Host Species and Captivity Distinguish the Microbiome Compositions of a Diverse Zoo-Resident Non-Human Primate Population

Vast numbers of microorganisms inhabit the mammalian gastrointestinal tract in a complex community referred to as the gut microbiome. An individual’s microbiome may be impacted by genetics, diet, and various environmental factors, and has been associated with many health states and diseases, though specific explanations are lacking. While these communities are well-studied in human populations, non-human primates (NHPs), in particular zoo-resident or captive NHPs, offer distinct advantages to increasing our understanding of factors that influence gut microbiome composition. Here, we characterize the gut microbiome composition of a phylogenetically diverse cohort of NHPs residing in the same urban zoo. We show that despite overlapping and controlled environmental contexts, gut microbiomes are still distinguished between NHP host species. However, when comparing the zoo cohort to wild NHPs, we show that captivity status strongly distinguishes zoo-resident NHPs from their wild counterparts, regardless of host phylogeny. Microbial orders unique to captive NHPs include taxa commonly present in human gut microbiomes. Together, these results demonstrate that differences between NHP species are strongly associated with gut microbiome composition and diversity, suggesting that species-specific approaches should be considered when investigating environmental factors’ influence on gut microbiome composition

Human metabolome-derived cofactors are required for the antibacterial activity of siderocalin in urine

In human urinary tract infections, host cells release the antimicrobial protein siderocalin (SCN; also known as lipocalin-2, neutrophil gelatinase-associated lipocalin, or 24p3) into the urinary tract. By binding to ferric catechol complexes, SCN can sequester iron, a growth-limiting nutrient for most bacterial pathogens. Recent evidence links the antibacterial activity of SCN in human urine to iron sequestration and metabolomic variation between individuals. To determine whether these metabolomic associations correspond to functional Fe(III)-binding SCN ligands, we devised a biophysical protein binding screen to identify SCN ligands through direct analysis of human urine. This screen revealed a series of physiologic unconjugated urinary catechols that were able to function as SCN ligands of which pyrogallol in particular was positively associated with high urinary SCN activity. In a purified, defined culture system, these physiologic SCN ligands were sufficient to activate SCN antibacterial activity against Escherichia coli. In the presence of multiple SCN ligands, native mass spectrometry demonstrated that SCN may preferentially combine different ligands to coordinate iron, suggesting that availability of specific ligand combinations affects in vivo SCN antibacterial activity. These results support a mechanistic link between the human urinary metabolome and innate immune function

SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies

Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies

SHOGUN: a modular, accurate and scalable framework for microbiome quantification

SummaryThe software pipeline SHOGUN profiles known taxonomic and gene abundances of short-read shotgun metagenomics sequencing data. The pipeline is scalable, modular and flexible. Data analysis and transformation steps can be run individually or together in an automated workflow. Users can easily create new reference databases and can select one of three DNA alignment tools, ranging from ultra-fast low-RAM k-mer-based database search to fully exhaustive gapped DNA alignment, to best fit their analysis needs and computational resources. The pipeline includes an implementation of a published method for taxonomy assignment disambiguation with empirical Bayesian redistribution. The software is installable via the conda resource management framework, has plugins for the QIIME2 and QIITA packages and produces both taxonomy and gene abundance profile tables with a single command, thus promoting convenient and reproducible metagenomics research.Availability and implementationhttps://github.com/knights-lab/SHOGUN

Data_Sheet_3_SplinectomeR Enables Group Comparisons in Longitudinal Microbiome Studies.PDF

<p>Longitudinal, prospective studies often rely on multi-omics approaches, wherein various specimens are analyzed for genomic, metabolomic, and/or transcriptomic profiles. In practice, longitudinal studies in humans and other animals routinely suffer from subject dropout, irregular sampling, and biological variation that may not be normally distributed. As a result, testing hypotheses about observations over time can be statistically challenging without performing transformations and dramatic simplifications to the dataset, causing a loss of longitudinal power in the process. Here, we introduce splinectomeR, an R package that uses smoothing splines to summarize data for straightforward hypothesis testing in longitudinal studies. The package is open-source, and can be used interactively within R or run from the command line as a standalone tool. We present a novel in-depth analysis of a published large-scale microbiome study as an example of its utility in straightforward testing of key hypotheses. We expect that splinectomeR will be a useful tool for hypothesis testing in longitudinal microbiome studies.</p

Human Urinary Composition Controls Siderocalin's Antibacterial Activity.

