Robust species delimitations provide a foundation for investigating speciation, phylogeography, and conservation. Here we attempted to elucidate species boundaries in the cosmopolitan lichen-forming fungal taxon Lecanora polytropa. This nominal taxon is morphologically variable, with distinct populations occurring on all seven continents. To delimit candidate species, we compiled ITS sequence data from populations worldwide. For a subset of the samples, we also generated alignments for 1209 single-copy nuclear genes and an alignment spanning most of the mitochondrial genome to assess concordance among the ITS, nuclear, and mitochondrial inferences. Species partitions were empirically delimited from the ITS alignment using ASAP and bPTP. We also inferred a phylogeny for the L. polytropa clade using a four-marker dataset. ASAP species delimitations revealed up to 103 species in the L. polytropa clade, with 75 corresponding to the nominal taxon L. polytropa. Inferences from phylogenomic alignments generally supported that these represent evolutionarily independent lineages or species. Less than 10% of the candidate species were comprised of specimens from multiple continents. High levels of candidate species were recovered at local scales but generally with limited overlap across regions. Lecanora polytropa likely ranks as one of the largest species complexes of lichen-forming fungi known to date.

Approximately 450,000 cases of Non-Hodgkin’s lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin’s Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.

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Defining the human factors associated with severe vs mild SARS-CoV-2 infection has become of increasing interest. Mining large numbers of public gene expression datasets is an effective way to identify genes that contribute to a given phenotype. Combining RNA-sequencing data with the associated clinical metadata describing disease severity can enable earlier identification of patients who are at higher risk of developing severe COVID-19 disease. We consequently identified 356 public RNA-seq human transcriptome samples from the Gene Expression Omnibus database that had disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to quantify gene expression in each patient. This process involved using Salmon to map the reads to the reference transcriptomes, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then applied a machine learning algorithm to the read counts data to identify features that best differentiated samples based on COVID-19 severity phenotype. Ultimately, we produced a ranked list of genes based on their Gini importance values that includes GIMAP7 and S1PR2, which are associated with immunity and inflammation (respectively). Our results show that these two genes can potentially predict people with severe COVID-19 at up to ~90% accuracy. We expect that our findings can help contribute to the development of improved prognostics for severe COVID-19.