Publications by authors named "Gavin M Douglas"

21 Publications

  • Page 1 of 1

Transcriptomic profiling of responses to .

Innate Immun 2021 Feb 22;27(2):143-157. Epub 2020 Dec 22.

Department of Microbiology and Immunology, Dalhousie University, Canada.

is an opportunistic bacterial pathogen of plants. Unlike the well-characterized plant defense responses to highly adapted bacterial phytopathogens, little is known about plant response to infection. In this study, we examined the (canola) tissue-specific response to infection using RNA sequencing. Transcriptomic analysis of canola seedlings over a 5 day infection revealed that many molecular processes involved in plant innate immunity were up-regulated, whereas photosynthesis was down-regulated. Phytohormones control many vital biological processes within plants, including growth and development, senescence, seed setting, fruit ripening, and innate immunity. The three main phytohormones involved in plant innate immunity are salicylic acid (SA), jasmonic acid (JA), and ethylene (ET). Many bacterial pathogens have evolved multiple strategies to manipulate these hormone responses in order to infect plants successfully. Interestingly, gene expression within all three phytohormone (SA, JA, and ET) signaling pathways was up-regulated in response to infection. This study identified a unique plant hormone response to the opportunistic bacterial pathogen infection.
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http://dx.doi.org/10.1177/1753425920980512DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882811PMC
February 2021

Re-evaluating the relationship between missing heritability and the microbiome.

Microbiome 2020 06 8;8(1):87. Epub 2020 Jun 8.

Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.

Human genome-wide association studies (GWASs) have recurrently estimated lower heritability estimates than familial studies. Many explanations have been suggested to explain these lower estimates, including that a substantial proportion of genetic variation and gene-by-environment interactions are unmeasured in typical GWASs. The human microbiome is potentially related to both of these explanations, but it has been more commonly considered as a source of unmeasured genetic variation. In particular, it has recently been argued that the genetic variation within the human microbiome should be included when estimating trait heritability. We outline issues with this argument, which in its strictest form depends on the holobiont model of human-microbiome interactions. Instead, we argue that the microbiome could be leveraged to help control for environmental variation across a population, although that remains to be determined. We discuss potential approaches that could be explored to determine whether integrating microbiome sequencing data into GWASs is useful. Video abstract.
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http://dx.doi.org/10.1186/s40168-020-00839-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282175PMC
June 2020

PICRUSt2 for prediction of metagenome functions.

Nat Biotechnol 2020 06;38(6):685-688

Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada.

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http://dx.doi.org/10.1038/s41587-020-0548-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365738PMC
June 2020

The human tumor microbiome is composed of tumor type-specific intracellular bacteria.

Science 2020 05;368(6494):973-980

Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients' smoking status, and the response to immunotherapy.
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http://dx.doi.org/10.1126/science.aay9189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757858PMC
May 2020

Detection of Microevolution and Multiple Infection from Gastric Biopsies by Housekeeping Gene Amplicon Sequencing.

Pathogens 2020 Feb 5;9(2). Epub 2020 Feb 5.

Secció de Microbiologia, Departament de Biologia, Sanitat i Medi Ambient, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona, Av. Joan XXIII 27-31, 08028 Barcelona, Catalonia, Spain.

Despite the great efforts devoted to research on , the prevalence of single-strain infection or mixed infection and its implications in the mode of transmission of this bacterium are still controversial. In this study, we explored the usefulness of housekeeping gene amplicon sequencing in the detection of microevolution and multiple infections. DNA was extracted from five gastric biopsies from four patients infected with distinct histopathological diagnoses. PCR amplification of six -specific housekeeping genes was then assessed on each sample. Optimal results were obtained for the and genes, which were selected for amplicon sequencing. A total of 11,833 and 403 amplicon sequences were obtained, 2042 and 112 of which were unique sequences, respectively. All and sequences were clustered at 97% to 9 and 13 operational taxonomic units (OTUs), respectively. For each sample from a different patient, a single OTU comprised the majority of sequences in both genes, but more than one OTU was detected in all samples. These results suggest that multiple infections with a predominant strain together with other minority strains are the main way by which colonizes the human stomach.
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http://dx.doi.org/10.3390/pathogens9020097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7168683PMC
February 2020

Current and Promising Approaches to Identify Horizontal Gene Transfer Events in Metagenomes.

