RESUMO
BACKGROUND: The transition to a biobased economy involving the depolymerization and fermentation of renewable agro-industrial sources is a challenge that can only be met by achieving the efficient hydrolysis of biomass to monosaccharides. In nature, lignocellulosic biomass is mainly decomposed by fungi. We recently identified six efficient cellulose degraders by screening fungi from Vietnam. RESULTS: We characterized a high-performance cellulase-producing strain, with an activity of 0.06 U/mg, which was identified as a member of the Fusarium solani species complex linkage 6 (Fusarium metavorans), isolated from mangrove wood (FW16.1, deposited as DSM105788). The genome, representing nine potential chromosomes, was sequenced using PacBio and Illumina technology. In-depth secretome analysis using six different synthetic and artificial cellulose substrates and two agro-industrial waste products identified 500 proteins, including 135 enzymes assigned to five different carbohydrate-active enzyme (CAZyme) classes. The F. metavorans enzyme cocktail was tested for saccharification activity on pre-treated sugarcane bagasse, as well as untreated sugarcane bagasse and maize leaves, where it was complemented with the commercial enzyme mixture Accellerase 1500. In the untreated sugarcane bagasse and maize leaves, initial cell wall degradation was observed in the presence of at least 196 µg/mL of the in-house cocktail. Increasing the dose to 336 µg/mL facilitated the saccharification of untreated sugarcane biomass, but had no further effect on the pre-treated biomass. CONCLUSION: Our results show that F. metavorans DSM105788 is a promising alternative pre-treatment for the degradation of agro-industrial lignocellulosic materials. The enzyme cocktail promotes the debranching of biopolymers surrounding the cellulose fibers and releases reduced sugars without process disadvantages or loss of carbohydrates.
RESUMO
BACKGROUND: The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software. RESULTS: Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources. CONCLUSIONS: With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP's post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.
