Diversity, Productivity and Stability of an Industrial Microbial Ecosystem

Transcription

1 Diversity, Productivity and Stability of an Industrial Microbial Ecosystem Doruk Beyter 1, Pei-Zhong Tang 2, Scott Becker 2, Tony Hoang 3, Damla Bilgin 3, Yan Wei Lim 4, Todd C. Peterson 2, Stephen Mayfield 5, Farzad Haerizadeh 2, Jonathan B. Shurin 6, Vineet Bafna 1, and Robert McBride 3 1 Computer Science and Engineering Department, UCSD, 2 Life Technologies, 3 Sapphire Energy, 4 Biology Department, SDSU, 5 Division of Biological Sciences, UCSD, 6 Section of Ecology, Behavior and Evolution, University of California San Diego October, 2015

2 Motivation Managing ecosystems to maintain biodiversity may be one approach to ensuring: stability, productivity, and delivery of vital services. Journal of Applied Phycology, 2012 Stockenreiter et al. Applicability of this approach to industrial ecosystems has been proposed but never been tested at relevant scales.

3 Data Data collection site: Open pond managed for bioenergy production by micro-algae at Las Cruces, NM Test Site of Sapphire Energy Inc. Ecology Letters, 2013 Shurin et al.

4 Data Monitored a number of aspects of ecosystem structure and function (Temperature, Fv/Fm, Urea, NO 3, NO 2...). Amplicon sequencing: 16S (bacteria) and ITS2 (eukaryotes) regions to assess taxonomy. Both data collected over a year long period to examine the relationships between ecosystem variables and genomic content, particularly microbial diversity and biomass productivity. genomic sampling 2 weeks

5 Sequencing data properties 26 biweekly temporal samples for 16S and ITS2 sequences. 4 samples had triplicates to be used as technical replicates. 16S and ITS2 samples analyzed on 3 and 4 chips, respectively, to asses reproducibility. 1.6, 3.8, and 4.4 million reads for 16S chips. 3.7, 4.7, 4.6, and 3.7 million reads for ITS2 data. Sequenced using Ion Torrent PGM from Life Technologies.

6 What is an amplicon sequencing sample? Amplicon sequencing reads length and quality filtering TMAP amplicon database R1 R2 R3 R4 RN REF ID Hit counts R1 145 R2 34 R3 9 pick top N Query coverage cutoff = 70% Percent identity cutoff = 95%

7 Temporal taxonomic composition (bacterial) phylum Grey shading: pesticide application class Key events: Introduction of new phylum at the post pesticide samples: Cyanobacteria. Proteobacteria decline, Verrucomicbrobia rise. Alphaproteobacteria decline. Dominance by single genus: Luteolibacter. genus

8 Temporal taxonomic composition (eukaryotic) kingdo m class Grey shading: pesticide application Key events: Overall Viridiplantae (algae) dominance. Fungal abundance fluctuations. Dominance of class Chlorophyceae. Sharp decline in previously dominating Coelastrum post pesticide application. Dominance by Scenedesmus, at the last 50 days. genus

9 16S data Bacterial-eukaryotic diversity (P = ) relationship (P = ) ITS2 data Negative Correlation: Pearson R = 0.56, p-value = Controlling for temperature revealed eukaryotic and bacterial diversities had no explanatory value on each other. (P = ) (P = )

10 Ecosystem Structure and Function Various ecosystem variables collected on a regular basis, ranging from every day for some variables to a few times a week for others, over a year. We clustered the ecosystem variables using the Cluster Affinity Search Technique (CAST) algorithm to find highly connected variable components/cliques. We represented each cluster with their first principle component.

11 Existing ecosystem variable relationship in literature and our data Ecology Letters, 2013 Edwards et al.

12 Kingdom diversities versus ecosystem variables

13 Bacteria phyla relative abundances vs. ecosystem variables

14 Algal diversity vs. productivity mean: R = 0.33, P = median: R = 0.43, P = variance: R = 0.57, P = 3.10 Controlling for temperature, algal diversity 10 3 had significant explanatory value on productivity mean, median and variance (P = , , and )

15 Conclusion Periods of high algal diversity were associated with high median and low temporal variation in biomass productivity. Prokaryotic and eukaryotic diversity were inversely correlated over time, due to their different responses to temperature. Results indicate that maintaining diverse communities may be essential to engineering stable and productive bioenergy ecosystems using micro-organisms.

16 Acknowledgements Coauthors UCSD Prof. Vineet Bafna Prof. Stephen Mayfield Prof. Jonathan B. Shurin Coauthors Life Technologies Scott Becker Farzad Haerizadeh Todd C. Peterson Pei-Zhong Tang Coauthors - Sapphire Energy Damla Bilgin Tony Hoang Robert McBride Coauthors - SDSU Yan Wei Lim Lab members Ali Akbari Stefano Bonissone Viraj Deshpande Mikhail Kolmogorov Eric Scott JCVI Shibu Yooseph

17 Thank you.