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Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.
PUBLISHED: 04-15-2010
Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.
Authors: Alla Gagarinova, Mohan Babu, Jack Greenblatt, Andrew Emili.
Published: 11-12-2012
Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10. Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.
23 Related JoVE Articles!
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The ITS2 Database
Authors: Benjamin Merget, Christian Koetschan, Thomas Hackl, Frank Förster, Thomas Dandekar, Tobias Müller, Jörg Schultz, Matthias Wolf.
Institutions: University of Würzburg, University of Würzburg.
The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution1 and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation2-8. The ITS2 Database9 presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank11 accurately reannotated10. Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold12 (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling13. In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold. The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST14 search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE15,16 and ProfDistS17 for multiple sequence-structure alignment calculation and Neighbor Joining18 tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure. In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.
Genetics, Issue 61, alignment, internal transcribed spacer 2, molecular systematics, secondary structure, ribosomal RNA, phylogenetic tree, homology modeling, phylogeny
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High-throughput Yeast Plasmid Overexpression Screen
Authors: Michael S. Fleming, Aaron D. Gitler.
Institutions: University of Pennsylvania School of Medicine , University of Pennsylvania School of Medicine .
The budding yeast, Saccharomyces cerevisiae, is a powerful model system for defining fundamental mechanisms of many important cellular processes, including those with direct relevance to human disease. Because of its short generation time and well-characterized genome, a major experimental advantage of the yeast model system is the ability to perform genetic screens to identify genes and pathways that are involved in a given process. Over the last thirty years such genetic screens have been used to elucidate the cell cycle, secretory pathway, and many more highly conserved aspects of eukaryotic cell biology 1-5. In the last few years, several genomewide libraries of yeast strains and plasmids have been generated 6-10. These collections now allow for the systematic interrogation of gene function using gain- and loss-of-function approaches 11-16. Here we provide a detailed protocol for the use of a high-throughput yeast transformation protocol with a liquid handling robot to perform a plasmid overexpression screen, using an arrayed library of 5,500 yeast plasmids. We have been using these screens to identify genetic modifiers of toxicity associated with the accumulation of aggregation-prone human neurodegenerative disease proteins. The methods presented here are readily adaptable to the study of other cellular phenotypes of interest.
Cell Biology, Issue 53, Yeast, plasmid, transformation, PEG/LioAc, high-throughput screen
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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
Authors: Takayuki Tohge, Alisdair R. Fernie.
Institutions: Max-Planck-Institut.
Given the ever expanding number of model plant species for which complete genome sequences are available and the abundance of bio-resources such as knockout mutants, wild accessions and advanced breeding populations, there is a rising burden for gene functional annotation. In this protocol, annotation of plant gene function using combined co-expression gene analysis, metabolomics and informatics is provided (Figure 1). This approach is based on the theory of using target genes of known function to allow the identification of non-annotated genes likely to be involved in a certain metabolic process, with the identification of target compounds via metabolomics. Strategies are put forward for applying this information on populations generated by both forward and reverse genetics approaches in spite of none of these are effortless. By corollary this approach can also be used as an approach to characterise unknown peaks representing new or specific secondary metabolites in the limited tissues, plant species or stress treatment, which is currently the important trial to understanding plant metabolism.
Plant Biology, Issue 64, Genetics, Bioinformatics, Metabolomics, Plant metabolism, Transcriptome analysis, Functional annotation, Computational biology, Plant biology, Theoretical biology, Spectroscopy and structural analysis
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High Content Screening in Neurodegenerative Diseases
Authors: Shushant Jain, Ronald E. van Kesteren, Peter Heutink.
Institutions: VU University Medical Center, Neuroscience Campus Amsterdam.
The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency1-4. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation5. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known6, 7. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long. There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity8. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention. High-throughput screening (HTS) is an ideal methodology to address these issues9-12. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties13, 14. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS)4, 15-17. HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS. HCS has many advantages over HTS18, 19, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility. Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing20 and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism21. Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets.
