Background: The fungal genus Aspergillus is of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus. Results: We have generated genome sequences for ten novel, highly diverse Aspergillus species and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli. Conclusions: Many aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi.

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Comparative genomics reveals high biological diversity and specific adaptations in the industrially and medically important fungal genus Aspergillus

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au.

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.13794

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

SUMMARY Basidiomycete fungi subsist on various types of plant material in diverse environments, from living and dead trees and forest litter to crops and grasses and to decaying plant matter in soils. Due to the variation in their natural carbon sources, basidiomycetes have highly varied plant-polysaccharide-degrading capabilities. This topic is not as well studied for basidiomycetes as for ascomycete fungi, which are the main sources of knowledge on fungal plant polysaccharide degradation. Research on plant-biomass-decaying fungi has focused on isolating enzymes for current and future applications, such as for the production of fuels, the food industry, and waste treatment. More recently, genomic studies of basidiomycete fungi have provided a profound view of the plant-biomass-degrading potential of wood-rotting, litter-decomposing, plant-pathogenic, and ectomycorrhizal (ECM) basidiomycetes. This review summarizes the current knowledge on plant polysaccharide depolymerization by basidiomycete species from diverse habitats. In addition, these data are compared to those for the most broadly studied ascomycete genus, Aspergillus, to provide insight into specific features of basidiomycetes with respect to plant polysaccharide degradation.

Plant-Polysaccharide-Degrading Enzymes from Basidiomycetes

FungiDB (http://FungiDB.org) is a functional genomic resource for pan-fungal genomes that was developed in partnership with the Eukaryotic Pathogen Bioinformatic resource center (http://EuPathDB.org). FungiDB uses the same infrastructure and user interface as EuPathDB, which allows for sophisticated and integrated searches to be performed using an intuitive graphical system. The current release of FungiDB contains genome sequence and annotation from 18 species spanning several fungal classes, including the Ascomycota classes, Eurotiomycetes, Sordariomycetes, Saccharomycetes and the Basidiomycota orders, Pucciniomycetes and Tremellomycetes, and the basal 'Zygomycete' lineage Mucormycotina. Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yeast two hybrid interaction data. The underlying genomic sequence and annotation combined with functional data, additional data from the FungiDB standard analysis pipeline and the ability to leverage orthology provides a powerful resource for in silico experimentation.

FungiDB: an integrated functional genomics database for fungi

Plant-pathogenic fungi secrete effector proteins to facilitate infection We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction EffectorP 20 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification EffectorP 20 achieves an accuracy of 89%, compared with 82% for EffectorP 10 and 598% for a small size classifier Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine EffectorP 20 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared with EffectorP 10 However, EffectorP 10 retains value, and combining EffectorP 10 and 20 results in a stringent classifier with a low false positive rate of 9% EffectorP 20 predicts significant enrichments of effectors in 12 of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment in only seven of 13 EffectorP 20 will fast track the prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology EffectorP 20 is available at http://effectorpcsiroau

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0.

The filamentous fungus Aspergillus niger exhibits great diversity in its phenotype. It is found globally, both as marine and terrestrial strains, produces both organic acids and hydrolytic enzymes in high amounts, and some isolates exhibit pathogenicity. Although the genome of an industrial enzyme-producing A. niger strain (CBS 513.88) has already been sequenced, the versatility and diversity of this species compel additional exploration. We therefore undertook whole-genome sequencing of the acidogenic A. niger wild-type strain (ATCC 1015) and produced a genome sequence of very high quality. Only 15 gaps are present in the sequence, and half the telomeric regions have been elucidated. Moreover, sequence information from ATCC 1015 was used to improve the genome sequence of CBS 513.88. Chromosome-level comparisons uncovered several genome rearrangements, deletions, a clear case of strain-specific horizontal gene transfer, and identification of 0.8 Mb of novel sequence. Single nucleotide polymorphisms per kilobase (SNPs/kb) between the two strains were found to be exceptionally high (average: 7.8, maximum: 160 SNPs/kb). High variation within the species was confirmed with exo-metabolite profiling and phylogenetics. Detailed lists of alleles were generated, and genotypic differences were observed to accumulate in metabolic pathways essential to acid production and protein synthesis. A transcriptome analysis supported up-regulation of genes associated with biosynthesis of amino acids that are abundant in glucoamylase A, tRNA-synthases, and protein transporters in the protein producing CBS 513.88 strain. Our results and data sets from this integrative systems biology analysis resulted in a snapshot of fungal evolution and will support further optimization of cell factories based on filamentous fungi.

/pdf/comparative-genomics-of-citric-acid-producing-aspergillus-19wpab7k4t.pdf

Yigong Lou

Papers

Comparative genomics of citric-acid-producing Aspergillus niger ATCC 1015 versus enzyme-producing CBS 513.88