A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

2020 ◽  
Vol 48 (2) ◽  
pp. 399-409
Author(s):  
Baizhen Gao ◽  
Rushant Sabnis ◽  
Tommaso Costantini ◽  
Robert Jinkerson ◽  
Qing Sun
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Environmental Remediation ◽  
Real Life ◽  
Design Principles ◽  
Microbial Interactions ◽  
Potential Applications ◽  
New Gene ◽  
Global Biogeochemical Cycles ◽  
Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

scholarly journals Massively parallel screening of synthetic microbial communities

2019 ◽  
Vol 116 (26) ◽  
pp. 12804-12809 ◽  
Author(s):  
Jared Kehe ◽  
Anthony Kulesa ◽  
Anthony Ortiz ◽  
Cheri M. Ackerman ◽  
Sri Gowtham Thakku ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Massively Parallel ◽  
Phenotypic Characterization ◽  
Interspecies Interactions ◽  
Additional Species ◽  
Plant Symbiont ◽  
Complementary Approach ◽  
Potential Applications ◽  
Synthetic Microbial Communities

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiontHerbaspirillum frisingensein a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.

scholarly journals How sample heterogeneity can obscure the signal of microbial interactions

2019 ◽  
Author(s):  
David W. Armitage ◽  
Stuart E. Jones
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Species Interactions ◽  
Dynamic Models ◽  
Spatial Scales ◽  
Microbial Interactions ◽  
Vital Rates ◽  
Ecological Processes ◽  
Correlation Networks ◽  
Community Data

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.

scholarly journals METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

2020 ◽  
Author(s):  
Zhichao Zhou ◽  
Patricia Q Tran ◽  
Adam M Breister ◽  
Yang Liu ◽  
Kristopher Kieft ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Single Cell ◽  
Metabolic Networks ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Microbial Genomes ◽  
Biogeochemical Models ◽  
Genome Scale

Abstract Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

scholarly journals PCR Amplification-Independent Methods for Detection of Microbial Communities by the High-Density Microarray PhyloChip

2011 ◽  
Vol 77 (18) ◽  
pp. 6313-6322 ◽  
Author(s):  
Kristen M. DeAngelis ◽  
Cindy H. Wu ◽  
Harry R. Beller ◽  
Eoin L. Brodie ◽  
Romy Chakraborty ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Pcr Amplification ◽  
Community Analysis ◽  
High Density ◽  
Microbial Community Analysis ◽  
Taxon Richness ◽  
Tropical Forest Soil ◽  
Global Biogeochemical Cycles ◽  
Direct Hybridization

ABSTRACTEnvironmental microbial community analysis typically involves amplification by PCR, despite well-documented biases. We have developed two methods of PCR-independent microbial community analysis using the high-density microarray PhyloChip: direct hybridization of 16S rRNA (dirRNA) or rRNA converted to double-stranded cDNA (dscDNA). We compared dirRNA and dscDNA communities to PCR-amplified DNA communities using a mock community of eight taxa, as well as experiments derived from three environmental sample types: chromium-contaminated aquifer groundwater, tropical forest soil, and secondary sewage in seawater. Community profiles by both direct hybridization methods showed differences that were expected based on accompanying data but that were missing in PCR-amplified communities. Taxon richness decreased in RNA compared to that in DNA communities, suggesting a subset of 20% in soil and 60% in groundwater that is active; secondary sewage showed no difference between active and inactive populations. Direct hybridization of dscDNA and RNA is thus a viable alternative to PCR-amplified microbial community analysis, providing identification of the active populations within microbial communities that attenuate pollutants, drive global biogeochemical cycles, or proliferate disease states.

Rhizosphere Microbial Community Dynamics in Glyphosate-Treated Susceptible and Resistant Biotypes of Giant Ragweed (Ambrosia trifida)

Weed Science ◽  
2014 ◽  
Vol 62 (2) ◽  
pp. 370-381 ◽  
Author(s):  
Jessica R. Schafer ◽  
Steven G. Hallett ◽  
William G. Johnson
Keyword(s):  
Microbial Community ◽  
Next Generation Sequencing ◽  
Microbial Communities ◽  
Soil Microbial Communities ◽  
Microbial Interactions ◽  
Field Soil ◽  
Next Generation ◽  
Giant Ragweed ◽  
Generation Sequencing ◽  
Rhizosphere Microbial Community

