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This study aims to demonstrate the benefits of augmenting commercially available, real‐time, in vitro glycolysis assays with phenomenological rate equation‐based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell‐lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two‐phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway‐modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end‐product. This study demonstrates that augmenting a relatively simple, real‐time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.

This study aims to demonstrate the benefits of augmenting commercially available, real‐time, in vitro glycolysis assays with phenomenological rate equation‐based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell‐lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two‐phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway‐modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end‐product. This study demonstrates that augmenting a relatively simple, real‐time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems. [ABSTRACT FROM AUTHOR]

The incidence of prostate cancer (PCa) among African-Americans (AfA) is significantly higher than Caucasian-Americans (CaA) but the genetic basis for this disparity is not known. To address this problem, we analyzed miRNA expression in AfA (n = 81) and CaA (n = 51) PCa patients. Here, we found that miR-24 is differentially expressed in AfA and CaA PCa patients and attempt to clarify its role in AfA patients. Also, the public sequencing data of the miR-24 promoter confirmed that it was highly methylated and down-regulated in PCa patients. Utilizing a VAMCSF and NDRI patient cohorts, we discovered that miR-24 expression was linked to a racial difference between AfA/CaA PCa patients. Interestingly, miR-24 was restored after treatment of PCa cells with 5Aza-CdR in an AfA cell line (MDA-PCa-2b), while restoration of miR-24 was not observed in CaA cells, DU-145. Ectopic expression of miR-24 showed decreased growth and induced apoptosis, though the effect was less in the CaA cell line compared to the AfA cell line. Finally, we found unique changes in biological pathways and processes associated with miR-24 transfected AfA cells by quantitative PCR-based gene expression array. Evaluation of the altered pathways showed that AR, IGF1, IGFBP5 and ETV1 were markedly decreased in the AfA derived cell line compared with CaA cells, and there was a reciprocal regulatory relationship of miR-24/target expression in prostate cancer patients. These results demonstrate that miR-24 may be a central regulator of key events that contribute to race-related tumorigenesis and has potential to be a therapeutic agent for PCa treatment.

Morphogen gradients need to be robust, but may also need to be tailored for specific tissues. Often this type of regulation is carried out by negative regulators and negative feedback loops. In the Hedgehog (Hh) pathway, activation of patched (ptc) in response to Hh is part of a negative feedback loop limiting the range of the Hh morphogen. Here, we show that in the Drosophila wing imaginal disc two other known Hh targets genes feed back to modulate Hh signaling. First, anterior expression of the transcriptional repressor Engrailed modifies the Hh gradient by attenuating the expression of the Hh pathway transcription factor cubitus interruptus (ci), leading to lower levels of ptc expression. Second, the E-3 ligase Roadkill shifts the competition between the full-length activator and truncated repressor forms of Ci by preferentially targeting full-length Ci for degradation. Finally, we provide evidence that Suppressor of fused, a negative regulator of Hh signaling, has an unexpected positive role, specifically protecting full-length Ci but not the Ci repressor from Roadkill. [ABSTRACT FROM AUTHOR]

Reagents that target protein–protein interactions to rewire signaling are of great relevance in biological research. Computational protein design may offer a means of creating such reagents on demand, but methods for encoding targeting selectivity are sorely needed. This is especially challenging when targeting interactions with ubiquitous recognition modules—for example, PDZ domains, which bind C-terminal sequences of partner proteins. Here we consider the problem of designing selective PDZ inhibitor peptides in the context of an oncogenic signaling pathway, in which two PDZ domains (NHERF-2 PDZ2—N2P2 and MAGI-3 PDZ6—M3P6) compete for a receptor C-terminus to differentially modulate oncogenic activities. Because N2P2 has been shown to increase tumorigenicity and M3P6 to decreases it, we sought to design peptides that inhibit N2P2 without affecting M3P6. We developed a structure-based computational design framework that models peptide flexibility in binding yet is efficient enough to rapidly analyze tradeoffs between affinity and selectivity. Designed peptides showed low-micromolar inhibition constants for N2P2 and no detectable M3P6 binding. Peptides designed for reverse discrimination bound M3P6 tighter than N2P2, further testing our technology. Experimental and computational analysis of selectivity determinants revealed significant indirect energetic coupling in the binding site. Successful discrimination between N2P2 and M3P6, despite their overlapping binding preferences, is highly encouraging for computational approaches to selective PDZ targeting, especially because design relied on a homology model of M3P6. Still, we demonstrate specific deficiencies of structural modeling that must be addressed to enable truly robust design. The presented framework is general and can be applied in many scenarios to engineer selective targeting. [ABSTRACT FROM AUTHOR]