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Bacteria on and inside leaves can influence forest tree health and resilience. The distribution and limits of a tree species' range can be influenced by various factors, with biological interactions among the most significant. We investigated the processes shaping the bacterial needle community across the species distribution of limber pine, a widespread Western conifer inhabiting a range of extreme habitats. We tested four hypotheses: (i) Needle community structure varies across sites, with site-specific factors more important to microbial assembly than host species selection; (ii) dispersal limitation structures foliar communities across the range of limber pine; (iii) the relative significance of dispersal and selection differs across sites in the tree species range; and (iv) needle age structures bacterial communities. We characterized needle communities from the needle surface and tissue of limber pine and co-occurring conifers across 16 sites in the limber pine distribution. Our findings confirmed that site characteristics shape the assembly of bacterial communities across the host species range and showed that these patterns are not driven by dispersal limitation. Furthermore, the strength of selection by the host varied by site, possibly due to differences in available microbes. Our study, by focusing on trees in their natural setting, reveals real needle bacterial dynamics in forests, which is key to understanding the balance between stochastic and deterministic processes in shaping forest tree-microbe interactions. Such understanding will be necessary to predict or manipulate these interactions to support forest ecosystem productivity or assist plant migration and adaptation in the face of global change.

Peatlands are crucial sinks for atmospheric carbon but are critically threatened due to warming climates. Sphagnum (peat moss) species are keystone members of peatland communities where they actively engineer hyperacidic conditions, which improves their competitive advantage and accelerates ecosystem-level carbon sequestration. To dissect the molecular and physiological sources of this unique biology, we generated chromosome-scale genomes of two Sphagnum species: S. divinum and S. angustifolium. Sphagnum genomes show no gene colinearity with any other reference genome to date, demonstrating that Sphagnum represents an unsampled lineage of land plant evolution. The genomes also revealed an average recombination rate an order of magnitude higher than vascular land plants and short putative U/V sex chromosomes. These newly described sex chromosomes interact with autosomal loci that significantly impact growth across diverse pH conditions. This discovery demonstrates that the ability of Sphagnum to sequester carbon in acidic peat bogs is mediated by interactions between sex, autosomes and environment.

We present 49 metagenome assemblies of the microbiome associated with Sphagnum (peat moss) collected from ambient, artificially warmed, and geothermally warmed conditions across Europe. These data will enable further research regarding the impact of climate change on plant-microbe symbiosis, ecology, and ecosystem functioning of northern peatland ecosystems.

Mucins are large gel-forming polymers inside the mucus barrier that inhibit the yeast-to-hyphal transition of Candida albicans, a key virulence trait of this important human fungal pathogen. However, the molecular motifs in mucins that inhibit filamentation remain unclear despite their potential for therapeutic interventions. Here, we determined that mucins display an abundance of virulence-attenuating molecules in the form of mucin O-glycans. We isolated and cataloged >100 mucin O-glycans from three major mucosal surfaces and established that they suppress filamentation and related phenotypes relevant to infection, including surface adhesion, biofilm formation and cross-kingdom competition between C. albicans and the bacterium Pseudomonas aeruginosa. Using synthetic O-glycans, we identified three structures (core 1, core 1 + fucose and core 2 + galactose) that are sufficient to inhibit filamentation with potency comparable to the complex O-glycan pool. Overall, this work identifies mucin O-glycans as host molecules with untapped therapeutic potential to manage fungal pathogens.

Interactions between Sphagnum (peat moss) and cyanobacteria play critical roles in terrestrial carbon and nitrogen cycling processes. Knowledge of the metabolites exchanged, the physiological processes involved, and the environmental conditions allowing the formation of symbiosis is important for a better understanding of the mechanisms underlying these interactions. In this study, we used a cross-feeding approach with spatially resolved metabolite profiling and metatranscriptomics to characterize the symbiosis between Sphagnum and Nostoc cyanobacteria. A pH gradient study revealed that the Sphagnum-Nostoc symbiosis was driven by pH, with mutualism occurring only at low pH. Metabolic cross-feeding studies along with spatially resolved matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) identified trehalose as the main carbohydrate source released by Sphagnum, which were depleted by Nostoc along with sulfur-containing choline-O-sulfate, taurine and sulfoacetate. In exchange, Nostoc increased exudation of purines and amino acids. Metatranscriptome analysis indicated that Sphagnum host defense was downregulated when in direct contact with the Nostoc symbiont, but not as a result of chemical contact alone. The observations in this study elucidated environmental, metabolic, and physiological underpinnings of the widespread plant-cyanobacterial symbioses with important implications for predicting carbon and nitrogen cycling in peatland ecosystems as well as the basis of general host-microbe interactions.

BackgroundEukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data.ResultsUsing Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies ("phyloclassifies") cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data.ConclusionsPhylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies.

CD8 T cells can play both a protective and pathogenic role in inflammation and autoimmune development. Recent studies have highlighted the ability of CD8 T cells to function as T follicular helper (Tfh) cells in the germinal center in the context of infection. However, whether this phenomenon occurs in autoimmunity and contributes to autoimmune pathogenesis is largely unexplored. In this study, we show that CD8 T cells acquire a CD4 Tfh profile in the absence of functional regulatory T cells in both the IL-2-deficient and scurfy mouse models. Depletion of CD8 T cells mitigates autoimmune pathogenesis in IL-2-deficient mice. CD8 T cells express the B cell follicle-localizing chemokine receptor CXCR5, a principal Tfh transcription factor Bcl6, and the Tfh effector cytokine IL-21. CD8 T cells localize to the B cell follicle, express B cell costimulatory proteins, and promote B cell differentiation and Ab isotype class switching. These data reveal a novel contribution of autoreactive CD8 T cells to autoimmune disease, in part, through CD4 follicular-like differentiation and functionality.

Candida albicans is the most common cause of life-threatening fungal infections in humans, especially in immunocompromised individuals. Crucial to its success as an opportunistic pathogen is the considerable dynamism of its genome, which readily undergoes genetic changes generating new phenotypes and shaping the evolution of new strains. Candida africana is an intriguing C. albicans biovariant strain that exhibits remarkable genetic and phenotypic differences when compared with standard C. albicans isolates. Candida africana is well-known for its low degree of virulence compared with C. albicans and for its inability to produce chlamydospores that C. albicans, characteristically, produces under certain environmental conditions. Chlamydospores are large, spherical structures, whose biological function is still unknown. For this reason, we have sequenced, assembled, and annotated the whole transcriptomes obtained from an efficient C. albicans chlamydospore-producing clinical strain (GE1), compared with the natural chlamydospore-negative C. africana clinical strain (CBS 11016). The transcriptomes of both C. albicans (GE1) and C. africana (CBS 11016) clinical strains, grown under chlamydospore-inducing conditions, were sequenced and assembled into 7,442 (GE1 strain) and 8,370 (CBS 11016 strain) high quality transcripts, respectively. The release of the first assembly of the C. africana transcriptome will allow future comparative studies to better understand the biology and evolution of this important human fungal pathogen.

FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNU's Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought.

A common complaint in work spaces is lack of speech privacy in open plan and private offices. Standard methods exist to quantify speech privacy by measurement and calculation. Performing these methods, although accurate, requires the precision of expensive hardware and the experience of professional acousticians. Robert Young (1965) developed a simple method using common variables to accurately determine speech privacy. These variables can be estimated during design or measured after construction. A side by side evaluation of the standard speech privacy methods were compared to a method based on Young’s research. These evaluations determined that the methods had comparative levels of accuracy and the Young method costs substantially less to utilize. The measurements required by all of the evaluated methods were performed within three types of private office and three types of open plan office spaces. Following the testing of the private offices there was a review of the sound isolation of the shared partition and the background noise in the receiving office. Measurements in the open plan spaces were conducted in spaces with different height partitions to identify if this was a factor in the measured speech privacy level. A pink noise signal was used as the source noise for measurement between all of the tested spaces. The pink noise signal was adjusted to have a frequency response similar to a human voice. The constant level of pink noise was produced as a test signal for playback on a compact disc or MP3 player and an appropriate loudspeaker. The appendix summarizes all measurements.

Summary Antimicrobial peptides (AMPs) are promising alternative antimicrobial agents. Currently, however, portable, user-friendly and efficient methods for predicting AMP sequences from genome-scale data are not readily available. Here we present amPEPpy, an open-source, multi-threaded command-line application for predicting AMP sequences using a random forest classifier. Availability and implementation amPEPpy is implemented in Python 3 and is freely available through GitHub (https://github.com/tlawrence3/amPEPpy). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]