Gavin M. Douglas

Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research

As the microbiome field continues to grow, a multitude of researchers are learning how to conduct proper microbiome experiments. We outline here a streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures. This allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results. Our sequencing protocols, bioinformatic tutorials, and bundled software are freely available through Microbiome Helper. As the microbiome research field continues to evolve, Microbiome Helper will be updated with new protocols, scripts, and training materials. ABSTRACT Sequence-based approaches to study microbiomes, such as 16S rRNA gene sequencing and metagenomics, are uncovering associations between microbial taxa and a myriad of factors. A drawback of these approaches is that the necessary sequencing library preparation and bioinformatic analyses are complicated and continuously changing, which can be a barrier for researchers new to the field. We present three essential components to conducting a microbiome experiment from start to finish: first, a simplified and step-by-step custom gene sequencing protocol that requires limited lab equipment, is cost-effective, and has been thoroughly tested and utilized on various sample types; second, a series of scripts to integrate various commonly used bioinformatic tools that is available as a standalone installation or as a single downloadable virtual image; and third, a set of bioinformatic workflows and tutorials to provide step-by-step guidance and education for those new to the microbiome field. This resource will provide the foundations for those newly entering the microbiome field and will provide much-needed guidance and best practices to ensure that quality microbiome research is undertaken. All protocols, scripts, workflows, tutorials, and virtual images are freely available through the Microbiome Helper website (https://github.com/mlangill/microbiome_helper/wiki ). IMPORTANCE As the microbiome field continues to grow, a multitude of researchers are learning how to conduct proper microbiome experiments. We outline here a streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures. This allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results. Our sequencing protocols, bioinformatic tutorials, and bundled software are freely available through Microbiome Helper. As the microbiome research field continues to evolve, Microbiome Helper will be updated with new protocols, scripts, and training materials.

Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease

BackgroundCrohn’s disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients’ ongoing treatments. Additionally, most analyses of CD patients’ microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples.ResultsWe sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients’ response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response.ConclusionsWe demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers’ microbiome in the future.The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)—CAF/08/01.

Variation in Bacterial and Eukaryotic Communities Associated with Natural and Managed Wild Blueberry Habitats

We examined the differences between bacterial and eukaryotic soil communities associated with natural and managed habitats of wild blueberry, Vaccinium angustifolium. In total, 138 bacterial and 130 eukaryotic soil and rhizosphere communities across seven blueberry fields, all established at least 30 years ago and from two forest areas adjacent to some of these fields, were analyzed. We analyzed correlations between soil chemical factors and the structure of eukaryotic and bacterial communities, including differences in the microbiome between bulk and rhizosphere soils, and between rhizospheres of plants growing in natural and managed habitats. Characterization of a broad selection of fields across the province of Nova Scotia, Canada, allowed us to tentatively identify specific signatures from several distinct soil niches. Our data indicate that bacterial and eukaryotic communities differ in how they correlate with soil chemical properties. Also, while eukaryotic communities correlate stronger with soil f...

A Genome-Wide Association Study of Apple Quality and Scab Resistance

Well‐studied and novel associations detected using next‐generation sequencing data and genome‐wide association studies in apples. Several notable associations detected for apple scab using historical data. More studies in wider breeding material needed to assess suitability for marker‐assisted selection.

Processing a 16S rRNA Sequencing Dataset with the Microbiome Helper Workflow

Sequencing microbiome samples has recently become a fast and cost-effective method to taxonomically profile communities. The growing interest in analyzing microbial sequencing data has attracted many new researchers to the field. Here, we present a straightforward bioinformatic pipeline that aims to streamline the processing of 16S rRNA sequencing data. This workflow is part of the larger project called Microbiome Helper (Comeau et al. mSyst 2:e00127-16, 2017), which includes other bioinformatic workflows, tutorials, and scripts available here: https://github.com/mlangill/microbiome_helper/wiki .

