Biology subjects

Steindorff, A. S.

Publications and source records attributed to Steindorff, A. S..

3 recordsLinked to original sources

bioRxiv2025

MAESTRO uncovers tandem paralog dynamics as a core driver of fungal stress adaptation

Understanding how fungi adapt to diverse stresses is critical for mitigating emerging drug resistance and harnessing their robustness for biotechnology. Pichia kudriavzevii is a stress-tolerant and intrinsically drug-resistant yeast, with dual industrial and clinical importance. We analyzed 170 strains using MAESTRO, a machine-learning-assisted GWAS pipeline optimized for small cohorts. MAESTRO identified biologically meaningful features and revealed that copy number variation (CNV) of tandem paralogs (TPs) is a core mechanism of multi-stress adaptation. TPs were recurrently linked to tolerance of industrial inhibitors (HMF, phenolics, heat) and antifungal drugs (fluconazole, azoles), and deletion of the TP pair gene4260/gene4261 confirmed pleiotropic effects across stresses. These findings support a TP CNV model where recombination-driven TP CNVs and gene fusions enable rapid stress adaptation. Importantly, our results suggest that antifungal resistance can arise through co-option of mechanisms originally evolved for environmental stressors, raising a One Health concern about the environmental origins of drug-resistant pathogens.

genomics↗

bioRxiv2023

Improved gene annotation of the fungal wheat pathogen Zymoseptoria tritici based on combined Iso-Seq and RNA-Seq evidence

Despite large omics datasets, the establishment of a reliable gene annotation is still challenging for eukaryotic genomes. Here, we used the reference genome of the major fungal wheat pathogen Zymoseptoria tritici (isolate IPO323) as a case study to develop methods to improve eukaryotic gene prediction. Four previous IPO323 annotations identified 10,933 to 13,260 gene models, but only one third of these coding sequences (CDS) have identical structures. To resolve these discrepancies and improve gene models, we generated full-length transcripts using long-read sequencing. This dataset was used together with other evidence (RNA-Seq transcripts and protein sequences) to generate novel ab initio gene models. The selection of the best structure among novel and existing gene models was performed according to transcript and protein evidence using InGenAnnot, a novel bioinformatics suite. Overall, 13,414 re-annotated gene models (RGMs) were predicted, including 671 new genes among which 53 encoded effector candidates. This process corrected many of the errors (15%) observed in previous gene models (coding sequence fusions, false introns, missing exons). While fungal genomes have poor annotations of untranslated regions (UTRs), our Iso-Seq long-read sequences outlined 5 and 3UTRs for 73% of the RGMs. Alternative transcripts were identified for 13% of RGMs, mostly due to intron retention (75%), likely corresponding to unprocessed pre-mRNAs. A total of 353 genes displayed alternative transcripts with combinations of previously predicted or novel exons. Long non-coding transcripts (lncRNAs) and double-stranded RNAs from two fungal viruses were also identified. Most lncRNAs corresponded to antisense transcripts of genes (52%). lncRNAs that were up or down regulated during infection were enriched in antisense transcripts (70%), suggesting their involvement in the control of gene expression. Our results showed that combining different ab initio gene predictions and evidence-driven curation using InGenAnnot improved the quality of gene annotations of a compact eukaryotic genome. Our analysis also provided new insights into the transcriptional landscape of Z. tritici, helping develop an increasingly complex picture of its biology.

genomics↗

bioRxiv2021

Playing with FiRE: A genome resolved view of the soil microbiome responses to high severity forest wildfire

Warming climate has increased the frequency and size of high severity wildfires in the western United States, with deleterious impacts on forest ecosystem resilience. Although forest soil microbiomes provide a myriad of ecosystem functions, little is known regarding the impact of high severity fire on microbially-mediated processes. Here, we characterized functional shifts in the soil microbiome (bacterial, fungal, and viral) across wildfire burn severity gradients one year post-fire in coniferous forests (Colorado and Wyoming, USA). We generated the Fire Responding Ecogenomic database (FiRE-db), consisting of 637 metagenome-assembled bacterial genomes, 2490 viral populations, and 2 fungal genomes complemented by 12 metatranscriptomes from soils affected by low and high-severity, and complementary marker gene sequencing and metabolomics data. Actinobacteria dominated the fraction of enriched and active taxa across burned soils. Taxa within surficial soils impacted by high severity wildfire exhibited traits including heat resistance, sporulation and fast growth that enhanced post-fire survival. Carbon cycling within this system was predicted to be influenced by microbial processing of pyrogenic compounds and turnover of dominant bacterial community members by abundant viruses. These genome-resolved analyses across trophic levels reveal the complexity of post-fire soil microbiome activity and offer opportunities for restoration strategies that specifically target these communities.

microbiology↗