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PubMed2006

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.

Animals↗
PubMed2005

Biochemical and genetic analysis of the yeast proteome with a movable ORF collection.

Functional analysis of the proteome is an essential part of genomic research. To facilitate different proteomic approaches, a MORF (moveable ORF) library of 5854 yeast expression plasmids was constructed, each expressing a sequence-verified ORF as a C-terminal ORF fusion protein, under regulated control. Analysis of 5573 MORFs demonstrates that nearly all verified ORFs are expressed, suggests the authenticity of 48 ORFs characterized as dubious, and implicates specific processes including cytoskeletal organization and transcriptional control in growth inhibition caused by overexpression. Global analysis of glycosylated proteins identifies 109 new confirmed N-linked and 345 candidate glycoproteins, nearly doubling the known yeast glycome.

Cell Growth Processes↗
PubMed2005

Three-dimensional structure determination of proteins related to human health in their functional context at The Israel Structural Proteomics Center (ISPC). This paper was presented at ICCBM10.

The principal goal of the Israel Structural Proteomics Center (ISPC) is to determine the structures of proteins related to human health in their functional context. Emphasis is on the solution of structures of proteins complexed with their natural partner proteins and/or with DNA. To date, the ISPC has solved the structures of 14 proteins, including two protein complexes. It has adopted automated high-throughput (HTP) cloning and expression techniques and is now expressing in Escherichia coli, Pichia pastoris and baculovirus, and in a cell-free E. coli system. Protein expression in E. coli is the primary system of choice in which different parameters are tested in parallel. Much effort is being devoted to development of automated refolding of proteins expressed as inclusion bodies in E. coli. The current procedure utilizes tagged proteins from which the tag can subsequently be removed by TEV protease, thus permitting streamlined purification of a large number of samples. Robotic protein crystallization screens and optimization utilize both the batch method under oil and vapour diffusion. In order to record and organize the data accumulated by the ISPC, a laboratory information-management system (LIMS) has been developed which facilitates data monitoring and analysis. This permits optimization of conditions at all stages of protein production and structure determination. A set of bioinformatics tools, which are implemented in our LIMS, is utilized to analyze each target.

Automation↗
PubMed2002

Structural proteomics: developments in structure-to-function predictions.

The major challenge for post-genomic research is to functionally assign and validate a large number of novel target genes and their corresponding proteins. Functional genomics approaches have, therefore, gained considerable attention in the quest to convert this massive data set into useful information. One of the crucial components for the functional understanding of unassigned proteins is the analysis of their experimental or modeled 3D structures. Structural proteomics initiatives are generating protein structures at an unprecedented rate but our current knowledge of 3D-structural space is still limited. Estimates on the completeness of the 3D-structural coverage of proteins vary but it is generally accepted that only a minority of the structural proteome has a template structure from which reliable conclusions can be drawn. Thus, structural proteomics has set out to build a map of protein structures that will represent all protein folds included in the 'global proteome'.

Amino Acid Sequence↗
PubMed2005

Proteomic analysis of human natural killer cells: insights on new potential NK immune functions.

Applying high-throughput proteomic analysis of mammalian cells can facilitate the identification of a large number of proteins expressed in the examined samples. Moreover, extensive research efforts are being made to perform large-scale characterization of membrane proteins. Here we use mass spectrometry-based proteomic strategy to characterize protein expression in membrane-enriched fractions derived from human NK lymphoma cell line YTS. This query yielded a list of over 1000 identified proteins, and provided us with new insights on NK cell biology. We highlight the expression of CD86 on YTS and its ability to co-stimulate TCR responses of human CD4+ T-cells, providing an unexpected link between innate and adaptive immune systems.

Antigens, CD↗
PubMed2001

Interrelating different types of genomic data, from proteome to secretome: 'oming in on function.

With the completion of genome sequences, the current challenge for biology is to determine the functions of all gene products and to understand how they contribute in making an organism viable. For the first time, biological systems can be viewed as being finite, with a limited set of molecular parts. However, the full range of biological processes controlled by these parts is extremely complex. Thus, a key approach in genomic research is to divide the cellular contents into distinct sub-populations, which are often given an "-omic" term. For example, the proteome is the full complement of proteins encoded by the genome, and the secretome is the part of it secreted from the cell. Carrying this further, we suggest the term "translatome" to describe the members of the proteome weighted by their abundance, and the "functome" to describe all the functions carried out by these. Once the individual sub-populations are defined and analyzed, we can then try to reconstruct the full organism by interrelating them, eventually allowing for a full and dynamic view of the cell. All this is, of course, made possible because of the increasing amount of large-scale data resulting from functional genomics experiments. However, there are still many difficulties resulting from the noisiness and complexity of the information. To some degree, these can be overcome through averaging with broad proteomic categories such as those implicit in functional and structural classifications. For illustration, we discuss one example in detail, interrelating transcript and cellular protein populations (transcriptome and translatome). Further information is available at http://bioinfo.mbb.yale.edu/what-is-it.

