scholarly journals Precise genotyping of circular mobile elements uncovers human associated plasmids with surprisingly recent common ancestors

2021 ◽  
Author(s):  
Nitan Shalon ◽  
David Relman ◽  
Eitan Yaffe
Keyword(s):  
Microbial Communities ◽  
Sequence Data ◽  
Mobile Elements ◽  
Natural Environments ◽  
Data Sets ◽  
Clonal Population ◽  
Data Application ◽  
Population Structures ◽  
Natural Settings ◽  
Common Ancestors

Mobile genetic elements with circular genomes play a key role in the evolution of microbial communities. These circular genomes correspond to cyclic paths in metagenome graphs, and yet, assemblies derived from natural microbial communities produce graphs riddled with spurious cycles, complicating the accurate reconstruction of circular genomes. We present an algorithm that reconstructs true circular genomes based on the identification of so-called ′dominant′ cycles. Our algorithm leverages paired reads to bridge gaps between assembly contigs and scrutinizes cycles through a nucleotide-level analysis, making the approach robust to mis-assembly artifacts. We validated the approach using simulated and reference data. Application of this approach to 32 publicly available DNA shotgun sequence data sets from diverse natural environments led to the reconstruction of hundreds of circular mobile genomes. Clustering revealed 20 clusters of cryptic, prevalent, and abundant plasmids that have clonal population structures with surprisingly recent common ancestors. This work enables the robust study of evolution and spread of mobile elements in natural settings.

scholarly journals Assessment of Microbial Communities by Graph Partitioning in a Study of Soil Fungi in Two Alpine Meadows

2009 ◽  
Vol 75 (18) ◽  
pp. 5863-5870 ◽  
Author(s):  
L. Zinger ◽  
E. Coissac ◽  
P. Choler ◽  
R. A. Geremia
Keyword(s):  
Microbial Communities ◽  
Graph Partitioning ◽  
Sequence Data ◽  
Large Subunit ◽  
Fungal Communities ◽  
Rrna Gene ◽  
Data Sets ◽  
Alignment Algorithm ◽  
Single Linkage ◽  
Phylogenetic Structure

ABSTRACT Understanding how microbial community structure and diversity respond to environmental conditions is one of the main challenges in environmental microbiology. However, there is often confusion between determining the phylogenetic structure of microbial communities and assessing the distribution and diversity of molecular operational taxonomic units (MOTUs) in these communities. This has led to the use of sequence analysis tools such as multiple alignments and hierarchical clustering that are not adapted to the analysis of large and diverse data sets and not always justified for characterization of MOTUs. Here, we developed an approach combining a pairwise alignment algorithm and graph partitioning by using MCL (Markov clustering) in order to generate discrete groups for nuclear large-subunit rRNA gene and internal transcript spacer 1 sequence data sets obtained from a yearly monitoring study of two spatially close but ecologically contrasting alpine soils (namely, early and late snowmelt locations). We compared MCL with a classical single-linkage method (Ccomps) and showed that MCL reduced bias such as the chaining effect. Using MCL, we characterized fungal communities in early and late snowmelt locations. We found contrasting distributions of MOTUs in the two soils, suggesting that there is a high level of habitat filtering in the assembly of alpine soil fungal communities. However, few MOTUs were specific to one location.

scholarly journals Metagenomic Analyses: Past and Future Trends

2010 ◽  
Vol 77 (4) ◽  
pp. 1153-1161 ◽  
Author(s):  
Carola Simon ◽  
Rolf Daniel
Keyword(s):  
Microbial Communities ◽  
Sequence Data ◽  
Extreme Environments ◽  
Data Sets ◽  
Metagenomic Dna ◽  
Functional Dynamics ◽  
Dna Libraries ◽  
Water Analyses ◽  
Independent Assessment ◽  
Generation Sequencing

ABSTRACTMetagenomics has revolutionized microbiology by paving the way for a cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. Metagenomics comprising construction and screening of metagenomic DNA libraries has proven to be a powerful tool to isolate new enzymes and drugs of industrial importance. So far, the majority of the metagenomically exploited habitats comprised temperate environments, such as soil and marine environments. Recently, metagenomes of extreme environments have also been used as sources of novel biocatalysts. The employment of next-generation sequencing techniques for metagenomics resulted in the generation of large sequence data sets derived from various environments, such as soil, the human body, and ocean water. Analyses of these data sets opened a window into the enormous taxonomic and functional diversity of environmental microbial communities. To assess the functional dynamics of microbial communities, metatranscriptomics and metaproteomics have been developed. The combination of DNA-based, mRNA-based, and protein-based analyses of microbial communities present in different environments is a way to elucidate the compositions, functions, and interactions of microbial communities and to link these to environmental processes.

scholarly journals mtDNAcombine: tools to combine sequences from multiple studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Eleanor F. Miller ◽  
Andrea Manica
Keyword(s):  
Sequence Data ◽  
Data Extraction ◽  
Bayesian Skyline Plot ◽  
Model Organisms ◽  
Data Sets ◽  
Data Handling ◽  
Online Database ◽  
Genetic Studies ◽  
Wide Range ◽  
Existing Data

