Pioneering the new genomics era in environmental microbiology for engineering design
Lead Research Organisation:
University of Glasgow
Department Name: Civil Engineering
Abstract
Natural microbial communities perform many vital engineering functions in wastewater treatment, bioenergy production, and bioremediation, but our understanding of how these complex communities assemble, function and respond to environmental change is limited. In the past we have used an empirical approach to utilising these systems based on accumulated knowledge. This has had some success, for example in wastewater treatment, but previous experience will not help us deal with novel environments and problems. To start engineering these systems and optimise them for different applications we need mathematical models that are capable of predicting their behaviour. The urgent need to harness microbes to their full potential has been brought into sharp focus by the climate, energy, pollution, and water crises we now face. Development of predicative models has been constrained by the difficulty of obtaining information on these communities. Historically analysis was restricted to those microbes that could be isolated and grown in the laboratory but these represent only a fraction of the community. The new science of environmental genomics, by direct extraction and amplification of DNA, has sidestepped the need to culture organisms, but until the last few years actual sequencing of this DNA was slow and expensive. Consequently sample sizes were small compared to the huge diversity and numbers of microbes. Now new high throughput sequencing technologies are available, which have increased the rate of data acquisition by orders of magnitude, allowing us for the first time to obtain a detailed picture of the composition of these communities and how they vary through space and time. Using metagenomics we can also start linking the identity of the community members to their metabolic functions. Finally, we have sufficient data to start constructing the models we desperately need. This fellowship will exploit this opportunity, through an integrated approach, to develop a new combined genomics modelling paradigm for the study of microbial systems. New statistical tools and software will be developed to filter noise from the sequencing data, and extract information which can then be fed into multi-scale mathematical models. At the most fundamental level these models will have an explicit description of individuals moving, reproducing and interacting through the consumption and production of chemical substrates. Using advanced mathematical techniques they will be scaled-up to a description of whole populations. This will enable us to define the models in terms of processes operating on the level of individuals but validate them with the genomics data which provides a population level picture. It will also allow these models to be applied on the whole system scales necessary for their industrial application. The statistical tools, and mathematical models developed will be completely generic but we will illustrate the approach by focusing on two specific case studies: low temperature anaerobic wastewater treatment and microbial fuel cells. The former has the potential to reduce the energetic costs and carbon footprint of the treatment of wastewater in the UK and other temperate countries. The latter could provide a cheap, clean source of electricity in remote locations using virtually any organic substance as fuel. They could be particularly useful in the developing world. We will explore further applications of our paradigm to other vital microbial based engineering problems. In addition, the tools and models developed will be applicable to the study of microbial communities in any area, from human health to the biogeochemical cycles that sustain life on this planet.
People |
ORCID iD |
Christopher Quince (Principal Investigator) |
Publications
Alneberg J
(2014)
Binning metagenomic contigs by coverage and composition
in Nature Methods
Besemer K
(2013)
Headwaters are critical reservoirs of microbial diversity for fluvial networks.
in Proceedings. Biological sciences
Cho SK
(2016)
Low-strength ultrasonication positively affects methanogenic granules toward higher AD performance: Implications from microbial community shift.
in Ultrasonics sonochemistry
Chronáková A
(2015)
Response of Archaeal and Bacterial Soil Communities to Changes Associated with Outdoor Cattle Overwintering.
in PloS one
Coolen MJ
(2013)
Evolution of the plankton paleome in the Black Sea from the Deglacial to Anthropocene.
in Proceedings of the National Academy of Sciences of the United States of America
Edgar RC
(2011)
UCHIME improves sensitivity and speed of chimera detection.
in Bioinformatics (Oxford, England)
Fonseca V
(2014)
Metagenetic analysis of patterns of distribution and diversity of marine meiobenthic eukaryotes
in Global Ecology and Biogeography
Fonseca VG
(2012)
Sample richness and genetic diversity as drivers of chimera formation in nSSU metagenetic analyses.