In a previous study, glyphosate-susceptible and -resistant giant ragweed biotypes grown in sterile field soil survived a higher rate of glyphosate than those grown in unsterile field soil, and the roots of the susceptible biotype were colonized by a larger number of soil microorganisms than those of the resistant biotype when treated with 1.6 kg ae ha−1glyphosate. Thus, we concluded that soil-borne microbes play a role in glyphosate activity and now hypothesize that the ability of the resistant biotype to tolerate glyphosate may involve microbial interactions in the rhizosphere. The objective of this study was to evaluate differences in the rhizosphere microbial communities of glyphosate-susceptible and -resistant giant ragweed biotypes 3 d after a glyphosate treatment. Giant ragweed biotypes were grown in the greenhouse in unsterile field soil and glyphosate was applied at either 0 or 1.6 kg ha−1. Rhizosphere soil was sampled 3 d after the glyphosate treatment, and DNA was extracted, purified, and sequenced with the use of Illumina Genome Analyzer next-generation sequencing. The taxonomic distribution of the microbial community, diversity, genera abundance, and community structure within the rhizosphere of the two giant ragweed biotypes in response to a glyphosate application was evaluated by metagenomics analysis. Bacteria comprised approximately 96% of the total microbial community in both biotypes, and differences in the distribution of some microbes at the phyla level were observed. Select soil-borne plant pathogens (VerticilliumandXanthomonas) and plant-growth–promoting rhizobacteria (Burkholderia) present in the rhizosphere were influenced by either biotype or glyphosate application. We did not, however, observe large differences in the diversity or structure of soil microbial communities among our treatments. The results of this study indicate that challenging giant ragweed biotypes with glyphosate causes perturbations in rhizosphere microbial communities and that the perturbations differ between the susceptible and resistant biotypes. However, biological relevance of the rhizosphere microbial community data that we obtained by next-generation sequencing remains unclear.

scholarly journals Microbial Interactions within the Cheese Ecosystem and Their Application to Improve Quality and Safety

Foods ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 602
Author(s):  
Baltasar Mayo ◽  
Javier Rodríguez ◽  
Lucía Vázquez ◽  
Ana Belén Flórez
Keyword(s):  
Microbial Communities ◽  
Abiotic Factors ◽  
Microbial Interactions ◽  
Competitive Interactions ◽  
Quality And Safety ◽  
Improve Quality ◽  
Basic Principles ◽  
Functional Consequences ◽  
Experimental Use ◽  
Synthetic Microbial Communities

The cheese microbiota comprises a consortium of prokaryotic, eukaryotic and viral populations, among which lactic acid bacteria (LAB) are majority components with a prominent role during manufacturing and ripening. The assortment, numbers and proportions of LAB and other microbial biotypes making up the microbiota of cheese are affected by a range of biotic and abiotic factors. Cooperative and competitive interactions between distinct members of the microbiota may occur, with rheological, organoleptic and safety implications for ripened cheese. However, the mechanistic details of these interactions, and their functional consequences, are largely unknown. Acquiring such knowledge is important if we are to predict when fermentations will be successful and understand the causes of technological failures. The experimental use of “synthetic” microbial communities might help throw light on the dynamics of different cheese microbiota components and the interplay between them. Although synthetic communities cannot reproduce entirely the natural microbial diversity in cheese, they could help reveal basic principles governing the interactions between microbial types and perhaps allow multi-species microbial communities to be developed as functional starters. By occupying the whole ecosystem taxonomically and functionally, microbiota-based cultures might be expected to be more resilient and efficient than conventional starters in the development of unique sensorial properties.

scholarly journals The role of competition versus cooperation in microbial community coalescence

2021 ◽  
Author(s):  
Pablo Lechon Alonso ◽  
Tom Clegg ◽  
Jacob Cook ◽  
Thomas P Smith ◽  
Samraat Pawar
Keyword(s):  
Microbial Community ◽  
Real Life ◽  
Biotic Interactions ◽  
Basic Research ◽  
Theoretical Work ◽  
Microbial Interactions ◽  
Competition And Cooperation ◽  
Species Invasions ◽  
Determine Role

Microbial communities are ubiquitous in nature. Although processes driving the assembly of these consortia are not yet well understood, new communities frequently emerge when two or more microbial ensembles encounter each other and mix to yield a new functioning aggregation, termed "community coalescence". Despite recent advances in our understanding of coalescence, theoretical work has focused mainly on competition, and more work is necessary to determine role of other common microbial interactions, such as cooperation. In this work, we study the combined effects that competitive and cooperative interactions have in the outcome of coalescence events. We simulate communities with varying levels of each type of interaction using a consumer-resource model with cross-feeding on metabolic by-products. We then perform coalescence simulations and measure interaction levels on the pre- and post-coalescence communities using new metrics of competition and cooperation that we present previously. We find that when both interactions are present, the less competitive community tends to succeed in community coalescence, regardless of its cooperativity, suggesting that minimizing competition is the main driving force of this process. When competition is weak however, simulations show that highly cooperative communities are at a disadvantage in coalescence events, indicating that multi-species invasions tend to intercept cooperative links. Microbial community coalescence is gaining popularity in applied and basic research due to its multiple advantages. In the absence of theory that supports real life observations, here we develop a framework to understand and predict the result of community coalescence events from the perspective of biotic interactions present in the community.

