Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Erratum to: The sponge microbiome project
    Moitinho-Silva, Lucas ; Nielsen, Shaun ; Amir, Amnon ; Gonzalez, Antonio ; Ackermann, Gail L. ; Cerrano, Carlo ; Astudillo-Garcia, Carmen ; Easson, Cole ; Sipkema, Detmer ; Liu, Fang ; Steinert, Georg ; Kotoulas, Giorgos ; McCormack, Grace P. ; Feng, Guofang ; Bell, James J. ; Vicente, Jan ; Björk, Johannes R. ; Montoya, Jose M. ; Olson, Julie B. ; Reveillaud, Julie ; Steindler, Laura ; Pineda, Mari Carmen ; Marra, Maria V. ; Ilan, Micha ; Taylor, Michael W. ; Polymenakou, Paraskevi ; Erwin, Patrick M. ; Schupp, Peter J. ; Simister, Rachel L. ; Knight, Rob ; Thacker, Robert W. ; Costa, Rodrigo ; Hill, Russell T. ; Lopez-Legentil, Susanna ; Dailianis, Thanos ; Ravasi, Timothy ; Hentschel, Ute ; Li, Zhiyong ; Webster, Nicole S. ; Thomas, Torsten - \ 2018
    GigaScience 7 (2018)12. - ISSN 2047-217X
    The sponge microbiome project
    Moitinho-Silva, Lucas ; Nielsen, Shaun ; Amir, Amnon ; Gonzalez, Antonio ; Ackermann, Gail L. ; Cerrano, Carlo ; Astudillo-Garcia, Carmen ; Easson, Cole ; Sipkema, Detmer ; Liu, Fang ; Steinert, Georg ; Kotoulas, Giorgos ; McCormack, Grace P. ; Feng, Guofang ; Bell, James J. ; Vicente, Jan ; Björk, Johannes R. ; Montoya, Jose M. ; Olson, Julie B. ; Reveillaud, Julie ; Steindler, Laura ; Pineda, Mari Carmen ; Marra, Maria V. ; Ilan, Micha ; Taylor, Michael W. ; Polymenakou, Paraskevi ; Erwin, Patrick M. ; Schupp, Peter J. ; Simister, Rachel L. ; Knight, Rob ; Thacker, Robert W. ; Costa, Rodrigo ; Hill, Russell T. ; Lopez-Legentil, Susanna ; Dailianis, Thanos ; Ravasi, Timothy ; Hentschel, Ute ; Li, Zhiyong ; Webster, Nicole S. ; Thomas, Torsten - \ 2017
    GigaScience 6 (2017)10. - ISSN 2047-217X
    16S rRNA gene - Archaea - Bacteria - Marine sponges - Microbial diversity - Microbiome - Symbiosis
    Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.
    Predicting the HMA-LMA status in marine sponges by machine learning
    Moitinho-Silva, Lucas ; Steinert, Georg ; Nielsen, Shaun ; Hardoim, Cristiane C.P. ; Wu, Yu Chen ; McCormack, Grace P. ; López-Legentil, Susanna ; Marchant, Roman ; Webster, Nicole ; Thomas, Torsten ; Hentschel, Ute - \ 2017
    Frontiers in Microbiology 8 (2017). - ISSN 1664-302X - 14 p.
    16S rRNA gene - Marine sponges - Microbial diversity - Microbiome - Random forest - Symbiosis

    The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered "HMA indicators" and "LMA indicators." Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n = 44) and LMA (n = 74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.

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