Teaching the design principles of metabolism

Blended learning in biochemistry: The development of pre-class and post-class learning aids for electron transport chain and oxidative phosphorylation

Electron transport chain and oxidative phosphorylation are always a challenging topic for students studying metabolism. We had adopted blended learning in metabolism teaching and evaluated the learning experiences of students. In this project, a pre-class learning aid the Story Mode and a post-class learning aid the Revision Mode in the Powerland was developed that facilitated students learning electron transport chain and oxidative phosphorylation. In the Story Mode, pathways were presented by short animations and simplified diagram that allowed students to understand basic concepts and recall simple facts of the topic. Students were asked to watch the animations before class to acquire lower level of cognitive learning first, and this facilitated students in understanding more complicated concepts later on during class. Another challenge that students faced was that they were especially weak at integrating metabolic pathways and understand the relationships between these pathways. A metro map was designed in the Revision Mode that aided students in knowledge integration, and the functions of biomolecules were summarized in flashcards that helped students in revising the concepts. This interactive self-learning tool was packaged as a courseware using the Articulate Storyline.

Analysis of steady-state carbon tracer experiments using akaike information criteria

INTRODUCTION

Carbon isotope tracers have been used to determine relative rates of tricarboxylic acid cycle (TCA) cycle pathways since the 1950s. Steady-state experimental data are typically fit to a single mathematical model of metabolism to determine metabolic fluxes. Whether the chosen model is appropriate for the biological system has generally not been evaluated systematically. An overly-simple model omits known pathways while an overly-complex model may produce incorrect results due to overfitting.

OBJECTIVES

The objectives were to develop and study a method that systematically evaluates multiple TCA cycle mathematical models as part of the fitting process.

METHODS

The problem of choosing overly-simple or overly-complex models was approached by developing software that automatically explores all possible combinations of flux through pyruvate dehydrogenase, pyruvate kinase, pyruvate carboxylase and anaplerosis at propionyl-CoA carboxylase, and equivalent pathways, all relative to TCA cycle flux. Typical TCA cycle metabolic tracer experiments that use ¹³C nuclear magnetic resonance for detection and quantification of ¹³C-enriched glutamate products were simulated and analyzed. By evaluating the multiple model fits with both the conventional sum-of-squares residual error (SSRE) and the Akaike Information Criterion (AIC), the software helps the investigator understand the interaction between model complexity and goodness of fit.

RESULTS

When fitting alternative models of the TCA cycle metabolism, the SSRE may identify more than one model that fits the data well. Among those models, the AIC provides guidance as to which is the simplest of the candidate models is sufficient to describe the observed data. However under some conditions, AIC used alone inappropriately discriminates against necessary metabolic complexity.

CONCLUSION

In combination, the SSRE and AIC help the investigator identify the model that best describes the metabolism of a biological system.

Searching for principles of microbial physiology

Why do evolutionarily distinct microorganisms display similar physiological behaviours? Why are transitions from high-ATP yield to low(er)-ATP yield metabolisms so widespread across species? Why is fast growth generally accompanied with low stress tolerance? Do these regularities occur because most microbial species are subject to the same selective pressures and physicochemical constraints? If so, a broadly-applicable theory might be developed that predicts common microbiological behaviours. Microbial systems biologists have been working out the contours of this theory for the last two decades, guided by experimental data. At its foundations lie basic principles from evolutionary biology, enzyme biochemistry, metabolism, cell composition and steady-state growth. The theory makes predictions about fitness costs and benefits of protein expression, physicochemical constraints on cell growth and characteristics of optimal metabolisms that maximise growth rate. Comparisons of the theory with experimental data indicates that microorganisms often aim for maximisation of growth rate, also in the presence of stresses; they often express optimal metabolisms and metabolic proteins at optimal concentrations. This review explains the current status of the theory for microbiologists; its roots, predictions, experimental evidence and future directions.

Student-directed interactive animation for learning cytochrome P450-mediated drug metabolism

INTRODUCTION

In this study, we introduced a student self-directed interactive animation tool created to enhance student understanding of cytochrome P450 (CYP450) mediated drug metabolism.

METHODS

The online learning tool was constructed in HTML5 computer code. It was implemented over four years in a second year pharmacy degree course where CYP450 metabolism was taught. Assessment was by comparing test scores of students using the learning tool with a previous class that did not and through survey data from the student users.

RESULTS

Use of the Cyber-CYP learning tool enhanced test performance on CYP450 metabolism-related questions in all years tested. Survey responses indicated that the learning tool was easy to use and facilitated student learning of CYP450-mediated drug metabolism.

CONCLUSIONS

This study has shown that complex and dynamic processes, such as CYP450 metabolism, can be more effectively communicated using student-centered, self-paced and interactive animations.