Medicine, Issue 59, High-throughput screening, high-content screening, neurodegeneration, automated cell culturing, Parkinson’s disease
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Authors: Phoebe Spetsieris, Yilong Ma, Shichun Peng, Ji Hyun Ko, Vijay Dhawan, Chris C. Tang, David Eidelberg.
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4 is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8. Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects5,6. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11. These standardized values can in turn be used to assist in differential diagnosis12,13 and to assess disease progression and treatment effects at the network level7,14-16. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
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Split-Ubiquitin Based Membrane Yeast Two-Hybrid (MYTH) System: A Powerful Tool For Identifying Protein-Protein Interactions
Authors: Jamie Snider, Saranya Kittanakom, Jasna Curak, Igor Stagljar.
Institutions: University of Toronto, University of Toronto, University of Toronto.
The fundamental biological and clinical importance of integral membrane proteins prompted the development of a yeast-based system for the high-throughput identification of protein-protein interactions (PPI) for full-length transmembrane proteins. To this end, our lab developed the split-ubiquitin based Membrane Yeast Two-Hybrid (MYTH) system. This technology allows for the sensitive detection of transient and stable protein interactions using Saccharomyces cerevisiae as a host organism. MYTH takes advantage of the observation that ubiquitin can be separated into two stable moieties: the C-terminal half of yeast ubiquitin (Cub) and the N-terminal half of the ubiquitin moiety (Nub). In MYTH, this principle is adapted for use as a 'sensor' of protein-protein interactions. Briefly, the integral membrane bait protein is fused to Cub which is linked to an artificial transcription factor. Prey proteins, either in individual or library format, are fused to the Nub moiety. Protein interaction between the bait and prey leads to reconstitution of the ubiquitin moieties, forming a full-length 'pseudo-ubiquitin' molecule. This molecule is in turn recognized by cytosolic deubiquitinating enzymes, resulting in cleavage of the transcription factor, and subsequent induction of reporter gene expression. The system is highly adaptable, and is particularly well-suited to high-throughput screening. It has been successfully employed to investigate interactions using integral membrane proteins from both yeast and other organisms.
Cellular Biology, Issue 36, protein-protein interaction, membrane, split-ubiquitin, yeast, library screening, Y2H, yeast two-hybrid, MYTH
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Genetic Manipulation in Δku80 Strains for Functional Genomic Analysis of Toxoplasma gondii
Authors: Leah M. Rommereim, Miryam A. Hortua Triana, Alejandra Falla, Kiah L. Sanders, Rebekah B. Guevara, David J. Bzik, Barbara A. Fox.
Institutions: The Geisel School of Medicine at Dartmouth.
Targeted genetic manipulation using homologous recombination is the method of choice for functional genomic analysis to obtain a detailed view of gene function and phenotype(s). The development of mutant strains with targeted gene deletions, targeted mutations, complemented gene function, and/or tagged genes provides powerful strategies to address gene function, particularly if these genetic manipulations can be efficiently targeted to the gene locus of interest using integration mediated by double cross over homologous recombination. Due to very high rates of nonhomologous recombination, functional genomic analysis of Toxoplasma gondii has been previously limited by the absence of efficient methods for targeting gene deletions and gene replacements to specific genetic loci. Recently, we abolished the major pathway of nonhomologous recombination in type I and type II strains of T. gondii by deleting the gene encoding the KU80 protein1,2. The Δku80 strains behave normally during tachyzoite (acute) and bradyzoite (chronic) stages in vitro and in vivo and exhibit essentially a 100% frequency of homologous recombination. The Δku80 strains make functional genomic studies feasible on the single gene as well as on the genome scale1-4. Here, we report methods for using type I and type II Δku80Δhxgprt strains to advance gene targeting approaches in T. gondii. We outline efficient methods for generating gene deletions, gene replacements, and tagged genes by targeted insertion or deletion of the hypoxanthine-xanthine-guanine phosphoribosyltransferase (HXGPRT) selectable marker. The described gene targeting protocol can be used in a variety of ways in Δku80 strains to advance functional analysis of the parasite genome and to develop single strains that carry multiple targeted genetic manipulations. The application of this genetic method and subsequent phenotypic assays will reveal fundamental and unique aspects of the biology of T. gondii and related significant human pathogens that cause malaria (Plasmodium sp.) and cryptosporidiosis (Cryptosporidium).
