Characteristics of Wetting-Induced Bacteriophage Blooms in Biological Soil Crust.

Biological soil crusts (biocrusts) are photosynthetic "hot spots" in deserts and cover ∼12% of the Earth's terrestrial surface, and yet they face an uncertain future given expected shifts in rainfall events. Laboratory wetting of biocrust communities is known to cause a bloom of Firmicutes which rapidly become dominant community members within 2 days after emerging from a sporulated state. We hypothesized that their bacteriophages (phages) would respond to such a dramatic increase in their host's abundance. In our experiment, wetting caused Firmicutes to bloom and triggered a significant depletion of cyanobacterial diversity. We used genome-resolved metagenomics to link phage to their hosts and found that the bloom of the genus Bacillus correlated with a dramatic increase in the number of Caudovirales phages targeting these diverse spore-formers (r = 0.762). After 2 days, we observed dramatic reductions in the relative abundances of Bacillus, while the number of Bacillus phages continued to increase, suggestive of a predator-prey relationship. We found predicted auxiliary metabolic genes (AMGs) associated with sporulation in several Caudovirales genomes, suggesting that phages may influence and even benefit from sporulation dynamics in biocrusts. Prophage elements and CRISPR-Cas repeats in Firmicutes metagenome-assembled genomes (MAGs) provide evidence of recent infection events by phages, which were corroborated by mapping viral contigs to their host MAGs. Combined, these findings suggest that the blooming Firmicutes become primary targets for biocrust Caudovirales phages, consistent with the classical "kill-the-winner" hypothesis.IMPORTANCE This work forms part of an overarching research theme studying the effects of a changing climate on biological soil crust (biocrust) in the Southwestern United States. To our knowledge, this study was the first to characterize bacteriophages in biocrust and offers a view into the ecology of phages in response to a laboratory wetting experiment. The phages identified here represent lineages of Caudovirales, and we found that the dynamics of their interactions with their Firmicutes hosts explain the collapse of a bacterial bloom that was induced by wetting. Moreover, we show that phages carried host-altering metabolic genes and found evidence of proviral infection and CRISPR-Cas repeats within host genomes. Our results suggest that phages exert controls on population density by lysing dominant bacterial hosts and that they further impact biocrust by acquiring host genes for sporulation. Future research should explore how dominant these phages are in other biocrust communities and quantify how much the control and lysis of blooming populations contributes to nutrient cycling in biocrusts

The core metabolome and root exudation dynamics of three phylogenetically distinct plant species

Root exudates are plant-derived, exported metabolites likely shaping root-associated microbiomes by acting as nutrients and signals. However, root exudation dynamics are unclear and thus also, if changes in exudation are reflected in changes in microbiome structure. Here, we assess commonalities and differences between exudates of different plant species, diurnal exudation dynamics, as well as the accompanying methodological aspects of exudate sampling. We find that exudates should be collected for hours rather than days as many metabolite abundances saturate over time. Plant growth in sterile, nonsterile, or sugar-supplemented environments significantly alters exudate profiles. A comparison of Arabidopsis thaliana, Brachypodium distachyon, and Medicago truncatula shoot, root, and root exudate metabolite profiles reveals clear differences between these species, but also a core metabolome for tissues and exudates. Exudate profiles also exhibit a diurnal signature. These findings add to the methodological and conceptual groundwork for future exudate studies to improve understanding of plant-microbe interactions

Improved genome annotation through untargeted detection of pathway-specific metabolites

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.</p> <p>Results</p> <p>Of 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.</p> <p>Conclusions</p> <p>To facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (¹⁵N) or carbon (¹³C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.</p

Need for Laboratory Ecosystems To Unravel the Structures and Functions of Soil Microbial Communities Mediated by Chemistry

The chemistry underpinning microbial interactions provides an integrative framework for linking the activities of individual microbes, microbial communities, plants, and their environments. Currently, we know very little about the functions of genes and metabolites within these communities because genome annotations and functions are derived from the minority of microbes that have been propagated in the laboratory. Yet the diversity, complexity, inaccessibility, and irreproducibility of native microbial consortia limit our ability to interpret chemical signaling and map metabolic networks. In this perspective, we contend that standardized laboratory ecosystems are needed to dissect the chemistry of soil microbiomes. We argue that dissemination and application of standardized laboratory ecosystems will be transformative for the field, much like how model organisms have played critical roles in advancing biochemistry and molecular and cellular biology. Community consensus on fabricated ecosystems (“EcoFABs”) along with protocols and data standards will integrate efforts and enable rapid improvements in our understanding of the biochemical ecology of microbial communities

Impacts of Maize Domestication and Breeding on Rhizosphere Microbial Community Recruitment from a Nutrient Depleted Agricultural Soil.

Maize domestication and breeding have resulted in drastic and well documented changes in aboveground traits, but belowground effects on root system functioning and rhizosphere microbial communities remain poorly understood, despite their critical importance for nutrient and water acquisition. We investigated the rhizosphere microbial community composition and structure of ten Zea mays accessions along an evolutionary transect (two teosinte, three inbred maize lines, and five modern maize hybrids) grown in nutrient depleted soil from a low input agricultural system. Microbial community analysis revealed significant differences in community composition between soil compartments (proximal vs. distal rhizosphere) and between plant genetic groups (teosinte, inbred, and modern hybrid). Only a small portion of the microbial community was differentially selected across plant genetic groups: 3.7% of prokaryotic community members and 4.9% of fungal community members were significantly associated with a specific plant genetic group. Indicator species analysis showed the greatest differentiation between modern hybrids and the other two plant genetic groups. Co-occurrence network analysis revealed that microbial co-occurrence patterns of the inbred maize lines' rhizosphere were significantly more similar to those of the teosintes than to the modern hybrids. Our results suggest that advances in hybrid development significantly impacted rhizosphere microbial communities and network assembly

