkably, the simulation of lake thermal dynamics, driven by the forcings from the North American Land Data Assimilation System-2 (NLDAS-2) and incorporating modeled surface runoff from Noah-MP, exhibited a capacity to reproduce reservoir thermal regimes that surpassed field measurements, albeit marginally. Overall, this dissertation offers a thorough assessment of the performance of the state-of-the-art land surface model, Noah-MP, providing valuable insights into the integration of the GLM within this framework at Marion Reservoir in Kansas
Bio-inspired, AI-based Systems for Intelligent Counter Swarm- BASICS
No full textSwarming unmanned aerial systems (UAS) are quickly becoming a preferredtactic for adversarial powers, with attacks on key U.S. allies showcasing theirpotential against soft targets. Improved endurance leads to UAS flying low andslow, arriving from unique routes, complicating ground based detection. Currently,swarm operators employ rudimentary navigation tactics which rely on a numbersadvantage, or repeated waves of assaults. However, swarm tactics using advancedalgorithms, like machine learning, are emerging, and defense methods will bemore routinely asked to operate against complex strategies.Many existing counter swarm algorithms suffer from a glaring problem, in whichthe number of intruders, their positions and trajectories, their goal(s), and theirmaneuverability are assumed to be known. Looking to nature for inspiration,a bio-inspired algorithm is developed, mimicking the hunting patterns of theHarris Hawk. This American raptor employs collaborative hunting strategies tomaximize prey exploitation in the scarce desert environment. The presented bioinspired, counter swarm algorithm uniquely captures the interplay between eachagent’s autonomy and multi-agent collaborative task allocation, maximizing theeffectiveness and providing the flexibility required for such a complex optimizationproblem. This work uses a two-part strategy: (1) Initial search: where a globalheat map is developed in real-time, with each agent contributing search knowledge.The heat map allows the integration of memory structures on a global scale andalso discourages searching recently visited areas. (2) Intruder information sharingand collaborative navigation strategies: which are used to influence collectivedecision-making towards successful agents to avoid falling into a local minimum.Within the dynamically changing environment of counter swarm, a fixed strategyof deterrence is costly and inefficient, and could be subverted by an intelligentintruder. The heat map provides a nonlinear and flexible approach to searching. Itprevents the premature re-visitation of previously explored areas and can also beused to return to suitable navigation paths to localize and intensify targeted search.Intruders identified through this search are then exploited through collaborativetactics, which look to leverage shared information between agents and their successin "finding and killing" to produce more favorable attack strategies.Due to the highly dimensionalized nature of this environment, and the complexityof balancing exploration and the development of search tactics, which need to findand kill an unknown number of intruders using multiple agents, a reinforcementlearning-based approach is uniquely adopted. The resultant algorithm is validatedusing a large number of randomly selected scenarios to assess its ability togeneralize the find-and-kill policy (a.k.a. tactics). Tests are conducted in largerareas, with varying numbers of agents and different velocity ratios, while alsoconsidering unique behaviors like a central swarm goal or split swarm. Otherwell-known methods (e.g., grid search, etc.) are used as the base for quantificationto assess the developed algorithms. The results demonstrate the effectiveness ofreinforcement learning-based algorithm to find and kill intruders compared toother commonly used algorithm
Population pharmacokinetic analysis of enrofloxacin and its active metabolite ciprofloxacin after intravenous injection to cats with reduced kidney function
No full textBackground
It is unknown if enrofloxacin accumulates in plasma of cats with reduced kidney function.
Hypothesis
To determine if enrofloxacin and its active metabolite ciprofloxacin have reduced clearance in azotemic cats.
Animals
Thirty‐four cats hospitalized for clinical illness with variable degree of kidney function.
Methods
Prospective study. After enrofloxacin (dose 5 mg/kg) administration to cats, sparse blood sampling was used to obtain 2 compartment population pharmacokinetic results using nonlinear mixed‐effects modeling. Plasma enrofloxacin and ciprofloxacin concentrations were measured and summed to obtain the total fluoroquinolone concentration. A model of ciprofloxacin metabolism from enrofloxacin was created and evaluated for covariate effects on clearance, volume of distribution, and the metabolic rate of ciprofloxacin generation from enrofloxacin.
