Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf.

The planktonic diversity throughout the oceans is vital to ecosystem functioning and linked to environmental change. Plankton monitoring tools have advanced considerably with high-throughput in-situ digital cameras and genomic sequencing, opening new challenges for high-frequency observations of community composition, structure, and species discovery. Here, we combine multi-marker metabarcoding based on nuclear 18S (V4) and plastidial 16S (V4-V5) rRNA gene amplicons with a digital in-line holographic microscope to provide a synoptic diversity survey of eukaryotic plankton along the Newfoundland Shelf (Canada) during the winter transition phase of the North Atlantic bloom phenomenon. Metabarcoding revealed a rich eukaryotic diversity unidentifiable in the imaging samples, confirming the presence of ecologically important saprophytic protists which were unclassifiable in matching images, and detecting important groups unobserved or taxonomically unresolved during similar sequencing campaigns in the Northwest Atlantic Ocean. In turn, imaging analysis provided quantitative observations of widely prevalent plankton from every trophic level. Despite contrasting plankton compositions portrayed by each sampling method, both capture broad spatial differences between the northern and southern sectors of the Newfoundland Shelf and suggest complementary estimations of important features in eukaryotic assemblages. Future tasks will involve standardizing digital imaging and metabarcoding for wider use and consistent, comparable ocean observations.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study.

BACKGROUND: Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes ('Hole finding protocol') coupled with the identification of candidate proteins for the predicted orphan enzyme ('Hole filling protocol'). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function. RESULTS: The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using 'Hole finding protocol'. The 'Hole-filling protocol' was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes. CONCLUSIONS: We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater.

We present the first geomicrobiological characterization of the meromictic water column of Powell Lake (British Columbia, Canada), a former fjord, which has been stably stratified since the last glacial period. Its deepest layers (300-350 m) retain isolated, relict seawater from that period. Fine-scale vertical profiling of the water chemistry and microbial communities allowed subdivision of the water column into distinct geomicrobiological zones. These zones were further characterized by phylogenetic and functional marker genes from amplicon and shotgun metagenome sequencing. Binning of metagenomic reads allowed the linkage of function to specific taxonomic groups. Statistical analyses (analysis of similarities, Bray-Curtis similarity) confirmed that the microbial community structure followed closely the geochemical zonation. Yet, our characterization of the genetic potential relevant to carbon, nitrogen and sulphur cycling of each zone revealed unexpected features, including potential for facultative anaerobic methylotrophy, nitrogen fixation despite high ammonium concentrations and potential micro-aerobic nitrifiers within the chemocline. At the oxic-suboxic interface, facultative anaerobic potential was found in the widespread freshwater lineage acI (Actinobacteria), suggesting intriguing ecophysiological similarities to the marine SAR11. Evolutionary divergent lineages among diverse phyla were identified in the ancient seawater zone and may indicate novel adaptations to this unusual environment.

Regulation of the Phytoplankton Heme b Iron Pool During the North Atlantic Spring Bloom.

Heme b is an iron-containing co-factor in hemoproteins. Heme b concentrations are low (<1 pmol L-1) in iron limited phytoplankton in cultures and in the field. Here, we determined heme b in marine particulate material (>0.7 µm) from the North Atlantic Ocean (GEOVIDE cruise - GEOTRACES section GA01), which spanned several biogeochemical regimes. We examined the relationship between heme b abundance and the microbial community composition, and its utility for mapping iron limited phytoplankton. Heme b concentrations ranged from 0.16 to 5.1 pmol L-1 (median = 2.0 pmol L-1, n = 62) in the surface mixed layer (SML) along the cruise track, driven mainly by variability in biomass. However, in the Irminger Basin, the lowest heme b levels (SML: median = 0.53 pmol L-1, n = 12) were observed, whilst the biomass was highest (particulate organic carbon, median = 14.2 µmol L-1, n = 25; chlorophyll a: median = 2.0 nmol L-1, n = 23) pointing to regulatory mechanisms of the heme b pool for growth conservation. Dissolved iron (DFe) was not depleted (SML: median = 0.38 nmol L-1, n = 11) in the Irminger Basin, but large diatoms (Rhizosolenia sp.) dominated. Hence, heme b depletion and regulation is likely to occur during bloom progression when phytoplankton class-dependent absolute iron requirements exceed the available ambient concentration of DFe. Furthermore, high heme b concentrations found in the Iceland Basin and Labrador Sea (median = 3.4 pmol L-1, n = 20), despite having similar DFe concentrations to the Irminger Basin, were attributed to an earlier growth phase of the extant phytoplankton populations. Thus, heme b provides a snapshot of the cellular activity in situ and could both be used as indicator of iron limitation and contribute to understanding phytoplankton adaptation mechanisms to changing iron supplies.

Aerobic Microbial Respiration In Oceanic Oxygen Minimum Zones.

Oxygen minimum zones are major sites of fixed nitrogen loss in the ocean. Recent studies have highlighted the importance of anaerobic ammonium oxidation, anammox, in pelagic nitrogen removal. Sources of ammonium for the anammox reaction, however, remain controversial, as heterotrophic denitrification and alternative anaerobic pathways of organic matter remineralization cannot account for the ammonium requirements of reported anammox rates. Here, we explore the significance of microaerobic respiration as a source of ammonium during organic matter degradation in the oxygen-deficient waters off Namibia and Peru. Experiments with additions of double-labelled oxygen revealed high aerobic activity in the upper OMZs, likely controlled by surface organic matter export. Consistently observed oxygen consumption in samples retrieved throughout the lower OMZs hints at efficient exploitation of vertically and laterally advected, oxygenated waters in this zone by aerobic microorganisms. In accordance, metagenomic and metatranscriptomic analyses identified genes encoding for aerobic terminal oxidases and demonstrated their expression by diverse microbial communities, even in virtually anoxic waters. Our results suggest that microaerobic respiration is a major mode of organic matter remineralization and source of ammonium (~45-100%) in the upper oxygen minimum zones, and reconcile hitherto observed mismatches between ammonium producing and consuming processes therein.

Giant hydrogen sulfide plume in the oxygen minimum zone off Peru supports chemolithoautotrophy.

In Eastern Boundary Upwelling Systems nutrient-rich waters are transported to the ocean surface, fuelling high photoautotrophic primary production. Subsequent heterotrophic decomposition of the produced biomass increases the oxygen-depletion at intermediate water depths, which can result in the formation of oxygen minimum zones (OMZ). OMZs can sporadically accumulate hydrogen sulfide (H2S), which is toxic to most multicellular organisms and has been implicated in massive fish kills. During a cruise to the OMZ off Peru in January 2009 we found a sulfidic plume in continental shelf waters, covering an area >5500 km(2), which contained ∼2.2×10(4) tons of H2S. This was the first time that H2S was measured in the Peruvian OMZ and with ∼440 km(3) the largest plume ever reported for oceanic waters. We assessed the phylogenetic and functional diversity of the inhabiting microbial community by high-throughput sequencing of DNA and RNA, while its metabolic activity was determined with rate measurements of carbon fixation and nitrogen transformation processes. The waters were dominated by several distinct γ-, δ- and ε-proteobacterial taxa associated with either sulfur oxidation or sulfate reduction. Our results suggest that these chemolithoautotrophic bacteria utilized several oxidants (oxygen, nitrate, nitrite, nitric oxide and nitrous oxide) to detoxify the sulfidic waters well below the oxic surface. The chemolithoautotrophic activity at our sampling site led to high rates of dark carbon fixation. Assuming that these chemolithoautotrophic rates were maintained throughout the sulfidic waters, they could be representing as much as ∼30% of the photoautotrophic carbon fixation. Postulated changes such as eutrophication and global warming, which lead to an expansion and intensification of OMZs, might also increase the frequency of sulfidic waters. We suggest that the chemolithoautotrophically fixed carbon may be involved in a negative feedback loop that could fuel further sulfate reduction and potentially stabilize the sulfidic OMZ waters.

Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes.

