scholarly journals Welcome to Phytobiomes

2017 ◽  
Vol 1 (1) ◽  
pp. 3-4 ◽  
Author(s):  
Carolyn A. Young ◽  
Linda Kinkel
Keyword(s):  
Spatial Scales ◽  
Crop Improvement ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Crop Productivity ◽  
Complex Data ◽  
New Paradigm ◽  
New Vision ◽  
Generate Plant ◽  
Interacting Components

In 2014, members of The American Phytopathological Society (APS) began to consider strategically the ways in which the accelerating flow of diverse types of complex data are fundamentally changing the ways in which we study, think about, and manage plant systems. Advancing technical capacities to generate plant, microbial and other organismal ‘omics’, environmental, and data produced at very fine spatial scales, coupled with expanding capabilities to integrate data across diverse scales of space and time, are providing novel insights into the networks of interaction that mediate plant productivity. In response to these enormous advances in technical capacities, APS created a Phytobiomes Initiative to chart a path forward. In 2015, APS brought together a diverse community of scientists in Washington DC for a strategically timed meeting to spearhead a new paradigm for crop improvement focusing on phytobiome-based approaches. The success of the human microbiome project paved the way, challenging us to push the boundaries to expand our knowledge of sustainable agriculture to meet the demands of feeding the growing global population. This meeting was instrumental in clarifying the vision of phytobiomes research, encapsulating systems-level understanding of diverse interacting components spanning multiple disciplines, species, and environments. To fully appreciate the breadth of scope of the Phytobiomes Initiative, we encourage you to review the Phytobiomes Roadmap ( http://www.phytobiomes.org/roadmap ), released in March 2016. The Phytobiomes journal was launched as an integral but independent part of the Phytobiomes Initiative. The Phytobiomes journal provides an international platform for fundamental, translational, and integrated research that accomplishes the overarching objective of offering a new vision for agriculture in which sustainable crop productivity is achieved through a systems-level understanding of the diverse interacting components of the phytobiome. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY Attribution 4.0 International license .

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

scholarly journals Sequence Comparison of Vaginolysin from Different Gardnerella Species

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 86
Author(s):  
Erin M. Garcia ◽  
Myrna G. Serrano ◽  
Laahirie Edupuganti ◽  
David J. Edwards ◽  
Gregory A. Buck ◽  
...  
Keyword(s):  
Amino Acid ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Distinct Species ◽  
Vaginal Swab ◽  
Metagenomic Data ◽  
Gardnerella Vaginalis ◽  
Vaginal Epithelial Cells ◽  
Species Specific

Gardnerella vaginalis has recently been split into 13 distinct species. In this study, we tested the hypotheses that species-specific variations in the vaginolysin (VLY) amino acid sequence could influence the interaction between the toxin and vaginal epithelial cells and that VLY variation may be one factor that distinguishes less virulent or commensal strains from more virulent strains. This was assessed by bioinformatic analyses of publicly available Gardnerella spp. sequences and quantification of cytotoxicity and cytokine production from purified, recombinantly produced versions of VLY. After identifying conserved differences that could distinguish distinct VLY types, we analyzed metagenomic data from a cohort of female subjects from the Vaginal Human Microbiome Project to investigate whether these different VLY types exhibited any significant associations with symptoms or Gardnerella spp.-relative abundance in vaginal swab samples. While Type 1 VLY was most prevalent among the subjects and may be associated with increased reports of symptoms, subjects with Type 2 VLY dominant profiles exhibited increased relative Gardnerella spp. abundance. Our findings suggest that amino acid differences alter the interaction of VLY with vaginal keratinocytes, which may potentiate differences in bacterial vaginosis (BV) immunopathology in vivo.

scholarly journals Competition among Nasal Bacteria Suggests a Role for Siderophore-Mediated Interactions in Shaping the Human Nasal Microbiota

2018 ◽  
Vol 85 (10) ◽  
Author(s):  
Reed M. Stubbendieck ◽  
Daniel S. May ◽  
Marc G. Chevrette ◽  
Mia I. Temkin ◽  
Evelyn Wendt-Pienkowski ◽  
...  
Keyword(s):  
Nasal Cavity ◽  
Iron Acquisition ◽  
Interference Competition ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Siderophore Production ◽  
Coagulase Negative Staphylococci ◽  
Exploitation Competition ◽  
Content Type