Infectious Diseases, Issue 77, Genetics, Microbiology, Infection, Medicine, Immunology, Molecular Biology, Cellular Biology, Biomedical Engineering, Bioengineering, Genomics, Parasitology, Pathology, Apicomplexa, Coccidia, Toxoplasma, Genetic Techniques, Gene Targeting, Eukaryota, Toxoplasma gondii, genetic manipulation, gene targeting, gene deletion, gene replacement, gene tagging, homologous recombination, DNA, sequencing
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Cortical Source Analysis of High-Density EEG Recordings in Children
Authors: Joe Bathelt, Helen O'Reilly, Michelle de Haan.
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2, because the composition and spatial configuration of head tissues changes dramatically over development3.  In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis. 
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials 
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
Authors: Nikki M. Curthoys, Michael J. Mlodzianoski, Dahan Kim, Samuel T. Hess.
Institutions: University of Maine.
Localization-based super resolution microscopy can be applied to obtain a spatial map (image) of the distribution of individual fluorescently labeled single molecules within a sample with a spatial resolution of tens of nanometers. Using either photoactivatable (PAFP) or photoswitchable (PSFP) fluorescent proteins fused to proteins of interest, or organic dyes conjugated to antibodies or other molecules of interest, fluorescence photoactivation localization microscopy (FPALM) can simultaneously image multiple species of molecules within single cells. By using the following approach, populations of large numbers (thousands to hundreds of thousands) of individual molecules are imaged in single cells and localized with a precision of ~10-30 nm. Data obtained can be applied to understanding the nanoscale spatial distributions of multiple protein types within a cell. One primary advantage of this technique is the dramatic increase in spatial resolution: while diffraction limits resolution to ~200-250 nm in conventional light microscopy, FPALM can image length scales more than an order of magnitude smaller. As many biological hypotheses concern the spatial relationships among different biomolecules, the improved resolution of FPALM can provide insight into questions of cellular organization which have previously been inaccessible to conventional fluorescence microscopy. In addition to detailing the methods for sample preparation and data acquisition, we here describe the optical setup for FPALM. One additional consideration for researchers wishing to do super-resolution microscopy is cost: in-house setups are significantly cheaper than most commercially available imaging machines. Limitations of this technique include the need for optimizing the labeling of molecules of interest within cell samples, and the need for post-processing software to visualize results. We here describe the use of PAFP and PSFP expression to image two protein species in fixed cells. Extension of the technique to living cells is also described.
Basic Protocol, Issue 82, Microscopy, Super-resolution imaging, Multicolor, single molecule, FPALM, Localization microscopy, fluorescent proteins
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Designing Silk-silk Protein Alloy Materials for Biomedical Applications
Authors: Xiao Hu, Solomon Duki, Joseph Forys, Jeffrey Hettinger, Justin Buchicchio, Tabbetha Dobbins, Catherine Yang.
Institutions: Rowan University, Rowan University, Cooper Medical School of Rowan University, Rowan University.
Fibrous proteins display different sequences and structures that have been used for various applications in biomedical fields such as biosensors, nanomedicine, tissue regeneration, and drug delivery. Designing materials based on the molecular-scale interactions between these proteins will help generate new multifunctional protein alloy biomaterials with tunable properties. Such alloy material systems also provide advantages in comparison to traditional synthetic polymers due to the materials biodegradability, biocompatibility, and tenability in the body. This article used the protein blends of wild tussah silk (Antheraea pernyi) and domestic mulberry silk (Bombyx mori) as an example to provide useful protocols regarding these topics, including how to predict protein-protein interactions by computational methods, how to produce protein alloy solutions, how to verify alloy systems by thermal analysis, and how to fabricate variable alloy materials including optical materials with diffraction gratings, electric materials with circuits coatings, and pharmaceutical materials for drug release and delivery. These methods can provide important information for designing the next generation multifunctional biomaterials based on different protein alloys.