Genome Biol Evol 2019 10;11(10):2750-2766

Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada.

High-throughput shotgun metagenomics sequencing has enabled the profiling of myriad natural communities. These data are commonly used to identify gene families and pathways that were potentially gained or lost in an environment and which may be involved in microbial adaptation. Despite the widespread interest in these events, there are no established best practices for identifying gene gain and loss in metagenomics data. Horizontal gene transfer (HGT) represents several mechanisms of gene gain that are especially of interest in clinical microbiology due to the rapid spread of antibiotic resistance genes in natural communities. Several additional mechanisms of gene gain and loss, including gene duplication, gene loss-of-function events, and de novo gene birth are also important to consider in the context of metagenomes but have been less studied. This review is largely focused on detecting HGT in prokaryotic metagenomes, but methods for detecting these other mechanisms are first discussed. For this article to be self-contained, we provide a general background on HGT and the different possible signatures of this process. Lastly, we discuss how improved assembly of genomes from metagenomes would be the most straight-forward approach for improving the inference of gene gain and loss events. Several recent technological advances could help improve metagenome assemblies: long-read sequencing, determining the physical proximity of contigs, optical mapping of short sequences along chromosomes, and single-cell metagenomics. The benefits and limitations of these advances are discussed and open questions in this area are highlighted.
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http://dx.doi.org/10.1093/gbe/evz184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777429PMC
October 2019

Metagenomic Functional Shifts to Plant Induced Environmental Changes.

Front Microbiol 2019 26;10:1682. Epub 2019 Jul 26.

Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.

The (wild blueberry) agricultural system involves transformation of the environment surrounding the plant to intensify plant propagation and to improve fruit yield, and therefore is an advantageous model to study the interaction between soil microorganisms and plant-host interactions. We studied this system to address the question of a trade-off between microbial adaptation to a plant-influenced environment and its general metabolic capabilities. We found that many basic metabolic functions were similarly represented in bulk soil and rhizosphere microbiomes overall. However, we identified a niche-specific difference in functions potentially beneficial for microbial survival in the rhizosphere but that might also reduce the ability of microbes to withstand stresses in bulk soils. These functions could provide the microbiome with additional capabilities to respond to environmental fluctuations in the rhizosphere triggered by changes in the composition of root exudates. Based on our analysis we hypothesize that the rhizosphere-specific pathways involved in xenobiotics biodegradation could provide the microbiome with functional flexibility to respond to plant stress status.
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http://dx.doi.org/10.3389/fmicb.2019.01682DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6676915PMC
July 2019

Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Authors:
Evan Bolyen Jai Ram Rideout Matthew R Dillon Nicholas A Bokulich Christian C Abnet Gabriel A Al-Ghalith Harriet Alexander Eric J Alm Manimozhiyan Arumugam Francesco Asnicar Yang Bai Jordan E Bisanz Kyle Bittinger Asker Brejnrod Colin J Brislawn C Titus Brown Benjamin J Callahan Andrés Mauricio Caraballo-Rodríguez John Chase Emily K Cope Ricardo Da Silva Christian Diener Pieter C Dorrestein Gavin M Douglas Daniel M Durall Claire Duvallet Christian F Edwardson Madeleine Ernst Mehrbod Estaki Jennifer Fouquier Julia M Gauglitz Sean M Gibbons Deanna L Gibson Antonio Gonzalez Kestrel Gorlick Jiarong Guo Benjamin Hillmann Susan Holmes Hannes Holste Curtis Huttenhower Gavin A Huttley Stefan Janssen Alan K Jarmusch Lingjing Jiang Benjamin D Kaehler Kyo Bin Kang Christopher R Keefe Paul Keim Scott T Kelley Dan Knights Irina Koester Tomasz Kosciolek Jorden Kreps Morgan G I Langille Joslynn Lee Ruth Ley Yong-Xin Liu Erikka Loftfield Catherine Lozupone Massoud Maher Clarisse Marotz Bryan D Martin Daniel McDonald Lauren J McIver Alexey V Melnik Jessica L Metcalf Sydney C Morgan Jamie T Morton Ahmad Turan Naimey Jose A Navas-Molina Louis Felix Nothias Stephanie B Orchanian Talima Pearson Samuel L Peoples Daniel Petras Mary Lai Preuss Elmar Pruesse Lasse Buur Rasmussen Adam Rivers Michael S Robeson Patrick Rosenthal Nicola Segata Michael Shaffer Arron Shiffer Rashmi Sinha Se Jin Song John R Spear Austin D Swafford Luke R Thompson Pedro J Torres Pauline Trinh Anupriya Tripathi Peter J Turnbaugh Sabah Ul-Hasan Justin J J van der Hooft Fernando Vargas Yoshiki Vázquez-Baeza Emily Vogtmann Max von Hippel William Walters Yunhu Wan Mingxun Wang Jonathan Warren Kyle C Weber Charles H D Williamson Amy D Willis Zhenjiang Zech Xu Jesse R Zaneveld Yilong Zhang Qiyun Zhu Rob Knight J Gregory Caporaso

