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Multi-edge gene set networks reveal novel insights into global relationships between biological themes.
Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at
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.
21 Related JoVE Articles!
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A Manual Small Molecule Screen Approaching High-throughput Using Zebrafish Embryos
Authors: Shahram Jevin Poureetezadi, Eric K. Donahue, Rebecca A. Wingert.
Institutions: University of Notre Dame.
Zebrafish have become a widely used model organism to investigate the mechanisms that underlie developmental biology and to study human disease pathology due to their considerable degree of genetic conservation with humans. Chemical genetics entails testing the effect that small molecules have on a biological process and is becoming a popular translational research method to identify therapeutic compounds. Zebrafish are specifically appealing to use for chemical genetics because of their ability to produce large clutches of transparent embryos, which are externally fertilized. Furthermore, zebrafish embryos can be easily drug treated by the simple addition of a compound to the embryo media. Using whole-mount in situ hybridization (WISH), mRNA expression can be clearly visualized within zebrafish embryos. Together, using chemical genetics and WISH, the zebrafish becomes a potent whole organism context in which to determine the cellular and physiological effects of small molecules. Innovative advances have been made in technologies that utilize machine-based screening procedures, however for many labs such options are not accessible or remain cost-prohibitive. The protocol described here explains how to execute a manual high-throughput chemical genetic screen that requires basic resources and can be accomplished by a single individual or small team in an efficient period of time. Thus, this protocol provides a feasible strategy that can be implemented by research groups to perform chemical genetics in zebrafish, which can be useful for gaining fundamental insights into developmental processes, disease mechanisms, and to identify novel compounds and signaling pathways that have medically relevant applications.
Developmental Biology, Issue 93, zebrafish, chemical genetics, chemical screen, in vivo small molecule screen, drug discovery, whole mount in situ hybridization (WISH), high-throughput screening (HTS), high-content screening (HCS)
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Authors: Hans-Peter Müller, Jan Kassubek.
Institutions: University of Ulm.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels. In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
Medicine, Issue 77, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Neurodegenerative Diseases, nuclear magnetic resonance, NMR, MR, MRI, diffusion tensor imaging, fiber tracking, group level comparison, neurodegenerative diseases, brain, imaging, clinical techniques
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Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS
Authors: Christopher J. Zopf, Narendra Maheshri.
Institutions: Massachusetts Institute of Technology.
Fluorescence time-lapse microscopy has become a powerful tool in the study of many biological processes at the single-cell level. In particular, movies depicting the temporal dependence of gene expression provide insight into the dynamics of its regulation; however, there are many technical challenges to obtaining and analyzing fluorescence movies of single cells. We describe here a simple protocol using a commercially available microfluidic culture device to generate such data, and a MATLAB-based, graphical user interface (GUI) -based software package to quantify the fluorescence images. The software segments and tracks cells, enables the user to visually curate errors in the data, and automatically assigns lineage and division times. The GUI further analyzes the time series to produce whole cell traces as well as their first and second time derivatives. While the software was designed for S. cerevisiae, its modularity and versatility should allow it to serve as a platform for studying other cell types with few modifications.
Microbiology, Issue 77, Cellular Biology, Molecular Biology, Genetics, Biophysics, Saccharomyces cerevisiae, Microscopy, Fluorescence, Cell Biology, microscopy/fluorescence and time-lapse, budding yeast, gene expression dynamics, segmentation, lineage tracking, image tracking, software, yeast, cells, imaging
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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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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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Analysis of Nephron Composition and Function in the Adult Zebrafish Kidney
Authors: Kristen K. McCampbell, Kristin N. Springer, Rebecca A. Wingert.
Institutions: University of Notre Dame.
