A Novel Analysis Method for Paired-Sample Microbial Ecology Experiments

Cross-study analyses of microbial abundance using generalized common factor methods

BACKGROUND

By creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in the environmental and biomedical sciences. However, this is challenging, as genomic data are high-dimensional, sparse, and noisy. Much of this noise reflects the exact conditions under which sequencing took place, and is so significant that it limits consensus-based validation of study results.

RESULTS

We propose an ensemble approach for cross-study exploratory analyses of microbial abundance data in which we first estimate the variance-covariance matrix of the underlying abundances from each dataset on the log scale assuming Poisson sampling, and subsequently model these covariances jointly so as to find a shared low-dimensional subspace of the feature space.

CONCLUSIONS

By viewing the projection of the latent true abundances onto this common structure, the variation is pared down to that which is shared among all datasets, and is likely to reflect more generalizable biological signal than can be inferred from individual datasets. We investigate several ways of achieving this, demonstrate that they work well on simulated and real metagenomic data in terms of signal retention and interpretability, and recommend a particular implementation.

DNA extraction protocol impacts ocular surface microbiome profile

Purpose

The aim of this study is to provide a reference frame to allow the comparison and interpretation of currently published studies on 16S ribosomal ribonucleic acid amplicon sequencing of ocular microbiome samples using different DNA extraction protocols. Alongside, the quantitative and qualitative yield and the reproducibility of different protocols has been assessed.

Methods

Both eyes of 7 eligible volunteers were sampled. Five commercially available DNA extraction protocols were selected based on previous publications in the field of the ocular surface microbiome and 2 host DNA depletion protocols were added based on their reported effective host DNA depletion without significant reduction in bacterial DNA concentration. The V3-V4 region of the 16S rRNA gene was targeted using Illumina MiSeq sequencing. The DADA2 pipeline in R was used to perform the bio-informatic processing and taxonomical assignment was done using the SILVA v132 database. The Vegdist function was used to calculate Bray-Curtis distances and the Galaxy web application was used to identify potential metagenomic biomarkers via linear discriminant analysis Effect Size (LEfSe). The R package Decontam was applied to control for potential contaminants.

Results

Samples analysed with PowerSoil, RNeasy and NucleoSpin had the highest DNA yield. The host DNA depletion kits showed a very low microbial DNA yield; and these samples were pooled per kit before sequencing. Despite pooling, 1 of both failed to construct a library.Looking at the beta-diversity, clear microbial compositional differences - dependent on the extraction protocol used - were observed and remained present after decontamination. Eighteen genera were consistently retrieved from the ocular surface of every volunteer by all non-pooled extraction kits and a comprehensive list of differentially abundant bacteria per extraction method was generated using LefSe analysis.

Conclusion

High-quality papers have been published in the field of the ocular surface microbiome but consensus on the importance of the extraction protocol used are lacking. Potential contaminants and discriminative genera per extraction protocol used, were introduced and a reference frame was built to facilitate both the interpretation of currently published papers and to ease future choice - making based on the research question at hand.

Oil Hydrocarbon Degradation by Caspian Sea Microbial Communities

The Caspian Sea, which is the largest landlocked body of water on the planet, receives substantial annual hydrocarbon input from anthropogenic sources (e.g., industry, agriculture, oil exploration, and extraction) and natural sources (e.g., mud volcanoes and oil seeps). The Caspian Sea also receives substantial amounts of runoff from agricultural and municipal sources, containing nutrients that have caused eutrophication and subsequent hypoxia in the deep, cold waters. The effect of decreasing oxygen saturation and cold temperatures on oil hydrocarbon biodegradation by a microbial community is not well characterized. The purpose of this study was to investigate the effect of oxic and anoxic conditions on oil hydrocarbon biodegradation at cold temperatures by microbial communities derived from the Caspian Sea. Water samples were collected from the Caspian Sea for study in experimental microcosms. Major taxonomic orders observed in the ambient water samples included Flavobacteriales, Actinomycetales, and Oceanospirillales. Microcosms were inoculated with microbial communities from the deepest waters and amended with oil hydrocarbons for 17 days. Hydrocarbon degradation and shifts in microbial community structure were measured. Surprisingly, oil hydrocarbon biodegradation under anoxic conditions exceeded that under oxic conditions; this was particularly evident in the degradation of aromatic hydrocarbons. Important microbial taxa associated with the anoxic microcosms included known oil degraders such as Oceanospirillaceae. This study provides knowledge about the ambient community structure of the Caspian Sea, which serves as an important reference point for future studies. Furthermore, this may be the first report in which anaerobic biodegradation of oil hydrocarbons exceeds aerobic biodegradation.

