A high throughput Nile red method for quantitative measurement of neutral lipids in microalgae

Isolation of high neutral lipid-containing microalgae is key to the commercial success of microalgae-based biofuel production. The Nile red fluorescence method has been successfully applied to the determination of lipids in certain microalgae, but has been unsuccessful in many others, particularly those with thick, rigid cell walls that prevent the penetration of the fluorescence dye. The conventional "one sample at a time" method was also time-consuming. In this study, the solvent dimethyl sulfoxide (DMSO) was introduced to microalgal samples as the stain carrier at an elevated temperature. The cellular neutral lipids were determined and quantified using a 96-well plate on a fluorescence spectrophotometer with an excitation wavelength of 530 nm and an emission wavelength of 575 run. An optimized procedure yielded a high correlation coefficient (R-2 = 0.998) with the lipid standard triolein and repeated measurements of replicates. Application of the improved method to several green algal strains gave very reproducible results with relative standard errors of 8.5%, 3.9% and 8.6%, 4.5% for repeatability and reproducibility at two concentration levels (2.0 mu g/mL and 20 mu g/mL), respectively. Moreover, the detection and quantification limits of the improved Nile red staining method were 0.8 mu g/mL and 2.0 mu g/mL for the neutral lipid standard triolein, respectively. The modified method and a conventional gravimetric determination method provided similar results on replicate samples. The 96-well plate-based Nile red method can be used as a high throughput technique for rapid screening of a broader spectrum of naturally-occurring and genetically-modified algal strains and mutants for high neutral lipid/oil production. (C) 2009 Published by Elsevier B.V.

High-throughput characterization of heterogeneous catalysts by temperature-programmed analysis method

A multistream reactor for high-throughput examining the surface acidity by NH3-TPD method by application of multistream mass spectrometer screening (MSMSS) technique has been developed. This method allows for examining the surface acidity of 10 catalyst samples in about 6 h, which is an improvement over the traditional process. The demonstration of the feasibility of high-throughput TPD can be significant in convincing the hardened traditionalists in the heterogeneous catalysis community that, combinatorial methods indeed should have an important place in scientific catalyst research and development. The developed method could also be used for almost all the temperature-programmed analysis theoretically with careful designed multistream reactors. (C) 2003 Elsevier B.V. All rights reserved.

High-throughput synergy screening identifies microbial metabolites as combination agents for the treatment of fungal infections

The high mortality rate of immunocompromised patients with fungal infections and the limited availability of highly efficacious and safe agents demand the development of new antifungal therapeutics. To rapidly discover such agents, we developed a high-throughput synergy screening (HTSS) strategy for novel microbial natural products. Specifically, a microbial natural product library was screened for hits that synergize the effect of a low dosage of ketoconazole (KTC) that alone shows little detectable fungicidal activity. Through screening of approximate to 20,000 microbial extracts, 12 hits were identified with broadspectrum antifungal activity. Seven of them showed little cytotoxicity against human hepatoma cells. Fractionation of the active extracts revealed beauvericin (BEA) as the most potent component, because it dramatically synergized KTC activity against diverse fungal pathogens by a checkerboard assay. Significantly, in our immunocompromised mouse model, combinations of BEA (0.5 mg/kg) and KTC (0.5 mg/kg) prolonged survival of the host infected with Candida parapsilosis and reduced fungal colony counts in animal organs including kidneys, lungs, and brains. Such an effect was not achieved even with the high dose of 50 mg/kg KTC. These data support synergism between BEA and KTC and thereby a prospective strategy for antifungal therapy.

High-throughput screening of HZSM-5 supported metal-oxides catalysts for the coupling of methane with CO to benzene and naphthalene

High-throughput screening of HZSM-5 supported metal-oxides catalysts were carried out for the coupling reaction of methane with CO to aromatics in a multi-stream reactor system. Zn/HZSM-5 and Mo/HZSM-5 were observed to be rather effective for the catalytic formation of aromatics from the coupling reaction of methane with CO. Temperature-programmed reaction has further proven the efficiency of the coupling of methane and CO over Zn/HZSM-5 catalyst. The results were also validated in a conventional fixed-bed reactor coupled with GC. The results propose that the coupling methane with CO toward benzene and naphthalene can be catalyzed by Zn/HZSM-5 at 500 ° C. Both methane and CO are needed for the formation of reactive coke on the catalyst, and the reactive coke may be the initial product in the producing of hydrocarbons. © 2005 Elsevier B.V. All rights reserved.

Thermodynamic analysis of ligand-induced changes in protein thermal unfolding applied to high-throughput determination of ligand affinities with extrinsic fluorescent dyes.

The quantification of protein-ligand interactions is essential for systems biology, drug discovery, and bioengineering. Ligand-induced changes in protein thermal stability provide a general, quantifiable signature of binding and may be monitored with dyes such as Sypro Orange (SO), which increase their fluorescence emission intensities upon interaction with the unfolded protein. This method is an experimentally straightforward, economical, and high-throughput approach for observing thermal melts using commonly available real-time polymerase chain reaction instrumentation. However, quantitative analysis requires careful consideration of the dye-mediated reporting mechanism and the underlying thermodynamic model. We determine affinity constants by analysis of ligand-mediated shifts in melting-temperature midpoint values. Ligand affinity is determined in a ligand titration series from shifts in free energies of stability at a common reference temperature. Thermodynamic parameters are obtained by fitting the inverse first derivative of the experimental signal reporting on thermal denaturation with equations that incorporate linear or nonlinear baseline models. We apply these methods to fit protein melts monitored with SO that exhibit prominent nonlinear post-transition baselines. SO can perturb the equilibria on which it is reporting. We analyze cases in which the ligand binds to both the native and denatured state or to the native state only and cases in which protein:ligand stoichiometry needs to treated explicitly.

