The role of biofuels in the future energy supply

In recent years several different arguments have been raised against the use of biofuels and their role in our future energy supply. These arguments can be divided into issues related to costs, food versus fuel, and lack of sustainability. Here we address these three points and argue that biofuels represent an essential contribution to our future energy supply and more importantly will contribute to a reduction in carbon dioxide emissions

Penicillium arizonense, a new, genome sequenced fungal species, reveals a high chemical diversity in secreted metabolites

A new soil-borne species belonging to the Penicillium section Canescentia is described, Penicillium arizonense sp. nov. (type strain CBS 141311(T) = IBT 12289(T)). The genome was sequenced and assembled into 33.7 Mb containing 12,502 predicted genes. A phylogenetic assessment based on marker genes confirmed the grouping of P. arizonense within section Canescentia. Compared to related species, P. arizonense proved to encode a high number of proteins involved in carbohydrate metabolism, in particular hemicellulases. Mining the genome for genes involved in secondary metabolite biosynthesis resulted in the identification of 62 putative biosynthetic gene clusters. Extracts of P. arizonense were analysed for secondary metabolites and austalides, pyripyropenes, tryptoquivalines, fumagillin, pseurotin A, curvulinic acid and xanthoepocin were detected. A comparative analysis against known pathways enabled the proposal of biosynthetic gene clusters in P. arizonense responsible for the synthesis of all detected compounds except curvulinic acid. The capacity to produce biomass degrading enzymes and the identification of a high chemical diversity in secreted bioactive secondary metabolites, offers a broad range of potential industrial applications for the new species P. arizonense. The description and availability of the genome sequence of P. arizonense, further provides the basis for biotechnological exploitation of this species

Finding directionality and gene-disease predictions in disease associations

Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders. Methods: The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases. Results: 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology. Conclusions: The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications

Biobased production of alkanes and alkenes through metabolic engineering of microorganisms

Advancement in metabolic engineering of microorganisms has enabled bio-based production of a range of chemicals, and such engineered microorganism can be used for sustainable production leading to reduced carbon dioxide emission there. One area that has attained much interest is microbial hydrocarbon biosynthesis, and in particular, alkanes and alkenes are important high-value chemicals as they can be utilized for a broad range of industrial purposes as well as 'drop-in' biofuels. Some microorganisms have the ability to biosynthesize alkanes and alkenes naturally, but their production level is extremely low. Therefore, there have been various attempts to recruit other microbial cell factories for production of alkanes and alkenes by applying metabolic engineering strategies. Here we review different pathways and involved enzymes for alkane and alkene production and discuss bottlenecks and possible solutions to accomplish industrial level production of these chemicals by microbial fermentation

Functional expression and evaluation of heterologous phosphoketolases in Saccharomyces cerevisiae

Phosphoketolases catalyze an energy-and redox-independent cleavage of certain sugar phosphates. Hereby, the two-carbon (C2) compound acetyl-phosphate is formed, which enzymatically can be converted into acetyl-CoA-a key precursor in central carbon metabolism. Saccharomyces cerevisiae does not demonstrate efficient phosphoketolase activity naturally. In this study, we aimed to compare and identify efficient heterologous phosphoketolase enzyme candidates that in yeast have the potential to reduce carbon loss compared to the native acetyl-CoA producing pathway by redirecting carbon flux directly from C5 and C6 sugars towards C2-synthesis. Nine phosphoketolase candidates were expressed in S. cerevisiae of which seven produced significant amounts of acetyl-phosphate after provision of sugar phosphate substrates in vitro. The candidates showed differing substrate specificities, and some demonstrated activity levels significantly exceeding those of candidates previously expressed in yeast. The conducted studies also revealed that S. cerevisiae contains endogenous enzymes capable of breaking down acetyl-phosphate, likely into acetate, and that removal of the phosphatases Gpp1 and Gpp2 could largely prevent this breakdown. An evaluation of in vivo function of a subset of phosphoketolases was conducted by monitoring acetate levels during growth, confirming that candidates showing high activity in vitro indeed showed increased acetate accumulation, but expression also decreased cellular fitness. The study shows that expression of several bacterial phosphoketolase candidates in S. cerevisiae can efficiently divert intracellular carbon flux toward C2-synthesis, thus showing potential to be used in metabolic engineering strategies aimed to increase yields of acetyl-CoA derived compounds

Human metabolic atlas: an online resource for human metabolism

Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License

Adaptation to different types of stress converge on mitochondrial metabolism

Yeast cell factories encounter physical and chemical stresses when used for industrial production of fuels and chemicals. These stresses reduce productivity and increase bioprocess costs. Understanding the mechanisms of the stress response is essential for improving cellular robustness in platform strains. We investigated the three most commonly encountered industrial stresses for yeast (ethanol, salt, and temperature) to identify the mechanisms of general and stress-specific responses under chemostat conditions in which specific growth rate-dependent changes are eliminated. By applying systems-level analysis, we found that most stress responses converge on mitochondrial processes. Our analysis revealed that stress-specific factors differ between applied stresses; however, they are underpinned by an increased ATP demand. We found that when ATP demand increases to high levels, respiration cannot provide sufficient ATP, leading to onset of respirofermentative metabolism. Although stress-specific factors increase ATP demand for cellular growth under stressful conditions, increased ATP demand for cellular maintenance underpins a general stress response and is responsible for the onset of overflow metabolism

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods. To address this, we have developed the R package Piano that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on we refine the GSA workflow by using modifications of the gene-level statistics. This enables us to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level. We use our fully implemented workflow to investigate the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, we suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation