The development of computational biology in South Africa: successes achieved and lessons learnt

Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt

The logic of kinetic regulation in the thioredoxin system

<p>Abstract</p> <p>Background</p> <p>The thioredoxin system consisting of NADP(H), thioredoxin reductase and thioredoxin provides reducing equivalents to a large and diverse array of cellular processes. Despite a great deal of information on the kinetics of individual thioredoxin-dependent reactions, the kinetic regulation of this system as an integrated whole is not known. We address this by using kinetic modeling to identify and describe kinetic behavioral motifs found within the system.</p> <p>Results</p> <p>Analysis of a realistic computational model of the <it>Escherichia coli </it>thioredoxin system revealed several modes of kinetic regulation in the system. In keeping with published findings, the model showed that thioredoxin-dependent reactions were adaptable (i.e. changes to the thioredoxin system affected the kinetic profiles of these reactions). Further and in contrast to other systems-level descriptions, analysis of the model showed that apparently unrelated thioredoxin oxidation reactions can affect each other via their combined effects on the thioredoxin redox cycle. However, the scale of these effects depended on the kinetics of the individual thioredoxin oxidation reactions with some reactions more sensitive to changes in the thioredoxin cycle and others, such as the Tpx-dependent reduction of hydrogen peroxide, less sensitive to these changes. The coupling of the thioredoxin and Tpx redox cycles also allowed for ultrasensitive changes in the thioredoxin concentration in response to changes in the thioredoxin reductase concentration. We were able to describe the kinetic mechanisms underlying these behaviors precisely with analytical solutions and core models.</p> <p>Conclusions</p> <p>Using kinetic modeling we have revealed the logic that underlies the functional organization and kinetic behavior of the thioredoxin system. The thioredoxin redox cycle and associated reactions allows for a system that is adaptable, interconnected and able to display differential sensitivities to changes in this redox cycle. This work provides a theoretical, systems-biological basis for an experimental analysis of the thioredoxin system and its associated reactions.</p

The thioredoxin system and not the Michaelis–Menten equation should be fitted to substrate saturation datasets from the thioredoxin insulin assay

<p><b>Introduction</b>: The thioredoxin system, consisting of thioredoxin reductase, thioredoxin and NADPH, is present in most living organisms and reduces a large array of target protein disulfides.</p> <p><b>Objective</b>: The insulin reduction assay is commonly used to characterise thioredoxin activity <i>in vitro</i>, but it is not clear whether substrate saturation datasets from this assay should be fitted and modeled with the Michaelis-Menten equation (thioredoxin enzyme model), or fitted to the thioredoxin system with insulin reduction described by mass-action kinetics (redox couple model).</p> <p><b>Methods</b>: We utilized computational modeling and <i>in vitro</i> assays to determine which of these approaches yield consistent and accurate kinetic parameter sets for insulin reduction.</p> <p><b>Results</b>: Using computational modeling, we found that fitting to the redox couple model, rather than to the thioredoxin enzyme model, resulted in consistent parameter sets over a range of thioredoxin reductase concentrations. Furthermore, we established that substrate saturation in this assay was due to the progressive redistribution of the thioredoxin moiety into its oxidised form. We then confirmed these results in vitro using the yeast thioredoxin system.</p> <p><b>Discussion</b>: This study shows how consistent parameter sets for thioredoxin activity can be obtained regardless of the thioredoxin reductase concentration used in the insulin reduction assay, and validates computational systems biology modeling studies that have described the thioredoxin system with the redox couple modeling approach.</p

The glutaredoxin mono- and di-thiol mechanisms for deglutathionylation are functionally equivalent: implications for redox systems biology

Glutathionylation plays a central role in cellular redox regulation and anti-oxidative defence. Grx (Glutaredoxins) are primarily responsible for reversing glutathionylation and their activity therefore affects a range of cellular processes, making them prime candidates for computational systems biology studies. However, two distinct kinetic mechanisms involving either one (monothiol) or both (dithiol) active-site cysteines have been proposed for their deglutathionylation activity and initial studies predicted that computational models based on either of these mechanisms will have different structural and kinetic properties. Further, a number of other discrepancies including the relative activity of active-site mutants and contrasting reciprocal plot kinetics have also been reported for these redoxins. Using kinetic modelling, we show that the dithiol and monothiol mechanisms are identical and, we were also able to explain much of the discrepant data found within the literature on Grx activity and kinetics. Moreover, our results have revealed how an apparently futile side-reaction in the monothiol mechanism may play a significant role in regulating Grx activity in vivo

Modelling the Decamerisation Cycle of PRDX1 and the Inhibition-like Effect on Its Peroxidase Activity

Peroxiredoxins play central roles in the detoxification of reactive oxygen species and have been modelled across multiple organisms using a variety of kinetic methods. However, the peroxiredoxin dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between these forms. This is due to the lack of available kinetics and a theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we obtained association and dissociation rate constants of 0.050 µM−4·s−1 and 0.055 s−1, respectively, for the dimer–decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer–decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer–decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation state to hydrogen peroxide insult. By accounting for the dimer–decamer transition of peroxiredoxins, we were able to resolve several discrepancies between experimental data and available kinetic models

Identifying the conditions necessary for the thioredoxin ultrasensitive response

Thioredoxin, glutaredoxin, and peroxiredoxin systems (collectively called redoxins) play critical roles in a large number of redox-sensitive cellular processes. These systems are linked to each other by coupled redox cycles and by common reaction intermediates into a larger network. Previous results from a realistic computational model of the Escherichia coli thioredoxin system developed in our group have revealed several modes of kinetic regulation in the system. Amongst others, the coupling of the thioredoxin and peroxiredoxin redox cycles was shown to exhibit the potential for ultrasensitive changes in the thioredoxin concentration and the flux through other thioredoxin-dependent processes in response to changes in the thioredoxin reductase level. Here, we analyse the basis for this ultrasensitive response using kinetic modelling and metabolic control analysis and derive quantitative conditions that must be fulfilled for ultrasensitivity to occur

Atypical network topologies enhance the reductive capacity of pathogen thiol antioxidant defense networks

Infectious diseases are a significant health burden for developing countries, particularly with the rise of multidrug resistance. There is an urgent need to elucidate the factors underlying the persistence of pathogens such as Mycobacterium tuberculosis, Plasmodium falciparum and Trypanosoma brucei. In contrast to host cells, these pathogens traverse multiple and varied redox environments during their infectious cycles, including exposure to high levels of host-derived reactive oxygen species. Pathogen antioxidant defenses such as the peroxiredoxin and thioredoxin systems play critical roles in the redox stress tolerance of these cells. However, many of the kinetic rate constants obtained for the pathogen peroxiredoxins are broadly similar to their mammalian homologs and therefore, their contributions to the redox tolerances within these cells are enigmatic. Using graph theoretical analysis, we show that compared to a canonical Escherichia coli redoxin network, pathogen redoxin networks contain unique network connections (motifs) between their thioredoxins and peroxiredoxins. Analysis of these motifs reveals that they increase the hydroperoxide reduction capacity of these networks and, in response to an oxidative insult, can distribute fluxes into specific thioredoxin-dependent pathways. Our results emphasize that the high oxidative stress tolerance of these pathogens depends on both the kinetic parameters for hydroperoxide reduction and the connectivity within their thioredoxin/peroxiredoxin systems