Discovery of biomarker candidates within disease by protein profiling: principles and concepts

Proteins and peptides present within clinical samples represent a valuable library of information regarding the ongoing processes within cells and tissues in health and disease. We have developed and validated novel technology applications that can be used to characterize the patterns of global protein expression in tissue and biofluids in either gel-based systems or by automated multidimensional nanocapillary liquid chromatography. Mass spectrophotometry platforms using MALDI MS and MS/MS or LTQ ion trap MS were capable of delivering sensitive and accurate identifications of hundreds of proteins contained in individual samples including individual forms of processing intermediates such as phospho peptides. The Systems Biology approach of integrating protein expression data with clinical data such as histopathology, clinical functional measurements, medical imaging scores, patient demographics, and clinical outcome provides a powerful tool for linking biomarker expression with biological processes that can be segmented and linked to disease presentation.     

Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules

Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply “vesicular”. We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.     

Wheat germ systems for cell-free protein expression

Cell-free protein expression plays an important role in biochemical research. However, only recent developments led to new methods to rapidly synthesize preparative amounts of protein that make cell-free protein expression an attractive alternative to cell-based methods. In particular the wheat germ system provides the highest translation efficiency among eukaryotic cell-free protein expression approaches and has a very high success rate for the expression of soluble proteins of good quality. As an open in vitro method, the wheat germ system is a preferable choice for many applications in protein research including options for protein labeling and the expression of difficult-to-express proteins like membrane proteins and multiple protein complexes. Here I describe wheat germ cell-free protein expression systems and give examples how they have been used in genome-wide expression studies, preparation of labeled proteins for structural genomics and protein mass spectroscopy, automated protein synthesis, and screening of enzymatic activities. Future directions for the use of cell-free expression methods are discussed.     

High throughput methods for gene cloning and expression

We outline a high throughput process for the production of bacterial expression clones using automated liquid handlers. The protocol consists of a series of interlinked methods representing liquid manipulations or incubations on various stations of the automation system. The methods employ the ligation-independent cloning approach that enables the simultaneous production of plasmids for different expression systems. The current cloning protocol spans 3 days with a linear throughput of 400 targets per production run. This automated approach enables the production of large numbers of bacterial expression clones and ultimately purified proteins. Although they were developed for structural genomics, these molecular protocols can also be applied in high throughput strategies such as those used for site-specific mutagenesis or protein interaction studies.     

Precision medicine in hearing loss

Precision medicine (PM) proposes customized medical care based on a patient's unique genome, biomarkers, environment and behaviors. Hearing loss (HL) is the most common sensorineural disorder worldwide and is frequently caused by a single genetic mutation. With recent advances in PM tools such as genetic sequencing and data analysis, the field of HL is ideally positioned to adopt the strategies of PM. Here, we review current and future applications of PM in HL as they relate to the four core qualities of PM (P4): predictive, personalized, patient-centered, and participatory. We then introduce a strategy for effective incorporation of HL PM into the design of future research studies, electronic medical records, and clinical practice to improve diagnostics, prognostics, and, ultimately, individualized patient treatment. Finally, specific anticipated ethical and economic concerns in this growing era of genomics-based HL treatment are discussed. By integrating PM principles into translational HL research and clinical practice, hearing specialists are uniquely positioned to effectively treat the heterogeneous causes and manifestations of HL on an individualized basis.     

Three-dimensional-segmentation of the spine with specially adapted tools]

Segmented 3-D data of the spine form the basis for various modern clinical applications. Among these, multisegmental image fusion, image registration and finite element modeling for biomechanical analysis are promising innovative tools capable of facilitating treatment decisions and optimization of individual therapy in the future. However, the complex anatomy of the spine and the often extensive degenerative deformation presenting in clinical practice, generally limit the application of fully automated segmentation. A newly developed software system is presented that meets the complex requirements for image segmentation of the spine through the use of specially adapted interactive tools that take account of its axial skeletal structure. Furthermore, a standardized protocol is introduced that combines the newly developed interactive tools (rotation transformation, warped dissection plane) and standard segmentation tools to permit rapid and accurate segmentation. To date, the software environment presented herein has been applied with success to the segmentation of cervical, thoracic and lumbar spine.     

