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Microbial ecologists use alpha diversity metrics for estimating species richness and evenness from data obtained by high-throughput sequencing of small subunit ribosomal RNA genes. This perspective argues that the nonparametric richness estimators Chao1 and ACE should never be used with ASV data because the default process of generating amplicon sequence variants (ASVs) removes singletons, which are specifically required for these estimate calculations. In addition, retaining singletons, if/when possible, will contribute a large proportion of artifacts to this abundance category, leading to extreme richness overestimation. We recommend the use of alternative sequence clustering strategies and/or diversity metrics to avoid generating meaningless richness estimates from ASV data.

Background A key step in microbiome sequencing analysis is read assignment to taxonomic units. This is often performed using one of four taxonomic classifications, namely SILVA, RDP, Greengenes or NCBI. It is unclear how similar these are and how to compare analysis results that are based on different taxonomies. Results We provide a method and software for mapping taxonomic entities from one taxonomy onto another. We use it to compare the four taxonomies and the Open Tree of life Taxonomy (OTT). Conclusions While we find that SILVA, RDP and Greengenes map well into NCBI, and all four map well into the OTT, mapping the two larger taxonomies on to the smaller ones is problematic.

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to \"classic\" open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, \"classic\" open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of \"classic\" open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by \"classic\" open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME's uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME's OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

Background Microorganisms are not only indispensable to ecosystem functioning, they are also keystones for emerging technologies. In the last 15 years, the number of studies on environmental microbial communities has increased exponentially due to advances in sequencing technologies, but the large amount of data generated remains difficult to analyze and interpret. Recently, metabarcoding analysis has shifted from clustering reads using Operational Taxonomical Units (OTUs) to Amplicon Sequence Variants (ASVs). Differences between these methods can seriously affect the biological interpretation of metabarcoding data, especially in ecosystems with high microbial diversity, as the methods are benchmarked based on low diversity datasets. Results In this work we have thoroughly examined the differences in community diversity, structure, and complexity between the OTU and ASV methods. We have examined culture-based mock and simulated datasets as well as soil- and plant-associated bacterial and fungal environmental communities. Four key findings were revealed. First, analysis of microbial datasets at family level guaranteed both consistency and adequate coverage when using either method. Second, the performance of both methods used are related to community diversity and sample sequencing depth. Third, differences in the method used affected sample diversity and number of detected differentially abundant families upon treatment; this may lead researchers to draw different biological conclusions. Fourth, the observed differences can mostly be attributed to low abundant (relative abundance < 0.1%) families, thus extra care is recommended when studying rare species using metabarcoding. The ASV method used outperformed the adopted OTU method concerning community diversity, especially for fungus-related sequences, but only when the sequencing depth was sufficient to capture the community complexity. Conclusions Investigation of metabarcoding data should be done with care. Correct biological interpretation depends on several factors, including in-depth sequencing of the samples, choice of the most appropriate filtering strategy for the specific research goal, and use of family level for data clustering.

16S rRNA gene sequencing has engendered significant interest in studying microbial communities. There has been tension between trying to classify 16S rRNA gene sequences to increasingly lower taxonomic levels and the reality that those levels were defined using more sequence and physiological information than is available from a fragment of the 16S rRNA gene. Amplicon sequencing variants (ASVs) have been proposed as an alternative to operational taxonomic units (OTUs) for analyzing microbial communities. ASVs have grown in popularity, in part because of a desire to reflect a more refined level of taxonomy since they do not cluster sequences based on a distance-based threshold. However, ASVs and the use of overly narrow thresholds to identify OTUs increase the risk of splitting a single genome into separate clusters. To assess this risk, I analyzed the intragenomic variation of 16S rRNA genes from the bacterial genomes represented in an rrn copy number database, which contained 20,427 genomes from 5,972 species. As the number of copies of the 16S rRNA gene increased in a genome, the number of ASVs also increased. There was an average of 0.58 ASVs per copy of the 16S rRNA gene for full-length 16S rRNA genes. It was necessary to use a distance threshold of 5.25% to cluster full-length ASVs from the same genome into a single OTU with 95% confidence for genomes with 7 copies of the 16S rRNA, such as Escherichia coli . This research highlights the risk of splitting a single bacterial genome into separate clusters when ASVs are used to analyze 16S rRNA gene sequence data. Although there is also a risk of clustering ASVs from different species into the same OTU when using broad distance thresholds, these risks are of less concern than artificially splitting a genome into separate ASVs and OTUs. IMPORTANCE 16S rRNA gene sequencing has engendered significant interest in studying microbial communities. There has been tension between trying to classify 16S rRNA gene sequences to increasingly lower taxonomic levels and the reality that those levels were defined using more sequence and physiological information than is available from a fragment of the 16S rRNA gene. Furthermore, the naming of bacterial taxa reflects the biases of those who name them. One motivation for the recent push to adopt ASVs in place of OTUs in microbial community analyses is to allow researchers to perform their analyses at the finest possible level that reflects species-level taxonomy. The current research is significant because it quantifies the risk of artificially splitting bacterial genomes into separate clusters. Far from providing a better representation of bacterial taxonomy and biology, the ASV approach can lead to conflicting inferences about the ecology of different ASVs from the same genome.

