RESUMO
Antimicrobial peptides (AMPs) are naturally produced by pro- and eukaryotes and are promising alternatives to antibiotics to fight multidrug-resistant microorganisms. However, despite thousands of AMP entries in respective databases, predictions about their structure-activity relationships are still limited. Similarly, common or dissimilar properties of AMPs that have evolved in different taxonomic groups are nearly unknown. We leveraged data entries for 10,987 peptides currently listed in the three antimicrobial peptide databases APD, DRAMP and DBAASP to aid structure-activity predictions. However, this number reduced to 3,828 AMPs that we could use for computational analyses, due to our stringent quality control criteria. The analysis uncovered a strong bias towards AMPs isolated from amphibians (1,391), whereas only 35 AMPs originate from fungi (0.9%), hindering evolutionary analyses on the origin and phylogenetic relationship of AMPs. The majority (62%) of the 3,828 AMPs consists of less than 40 amino acids but with a molecular weight higher than 2.5 kDa, has a net positive charge and shares a hydrophobic character. They are enriched in glycine, lysine and cysteine but are depleted in glutamate, aspartate and methionine when compared with a peptide set of the same size randomly selected from the UniProt database. The AMPs that deviate from this pattern (38%) can be found in different taxonomic groups, in particular in Gram-negative bacteria. Remarkably, the γ-core motif claimed so far as a unifying structural signature in cysteine-stabilised AMPs is absent in nearly 90% of the peptides, questioning its relevance as a prerequisite for antimicrobial activity. The disclosure of AMPs pattern and their variation in producing organism groups extends our knowledge of the structural diversity of AMPs and will assist future peptide screens in unexplored microorganisms. Structural design of peptide antibiotic drugs will benefit using natural AMPs as lead compounds. However, a reliable and statistically balanced database is missing which leads to a large knowledge gap in the AMP field. Thus, thorough evaluation of the available data, mitigation of biases and standardised experimental setups need to be implemented to leverage the full potential of AMPs for drug development programmes in the clinics and agriculture.
RESUMO
BACKGROUND: Fungal fermentation is used to produce a diverse repertoire of enzymes, chemicals, and drugs for various industries. During submerged cultivation, filamentous fungi form a range of macromorphologies, including dispersed mycelia, clumped aggregates, or pellets, which have critical implications for rheological aspects during fermentation, gas/nutrient transfer, and, thus, product titres. An important component of strain engineering efforts is the ability to quantitatively assess fungal growth phenotypes, which will drive novel leads for morphologically optimized production strains. RESULTS: In this study, we developed an automated image analysis pipeline to quantify the morphology of pelleted and dispersed growth (MPD) which rapidly and reproducibly measures dispersed and pelleted macromorphologies from any submerged fungal culture. It (i) enables capture and analysis of several hundred images per user/day, (ii) is designed to quantitatively assess heterogeneous cultures consisting of dispersed and pelleted forms, (iii) gives a quantitative measurement of culture heterogeneity, (iv) automatically generates key Euclidian parameters for individual fungal structures including particle diameter, aspect ratio, area, and solidity, which are also assembled into a previously described dimensionless morphology number MN, (v) has an in-built quality control check which enables end-users to easily confirm the accuracy of the automated calls, and (vi) is easily adaptable to user-specified magnifications and macromorphological definitions. To concomitantly provide proof of principle for the utility of this image analysis pipeline, and provide new leads for morphologically optimized fungal strains, we generated a morphological mutant in the cell factory Aspergillus niger based on CRISPR-Cas technology. First, we interrogated a previously published co-expression networks for A. niger to identify a putative gamma-adaptin encoding gene (aplD) that was predicted to play a role in endosome cargo trafficking. Gene editing was used to generate a conditional aplD expression mutant under control of the titratable Tet-on system. Reduced aplD expression caused a hyperbranched growth phenotype and diverse defects in pellet formation with a putative increase in protein secretion. This possible protein hypersecretion phenotype could be correlated with increased dispersed mycelia, and both decreased pellet diameter and MN. CONCLUSION: The MPD image analysis pipeline is a simple, rapid, and flexible approach to quantify diverse fungal morphologies. As an exemplar, we have demonstrated that the putative endosomal transport gene aplD plays a crucial role in A. niger filamentous growth and pellet formation during submerged culture. This suggests that endocytic components are underexplored targets for engineering fungal cell factories.