Publications

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Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are "keystone" OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

Large-scale open microalgae cultivation has tremendous potential to make a significant contribution to replacing petroleum-based fuels with biofuels. Open algal cultures are unavoidably inhabited with a diversity of microbes that live on, influence, and shape the fate of these ecosystems. However, there is little understanding of the resilience and stability of the microbial communities in engineered semicontinuous algal systems. To evaluate the dynamics and resilience of the microbial communities in microalgae biofuel cultures, we conducted a longitudinal study on open systems to compare the temporal profiles of the microbiota from two multigenerational algal cohorts, which include one seeded with the microbiota from an in-house culture and the other exogenously seeded with a natural-occurring consortia of bacterial species harvested from the Pacific Ocean. From these month-long, semicontinuous open microalga Nannochloropsis salina cultures, we sequenced a time-series of 46 samples, yielding 8804 operational taxonomic units derived from 9,160,076 high-quality partial 16S rRNA sequences. We provide quantitative evidence that clearly illustrates the development of microbial community is associated with microbiota ancestry. In addition, N. salina growth phases were linked with distinct changes in microbial phylotypes. Alteromonadeles dominated the community in the N. salina exponential phase whereas Alphaproteobacteria and Flavobacteriia were more prevalent in the stationary phase. We also demonstrate that the N. salina-associated microbial community in open cultures is diverse, resilient, and dynamic in response to environmental perturbations. This knowledge has general implications for developing and testing design principles of cultivated algal systems.

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We demonstrate that metal-organic frameworks (MOFs) can catalyze hydrogenolysis of aryl ether bonds under mild conditions. Mg-IRMOF-74(I) and Mg-IRMOF-74(II) are stable under reducing conditions and can cleave phenyl ethers containing β-O-4, α-O-4, and 4-O-5 linkages to the corresponding hydrocarbons and phenols. Reaction occurs at 10 bar H2 and 120 °C without added base. DFT-optimized structures and charge transfer analysis suggest that the MOF orients the substrate near Mg2+ ions on the pore walls. Ti and Ni doping further increase conversions to as high as 82% with 96% selectivity for hydrogenolysis versus ring hydrogenation. Repeated cycling induces no loss of activity, making this a promising route for mild aryl-ether bond scission.

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Sandia National Laboratories and DSM Innovation, Inc. collaborated on the investigation of the structure and function of cellulases from thermophilic fungi. Sandia's role was to use its expertise in protein structure determination and X-ray crystallography to solve the structure of these enzymes in their native state and in their substrate and product bound states. Sandia was also tasked to work with DSM to use the newly solved structure to, using computational approaches, analyze enzyme interactions with both bound substrate and bound product; the goal being to develop approaches for rationally designing improved cellulases for biomass deconstruction. We solved the structures of five cellulases from thermophilic fungi. Several of these were also solved with bound substrate/product, which allowed us to predict mutations that might enhance activity and stability.

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The innate immune system represents our first line of defense against microbial pathogens, and in many cases is activated by recognition of pathogen cellular components (dsRNA, flagella, LPS, etc.) by cell surface membrane proteins known as toll-like receptors (TLRs). As the initial trigger for innate immune response activation, TLRs also represent a means by which we can effectively control or modulate inflammatory responses. This proposal focused on TLR4, which is the cell-surface receptor primarily responsible for initiating the innate immune response to lipopolysaccharide (LPS), a major component of the outer membrane envelope of gram-negative bacteria. The goal was to better understand TLR4 activation and associated membrane proximal events, in order to enhance the design of small molecule therapeutics to modulate immune activation. Our approach was to reconstitute the receptor in biomimetic systems in-vitro to allow study of the structure and dynamics with biophysical methods. Structural studies were initiated in the first year but were halted after the crystal structure of the dimerized receptor was published early in the second year of the program. Methods were developed to determine the association constant for oligomerization of the soluble receptor. LPS-induced oligomerization was observed to be a strong function of buffer conditions. In 20 mM Tris pH 8.0 with 200 mM NaCl, the onset of receptor oligomerization occurred at 0.2 uM TLR4/MD2 with E coli LPS Ra mutant in excess. However, in the presence of 0.5 uM CD14 and 0.5 uM LBP, the onset of receptor oligomerization was observed to be less than 10 nM TLR4/MD2. Several methods were pursued to study LPS-induced oligomerization of the membrane-bound receptor, including CryoEM, FRET, colocalization and codiffusion followed by TIRF, and fluorescence correlation spectroscopy. However, there approaches met with only limited success.

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New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method is an extension of current ensemble classification techniques and has the advantage of allowing data to remain in heterogeneous databases. In this paper, we focus on the applicability of such a framework to the protein phosphorylation prediction problem. © Springer Science+Business Media, LLC 2008.

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Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine - learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein - chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine - learning techniques requiring binding information for individual reactions or individual targets. © 2007 The Author(s).