Identification of sample mix-ups and mixtures in microbiome data in Diversity Outbred mice

Abstract In a Diversity Outbred mouse project with genotype data on 500 mice, including 297 with microbiome data, we identified three sets of sample mix-ups (two pairs and one trio) as well as at least 15 microbiome samples that appear to be mixtures of pairs of mice. The microbiome data consisted of shotgun sequencing reads from fecal DNA, used to characterize the gut microbial communities present in these mice. These sequence reads included sufficient reads derived from the host mouse to identify the individual. A number of microbiome samples appeared to contain a mixture of DNA from two mice. We describe a method for identifying sample mix-ups in such microbiome data, as well as a method for evaluating sample mixtures in this context.

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Identification of sample mix-ups and mixtures in microbiome data in Diversity Outbred mice

10.1101/529040 ◽

2019 ◽

Author(s):

Alexandra K. Lobo ◽

Lindsay L. Traeger ◽

Mark P. Keller ◽

Alan D. Attie ◽

Federico E. Rey ◽

...

Keyword(s):

Microbial Communities ◽

Shotgun Sequencing ◽

Genotype Data ◽

Fecal Dna ◽

Diversity Outbred ◽

Outbred Mouse ◽

Host Mouse ◽

Outbred Mice ◽

The Individual ◽

Microbiome Data

AbstractIn a Diversity Outbred mouse project with genotype data on 500 mice, including 297 with microbiome data, we identified three sets of sample mix-ups (two pairs and one trio) as well as at least 15 microbiome samples that appear to be mixtures of pairs of mice. The microbiome data consisted of shotgun sequencing reads from fecal DNA, used to characterize the gut microbial communities present in these mice. These sequence reads included sufficient reads derived from the host mouse to identify the individual. A number of microbiome samples appeared to contain a mixture of DNA from two mice. We describe a method for identifying sample mix-ups in such microbiome data, as well as a method for evaluating sample mixtures in this context.

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Cleaning Genotype Data from Diversity Outbred Mice

G3 Genes|Genome|Genetics ◽

10.1534/g3.119.400165 ◽

2019 ◽

Vol 9 (5) ◽

pp. 1571-1579 ◽

Cited By ~ 4

Author(s):

Karl W. Broman ◽

Daniel M. Gatti ◽

Karen L. Svenson ◽

Śaunak Sen ◽

Gary A. Churchill

Keyword(s):

Genotype Data ◽

Diversity Outbred ◽

Outbred Mice

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Genetic factors contributing to extensive variability of sex-specific hepatic gene expression in Diversity Outbred mice

PLoS ONE ◽

10.1371/journal.pone.0242665 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242665

Author(s):

Tisha Melia ◽

David J. Waxman

Keyword(s):

Gene Expression ◽

Growth Hormone ◽

Sex Differences ◽

Genetic Factors ◽

Individual Variability ◽

Specific Gene ◽

Open Chromatin ◽

Diversity Outbred ◽

Outbred Mice ◽

The Individual

Sex-specific transcription characterizes hundreds of genes in mouse liver, many implicated in sex-differential drug and lipid metabolism and disease susceptibility. While the regulation of liver sex differences by growth hormone-activated STAT5 is well established, little is known about autosomal genetic factors regulating the sex-specific liver transcriptome. Here we show, using genotyping and expression data from a large population of Diversity Outbred mice, that genetic factors work in tandem with growth hormone to control the individual variability of hundreds of sex-biased genes, including many long non-coding RNA genes. Significant associations between single nucleotide polymorphisms and sex-specific gene expression were identified as expression quantitative trait loci (eQTLs), many of which showed strong sex-dependent associations. Remarkably, autosomal genetic modifiers of sex-specific genes were found to account for more than 200 instances of gain or loss of sex-specificity across eight Diversity Outbred mouse founder strains. Sex-biased STAT5 binding sites and open chromatin regions with strain-specific variants were significantly enriched at eQTL regions regulating correspondingly sex-specific genes, supporting the proposed functional regulatory nature of the eQTL regions identified. Binding of the male-biased, growth hormone-regulated repressor BCL6 was most highly enriched at trans-eQTL regions controlling female-specific genes. Co-regulated gene clusters defined by overlapping eQTLs included sets of highly correlated genes from different chromosomes, further supporting trans-eQTL action. These findings elucidate how an unexpectedly large number of autosomal factors work in tandem with growth hormone signaling pathways to regulate the individual variability associated with sex differences in liver metabolism and disease.