Uncovering compounds by synergy of cluster expansion and high-throughput methods.

Predicting from first-principles calculations whether mixed metallic elements phase-separate or form ordered structures is a major challenge of current materials research. It can be partially addressed in cases where experiments suggest the underlying lattice is conserved, using cluster expansion (CE) and a variety of exhaustive evaluation or genetic search algorithms. Evolutionary algorithms have been recently introduced to search for stable off-lattice structures at fixed mixture compositions. The general off-lattice problem is still unsolved. We present an integrated approach of CE and high-throughput ab initio calculations (HT) applicable to the full range of compositions in binary systems where the constituent elements or the intermediate ordered structures have different lattice types. The HT method replaces the search algorithms by direct calculation of a moderate number of naturally occurring prototypes representing all crystal systems and guides CE calculations of derivative structures. This synergy achieves the precision of the CE and the guiding strengths of the HT. Its application to poorly characterized binary Hf systems, believed to be phase-separating, defines three classes of alloys where CE and HT complement each other to uncover new ordered structures.

Nanofabrication at high throughput and low cost.

The task of nanofabrication can, in principle, be divided into two separate tracks: generation and replication of the patterned features. These two tracks are different in terms of characteristics, requirements, and aspects of emphasis. In general, generation of patterns is commonly achieved in a serial fashion using techniques that are typically slow, making this process only practical for making a small number of copies. Only when combined with a rapid duplication technique will fabrication at high-throughput and low-cost become feasible. Nanoskiving is unique in that it can be used for both generation and duplication of patterned nanostructures.

High-throughput isolation and mapping of C. elegans mutants susceptible to pathogen infection.

We present a novel strategy that uses high-throughput methods of isolating and mapping C. elegans mutants susceptible to pathogen infection. We show that C. elegans mutants that exhibit an enhanced pathogen accumulation (epa) phenotype can be rapidly identified and isolated using a sorting system that allows automation of the analysis, sorting, and dispensing of C. elegans by measuring fluorescent bacteria inside the animals. Furthermore, we validate the use of Amplifluor as a new single nucleotide polymorphism (SNP) mapping technique in C. elegans. We show that a set of 9 SNPs allows the linkage of C. elegans mutants to a 5-8 megabase sub-chromosomal region.

High-throughput identification of chemical inhibitors of E. coli Group 2 capsule biogenesis as anti-virulence agents.

Rising antibiotic resistance among Escherichia coli, the leading cause of urinary tract infections (UTIs), has placed a new focus on molecular pathogenesis studies, aiming to identify new therapeutic targets. Anti-virulence agents are attractive as chemotherapeutics to attenuate an organism during disease but not necessarily during benign commensalism, thus decreasing the stress on beneficial microbial communities and lessening the emergence of resistance. We and others have demonstrated that the K antigen capsule of E. coli is a preeminent virulence determinant during UTI and more invasive diseases. Components of assembly and export are highly conserved among the major K antigen capsular types associated with UTI-causing E. coli and are distinct from the capsule biogenesis machinery of many commensal E. coli, making these attractive therapeutic targets. We conducted a screen for anti-capsular small molecules and identified an agent designated "C7" that blocks the production of K1 and K5 capsules, unrelated polysaccharide types among the Group 2-3 capsules. Herein lies proof-of-concept that this screen may be implemented with larger chemical libraries to identify second-generation small-molecule inhibitors of capsule biogenesis. These inhibitors will lead to a better understanding of capsule biogenesis and may represent a new class of therapeutics.

WormSizer: high-throughput analysis of nematode size and shape.

The fundamental phenotypes of growth rate, size and morphology are the result of complex interactions between genotype and environment. We developed a high-throughput software application, WormSizer, which computes size and shape of nematodes from brightfield images. Existing methods for estimating volume either coarsely model the nematode as a cylinder or assume the worm shape or opacity is invariant. Our estimate is more robust to changes in morphology or optical density as it only assumes radial symmetry. This open source software is written as a plugin for the well-known image-processing framework Fiji/ImageJ. It may therefore be extended easily. We evaluated the technical performance of this framework, and we used it to analyze growth and shape of several canonical Caenorhabditis elegans mutants in a developmental time series. We confirm quantitatively that a Dumpy (Dpy) mutant is short and fat and that a Long (Lon) mutant is long and thin. We show that daf-2 insulin-like receptor mutants are larger than wild-type upon hatching but grow slow, and WormSizer can distinguish dauer larvae from normal larvae. We also show that a Small (Sma) mutant is actually smaller than wild-type at all stages of larval development. WormSizer works with Uncoordinated (Unc) and Roller (Rol) mutants as well, indicating that it can be used with mutants despite behavioral phenotypes. We used our complete data set to perform a power analysis, giving users a sense of how many images are needed to detect different effect sizes. Our analysis confirms and extends on existing phenotypic characterization of well-characterized mutants, demonstrating the utility and robustness of WormSizer.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.