Predicting the Functional Potential of the Microbiome from Marker Genes Using PICRUSt

Marker-gene sequencing is a cost-effective method of taxonomically profiling microbial communities. Unlike metagenomic approaches, marker-gene sequencing does not provide direct information about the functional genes that are present in the genomes of community members. However, by capitalizing on the rapid growth in the number of sequenced genomes, it is possible to infer which functions are likely associated with a marker gene based on its sequence similarity with a reference genome. The PICRUSt tool is based on this idea and can predict functional category abundances based on an input marker gene. In brief, this method requires a reference phylogeny with tips corresponding to taxa with reference genomes as well as taxa lacking sequenced genomes. A modified ancestral state reconstruction (ASR) method is then used to infer counts of functional categories for taxa without reference genomes. The predictions are written to pre-calculated files, which can be cross-referenced with other datasets to quickly generate predictions of functional potential for a community. This chapter will give an in-depth description of these methods and describe how PICRUSt should be used.

Prediction of Cacao (Theobroma cacao) Resistance to Moniliophthora spp. Diseases via Genome-Wide Association Analysis and Genomic Selection

Cacao (Theobroma cacao) is a globally important crop, and its yield is severely restricted by disease. Two of the most damaging diseases, witches’ broom disease (WBD) and frosty pod rot disease (FPRD), are caused by a pair of related fungi: Moniliophthora perniciosa and Moniliophthora roreri, respectively. Resistant cultivars are the most effective long-term strategy to address Moniliophthora diseases, but efficiently generating resistant and productive new cultivars will require robust methods for screening germplasm before field testing. Marker-assisted selection (MAS) and genomic selection (GS) provide two potential avenues for predicting the performance of new genotypes, potentially increasing the selection gain per unit time. To test the effectiveness of these two approaches, we performed a genome-wide association study (GWAS) and GS on three related populations of cacao in Ecuador genotyped with a 15K single nucleotide polymorphism (SNP) microarray for three measures of WBD infection (vegetative broom, cushion broom, and chirimoya pod), one of FPRD (monilia pod) and two productivity traits (total fresh weight of pods and % healthy pods produced). GWAS yielded several SNPs associated with disease resistance in each population, but none were significantly correlated with the same trait in other populations. Genomic selection, using one population as a training set to estimate the phenotypes of the remaining two (composed of different families), varied among traits, from a mean prediction accuracy of 0.46 (vegetative broom) to 0.15 (monilia pod), and varied between training populations. Simulations demonstrated that selecting seedlings using GWAS markers alone generates no improvement over selecting at random, but that GS improves the selection process significantly. Our results suggest that the GWAS markers discovered here are not sufficiently predictive across diverse germplasm to be useful for MAS, but that using all markers in a GS framework holds substantial promise in accelerating disease-resistance in cacao.

Dissecting Community Structure in Wild Blueberry Root and Soil Microbiome

A complex network of functions and symbiotic interactions between a eukaryotic host and its microbiome is a the foundation of the ecological unit holobiont. However, little is known about how the non-fungal eukaryotic microorganisms fit in this complex network of host–microbiome interactions. In this study, we employed a unique wild blueberry ecosystem to evaluate plant-associated microbiota, encompassing both eukaryotic and bacterial communities. We found that, while soil microbiome serves as a foundation for root microbiome, plant-influenced species sorting had stronger effect on eukaryotes than on bacteria. Our study identified several fungal and protist taxa, which are correlated with decreased fruit production in wild blueberry agricultural ecosystems. The specific effect of species sorting in root microbiome resulted in an increase in relative abundance of fungi adapted to plant-associated life-style, while the relative abundance of non-fungal eukaryotes was decreased along the soil-endosphere continuum in the root, probably because of low adaptation of these microorganisms to host–plant defense responses. Analysis of community correlation networks indicated that bacterial and eukaryotic interactions became more complex along the soil-endosphere continuum and, in addition to extensive mutualistic interactions, co-exclusion also played an important role in shaping wild blueberry associated microbiome. Our study identified several potential hub taxa with important roles in soil fertility and/or plant–microbe interaction, suggesting the key role of these taxa in the interconnection between soils and plant health and overall microbial community structure. This study also provides a comprehensive view of the role of non-fungal eukaryotes in soil ecosystem.