Nat Biotechnol 2019 Sep;37(9):1091

Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41587-019-0252-6DOI Listing
September 2019

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Authors:
Evan Bolyen Jai Ram Rideout Matthew R Dillon Nicholas A Bokulich Christian C Abnet Gabriel A Al-Ghalith Harriet Alexander Eric J Alm Manimozhiyan Arumugam Francesco Asnicar Yang Bai Jordan E Bisanz Kyle Bittinger Asker Brejnrod Colin J Brislawn C Titus Brown Benjamin J Callahan Andrés Mauricio Caraballo-Rodríguez John Chase Emily K Cope Ricardo Da Silva Christian Diener Pieter C Dorrestein Gavin M Douglas Daniel M Durall Claire Duvallet Christian F Edwardson Madeleine Ernst Mehrbod Estaki Jennifer Fouquier Julia M Gauglitz Sean M Gibbons Deanna L Gibson Antonio Gonzalez Kestrel Gorlick Jiarong Guo Benjamin Hillmann Susan Holmes Hannes Holste Curtis Huttenhower Gavin A Huttley Stefan Janssen Alan K Jarmusch Lingjing Jiang Benjamin D Kaehler Kyo Bin Kang Christopher R Keefe Paul Keim Scott T Kelley Dan Knights Irina Koester Tomasz Kosciolek Jorden Kreps Morgan G I Langille Joslynn Lee Ruth Ley Yong-Xin Liu Erikka Loftfield Catherine Lozupone Massoud Maher Clarisse Marotz Bryan D Martin Daniel McDonald Lauren J McIver Alexey V Melnik Jessica L Metcalf Sydney C Morgan Jamie T Morton Ahmad Turan Naimey Jose A Navas-Molina Louis Felix Nothias Stephanie B Orchanian Talima Pearson Samuel L Peoples Daniel Petras Mary Lai Preuss Elmar Pruesse Lasse Buur Rasmussen Adam Rivers Michael S Robeson Patrick Rosenthal Nicola Segata Michael Shaffer Arron Shiffer Rashmi Sinha Se Jin Song John R Spear Austin D Swafford Luke R Thompson Pedro J Torres Pauline Trinh Anupriya Tripathi Peter J Turnbaugh Sabah Ul-Hasan Justin J J van der Hooft Fernando Vargas Yoshiki Vázquez-Baeza Emily Vogtmann Max von Hippel William Walters Yunhu Wan Mingxun Wang Jonathan Warren Kyle C Weber Charles H D Williamson Amy D Willis Zhenjiang Zech Xu Jesse R Zaneveld Yilong Zhang Qiyun Zhu Rob Knight J Gregory Caporaso

Nat Biotechnol 2019 08;37(8):852-857

Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA.

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http://dx.doi.org/10.1038/s41587-019-0209-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015180PMC
August 2019

Enhancement of the gut barrier integrity by a microbial metabolite through the Nrf2 pathway.

Nat Commun 2019 01 9;10(1):89. Epub 2019 Jan 9.

Department of Microbiology and Immunology, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USA.

