Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks

Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms - the phenotypes - are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.     

Understanding mouse models of disease through metabolomics

Metabolomics is widely applicable to a number of fields including toxicology, plant metabolism and functional genomics. In the area of functional genomics, a number of studies have demonstrated the potential of this approach, which combines high-throughput metabolite profiling with computer-assisted pattern recognition approaches. In this review, recent applications of metabolomics to understanding mouse models of disease are considered. This includes studies on the impact of mouse strain on disease models, as well as metabolic profiling of cardiovascular, metabolic and neurodegenerative diseases. This versatile tool is set to increase in popularity as functional genomic approaches produce more mouse models for phenotyping.     

Quantitative proteomic view on secreted, cell surface-associated, and cytoplasmic proteins of the methicillin-resistant human pathogen Staphylococcus aureus under iron-limited conditions

Staphylococcus aureus is capable of colonizing and infecting humans by its arsenal of surface-exposed and secreted proteins. Iron-limited conditions in mammalian body fluids serve as a major environmental signal to bacteria to express virulence determinants. Here we present a comprehensive, gel-free, and GeLC-MS/MS-based quantitative proteome profiling of S. aureus under this infection-relevant situation. (14)N(15)N metabolic labeling and three complementing approaches were combined for relative quantitative analyses of surface-associated proteins. The surface-exposed and secreted proteome profiling approaches comprise trypsin shaving, biotinylation, and precipitation of the supernatant. By analysis of the outer subproteomic and cytoplasmic protein fraction, 1210 proteins could be identified including 221 surface-associated proteins. Thus, access was enabled to 70% of the predicted cell wall-associated proteins, 80% of the predicted sortase substrates, two/thirds of lipoproteins and more than 50% of secreted and cytoplasmic proteins. For iron-deficiency, 158 surface-associated proteins were quantified. Twenty-nine proteins were found in altered amounts showing particularly surface-exposed proteins strongly induced, such as the iron-regulated surface determinant proteins IsdA, IsdB, IsdC and IsdD as well as lipid-anchored iron compound-binding proteins. The work presents a crucial subject for understanding S. aureus pathophysiology by the use of methods that allow quantitative surface proteome profiling.     

Exploring the post-transcriptional RNA world with DNA microarrays

Genomic approaches are valuable for understanding the complex layer of gene regulation that involves the control of RNA processing, localization and stability. Recent work provides a prime example of the power of unbiased microarray-based methods to discover unexpected functions for proteins in the RNA world. The challenges ahead relate to extending such approaches to larger genomes and to integrating this type of information with that generated by standard expression profiling.     

MZmine 2: Modular framework for processing,visualizing, and analyzing mass spectrometry-based molecular profile data

Background  

Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.     

Methods of comparative proteomic profiling for disease diagnostics

The recent development of numerous technologies for proteome analysis holds the promise of new and more precise methods for disease diagnosis. In this review, we provide an overview of some of these technologies including two-dimensional gel electrophoresis (2DE), historically the workhorse of proteomic analysis, as well as some newer approaches such as liquid phase separations combined with mass spectrometry, and protein microarrays. It is evident that each method has its own strengths and weaknesses and no single method will be optimal in all applications. However, the continuing development of innovative strategies for protein separation and analysis is providing a wealth of new tools for multi-dimensional protein profiling that will advance our capabilities in disease diagnostics and our understanding of disease pathology.     

Unbiased vs. biased approaches to the identification of cancer signatures: the case of lung cancer

Expression profiling analysis of human cancers is a promising approach to obtain precise molecular classification of cancers, to develop stratification tools for therapeutic regimens, and to predict the biological behavior of neoplasia. Direct profiling of human cancers (herein defined as “the unbiased approach”) presents, however, intrinsic problems connected with the high genetic noise embedded in the system. This, in turn, leads to fitting of the noise in the data (the so-called “overtraining”) with consequent instability of the identified signatures, when applied on different cohorts of patients. To circumvent these problems, “biased approaches” - which exploit the molecular knowledge of cancer obtained in model systems - are being developed. Biased approaches, however, are not problem-free, in that they provide information limited to single oncogenic events, thereby failing, at least in principle, to capture the complex repertoire of alterations of human cancers. In this review, we compare the two approaches and provide a test case, from our studies, of how “integrated” strategies, which combine biased and unbiased approaches, might lead to the identification of stable and reliable predictive signatures in cancer.     

