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      Influence of fecal collection conditions and 16S rRNA gene sequencing at two centers on human gut microbiota analysis

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          Abstract

          To optimise fecal sampling for reproducible analysis of the gut microbiome, we compared different methods of sample collection and sequencing of 16S rRNA genes at two centers. Samples collected from six individuals on three consecutive days were placed in commercial collection tubes (OMNIgeneGut OMR-200) or in sterile screw-top tubes in a home fridge or home freezer for 6–24 h, before transfer and storage at −80 °C. Replicate samples were shipped to centers in Australia and the USA for DNA extraction and sequencing by their respective PCR protocols, and analysed with the same bioinformatic pipeline. Variation in gut microbiome was dominated by differences between individuals. Minor differences in the abundance of taxa were found between collection-processing methods and day of collection, and between the two centers. We conclude that collection with storage and transport at 4 °C within 24 h is adequate for 16S rRNA analysis of the gut microbiome. Other factors including differences in PCR and sequencing methods account for relatively minor variation compared to differences between individuals.

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          Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample.

          The ongoing revolution in high-throughput sequencing continues to democratize the ability of small groups of investigators to map the microbial component of the biosphere. In particular, the coevolution of new sequencing platforms and new software tools allows data acquisition and analysis on an unprecedented scale. Here we report the next stage in this coevolutionary arms race, using the Illumina GAIIx platform to sequence a diverse array of 25 environmental samples and three known "mock communities" at a depth averaging 3.1 million reads per sample. We demonstrate excellent consistency in taxonomic recovery and recapture diversity patterns that were previously reported on the basis of metaanalysis of many studies from the literature (notably, the saline/nonsaline split in environmental samples and the split between host-associated and free-living communities). We also demonstrate that 2,000 Illumina single-end reads are sufficient to recapture the same relationships among samples that we observe with the full dataset. The results thus open up the possibility of conducting large-scale studies analyzing thousands of samples simultaneously to survey microbial communities at an unprecedented spatial and temporal resolution.
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            Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples.

            Storage conditions are considered to be a critical component of DNA-based microbial community analysis methods. However, whether differences in short-term sample storage conditions impact the assessment of bacterial community composition and diversity requires systematic and quantitative assessment. Therefore, we used barcoded pyrosequencing of bacterial 16S rRNA genes to survey communities, harvested from a variety of habitats [soil, human gut (feces) and human skin] and subsequently stored at 20, 4, -20 and -80 degrees C for 3 and 14 days. Our results indicate that the phylogenetic structure and diversity of communities in individual samples were not significantly influenced by the storage temperature or the duration of storage. Likewise, the relative abundances of most taxa were largely unaffected by temperature even after 14 days of storage. Our results indicate that environmental factors and biases in molecular techniques likely confer greater amounts of variation to microbial communities than do differences in short-term storage conditions, including storage for up to 2 weeks at room temperature. These results suggest that many samples collected and stored under field conditions without refrigeration may be useful for microbial community analyses.
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              An inducible lentiviral guide RNA platform enables the identification of tumor-essential genes and tumor-promoting mutations in vivo.

              The CRISPR/Cas9 technology enables the introduction of genomic alterations into almost any organism; however, systems for efficient and inducible gene modification have been lacking, especially for deletion of essential genes. Here, we describe a drug-inducible small guide RNA (sgRNA) vector system allowing for ubiquitous and efficient gene deletion in murine and human cells. This system mediates the efficient, temporally controlled deletion of MCL-1, both in vitro and in vivo, in human Burkitt lymphoma cell lines that require this anti-apoptotic BCL-2 protein for sustained survival and growth. Unexpectedly, repeated induction of the same sgRNA generated similar inactivating mutations in the human Mcl-1 gene due to low mutation variability exerted by the accompanying non-homologous end-joining (NHEJ) process. Finally, we were able to generate hematopoietic cell compartment-restricted Trp53-knockout mice, leading to the identification of cancer-promoting mutants of this critical tumor suppressor.
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                Author and article information

                Contributors
                papenfuss@wehi.edu.au
                harrison@wehi.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 March 2018
                12 March 2018
                2018
                : 8
                : 4386
                Affiliations
                [1 ]GRID grid.1042.7, The Walter and Eliza Hall Institute of Medical Research, ; Parkville, Victoria 3052 Australia
                [2 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Medical Biology, , University of Melbourne, ; Victoria, 3010 Australia
                [3 ]ISNI 0000 0004 1936 7304, GRID grid.1010.0, Robinson Research Institute, , University of Adelaide, ; Adelaide, 5006 South Australia Australia
                [4 ]ISNI 0000 0001 2160 926X, GRID grid.39382.33, Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, , Baylor College of Medicine, ; Houston, TX 77030 USA
                [5 ]ISNI 0000 0004 0624 1200, GRID grid.416153.4, Department of Diabetes and Endocrinology, , Royal Melbourne Hospital, ; Parkville, Victoria Australia
                [6 ]GRID grid.240562.7, Endocrinology Department, , Lady Cilento Children’s Hospital, ; Brisbane, Queensland Australia
                [7 ]ISNI 0000 0000 9690 854X, GRID grid.413973.b, Institute of Endocrinology and Diabetes, , The Children’s Hospital at Westmead, ; Westmead, New South Wales Australia
                [8 ]GRID grid.415193.b, Serology and Virology Division, , NSW Health Pathology, Prince of Wales Hospital, ; Randwick, New South Wales Australia
                [9 ]ISNI 0000 0004 4902 0432, GRID grid.1005.4, University of NSW, ; Sydney, Australia
                [10 ]ISNI 0000 0004 0625 8600, GRID grid.410667.2, Department of Endocrinology & Diabetes, , Princess Margaret Hospital for Children, ; Perth, Western Australia Australia
                [11 ]ISNI 0000 0000 8828 1230, GRID grid.414659.b, Telethon Kids Institute, ; Subiaco, Western Australia Australia
                [12 ]ISNI 0000 0004 1936 7304, GRID grid.1010.0, School of Public Health, , University of Adelaide, ; Adelaide, South Australia Australia
                [13 ]GRID grid.431595.f, Centre for Diabetes Research, , Harry Perkins Institute of Medical Research, ; Nedlands, Western Australia Australia
                [14 ]Hunter Diabetes Centre, Mereweather, New South Wales Australia
                [15 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Melbourne eResearch Group, , University of Melbourne, ; Parkville, Victoria Australia
                [16 ]ISNI 0000 0000 9295 3933, GRID grid.419789.a, Diabetes and Vascular Medicine Unit, , Monash Health, ; Clayton, Victoria Australia
                [17 ]ISNI 0000 0004 1936 7304, GRID grid.1010.0, Adelaide Medical School, , University of Adelaide, ; Adelaide, South Australia Australia
                [18 ]ISNI 0000 0004 0540 0062, GRID grid.414257.1, Child Health Research Unit, , Barwon Health, ; Geelong, Victoria Australia
                Author information
                http://orcid.org/0000-0003-1561-0074
                http://orcid.org/0000-0002-3808-8576
                http://orcid.org/0000-0002-1102-8506
                Article
                22491
                10.1038/s41598-018-22491-7
                5847573
                29531234
                21b407b7-62b1-4ad6-9049-549202b0f149
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 September 2017
                : 22 February 2018
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