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.     

Simplified Automated Image Analysis for Detection and Phenotyping of Mycobacterium tuberculosis on Porous Supports by Monitoring Growing Microcolonies

Background

Even with the advent of nucleic acid (NA) amplification technologies the culture of mycobacteria for diagnostic and other applications remains of critical importance. Notably microscopic observed drug susceptibility testing (MODS), as opposed to traditional culture on solid media or automated liquid culture, has shown potential to both speed up and increase the provision of mycobacterial culture in high burden settings.

Methods

Here we explore the growth of Mycobacterial tuberculosis microcolonies, imaged by automated digital microscopy, cultured on a porous aluminium oxide (PAO) supports. Repeated imaging during colony growth greatly simplifies “computer vision” and presumptive identification of microcolonies was achieved here using existing publically available algorithms. Our system thus allows the growth of individual microcolonies to be monitored and critically, also to change the media during the growth phase without disrupting the microcolonies. Transfer of identified microcolonies onto selective media allowed us, within 1-2 bacterial generations, to rapidly detect the drug susceptibility of individual microcolonies, eliminating the need for time consuming subculturing or the inoculation of multiple parallel cultures.

Significance

Monitoring the phenotype of individual microcolonies as they grow has immense potential for research, screening, and ultimately M. tuberculosis diagnostic applications. The method described is particularly appealing with respect to speed and automation.     

Automated system for high-throughput protein production using the dialysis cell-free method

High-throughput protein production systems have become an important issue, because protein production is one of the bottleneck steps in large-scale structural and functional analyses of proteins. We have developed a dialysis reactor and a fully automated system for protein production using the dialysis cell-free synthesis method, which we previously established to produce protein samples on a milligram scale in a high-throughput manner. The dialysis reactor was designed to be suitable for an automated system and has six dialysis cups attached to a flat dialysis membrane. The automated system is based on a Tecan Freedom EVO 200 workstation in a three-arm configuration, and is equipped with shaking incubators, a vacuum module, a robotic centrifuge, a plate heat sealer, and a custom-made tilting carrier for collection of reaction solutions from the flat-bottom cups with dialysis membranes. The consecutive process, from the dialysis cell-free protein synthesis to the partial purification by immobilized metal affinity chromatography on a 96-well filtration plate, was performed within ca. 14 h, including 8 h of cell-free protein synthesis. The proteins were eluted stepwise in a high concentration using EDTA by centrifugation, while the resin in the filtration plate was washed on the vacuum manifold. The system was validated to be able to simultaneously and automatically produce up to 96 proteins in yields of several milligrams with high well-to-well reliability, sufficient for structural and functional analyses of proteins. The protein samples produced by the automated system have been utilized for NMR screening to judge the protein foldedness and for structure determinations using heteronuclear multi-dimensional NMR spectroscopy. The automated high-throughput protein production system represents an important breakthrough in the structural and functional studies of proteins and has already contributed a massive amount of results in the structural genomics project at the RIKEN Structural Genomics/Proteomics Initiative (RSGI).     

Integration of genome data and protein structures: prediction of protein folds, protein interactions and "molecular phenotypes" of single nucleotide polymorphisms

With the massive amount of sequence and structural data being produced, new avenues emerge for exploiting the information therein for applications in several fields. Fold distributions can be mapped onto entire genomes to learn about the nature of the protein universe and many of the interactions between proteins can now be predicted solely on the basis of the genomic context of their genes. Furthermore, by utilising the new incoming data on single nucleotide polymorphisms by mapping them onto three-dimensional structures of proteins, problems concerning population, medical and evolutionary genetics can be addressed.     

