Fluorescence strategies for high-throughput quantification of protein interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Hieb AR
PROVIDER: S-EPMC3299996 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMicrobes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness in terms of birth rate and death rate in a wide range of environments is critical for understanding microbial evolution and ecology.MethodsHere, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite (auxotrophs) grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing.ResultsFor lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate compared to no lysine. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates can vary with metabolite concentration and time.ConclusionsOur work reveals how time-lapse microscopy can be used to discover non-intuitive microbial birth and death dynamics and to quantify growth rates in many environments.
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Project description:The quantification of bacteria in cell culture infection models is of paramount importance for the characterization of host-pathogen interactions and pathogenicity factors involved. The standard to enumerate bacteria in these assays is plating of a dilution series on solid agar and counting of the resulting colony forming units (CFU). In contrast, the virtual colony count (VCC) method is a high-throughput compatible alternative with minimized manual input. Based on the recording of quantitative growth kinetics, VCC relates the time to reach a given absorbance threshold to the initial cell count using a series of calibration curves. Here, we adapted the VCC method using the model organism Salmonella enterica sv. Typhimurium (S. Typhimurium) in combination with established cell culture-based infection models. For HeLa infections, a direct side-by-side comparison showed a good correlation of VCC with CFU counting after plating. For MDCK cells and RAW macrophages we found that VCC reproduced the expected phenotypes of different S. Typhimurium mutants. Furthermore, we demonstrated the use of VCC to test the inhibition of Salmonella invasion by the probiotic E. coli strain Nissle 1917. Taken together, VCC provides a flexible, label-free, automation-compatible methodology to quantify bacteria in in vitro infection assays.
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Project description:RNA-binding proteins (RBPs) are important regulators of eukaryotic gene expression. Genomes typically encode dozens to hundreds of proteins containing RNA-binding domains, which collectively recognize diverse RNA sequences and structures. Recent advances in high-throughput methods for assaying the targets of RBPs in vitro and in vivo allow large-scale derivation of RNA-binding motifs as well as determination of RNA-protein interactions in living cells. In parallel, many computational methods have been developed to analyze and interpret these data. The interplay between RNA secondary structure and RBP binding has also been a growing theme. Integrating RNA-protein interaction data with observations of post-transcriptional regulation will enhance our understanding of the roles of these important proteins.
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Project description:Human biology is regulated by a complex network of protein-protein interactions (PPIs), and disruption of this network has been implicated in many diseases. However, the targeting of PPIs remains a challenging area for chemical probe and drug discovery. Although many methodologies have been put forth to facilitate these efforts, new technologies are still needed. Current biochemical assays for PPIs are typically limited to motif-domain and domain-domain interactions, and assays that will enable the screening of full-length protein systems, which are more biologically relevant, are sparse. To overcome this barrier, we have developed a new assay technology, "PPI catalytic enzyme-linked click chemistry assay" or PPI cat-ELCCA, which utilizes click chemistry to afford catalytic signal amplification. To validate this approach, we have applied PPI cat-ELCCA to the eIF4E-4E-BP1 and eIF4E-eIF4G PPIs, key regulators of cap-dependent mRNA translation. Using these examples, we have demonstrated that PPI cat-ELCCA is amenable to full-length proteins, large (>200 kDa) and small (?12 kDa), and is readily adaptable to automated high-throughput screening. Thus, PPI cat-ELCCA represents a powerful new tool in the toolbox of assays available to scientists interested in the targeting of disease-relevant PPIs.