Flux-dependent graphs for metabolic networks

Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic flows via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic flows and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

Cellular metabolism is the result of a highly enmeshed set of biochemical reactions that is naturally amenable to graph-based analyses. Yet there are multiple ways to construct a graph representation from any given metabolic model. Here an international research team of UK and Spain scientists presents a principled approach to study metabolic models through the lens of network science. They propose a framework to construct graphs for genome-scale metabolic models that resolve various challenges, such as the incorporation of pool metabolites, the preservation of the directionality of metabolic flows, and the capability to incorporate specific flux information. The method can be integrated into pipelines based on flux balance analysis and provides a systematic framework to explore changes in network connectivity as a result of environmental shifts or genetic perturbations. The framework thus allows to interrogate context-specific metabolic responses beyond standard pathway descriptions. The authors illustrate the approach through the analysis of Escherichia coli's core metabolism in different growth conditions, as well as a rare metabolic disease affecting human hepatocytes.

Converting inert plastic waste into energetic materials: A study on the light-accelerated decomposition of plastic waste with the Fenton reaction

Better treatment and management strategies than landfilling are needed to address the large quantities of unrecycled plastic waste generated by daily human activities. Waste-to-energy conversion is an ideal benchmark for developing future large-scale waste management technologies. The present study explores a new approach for producing energetic materials by converting inert plastic waste into energy (thermal and mechanical energies) via a light-controlled process through the simple chemical activation of plastic waste, including polyethylene, polypropylene, and polyvinyl chloride. The inert and non-polar polymer surfaces of the plastics were modified by generating a number of sulfonic groups (SO₃^-) using chlorosulfuric acid, followed by grafting of Fe(III) catalyst onto the polymer chains to obtain activated polymer. Elemental analyses of these activated materials showed that the carbon-to-sulfur ratio ranged from 3:1 to 5:1. The FTIR spectra indicated the presence of CC bonds (v_C=C: 1615-1630 cm^-1) and SO bonds (v_S=O: 1151-1167 cm^-1) in the activated polymers after chemical reaction. These activated materials were energetic, as light could be used to convert them into thermal (1800-3200 J/g) and mechanical energies (380-560 kPa/g) using hydrogen peroxide as the oxidant under ambient conditions within 1 h.

New concepts in feedback regulation of glucose metabolism

Glycolysis, the breakdown of glucose, is one of the most conserved and extensively studied biochemical pathways. Designing principles from chemistry and thermodynamics allow for energy production, biosynthesis and cellular communication. However, the kinetics or metabolic flux through the pathway also determines its function. Recently, there have been numerous developments that establish new allosteric interactions of glycolytic enzymes with small molecule metabolites and other mechanisms that may cooperate to allow for addition complex regulation of glycolysis. This review surveys these newfound sources of glycolysis regulation and discusses their possible roles in establishing kinetic design principles of glycolysis.

Glycolytic Metabolism Plays a Functional Role in Regulating Human Pluripotent Stem Cell State

The rate of glycolytic metabolism changes during differentiation of human embryonic stem cells (hESCs) and reprogramming of somatic cells to pluripotency. However, the functional contribution of glycolytic metabolism to the pluripotent state is unclear. Here we show that naive hESCs exhibit increased glycolytic flux, MYC transcriptional activity, and nuclear N-MYC localization relative to primed hESCs. This status is consistent with the inner cell mass of human blastocysts, where MYC transcriptional activity is higher than in primed hESCs and nuclear N-MYC levels are elevated. Reduction of glycolysis decreases self-renewal of naive hESCs and feeder-free primed hESCs, but not primed hESCs grown in feeder-supported conditions. Reduction of glycolysis in feeder-free primed hESCs also enhances neural specification. These findings reveal associations between glycolytic metabolism and human naive pluripotency and differences in the metabolism of feeder-/feeder-free cultured hESCs. They may also suggest methods for regulating self-renewal and initial cell fate specification of hESCs.

Making metabolism accessible and meaningful: is the definition of a central metabolic dogma within reach?

Intermediary metabolism, a dominant research area before the emergence of molecular biology, is attracting renewed interest for fundamental and applied reasons as documented here. Nonetheless, the field may appear to be a thicket precluding entry to all but the most determined. Here we present a metabolic overview that makes this important and fascinating area accessible to a broad range of the molecular biological and biotechnological communities that are being attracted to biological problems crying out for metabolic solutions. This is accomplished by identifying seven key concepts, a so-called metabolic central dogma, that provide a core understanding analogous to the "Central Dogma of Molecular Biology" which focused upon maintenance and flow of genetic information.

The Nrf2 regulatory network provides an interface between redox and intermediary metabolism

Nuclear factor-erythroid 2 p45-related factor 2 (Nrf2, also called Nfe2l2) is a transcription factor that regulates the cellular redox status. Nrf2 is controlled through a complex transcriptional/epigenetic and post-translational network that ensures its activity increases during redox perturbation, inflammation, growth factor stimulation and nutrient/energy fluxes, thereby enabling the factor to orchestrate adaptive responses to diverse forms of stress. Besides mediating stress-stimulated induction of antioxidant and detoxification genes, Nrf2 contributes to adaptation by upregulating the repair and degradation of damaged macromolecules, and by modulating intermediary metabolism. In the latter case, Nrf2 inhibits lipogenesis, supports β-oxidation of fatty acids, facilitates flux through the pentose phosphate pathway, and increases NADPH regeneration and purine biosynthesis; these observations suggest Nrf2 directs metabolic reprogramming during stress.