Bioengineering, Issue 90, protein alloys, biomaterials, biomedical, silk blends, computational simulation, implantable electronic devices
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Authors: James Smadbeck, Meghan B. Peterson, George A. Khoury, Martin S. Taylor, Christodoulos A. Floudas.
Institutions: Princeton University.
The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (, a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
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Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer
Authors: Pelagia Deriziotis, Sarah A. Graham, Sara B. Estruch, Simon E. Fisher.
Institutions: Max Planck Institute for Psycholinguistics, Donders Institute for Brain, Cognition and Behaviour.
Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live cells. BRET is the non-radiative transfer of energy from a 'donor' luciferase enzyme to an 'acceptor' fluorescent protein. In the most common configuration of this assay, the donor is Renilla reniformis luciferase and the acceptor is Yellow Fluorescent Protein (YFP). Because the efficiency of energy transfer is strongly distance-dependent, observation of the BRET phenomenon requires that the donor and acceptor be in close proximity. To test for an interaction between two proteins of interest in cultured mammalian cells, one protein is expressed as a fusion with luciferase and the second as a fusion with YFP. An interaction between the two proteins of interest may bring the donor and acceptor sufficiently close for energy transfer to occur. Compared to other techniques for investigating protein-protein interactions, the BRET assay is sensitive, requires little hands-on time and few reagents, and is able to detect interactions which are weak, transient, or dependent on the biochemical environment found within a live cell. It is therefore an ideal approach for confirming putative interactions suggested by yeast two-hybrid or mass spectrometry proteomics studies, and in addition it is well-suited for mapping interacting regions, assessing the effect of post-translational modifications on protein-protein interactions, and evaluating the impact of mutations identified in patient DNA.
Cellular Biology, Issue 87, Protein-protein interactions, Bioluminescence Resonance Energy Transfer, Live cell, Transfection, Luciferase, Yellow Fluorescent Protein, Mutations
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Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy
Authors: Matthew Rames, Yadong Yu, Gang Ren.
Institutions: The Molecular Foundry.
Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of natural proteins have a molecular mass between 40 - 200 kDa1,2, a robust and high-throughput method with a nanometer resolution capability is needed. Negative staining (NS) electron microscopy (EM) is an easy, rapid, and qualitative approach which has frequently been used in research laboratories to examine protein structure and protein-protein interactions. Unfortunately, conventional NS protocols often generate structural artifacts on proteins, especially with lipoproteins that usually form presenting rouleaux artifacts. By using images of lipoproteins from cryo-electron microscopy (cryo-EM) as a standard, the key parameters in NS specimen preparation conditions were recently screened and reported as the optimized NS protocol (OpNS), a modified conventional NS protocol 3 . Artifacts like rouleaux can be greatly limited by OpNS, additionally providing high contrast along with reasonably high‐resolution (near 1 nm) images of small and asymmetric proteins. These high-resolution and high contrast images are even favorable for an individual protein (a single object, no average) 3D reconstruction, such as a 160 kDa antibody, through the method of electron tomography4,5. Moreover, OpNS can be a high‐throughput tool to examine hundreds of samples of small proteins. For example, the previously published mechanism of 53 kDa cholesteryl ester transfer protein (CETP) involved the screening and imaging of hundreds of samples 6. Considering cryo-EM rarely successfully images proteins less than 200 kDa has yet to publish any study involving screening over one hundred sample conditions, it is fair to call OpNS a high-throughput method for studying small proteins. Hopefully the OpNS protocol presented here can be a useful tool to push the boundaries of EM and accelerate EM studies into small protein structure, dynamics and mechanisms.
Environmental Sciences, Issue 90, small and asymmetric protein structure, electron microscopy, optimized negative staining
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Authors: Mandy Muller, Patricia Cassonnet, Michel Favre, Yves Jacob, Caroline Demeret.
Institutions: Institut Pasteur , Université Sorbonne Paris Cité, Dana Farber Cancer Institute.
Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors. This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells. A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.
