Programmed cell death 6 interacting protein (PDCD6IP) and Rabenosyn-5 (ZFYVE20) are potential urinary biomarkers for upper gastrointestinal cancer

PURPOSE: Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers. EXPERIMENTAL DESIGN: Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples. RESULTS: We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting. CONCLUSIONS AND CLINICAL RELEVANCE: We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening

A Robust and Universal Metaproteomics Workflow for Research Studies and Routine Diagnostics Within 24 h Using Phenol Extraction, FASP Digest, and the MetaProteomeAnalyzer

The investigation of microbial proteins by mass spectrometry (metaproteomics) is a key technology for simultaneously assessing the taxonomic composition and the functionality of microbial communities in medical, environmental, and biotechnological applications. We present an improved metaproteomics workflow using an updated sample preparation and a new version of the MetaProteomeAnalyzer software for data analysis. High resolution by multidimensional separation (GeLC, MudPIT) was sacrificed to aim at fast analysis of a broad range of different samples in less than 24 h. The improved workflow generated at least two times as many protein identifications than our previous workflow, and a drastic increase of taxonomic and functional annotations. Improvements of all aspects of the workflow, particularly the speed, are first steps toward potential routine clinical diagnostics (i.e., fecal samples) and analysis of technical and environmental samples. The MetaProteomeAnalyzer is provided to the scientific community as a central remote server solution at www.mpa.ovgu.de.Peer Reviewe

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Deciphering Proteomic Signatures of Early Diapause in Nasonia

Insect diapause is an alternative life-history strategy used to increase longevity and survival in harsh environmental conditions. Even though some aspects of diapause are well investigated, broader scale studies that elucidate the global metabolic adjustments required for this remarkable trait, are rare. In order to better understand the metabolic changes during early insect diapause, we used a shotgun proteomics approach on early diapausing and non-diapausing larvae of the recently sequenced hymenopteran model organism Nasonia vitripennis. Our results deliver insights into the molecular underpinnings of diapause in Nasonia and corroborate previously reported diapause-associated features for invertebrates, such as a diapause-dependent abundance change for heat shock and storage proteins. Furthermore, we observed a diapause-dependent switch in enzymes involved in glycerol synthesis and a vastly changed capacity for protein synthesis and degradation. The abundance of structural proteins and proteins involved in protein synthesis decreased with increasing diapause duration, while the abundance of proteins likely involved in diapause maintenance (e.g. ferritins) increased. Only few potentially diapause-specific proteins were identified suggesting that diapause in Nasonia relies to a large extent on a modulation of pre-existing pathways. Studying a diapause syndrome on a proteomic level rather than isolated pathways or physiological networks, has proven to be an efficient and successful avenue to understand molecular mechanisms involved in diapause

Development and Integration of Informatic Tools for Qualitative and Quantitative Characterization of Proteomic Datasets Generated by Tandem Mass Spectrometry

Shotgun proteomic experiments provide qualitative and quantitative analytical information from biological samples ranging in complexity from simple bacterial isolates to higher eukaryotes such as plants and humans and even to communities of microbial organisms. Improvements to instrument performance, sample preparation, and informatic tools are increasing the scope and volume of data that can be analyzed by mass spectrometry (MS). To accommodate for these advances, it is becoming increasingly essential to choose and/or create tools that can not only scale well but also those that make more informed decisions using additional features within the data. Incorporating novel and existing tools into a scalable, modular workflow not only provides more accurate, contextualized perspectives of processed data, but it also generates detailed, standardized outputs that can be used for future studies dedicated to mining general analytical or biological features, anomalies, and trends. This research developed cyber-infrastructure that would allow a user to seamlessly run multiple analyses, store the results, and share processed data with other users. The work represented in this dissertation demonstrates successful implementation of an enhanced bioinformatics workflow designed to analyze raw data directly generated from MS instruments and to create fully-annotated reports of qualitative and quantitative protein information for large-scale proteomics experiments. Answering these questions requires several points of engagement between informatics and analytical understanding of the underlying biochemistry of the system under observation. Deriving meaningful information from analytical data can be achieved through linking together the concerted efforts of more focused, logistical questions. This study focuses on the following aspects of proteomics experiments: spectra to peptide matching, peptide to protein mapping, and protein quantification and differential expression. The interaction and usability of these analyses and other existing tools are also described. By constructing a workflow that allows high-throughput processing of massive datasets, data collected within the past decade can be standardized and updated with the most recent analyses