BONCAT-FACS-Seq reveals the active fraction of a biocrust community undergoing a wet-up event

Determining which microorganisms are active within soil communities remains a major technical endeavor in microbial ecology research. One promising method to accomplish this is coupling bioorthogonal non-canonical amino acid tagging (BONCAT) with fluorescence activated cell sorting (FACS) which sorts cells based on whether or not they are producing new proteins. Combined with shotgun metagenomic sequencing (Seq), we apply this method to profile the diversity and potential functional capabilities of both active and inactive microorganisms in a biocrust community after being resuscitated by a simulated rain event. We find that BONCAT-FACS-Seq is capable of discerning the pools of active and inactive microorganisms, especially within hours of applying the BONCAT probe. The active and inactive components of the biocrust community differed in species richness and composition at both 4 and 21 h after the wetting event. The active fraction of the biocrust community is marked by taxa commonly observed in other biocrust communities, many of which play important roles in species interactions and nutrient transformations. Among these, 11 families within the Firmicutes are enriched in the active fraction, supporting previous reports indicating that the Firmicutes are key early responders to biocrust wetting. We highlight the apparent inactivity of many Actinobacteria and Proteobacteria through 21 h after wetting, and note that members of the Chitinophagaceae, enriched in the active fraction, may play important ecological roles following wetting. Based on the enrichment of COGs in the active fraction, predation by phage and other bacterial members, as well as scavenging and recycling of labile nutrients, appear to be important ecological processes soon after wetting. To our knowledge, this is the first time BONCAT-FACS-Seq has been applied to biocrust samples, and therefore we discuss the potential advantages and shortcomings of coupling metagenomics to BONCAT to intact soil communities such as biocrust. In all, by pairing BONCAT-FACS and metagenomics, we are capable of highlighting the taxa and potential functions that typifies the microbes actively responding to a rain event

A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g., sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a two real data sets of 16S variable region amplicon sequences that were expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats

Tunnelivadelman kirvatorjunnan tehostaminen Anthocoris nemoralis -petoluteen avulla

Tunneli suojaa vadelmakasvustoa monilta kasvitaudeilta ja tuholaisilta, mutta tunneliin päästessään esimerkiksi kirvat lisääntyvät nopeasti, eivätkä biologiset torjuntaeliöt aina torju kirvoja riittävän tehokkaasti. Tunnelivadelmalla esiintyy isovattukirvaa sekä pikkuvattukirvaa. Kirvatorjun-nan tehostamiseen lähdettiin hakemaan apua kenttäkokeella, jossa kirvatorjuntaan käytettiin tyrninokkaludetta (Anthocoris nemoralis). Toimeksiantajana opinnäytetyössä oli biologinen torjuntafirma Biotus Oy. Koe suoritettiin uusimaalaisella tilalla kahdessa vadelmatunnelissa kesän 2017 aikana. Tunneleihin levitettiin kolmen viikon välein hyötyeliöitä. Hyötyeliöinä kokeessa käytettiin vainokaisia, kirvasääskiä sekä tyrninokkaluteita. Kirvojen määrä tunneleista laskettiin joka viikko ja samalla havainnoitiin hyötyeliöiden sekä tuhoeliöiden esiintyvyyttä tunneleissa. Ensimmäiset kirvahavainnot tunneleissa tehtiin heinäkuussa. Suurin osa havaituista kirvoista oli pikkuvattukirvan siivetöntä muotoa. BerryProtect -putkilo oli toimiva ennakkotorjunnassa. Kirvasääski torjui tehokkaammin toisessa tunnelissa, kun taas toisessa tyrninokkalude oli tehokkaampi. Näillä levitysstrategioilla saatiin kuitenkin Wennborgin tilan kannalta toimiva lopputulos

Toward a predictive understanding of Earth’s microbiomes to address 21st century challenges

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mBio 7 (2016): e00714-16, doi:10.1128/mBio.00714-16.Microorganisms have shaped our planet and its inhabitants for over 3.5 billion years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we propose that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function. From the parallelization of gene function testing to precision manipulation of genes, communities, and model ecosystems and development of novel analytical and simulation approaches, we outline strategies to move microbiome research into an era of causality. These efforts will improve prediction of ecosystem response and enable the development of new, responsible, microbiome-based solutions to significant challenges of our time.E.L.B. is supported by the Genomes-to-Watersheds Subsurface Biogeochemical Research Scientific Focus Area, and T.R.N. is supported by ENIGMA-Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov) Scientific Focus Area, funded by the U.S. Department of Energy (US DOE), Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02- 05CH11231 to Lawrence Berkeley National Laboratory (LBNL). M.E.M. is also supported by the US DOE, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. Z.G.C. is supported by National Science Foundation Integrative Organismal Systems grant #1355085, and by US DOE, Office of Biological and Environmental Research grant # DE-SC0008182 ER65389 from the Terrestrial Ecosystem Science Program. M.J.B. is supported by R01 DK 090989 from the NIH. T.J.D. is supported by the US DOE Office of Science’s Great Lakes Bioenergy Research Center, grant DE-FC02- 07ER64494. J.L.G. is supported by Alfred P. Sloan Foundation G 2-15-14023. R.K. is supported by grants from the NSF (DBI-1565057) and NIH (U01AI24316, U19AI113048, P01DK078669, 1U54DE023789, U01HG006537). K.S.P. is supported by grants from the NSF DMS- 1069303 and the Gordon & Betty Moore Foundation (#3300)