ALBERT

Description: RNA silencing at the transcriptional and posttranscriptional levels regulates endogenous gene expression, controls invading transposable elements (TEs), and protects the cell against viruses. Key components of the mechanism are small RNAs (sRNAs) of 21–24 nt that guide the silencing machinery to their nucleic acid targets in a nucleotide sequence-specific manner. Transcriptional gene silencing is associated with 24-nt sRNAs and RNA-directed DNA methylation (RdDM) at cytosine residues in three DNA sequence contexts (CG, CHG, and CHH). We previously demonstrated that 24-nt sRNAs are mobile from shoot to root in Arabidopsis thaliana and confirmed that they mediate DNA methylation at three sites in recipient cells. In this study, we extend this finding by demonstrating that RdDM of thousands of loci in root tissues is dependent upon mobile sRNAs from the shoot and that mobile sRNA-dependent DNA methylation occurs predominantly in non-CG contexts. Mobile sRNA-dependent non-CG methylation is largely dependent on the DOMAINS REARRANGED METHYLTRANSFERASES 1/2 (DRM1/DRM2) RdDM pathway but is independent of the CHROMOMETHYLASE (CMT)2/3 DNA methyltransferases. Specific superfamilies of TEs, including those typically found in gene-rich euchromatic regions, lose DNA methylation in a mutant lacking 22- to 24-nt sRNAs (dicer-like 2, 3, 4 triple mutant). Transcriptome analyses identified a small number of genes whose expression in roots is associated with mobile sRNAs and connected to DNA methylation directly or indirectly. Finally, we demonstrate that sRNAs from shoots of one accession move across a graft union and target DNA methylation de novo at normally unmethylated sites in the genomes of root cells from a different accession.

Description: Arctic permafrost soils store large amounts of soil organic carbon (SOC) that could be released into the atmosphere as methane (CH4) in a future warmer climate. How warming affects the complex microbial network decomposing SOC is not understood. We studied CH4 production of Arctic peat soil microbiota in anoxic microcosms...

Description: Arctic permafrost soils store large amounts of soil organic carbon (SOC) that could be released into the atmosphere as methane (CH4) in a future warmer climate. How warming affects the complex microbial network decomposing SOC is not understood. We studied CH4production of Arctic peat soil microbiota in anoxic microcosms over a temperature gradient from 1 to 30 °C, combining metatranscriptomic, metagenomic, and targeted metabolic profiling. The CH4production rate at 4 °C was 25% of that at 25 °C and increased rapidly with temperature, driven by fast adaptations of microbial community structure, metabolic network of SOC decomposition, and trophic interactions. Below 7 °C, syntrophic propionate oxidation was the rate-limiting step for CH4production; above this threshold temperature, polysaccharide hydrolysis became rate limiting. This change was associated with a shift within the functional guild for syntrophic propionate oxidation, with Firmicutes being replaced by Bacteroidetes. Correspondingly, there was a shift from the formate- and H2-using Methanobacteriales to Methanomicrobiales and from the acetotrophic Methanosarcinaceae to Methanosaetaceae.Methanogenesis from methylamines, probably stemming from degradation of bacterial cells, became more important with increasing temperature and corresponded with an increased relative abundance of predatory protists of the phylum Cercozoa. We concluded that Arctic peat microbiota responds rapidly to increased temperatures by modulating metabolic and trophic interactions so that CH4is always highly produced: The microbial community adapts through taxonomic shifts, and cascade effects of substrate availability cause replacement of functional guilds and functional changes within taxa.

Description: Metatranscriptomic census of active protists in soils The ISME Journal 9, 2178 (October 2015). doi:10.1038/ismej.2015.30 Authors: Stefan Geisen, Alexander T Tveit, Ian M Clark, Andreas Richter, Mette M Svenning, Michael Bonkowski & Tim Urich

Description: Methanogenic Thermoplasmata of the novel order Methanomassiliicoccales were recently discovered in human and animal gastro-intestinal tracts (GITs). However, their distribution in other methanogenic environments has not been addressed systematically. Here, we surveyed Methanomassiliicoccales presence in wetland soils, a globally important source of methane emissions to the atmosphere, and in the GITs of different animals by PCR targeting their 16S rRNA and methyl:coenzyme M reductase (α-subunit) genes. We detected Methanomassiliicoccales in all 16 peat soils investigated, indicating their wide distribution in these habitats. Additionally, we detected their genes in various animal faeces. Methanomassiliicoccales were subdivided in two broad phylogenetic clades designated ‘environmental’ and ‘GIT’ clades based on differential, although non-exclusive, habitat preferences of their members. A well-supported cluster within the environmental clade comprised more than 80% of all wetland 16S rRNA gene sequences. Metagenome assembly from bovine rumen fluid enrichments resulted in two almost complete genomes of both Methanomassiliicoccales clades. Comparative genomics revealed that members of the environmental clade contain larger genomes and a higher number of genes encoding anti-oxidative enzymes than animal GIT clade representatives. This study highlights the wide distribution of Methanomassiliicoccales in wetlands, which suggests that they contribute to methane emissions from these climate-relevant ecosystems.