Bacillus subtilis↗
PubMed2005

Role-similarity based functional prediction in networked systems: application to the yeast proteome.

We propose a general method to predict functions of vertices where (i) the wiring of the network is somehow related to the vertex functionality and (ii) a fraction of the vertices are functionally classified. The method is influenced by role-similarity measures of social network analysis. The two versions of our prediction scheme are tested on model networks where the functions of the vertices are designed to match their network surroundings. We also apply these methods to the proteome of the yeast Saccharomyces cerevisiae and find the results compatible with more specialized methods.

Animals↗
PubMed2004

Proteomic mapping provides powerful insights into functional myelin biology.

Myelin is a dynamic, functionally active membrane necessary for rapid action potential conduction, axon survival, and cytoarchitecture. The number of debilitating neurological disorders that occur when myelin is disrupted emphasizes its importance. Using high-resolution 2D gel electrophoresis, mass spectrometry, and immunoblotting, we have developed an extensive proteomic map of proteins present in myelin, identifying 98 proteins corresponding to at least 130 of the approximately 200 spots on the map. This proteomic map has been applied to analyses of the localization and function of selected proteins, providing a powerful tool to investigate the diverse functions of myelin.

Animals↗
PubMed2003

The proteome: anno Domini 2002.

We present some current definitions related to functional and structural proteomics and the human proteome, and we review the following aspects of proteome analysis: Classical 2-D map analysis (isoelectric focusing (IEF) followed by SDS-PAGE); Quantitative proteomics (isotope-coded affinity tag (ICAT), fluorescent stains) and their use in e.g., tumor analysis and identification of new target proteins for drug development; Electrophoretic pre-fractionation (how to see the hidden proteome!); Multidimensional separations, such as: (a) coupled size-exclusion and reverse-phase (RP)-HPLC; (b) coupled ion-exchange and RP-HPLC; (c) coupled RP-HPLC and RP-HPLC at 25/60 degrees C; (d) coupled RP-HPLC and capillary electrophoresis (CE); (e) metal affinity chromatography coupled with CE; Protein chips. Some general conclusions are drawn on proteome analysis and we end this review by trying to decode the glass ball of the aruspex and answer the question: "Quo vadis, proteome"?

Animals↗
PubMed2006

Proteomics of the human brain: sub-proteomes might hold the key to handle brain complexity.

Proteomics is a promising approach, which provides information about the expression of proteins and increasingly finds application in life science and disease research. Meanwhile, proteomics has proven to be applicable even on post mortem human brain tissue and has opened a new area in neuroproteomics. Thereby, neuroproteomics is usually employed to generate large protein profiles of brain tissue, which mostly reflect the expression of highly abundant proteins. As a complementary approach, the focus on sub-proteomes would enhance more specific insight into brain function. Sub-proteomes are accessible via several strategies, including affinity pull-down approaches, immunoprecipitation or subcellular fractionation. The extraordinary potential of subcellular proteomics to reveal even minute differences in the protein constitution of related cellular organelles is exemplified by a recent global description of neuromelanin granules from the human brain, which could be identified as pigmented lysosome-related organelles.

Brain Chemistry↗
PubMed2002

Central functions of the lumenal and peripheral thylakoid proteome of Arabidopsis determined by experimentation and genome-wide prediction.

Experimental proteome analysis was combined with a genome-wide prediction screen to characterize the protein content of the thylakoid lumen of Arabidopsis chloroplasts. Soluble thylakoid proteins were separated by two-dimensional electrophoresis and identified by mass spectrometry. The identities of 81 proteins were established, and N termini were sequenced to validate localization prediction. Gene annotation of the identified proteins was corrected by experimental data, and an interesting case of alternative splicing was discovered. Expression of a surprising number of paralogs was detected. Expression of five isomerases of different classes suggests strong (un)folding activity in the thylakoid lumen. These isomerases possibly are connected to a network of peripheral and lumenal proteins involved in antioxidative response, including peroxiredoxins, m-type thioredoxins, and a lumenal ascorbate peroxidase. Characteristics of the experimentally identified lumenal proteins and their orthologs were used for a genome-wide prediction of the lumenal proteome. Lumenal proteins with a typical twin-arginine translocation motif were predicted with good accuracy and sensitivity and included additional isomerases and proteases. Thus, prime functions of the lumenal proteome include assistance in the folding and proteolysis of thylakoid proteins as well as protection against oxidative stress. Many of the predicted lumenal proteins must be present at concentrations at least 10,000-fold lower than proteins of the photosynthetic apparatus.