MOTIVATION: The sheer scale of the metagenomic and metatranscriptomic datasets that are now available warrants the development of automated protocols for organizing, annotating and comparing the samples in terms of their metabolic profiles. We describe a user-friendly java program FROMP (Fragment Recruitment on Metabolic Pathways) for mapping and visualizing enzyme annotations onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways or custom-made pathways and comparing the samples in terms of their Pathway Completeness Scores, their relative Activity Scores or enzyme enrichment odds ratios. This program along with our fully configurable PERL-based annotation organization pipeline Meta2Pro (METAbolic PROfiling of META-omic data) offers a quick and accurate standalone solution for metabolic profiling of environmental samples or cultures from different treatments. Apart from pictorial comparisons, FROMP can also generate score matrices for multiple meta-omics samples, which can be used directly by other statistical programs.

Factors influencing the diversity of iron uptake systems in aquatic microorganisms.

Iron (Fe) is an essential micronutrient for many processes in all living cells. Dissolved Fe (dFe) concentrations in the ocean are of the order of a few nM, and Fe is often a factor limiting primary production. Bioavailability of Fe in aquatic environments is believed to be primarily controlled through chelation by Fe-binding ligands. Marine microbes have evolved different mechanisms to cope with the scarcity of bioavailable dFe. Gradients in dFe concentrations and diversity of the Fe-ligand pool from coastal to open ocean waters have presumably imposed selection pressures that should be reflected in the genomes of microbial communities inhabiting the pelagic realm. We applied a hidden Markov model (HMM)-based search for proteins related to cellular iron metabolism, and in particular those involved in Fe uptake mechanisms in 164 microbial genomes belonging to diverse taxa and occupying different aquatic niches. A multivariate statistical approach demonstrated that in phototrophic organisms, there is a clear influence of the ecological niche on the diversity of Fe uptake systems. Extending the analyses to the metagenome database from the Global Ocean Sampling expedition, we demonstrated that the Fe uptake and homeostasis mechanisms differed significantly across marine niches defined by temperatures and dFe concentrations, and that this difference was linked to the distribution of microbial taxa in these niches. Using the dN/dS ratios (which signify the rate of non-synonymous mutations) of the nucleotide sequences, we identified that genes encoding for TonB, Ferritin, Ferric reductase, IdiA, ZupT, and Fe(2+) transport proteins FeoA and FeoB were evolving at a faster rate (positive selection pressure) while genes encoding ferrisiderophore, heme and Vitamin B12 uptake systems, siderophore biosynthesis, and IsiA and IsiB were under purifying selection pressure (evolving slowly).

ModEnzA: Accurate Identification of Metabolic Enzymes Using Function Specific Profile HMMs with Optimised Discrimination Threshold and Modified Emission Probabilities.

Various enzyme identification protocols involving homology transfer by sequence-sequence or profile-sequence comparisons have been devised which utilise Swiss-Prot sequences associated with EC numbers as the training set. A profile HMM constructed for a particular EC number might select sequences which perform a different enzymatic function due to the presence of certain fold-specific residues which are conserved in enzymes sharing a common fold. We describe a protocol, ModEnzA (HMM-ModE Enzyme Annotation), which generates profile HMMs highly specific at a functional level as defined by the EC numbers by incorporating information from negative training sequences. We enrich the training dataset by mining sequences from the NCBI Non-Redundant database for increased sensitivity. We compare our method with other enzyme identification methods, both for assigning EC numbers to a genome as well as identifying protein sequences associated with an enzymatic activity. We report a sensitivity of 88% and specificity of 95% in identifying EC numbers and annotating enzymatic sequences from the E. coli genome which is higher than any other method. With the next-generation sequencing methods producing a huge amount of sequence data, the development and use of fully automated yet accurate protocols such as ModEnzA is warranted for rapid annotation of newly sequenced genomes and metagenomic sequences.

HMM-ModE--improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying emission probabilities with negative training sequences.

BACKGROUND: Profile Hidden Markov Models (HMM) are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives). Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. RESULTS: The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors sub-classified with respect to their substrate specificity, though the average sequence identity across the sub-families is just 20.6%. The protocol is applied in a high-throughput classification exercise on protein kinases. CONCLUSION: The protocol has the potential to maximise the contributions of discriminating residues to classify proteins based on their molecular function, using pre-classified positive and negative sequence training data. The high specificity of the method, and increasing availability of pre-classified sequence data holds the potential for its application in sequence annotation.