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      Geochemical-Compositional-Functional Changes in Arctic Soil Microbiomes Post Land Submergence Revealed by Metagenomics

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          Abstract

          Lakes of meltwater in the Artic have become one of the transforming landscape changes under global warming. We herein compared microbial communities between sediments and bank soils at an arctic lake post land submergence using geochemistry, 16S rRNA amplicons, and metagenomes. The results obtained showed that each sample had approximately 2,609 OTUs on average and shared 1,716 OTUs based on the 16S rRNA gene V3–V4 region. Dominant phyla in sediments and soils included Proteobacteria, Acidobacteria, Actinobacteria, Gemmatimonadetes, and Nitrospirae; sediments contained a unique phylum, Euryarchaeota, with the phylum Thaumarchaeota being primarily present in bank soils. Among the top 35 genera across all sites, 17 were more abundant in sediments, while the remaining 18 were more abundant in bank soils; seven out of the top ten genera across all sites were only from sediments. A redundancy analysis separated sediment samples from soil samples based on the components of nitrite and ammonium. Metagenome results supported the role of nitrite because most of the genes for denitrification and methane metabolic genes were more abundant in sediments than in soils, while the abundance of phosphorus-utilizing genes was similar and, thus, was not a significant explanatory factor. We identified several modules from the global networks of OTUs that were closely related to some geochemical factors, such as pH and nitrite. Collectively, the present results showing consistent changes in geochemistry, microbiome compositions, and functional genes suggest an ecological mechanism across molecular and community levels that structures microbiomes post land submergence.

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          Most cited references34

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          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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            Cross-biome metagenomic analyses of soil microbial communities and their functional attributes.

            For centuries ecologists have studied how the diversity and functional traits of plant and animal communities vary across biomes. In contrast, we have only just begun exploring similar questions for soil microbial communities despite soil microbes being the dominant engines of biogeochemical cycles and a major pool of living biomass in terrestrial ecosystems. We used metagenomic sequencing to compare the composition and functional attributes of 16 soil microbial communities collected from cold deserts, hot deserts, forests, grasslands, and tundra. Those communities found in plant-free cold desert soils typically had the lowest levels of functional diversity (diversity of protein-coding gene categories) and the lowest levels of phylogenetic and taxonomic diversity. Across all soils, functional beta diversity was strongly correlated with taxonomic and phylogenetic beta diversity; the desert microbial communities were clearly distinct from the nondesert communities regardless of the metric used. The desert communities had higher relative abundances of genes associated with osmoregulation and dormancy, but lower relative abundances of genes associated with nutrient cycling and the catabolism of plant-derived organic compounds. Antibiotic resistance genes were consistently threefold less abundant in the desert soils than in the nondesert soils, suggesting that abiotic conditions, not competitive interactions, are more important in shaping the desert microbial communities. As the most comprehensive survey of soil taxonomic, phylogenetic, and functional diversity to date, this study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.
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              Lakes as sentinels of climate change.

              While there is a general sense that lakes can act as sentinels of climate change, their efficacy has not been thoroughly analyzed. We identified the key response variables within a lake that act as indicators of the effects of climate change on both the lake and the catchment. These variables reflect a wide range of physical, chemical, and biological responses to climate. However, the efficacy of the different indicators is affected by regional response to climate change, characteristics of the catchment, and lake mixing regimes. Thus, particular indicators or combinations of indicators are more effective for different lake types and geographic regions. The extraction of climate signals can be further complicated by the influence of other environmental changes, such as eutrophication or acidification, and the equivalent reverse phenomena, in addition to other land-use influences. In many cases, however, confounding factors can be addressed through analytical tools such as detrending or filtering. Lakes are effective sentinels for climate change because they are sensitive to climate, respond rapidly to change, and integrate information about changes in the catchment.
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                Author and article information

                Journal
                Microbes Environ
                Microbes Environ
                Microbes and Environments
                the Japanese Society of Microbial Ecology (JSME)/the Japanese Society of Soil Microbiology (JSSM)/the Taiwan Society of Microbial Ecology (TSME)/the Japanese Society of Plant Microbe Interactions (JSPMI)
                1342-6311
                1347-4405
                June 2019
                07 June 2019
                : 34
                : 2
                : 180-190
                Affiliations
                [1 ] Key Lab of Marine Bioactive Substances, First Institute of Oceanography, State Oceanic Administration Qingdao 266061 China
                [2 ] Department of Bioengineering, College of Marine Sciences and Biological Engineering, Qingdao University of Science & Technology Qingdao 266042 China
                [3 ] College of Computer Science and Technology, Jilin University Changchun, Jilin 100012 China
                [4 ] Jilin University First Hospital Changchun, Jilin 100012 China
                [5 ] College of Chemistry and Chemical Engineering, Qingdao University Qingdao 266071 China
                [6 ] Center for Fundamental and Applied Microbiomics, Biodesign Institute, Arizona State University Tempe, AZ 85287 USA
                Author notes
                [* ]Corresponding author. E-mail: hshcao@ 123456asu.edu ; Tel: +1 480–727–8381.
                Article
                34_180
                10.1264/jsme2.ME18091
                6594734
                31178526
                5e0946a1-9605-4aa0-a089-ad9501654ecd
                Copyright © 2019 by Japanese Society of Microbial Ecology / Japanese Society of Soil Microbiology / Taiwan Society of Microbial Ecology / Japanese Society of Plant Microbe Interactions.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 June 2018
                : 23 February 2019
                Categories
                Articles

                16s rrna gene,the arctic,soil microbiomes,metagenome,meltwater

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