LyGo: A platform for rapid screening of lytic polysaccharide monooxygenase production

Environmentally friendly sources of energy and chemicals are essential constituents of a sustainable society. An important step towards this goal is the utilization of non-edible biomass as supply of building blocks for future biorefineries. Lytic polysaccharide monooxygenases (LPMOs) are enzymes that play a critical role in breaking the chemical bonds in the most abundant polymers found in recalcitrant biomass, such as cellulose and chitin. Predicting optimal strategies for producing LPMOs is often non-trivial, and methods allowing for screening several strategies simultaneously are therefore needed. Here, we present a standardized platform for cloning LPMOs. The platform allows users to combine gene fragments with different expression vectors in a simple 15-minute reaction, thus enabling rapid exploration of several gene contexts, hosts and expression strategies in parallel. The open-source LyGo platform is accompanied by easy-to-follow online protocols for both cloning and expression. As a demonstration, we utilize the LyGo platform to explore different strategies for expressing several different LPMOs in Escherichia coli, Bacillus subtilis, and Komagataella phaffii.

A comparative study of SVDquartets and other coalescent-based species tree estimation methods

Background: Species tree estimation is challenging in the presence of incomplete lineage sorting (ILS), which can make gene trees different from the species tree. Because ILS is expected to occur and the standard concatenation approach can return incorrect trees with high support in the presence of ILS, “coalescent-based” summary methods (which first estimate gene trees and then combine gene trees into a species tree) have been developed that have theoretical guarantees of robustness to arbitrarily high amounts of ILS. Some studies have suggested that summary methods should only be used on “c-genes” (i.e., recombination-free loci) that can be extremely short (sometimes fewer than 100 sites). However, gene trees estimated on short alignments can have high estimation error, and summary methods tend to have high error on short c-genes. To address this problem, Chifman and Kubatko introduced SVDquartets, a new coalescent-based method. SVDquartets takes multi-locus unlinked single-site data, infers the quartet trees for all subsets of four species, and then combines the set of quartet trees into a species tree using a quartet amalgamation heuristic. Yet, the relative accuracy of SVDquartets to leading coalescent-based methods has not been assessed. Results: We compared SVDquartets to two leading coalescent-based methods (ASTRAL-2 and NJst), and to concatenation using maximum likelihood. We used a collection of simulated datasets, varying ILS levels, numbers of taxa, and number of sites per locus. Although SVDquartets was sometimes more accurate than ASTRAL-2 and NJst, most often the best results were obtained using ASTRAL-2, even on the shortest gene sequence alignments we explored (with only 10 sites per locus). Finally, concatenation was the most accurate of all methods under low ILS conditions. Conclusions: ASTRAL-2 generally had the best accuracy under higher ILS conditions, and concatenation had the best accuracy under the lowest ILS conditions. However, SVDquartets was competitive with the best methods under conditions with low ILS and small numbers of sites per locus. The good performance under many conditions of ASTRAL-2 in comparison to SVDquartets is surprising given the known vulnerability of ASTRAL-2 and similar methods to short gene sequences.

Potential of Neural Stem Cells for the Treatment of Brain Tumors

Neural stem cells (NSCs) are self-renewing multipotent cells that generate the main phenotypes of the nervous system, neurons, astrocytes and oligodendrocytes. As such they hold the promise to treat a broad range of neurological diseases and injuries. Neural progenitor and stem cells have been isolated and characterized in vitro, from adult, fetal and post-mortem tissues, providing sources of material for cellular therapy. However, NSCs are still elusive cells and remain to be unequivocally identified and characterized, limiting their potential use for therapy. Neural progenitor and stem cells, isolated and cultured in vitro, can be genetically modified and when transplanted migrate to tumor sites in the brain. These intrinsic properties of neural progenitor and stem cells provide tremendous potential to bolster the translation of NSC research to therapy. It is proposed to combine gene therapy and cellular therapy to treat brain cancers. Hence, neural progenitor and stem cells provide new opportunities for the treatment of brain cancers.