Elucidation of primary metabolic pathways in <i>Aspergillus </i>species: Orphaned research in characterizing orphan genes

Primary metabolism affects all phenotypical traits of filamentous fungi. Particular examples include reacting to extracellular stimuli, producing precursor molecules required for cell division and morphological changes as well as providing monomer building blocks for production of secondary metabolites and extracellular enzymes. In this review, all annotated genes from four Aspergillus species have been examined. In this process, it becomes evident that 80–96% of the genes (depending on the species) are still without verified function. A significant proportion of the genes with verified metabolic functions are assigned to secondary or extracellular metabolism, leaving only 2–4% of the annotated genes within primary metabolism. It is clear that primary metabolism has not received the same attention in the post-genomic area as many other research areas—despite its role at the very centre of cellular function. However, several methods can be employed to use the metabolic networks in tandem with comparative genomics to accelerate functional assignment of genes in primary metabolism. In particular, gaps in metabolic pathways can be used to assign functions to orphan genes. In this review, applications of this from the Aspergillus genes will be examined, and it is proposed that, where feasible, this should be a standard part of functional annotation of fungal genomes

Curation of a CHO DG44 genome scale model and application to support cell culture development process

Genome scale models (GSM) have become a useful tool to connect different omics dataset into a single computational framework, thus giving a good overview of the flux distribution and metabolites interconnections in a specific environmental condition. A community genome-scale metabolic network reconstruction of Cricetulus griseus and cell line specific models have been recently developed 1. The main objectives with the use of the published CHO DG44 model were to enhance industrial bioprocess performance by suggesting genetic or metabolic targets, as well as strategies for medium optimization, and by bringing more fundamental knowledge about CHO cell metabolism. In a first step, some corrections were required in order to improve the biological relevancy of the predicted intracellular fluxes. The optimization method chosen was Parsimonious Flux Balance Analysis, based on the assumption that the cell is using a minimum amount of enzymes to reach a maximized objective value, under steady state. As the predictions were generating a lot of infeasible cycles, silencing of amino acid transporters that do not involve protons or sodium in the model allowed to reduce the incoming flow of amino acids and led to disappearance of infeasible cycles in the flux distribution solution. Four reactions involved in central carbon metabolism were manually added in the model, and some reactions were removed from the model to improve predictions such as the cytosolic enzyme fumarase, mainly localized in mitochondria, or L-asparaginase which is not reported to be present in CHO cells. As initial predictions for lactate production rate were overestimated compared to experimental measurements, the assumption of lipid accumulation during cell culture was added in the form of a constraint for a minimal level of triglyceride synthesis in the model (Figure 1). In a second step, the accuracy of the prediction from the curated model was tested with three independent data set obtained from a fed-batch experiment with a CHO DG44 cell line producing a monoclonal antibody in 2L stirred tank glass bioreactors. For modelling with GSM, pre-calculated input values are required in order to constraint the model with the environmental conditions, and thus to give a prediction that is representative of the experimental condition. Uptake rates of essential nutrients initially present in extracellular medium and consumed as the cells grow were used as the limit for a maximum uptake rate in the model. The objective function chosen was maximization of growth rate or maximization of specific productivity. As a result, correlation coefficients between experimental value and prediction indicate a good fit for growth rate and specific productivity (Qp) (Figure 2). Predicted amino acid consumption rates were comparable to experimental values, indicating the accuracy of the predicted stoichiometric requirements for cell growth and energy, except for 19% of the fluxes studied (Figure 3). As the extracellular flux values seem to correlate with experimental data, prediction of intracellular flux rates were analyzed at different timepoints of the culture, showing the activation of multiple metabolic pathways. Based on the results obtained, optimization of cell culture medium was performed to identify the limiting metabolites during the process that could lead to an increased growth rate and Qp. Please click Additional Files below to see the full abstract

FunGeneClusterS:Predicting fungal gene clusters from genome and transcriptome data

Introduction: Secondary metabolites of fungi are receiving an increasing amount of interest due to their prolific bioactivities and the fact that fungal biosynthesis of secondary metabolites often occurs from co-regulated and co-located gene clusters. This makes the gene clusters attractive for synthetic biology and industrial biotechnology applications. We have previously published a method for accurate prediction of clusters from genome and transcriptome data, which could also suggest cross-chemistry, however, this method was limited both in the number of parameters which could be adjusted as well as in user-friendliness. Furthermore, sensitivity to the transcriptome data required manual curation of the predictions. In the present work, we have aimed at improving these features. Results: FunGeneClusterS is an improved implementation of our previous method with a graphical user interface for off- and on-line use. The new method adds options to adjust the size of the gene cluster(s) being sought as well as an option for the algorithm to be flexible with genes in the cluster which may not seem to be co-regulated with the remainder of the cluster. We have benchmarked the method using data from the well-studied Aspergillus nidulans and found that the method is an improvement over the previous one. In particular, it makes it possible to predict clusters with more than 10 genes more accurately, and allows identification of co-regulated gene clusters irrespective of the function of the genes. It also greatly reduces the need for manual curation of the prediction results. We furthermore applied the method to transcriptome data from A. niger. Using the identified best set of parameters, we were able to identify clusters for 31 out of 76 previously predicted secondary metabolite synthases/synthetases. Furthermore, we identified additional putative secondary metabolite gene clusters. In total, we predicted 432 co-transcribed gene clusters in A. niger (spanning 1.323 genes, 12% of the genome). Some of these had functions related to primary metabolism, e.g. we have identified a cluster for biosynthesis of biotin, as well as several for degradation of aromatic compounds. The data identifies that suggests that larger parts of the fungal genome than previously anticipated operates as gene clusters. This includes both primary and secondary metabolism as well as other cellular maintenance functions. Conclusion: We have developed FunGeneClusterS in a graphical implementation and made the method capable of adjustments to different datasets and target clusters. The method is versatile in that it can predict co-regulated clusters not limited to secondary metabolism. Our analysis of data has shown not only the validity of the method, but also strongly suggests that large parts of fungal primary metabolism and cellular functions are both co-regulated and co-located

A new view of responses to first-time barefoot running.

We examined acute alterations in gait and oxygen cost from shod-to-barefoot running in habitually-shod well-trained runners with no prior experience of running barefoot. Thirteen runners completed six-minute treadmill runs shod and barefoot on separate days at a mean speed of 12.5 km·h-1. Steady-state oxygen cost in the final minute was recorded. Kinematic data were captured from 30-consecutive strides. Mean differences between conditions were estimated with 90% confidence intervals. When barefoot, stride length and ground-contact time decreased while stride rate increased. Leg-and vertical stiffness and ankle-mid-stance dorsi-flexion angle increased when barefoot while horizontal distance between point of contact and the hip decreased. Mean oxygen cost decreased in barefoot compared to shod running (90% CI -11% to -3%) and was related to change in ankle angle and point-of-contact distance, though individual variability was high (-19% to +8%). The results suggest that removal of shoes produces an alteration in running gait and a potentially-practically-beneficial reduction in mean oxygen cost of running in trained-habitually-shod runners new to running barefoot. However, high variability suggests an element of skill in adapting to the novel task and that caution be exercised in assuming the mean response applies to all runners