Visual analysis of gel-free proteome data

Differential Protein Expression in Shewanella seohaensis Decolorizing Azo Dyes

Background:

Microbial remediation is an ecologically safe alternative to controlling environmental pollution caused by toxic aromatic compounds including azo dyes. Marine bacteria show excellent potential as agents of bioremediation. However, a lack of understanding of the entailing mechanisms of microbial degradation often restricts its wide-scale and effective application.

Objective:

To understand the changes in a bacterial proteome profile during azo dye decolorization.

Methods:

In this study, we tested a Gram-negative bacterium, Shewanella seohaensis NIODMS14 isolated from an estuarine environment and grown in three different azo dyes (Reactive Black 5 (RB5), Reactive Green 19 (RG19) and Reactive Red 120 (RR120)). The unlabeled bacterial protein samples extracted during the process of dye decolorization were subject to mass spectrometry. Relative protein quantification was determined by comparing the resultant MS/MS spectra for each protein.

Results:

Maximum dye decolorization of 98.31% for RB5, 91.49% for RG19 and 97.07% for RR120 at a concentration of 100 mg L-1 was observed. The liquid chromatography-mass spectrometry - Quadrupole Time of Flight (LCMS-QToF) analysis revealed that as many as 29 proteins were up-regulated by 7 hours of growth and 17 by 24 hours of growth. Notably, these were common across the decolorized solutions of all three azo dyes. In cultures challenged with the azo dyes, the major class of upregulated proteins was cellular oxidoreductases and an alkyl hydroperoxide reductase (SwissProt ID: A9KY42).

Conclusion:

The findings of this study on the bacterial proteome profiling during the azo dye decolorization process are used to highlight the up-regulation of important proteins that are involved in energy metabolism and oxido-reduction pathways. This has important implications in understanding the mechanism of azo dye decolorization by Shewanella seohaensis.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/.

Data visualization in environmental proteomics

From raw data to gene expression profiles, from single cultures to complex microbial communities, environmental proteomics works with data of different complexity levels that need to be interpreted in detail or in its entirety. Although data visualization is closely connected with data analysis approaches, this work will solely focus on data visualization. Complementing traditional tools such as bar charts or line graphs, scientists and visualization professionals have been provided sophisticated visualization tools. Many rules and concerns regarding the display of single but also complex data will be reviewed and discussed. Visual approaches such as microcharts, heat maps, stream graphs, and tree maps will be brought to the reader's attention and demonstrated by utilizing real data sets.

Metaproteomics to unravel major microbial players in leaf litter and soil environments: challenges and perspectives

Soil- and litter-borne microorganisms vitally contribute to biogeochemical cycles. However, changes in environmental parameters but also human interferences may alter species composition and elicit alterations in microbial activities. Soil and litter metaproteomics, implying the assignment of soil and litter proteins to specific phylogenetic and functional groups, has a great potential to provide essential new insights into the impact of microbial diversity on soil ecosystem functioning. This article will illuminate challenges and perspectives of current soil and litter metaproteomics research, starting with an introduction to an appropriate experimental design and state-of-the-art proteomics methodologies. This will be followed by a summary of important studies aimed at (i) the discovery of the major biotic drivers of leaf litter decomposition, (ii) metaproteomics analyses of rhizosphere-inhabiting microbes, and (iii) global approaches to study bioremediation processes. The review will be closed by a brief outlook on future developments and some concluding remarks, which should assist the reader to develop successful concepts for soil and litter metaproteomics studies.

OmicsVis: an interactive tool for visually analyzing metabolomics data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery.

SVM-based spectral matching for metabolite identification

Mass spectrometry-based metabolomics is getting mature and playing an ever important role in the systematic understanding of biological process in conjunction with other members of "-omics" family. However, the identification of metabolites in untargeted metabolomics profiling remains a challenge. In this paper, we propose a support vector machine (SVM)-based spectral matching algorithm to combine multiple similarity measures for accurate identification of metabolites. We compared the performance of this approach with several existing spectral matching algorithms on a spectral library we constructed. The results demonstrate that our proposed method is very promising in identifying metabolites in the face of data heterogeneity caused by different experimental parameters and platforms.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.