Development of fungal cell factories for the production of secondary metabolites: Linking genomics and metabolism

The genomic era has revolutionized research on secondary metabolites and bioinformatics methods have in recent years revived the antibiotic discovery process after decades with only few new active molecules being identified. New computational tools are driven by genomics and metabolomics analysis, and enables rapid identification of novel secondary metabolites. To translate this increased discovery rate into industrial exploitation, it is necessary to integrate secondary metabolite pathways in the metabolic engineering process. In this review, we will describe the novel advances in discovery of secondary metabolites produced by filamentous fungi, highlight the utilization of genome-scale metabolic models (GEMs) in the design of fungal cell factories for the production of secondary metabolites and review strategies for optimizing secondary metabolite production through the construction of high yielding platform cell factories

Innovation trends in industrial biotechnology

Microbial fermentations are used for the sustainable production of a range of products. Due to increasing trends in the food sector toward plant-based foods and meat and dairy product substitutes, microbial fermentation will have an increasing role in this sector, as it will enable a sustainable and scalable production of valuable foods and food ingredients. Microbial fermentation will also be used to advance and expand the production of sustainable chemicals and natural products. Much of this market expansion will come from new start-ups that translate academic research into novel processes and products using state-of-the art technologies. Here, we discuss the trends in innovation and technology and provide recommendations for how to successfully start and grow companies in industrial biotechnology

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

Multi-elemental speciation analysis of barley genotypes diering in tolerance to cadmium toxicity using SEC-ICP-MS and ESI-TOF-MS

Plants respond to Cd exposure by synthesizing heavy-metal-binding oligopeptides, called phytochelatins (PCs). These peptides reduce the activity of Cd2+ ions in the plant tissues by forming Cd chelates. The main objective of the present work was to develop an analytical technique, which allowed identication of the most prominent Cd species in plant tissue by SEC-ICP-MS and ESI-TOF-MS. An integrated part of the method development was to test the hypothesis that dierential Cd tolerance between two barley genotypes was linked to dierences in Cd speciation. Only one fraction of Cd species, ranging from 7001800 Da, was detected in the shoots of both genotypes. In the roots, two additional fractions ranging from 29004600 and 670015 000 Da were found. The Cd-rich SEC fractions were heart-cut, de-salted and demetallized using reversed-phase chromatography (RPC), followed by ESI-MS-TOF to identify the ligands. Three dierent families of PCs, viz. (gGlu-Cys)n-Gly (PCn), (gGlu-Cys)n-Ser (iso-PCn) and Cys-(gGlu-Cys)n-Gly (des-gGlu-PCn), the last lacking the N-terminal amino acid, were identied. The PCs induced by Cd toxicity also bound several essential trace elements in plants, including Zn, Cu, and Ni, whereas no Mn species were detected. Zn, Cu and Ni-species were distributed between the 7001800 Da and 670015 000 Da fractions, whereas only Cd species were found in the 29004600 Da fraction dominated by PC3 ligands. Although the total tissue concentration of Cd was similar for the two species, the tolerant barley genotype synthesized signicantly more CdPC3 species with a high Cd specicity than the intolerant genotype, clearly indicating a correlation between Cd tolerance and the CdPC speciation

Quantum Computing with Continuous-Variable Clusters

Continuous-variable cluster states offer a potentially promising method of implementing a quantum computer. This paper extends and further refines theoretical foundations and protocols for experimental implementation. We give a cluster-state implementation of the cubic phase gate through photon detection, which, together with homodyne detection, facilitates universal quantum computation. In addition, we characterize the offline squeezed resources required to generate an arbitrary graph state through passive linear optics. Most significantly, we prove that there are universal states for which the offline squeezing per mode does not increase with the size of the cluster. Simple representations of continuous-variable graph states are introduced to analyze graph state transformations under measurement and the existence of universal continuous-variable resource states.Comment: 17 pages, 5 figure

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm