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1. Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments

   https://doi.org/10.7717/peerj.1913  

   Trembath-Reichert, Elizabeth ; Case, David H. ; Orphan, Victoria J. ( April 2016 , PeerJ)

   Methane seep systems along continental margins host diverse and dynamic microbial assemblages, sustained in large part through the microbially mediated process of sulfate-coupled Anaerobic Oxidation of Methane (AOM). This methanotrophic metabolism has been linked to consortia of anaerobic methane-oxidizing archaea (ANME) and sulfate-reducing bacteria (SRB). These two groups are the focus of numerous studies; however, less is known about the wide diversity of other seep associated microorganisms. We selected a hierarchical set of FISH probes targeting a range ofDeltaproteobacteriadiversity. Using the Magneto-FISH enrichment technique, we then magnetically captured CARD-FISH hybridized cells and their physically associated microorganisms from a methane seep sediment incubation. DNA from nested Magneto-FISH experiments was analyzed using Illumina tag 16S rRNA gene sequencing (iTag). Enrichment success and potential bias with iTag was evaluated in the context of full-length 16S rRNA gene clone libraries, CARD-FISH, functional gene clone libraries, and iTag mock communities. We determined commonly used Earth Microbiome Project (EMP) iTAG primers introduced bias in some common methane seep microbial taxa that reduced the ability to directly compare OTU relative abundances within a sample, but comparison of relative abundances between samples (in nearly all cases) and whole community-based analyses were robust. The iTag dataset was subjected to statistical co-occurrence measures of the most abundant OTUs to determine which taxa in this dataset were most correlated across all samples. Many non-canonical microbial partnerships were statistically significant in our co-occurrence network analysis, most of which were not recovered with conventional clone library sequencing, demonstrating the utility of combining Magneto-FISH and iTag sequencing methods for hypothesis generation of associations within complex microbial communities. Network analysis pointed to many co-occurrences containing putatively heterotrophic, candidate phyla such as OD1,Atribacteria, MBG-B, and Hyd24-12 and the potential for complex sulfur cycling involvingEpsilon-,Delta-, andGammaproteobacteriain methane seep ecosystems.

    

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2. DeepMicroGen: a generative adversarial network-based method for longitudinal microbiome data imputation

   https://doi.org/10.1093/bioinformatics/btad286  

   Choi, Joung Min ; Ji, Ming ; Watson, Layne T. ; Zhang, Liqing ; Boeva, ed., Valentina ( April 2023 , Bioinformatics)

   The human microbiome, which is linked to various diseases by growing evidence, has a profound impact on human health. Since changes in the composition of the microbiome across time are associated with disease and clinical outcomes, microbiome analysis should be performed in a longitudinal study. However, due to limited sample sizes and differing numbers of timepoints for different subjects, a significant amount of data cannot be utilized, directly affecting the quality of analysis results. Deep generative models have been proposed to address this lack of data issue. Specifically, a generative adversarial network (GAN) has been successfully utilized for data augmentation to improve prediction tasks. Recent studies have also shown improved performance of GAN-based models for missing value imputation in a multivariate time series dataset compared with traditional imputation methods.

   This work proposes DeepMicroGen, a bidirectional recurrent neural network-based GAN model, trained on the temporal relationship between the observations, to impute the missing microbiome samples in longitudinal studies. DeepMicroGen outperforms standard baseline imputation methods, showing the lowest mean absolute error for both simulated and real datasets. Finally, the proposed model improved the predicted clinical outcome for allergies, by providing imputation for an incomplete longitudinal dataset used to train the classifier.

   DeepMicroGen is publicly available at https://github.com/joungmin-choi/DeepMicroGen.

    

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3. MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome

   https://doi.org/10.1093/bioinformatics/btz414  

   Ma, Anjun ; Sun, Minxuan ; McDermaid, Adam ; Liu, Bingqiang ; Ma, Qin ; Valencia, ed., Alfonso ( May 2019 , Bioinformatics)

   Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions.

   Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies.

   The package is freely available at https://github.com/OSU-BMBL/metaqubic.

   Supplementary data are available at Bioinformatics online.

    

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4. Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

   https://doi.org/10.1038/s41564-022-01266-x  

   Shaffer, Justin P. ; Nothias, Louis-Félix ; Thompson, Luke R. ; Sanders, Jon G. ; Salido, Rodolfo A. ; Couvillion, Sneha P. ; Brejnrod, Asker D. ; Lejzerowicz, Franck ; Haiminen, Niina ; Huang, Shi ; et al ( December 2022 , Nature Microbiology)

   Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment. 

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5. A multi‐study analysis of gut microbiome data from the blue mussel ( Mytilus edulis ) emphasises the impact of depuration on biological interpretation

   https://doi.org/10.1111/1462-2920.16537  

   Griffin, Tyler W. ; Darsan, Mya A. ; Collins, Hannah I. ; Holohan, Bridget A. ; Pierce, Melissa L. ; Ward, J. Evan ( November 2023 , Environmental Microbiology)

   The blue mussel (Mytilus edulis) is a suspension feeder which has been used in gut‐microbiome surveys. Although raw 16S sequence data are often publicly available, unifying secondary analyses are lacking. The present work analysed raw data from seven projects conducted by one group over 7 years. Although each project had different motivations, experimental designs and conclusions, all selected samples were from the guts ofM. eduliscollected from a single location in Long Island Sound. The goal of this analysis was to determine which independent factors (e.g., collection date, depuration status) were responsible for governing composition and diversity in the gut microbiomes. Results indicated that whether mussels had undergone depuration, defined here as voidance of faeces in a controlled, no‐food period, was the primary factor that governed gut microbiome composition. Gut microbiomes from non‐depurated mussels were mixtures of resident and transient communities and were influenced by temporal factors. Resident communities from depurated mussels were influenced by the final food source and length of time host mussels were held under laboratory conditions. These findings reinforce the paradigm that gut microbiota are divided into resident and transient components and suggest that depuration status should be taken into consideration when designing and interpreting future experiments.

    

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