The importance of gut microbiota in human health and pathophysiology is undisputable. Despite the abundance of metagenomics data, the functional dynamics of gut microbiota in human health and disease remain elusive. Urolithin A (UroA), a major microbial metabolite derived from polyphenolics of berries and pomegranate fruits displays anti-inflammatory, anti-oxidative, and anti-ageing activities. Here, we show that UroA and its potent synthetic analogue (UAS03) significantly enhance gut barrier function and inhibit unwarranted inflammation. We demonstrate that UroA and UAS03 exert their barrier functions through activation of aryl hydrocarbon receptor (AhR)- nuclear factor erythroid 2-related factor 2 (Nrf2)-dependent pathways to upregulate epithelial tight junction proteins. Importantly, treatment with these compounds attenuated colitis in pre-clinical models by remedying barrier dysfunction in addition to anti-inflammatory activities. Cumulatively, the results highlight how microbial metabolites provide two-pronged beneficial activities at gut epithelium by enhancing barrier functions and reducing inflammation to protect from colonic diseases.
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http://dx.doi.org/10.1038/s41467-018-07859-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327034PMC
January 2019

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

Methods Mol Biol 2018 ;1849:169-177

Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.

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.
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http://dx.doi.org/10.1007/978-1-4939-8728-3_11DOI Listing
July 2019

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

Methods Mol Biol 2018 ;1849:131-141

Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.

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 .
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http://dx.doi.org/10.1007/978-1-4939-8728-3_9DOI Listing
July 2019

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches.

PeerJ 2018 8;6:e5364. Epub 2018 Aug 8.

Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada.

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel "denoising" pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray-Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.
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http://dx.doi.org/10.7717/peerj.5364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6087418PMC
August 2018

Dissecting Community Structure in Wild Blueberry Root and Soil Microbiome.

Front Microbiol 2018 6;9:1187. Epub 2018 Jun 6.

Department of Pharmacology, Dalhousie University, Halifax, NS, Canada.

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.
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http://dx.doi.org/10.3389/fmicb.2018.01187DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996171PMC
June 2018

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

Front Plant Sci 2018 20;9:343. Epub 2018 Mar 20.

MARS, Incorporated c/o United States Department of Agriculture - Agricultural Research Service, Miami, FL, United States.

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: and , respectively. Resistant cultivars are the most effective long-term strategy to address 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.
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http://dx.doi.org/10.3389/fpls.2018.00343DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5890178PMC
March 2018

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

Plant Genome 2018 03;11(1)

The apple ( × Borkh.) is an economically and culturally important crop grown worldwide. Growers of this long-lived perennial must produce fruit of adequate quality while also combatting abiotic and biotic stress. Traditional apple breeding can take up to 20 yr from initial cross to commercial release, but genomics-assisted breeding can help accelerate this process. To advance genomics-assisted breeding in apple, we performed genome-wide association studies (GWAS) and genomic prediction in a collection of 172 apple accessions by linking over 55,000 single nucleotide polymorphisms (SNPs) with 10 phenotypes collected over 2 yr. Genome-wide association studies revealed several known loci for skin color, harvest date and firmness at harvest. Several significant GWAS associations were detected for resistance to a major fungal pathogen, apple scab ( [Cke.] Wint.), but we demonstrate that these hits likely represent a single ancestral source. Using genomic prediction, we show that most phenotypes are sufficiently predictable using genome-wide SNPs to be candidates for genomic selection. Finally, we detect a signal for firmness retention after storage on chromosome 10 and show that it may not stem from variation in , a gene repeatedly identified in bi-parental mapping studies and widely believed to underlie a major QTL for firmness on chromosome 10. We provide evidence that this major QTL is more likely due to variation in a neighboring ethylene response factor (ERF) gene. The present study showcases the superior mapping resolution of GWAS compared to bi-parental linkage mapping by identifying a novel candidate gene underlying a well-studied, major QTL involved in apple firmness.
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http://dx.doi.org/10.3835/plantgenome2017.08.0075DOI Listing
March 2018

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

Microbiome 2018 01 15;6(1):13. Epub 2018 Jan 15.

Department of Pediatrics, Dalhousie University, Halifax, NS, Canada.

Background: Crohn'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.

Results: We 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.

Conclusions: We 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.
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http://dx.doi.org/10.1186/s40168-018-0398-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769311PMC
January 2018

Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research.

mSystems 2017 Jan-Feb;2(1). Epub 2017 Jan 3.

CGEB-Integrated Microbiome Resource (IMR) and Department of Pharmacology, Dalhousie University, Halifax, Canada.

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). 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.
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http://dx.doi.org/10.1128/mSystems.00127-16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5209531PMC
January 2017

Decreased Transcription Factor Binding Levels Nearby Primate Pseudogenes Suggest Regulatory Degeneration.