A simple topological representation of protein structure: implications for new, fast, and robust structural classification

A topological representation of proteins is developed that makes use of two metrics: the Euclidean metric for identifying natural nearest neighboring residues via the Delaunay tessellation in Cartesian space and the distance between residues in sequence space. Using this representation, we introduce a quantitative and computationally inexpensive method for the comparison of protein structural topology. The method ultimately results in a numerical score quantifying the distance between proteins in a heuristically defined topological space. The properties of this scoring scheme are investigated and correlated with the standard Calpha distance root-mean-square deviation measure of protein similarity calculated by rigid body structural alignment. The topological comparison method is shown to have a characteristic dependence on protein conformational differences and secondary structure. This distinctive behavior is also observed in the comparison of proteins within families of structural relatives. The ability of the comparison method to successfully classify proteins into classes, superfamilies, folds, and families that are consistent with standard classification methods, both automated and human-driven, is demonstrated. Furthermore, it is shown that the scoring method allows for a fine-grained classification on the family, protein, and species level that agrees very well with currently established phylogenetic hierarchies. This fine classification is achieved without requiring visual inspection of proteins, sequence analysis, or the use of structural superimposition methods. Implications of the method for a fast, automated, topological hierarchical classification of proteins are discussed.     

From structure to application: Progress and opportunities in peptide materials development

For over 20 years, peptide materials in their hydrogel or soluble fibril form have been used for biomedical applications such as drug delivery, cell culture, vaccines, and tissue regeneration. To facilitate the translation of these materials, key areas of research still need to be addressed. Their structural characterization lags compared to amyloid proteins. Many of the structural features designed to guide materials formation are primarily being characterized by their observation in atomic resolution structures of amyloid assemblies. Herein, these motifs are examined in relation to peptide designs identifying common interactions that drive assembly and provide structural specificity. Current efforts to design complex structures, as reviewed here, highlight the need to extend the structural revolution of amyloid proteins to peptide assemblies to validate design principles. With respect to clinical applications, the fundamental interactions and responses of proteins, cells, and the immune system to peptide materials are still not well understood. Only a few trends are just now emerging for peptide materials interactions with biological systems. Understanding how peptide material properties influence these interactions will enable the translation of materials towards current and emerging applications.     

Mapping SNPs to protein sequence and structure data

MOTIVATION: Data on both single nucleotide polymorphisms and disease-related mutations are being collected at ever-increasing rates. To understand the structural effects of missense mutations, we consider both classes under the term single amino acid polymorphisms (SAAPs) and we wish to map these to protein structure where their effects can be analyzed. Our initial aim therefore is to create a completely automatically maintained database of SAAPs mapped to individual residues in the Protein Data Bank (PDB) updated as new mutations or structures become available. RESULTS: We present an integrated pipeline for the automated mapping of SAAP data from HGVbase to individual PDB residues. Achieving this in a completely automated and reliable manner is a complex task. Data extracted from HGVbase are mapped to EMBL entries to confirm whether the mutation occurs in an exon and, if so, where in the sequence it occurs. From there we map to Swiss-Prot entries and thence to the PDB. AVAILABILITY: The resulting database may be accessed over the web at http://www.bioinf.org.uk/saap/ or http://acrmwww.biochem.ucl.ac.uk/saap/ CONTACT: a.martin@biochem.ucl.ac.uk.     

Live sequence charts to model medical information

ABSTRACT: BACKGROUND: Medical records accumulate data concerning patient health and the natural history of disease progression. However, methods to mine information systematically in a form other than an electronic health record are not yet available. The purpose of this study was to develop an object modeling technique as a first step towards a formal database of medical records. METHOD: Live Sequence Charts (LSC) were used to formalize the narrative text obtained during a patient interview. LSCs utilize a visual scenario-based programming language to build object models. LSC extends the classical language of UML message sequence charts (MSC), predominantly through addition of modalities and providing executable semantics. Interobject scenarios were defined to specify natural history event interactions and different scenarios in the narrative text. Result A simulated medical record was specified into LSC formalism by translating the text into an object model that comprised a set of entities and events. The entities described the participating components (i.e., doctor, patient and record) and the events described the interactions between elements. A conceptual model is presented to illustrate the approach. An object model was generated from data extracted from an actual new patient interview, where the individual was eventually diagnosed as suffering from Chronic Fatigue Syndrome (CFS). This yielded a preliminary formal designated vocabulary for CFS development that provided a basis for future formalism of these records. CONCLUSIONS: Translation of medical records into object models created the basis for a formal database of the patient narrative that temporally depicts the events preceding disease, the diagnosis and treatment approach. The LSCs object model of the medical narrative provided an intuitive, visual representation of the natural history of the patient's disease.     

