Youngblut, N. D. | bioRxiv Science

bioRxiv2018

Human salivary amylase gene copy number impacts oral and gut microbiomes

Host genetic variation influences the composition of the human microbiome. While studies have focused on associations between the microbiome and single nucleotide polymorphisms in genes, their copy number (CN) can also vary. Here, in a study of human subjects including a 2-week standard diet, we relate oral and gut microbiome to CN at the AMY1 locus, which encodes the gene for salivary amylase, active in starch degradation. We show that although diet standardization drove gut microbiome convergence, AMY1-CN influenced oral and gut microbiome composition and function. The gut microbiomes of low-AMY1-CN subjects had an enhanced capacity for breakdown of complex carbohydrates. Those of high-AMY1 subjects were enriched in microbiota linked to resistant starch fermentation, had higher fecal SCFAs, and drove higher adiposity when transferred to germfree mice. Gut microbiota results were validated in a larger separate population. This study establishes AMY1-CN as a genetic factor patterning microbiome composition and function.

genetics↗

bioRxiv2017

HTSSIP: an R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments

Combining high throughput sequencing with stable isotope probing (HTS-SIP) is a powerful method for mapping in situ metabolic processes to thousands of microbial taxa. However, accurately mapping metabolic processes to taxa is complex and challenging. Multiple HTS-SIP data analysis methods have been developed, including high-resolution stable isotope probing (HR-SIP), multi-window high-resolution stable isotope probing (MW-HR-SIP), quantitative stable isotope probing (q-SIP), and {Delta}BD. Currently, the computational tools to perform these analyses are either not publicly available or lack documentation, testing, and developer support. To address this shortfall, we have developed the HTSSIP R package, a toolset for conducting HTS-SIP analyses in a straightforward and easily reproducible manner. The HTSSIP package, along with full documentation and examples, is available from CRAN at https://cran.r-project.org/web/packages/HTSSIP/index.html and Github at https://github.com/nick-youngblut/HTSSIP.

bioinformatics↗

bioRxiv2017

Evaluating The Accuracy Of DNA Stable Isotope Probing

Originality-Significance StatementBy combining DNA Stable Isotope Probing (DNA-SIP) with multiplexed high throughput DNA sequencing (HTS-DNA-SIP), it is now possible to identify patterns of isotope incorporation for thousands of microbial taxa. HTS-DNA-SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. A current limitation is that, due to the expense of these experiments, it has been impossible to evaluate the accuracy of DNA-SIP methods. We have developed a model that simulates DNA-SIP data, and we use the model to systematically evaluate and validate the accuracy of DNA-SIP analyses. This model can determine the analytical accuracy of DNA-SIP experiments in a range of contexts. Furthermore, the ability to predict experimental outcomes, as a function of experimental design and community characteristics, should be of great use in the design and interpretation DNA-SIP experiments.\n\nSummaryDNA Stable isotope probing (DNA-SIP) is a powerful method that identifies in situ isotope assimilation by microbial taxa. Combining DNA-SIP with multiplexed high throughput DNA sequencing (HTS-DNA-SIP) creates the potential to map in situ assimilation dynamics for thousands of microbial taxonomic units. However, the accuracy of methods for analyzing DNA-SIP data has never been evaluated. We have developed a toolset (SIPSim) for simulating HTS-DNA-SIP datasets and evaluating the accuracy of methods for analyzing HTS-DNA-SIP data. We evaluated two different approaches to analyzing HTS-DNA-SIP data: \"high resolution stable isotope probing\" (HR-SIP) and \"quantitative stable isotope probing\" (q-SIP). HR-SIP was highly specific and moderately sensitive, with very few false positives but potential for false negatives. In contrast, q-SIP had fewer false negatives but many false positives. We also found HR-SIP more robust than q-SIP with respect to experimental variance. Furthermore, we found that the detection sensitivity of HTS-DNA-SIP can be increased without compromising specificity by evaluating evidence of isotope incorporation over multiple windows of buoyant density (MW-HR-SIP). SIPSim provides a platform for determining the accuracy of HTS-DNA-SIP methods across a range of experimental parameters, which will be useful in the design, analysis, and validation of DNA-SIP experiments.

microbiology↗