Emerging Priorities for Microbiome Research

Cullen, Chad M.; Aneja, Kawalpreet K.; Beyhan, Sinem; Cho, Clara E.; Woloszynek, Stephen; Convertino, Matteo; McCoy, Sophie J.; Zhang, Yanyan; Anderson, Matthew; Alvarez‐Ponce, David; Smirnova, Ekaterina; Karstens, Lisa; Dorrestein, Pieter C.; Li, Hongzhe; Gupta, Ananya Sen; Cheung, Kevin; Powers, Jennifer G.; Zhao, Zhengqiao; Rosen, Gail

doi:10.3389/fmicb.2020.00136

Cited by 131 publications

(109 citation statements)

References 281 publications

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“…An alternative is to use cultivation-independent methods such as metagenomics and NGS in combination. Such studies will provide comprehensive information regarding the microbial populations in communities and species richness and the ecological role in the environment [ 86 ]. It is important to understand biological processes and changes in the bacterial dynamics in the aquatic ecosystems for the assessment of human-driven changes and natural selection.…”

Section: Edna and Environmental Microbiomesmentioning

confidence: 99%

Extracellular DNA (eDNA): Neglected and Potential Sources of Antibiotic Resistant Genes (ARGs) in the Aquatic Environments

Sivalingam

Poté

Prabakar³

2020

Pathogens

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Over the past decades, the rising antibiotic resistance bacteria (ARB) are continuing to emerge as a global threat due to potential public health risk. Rapidly evolving antibiotic resistance and its persistence in the environment, have underpinned the need for more studies to identify the possible sources and limit the spread. In this context, not commonly studied and a neglected genetic material called extracellular DNA (eDNA) is gaining increased attention as it can be one of the significant drivers for transmission of extracellular ARGS (eARGs) via horizontal gene transfer (HGT) to competent environmental bacteria and diverse sources of antibiotic-resistance genes (ARGs) in the environment. Consequently, this review highlights the studies that address the environmental occurrence of eDNA and encoding eARGs and its impact on the environmental resistome. In this review, we also brief the recent dedicated technological advancements that are accelerating extraction of eDNA and the efficiency of treatment technologies in reducing eDNA that focuses on environmental antibiotic resistance and potential ecological health risk.

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Section: Edna and Environmental Microbiomesmentioning

confidence: 99%

Extracellular DNA (eDNA): Neglected and Potential Sources of Antibiotic Resistant Genes (ARGs) in the Aquatic Environments

Sivalingam

Poté

Prabakar³

2020

Pathogens

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“…The statistical methodology literature on quality control for the derived feature data is scarce. Most previous studies either recommended filtering without thorough evaluation of its effects [19,20], or focused on the number of taxa removed from the mock artificial community studies [14] and on contaminant identification [16,10]. We have previously demonstrated that filtering methods were effective in identifying true species in mock data [14].…”

Section: Discussionmentioning

confidence: 99%

“…However, in practice, filtering is used as an intermediary step applied to the derived taxonomic feature table prior to data analysis. While filtering is a commonly used and recommended approach [19,20], its benefits on data analysis and the effects on the scientific conclusions drawn from filtered and unfiltered data have not been reported.…”

Section: Introductionmentioning

confidence: 99%

Effects of microbiome rare taxa filtering on statistical analysis

Cao

Sun

Rajesh

et al. 2020

Preprint

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Abstract Background: Accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. This approach reduces extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation, as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs, while preserving between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially expressed across groups of samples, and random forest models to rank features with largest contribution towards disease classiffcation. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between filtering and contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data, while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

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“…Microbiome research is a highly transdisciplinary field with a wide range of applications and methods for studying it, involving different computational approaches and models [11].…”

Section: Introductionmentioning

confidence: 99%

Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?

et al. 2020

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Microbiome research is a highly transdisciplinary field with a wide range of applications and methods for studying it, involving different computational approaches and models. The fact that different people host radically different microbiota highlights forensic perspectives in understanding what leads to this variation and what regulates it, in order to effectively use microbes as forensic evidence. This narrative review provides an overview of some of the main scientific works so far produced, focusing on the potentiality of using skin microbiome profiling for human identification in forensics. This review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The examined literature clearly ascertains that skin microbial communities, although personalized, vary systematically across body sites and time, with intrapersonal differences over time smaller than interpersonal ones, showing such a high degree of spatial and temporal variability that the degree and nature of this variability can constitute in itself an important parameter useful in distinguishing individuals from one another. Even making the effort to organically synthesize all results achieved until now, it is quite evident that these results are still the pieces of a puzzle, which is not yet complete.

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Emerging Priorities for Microbiome Research

Cited by 131 publications

References 281 publications

Extracellular DNA (eDNA): Neglected and Potential Sources of Antibiotic Resistant Genes (ARGs) in the Aquatic Environments

Extracellular DNA (eDNA): Neglected and Potential Sources of Antibiotic Resistant Genes (ARGs) in the Aquatic Environments

Effects of microbiome rare taxa filtering on statistical analysis

Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?

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