scholarly journals Auxotrophic interactions: a stabilizing attribute of aquatic microbial communities?

2020 ◽  
Vol 96 (11) ◽  
Author(s):  
Winifred M Johnson ◽  
Harriet Alexander ◽  
Raven L Bier ◽  
Dan R Miller ◽  
Mario E Muscarella ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Theoretical Work ◽  
Microbial Interactions ◽  
Ecosystem Structure ◽  
Community Members ◽  
Aquatic Microorganisms ◽  
The Stability

ABSTRACT Auxotrophy, or an organism's requirement for an exogenous source of an organic molecule, is widespread throughout species and ecosystems. Auxotrophy can result in obligate interactions between organisms, influencing ecosystem structure and community composition. We explore how auxotrophy-induced interactions between aquatic microorganisms affect microbial community structure and stability. While some studies have documented auxotrophy in aquatic microorganisms, these studies are not widespread, and we therefore do not know the full extent of auxotrophic interactions in aquatic environments. Current theoretical and experimental work suggests that auxotrophy links microbial community members through a complex web of metabolic dependencies. We discuss the proposed ways in which auxotrophy may enhance or undermine the stability of aquatic microbial communities, highlighting areas where our limited understanding of these interactions prevents us from being able to predict the ecological implications of auxotrophy. Finally, we examine an example of auxotrophy in harmful algal blooms to place this often theoretical discussion in a field context where auxotrophy may have implications for the development and robustness of algal bloom communities. We seek to draw attention to the relationship between auxotrophy and community stability in an effort to encourage further field and theoretical work that explores the underlying principles of microbial interactions.

Microbial analyses of blood spot surfaces collected from a laboratory and the bathroom of a female single-person household under different environmental conditions

2021 ◽  
Vol 368 (5) ◽  
Author(s):  
Hee Sang You ◽  
Song Hee Lee ◽  
Young Ju Lee ◽  
Ho Joong Sung ◽  
Hee-Gyoo Kang ◽  
...  
Keyword(s):  
Microbial Community ◽  
Bacterial Community ◽  
Microbial Communities ◽  
Community Analysis ◽  
Personal Identification ◽  
Microbial Interactions ◽  
Microbial Community Analysis ◽  
Single Person ◽  
Blood Spot ◽  
Over Time

Abstract Many people spend most of their time indoors, thereby exposing themselves to indoor environmental microbial communities that might interact with the human microbiota. These potential interactions have only been considered for personal identification; however, accumulating evidence indicates that these microbial interactions are potentially implicated with the identification of human interactions and location-specific factors including time and seasonal variations in the microbial community. To augment the potential of metagenomics-based forensic tools, we compared the composition of microbial communities in blood spot surfaces from healthy adults placed in different environments, such as in the bathroom of a female single-person household and on a laboratory, which were sampled across seasons and time points. The laboratory samples showed more changes in the bacterial community over time owing to the higher number of individuals using the laboratory, whereas the microbial communities in the bathroom samples remained relatively stable over time. Moreover, the two locations could be distinguished according to their specific bacterial community compositions. Variations were also observed related to changes in temperature and humidity, allowing for prediction of season-based microbial community. These findings offer a new perspective regarding the use of microbial community analysis in forensic science.

scholarly journals METABOLIC: High-throughput profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks

2019 ◽  
Author(s):  
Zhichao Zhou ◽  
Patricia Q. Tran ◽  
Adam M. Breister ◽  
Yang Liu ◽  
Kristopher Kieft ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Single Cell ◽  
Metabolic Networks ◽  
Biogeochemical Cycling ◽  
Microbial Interactions ◽  
Microbial Genomes ◽  
Biogeochemical Models ◽  
Genome Scale

ABSTRACTBackgroundAdvances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling.ResultsWe present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ∼3 hours with 40 CPU threads to process ∼100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ∼45 mins, while it takes ∼5 hours to complete hmmsearch for ∼3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.ConclusionMETABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

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