Abstract Background Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. Results Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. Conclusions There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.

scholarly journals Deconstruction of Lignin: From Enzymes to Microorganisms

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2299
Author(s):  
Jéssica P. Silva ◽  
Alonso R. P. Ticona ◽  
Pedro R. V. Hamann ◽  
Betania F. Quirino ◽  
Eliane F. Noronha
Keyword(s):  
Microbial Communities ◽  
Metabolic Pathways ◽  
Low Cost ◽  
Direct Analysis ◽  
Industrial Applications ◽  
Natural Environments ◽  
Lignocellulosic Residues ◽  
Complementary Techniques ◽  
Plant Polymer ◽  
Powerful Strategy

Lignocellulosic residues are low-cost abundant feedstocks that can be used for industrial applications. However, their recalcitrance currently makes lignocellulose use limited. In natural environments, microbial communities can completely deconstruct lignocellulose by synergistic action of a set of enzymes and proteins. Microbial degradation of lignin by fungi, important lignin degraders in nature, has been intensively studied. More recently, bacteria have also been described as able to break down lignin, and to have a central role in recycling this plant polymer. Nevertheless, bacterial deconstruction of lignin has not been fully elucidated yet. Direct analysis of environmental samples using metagenomics, metatranscriptomics, and metaproteomics approaches is a powerful strategy to describe/discover enzymes, metabolic pathways, and microorganisms involved in lignin breakdown. Indeed, the use of these complementary techniques leads to a better understanding of the composition, function, and dynamics of microbial communities involved in lignin deconstruction. We focus on omics approaches and their contribution to the discovery of new enzymes and reactions that impact the development of lignin-based bioprocesses.

scholarly journals Psychological Restoration and the Effect of People in Nature and Urban Scenes: A Laboratory Experiment

2021 ◽  
Vol 13 (11) ◽  
pp. 6464
Author(s):  
Chris Neale ◽  
Stephanie Lopez ◽  
Jenny Roe
Keyword(s):  
Outcome Measures ◽  
Amazon Mechanical Turk ◽  
Natural Environments ◽  
Urban Scenes ◽  
Social Wellbeing ◽  
Environmental Interactions ◽  
Social Belonging ◽  
Restorative Environment ◽  
Social Environmental ◽  
Natural Settings

It is well-evidenced that exposure to natural environments increases psychological restoration as compared to non-natural settings, increasing our ability to recover from stress, low mood, and mental fatigue and encouraging positive social interactions that cultivate social cohesion. However, very few studies have explored how the inclusion of people within a given environment—either urban or natural settings—affect restorative health outcomes. We present three laboratory-based studies examining, first, the effect of nature vs. urban scenes, and second, investigating nature ‘with’ vs. ‘without’ people—using static and moving imagery—on psychological restoration and social wellbeing. Our third study explores differences between urban and natural settings both with vs. without people, using video stimuli to understand potential restorative and social wellbeing effects. Outcome measures across all studies included perceived social belonging, loneliness, subjective mood, and perceived restorativeness. Studies 1 and 2 both used a within group, randomized crossover design. Study 1 (n = 45, mean age = 20.7) explored static imagery of environmental conditions without people; findings were consistent with restorative theories showing a positive effect of nature exposure on all outcome measures. Study 2 compared nature scenes with vs. without people (n = 47, mean age = 20.9) and we found no significant differences on our outcome measures between either social scenario, though both scenarios generated positive wellbeing outcomes. Study 3, conducted on Amazon Mechanical Turk, employed an independent group design with subjects randomly assigned to one of four conditions; an urban vs. nature setting, with vs. without people. We explored the effect of moving imagery on psychological restoration (n = 200, mean age = 35.7) and our findings showed no impact on belonging, loneliness, or mood between conditions, but did show that—regardless of the inclusion of people—the nature settings were more restorative than the urban. There were no differences in psychological restoration between nature conditions with vs. without people. We discuss the implications for restorative environment research exploring social-environmental interactions.

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

scholarly journals A Microbial Perspective on the Grand Challenges in Comparative Animal Physiology

mSystems ◽  
2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Kevin D. Kohl
Keyword(s):  
Microbial Communities ◽  
Vertical Integration ◽  
Temporal Integration ◽  
Data Sets ◽  
Grand Challenges ◽  
Animal Physiology ◽  
Physiological Processes ◽  
Microbe Interactions ◽  
Animal Hosts ◽  
Host Microbe Interactions

ABSTRACTInteractions with microbial communities can have profound influences on animal physiology, thereby impacting animal performance and fitness. Therefore, it is important to understand the diversity and nature of host-microbe interactions in various animal groups (invertebrates, fish, amphibians, reptiles, birds, and mammals). In this perspective, I discuss how the field of host-microbe interactions can be used to address topics that have been identified as grand challenges in comparative animal physiology: (i) horizontal integration of physiological processes across organisms, (ii) vertical integration of physiological processes across organizational levels within organisms, and (iii) temporal integration of physiological processes during evolutionary change. Addressing these challenges will require the use of a variety of animal models and the development of systems approaches that can integrate large, multiomic data sets from both microbial communities and animal hosts. Integrating host-microbe interactions into the established field of comparative physiology represents an exciting frontier for both fields.

