Tasting Soil Fungal Diversity with Earth Tongues: Phylogenetic Test of SATé Alignments for Environmental ITS Data

An abundance of novel fungal lineages have been indicated by DNA sequencing of the nuclear ribosomal ITS region from environmental samples such as soil and wood. Although phylogenetic analysis of these novel lineages is a key component of unveiling the structure and diversity of complex communities, such analyses are rare for environmental ITS data due to the difficulties of aligning this locus across significantly divergent taxa. One potential approach to this issue is simultaneous alignment and tree estimation. We targeted divergent ITS sequences of the earth tongue fungi (Geoglossomycetes), a basal class in the Ascomycota, to assess the performance of SATé, recent software that combines progressive alignment and tree building. We found that SATé performed well in generating high-quality alignments and in accurately estimating the phylogeny of earth tongue fungi. Drawing from a data set of 300 sequences of earth tongues and progressively more distant fungal lineages, 30 insufficiently identified ITS sequences from the public sequence databases were assigned to the Geoglossomycetes. The association between earth tongues and plants has been hypothesized for a long time, but hard evidence is yet to be collected. The ITS phylogeny showed that four ectomycorrhizal isolates shared a clade with Geoglossum but not with Trichoglossum earth tongues, pointing to the significant potential inherent to ecological data mining of environmental samples. Environmental sampling holds the key to many focal questions in mycology, and simultaneous alignment and tree estimation, as performed by SATé, can be a highly efficient companion in that pursuit

A narrative of the life of Mary Jemison : De-he-wä-mis, the white woman of the Genesee /

"This edition also includes numerous illustrations, further particulars of the history of De-he-wä-mis, and other interesting matter collected and arranged by Wm. Pryor Letchworth."First edition: A narrative of the life of Mrs. Mary Jemison ... / Carefully taken from her own words ... by James E. Seaver. Canandaigua [N.Y.] : Printed by J.D. Bemis and Co., 1824.Mode of access: Internet

<b>IL-33 mediates multi-walled carbon nanotube (MWCNT)-induced airway hyper-reactivity via the mobilization of innate helper cells in the lung</b>

Allergic asthma is a chronic inflammatory disorder of the airway associated with bronchial obstruction, airway hyper-reactivity (AHR), and mucus production. The epithelium may direct and propagate asthmatic-like responses. Central to this theory is the observation that viruses, air pollution, and allergens promote epithelial damage and trigger the generation of IL-25, IL-33, and TSLP via innate pathways such as TLRs and purinergic receptors. Similarly, engineered nanomaterials promote a Th2-associated pathophysiology. In this study, we tested the hypothesis that instillation of multi-walled carbon nanotubes (MWCNT) impair pulmonary function in C57Bl/6 mice due to the development of IL-33-dependent Th2-associated inflammation. MWCNT exposure resulted in elevated levels of IL-33 in the lavage fluid (likely originating from airway epithelial cells), enhanced AHR, eosinophil recruitment, and production of Th2-associated cytokines and chemokines. Moreover, these events were dependent on IL-13 signaling and the IL-33/ST2 axis, but independent of T and B cells. Finally, MWCNT exposure resulted in the recruitment of innate lymphoid cells. Collectively, our data suggest that MWCNT induce epithelial damage that results in release of IL-33, which in turn promotes innate lymphoid cell recruitment and the development of IL-13-dependent inflammatory response.</p

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes.

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.ISSN:1362-4962ISSN:0301-561