ABSTRACTResources available in the human nasal cavity are limited. Therefore, to successfully colonize the nasal cavity, bacteria must compete for scarce nutrients. Competition may occur directly through interference (e.g., antibiotics) or indirectly by nutrient sequestration. To investigate the nature of nasal bacterial competition, we performed coculture inhibition assays between nasalActinobacteriaandStaphylococcusspp. We found that isolates of coagulase-negative staphylococci (CoNS) were sensitive to growth inhibition byActinobacteriabut thatStaphylococcus aureusisolates were resistant to inhibition. AmongActinobacteria, we observed thatCorynebacteriumspp. were variable in their ability to inhibit CoNS. We sequenced the genomes of 10Corynebacteriumspecies isolates, including 3Corynebacterium propinquumisolates that strongly inhibited CoNS and 7 otherCorynebacteriumspecies isolates that only weakly inhibited CoNS. Using a comparative genomics approach, we found that theC. propinquumgenomes were enriched in genes for iron acquisition and harbored a biosynthetic gene cluster (BGC) for siderophore production, absent in the noninhibitoryCorynebacteriumspecies genomes. Using a chrome azurol S assay, we confirmed thatC. propinquumproduced siderophores. We demonstrated that iron supplementation rescued CoNS from inhibition byC. propinquum, suggesting that inhibition was due to iron restriction through siderophore production. Through comparative metabolomics and molecular networking, we identified the siderophore produced byC. propinquumas dehydroxynocardamine. Finally, we confirmed that the dehydroxynocardamine BGC is expressedin vivoby analyzing human nasal metatranscriptomes from the NIH Human Microbiome Project. Together, our results suggest that bacteria produce siderophores to compete for limited available iron in the nasal cavity and improve their fitness.IMPORTANCEWithin the nasal cavity, interference competition through antimicrobial production is prevalent. For instance, nasalStaphylococcusspecies strains can inhibit the growth of other bacteria through the production of nonribosomal peptides and ribosomally synthesized and posttranslationally modified peptides. In contrast, bacteria engaging in exploitation competition modify the external environment to prevent competitors from growing, usually by hindering access to or depleting essential nutrients. As the nasal cavity is a nutrient-limited environment, we hypothesized that exploitation competition occurs in this system. We determined thatCorynebacterium propinquumproduces an iron-chelating siderophore, and this iron-sequestering molecule correlates with the ability to inhibit the growth of coagulase-negative staphylococci. Furthermore, we found that the genes required for siderophore production are expressedin vivo. Thus, although siderophore production by bacteria is often considered a virulence trait, our work indicates that bacteria may produce siderophores to compete for limited iron in the human nasal cavity.

scholarly journals MetaCompass: Reference-guided Assembly of Metagenomes

2017 ◽  
Author(s):  
Victoria Cepeda ◽  
Bo Liu ◽  
Mathieu Almeida ◽  
Christopher M. Hill ◽  
Sergey Koren ◽  
...  
Keyword(s):  
De Novo ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Bacterial Genomes ◽  
Guided Assembly ◽  
Metagenomic Assembly

ABSTRACTMetagenomic studies have primarily relied on de novo approaches for reconstructing genes and genomes from microbial mixtures. While database driven approaches have been employed in certain analyses, they have not been used in the assembly of metagenomes. Here we describe the first effective approach for reference-guided metagenomic assembly of low-abundance bacterial genomes that can complement and improve upon de novo metagenomic assembly methods. When combined with de novo assembly approaches, we show that MetaCompass can generate more complete assemblies than can be obtained by de novo assembly alone, and improve on assemblies from the Human Microbiome Project (over 2,000 samples).

scholarly journals Misclassification of a whole genome sequence reference defined by the Human Microbiome Project: a detrimental carryover effect to microbiome studies

2019 ◽  
Author(s):  
DJ Darwin R. Bandoy ◽  
B Carol Huang ◽  
Bart C. Weimer
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Outbreak Detection ◽  
Whole Genome Sequence ◽  
Reference Database ◽  
Whole Genome ◽  
Reference Species ◽  
Genome Sequences ◽  
Genome Homology ◽  
Microbiome Data

AbstractTaxonomic classification is an essential step in the analysis of microbiome data that depends on a reference database of whole genome sequences. Taxonomic classifiers are built on established reference species, such as the Human Microbiome Project database, that is growing rapidly. While constructing a population wide pangenome of the bacterium Hungatella, we discovered that the Human Microbiome Project reference species Hungatella hathewayi (WAL 18680) was significantly different to other members of this genus. Specifically, the reference lacked the core genome as compared to the other members. Further analysis, using average nucleotide identity (ANI) and 16s rRNA comparisons, indicated that WAL18680 was misclassified as Hungatella. The error in classification is being amplified in the taxonomic classifiers and will have a compounding effect as microbiome analyses are done, resulting in inaccurate assignment of community members and will lead to fallacious conclusions and possibly treatment. As automated genome homology assessment expands for microbiome analysis, outbreak detection, and public health reliance on whole genomes increases this issue will likely occur at an increasing rate. These observations highlight the need for developing reference free methods for epidemiological investigation using whole genome sequences and the criticality of accurate reference databases.

