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  • Adaptation
  • Animal evolution
  • Bacteria
  • Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu  (5)
  • Oxford University Press  (3)
  • 2020-2023  (8)
  • 1970-1974
  • 1950-1954
  • 1935-1939
Collection
Publisher
Years
  • 2020-2023  (8)
  • 1970-1974
  • 1950-1954
  • 1935-1939
  • 2015-2019  (3)
Year
  • 1
    Publication Date: 2022-10-26
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Keller, A. G., Apprill, A., Lebaron, P., Robbins, J., Romano, T. A., Overton, E., Rong, Y., Yuan, R., Pollara, S., & Whalen, K. E. Characterizing the culturable surface microbiomes of diverse marine animals. FEMS Microbiology Ecology, 97(4), (2021): fiab040, https://doi.org/10.1093/femsec/fiab040.
    Description: Biofilm-forming bacteria have the potential to contribute to the health, physiology, behavior and ecology of the host and serve as its first line of defense against adverse conditions in the environment. While metabarcoding and metagenomic information furthers our understanding of microbiome composition, fewer studies use cultured samples to study the diverse interactions among the host and its microbiome, as cultured representatives are often lacking. This study examines the surface microbiomes cultured from three shallow-water coral species and two whale species. These unique marine animals place strong selective pressures on their microbial symbionts and contain members under similar environmental and anthropogenic stress. We developed an intense cultivation procedure, utilizing a suite of culture conditions targeting a rich assortment of biofilm-forming microorganisms. We identified 592 microbial isolates contained within 15 bacterial orders representing 50 bacterial genera, and two fungal species. Culturable bacteria from coral and whale samples paralleled taxonomic groups identified in culture-independent surveys, including 29% of all bacterial genera identified in the Megaptera novaeangliae skin microbiome through culture-independent methods. This microbial repository provides raw material and biological input for more nuanced studies which can explore how members of the microbiome both shape their micro-niche and impact host fitness.
    Description: Funding was provided by the National Science Foundation (Biological Oceanography) award #1657808 and National Institutes of Health grants 1R21-AI119311–01 to K. E. Whalen, as well as funding from the Koshland Integrated Natural Science Center and Green Fund at Haverford College. This constitutes scientific manuscript #298 from the Sea Research Foundation.
    Keywords: Bacteria ; SSU rRNA ; Coral ; Whale ; Microbiome ; Skin
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: MO - biogeochemical and microbial field surveys
    Description: Biogeochemical and microbial field surveys from the BATS site, Bermuda from R/V Atlantic Explorer cruises from 2009-2013. This dataset includes water samples collected from 2009-2013 at the Bermuda Hydrostation that were analyzed for DOC, POC, bacterial abundance, leucine, and thymidine incorporation. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/543314
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-0802004
    Keywords: Hydrography ; Bacteria ; Organic matter ; Sargasso Sea
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 3
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Diatom Matrix RNAseq
    Description: Transcriptome data for bacteria Ruegeria pomeroyi DSS-3, Stenotrophomonas sp. SKA14, Polaribacter dokdonensis MED152, and Dokdonia MED134 collected eight hours after individual inoculation into a diatom Thalassiosira psuedonana culture. The sequence data description for PRHNA448168 is at https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA448168. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/818765
    Description: NSF Division of Integrative Organismal Systems (NSF IOS) IOS-1656311
    Keywords: Phytoplankton ; Bacteria ; Co-cultures ; Transcriptomes ; RNAseq
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 4
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: BLOOFINZ-GoM flow cytometry abundance
    Description: Phytoplankton and bacteria abundance from flow cytometry from samples collected in the Gulf of Mexico on R/V Nancy Foster cruises NF1704 and NF1802 in May 2017 and May 2018 For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/835414
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1851558, National Oceanic and Atmospheric Administration (NOAA) NA16NMF4320058
    Keywords: Phytoplankton ; Bacteria ; Prochlorococcus ; Synechococcus ; Gulf of Mexico
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 5
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    Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu
    Publication Date: 2022-10-31
    Description: Dataset: Coral Associated Microbes on Mo'orean Coral Reefs
    Description: Three species of coral, plus water and sediment, were sampled at 21 sites around the island of Mo’orea, French Polynesia during the dry and rainy seasons in 2017 and 2018. Coral associated microbes (bacteria and archaea) were investigated and their community composition characterized through sequencing of the 16S rRNA gene. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/845039
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1635798, NSF Division of Ocean Sciences (NSF OCE) OCE-1635913
    Keywords: Bacteria ; Archaea ; Moorea Coral Reef LTER ; Holobiont ; Microbiome ; Earth Microbiome Project
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 6
    Publication Date: 2022-10-31
    Description: Dataset: CTD Profile Casts
    Description: CTD Depth Profile Cast Data for the InVirT-2019-BATS (Bermuda Atlantic Time Series) project taken in the on board of the R/V Atlantic Explorer AE1926 in 2019. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/835593
    Description: NSF Division of Ocean Sciences (NSF OCE) OCE-1829636
    Keywords: Bermuda Atlantic Time Series ; BATS ; Virus ; Phage ; Metatranscriptomics ; Bacteria ; Diel ; Sargasso Sea
    Repository Name: Woods Hole Open Access Server
    Type: Dataset
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  • 7
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tassia, M. G., David, K. T., Townsend, J. P., & Halanych, K. M. TIAMMAt: leveraging biodiversity to revise protein domain models, evidence from innate immunity. Molecular Biology and Evolution, 38(12), (2021): 5806–5818, https://doi.org/10.1093/molbev/msab258.
    Description: Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species.
    Description: This work was supported by The National Science Foundation (Grant No. IOS—1755377 to K.M.H., Rita Graze, and Elizabeth Hiltbold Schwartz), and K.T.D. was supported by The National Science Foundation’s Graduate Research Fellowship Program.
    Keywords: Protein evolution ; Domain annotation ; Animal evolution ; Innate immunity
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 8
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Shoshan, Y., Liscovitch-Brauer, N., Rosenthal, J. J. C., & Eisenberg, E. Adaptive proteome diversification by nonsynonymous A-to-I RNA editing in coleoid cephalopods. Molecular Biology and Evolution, 38(9), (2021): 3775–3788, https://doi.org/10.1093/molbev/msab154.
    Description: RNA editing by the ADAR enzymes converts selected adenosines into inosines, biological mimics for guanosines. By doing so, it alters protein-coding sequences, resulting in novel protein products that diversify the proteome beyond its genomic blueprint. Recoding is exceptionally abundant in the neural tissues of coleoid cephalopods (octopuses, squids, and cuttlefishes), with an over-representation of nonsynonymous edits suggesting positive selection. However, the extent to which proteome diversification by recoding provides an adaptive advantage is not known. It was recently suggested that the role of evolutionarily conserved edits is to compensate for harmful genomic substitutions, and that there is no added value in having an editable codon as compared with a restoration of the preferred genomic allele. Here, we show that this hypothesis fails to explain the evolutionary dynamics of recoding sites in coleoids. Instead, our results indicate that a large fraction of the shared, strongly recoded, sites in coleoids have been selected for proteome diversification, meaning that the fitness of an editable A is higher than an uneditable A or a genomically encoded G.
    Description: This research was supported by a grants from the United States–Israel Binational Science Foundation (BSF), Jerusalem, Israel (BSF2017262 to J.J.C.R. and E.E.), the Israel Science Foundation (3371/20 to E.E.) and the National Science Foundation (IOS 1827509 and 1557748 to J.J.C.R).
    Keywords: RNA editing ; Adaptation ; Evolution
    Repository Name: Woods Hole Open Access Server
    Type: Article
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