Kantor, R. | bioRxiv Science

bioRxiv2018

Hydrogen-based metabolism - An ancestral trait in lineages sibling to the Cyanobacteria

The metabolic machinery from which microbial aerobic respiration evolved is tightly linked to the origins of oxygenic Cyanobacteria (Oxyphotobacteria). Even though the majority of Oxyphotobacteria are photoautotrophs and can use carbohydrates with oxygen (O2) as the electron acceptor, all are fermenters under dark anoxic conditions. Studies suggest that the ancestor of Oxyphotobacteria may have used hydrogen (H2) as an electron donor and that two types of NiFe hydrogenases are essential for its oxidation. Melainabacteria and Sericytochromatia, close phylogenetic neighbors to Oxyphotobacteria comprise fermentative and aerobic representatives, or organisms capable of both. Margulisbacteria (candidate divisions RBX-1 and ZB3) and Saganbacteria (candidate division WOR-1), a novel cluster of bacteria phylogenetically related to Melainabacteria, Sericytochromatia and Oxyphotobacteria may further constrain the metabolic platform in which oxygenic photosynthesis and aerobic respiration arose. Here, we predict the metabolisms of Margulisbacteria and Saganbacteria from new and published metagenome-assembled genomes (MAGs) and single amplified genomes (SAGs), and compare them to their phylogenetic neighbors. Sediment-associated Margulisbacteria are predicted to have a fermentation-based metabolism featuring a variety of hydrogenases, a nitrogenase for nitrogen (N2) fixation, and electron bifurcating complexes involved in cycling of ferredoxin and NAD(P)H. Overall, the genomic features suggest the capacity for metabolic fine-tuning under strictly anoxic conditions. In contrast, the genomes of Margulisbacteria from the ocean ecosystem encode an electron transport chain that supports aerobic growth. Similarly, some Saganbacteria genomes encode various hydrogenases, and others may have the ability to use O2 under certain conditions via a putative novel type of heme copper O2 reductase. Like Melainabacteria and Sericytochromatia, Margulisbacteria and Saganbacteria have diverse energy metabolisms capable of fermentation, and aerobic or anaerobic respiration. In summary, our findings support the hypothesis that the ancestor of these groups was an anaerobe in which fermentation and H2 metabolism were central metabolic features. Our genomic data also suggests that contemporary lineages sibling to the Oxyphotobacteria may have acquired the ability to use O2 as a terminal electron acceptor under certain environmental conditions.

microbiology↗

bioRxiv2018

Measurement error and variant-calling in deep Illumina sequencing of HIV

MotivationNext-generation deep sequencing of viral genomes, particularly on the Illumina platform, is increasingly applied in HIV research. Yet, there is no standard protocol or method used by the research community to account for measurement errors that arise during sample preparation and sequencing. Correctly calling high and low frequency variants while controlling for erroneous variant calls is an important precursor to downstream interpretation, such as studying the emergence of HIV drug-resistance mutations, which in turn has clinical applications and can improve patient care.\n\nResultsWe developed a new variant-calling pipeline, hivmmer, for Illumina sequences from HIV viral genomes. First, we validated hivmmer by comparing it to other variant-calling pipelines on real HIV plasmid data sets, which have known sequences. We found that hivmmer achieves a lower rate of erroneous variant calls, and that all methods agree on the frequency of correctly called variants. Next, we compared the methods on an HIV plasmid data set that was sequenced using an amplicon-tagging protocol called Primer ID, which is designed to reduce errors and amplification bias during library preparation. We show that the Primer ID consensus does indeed have fewer erroneous variant calls compared to the variant-calling pipelines, and that hivmmer more closely approaches this low error rate compared to the other pipelines. Surprisingly, the frequency estimates from the Primer ID consensus do not differ significantly from those of the variant-calling pipelines. Finally, we built a predictive model for classifying errors in the hivmmer alignment, and show that it achieves high accuracy for identifying erroneous variant calls.\n\nAvailabilityhivmmer is freely available for non-commercial use from https://github.com/mhowison/hivmmer.\n\nContactmhowison@brown.edu

bioinformatics↗