The zebrafish model has emerged as a relevant system to study kidney development, regeneration and disease. Both the embryonic and adult zebrafish kidneys are composed of functional units known as nephrons, which are highly conserved with other vertebrates, including mammals. Research in zebrafish has recently demonstrated that two distinctive phenomena transpire after adult nephrons incur damage: first, there is robust regeneration within existing nephrons that replaces the destroyed tubule epithelial cells; second, entirely new nephrons are produced from renal progenitors in a process known as neonephrogenesis. In contrast, humans and other mammals seem to have only a limited ability for nephron epithelial regeneration. To date, the mechanisms responsible for these kidney regeneration phenomena remain poorly understood. Since adult zebrafish kidneys undergo both nephron epithelial regeneration and neonephrogenesis, they provide an outstanding experimental paradigm to study these events. Further, there is a wide range of genetic and pharmacological tools available in the zebrafish model that can be used to delineate the cellular and molecular mechanisms that regulate renal regeneration. One essential aspect of such research is the evaluation of nephron structure and function. This protocol describes a set of labeling techniques that can be used to gauge renal composition and test nephron functionality in the adult zebrafish kidney. Thus, these methods are widely applicable to the future phenotypic characterization of adult zebrafish kidney injury paradigms, which include but are not limited to, nephrotoxicant exposure regimes or genetic methods of targeted cell death such as the nitroreductase mediated cell ablation technique. Further, these methods could be used to study genetic perturbations in adult kidney formation and could also be applied to assess renal status during chronic disease modeling.
Cellular Biology, Issue 90, zebrafish; kidney; nephron; nephrology; renal; regeneration; proximal tubule; distal tubule; segment; mesonephros; physiology; acute kidney injury (AKI)
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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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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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Multimodal Optical Microscopy Methods Reveal Polyp Tissue Morphology and Structure in Caribbean Reef Building Corals
Authors: Mayandi Sivaguru, Glenn A. Fried, Carly A. H. Miller, Bruce W. Fouke.
Institutions: University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign, University of Illinois at Urbana-Champaign.
An integrated suite of imaging techniques has been applied to determine the three-dimensional (3D) morphology and cellular structure of polyp tissues comprising the Caribbean reef building corals Montastraeaannularis and M. faveolata. These approaches include fluorescence microscopy (FM), serial block face imaging (SBFI), and two-photon confocal laser scanning microscopy (TPLSM). SBFI provides deep tissue imaging after physical sectioning; it details the tissue surface texture and 3D visualization to tissue depths of more than 2 mm. Complementary FM and TPLSM yield ultra-high resolution images of tissue cellular structure. Results have: (1) identified previously unreported lobate tissue morphologies on the outer wall of individual coral polyps and (2) created the first surface maps of the 3D distribution and tissue density of chromatophores and algae-like dinoflagellate zooxanthellae endosymbionts. Spectral absorption peaks of 500 nm and 675 nm, respectively, suggest that M. annularis and M. faveolata contain similar types of chlorophyll and chromatophores. However, M. annularis and M. faveolata exhibit significant differences in the tissue density and 3D distribution of these key cellular components. This study focusing on imaging methods indicates that SBFI is extremely useful for analysis of large mm-scale samples of decalcified coral tissues. Complimentary FM and TPLSM reveal subtle submillimeter scale changes in cellular distribution and density in nondecalcified coral tissue samples. The TPLSM technique affords: (1) minimally invasive sample preparation, (2) superior optical sectioning ability, and (3) minimal light absorption and scattering, while still permitting deep tissue imaging.
Environmental Sciences, Issue 91, Serial block face imaging, two-photon fluorescence microscopy, Montastraea annularis, Montastraea faveolata, 3D coral tissue morphology and structure, zooxanthellae, chromatophore, autofluorescence, light harvesting optimization, environmental change
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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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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Authors: Haipeng Xing, Willey Liao, Yifan Mo, Michael Q. Zhang.
Institutions: Stony Brook University, Cold Spring Harbor Laboratory, University of Texas at Dallas.
ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work. Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized. Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types. To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.
Genetics, Issue 70, Bioinformatics, Genomics, Molecular Biology, Cellular Biology, Immunology, Chromatin immunoprecipitation, ChIP-Seq, histone modifications, segmentation, Bayesian, Hidden Markov Models, epigenetics
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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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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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A Protocol for Computer-Based Protein Structure and Function Prediction
Authors: Ambrish Roy, Dong Xu, Jonathan Poisson, Yang Zhang.
