Home LiteratureArticle Details
PMID: 24030600 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Genomic properties of Marine Group A bacteria indicate a role in the marine sulfur cycle.

The ISME journal ·Vol. 8 ·No. 2 ·2014-02-00 ·Pages 455-68

Wright JJ, Mewis K, Hanson NW, Konwar KM, Maas KR, Hallam SJ

Abstract

Marine Group A (MGA) is a deeply branching and uncultivated phylum of bacteria. Although their functional roles remain elusive, MGA subgroups are particularly abundant and diverse in oxygen minimum zones and permanent or seasonally stratified anoxic basins, suggesting metabolic adaptation to oxygen-deficiency. Here, we expand a previous survey of MGA diversity in O2-deficient waters of the Northeast subarctic Pacific Ocean (NESAP) to include Saanich Inlet (SI), an anoxic fjord with seasonal O2 gradients and periodic sulfide accumulation. Phylogenetic analysis of small subunit ribosomal RNA (16S rRNA) gene clone libraries recovered five previously described MGA subgroups and defined three novel subgroups (SHBH1141, SHBH391, and SHAN400) in SI. To discern the functional properties of MGA residing along gradients of O2 in the NESAP and SI, we identified and sequenced to completion 14 fosmids harboring MGA-associated 16S RNA genes from a collection of 46 fosmid libraries sourced from NESAP and SI waters. Comparative analysis of these fosmids, in addition to four publicly available MGA-associated large-insert DNA fragments from Hawaii Ocean Time-series and Monterey Bay, revealed widespread genomic differentiation proximal to the ribosomal RNA operon that did not consistently reflect subgroup partitioning patterns observed in 16S rRNA gene clone libraries. Predicted protein-coding genes associated with adaptation to O2-deficiency and sulfur-based energy metabolism were detected on multiple fosmids, including polysulfide reductase (psrABC), implicated in dissimilatory polysulfide reduction to hydrogen sulfide and dissimilatory sulfur oxidation. These results posit a potential role for specific MGA subgroups in the marine sulfur cycle.

MeSH Terms
Aquatic Organisms/classification,genetics,metabolism Bacteria/classification,genetics,metabolism Biodiversity Genome, Bacterial/genetics Genomics Molecular Sequence Data Oxygen/analysis Pacific Ocean Phylogeny RNA, Ribosomal, 16S/genetics Seawater/chemistry
Chemicals
RNA, Ribosomal, 16S Oxygen
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Wright Jody J
Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.
Mewis Keith
Genome Science and Technology Program, University of British Columbia, Vancouver, BC, Canada.
Hanson Niels W
Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada.
Konwar Kishori M
Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.
Maas Kendra R
Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada.
Hallam Steven J
1] Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada [2] Graduate Program in Bioinformatics, University of British Columbia, Vancouver, BC, Canada.
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Article Info
Journal
The ISME journal
Abbr.
ISME J
ISSN
1751-7370
DOI
10.1038/ismej.2013.152
Published
2014-02-00
Epub
2013-00-12
Pages
455-68
Language
English
Region
England
NLM ID
101301086
PMCID
PMC3906813
Subset
IM
Databases
GENBANK
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KG089756, KG089757, KG089758, KG089759, KG089760, KG089761, KG089762, KG089763, KG089764, KG089765, KG089766, KG089767, KG089768, KG089769, KG089770, KG089771, KG089772, KG089773, KG089774, KG089775, KG089776, KG089777, KG089778, KG089779, KG089780, KG089781, KG089782, KG089783, KG089784, KG089785, KG089786, KG089787, KG089788, KG089789, KG089790, KG089791, KG089792, KG089793, KG089794, KG089795, KG089796, KG089797, KG089798, KG089799, KG089800, KG089801, KG089802, KG089803, KG089804, KG089805, KG089806, KG089807, KG089808, KG089809, KG089810, KG089811, KG089812, KG089813, KG089814, KG089815, KG089816, KG089817, KG089818, KG089819, KG089820, KG089821, KG089822, KG089823, KG089824, KG089825, KG089826, KG089827, KG089828, KG089829, KG089830, KG089831, KG089832, KG089833, KG089834, KG089835, KG089836, KG089837, KG089838, KG089839, KG089840, KG089841, KG089842, KG089843, KG089844, KG089845, KG089846, KG089847, KG089848, KG089849, KG089850, KG089851, KG089852, KG089853, KG089854, KG089855, KG089856, KG089857, KG089858, KG089859, KG089860, KG089861, KG089862, KG089863, KG089864, KG089865, KG089866, KG089867, KG089868, KG089869, KG089870, KG089871, KG089872, KG089873, KG089874, KG089875, KG089876, KG089877, KG089878, KG089879, KG089880, KG089881, KG089882, KG089883, KG089884, KG089885, KG089886, KG089887, KG089888, KG089889, KG089890, KG089891, KG089892, KG089893, KG089894, KG089895, KG089896, KG089897, KG089898, KG089899, KG089900, KG089901, KG089902, KG089903, KG089904, KG089905, KG089906, KG089907, KG089908, KG089909, KG089910, KG089911, KG089912, KG089913, KG089914, KG089915, KG089916, KG089917, KG089918, KG089919, KG089920, KG089921, KG089922, KG089923, KG089924, KG089925, KG089926, KG089927, KG089928, KG089929, KG089930, KG089931, KG089932, KG089933, KG089934, KG089935, KG089936, KG089937, KG089938, KG089939, KG089940, KG089941, KG089942, KG089943, KG089944, KG089945, KG089946, KG089947, KG089948, KG089949, KG089950, KG089951, KG089952, KG089953, KG089954, KG089955
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PubMed source DOI: 10.1038/ismej.2013.152 PMC Full Text View journal details
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