Results
Body weight was the only covariate found to affect total fluoroquinolone volume of distribution (effect 1.63, SE 0.19, P < .01) and clearance (effect 1.63, SE 0.27, P < .01). Kidney function did not have a significant effect on total fluoroquinolone clearance (median 440.8 mL/kg/h (range 191.4‐538.0 mL/kg/h) in cats with normal kidney function, 365.8 mL/kg/h (range 89.49‐1092.0 mL/kg/h) in cats with moderate kidney dysfunction, and 308.5 mL/kg/h (range 140.20‐480.0 mL/kg/h) in cats with severe kidney dysfunction (P = .64). Blood urea nitrogen concentration influenced the metabolic generation of ciprofloxacin from enrofloxacin (effect 0.51, SE 0.08, P < .01), but other markers of kidney function did not.
Conclusions and clinical importance.
Adjustment of enrofloxacin dosage is not indicated for azotemic cats
Strategies for Mitigating Commercial Sensor Chip Variability with Experimental Design Controls
No full textSurface plasmon resonance (SPR) is a popular real-time technique for the measurement of binding affinity and kinetics, and bench-top instruments combine affordability and ease of use with other benefits of the technique. Biomolecular ligands labeled with the 6xHis tag can be immobilized onto sensing surfaces presenting the Ni2+-nitrilotriacetic acid (NTA) functional group. While Ni-NTA immobilization offers many advantages, including the ability to regenerate and reuse the sensors, its use can lead to signal variability between experimental replicates. We report here a study of factors contributing to this variability using the Nicoya OpenSPR as a model system and suggest ways to control for those factors, increasing the reproducibility and rigor of the data. Our model ligand/analyte pairs were two ovarian cancer biomarker proteins (MUC16 and HE4) and their corresponding monoclonal antibodies. We observed a broad range of non-specific binding across multiple NTA chips. Experiments run on the same chips had more consistent results in ligand immobilization and analyte binding than experiments run on different chips. Further assessment showed that different chips demonstrated different maximum immobilizations for the same concentration of injected protein. We also show a variety of relationships between ligand immobilization level and analyte response, which we attribute to steric crowding at high ligand concentrations. Using this calibration to inform experimental design, researchers can choose protein concentrations for immobilization corresponding to the linear range of analyte response. We are the first to demonstrate calibration and normalization as a strategy to increase reproducibility and data quality of these chips. Our study assesses a variety of factors affecting chip variability, addressing a gap in knowledge about commercially available sensor chips. Controlling for these factors in the process of experimental design will minimize variability in analyte signal when using these important sensing platforms
Evidence-based practice models and frameworks in the healthcare setting: a scoping review
No full textA grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Objectives The aim of this scoping review was to identify and review current evidence-based practice (EBP) models and frameworks. Specifically, how EBP models and frameworks used in healthcare settings align with the original model of (1) asking the question, (2) acquiring the best evidence, (3) appraising the evidence, (4) applying the findings to clinical practice and (5) evaluating the outcomes of change, along with patient values and preferences and clinical skills.
Design A Scoping review.
Included sources and articles Published articles were identified through searches within electronic databases (MEDLINE, EMBASE, Scopus) from January 1990 to April 2022. The English language EBP models and frameworks included in the review all included the five main steps of EBP. Excluded were models and frameworks focused on one domain or strategy (eg, frameworks focused on applying findings).
Results Of the 20 097 articles found by our search, 19 models and frameworks met our inclusion criteria. The results showed a diverse collection of models and frameworks. Many models and frameworks were well developed and widely used, with supporting validation and updates. Some models and frameworks provided many tools and contextual instruction, while others provided only general process instruction. The models and frameworks reviewed demonstrated that the user must possess EBP expertise and knowledge for the step of assessing evidence. The models and frameworks varied greatly in the level of instruction to assess the evidence. Only seven models and frameworks integrated patient values and preferences into their processes.
Conclusion Many EBP models and frameworks currently exist that provide diverse instructions on the best way to use EBP. However, the inclusion of patient values and preferences needs to be better integrated into EBP models and frameworks. Also, the issues of EBP expertise and knowledge to assess evidence must be considered when choosing a model or framework