Profiling the global tyrosine phosphorylation state

Protein tyrosine kinases and protein tyrosine phosphatases play a key role in cell signaling, and the recent success of specific tyrosine kinase inhibitors in cancer treatment strongly validates the clinical relevance of basic research on tyrosine phosphorylation. Functional profiling of the tyrosine phosphoproteome is likely to lead to the identification of novel targets for drug discovery and provide a basis for novel molecular diagnostic approaches. The ultimate aim of current mass spectrometry-based phosphoproteomic approaches is the comprehensive characterization of the phosphoproteome. However, current methods are not yet sensitive enough for routine detection of a large percentage of tyrosine-phosphorylated proteins, which are generally of low abundance. In this article, we discuss alternative methods that exploit Src homology 2 (SH2) domains for profiling the tyrosine phosphoproteome. SH2 domains are small protein modules that bind specifically to tyrosine-phosphorylated peptides; there are more than 100 SH2 domains in the human genome, and different SH2 domains bind to different classes of tyrosine-phosphorylated ligands. These domains play a critical role in the propagation of signals in the cell, mediating the relocalization and complex formation of proteins in response to changes in tyrosine phosphorylation. We have developed an SH2 profiling method based on far-Western blotting, in which a battery of SH2 domains is used to probe the global state of tyrosine phosphorylation. Application to the classification of human malignancies suggests that this approach has potential as a molecular diagnostic tool. We also describe ongoing efforts to modify and improve SH2 profiling, including the development of a multiplexed assay system that will allow high-throughput functional profiling of the tyrosine phosphoproteome.     

Myelin,DIGs, and membrane rafts in the central nervous system

Over the past 40 years our understanding of the organization of cell membranes has changed dramatically. Membranes are no longer viewed as a homogenous sea of phospholipids studded with randomly positioned islands of proteins. Our current view of the membrane involves the formation of small lipid clusters, comprised mainly of cholesterol and sphingolipids, known as membrane rafts. These lipid clusters apparently include and exclude specific proteins leading to the hypothesis that these domains (1) regulate cellular polarity and compartmentalization through trafficking and sorting, (2) provide platforms for cellular signaling and adhesion, and (3) function as cellular gate keepers. Tremendous controversy surrounds the concept of membrane rafts primarily because these small, highly dynamic entities are too small to be observed with traditional microscopic methods and the most utilized approach for raft analysis relies on poorly quantified, inconsistent biochemical extractions. New analytical approaches are being developed and applied to the study of membrane rafts and these techniques provide great promise for furthering our understanding of these enigmatic domains. In this review we will provide a brief summary of the current understanding of membrane rafts, utilizing the CNS myelin literature for illustrative purposes, and present caveats that should be considered when studying these domains.     

From modules to medicine: How modular domains and their associated networks can enable personalized medicine

Unveiling of cancer genomes is unleashing new therapeutic strategies for cancer. With cancer parts lists in hand, new approaches to personalized medicine can be developed by understanding the assembly of cancer machines using modular domains in proteins and their associated networks. Using the Src-homology-2 (SH2) domain as an example, new profiling approaches can discern global patterns of tyrosine phosphorylation in cancer cells that can enable molecular cancer medicine. Identifying and quantifying protein-protein interactions also has the potential to subtype tumors and guide clinical decision making. These approaches should extend the impact of genomics through viewing the architecture of cancer systems and improve predictions of patient outcome and therapeutic response, as well as guide combination therapy approaches that attack cancer systems.     

Vaccinomics and a new paradigm for the development of preventive vaccines against viral infections

In this article we define vaccinomics as the integration of immunogenetics and immunogenomics with systems biology and immune profiling. Vaccinomics is based on the use of cutting edge, high-dimensional (so called "omics") assays and novel bioinformatics approaches to the development of next-generation vaccines and the expansion of our capabilities in individualized medicine. Vaccinomics will allow us to move beyond the empiric "isolate, inactivate, and inject" approach characterizing past vaccine development efforts, and toward a more detailed molecular and systemic understanding of the carefully choreographed series of biological processes involved in developing viral vaccine-induced "immunity." This enhanced understanding will then be applied to overcome the obstacles to the creation of effective vaccines to protect against pathogens, particularly hypervariable viruses, with the greatest current impact on public health. Here we provide an overview of how vaccinomics will inform vaccine science, the development of new vaccines and/or clinically relevant biomarkers or surrogates of protection, vaccine response heterogeneity, and our understanding of immunosenescence.     

Metagenomic microbial community profiling using unique clade-specific marker genes

Metagenomic shotgun sequencing data can identify microbes populating a microbial community and their proportions, but existing taxonomic profiling methods are inefficient for increasingly large data sets. We present an approach that uses clade-specific marker genes to unambiguously assign reads to microbial clades more accurately and >50× faster than current approaches. We validated our metagenomic phylogenetic analysis tool, MetaPhlAn, on terabases of short reads and provide the largest metagenomic profiling to date of the human gut. It can be accessed at http://huttenhower.sph.harvard.edu/metaphlan/.