Are stress responses to geomagnetic storms mediated by the cryptochrome compass system?

A controversial body of literature demonstrates associations of geomagnetic storms (GMS) with numerous cardiovascular, psychiatric and behavioural outcomes. Various melatonin hypotheses of GMS have suggested that temporal variation in the geomagnetic field (GMF) may be acting as an additional zeitgeber (a temporal synchronizer) for circadian rhythms, with GMS somehow interfering with the hypothesized system. The cryptochrome genes are known primarily as key components of the circadian pacemaker, ultimately involved in controlling the expression of the hormone melatonin. Cryptochrome is identified as a clear candidate for mediating the effect of GMS on humans, demonstrating the prior existence of several crucial pieces of evidence. A distinct scientific literature demonstrates the widespread use of geomagnetic information for navigation across a range of taxa. One mechanism of magnetoreception is thought to involve a light-dependent retinal molecular system mediated by cryptochrome, acting in a distinct functionality to its established role as a circadian oscillator. There is evidence suggesting that such a magnetosense--or at least the vestiges of it--may exist in humans. This paper argues that cryptochrome is not acting as secondary geomagnetic zeitgeber to influence melatonin synthesis. Instead, it is hypothesized that the cryptochrome compass system is mediating stress responses more broadly across the hypothalamic-pituitary-adrenal (HPA) axis (including alterations to circadian behaviour) in response to changes in the GMF. Two conceptual models are outlined for the existence of such responses--the first as a generalized migrational/dispersal strategy, the second as a stress response to unexpected signals to the magnetosense. It is therefore proposed that GMS lead to disorientation of hormonal systems in animals and humans, thus explaining the effects of GMS on human health and behaviour.     

Regulatory interactions between IRG resistance GTPases in the cellular response to Toxoplasma gondii

Members of the immunity-related GTPase (IRG) family are interferon-inducible resistance factors against a broad spectrum of intracellular pathogens including Toxoplasma gondii. The molecular mechanisms governing the function and regulation of the IRG resistance system are largely unknown. We find that IRG proteins function in a system of direct, nucleotide-dependent regulatory interactions between family members. After interferon induction but before infection, the three members of the GMS subfamily of IRG proteins, Irgm1, Irgm2 and Irgm3, which possess an atypical nucleotide-binding site, regulate the intracellular positioning of the conventional GKS subfamily members, Irga6 and Irgb6. Following infection, the normal accumulation of Irga6 protein at the parasitophorous vacuole membrane (PVM) is nucleotide dependent and also depends on the presence of all three GMS proteins. We present evidence that an essential role of the GMS proteins in this response is control of the nucleotide-bound state of the GKS proteins, preventing their GTP-dependent activation before infection. Accumulation of IRG proteins at the PVM has previously been shown to be associated with a block in pathogen replication: our results relate for the first time the enzymatic properties of IRG proteins to their role in pathogen resistance.     

miniTUBA: medical inference by network integration of temporal data using Bayesian analysis

MOTIVATION: Many biomedical and clinical research problems involve discovering causal relationships between observations gathered from temporal events. Dynamic Bayesian networks are a powerful modeling approach to describe causal or apparently causal relationships, and support complex medical inference, such as future response prediction, automated learning, and rational decision making. Although many engines exist for creating Bayesian networks, most require a local installation and significant data manipulation to be practical for a general biologist or clinician. No software pipeline currently exists for interpretation and inference of dynamic Bayesian networks learned from biomedical and clinical data. RESULTS: miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. AVAILABILITY: miniTUBA is available at http://www.minituba.org.     

Genetic polymorphisms of the endocannabinoid system

Disturbances in the endocannabinoid system has been linked to diseases and conditions such as Parkinson's, schizophrenia, pain, energy metabolism, immune modulation, and bone density. Since the early 1990s, a number of genetic polymorphisms in the genes and proteins of the endocannabinoid system have been characterized. Currently identified genetic polymorphisms of the endocannabinoid system are reviewed here with particular consideration given to polymorphisms linked to drug and alcohol abuse, schizophrenia, other mental disorders, and energy metabolism.