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CaptureSeq: Hybridization-Based Enrichment of cpn60 Gene Fragments Reveals the Community Structures of Synthetic and Natural Microbial Ecosystems

Microorganisms ◽

10.3390/microorganisms9040816 ◽

2021 ◽

Vol 9 (4) ◽

pp. 816

Author(s):

Matthew G. Links ◽

Tim J. Dumonceaux ◽

E. Luke McCarthy ◽

Sean M. Hemmingsen ◽

Edward Topp ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Dna Sequences ◽

Pcr Amplification ◽

Shotgun Sequencing ◽

Natural Ecosystems ◽

Gene Markers ◽

Microbial Profile ◽

Microbial Ecosystems ◽

Pcr Targeting

Background. The molecular profiling of complex microbial communities has become the basis for examining the relationship between the microbiome composition, structure and metabolic functions of those communities. Microbial community structure can be partially assessed with “universal” PCR targeting taxonomic or functional gene markers. Increasingly, shotgun metagenomic DNA sequencing is providing more quantitative insight into microbiomes. However, both amplicon-based and shotgun sequencing approaches have shortcomings that limit the ability to study microbiome dynamics. Methods. We present a novel, amplicon-free, hybridization-based method (CaptureSeq) for profiling complex microbial communities using probes based on the chaperonin-60 gene. Molecular profiles of a commercially available synthetic microbial community standard were compared using CaptureSeq, whole metagenome sequencing, and 16S universal target amplification. Profiles were also generated for natural ecosystems including antibiotic-amended soils, manure storage tanks, and an agricultural reservoir. Results. The CaptureSeq method generated a microbial profile that encompassed all of the bacteria and eukaryotes in the panel with greater reproducibility and more accurate representation of high G/C content microorganisms compared to 16S amplification. In the natural ecosystems, CaptureSeq provided a much greater depth of coverage and sensitivity of detection compared to shotgun sequencing without prior selection. The resulting community profiles provided quantitatively reliable information about all three domains of life (Bacteria, Archaea, and Eukarya) in the different ecosystems. The applications of CaptureSeq will facilitate accurate studies of host-microbiome interactions for environmental, crop, animal and human health. Conclusions: cpn60-based hybridization enriched for taxonomically informative DNA sequences from complex mixtures. In synthetic and natural microbial ecosystems, CaptureSeq provided sequences from prokaryotes and eukaryotes simultaneously, with quantitatively reliable read abundances. CaptureSeq provides an alternative to PCR amplification of taxonomic markers with deep community coverage while minimizing amplification biases.

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Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

Nature Communications ◽

10.1038/s41467-021-23821-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Verónica Lloréns-Rico ◽

Sara Vieira-Silva ◽

Pedro J. Gonçalves ◽

Gwen Falony ◽

Jeroen Raes

Keyword(s):

Microbial Communities ◽

Quantitative Methods ◽

Microbial Load ◽

Metagenomic Sequencing ◽

Sampling Depth ◽

Microbiome Research ◽

Microbiome Data ◽

The Impact ◽

Analytical Approaches ◽

Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

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Quantitative trait mapping in Diversity Outbred mice identifies novel genomic regions associated with the hepatic glutathione redox system

Redox Biology ◽

10.1016/j.redox.2021.102093 ◽

2021 ◽

pp. 102093

Author(s):

Rebecca L. Gould ◽

Steven W. Craig ◽

Susan McClatchy ◽

Gary A. Churchill ◽

Robert Pazdro

Keyword(s):

Quantitative Trait ◽

Redox System ◽

Glutathione Redox System ◽

Trait Mapping ◽

Quantitative Trait Mapping ◽

Diversity Outbred ◽

Hepatic Glutathione ◽

Outbred Mice ◽

Genomic Regions ◽

Glutathione Redox

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tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

BMC Bioinformatics ◽

10.1186/s12859-021-03967-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Charlie M. Carpenter ◽

Daniel N. Frank ◽

Kayla Williamson ◽

Jaron Arbet ◽

Brandie D. Wagner ◽

...

Keyword(s):

Microbial Communities ◽

Open Source ◽

Data Structures ◽

Negative Binomial ◽

Rocky Mountain ◽

R Package ◽

Microbiome Analysis ◽

External Data ◽

Data Tables ◽

Microbiome Data

Abstract Background The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. Results We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. Conclusions tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

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