Immunology, Issue 77, Genetics, Microbiology, Biochemistry, Molecular Biology, Cellular Biology, Biomedical Engineering, Infection, Cancer Biology, Virology, Medicine, Host-Pathogen Interactions, Host-Pathogen Interactions, Protein-protein interaction, High-throughput screening, Luminescence, Yeast two-hybrid, HT-GPCA, Network, protein, yeast, cell, culture
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Authors: Wen-Ting Tsai, Ahmed Hassan, Purbasha Sarkar, Joaquin Correa, Zoltan Metlagel, Danielle M. Jorgens, Manfred Auer.
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation. The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
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A Mouse Model for Pathogen-induced Chronic Inflammation at Local and Systemic Sites
Authors: George Papadopoulos, Carolyn D. Kramer, Connie S. Slocum, Ellen O. Weinberg, Ning Hua, Cynthia V. Gudino, James A. Hamilton, Caroline A. Genco.
Institutions: Boston University School of Medicine, Boston University School of Medicine.
Chronic inflammation is a major driver of pathological tissue damage and a unifying characteristic of many chronic diseases in humans including neoplastic, autoimmune, and chronic inflammatory diseases. Emerging evidence implicates pathogen-induced chronic inflammation in the development and progression of chronic diseases with a wide variety of clinical manifestations. Due to the complex and multifactorial etiology of chronic disease, designing experiments for proof of causality and the establishment of mechanistic links is nearly impossible in humans. An advantage of using animal models is that both genetic and environmental factors that may influence the course of a particular disease can be controlled. Thus, designing relevant animal models of infection represents a key step in identifying host and pathogen specific mechanisms that contribute to chronic inflammation. Here we describe a mouse model of pathogen-induced chronic inflammation at local and systemic sites following infection with the oral pathogen Porphyromonas gingivalis, a bacterium closely associated with human periodontal disease. Oral infection of specific-pathogen free mice induces a local inflammatory response resulting in destruction of tooth supporting alveolar bone, a hallmark of periodontal disease. In an established mouse model of atherosclerosis, infection with P. gingivalis accelerates inflammatory plaque deposition within the aortic sinus and innominate artery, accompanied by activation of the vascular endothelium, an increased immune cell infiltrate, and elevated expression of inflammatory mediators within lesions. We detail methodologies for the assessment of inflammation at local and systemic sites. The use of transgenic mice and defined bacterial mutants makes this model particularly suitable for identifying both host and microbial factors involved in the initiation, progression, and outcome of disease. Additionally, the model can be used to screen for novel therapeutic strategies, including vaccination and pharmacological intervention.
Immunology, Issue 90, Pathogen-Induced Chronic Inflammation; Porphyromonas gingivalis; Oral Bone Loss; Periodontal Disease; Atherosclerosis; Chronic Inflammation; Host-Pathogen Interaction; microCT; MRI
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Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
Authors: Eva Wagner, Sören Brandenburg, Tobias Kohl, Stephan E. Lehnart.
Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.
In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+ release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.
Bioengineering, Issue 92, cardiac myocyte, atria, ventricle, heart, primary cell isolation, fluorescence microscopy, membrane tubule, transverse-axial tubule system, image analysis, image processing, T-tubule, collagenase
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Microwave-assisted Functionalization of Poly(ethylene glycol) and On-resin Peptides for Use in Chain Polymerizations and Hydrogel Formation
Authors: Amy H. Van Hove, Brandon D. Wilson, Danielle S. W. Benoit.
Institutions: University of Rochester, University of Rochester, University of Rochester Medical Center.