Generalized Hotelling's test for paired compositional data with application to human microbiome studies

The human microbiome is a dynamic system that changes due to diseases, medication, change in diet, etc. The paired design is a common approach to evaluate the microbial changes while controlling for the inherent differences between people. For example, microbiome data may be collected from the same individuals before and after a treatment. Two challenges exist in analyzing this type of data. First, microbiome data are compositional such that the reads for all taxa in each sample are constrained to sum to a constant. Second, the number of taxa can be much larger than the sample size. Few statistical methods exist to analyze such data besides methods that test one taxon at a time. In this paper, we propose to first conduct a log-ratio transformation of the compositions, and then develop a generalized Hotelling's test (GHT) to evaluate whether the average microbiome compositions are equivalent in the paired samples. We replace the sample covariance matrix in standard Hotelling's statistic by a shrinkage-based covariance, calculated as a weighted average of the sample covariance and a positive definite target matrix. The optimal weighting can be obtained for many commonly used target matrices. We develop a permutation procedure to assess the statistical significance. Extensive simulations show that our proposed method has well-controlled type I error and better power than a few ad hoc approaches. We apply our method to examine the vaginal microbiome changes in response to treatments for menopausal hot flashes. An R package " GHT" is freely available at https://github.com/zhaoni153/GHT.

Modeling time-series data from microbial communities

As sequencing technologies have advanced, the amount of information regarding the composition of bacterial communities from various environments (for example, skin or soil) has grown exponentially. To date, most work has focused on cataloging taxa present in samples and determining whether the distribution of taxa shifts with exogenous covariates. However, important questions regarding how taxa interact with each other and their environment remain open thus preventing in-depth ecological understanding of microbiomes. Time-series data from 16S rDNA amplicon sequencing are becoming more common within microbial ecology, but methods to infer ecological interactions from these longitudinal data are limited. We address this gap by presenting a method of analysis using Poisson regression fit with an elastic-net penalty that (1) takes advantage of the fact that the data are time series; (2) constrains estimates to allow for the possibility of many more interactions than data; and (3) is scalable enough to handle data consisting of thousands of taxa. We test the method on gut microbiome data from white-throated woodrats (Neotoma albigula) that were fed varying amounts of the plant secondary compound oxalate over a period of 22 days to estimate interactions between OTUs and their environment.

Profiling Living Bacteria Informs Preparation of Fecal Microbiota Transplantations

Fecal microbiota transplantation is a compelling treatment for recurrent Clostridium difficile infections, with potential applications against other diseases associated with changes in gut microbiota. But variability in fecal bacterial communities—believed to be the therapeutic agent—can complicate or undermine treatment efficacy. To understand the effects of transplant preparation methods on living fecal microbial communities, we applied a DNA-sequencing method (PMA-seq) that uses propidium monoazide (PMA) to differentiate between living and dead fecal microbes, and we created an analysis pipeline to identify individual bacteria that change in abundance between samples. We found that oxygen exposure degraded fecal bacterial communities, whereas freeze-thaw cycles and lag time between donor defecation and transplant preparation had much smaller effects. Notably, the abundance of Faecalibacterium prausnitzii—an anti-inflammatory commensal bacterium whose absence is linked to inflammatory bowel disease—decreased with oxygen exposure. Our results indicate that some current practices for preparing microbiota transplant material adversely affect living fecal microbial content and highlight PMA-seq as a valuable tool to inform best practices and evaluate the suitability of clinical fecal material.