A proteomic approach to investigating gene cluster expression and secondary metabolite functionality in Aspergillus fumigatus.

A combined proteomics and metabolomics approach was utilised to advance the identification and characterisation of secondary metabolites in Aspergillus fumigatus. Here, implementation of a shotgun proteomic strategy led to the identification of non-redundant mycelial proteins (n = 414) from A. fumigatus including proteins typically under-represented in 2-D proteome maps: proteins with multiple transmembrane regions, hydrophobic proteins and proteins with extremes of molecular mass and pI. Indirect identification of secondary metabolite cluster expression was also achieved, with proteins (n = 18) from LaeA-regulated clusters detected, including GliT encoded within the gliotoxin biosynthetic cluster. Biochemical analysis then revealed that gliotoxin significantly attenuates H2O2-induced oxidative stress in A. fumigatus (p>0.0001), confirming observations from proteomics data. A complementary 2-D/LC-MS/MS approach further elucidated significantly increased abundance (p<0.05) of proliferating cell nuclear antigen (PCNA), NADH-quinone oxidoreductase and the gliotoxin oxidoreductase GliT, along with significantly attenuated abundance (p<0.05) of a heat shock protein, an oxidative stress protein and an autolysis-associated chitinase, when gliotoxin and H2O2 were present, compared to H2O2 alone. Moreover, gliotoxin exposure significantly reduced the abundance of selected proteins (p<0.05) involved in de novo purine biosynthesis. Significantly elevated abundance (p<0.05) of a key enzyme, xanthine-guanine phosphoribosyl transferase Xpt1, utilised in purine salvage, was observed in the presence of H2O2 and gliotoxin. This work provides new insights into the A. fumigatus proteome and experimental strategies, plus mechanistic data pertaining to gliotoxin functionality in the organism

Proteomics of industrial fungi: trends and insights for biotechnology

Filamentous fungi are widely known for their industrial applications, namely, the production of food-processing enzymes and metabolites such as antibiotics and organic acids. In the past decade, the full genome sequencing of filamentous fungi increased the potential to predict encoded proteins enormously, namely, hydrolytic enzymes or proteins involved in the biosynthesis of metabolites of interest. The integration of genome sequence information with possible phenotypes requires, however, the knowledge of all the proteins in the cell in a system-wise manner, given by proteomics. This review summarises the progress of proteomics and its importance for the study of biotechnological processes in filamentous fungi. A major step forward in proteomics was to couple protein separation with high-resolution mass spectrometry, allowing accurate protein quantification. Despite the fact that most fungal proteomic studies have been focused on proteins from mycelial extracts, many proteins are related to processes which are compartmentalised in the fungal cell, e.g. β-lactam antibiotic production in the microbody. For the study of such processes, a targeted approach is required, e.g. by organelle proteomics. Typical workflows for sample preparation in fungal organelle proteomics are discussed, including homogenisation and sub-cellular fractionation. Finally, examples are presented of fungal organelle proteomic studies, which have enlarged the knowledge on areas of interest to biotechnology, such as protein secretion, energy production or antibiotic biosynthesis

End-to-End Optimization of {LiDAR} Beam Configuration for {3D} Object Detection and Localization

Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distributed. Those fixed configurations are task-agnostic, so simply using them can lead to sub-optimal performance. In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application. Specifically, we propose a reinforcement learning-based learning-to-optimize (RL-L2O) framework to automatically optimize the beam configuration in an end-to-end manner for different LiDAR-based applications. The optimization is guided by the final performance of the target task and thus our method can be integrated easily with any LiDAR-based application as a simple drop-in module. The method is especially useful when a low-resolution (low-cost) LiDAR is needed, for instance, for system deployment at a massive scale. We use our method to search for the beam configuration of a low-resolution LiDAR for two important tasks: 3D object detection and localization. Experiments show that the proposed RL-L2O method improves the performance in both tasks significantly compared to the baseline methods. We believe that a combination of our method with the recent advances of programmable LiDARs can start a new research direction for LiDAR-based active perception. The code is publicly available at https://github.com/vniclas/lidar_beam_selectionComment: To appear in: IEEE Robotics and Automation Letters (RA-L

The Aspergillus nidulans nucAEndoG Homologue Is Not Involved in Cell Death ▿

Upon apoptosis induction, translocation of mammalian mitochondrial endonuclease G (EndoG) to the nucleus coincides with large-scale DNA fragmentation. Here, we describe for the first time a homologue of EndoG in filamentous fungi by investigating if the Aspergillus nidulans homologue of the EndoG gene, named nucAEndoG, is being activated during farnesol-induced cell death. Our results suggest that NucA is not involved in cell death, but it plays a role in the DNA-damaging response in A. nidulans

Dysregulated gliotoxin biosynthesis attenuates the production of unrelated biosynthetic gene cluster-encoded metabolites in Aspergillus fumigatus

Gliotoxin is an epipolythiodioxopiperazine (ETP) class toxin, contains a disulfide bridge that mediates its toxic effects via redox cycling and is produced by the opportunistic fungal pathogen Aspergillus fumigatus. The gliotoxin bis-thiomethyltransferase, GtmA, attenuates gliotoxin biosynthesis in A. fumigatus by conversion of dithiol gliotoxin to bis-thiomethylgliotoxin (BmGT). Here we show that disruption of dithiol gliotoxin bis-thiomethylation functionality in A. fumigatus results in significant remodelling of the A. fumigatus secondary metabolome upon extended culture. RP-HPLC and LC–MS/MS analysis revealed the reduced production of a plethora of unrelated biosynthetic gene cluster-encoded metabolites, including pseurotin A, fumagillin, fumitremorgin C and tryprostatin B, occurs in A. fumigatus ΔgtmA upon extended incubation. Parallel quantitative proteomic analysis of A. fumigatus wild-type and ΔgtmA during extended culture revealed cognate abundance alteration of proteins encoded by relevant biosynthetic gene clusters, allied to multiple alterations in hypoxia-related proteins. The data presented herein reveal a previously concealed functionality of GtmA in facilitating the biosynthesis of other BGC-encoded metabolites produced by A. fumigatus