Institutions: University of Michigan , University of Kansas.
Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server.
Biochemistry, Issue 57, On-line server, I-TASSER, protein structure prediction, function prediction
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Targeted Expression of GFP in the Hair Follicle Using Ex Vivo Viral Transduction
Authors: Robert M. Hoffman, Lingna Li.
Institutions: AntiCancer, Inc..
There are many cell types in the hair follicle, including hair matrix cells which form the hair shaft and stem cells which can initiate the hair shaft during early anagen, the growth phase of the hair cycle, as well as pluripotent stem cells that play a role in hair follicle growth but have the potential to differentiate to non-follicle cells such as neurons. These properties of the hair follicle are discussed. The various cell types of the hair follicle are potential targets for gene therapy. Gene delivery system for the hair follicle using viral vectors or liposomes for gene targeting to the various cell types in the hair follicle and the results obtained are also discussed.
Cellular Biology, Issue 13, Springer Protocols, hair follicles, liposomes, adenovirus, genes, stem cells
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Principles of Site-Specific Recombinase (SSR) Technology
Authors: Frank Bucholtz.
Institutions: Max Plank Institute for Molecular Cell Biology and Genetics, Dresden.
Site-specific recombinase (SSR) technology allows the manipulation of gene structure to explore gene function and has become an integral tool of molecular biology. Site-specific recombinases are proteins that bind to distinct DNA target sequences. The Cre/lox system was first described in bacteriophages during the 1980's. Cre recombinase is a Type I topoisomerase that catalyzes site-specific recombination of DNA between two loxP (locus of X-over P1) sites. The Cre/lox system does not require any cofactors. LoxP sequences contain distinct binding sites for Cre recombinases that surround a directional core sequence where recombination and rearrangement takes place. When cells contain loxP sites and express the Cre recombinase, a recombination event occurs. Double-stranded DNA is cut at both loxP sites by the Cre recombinase, rearranged, and ligated ("scissors and glue"). Products of the recombination event depend on the relative orientation of the asymmetric sequences. SSR technology is frequently used as a tool to explore gene function. Here the gene of interest is flanked with Cre target sites loxP ("floxed"). Animals are then crossed with animals expressing the Cre recombinase under the control of a tissue-specific promoter. In tissues that express the Cre recombinase it binds to target sequences and excises the floxed gene. Controlled gene deletion allows the investigation of gene function in specific tissues and at distinct time points. Analysis of gene function employing SSR technology --- conditional mutagenesis -- has significant advantages over traditional knock-outs where gene deletion is frequently lethal.
Cellular Biology, Issue 15, Molecular Biology, Site-Specific Recombinase, Cre recombinase, Cre/lox system, transgenic animals, transgenic technology
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Quantitative Real-Time PCR using the Thermo Scientific Solaris qPCR Assay
Authors: Christy Ogrean, Ben Jackson, James Covino.
Institutions: Thermo Scientific Solaris qPCR Products.
The Solaris qPCR Gene Expression Assay is a novel type of primer/probe set, designed to simplify the qPCR process while maintaining the sensitivity and accuracy of the assay. These primer/probe sets are pre-designed to >98% of the human and mouse genomes and feature significant improvements from previously available technologies. These improvements were made possible by virtue of a novel design algorithm, developed by Thermo Scientific bioinformatics experts. Several convenient features have been incorporated into the Solaris qPCR Assay to streamline the process of performing quantitative real-time PCR. First, the protocol is similar to commonly employed alternatives, so the methods used during qPCR are likely to be familiar. Second, the master mix is blue, which makes setting the qPCR reactions easier to track. Third, the thermal cycling conditions are the same for all assays (genes), making it possible to run many samples at a time and reducing the potential for error. Finally, the probe and primer sequence information are provided, simplifying the publication process. Here, we demonstrate how to obtain the appropriate Solaris reagents using the GENEius product search feature found on the ordering web site ( and how to use the Solaris reagents for performing qPCR using the standard curve method.