in Nucleic acids research
Gerasimidis K
(2014)
Role of Faecalibacterium prausnitzii in Crohn's Disease: friend, foe, or does not really matter?
in Inflammatory bowel diseases
Gilbert JA
(2010)
Meeting report: the terabase metagenomics workshop and the vision of an Earth microbiome project.
in Standards in genomic sciences
Gobet A
(2010)
Multivariate Cutoff Level Analysis (MultiCoLA) of large community data sets.
in Nucleic acids research
Gobet A
(2012)
Diversity and dynamics of rare and of resident bacterial populations in coastal sands.
in The ISME journal
Gubry-Rangin C
(2011)
Niche specialization of terrestrial archaeal ammonia oxidizers.
in Proceedings of the National Academy of Sciences of the United States of America
Haig S
(2014)
Replicating the microbial community and water quality performance of full-scale slow sand filters in laboratory-scale filters
in Water Research
Haig SJ
(2016)
Bioaugmentation Mitigates the Impact of Estrogen on Coliform-Grazing Protozoa in Slow Sand Filters.
in Environmental science & technology
Haig SJ
(2015)
Stable-isotope probing and metagenomics reveal predation by protozoa drives E. coli removal in slow sand filters.
in The ISME journal
Haig SJ
(2015)
The relationship between microbial community evenness and function in slow sand filters.
in mBio
Harris K
(2017)
Linking Statistical and Ecological Theory: Hubbell's Unified Neutral Theory of Biodiversity as a Hierarchical Dirichlet Process
in Proceedings of the IEEE
Hartmann M
(2011)
V-REVCOMP: automated high-throughput detection of reverse complementary 16S rRNA gene sequences in large environmental and taxonomic datasets.
in FEMS microbiology letters
Heidrich ES
(2018)
Temperature, inocula and substrate: Contrasting electroactive consortia, diversity and performance in microbial fuel cells.
in Bioelectrochemistry (Amsterdam, Netherlands)
Henrik Nilsson R
(2011)
Towards standardization of the description and publication of next-generation sequencing datasets of fungal communities
in New Phytologist
Holmes I
(2012)
Dirichlet multinomial mixtures: generative models for microbial metagenomics.
in PloS one
Description | This grant helped me develop bioinformatics tools for microbial community analysis using next-generation sequence data and apply them to engineering systems. Next-generation sequencing has transformed our ability to study microbes in situ by providing DNA sequence from the organisms present. This can be used to identify them and determine what they can do. During the grant I developed better tools to process that data. These tools include programs for removing errors from the sequences. Two of these papers collectively attracted over 2000 citations in the last four years. I also developed statistical methods for the probabilistic modelling of community structure. These have been applied to the modelling of the human gut. I also developed new tools for shotgun metagenomics extracting species and strain genomes from short read data. I am a major contributor to numerous bioinformatics tools including CONCOCT and v-search. I then went on to apply these methods to engineering of microbial communities. Furthering our understanding of the microbial communities present in water treatment systems such as slow sand filters and anaerobic digesters. This will help us better engineer these systems in future |
Exploitation Route | Yes my research will provide a basis for further work on the microbial communities in slow sand filters and anaerobic digesters. It will help researchers generally throughout microbial ecology and provide a basis for new developments in statistics and computing. |
Sectors | Agriculture Food and Drink Healthcare Pharmaceuticals and Medical Biotechnology |
Description | The software and algorithms developed during this award were used by Unilever to help understand the impact personal care products on the skin microbiota. This then led to a major TSB grant. |
First Year Of Impact | 2012 |
Sector | Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic |
Title | CONCOCT: Clustering cONtigs with COverage and ComposiTion |
Description | A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads. |
Type Of Technology | Software |
Year Produced | 2014 |
Open Source License? | Yes |
Impact | 55 citations and use in numerous studies. |
URL | https://github.com/BinPro/CONCOCT |