One of the main benefits to using poly(ethylene glycol) (PEG) macromers in hydrogel formation is synthetic versatility. The ability to draw from a large variety of PEG molecular weights and configurations (arm number, arm length, and branching pattern) affords researchers tight control over resulting hydrogel structures and properties, including Young’s modulus and mesh size. This video will illustrate a rapid, efficient, solvent-free, microwave-assisted method to methacrylate PEG precursors into poly(ethylene glycol) dimethacrylate (PEGDM). This synthetic method provides much-needed starting materials for applications in drug delivery and regenerative medicine. The demonstrated method is superior to traditional methacrylation methods as it is significantly faster and simpler, as well as more economical and environmentally friendly, using smaller amounts of reagents and solvents. We will also demonstrate an adaptation of this technique for on-resin methacrylamide functionalization of peptides. This on-resin method allows the N-terminus of peptides to be functionalized with methacrylamide groups prior to deprotection and cleavage from resin. This allows for selective addition of methacrylamide groups to the N-termini of the peptides while amino acids with reactive side groups (e.g. primary amine of lysine, primary alcohol of serine, secondary alcohols of threonine, and phenol of tyrosine) remain protected, preventing functionalization at multiple sites. This article will detail common analytical methods (proton Nuclear Magnetic Resonance spectroscopy (;H-NMR) and Matrix Assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-ToF)) to assess the efficiency of the functionalizations. Common pitfalls and suggested troubleshooting methods will be addressed, as will modifications of the technique which can be used to further tune macromer functionality and resulting hydrogel physical and chemical properties. Use of synthesized products for the formation of hydrogels for drug delivery and cell-material interaction studies will be demonstrated, with particular attention paid to modifying hydrogel composition to affect mesh size, controlling hydrogel stiffness and drug release.
Chemistry, Issue 80, Poly(ethylene glycol), peptides, polymerization, polymers, methacrylation, peptide functionalization, 1H-NMR, MALDI-ToF, hydrogels, macromer synthesis
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Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
Authors: Mohan Babu, Olga Kagan, Hongbo Guo, Jack Greenblatt, Andrew Emili.
Institutions: University of Toronto, University of Regina, University of Toronto.
Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the identification of the subunit composition of multi-protein complexes can provide insight into gene function and enhance understanding of biological systems1, 2. Physical interactions can be mapped with high confidence vialarge-scale isolation and characterization of endogenous protein complexes under near-physiological conditions based on affinity purification of chromosomally-tagged proteins in combination with mass spectrometry (APMS). This approach has been successfully applied in evolutionarily diverse organisms, including yeast, flies, worms, mammalian cells, and bacteria1-6. In particular, we have generated a carboxy-terminal Sequential Peptide Affinity (SPA) dual tagging system for affinity-purifying native protein complexes from cultured gram-negative Escherichia coli, using genetically-tractable host laboratory strains that are well-suited for genome-wide investigations of the fundamental biology and conserved processes of prokaryotes1, 2, 7. Our SPA-tagging system is analogous to the tandem affinity purification method developed originally for yeast8, 9, and consists of a calmodulin binding peptide (CBP) followed by the cleavage site for the highly specific tobacco etch virus (TEV) protease and three copies of the FLAG epitope (3X FLAG), allowing for two consecutive rounds of affinity enrichment. After cassette amplification, sequence-specific linear PCR products encoding the SPA-tag and a selectable marker are integrated and expressed in frame as carboxy-terminal fusions in a DY330 background that is induced to transiently express a highly efficient heterologous bacteriophage lambda recombination system10. Subsequent dual-step purification using calmodulin and anti-FLAG affinity beads enables the highly selective and efficient recovery of even low abundance protein complexes from large-scale cultures. Tandem mass spectrometry is then used to identify the stably co-purifying proteins with high sensitivity (low nanogram detection limits). Here, we describe detailed step-by-step procedures we commonly use for systematic protein tagging, purification and mass spectrometry-based analysis of soluble protein complexes from E. coli, which can be scaled up and potentially tailored to other bacterial species, including certain opportunistic pathogens that are amenable to recombineering. The resulting physical interactions can often reveal interesting unexpected components and connections suggesting novel mechanistic links. Integration of the PPI data with alternate molecular association data such as genetic (gene-gene) interactions and genomic-context (GC) predictions can facilitate elucidation of the global molecular organization of multi-protein complexes within biological pathways. The networks generated for E. coli can be used to gain insight into the functional architecture of orthologous gene products in other microbes for which functional annotations are currently lacking.
Genetics, Issue 69, Molecular Biology, Medicine, Biochemistry, Microbiology, affinity purification, Escherichia coli, gram-negative bacteria, cytosolic proteins, SPA-tagging, homologous recombination, mass spectrometry, protein interaction, protein complex
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In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces
Authors: Julian Weghuber, Stefan Sunzenauer, Mario Brameshuber, Birgit Plochberger, Clemens Hesch, Gerhard J. Schutz.