Cellular Biology, Issue 40, qPCR, probe, real-time PCR, molecular biology, Solaris, primer, gene expression assays
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
Authors: Adam M. McCoy, Claudia Litterst, Michelle L. Collins, Luis A. Ugozzoli.
Institutions: Bio-Rad Laboratories.
The use of siRNA mediated gene knockdown is continuing to be an important tool in studies of gene expression. siRNA studies are being conducted not only to study the effects of downregulating single genes, but also to interrogate signaling pathways and other complex interaction networks. These pathway analyses require both the use of relevant cellular models and methods that cause less perturbation to the cellular physiology. Electroporation is increasingly being used as an effective way to introduce siRNA and other nucleic acids into difficult to transfect cell lines and primary cells without altering the signaling pathway under investigation. There are multiple critical steps to a successful siRNA experiment, and there are ways to simplify the work while improving the data quality at several experimental stages. To help you get started with your siRNA mediated gene knockdown project, we will demonstrate how to perform a pathway study complete from collecting and counting the cells prior to electroporation through post transfection real-time PCR gene expression analysis. The following study investigates the role of the transcriptional activator STAT6 in IL-4 dependent gene expression of CCL17 in a Burkitt lymphoma cell line (Namalwa). The techniques demonstrated are useful for a wide range of siRNA-based experiments on both adherent and suspension cells. We will also show how to streamline cell counting with the TC10 automated cell counter, how to electroporate multiple samples simultaneously using the MXcell electroporation system, and how to simultaneously assess RNA quality and quantity with the Experion automated electrophoresis system.
Cellular Biology, Issue 38, Cell Counting, Gene Silencing, siRNA, Namalwa Cells, IL4, Gene Expression, Electroporation, Real Time PCR
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
Authors: Viktor Martyanov, Robert H. Gross.
Institutions: Dartmouth College.
SCOPE is an ensemble motif finder that uses three component algorithms in parallel to identify potential regulatory motifs by over-representation and motif position preference1. Each component algorithm is optimized to find a different kind of motif. By taking the best of these three approaches, SCOPE performs better than any single algorithm, even in the presence of noisy data1. In this article, we utilize a web version of SCOPE2 to examine genes that are involved in telomere maintenance. SCOPE has been incorporated into at least two other motif finding programs3,4 and has been used in other studies5-8. The three algorithms that comprise SCOPE are BEAM9, which finds non-degenerate motifs (ACCGGT), PRISM10, which finds degenerate motifs (ASCGWT), and SPACER11, which finds longer bipartite motifs (ACCnnnnnnnnGGT). These three algorithms have been optimized to find their corresponding type of motif. Together, they allow SCOPE to perform extremely well. Once a gene set has been analyzed and candidate motifs identified, SCOPE can look for other genes that contain the motif which, when added to the original set, will improve the motif score. This can occur through over-representation or motif position preference. Working with partial gene sets that have biologically verified transcription factor binding sites, SCOPE was able to identify most of the rest of the genes also regulated by the given transcription factor. Output from SCOPE shows candidate motifs, their significance, and other information both as a table and as a graphical motif map. FAQs and video tutorials are available at the SCOPE web site which also includes a "Sample Search" button that allows the user to perform a trial run. Scope has a very friendly user interface that enables novice users to access the algorithm's full power without having to become an expert in the bioinformatics of motif finding. As input, SCOPE can take a list of genes, or FASTA sequences. These can be entered in browser text fields, or read from a file. The output from SCOPE contains a list of all identified motifs with their scores, number of occurrences, fraction of genes containing the motif, and the algorithm used to identify the motif. For each motif, result details include a consensus representation of the motif, a sequence logo, a position weight matrix, and a list of instances for every motif occurrence (with exact positions and "strand" indicated). Results are returned in a browser window and also optionally by email. Previous papers describe the SCOPE algorithms in detail1,2,9-11.
Genetics, Issue 51, gene regulation, computational biology, algorithm, promoter sequence motif
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Gene-gun Transfection of Hippocampal Neurons
Authors: Powrnima Joshi, Anna Dunaevsky.
Institutions: Brown University.
Neuroscience, Issue 1, brain, hippocampus, neuron, transfection, gene-gun
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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