Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange mediates interactions among microbes, helping explain diversity in microbial communities. As these interactions often involve a fitness cost, it is unclear how stable cooperation can emerge. Here we use genome-scale metabolic models to investigate whether the release of “costless” metabolites (i.e. those that cause no fitness cost to the producer), can be a prominent driver of intermicrobial interactions. By performing over 2 million pairwise growth simulations of 24 species in a combinatorial assortment of environments, we identify a large space of metabolites that can be secreted without cost, thus generating ample cross-feeding opportunities. In addition to providing an atlas of putative interactions, we show that anoxic conditions can promote mutualisms by providing more opportunities for exchange of costless metabolites, resulting in an overrepresentation of stable ecological network motifs. These results may help identify interaction patterns in natural communities and inform the design of synthetic microbial consortia.We thank Dr. Niels Klitgord for pioneering ideas that inspired launch of this work. We are also grateful to David Bernstein, Joshua E. Goldford, Meghan Thommes, Demetrius DiMucci, and all members of the Segre Lab for helpful discussions. A.R.P. is supported by a National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship and a Howard Hughes Medical Institute Gilliam Fellowship. This work was supported by funding from the Defense Advanced Research Projects Agency (purchase request no. HR0011515303, contract no. HR0011-15-C-0091), the U.S. Department of Energy (grants DE-SC0004962 and DE-SC0012627), the NIH (grants 5R01DE024468, R01GM121950, and Sub_P30DK036836_P&F), the National Science Foundation (grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Inter-disciplinary Biomedical Research Office. (National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship; Howard Hughes Medical Institute Gilliam Fellowship; HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; Sub_P30DK036836_PF - NIH; 1457695 - National Science Foundation; NSFOCE-BSF 1635070 - National Science Foundation; W911NF-12-1-0390 - MURI Grant; RGP0020/2016 - Human Frontiers Science Program; Boston University Inter-disciplinary Biomedical Research Office)Published versio

Polyoxometalate (POM)-layered double hydroxides (LDH) composite materials: design and catalytic applications

Layered double hydroxides (LDHs) are an important large class of two-dimensional (2D) anionic lamellar materials that possess flexible modular structure, facile exchangeability of inter-lamellar guest anions and uniform distribution of metal cations in the layer. Owing to the modular accessible gallery and unique inter-lamellar chemical environment, polyoxometalates (POMs) intercalated with LDHs has shown a vast array of physical properties with applications in environment, energy, catalysis, etc. Here we describe how polyoxometalate clusters can be used as building components for the construction of systems with important catalytic properties. This review article mainly focuses on the discussion of new synthetic approaches developed recently that allow the incorporation of the element of design in the construction of a fundamentally new class of materials with pre-defined functionalities in catalytic applications. Introducing the element of design and taking control over the finally observed functionality we demonstrate the unique opportunity for engineering materials with modular properties for specific catalytic applications

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

Launching the Grand Challenges for Ocean Conservation

The ten most pressing Grand Challenges in Oceans Conservation were identified at the Oceans Big Think and described in a detailed working document:A Blue Revolution for Oceans: Reengineering Aquaculture for SustainabilityEnding and Recovering from Marine DebrisTransparency and Traceability from Sea to Shore:  Ending OverfishingProtecting Critical Ocean Habitats: New Tools for Marine ProtectionEngineering Ecological Resilience in Near Shore and Coastal AreasReducing the Ecological Footprint of Fishing through Smarter GearArresting the Alien Invasion: Combating Invasive SpeciesCombatting the Effects of Ocean AcidificationEnding Marine Wildlife TraffickingReviving Dead Zones: Combating Ocean Deoxygenation and Nutrient Runof