Comparative Proteomics Reveals Core vs. Unique Molecular Signatures for Dissimilatory Metal Reducing Bacteria Grown with Various Electron Acceptors

Dissimilatory metal reducing bacteria (DMRB) are probably one of the most respiratory versatile microorganisms on earth. Their ability to use metals as terminal electron acceptor allows them to survive in severe environments (e.g. radionuclide contaminated soil). In addition to metals, many other organic and inorganic substrates can be utilized as electron acceptors for DMRB respiration, including fumarate, nitrate, oxygen, etc. Genome information for many DMRB species is available, which reveals large numbers of c-type cytochrome encoding genes present in their genomes. For example, the genomes of three DMRBs, Anaeromyxobacter dehalogenans strain 2CP-C, Shewanella oneidensis strain MR-1, and Geobacter daltonii strain FRC-32, contain 69, 40, and 72 putative c-type cytochrome genes, respectively. Although mutagenesis techniques have determined the respiratory roles of several c-type cytochromes, gene disruption for majorities of the putative c-type cytochromes does not generate visible phenotypical alterations, and is not able to functionally link them to specific respirational activities. Thus, comprehensive proteome characterization for DMRBs is needed to elucidate the molecular mechanisms underlying their respirational versatilities. In this dissertation, a mass spectrometry-based proteomics approach was used to interrogate the proteomes of A. dehalogenans strain 2CP-C, S. oneidensis strain MR-1, and G. daltonii strain FRC-32. The proteomic responses of DMRBs to a wide range of electron acceptors were tested in this dissertation, including soluble and insoluble ferric iron, manganese oxide, fumarate, nitrate, oxygen, and nitrous oxide. The in-depth proteomic characterizations comparatively revealed the c-type cytochrome profiles of DMRBs, providing evidence for the identities and expressions of putative c-type cytochromes, and established the linkage between specific electron acceptor and individual c-type cytochromes. The entire proteome complements of DMRBs were also characterized, generating metabolic maps reflecting pathway-level activities responding to various electron acceptors. The results identified the core proteome carrying out the essential cellular machineries for each tested DMRB, and demonstrated clearly elevated energy metabolism for A. dehalogenans strain 2CP-C during respiration of metal electron acceptors. Comparative proteomics analysis between tested DMRB strains revealed the commonalities and differences of proteomic phenotypes displayed by different strains, and shed light into deeper understandings for DMRB metabolic activities

Investigating normal human gene expression in tissues with high-throughput transcriptomic and proteomic data.

