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  1. Heterotrophic bacteria initiate the degradation of high molecular weight organic matter by producing an array of extracellular enzymes to hydrolyze complex organic matter into sizes that can be taken up into the cell. These bacterial communities differ spatially and temporally in composition, and potentially also in their enzymatic complements. Previous research has shown that particle-associated bacteria can be considerably more active than bacteria in the surrounding bulk water, but most prior studies of particle-associated bacteria have been focused on the upper ocean - there are few measurements of enzymatic activities of particle-associated bacteria in the mesopelagic and bathypelagic ocean, although the bacterial communities in the deep are dependent upon degradation of particulate organic matter to fuel their metabolism. We used a broad suite of substrates to compare the glucosidase, peptidase, and polysaccharide hydrolase activities of particle-associated and unfiltered seawater microbial communities in epipelagic, mesopelagic, and bathypelagic waters across 11 stations in the western North Atlantic. We concurrently determined bacterial community composition of unfiltered seawater and of samples collected via gravity filtration (>3 μm). Overall, particle-associated bacterial communities showed a broader spectrum of enzyme activities compared with unfiltered seawater communities. These differences in enzymatic activities were greater at offshore than at coastal locations, and increased with increasing depth in the ocean. The greater differences in enzymatic function measured on particles with depth coincided with increasing differences in particle-associated community composition, suggesting that particles act as ‘specialty centers’ that are essential for degradation of organic matter even at bathypelagic depths. 
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  2. Abstract. Oceanic bacterial communities process a major fraction of marine organiccarbon. A substantial portion of this carbon transformation occurs in themesopelagic zone, and a further fraction fuels bacteria in the bathypelagiczone. However, the capabilities and limitations of the diverse microbialcommunities at these depths to degrade high-molecular-weight (HMW) organicmatter are not well constrained. Here, we compared the responses of distinctmicrobial communities from North Atlantic epipelagic (0–200 m), mesopelagic(200–1000 m), and bathypelagic (1000–4000 m) waters at two open-oceanstations to the same input of diatom-derived HMW particulate and dissolvedorganic matter. Microbial community composition and functional responses tothe input of HMW organic matter – as measured by polysaccharide hydrolase,glucosidase, and peptidase activities – were very similar between thestations, which were separated by 1370 km but showed distinct patterns withdepth. Changes in microbial community composition coincided with changes inenzymatic activities: as bacterial community composition changed in responseto the addition of HMW organic matter, the rate and spectrum of enzymaticactivities increased. In epipelagic mesocosms, the spectrum of peptidaseactivities became especially broad and glucosidase activities were veryhigh, a pattern not seen at other depths, which, in contrast, were dominatedby leucine aminopeptidase and had much lower peptidase and glucosidase ratesin general. The spectrum of polysaccharide hydrolase activities was enhancedparticularly in epipelagic and mesopelagic mesocosms, with fewerenhancements in rates or spectrum in bathypelagic waters. The timing andmagnitude of these distinct functional responses to the same HMW organicmatter varied with depth. Our results highlight the importance of residencetimes at specific depths in determining the nature and quantity of organicmatter reaching the deep sea. 
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  3. Abstract Background

    The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN.

    Results

    By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension.

    Conclusion

    Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

     
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  4. Abstract

    SAR86 is an abundant and ubiquitous heterotroph in the surface ocean that plays a central role in the function of marine ecosystems. We hypothesized that despite its ubiquity, different SAR86 subgroups may be endemic to specific ocean regions and functionally specialized for unique marine environments. However, the global biogeographical distributions of SAR86 genes, and the manner in which these distributions correlate with marine environments, have not been investigated. We quantified SAR86 gene content across globally distributed metagenomic samples and modeled these gene distributions as a function of 51 environmental variables. We identified five distinct clusters of genes within the SAR86 pangenome, each with a unique geographic distribution associated with specific environmental characteristics. Gene clusters are characterized by the strong taxonomic enrichment of distinct SAR86 genomes and partial assemblies, as well as differential enrichment of certain functional groups, suggesting differing functional and ecological roles of SAR86 ecotypes. We then leveraged our models and high-resolution, remote sensing-derived environmental data to predict the distributions of SAR86 gene clusters across the world’s oceans, creating global maps of SAR86 ecotype distributions. Our results reveal that SAR86 exhibits previously unknown, complex biogeography, and provide a framework for exploring geographic distributions of genetic diversity from other microbial clades.

     
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  5. Abstract Summary

    Sequencing data resources have increased exponentially in recent years, as has interest in large-scale meta-analyses of integrated next-generation sequencing datasets. However, curation of integrated datasets that match a user’s particular research priorities is currently a time-intensive and imprecise task. MetaSeek is a sequencing data discovery tool that enables users to flexibly search and filter on any metadata field to quickly find the sequencing datasets that meet their needs. MetaSeek automatically scrapes metadata from all publicly available datasets in the Sequence Read Archive, cleans and parses messy, user-provided metadata into a structured, standard-compliant database and predicts missing fields where possible. MetaSeek provides a web-based graphical user interface and interactive visualization dashboard, as well as a programmatic API to rapidly search, filter, visualize, save, share and download matching sequencing metadata.

    Availability and implementation

    The MetaSeek online interface is available at https://www.metaseek.cloud/. The MetaSeek database can also be accessed via API to programmatically search, filter and download all metadata. MetaSeek source code, metadata scrapers and documents are available at https://github.com/MetaSeek-Sequencing-Data-Discovery/metaseek/.

     
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  6. Summary

    The extent to which differences in microbial community structure result in variations in organic matter (OM) degradation is not well understood. Here, we tested the hypothesis that distinct marine microbial communities from North Atlantic surface and bottom waters would exhibit varying compositional succession and functional shifts in response to the same pool of complex high molecular weight (HMW‐OM). We also hypothesized that microbial communities would produce a broader spectrum of enzymes upon exposure to HMW‐OM, indicating a greater potential to degrade these compounds than reflected by initial enzymatic activities. Our results show that community succession in amended mesocosms was congruent with cell growth, increased bacterial production and most notably, with substantial shifts in enzymatic activities. In all amended mesocosms, closely related taxa that were initially rare became dominant at time frames during which a broader spectrum of active enzymes were detected compared to initial timepoints, indicating a similar response among different communities. However, succession on the whole‐community level, and the rates, spectra and progression of enzymatic activities, reveal robust differences among distinct communities from discrete water masses. These results underscore the crucial role of rare bacterial taxa in ocean carbon cycling and the importance of bacterial community structure for HMW‐OM degradation.

     
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