Microbial and chemical dynamics of a toxic dinoflagellate bloom

Fibrivirga algicola gen. nov., sp. nov., an algicidal bacterium isolated from a freshwater river

An aerobic, gram-stain-negative, pink-colored, non-motile and rod-shaped algicidal bacterium, designated as JA-25^T was isolated from freshwater in Geumgang River, Republic of Korea. Strain JA-25^T grew at 15-30 °C and pH 6-9, and did not require NaCl. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain JA-25^T belongs to the family 'Spirosomaceae' and is most closely related to Fibrella aestuarina BUZ 2^T (93.6%). Strain JA-25^T showed < 90% sequence similarity to other members of the family 'Spirosomaceae'. The average nucleotide identity(ANI), in silico DNA-DNA hybridization and average amino acid identity(AAI) values based on the genomic sequences of JA-25^T and F. aestuarina BUZ 2^T were 74.4, 20.5, and 73.6%, respectively. Strain JA-25^T showed an algicidal effect on the marine flagellate alga Heterocapsa triquetra, but no effect on fresh water cyanobacterium (Nostoc). In genome analysis, RIPP-like peptides were detected and predicted to resemble the indolmycin biosynthetic gene cluster, which possibly influence its algicidal effect. Furthermore, a bacteriorhodopsin gene with photoheterotrophic characteristics was detected. The genomic DNA G + C content was 52.5 mol%. The major cellular fatty acids were summed feature 3 (C16:1 ω6c/C16:1 ω7c), C16:1 ω5c, C16:0 (> 10%). The major respiratory quinone was menaquinone 7 and major polar lipids were phosphatidylethanolamine, two unidentified aminolipids, two phospholipids, and five unidentified lipids. Considering the phylogenetic inference, phenotypic, and chemotaxonomic data, strain JA-25^T should be classified as a novel species in the novel genus Fibrivirga, with the proposed name Fibrivirga algicola sp. nov. The type strain is JA-25^T (= KCCM 43334^T = NBRC 114259^T).

The Future Is Big-and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

Coupling remote sensing with microbial omics-based approaches provides a promising new frontier for scientists to scale microbial interactions across space and time. These data-rich, interdisciplinary methods allow us to better understand interactions between microbial communities and their environments and, in turn, their impact on ecosystem structure and function. Here, we highlight current and novel examples of applying remote sensing, machine learning, spatial statistics, and omics data approaches to marine, aquatic, and terrestrial systems. We emphasize the importance of integrating biochemical and spatiotemporal environmental data to move toward a predictive framework of microbiome interactions and their ecosystem-level effects. Finally, we emphasize lessons learned from our collaborative research with recommendations to foster productive and interdisciplinary teamwork.

How Do the Players Play? A Post-Genomic Analysis Paradigm to Understand Aquatic Ecosystem Processes

Culture-independent and meta-omics sequencing methods have shed considerable light on the so-called “microbial dark matter” of Earth’s environmental microbiome, improving our understanding of phylogeny, the tree of life, and the vast functional diversity of microorganisms. This influx of sequence data has led to refined and reimagined hypotheses about the role and importance of microbial biomass, that paradoxically, sequencing approaches alone are unable to effectively test. Post-genomic approaches such as metabolomics are providing more sensitive and insightful data to unravel the fundamental operations and intricacies of microbial communities within aquatic systems. We assert that the implementation of integrated post-genomic approaches, specifically metabolomics and metatranscriptomics, is the new frontier of environmental microbiology and ecology, expanding conventional assessments toward a holistic systems biology understanding. Progressing beyond siloed phylogenetic assessments and cataloging of metabolites, toward integrated analysis of expression (metatranscriptomics) and activity (metabolomics) is the most effective approach to provide true insight into microbial contributions toward local and global ecosystem functions. This data in turn creates opportunity for improved regulatory guidelines, biomarker discovery and better integration of modeling frameworks. To that end, critical aquatic environmental issues related to climate change, such as ocean warming and acidification, contamination mitigation, and macro-organism health have reasonable opportunity of being addressed through such an integrative approach. Lastly, we argue that the “post-genomics” paradigm is well served to proactively address the systemic technical issues experienced throughout the genomics revolution and focus on collaborative assessment of field-wide experimental standards of sampling, bioinformatics and statistical treatments.