Description: Article The mechanism of NLRP3 activation remains incompletely characterized. Here the authors show that it is dependent on vimentin, and that NLRP3-mediated lung injury and fibrosis induced by endotoxin, asbestos or bleomycin are reduced in vimentin-deficient mice. Nature Communications doi: 10.1038/ncomms7574 Authors: Gimena dos Santos, Micah R. Rogel, Margaret A. Baker, James R. Troken, Daniela Urich, Luisa Morales-Nebreda, Joseph A. Sennello, Mikhail A. Kutuzov, Albert Sitikov, Jennifer M. Davis, Anna P. Lam, Paul Cheresh, David Kamp, Dale K. Shumaker, G. R. Scott Budinger, Karen M. Ridge

Description: Understanding the diversity of human tissues is fundamental to disease and requires linking genetic information, which is identical in most of an individual's cells, with epigenetic mechanisms that could have tissue-specific roles. Surveys of DNA methylation in human tissues have established a complex landscape including both tissue-specific and invariant methylation patterns. Here we report high coverage methylomes that catalogue cytosine methylation in all contexts for the major human organ systems, integrated with matched transcriptomes and genomic sequence. By combining these diverse data types with each individuals' phased genome, we identified widespread tissue-specific differential CG methylation (mCG), partially methylated domains, allele-specific methylation and transcription, and the unexpected presence of non-CG methylation (mCH) in almost all human tissues. mCH correlated with tissue-specific functions, and using this mark, we made novel predictions of genes that escape X-chromosome inactivation in specific tissues. Overall, DNA methylation in several genomic contexts varies substantially among human tissues.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499021/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499021/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Schultz, Matthew D -- He, Yupeng -- Whitaker, John W -- Hariharan, Manoj -- Mukamel, Eran A -- Leung, Danny -- Rajagopal, Nisha -- Nery, Joseph R -- Urich, Mark A -- Chen, Huaming -- Lin, Shin -- Lin, Yiing -- Jung, Inkyung -- Schmitt, Anthony D -- Selvaraj, Siddarth -- Ren, Bing -- Sejnowski, Terrence J -- Wang, Wei -- Ecker, Joseph R -- F32 HL110473/HL/NHLBI NIH HHS/ -- F32HL110473/HL/NHLBI NIH HHS/ -- K99 HL119617/HL/NHLBI NIH HHS/ -- K99 NS080911/NS/NINDS NIH HHS/ -- K99HL119617/HL/NHLBI NIH HHS/ -- R00 NS080911/NS/NINDS NIH HHS/ -- R00NS080911/NS/NINDS NIH HHS/ -- R01 ES024984/ES/NIEHS NIH HHS/ -- T32 GM008666/GM/NIGMS NIH HHS/ -- U01 ES017166/ES/NIEHS NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2015 Jul 9;523(7559):212-6. doi: 10.1038/nature14465. Epub 2015 Jun 1.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Bioinformatics Program, University of California, San Diego, La Jolla, California 92093, USA [2] Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA. ; Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA. ; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA. ; 1] Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA [2] Department of Cognitive Science, University of California, San Diego, La Jolla, California 92037, USA. ; Ludwig Institute for Cancer Research, La Jolla, California 92093, USA. ; Department of Genetics, Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA. ; Department of Surgery, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8109, St Louis, Missouri 63110, USA. ; Bioinformatics Program, University of California, San Diego, La Jolla, California 92093, USA. ; 1] Ludwig Institute for Cancer Research, La Jolla, California 92093, USA [2] University of California, San Diego School of Medicine, Department of Cellular and Molecular Medicine, Institute of Genomic Medicine, La Jolla, California 92093, USA. ; 1] Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA [2] Division of Biological Sciences, University of California at San Diego, La Jolla, California 92037, USA [3] Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA. ; 1] Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, USA [2] Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California 92093, USA. ; 1] Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA [2] Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26030523" target="_blank"〉PubMed〈/a〉

Description: In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.

Description: RNA silencing at the transcriptional and posttranscriptional levels regulates endogenous gene expression, controls invading transposable elements (TEs), and protects the cell against viruses. Key components of the mechanism are small RNAs (sRNAs) of 21–24 nt that guide the silencing machinery to their nucleic acid targets in a nucleotide sequence-specific manner....