During Escherichia coli urinary tract infections, cells in the human urinary tract release the antimicrobial protein siderocalin (SCN; also known as lipocalin 2, neutrophil gelatinase-associated lipocalin/NGAL, or 24p3). SCN can interfere with E. coli iron acquisition by sequestering ferric iron complexes with enterobactin, the conserved E. coli siderophore. Here we find that human urinary constituents can reverse this relationship, instead making enterobactin critical for overcoming SCN-mediated growth restriction. Urinary control of SCN activity exhibits wide ranging individual differences. We used these differences to identify elevated urinary pH and aryl metabolites as key biochemical host factors controlling urinary SCN activity. These aryl metabolites are well-known products of intestinal microbial metabolism. Together, these results identify an innate antibacterial immune interaction that is critically dependent upon individualistic chemical features of human urine

Evaluating the Information Content of Shallow Shotgun Metagenomics

A common refrain in recent microbiome-related academic meetings is that the field needs to move away from broad taxonomic surveys using 16S sequencing and toward more powerful longitudinal studies using shotgun sequencing. However, performing deep shotgun sequencing in large longitudinal studies remains prohibitively expensive for all but the most well-funded research labs and consortia, which leads many researchers to choose 16S sequencing for large studies, followed by deep shotgun sequencing on a subset of targeted samples. Here, we show that shallow- or moderate-depth shotgun sequencing may be used by researchers to obtain species-level taxonomic and functional data at approximately the same cost as amplicon sequencing. While shallow shotgun sequencing is not intended to replace deep shotgun sequencing for strain-level characterization, we recommend that microbiome scientists consider using shallow shotgun sequencing instead of 16S sequencing for large-scale human microbiome studies.Although microbial communities are associated with human, environmental, plant, and animal health, there exists no cost-effective method for precisely characterizing species and genes in such communities. While deep whole-metagenome shotgun (WMS) sequencing provides high taxonomic and functional resolution, it is often prohibitively expensive for large-scale studies. The prevailing alternative, 16S rRNA gene amplicon (16S) sequencing, often does not resolve taxonomy past the genus level and provides only moderately accurate predictions of the functional profile; thus, there is currently no widely accepted approach to affordable, high-resolution, taxonomic, and functional microbiome analysis. To address this technology gap, we evaluated the information content of shallow shotgun sequencing with as low as 0.5 million sequences per sample as an alternative to 16S sequencing for large human microbiome studies. We describe a library preparation protocol enabling shallow shotgun sequencing at approximately the same per-sample cost as 16S sequencing. We analyzed multiple real and simulated biological data sets, including two novel human stool samples with ultradeep sequencing of 2.5 billion sequences per sample, and found that shallow shotgun sequencing recovers more-accurate species-level taxonomic and functional profiles of the human microbiome than 16S sequencing. We discuss the inherent limitations of shallow shotgun sequencing and note that 16S sequencing remains a valuable and important method for taxonomic profiling of novel environments. Although deep WMS sequencing remains the gold standard for high-resolution microbiome analysis, we recommend that researchers consider shallow shotgun sequencing as a useful alternative to 16S sequencing for large-scale human microbiome research studies where WMS sequencing may be cost-prohibitive

Evaluating the Information Content of Shallow Shotgun Metagenomics.

Although microbial communities are associated with human, environmental, plant, and animal health, there exists no cost-effective method for precisely characterizing species and genes in such communities. While deep whole-metagenome shotgun (WMS) sequencing provides high taxonomic and functional resolution, it is often prohibitively expensive for large-scale studies. The prevailing alternative, 16S rRNA gene amplicon (16S) sequencing, often does not resolve taxonomy past the genus level and provides only moderately accurate predictions of the functional profile; thus, there is currently no widely accepted approach to affordable, high-resolution, taxonomic, and functional microbiome analysis. To address this technology gap, we evaluated the information content of shallow shotgun sequencing with as low as 0.5 million sequences per sample as an alternative to 16S sequencing for large human microbiome studies. We describe a library preparation protocol enabling shallow shotgun sequencing at approximately the same per-sample cost as 16S sequencing. We analyzed multiple real and simulated biological data sets, including two novel human stool samples with ultradeep sequencing of 2.5 billion sequences per sample, and found that shallow shotgun sequencing recovers more-accurate species-level taxonomic and functional profiles of the human microbiome than 16S sequencing. We discuss the inherent limitations of shallow shotgun sequencing and note that 16S sequencing remains a valuable and important method for taxonomic profiling of novel environments. Although deep WMS sequencing remains the gold standard for high-resolution microbiome analysis, we recommend that researchers consider shallow shotgun sequencing as a useful alternative to 16S sequencing for large-scale human microbiome research studies where WMS sequencing may be cost-prohibitive. IMPORTANCE A common refrain in recent microbiome-related academic meetings is that the field needs to move away from broad taxonomic surveys using 16S sequencing and toward more powerful longitudinal studies using shotgun sequencing. However, performing deep shotgun sequencing in large longitudinal studies remains prohibitively expensive for all but the most well-funded research labs and consortia, which leads many researchers to choose 16S sequencing for large studies, followed by deep shotgun sequencing on a subset of targeted samples. Here, we show that shallow- or moderate-depth shotgun sequencing may be used by researchers to obtain species-level taxonomic and functional data at approximately the same cost as amplicon sequencing. While shallow shotgun sequencing is not intended to replace deep shotgun sequencing for strain-level characterization, we recommend that microbiome scientists consider using shallow shotgun sequencing instead of 16S sequencing for large-scale human microbiome studies