Symbolic Data Analysis

2014 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
Sandra Elizabeth González Císaro ◽  
Héctor Oscar Nigro
Keyword(s):  
Data Analysis ◽  
Complex Data ◽  
New Paradigm ◽  
Symbolic Data Analysis ◽  
Standard Data ◽  
Symbolic Data ◽  
The World ◽  
New Type ◽  
Mining Complex ◽  
Internal Variation

Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of symbolic object, which is a mathematical model of a concept. In this article the authors are going to present the fundamentals of the symbolic data analysis paradigm and the symbolic object concept. Theoretical aspects and examples allow the authors to understand the SDA paradigm as a tool for mining complex data.

scholarly journals Mouse Gut Microbiome-Encoded β-Glucuronidases Identified Using Metagenome Analysis Guided by Protein Structure

mSystems ◽  
2019 ◽  
Vol 4 (4) ◽  
Author(s):  
Benjamin C. Creekmore ◽  
Josh H. Gray ◽  
William G. Walton ◽  
Kristen A. Biernat ◽  
Michael S. Little ◽  
...  
Keyword(s):  
Protein Structure ◽  
Active Site ◽  
Human Microbiome ◽  
Drug Efficacy ◽  
Human Microbiome Project ◽  
Structural Features ◽  
Model Organisms ◽  
Mouse Strains ◽  
Sequencing Data ◽  
Metagenome Analysis

ABSTRACT Gut microbial β-glucuronidase (GUS) enzymes play important roles in drug efficacy and toxicity, intestinal carcinogenesis, and mammalian-microbial symbiosis. Recently, the first catalog of human gut GUS proteins was provided for the Human Microbiome Project stool sample database and revealed 279 unique GUS enzymes organized into six categories based on active-site structural features. Because mice represent a model biomedical research organism, here we provide an analogous catalog of mouse intestinal microbial GUS proteins—a mouse gut GUSome. Using metagenome analysis guided by protein structure, we examined 2.5 million unique proteins from a comprehensive mouse gut metagenome created from several mouse strains, providers, housing conditions, and diets. We identified 444 unique GUS proteins and organized them into six categories based on active-site features, similarly to the human GUSome analysis. GUS enzymes were encoded by the major gut microbial phyla, including Firmicutes (60%) and Bacteroidetes (21%), and there were nearly 20% for which taxonomy could not be assigned. No differences in gut microbial gus gene composition were observed for mice based on sex. However, mice exhibited gus differences based on active-site features associated with provider, location, strain, and diet. Furthermore, diet yielded the largest differences in gus composition. Biochemical analysis of two low-fat-associated GUS enzymes revealed that they are variable with respect to their efficacy of processing both sulfated and nonsulfated heparan nonasaccharides containing terminal glucuronides. IMPORTANCE Mice are commonly employed as model organisms of mammalian disease; as such, our understanding of the compositions of their gut microbiomes is critical to appreciating how the mouse and human gastrointestinal tracts mirror one another. GUS enzymes, with importance in normal physiology and disease, are an attractive set of proteins to use for such analyses. Here we show that while the specific GUS enzymes differ at the sequence level, a core GUSome functionality appears conserved between mouse and human gastrointestinal bacteria. Mouse strain, provider, housing location, and diet exhibit distinct GUSomes and gus gene compositions, but sex seems not to affect the GUSome. These data provide a basis for understanding the gut microbial GUS enzymes present in commonly used laboratory mice. Further, they demonstrate the utility of metagenome analysis guided by protein structure to provide specific sets of functionally related proteins from whole-genome metagenome sequencing data.

scholarly journals Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel R. Utter ◽  
Gary G. Borisy ◽  
A. Murat Eren ◽  
Colleen M. Cavanaugh ◽  
Jessica L. Mark Welch
Keyword(s):  
Buccal Mucosa ◽  
Habitat Preferences ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Oral Microbiome ◽  
Functional Enrichment ◽  
Habitat Adaptation ◽  
Haemophilus Parainfluenzae ◽  
Population Structures ◽  
Genome Content

Abstract Background The increasing availability of microbial genomes and environmental shotgun metagenomes provides unprecedented access to the genomic differences within related bacteria. The human oral microbiome with its diverse habitats and abundant, relatively well-characterized microbial inhabitants presents an opportunity to investigate bacterial population structures at an ecosystem scale. Results Here, we employ a metapangenomic approach that combines public genomes with Human Microbiome Project (HMP) metagenomes to study the diversity of microbial residents of three oral habitats: tongue dorsum, buccal mucosa, and supragingival plaque. For two exemplar taxa, Haemophilus parainfluenzae and the genus Rothia, metapangenomes reveal distinct genomic groups based on shared genome content. H. parainfluenzae genomes separate into three distinct subgroups with differential abundance between oral habitats. Functional enrichment analyses identify an operon encoding oxaloacetate decarboxylase as diagnostic for the tongue-abundant subgroup. For the genus Rothia, grouping by shared genome content recapitulates species-level taxonomy and habitat preferences. However, while most R. mucilaginosa are restricted to the tongue as expected, two genomes represent a cryptic population of R. mucilaginosa in many buccal mucosa samples. For both H. parainfluenzae and the genus Rothia, we identify not only limitations in the ability of cultivated organisms to represent populations in their native environment, but also specifically which cultivar gene sequences are absent or ubiquitous. Conclusions Our findings provide insights into population structure and biogeography in the mouth and form specific hypotheses about habitat adaptation. These results illustrate the power of combining metagenomes and pangenomes to investigate the ecology and evolution of bacteria across analytical scales.

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