Mol Biol Evol 2016 06 16;33(6):1478-85. Epub 2016 Feb 16.

Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada

Characteristics of pseudogene degeneration at the coding level are well-known, such as a shift toward neutral rates of nonsynonymous substitutions and gain of frameshift mutations. In contrast, degeneration of pseudogene transcriptional regulation is not well understood. Here, we test two predictions of regulatory degeneration along a pseudogenized lineage: 1) Decreased transcription factor (TF) binding and 2) accelerated evolution in putative cis-regulatory regions.We find evidence for decreased TF binding levels nearby two primate pseudogenes compared with functional liver genes. However, the majority of TF-bound sequences nearby pseudogenes do not show evidence for lineage-specific accelerated rates of evolution. We conclude that decreases in TF binding level could be a marker for regulatory degeneration, while sequence degeneration in primate cis-regulatory modules may be obscured by background rates of TF binding site turnover.
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http://dx.doi.org/10.1093/molbev/msw030DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868113PMC
June 2016

Polymorphism Analysis Reveals Reduced Negative Selection and Elevated Rate of Insertions and Deletions in Intrinsically Disordered Protein Regions.

Genome Biol Evol 2015 Jun 4;7(6):1815-26. Epub 2015 Jun 4.

Department of Cell & Systems Biology, University of Toronto, Ontario, Canada Department of Ecology & Evolutionary Biology, University of Toronto, Ontario, Canada Centre for the Analysis of Genome Evolution and Function, University of Toronto, Ontario, Canada

Intrinsically disordered protein regions are abundant in eukaryotic proteins and lack stable tertiary structures and enzymatic functions. Previous studies of disordered region evolution based on interspecific alignments have revealed an increased propensity for indels and rapid rates of amino acid substitution. How disordered regions are maintained at high abundance in the proteome and across taxa, despite apparently weak evolutionary constraints, remains unclear. Here, we use single nucleotide and indel polymorphism data in yeast and human populations to survey the population variation within disordered regions. First, we show that single nucleotide polymorphisms in disordered regions are under weaker negative selection compared with more structured protein regions and have a higher proportion of neutral non-synonymous sites. We also confirm previous findings that nonframeshifting indels are much more abundant in disordered regions relative to structured regions. We find that the rate of nonframeshifting indel polymorphism in intrinsically disordered regions resembles that of noncoding DNA and pseudogenes, and that large indels segregate in disordered regions in the human population. Our survey of polymorphism confirms patterns of evolution in disordered regions inferred based on longer evolutionary comparisons.
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http://dx.doi.org/10.1093/gbe/evv105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494057PMC
June 2015

Hybrid origins and the earliest stages of diploidization in the highly successful recent polyploid Capsella bursa-pastoris.

Proc Natl Acad Sci U S A 2015 Mar 17;112(9):2806-11. Epub 2015 Feb 17.

Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada M5S 3B2;

Whole-genome duplication (WGD) events have occurred repeatedly during flowering plant evolution, and there is growing evidence for predictable patterns of gene retention and loss following polyploidization. Despite these important insights, the rate and processes governing the earliest stages of diploidization remain poorly understood, and the relative importance of genetic drift, positive selection, and relaxed purifying selection in the process of gene degeneration and loss is unclear. Here, we conduct whole-genome resequencing in Capsella bursa-pastoris, a recently formed tetraploid with one of the most widespread species distributions of any angiosperm. Whole-genome data provide strong support for recent hybrid origins of the tetraploid species within the past 100,000-300,000 y from two diploid progenitors in the Capsella genus. Major-effect inactivating mutations are frequent, but many were inherited from the parental species and show no evidence of being fixed by positive selection. Despite a lack of large-scale gene loss, we observe a decrease in the efficacy of natural selection genome-wide due to the combined effects of demography, selfing, and genome redundancy from WGD. Our results suggest that the earliest stages of diploidization are associated with quantitative genome-wide decreases in the strength and efficacy of selection rather than rapid gene loss, and that nonfunctionalization can receive a "head start" through a legacy of deleterious variants and differential expression originating in parental diploid populations.
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http://dx.doi.org/10.1073/pnas.1412277112DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4352811PMC
March 2015