With the improvement of high-throughput technologies during the last decade, several studies exploring the normal gene expression in human tissues have been published. Many studies examine the transcriptome with RNA sequencing (RNA-Seq), and others probe the proteome with unlabelled bottom-up Mass Spectrometry. As the sampling of undiseased tissues is difficult, the community often refers to expression atlases, which are collating these studies, to support or validate new findings. Despite many overlapping tissues between the studies, few atlases attempt to integrate all the data. In this thesis, I investigate the consistency of gene expression across tissues and studies in human with the help of transcriptomics captured with high-throughput sequencing (RNA-Seq) and proteomics generated with label-free bottom-up Mass Spectrometry (MS). After describing the transcriptomic and proteomic data and their state-of-art processing (Chapter 2), I review several identified sources of biases and my approaches to limit their effects (Chapter 3). The integration of the various transcriptomic datasets (Chapter 4) shows that the biological signal dominates the technical noise for RNA-Seq data. Tissue samples display higher levels of correlation for identical tissues in other studies than for other tissues in the same datasets. In other words, interstudy correlations for identical tissues are higher than correlations between different tissues within the same study. Globally, genes show similar expression profiles across studies for a given set of tissues. All genes categories are involved, including the tissue-specific genes and the ubiquitously expressed ones. After briefly discussing comparisons of proteomic data, I introduce a new proteomic quantification method, PPKM (Chapter 5). The PPKM method allows me to quantify about twice as many proteins compared to usual methods. Limited numbers of previous studies have shown various correlation levels between the expression of protein and mRNA in studies combining high-throughput transcriptomics and proteomics. I show that, for most tissues, we can observe quite good correlation levels (i.e. significantly better than expected by chance), even when the samples have different biological and technical backgrounds as they have been independently sourced. Many genes share similar patterns of expression between the two biological layers, e.g. genes that have a protein detected in a single tissue are more likely to have their mRNA showing specificity for the same tissue. Additionally, three groups of genes present functional enrichments of biological processes. Genes having highly correlated protein and mRNA expressions across tissues are enriched in catabolic processes. Genes having the most anticorrelated expressions are enriched for ribosomes and ncRNAs regulation. Genes with a protein detected in a single tissue are enriched in signalling processes. Overall, this thesis describes a global picture of the current consolidated knowledge we can extract from the joint study of public transcriptomic and proteomic data. Beyond confirming or improving observations reported in the literature, this work provides new insights into the ubiquitous and tissue-specific genes. To the best of my knowledge, this work has also established the most extensive list of genes with robust transcriptomic and proteomic expression across tissues and studies. Furthermore, it shows that joint study approaches can help the development of new methods, like the new proteomic PPKM quantification method. Finally, the highlighting of distinct functional enrichment profiles for groups of genes across tissues and studies lays a framework for further research.EMBL International PhD Programm

DEVELOPMENT AND APPLICATION OF MASS SPECTROMETRY-BASED PROTEOMICS TO GENERATE AND NAVIGATE THE PROTEOMES OF THE GENUS POPULUS

Historically, there has been tremendous synergy between biology and analytical technology, such that one drives the development of the other. Over the past two decades, their interrelatedness has catalyzed entirely new experimental approaches and unlocked new types of biological questions, as exemplified by the advancements of the field of mass spectrometry (MS)-based proteomics. MS-based proteomics, which provides a more complete measurement of all the proteins in a cell, has revolutionized a variety of scientific fields, ranging from characterizing proteins expressed by a microorganism to tracking cancer-related biomarkers. Though MS technology has advanced significantly, the analysis of complicated proteomes, such as plants or humans, remains challenging because of the incongruity between the complexity of the biological samples and the analytical techniques available. In this dissertation, analytical methods utilizing state-of-the-art MS instrumentation have been developed to address challenges associated with both qualitative and quantitative characterization of eukaryotic organisms. In particular, these efforts focus on characterizing Populus, a model organism and potential feedstock for bioenergy. The effectiveness of pre-existing MS techniques, initially developed to identify proteins reliably in microbial proteomes, were tested to define the boundaries and characterize the landscape of functional genome expression in Populus. Although these approaches were generally successful, achieving maximal proteome coverage was still limited by a number of factors, including genome complexity, the dynamic range of protein identification, and the abundance of protein variants. To overcome these challenges, improvements were needed in sample preparation, MS instrumentation, and bioinformatics. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: 1) network-wide, 2) pathway-specific, and 3) protein-level viewpoints. In addition, we implemented two bioinformatic approaches that were capable of decoding the plasticity of the Populus proteome, facilitating the identification of single amino acid polymorphisms and generating a more accurate profile of protein expression. Though the methods and results presented in this dissertation have direct implications in the study of bioenergy research, more broadly this dissertation focuses on developing techniques to contend with the notorious challenges associated with protein characterization in all eukaryotic organisms