Alternative Splicing↗
PubMed2004

Genomics and clinical medicine: rationale for creating and effectively evaluating animal models.

Because resolving human complex diseases is difficult, appropriate biomedical models must be developed and validated. In the past, researchers have studied diseases either by characterizing a human clinical disease and choosing the most appropriate animal model, or by characterizing a naturally occurring or induced mutant animal and identifying which human disease it best resembled. Although there has been a great deal of progress through the use of these methods, such models have intrinsic faults that limit their relevance to clinical medicine. The recent advent of techniques in molecular biology, genomics, transgenesis, and cloning furnishes investigators with the ability to study vertebrates (e.g., pigs, cows, chickens, dogs) with greater precision and utilize them as model organisms. Comparative and functional genomics and proteomics provide effective approaches for identifying the genetic and environmental factors responsible for complex diseases and in the development of prevention and treatment strategies and therapeutics. By identifying and studying homologous genes across species, researchers are able to accurately translate and apply experimental data from animal experiments to humans. This review supports the hypothesis that associated enabling technologies can be used to create, de novo, appropriate animal models that recapitulate the human clinical manifestation. Comparative and functional genomic and proteomic techniques can then be used to identify gene and protein functions and the interactions responsible for disease phenotypes, which aids in the development of prevention and treatment strategies.

Animals↗
PubMed2004

A statistical framework for combining and interpreting proteomic datasets.

MOTIVATION: To identify accurately protein function on a proteome-wide scale requires integrating data within and between high-throughput experiments. High-throughput proteomic datasets often have high rates of errors and thus yield incomplete and contradictory information. In this study, we develop a simple statistical framework using Bayes' law to interpret such data and combine information from different high-throughput experiments. In order to illustrate our approach we apply it to two protein complex purification datasets. RESULTS: Our approach shows how to use high-throughput data to calculate accurately the probability that two proteins are part of the same complex. Importantly, our approach does not need a reference set of verified protein interactions to determine false positive and false negative error rates of protein association. We also demonstrate how to combine information from two separate protein purification datasets into a combined dataset that has greater coverage and accuracy than either dataset alone. In addition, we also provide a technique for estimating the total number of proteins which can be detected using a particular experimental technique. AVAILABILITY: A suite of simple programs to accomplish some of the above tasks is available at www.unm.edu/~compbio/software/DatasetAssess

Algorithms↗
PubMed2024

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Proteomics↗
PubMed2005

Mass spectrometry of the M. smegmatis proteome: protein expression levels correlate with function, operons, and codon bias.

The fast-growing bacterium Mycobacterium smegmatis is a model mycobacterial system, a nonpathogenic soil bacterium that nonetheless shares many features with the pathogenic Mycobacterium tuberculosis, the causative agent of tuberculosis. The study of M. smegmatis is expected to shed light on mechanisms of mycobacterial growth and complex lipid metabolism, and provides a tractable system for antimycobacterial drug development. Although the M. smegmatis genome sequence is not yet completed, we used multidimensional chromatography and tandem mass spectrometry, in combination with the partially completed genome sequence, to detect and identify a total of 901 distinct proteins from M. smegmatis over the course of 25 growth conditions, providing experimental annotation for many predicted genes with an approximately 5% false-positive identification rate. We observed numerous proteins involved in energy production (9.8% of expressed proteins), protein translation (8.7%), and lipid biosynthesis (5.4%); 33% of the 901 proteins are of unknown function. Protein expression levels were estimated from the number of observations of each protein, allowing measurement of differential expression of complete operons, and the comparison of the stationary and exponential phase proteomes. Expression levels are correlated with proteins' codon biases and mRNA expression levels, as measured by comparison with codon adaptation indices, principle component analysis of codon frequencies, and DNA microarray data. This observation is consistent with notions that either (1) prokaryotic protein expression levels are largely preset by codon choice, or (2) codon choice is optimized for consistency with average expression levels regardless of the mechanism of regulating expression.

Bacterial Proteins↗