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1. Correlation between postmortem microbial signatures and substance abuse disorders

   https://doi.org/10.1371/journal.pone.0274401  

   Javan, Gulnaz T.; Wells, Tiara; Allen, Jamese; Visona, Silvia; Moretti, Matteo; Tipton, Craig; Scott, Latia; Finley, Sheree J. (September 2022, PLOS ONE)

   Staley, Christopher (Ed.)

   The microbiota gut-brain-axis is a bidirectional circuit that links the neural, endocrine, and immunological systems with gut microbial communities. The gut microbiome plays significant roles in human mind and behavior, specifically pain perception, learning capacity, memory, and temperament. Studies have shown that disruptions in the gut microbiota have been associated with substance use disorders. The interplay of gut microbiota in substance abuse disorders has not been elucidated; however, postmortem microbiome profiles may produce promising avenues for future forensic investigations. The goal of the current study was to determine gut microbiome composition in substance abuse disorder cases using transverse colon tissues of 21 drug overdose versus 19 non-overdose-related cases. We hypothesized that postmortem samples of the same cause of death will reveal similar microbial taxonomic relationships. We compared microbial diversity profiles using amplicon-based sequencing of the 16S rRNA gene V4 hypervariable region. The results demonstrated that the microbial abundance in younger-aged cases were found to have significantly more operational taxonomic units than older cases. Using weighted UniFrac analysis, the influence of substances in overdose cases was found to be a significant factor in determining microbiome similarity. The results also revealed that samples of the same cause of death cluster together, showing a high degree of similarity between samples and a low degree of similarity among samples of different causes of death. In conclusion, our examination of human transverse colon microflora in decomposing remains extends emerging literature on postmortem microbial communities, which will ultimately contribute to advanced knowledge of human putrefaction. 

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2. OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

   https://doi.org/2021.04.04.438427  

   Qiyun Zhu; Shi Huang; Antonio Gonzalez; Imran McGrath; Daniel McDonald; Niina Haiminen; George Armstrong; Yoshiki Vázquez-Baeza; Julian Yu; Justin Kuczynski; et al (April 2021, bioRxiv)

   We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. Importance Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies. 

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3. GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data

   https://doi.org/10.3389/fbinf.2022.828703  

   Tataru, Christine; Eaton, Austin; David, Maude M. (April 2022, Frontiers in Bioinformatics)

   Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings . 

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4. Systematic review of cnidarian microbiomes reveals insights into the structure, specificity, and fidelity of marine associations

   https://doi.org/10.1038/s41467-023-39876-6  

   McCauley, M; Goulet, T L; Jackson, C R; Loesgen, S (December 2023, Nature Communications)

   Abstract Microorganisms play essential roles in the health and resilience of cnidarians. Understanding the factors influencing cnidarian microbiomes requires cross study comparisons, yet the plethora of protocols used hampers dataset integration. We unify 16S rRNA gene sequences from cnidarian microbiome studies under a single analysis pipeline. We reprocess 12,010 cnidarian microbiome samples from 186 studies, alongside 3,388 poriferan, 370 seawater samples, and 245 cultured Symbiodiniaceae, unifying ~6.5 billion sequence reads. Samples are partitioned by hypervariable region and sequencing platform to reduce sequencing variability. This systematic review uncovers an incredible diversity of 86 archaeal and bacterial phyla associated with Cnidaria, and highlights key bacteria hosted across host sub-phylum, depth, and microhabitat. Shallow (< 30 m) water Alcyonacea and Actinaria are characterized by highly shared and relatively abundant microbial communities, unlike Scleractinia and most deeper cnidarians. Utilizing the V4 region, we find that cnidarian microbial composition, richness, diversity, and structure are primarily influenced by host phylogeny, sampling depth, and ocean body, followed by microhabitat and sampling date. We identify host and geographical generalist and specificEndozoicomonasclades within Cnidaria and Porifera. This systematic review forms a framework for understanding factors governing cnidarian microbiomes and creates a baseline for assessing stress associated dysbiosis. 

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5. Ranking the biases: The choice of OTUs vs. ASVs in 16S rRNA amplicon data analysis has stronger effects on diversity measures than rarefaction and OTU identity threshold

   https://doi.org/10.1371/journal.pone.0264443  

   Chiarello, Marlène; McCauley, Mark; Villéger, Sébastien; Jackson, Colin R. (February 2022, PLOS ONE)

   Moreno-Hagelsieb, Gabriel (Ed.)

   Advances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from sequence identity-based clustering (producing Operational Taxonomic Units, OTUs) to denoising methods (producing amplicon sequence variants, ASVs). While denoising methods have several inherent properties that make them desirable compared to clustering-based methods, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e.g. rarefaction) and computing diversity indices. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. Mothur (a clustering method) on 16S rRNA gene amplicon datasets (hypervariable region v4), and compared such effects to the rarefaction of the community table and OTU identity threshold (97% vs. 99%) on the ecological signals detected. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. The choice of the pipeline significantly influenced alpha and beta diversities and changed the ecological signal detected, especially on presence/absence indices such as the richness index and unweighted Unifrac. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. The identification of major classes and genera also revealed significant discrepancies across pipelines. Compared to the pipeline’s effect, OTU threshold and rarefaction had a minimal impact on all measurements. 

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