scholarly journals MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 726
Author(s):  
Mike W.C. Thang ◽  
Xin-Yi Chua ◽  
Gareth Price ◽  
Dominique Gorse ◽  
Matt A. Field
Keyword(s):  
Microbial Communities ◽  
Sequence Data ◽  
Metagenomic Data ◽  
Marker Genes ◽  
Metagenomic Sequencing ◽  
Differential Analysis ◽  
Biomedical Sciences ◽  
Metagenomic Sequence ◽  
Differential Abundance ◽  
Differential Abundance Analysis

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences.  While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs.  Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics.  MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

scholarly journals Formation of Phage Lysis Patterns and Implications on Co-Propagation of Phages and Motile Host Bacteria

2019 ◽  
Author(s):  
Xiaochu Li ◽  
Floricel Gonzalez ◽  
Nathaniel Esteves ◽  
Birgit E. Scharf ◽  
Jing Chen
Keyword(s):  
Mathematical Model ◽  
Microbial Community ◽  
Microbial Communities ◽  
Biological Properties ◽  
Scientific Basis ◽  
Nutrient Level ◽  
Natural Environments ◽  
Extrinsic Factors ◽  
Inhibition Of Proliferation ◽  
Set Up

AbstractCoexistence of bacteriophages, or phages, and their host bacteria plays an important role in maintaining the microbial communities. In natural environments with limited nutrients, motile bacteria can actively migrate towards locations of richer resources. Although phages are not motile themselves, they can infect motile bacterial hosts and spread in space via the hosts. Therefore, in a migrating microbial community coexistence of bacteria and phages implies their co-propagation in space. Here, we combine an experimental approach and mathematical modeling to explore how phages and their motile host bacteria coexist and co-propagate. When lytic phages encountered motile host bacteria in our experimental set up, a sector-shaped lysis zone formed. Our mathematical model indicates that local nutrient depletion and the resulting inhibition of proliferation and motility of bacteria and phages are the key to formation of the observed lysis pattern. The model further reveals the straight radial boundaries in the lysis pattern as a tell-tale sign for coexistence and co-propagation of bacteria and phages. Emergence of such a pattern, albeit insensitive to extrinsic factors, requires a balance between intrinsic biological properties of phages and bacteria, which likely results from co-evolution of phages and bacteria.Author summaryCoexistence of phages and their bacterial hosts is important for maintaining the microbial communities. In a migrating microbial community, coexistence between phages and host bacteria implies that they co-propagate in space. Here we report a novel phage lysis pattern that is indicative of this co-propagation. The corresponding mathematical model we developed highlights a crucial dependence of the lysis pattern and implied phage-bacteria co-propagation on intrinsic properties allowing proliferation and spreading of the microbes in space. Remarkably, extrinsic factors, such as overall nutrient level, do not influence phage-bacteria coexistence and co-propagation. Findings from this work have strong implications for dispersal of phages mediated by motile bacterial communities, which will provide scientific basis for the fast-growing applications of phages.

scholarly journals Selective ancestral sorting and de novo evolution in the agricultural invasion of Amaranthus tuberculatus

2021 ◽  
Author(s):  
Julia M. Kreiner ◽  
Amalia Caballero ◽  
Stephen I. Wright ◽  
John R. Stinchcombe
Keyword(s):  
De Novo ◽  
Sequence Data ◽  
Sex Differentiation ◽  
Common Garden ◽  
Whole Genome Sequence ◽  
Secondary Contact ◽  
Natural Environments ◽  
Relative Role ◽  
Standing Variation ◽  
Amaranthus Tuberculatus

The relative role of hybridization, de novo evolution, and standing variation in weed adaptation to agricultural environments is largely unknown. In Amaranthus tuberculatus, a widespread North American agricultural weed, adaptation is likely influenced by recent secondary contact and admixture of two previously isolated subspecies. We characterized the extent of adaptation and phenotypic differentiation accompanying the spread of A. tuberculatus into agricultural environments and the contribution of subspecies divergence. We generated phenotypic and whole-genome sequence data from a manipulative common garden experiment, using paired samples from natural and agricultural populations. We found strong latitudinal, longitudinal, and sex differentiation in phenotypes, and subtle differences among agricultural and natural environments that were further resolved with ancestry-based inference. The transition into agricultural environments has favoured southwestern var. rudis ancestry that leads to higher biomass and environment-specific phenotypes: increased biomass and earlier flowering under reduced water availability, and reduced plasticity in fitness-related traits. We also detected de novo adaptation to agricultural habitats independent of ancestry effects, including marginally higher biomass and later flowering in agricultural populations, and a time to germination home advantage. Therefore, the invasion of A. tuberculatus into agricultural environments has drawn on adaptive variation across multiple timescales—through both preadaptation via the preferential sorting of var. rudis ancestry and de novo local adaptation.

Sign in / Sign up

Export Citation Format

Share Document