Biology subjects

Steenwyk, J. L.

Publications and source records attributed to Steenwyk, J. L..

3 recordsLinked to original sources

bioRxiv2018

Characterizing the pathogenic, genomic, and chemical traits of Aspergillus fischeri, the closest sequenced relative of the major human fungal pathogen Aspergillus fumigatus

Aspergillus fischeri is closely related to Aspergillus fumigatus, the major cause of invasive mold infections. Even though A. fischeri is commonly found in diverse environments, including hospitals, it rarely causes invasive disease; why that is so is unclear. Comparison of A. fischeri and A. fumigatus for diverse pathogenic, genomic, and secondary metabolic traits revealed multiple differences for pathogenesis-related phenotypes, including that A. fischeri is less virulent than A. fumigatus in multiple animal models of disease, grows slower in low oxygen environments, and is more sensitive to oxidative stress. In contrast, the two species exhibit high genomic similarity; ~90% of the A. fumigatus proteome is conserved in A. fischeri, including 48/49 genes known to be involved in A. fumigatus virulence. However, only 10/33 A. fumigatus biosynthetic gene clusters (BGCs) likely involved in secondary metabolite production are conserved in A. fischeri and only 13/48 A. fischeri BGCs are conserved in A. fumigatus. Detailed chemical characterization of A. fischeri cultures grown on multiple substrates identified multiple secondary metabolites, including two new compounds and one never before isolated as a natural product. Interestingly, an A. fischeri deletion mutant of laeA, a master regulator of secondary metabolism, produced fewer secondary metabolites and in lower quantities, suggesting that regulation of secondary metabolism is at least partially conserved. These results suggest that the non-pathogenic A. fischeri possesses many of the genes important for A. fumigatus pathogenicity but is divergent with respect to its ability to thrive under host-relevant conditions and its secondary metabolism.\n\nImportanceAspergillus fumigatus is the primary cause of aspergillosis, a devastating ensemble of diseases associated with severe morbidity and mortality worldwide. A. fischeri is a close relative of A. fumigatus, but is not generally observed to cause human disease. To gain insights into the underlying causes of this remarkable difference in pathogenicity, we compared two representative strains (one from each species) for a range of host-relevant biological and chemical characteristics. We found that disease progression in multiple A. fischeri mouse models was slower and caused less mortality than A. fumigatus. The two species also exhibited different growth profiles when placed in a range of stress-inducing conditions encountered during infection, such as low levels of oxygen and the presence of reactive oxygen species-inducing agents. Interestingly, we also found that the vast majority of A. fumigatus genes known to be involved in virulence are conserved in A. fischeri, whereas the two species differ significantly in their secondary metabolic pathways. These similarities and differences that we identified are the first step toward understanding the evolutionary origin of a major fungal pathogen.

microbiology↗

bioRxiv2018

A robust phylogenomic timetree for biotechnologically and medically important fungi from Aspergillaceae (Eurotiomycetes, Ascomycota)

AbbreviationsNT, nucleotide; AA, amino acid; CI, credible interval; RCV, relative composition variability; IC, internode certainty; GSF, gene support frequencies; GLS, gene-wise log-likelihood scores; DVMC, degree of violation of a molecular clock\n\nThe filamentous fungal family Aspergillaceae contains > 1,000 known species, mostly in the genera Aspergillus and Penicillium. Several species are used in the food, biotechnology, and drug industries (e.g., Aspergillus oryzae, Penicillium camemberti), while others are dangerous human and plant pathogens (e.g., Aspergillus fumigatus, Penicillium digitatum). To infer a robust phylogeny and pinpoint poorly resolved branches and their likely underlying contributors, we used 81 genomes spanning the diversity of Aspergillus and Penicillium to construct a 1,668-gene data matrix. Phylogenies of the nucleotide and amino acid versions of this full data matrix as well as of five additional 834-gene data matrices constructed by subsampling the top 50% of genes according to different criteria associated with strong phylogenetic signal were generated using three different maximum likelihood schemes (i.e., gene-partitioned, unpartitioned, and coalescence). Examination of the topological agreement among these 36 phylogenies and measures of internode certainty identified 12 / 78 (15.4%) bipartitions that were incongruent and pinpoint the likely underlying contributing factors (incomplete lineage sorting, hybridization or introgression, and reconstruction artifacts associated with poor taxon sampling). Relaxed molecular clock analyses suggest that Aspergillaceae likely originated in the lower Cretaceous and the Aspergillus and Penicillium genera in the upper Cretaceous. Our results shed light on the ongoing debate on Aspergillus systematics and taxonomy and provide a robust evolutionary and temporal framework for comparative genomic analyses in Aspergillaceae. More broadly, our approach provides a general template for phylogenomic identification of resolved and contentious branches in densely genome-sequenced lineages across the tree of life.

evolutionary biology↗

bioRxiv2018

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

BackgroundThe integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.\n\nMethodsTo better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).\n\nResultsWe developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).\n\nConclusionsDesirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

genomics↗