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Project description:IntroductionLC-MS-based methods for protein quantification have a stigma of being relatively expensive and low-throughput. This is partly due to the cost and speed of trypsin digestion, which has primarily focused on advancements in research-based biomarker discovery applications that rely on protein/peptide identifications rather than clinical biomarker quantification. However, there is a need for simple, fast, and reproducibly efficient surrogate peptide recovery in clinical biomarker quantification.MethodsMultiple methodologies were evaluated to enhance tryptic digestion for the analysis of thyroglobulin, a prototypical serum protein biomarker. The main criteria for assessment were the yield and speed of formation of surrogate peptides. Various factors such as different additives, types of trypsin, microwave- and pressure-assisted systems, and enzyme concentration were considered as key variables, in addition to digestion time.ResultsIt was observed that digestion additives/denaturants had a significant impact on the speed and yield of digestion for each surrogate peptide. Increasing the concentration of trypsin alone was found to accelerate digestions appreciably for most surrogate peptides, without affecting the yield. However, the use of sequencing-grade trypsins and microwave/pressure-assisted systems did not offer significant advantages over the use of 'standard-grade' TPCK-treated trypsin in combination with a conventional incubator, once digestion time and additive had been optimized.ConclusionWe have dispelled the notion that trypsin digestion is inherently slow and expensive for targeted quantification of serum proteins. Additionally, we have established a groundwork for experimentation that can pave the way for the creation of efficient trypsin digestion protocols, aiming to optimize yield, speed, and cost. It is our hope that these advancements will promote the wider incorporation of such assays in clinical laboratories.
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Project description:High-throughput methods for screening protein-protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of Saccharomyces cerevisiae can be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions with Kds ranging from below 500 pM to above 300 ?M. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein-protein interaction networks in a fully defined extracellular environment at a library-on-library scale.
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Project description:High throughput screening (HTS) projects like the U.S. Environmental Protection Agency's ToxCast program are required to address the large and rapidly increasing number of chemicals for which we have little to no toxicity measurements. Concentration-response parameters such as potency and efficacy are extracted from HTS data using nonlinear regression, and models and analyses built from these parameters are used to predict in vivo and in vitro toxicity of thousands of chemicals. How these predictions are impacted by uncertainties that stem from parameter estimation and propagated through the models and analyses has not been well explored. While data size and complexity makes uncertainty quantification computationally expensive for HTS datasets, continued advancements in computational resources have allowed these computational challenges to be met. This study uses nonparametric bootstrap resampling to calculate uncertainties in concentration-response parameters from a variety of HTS assays. Using the ToxCast estrogen receptor model for bioactivity as a case study, we highlight how these uncertainties can be propagated through models to quantify the uncertainty in model outputs. Uncertainty quantification in model outputs is used to identify potential false positives and false negatives and to determine the distribution of model values around semi-arbitrary activity cutoffs, increasing confidence in model predictions. At the individual chemical-assay level, curves with high variability are flagged for manual inspection or retesting, focusing subject-matter-expert time on results that need further input. This work improves the confidence of predictions made using HTS data, increasing the ability to use this data in risk assessment.
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Project description:The interactions of biological macromolecules with nanoparticles underlie a wide variety of current and future applications in the fields of biotechnology, medicine and bioremediation. The same interactions are also responsible for mediating potential biohazards of nanomaterials. Some applications require that proteins adsorb to the nanomaterial and that the protein resists or undergoes structural rearrangements. This article presents a screening method for detecting nanoparticle-protein partners and conformational changes on time scales ranging from milliseconds to days. Mobile fluorophores are used as reporters to study the interaction between proteins and nanoparticles in a high-throughput manner in multi-well format. Furthermore, the screening method may reveal changes in colloidal stability of nanomaterials depending on the physicochemical conditions.
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Project description:Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a human pathogen responsible for diarrhea, hemorrhagic colitis and hemolytic uremic syndrome (HUS). To promote a comprehensive insight into the molecular basis of EHEC O157:H7 physiology and pathogenesis, the combined proteome of EHEC O157:H7 strains, Clade 8 and Clade 6 isolated from cattle in Argentina, and the standard EDL933 (clade 3) strain has been analyzed. From shotgun proteomic analysis a total of 2,644 non-redundant proteins of EHEC O157:H7 were identified, which correspond approximately 47% of the predicted proteome of this pathogen. Normalized spectrum abundance factor analysis was performed to estimate the protein abundance. According this analysis, 50 proteins were detected as the most abundant of EHEC O157:H7 proteome. COG analysis showed that the majority of the most abundant proteins are associated with translation processes. A KEGG enrichment analysis revealed that Glycolysis / Gluconeogenesis was the most significant pathway. On the other hand, the less abundant detected proteins are those related to DNA processes, cell respiration and prophage. Among the proteins that composed the Type III Secretion System, the most abundant protein was EspA. Altogether, the results show a subset of important proteins that contribute to physiology and pathogenicity of EHEC O157:H7.
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