Despite the development of advanced technologies for interventional coronary reperfusion after myocardial infarction, a substantial number of patients experience high mortality due to myocardial ischemia‐reperfusion (MI/R) injury. An in‐depth understanding of the mechanisms underlying MI/R injury can provide crucial strategies for mitigating myocardial damage and improving patient survival. Here, it is discovered that the 4‐hydroxy‐2‐nonenal (4‐HNE) accumulates during MI/R, accompanied by high rates of myocardial ferroptosis. The loss‐of‐function of aldehyde dehydrogenase 2 (ALDH2), which dissipates 4‐HNE, aggravates myocardial ferroptosis, whereas the activation of ALDH2 mitigates ferroptosis. Mechanistically, 4‐HNE targets glutathione peroxidase 4 (GPX4) for K48‐linked polyubiquitin‐related degradation, which 4‐HNE‐GPX4 axis commits to myocyte ferroptosis and forms a positive feedback circuit. 4‐HNE blocks the interaction between GPX4 and ovarian tumor (OTU) deubiquitinase 5 (OTUD5) by directly carbonylating their cysteine residues at C93 of GPX4 and C247 of OTUD5, identifying OTUD5 as the novel deubiquitinase for GPX4. Consequently, the elevation of OTUD5 deubiquitinates and stabilizes GPX4 to reverse 4‐HNE‐induced ferroptosis and alleviate MI/R injury. The data unravel the mechanism of 4‐HNE in GPX4‐dependent ferroptosis and identify OTUD5 as a novel therapeutic target for the treatment of MI/R injury.

ABSTRACT Microbial metabarcoding is the standard approach to assess communities’ diversity. However reports are often limited to simple OTU abundances for each phylum, giving rather one-dimensional views of microbial assemblages, overlooking other accessible aspects. The first is masked by databases incompleteness; OTU picking involves clustering at 97% (near-species) sequence identity, but different OTUs regularly end up under a same taxon name. When expressing diversity as number of obtained taxonomical names, a large portion of the real diversity lying within the data remains underestimated. Using the 16S sequencing results of an environmental transect across a gradient of 17 coastal habitats we first extracted the number of OTUs hidden under the same name. Further, we observed which was the deepest rank yielded by annotation, revealing for which microbial groups are we missing most knowledge. Data were then used to infer an evolutionary aspect: what is, in each phylum the success of the present time individuals (abundances for each OTU) in relation to their prior evolutionary success in differentiation (number of OTUs). This information reveals whether the past speciation/diversification force is matched by the present competitiveness in reproduction/persistence. The final layer explored is functional diversity, i.e. abundances of groups involved in specific environmental processes. A novel view of the uneven evolutionary speed of different bacterial families and new indexes to measure their success in past and present times.

Background16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML.MethodsWe checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification.ResultsWe show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results.ConclusionsThe prepossessing of microbiome 16S data is crucial for optimal microbiome based Machine Learning. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand-alone version at:https://github.com/louzounlab/microbiome/tree/master/Preprocessor as a service athttp://mip-mlp.math.biu.ac.il/HomeBoth contain the code, and standard test sets.

The coastal ecosystems are considered as one of the most dynamic and vulnerable environments under various anthropogenic developments and the effects of climate change. Variations in the composition and diversity of microbial communities may be a good indicator for determining whether the marine ecosystems are affected by complex forcing stressors. DNA sequence-based metagenomics has recently emerged as a promising tool for analyzing the structure and diversity of microbial communities based on environmental DNA (eDNA). However, few studies have so far been performed using this approach to assess the impacts of human activities on the microbial communities in marine systems. In this study, using metagenomic DNA sequencing (16S ribosomal RNA gene), we analyzed and compared seawater and sediment communities between sand mining and control (natural) sites in southern coastal waters of Korea to assess whether anthropogenic activities have significantly affected the microbial communities. The sand mining sites harbored considerably lower levels of microbial diversities in the surface seawater community during spring compared with control sites. Moreover, the sand mining areas had distinct microbial taxonomic group compositions, particularly during spring season. The microbial groups detected solely in the sediment load/dredging areas (e.g., Marinobacter, Alcanivorax, Novosphingobium) are known to be involved in degradation of toxic chemicals such as hydrocarbon, oil, and aromatic compounds, and they also contain potential pathogens. This study highlights the versatility of metagenomics in monitoring and diagnosing the impacts of human disturbance on the environmental health of marine ecosystems from eDNA.