Institutions: Johannes Kepler Universitat Linz.
Unraveling the interaction network of molecules in-vivo is key to understanding the mechanisms that regulate cell function and metabolism. A multitude of methodological options for addressing molecular interactions in cells have been developed, but most of these methods suffer from being rather indirect and therefore hardly quantitative. On the contrary, a few high-end quantitative approaches were introduced, which however are difficult to extend to high throughput. To combine high throughput capabilities with the possibility to extract quantitative information, we recently developed a new concept for identifying protein-protein interactions (Schwarzenbacher et al., 2008). Here, we describe a detailed protocol for the design and the construction of this system which allows for analyzing interactions between a fluorophore-labeled protein ("prey") and a membrane protein ("bait") in-vivo. Cells are plated on micropatterned surfaces functionalized with antibodies against the bait exoplasmic domain. Bait-prey interactions are assayed via the redistribution of the fluorescent prey. The method is characterized by high sensitivity down to the level of single molecules, the capability to detect weak interactions, and high throughput capability, making it applicable as screening tool.
Bioengineering, Issue 37, protein-protein interactions, quantification, in-vivo, micro-contact-printing, micro-patterned surfaces
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A high-throughput method to globally study the organelle morphology in S. cerevisiae
Authors: Shabnam Tavassoli, Jesse Tzu-Cheng Chao, Christopher Loewen.
Institutions: University of British Columbia - UBC.
High-throughput methods to examine protein localization or organelle morphology is an effective tool for studying protein interactions and can help achieve an comprehensive understanding of molecular pathways. In Saccharomyces cerevisiae, with the development of the non-essential gene deletion array, we can globally study the morphology of different organelles like the endoplasmic reticulum (ER) and the mitochondria using GFP (or variant)-markers in different gene backgrounds. However, incorporating GFP markers in each single mutant individually is a labor-intensive process. Here, we describe a procedure that is routinely used in our laboratory. By using a robotic system to handle high-density yeast arrays and drug selection techniques, we can significantly shorten the time required and improve reproducibility. In brief, we cross a GFP-tagged mitochondrial marker (Apc1-GFP) to a high-density array of 4,672 nonessential gene deletion mutants by robotic replica pinning. Through diploid selection, sporulation, germination and dual marker selection, we recover both alleles. As a result, each haploid single mutant contains Apc1-GFP incorporated at its genomic locus. Now, we can study the morphology of mitochondria in all non-essential mutant background. Using this high-throughput approach, we can conveniently study and delineate the pathways and genes involved in the inheritance and the formation of organelles in a genome-wide setting.
Microbiology, Issue 25, High throughput, confocal microscopy, Acp1, mitochondria, endoplasmic reticulum, Saccharomyces cerevisiae
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Counting and Determining the Viability of Cultured Cells
Authors: Richard Ricardo, Katy Phelan.
Institutions: Molecular Pathology Laboratory Network, Inc.
Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. A hemacytometer is a thick glass slide with a central area designed as a counting chamber. Cell suspension is applied to a defined area and counted so cell density can be calculated.
Basic Protocols, Issue 16, Current Protocols Wiley, Cell Counting, Cell Culture, Trypan Blue, Cell Viability
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Trypsinizing and Subculturing Mammalian Cells
Authors: Richard Ricardo, Katy Phelan.
Institutions: Molecular Pathology Laboratory Network, Inc.
As cells reach confluency, they must be subcultured or passaged. Failure to subculture confluent cells results in reduced mitotic index and eventually in cell death. The first step in subculturing is to detach cells from the surface of the primary culture vessel by trypsinization or mechanical means. The resultant cell suspension is then subdivided, or reseeded, into fresh cultures. Secondary cultures are checked for growth and fed periodically, and may be subsequently subcultured to produce tertiary cultures. The time between passaging of cells varies with the cell line and depends on the growth rate.
Basic Protocols, Issue 16, Current Protocols Wiley, Cell Culture, Cell Passaging, Trypsinizing Cells, Adherent Cells, Suspension Cells
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