Conrad Bessant

Proposed minimum reporting standards for data analysis in metabolomics

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called meta-data). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.

Protein-Folding Location Can Regulate Manganese-Binding Versus Copper- or Zinc-Binding.

Metals are needed by at least one-quarter of all proteins. Although metallochaperones insert the correct metal into some proteins, they have not been found for the vast majority, and the view is that most metalloproteins acquire their metals directly from cellular pools. However, some metals form more stable complexes with proteins than do others. For instance, as described in the Irving–Williams series, Cu2+ and Zn2+ typically form more stable complexes than Mn2+. Thus it is unclear what cellular mechanisms manage metal acquisition by most nascent proteins. To investigate this question, we identified the most abundant Cu2+-protein, CucA (Cu2+-cupin A), and the most abundant Mn2+-protein, MncA (Mn2+-cupin A), in the periplasm of the cyanobacterium Synechocystis PCC 6803. Each of these newly identified proteins binds its respective metal via identical ligands within a cupin fold. Consistent with the Irving–Williams series, MncA only binds Mn2+ after folding in solutions containing at least a 104 times molar excess of Mn2+ over Cu2+ or Zn2+. However once MncA has bound Mn2+, the metal does not exchange with Cu2+. MncA and CucA have signal peptides for different export pathways into the periplasm, Tat and Sec respectively. Export by the Tat pathway allows MncA to fold in the cytoplasm, which contains only tightly bound copper or Zn2+ (refs 10–12) but micromolar Mn2+ (ref. 13). In contrast, CucA folds in the periplasm to acquire Cu2+. These results reveal a mechanism whereby the compartment in which a protein folds overrides its binding preference to control its metal content. They explain why the cytoplasm must contain only tightly bound and buffered copper and Zn2+.

Prospects for Clinical Application of Electronic-Nose Technology to Early Detection of Mycobacterium tuberculosis in Culture and Sputum

ABSTRACT Ziehl-Neelsen (ZN) staining for the diagnosis of tuberculosis (TB) is time-consuming and operator dependent and lacks sensitivity. A new method is urgently needed. We investigated the potential of an electronic nose (EN) (gas sensor array) comprising 14 conducting polymers to detect different Mycobacterium spp. and Pseudomonas aeruginosa in the headspaces of cultures, spiked sputa, and sputum samples from 330 culture-proven and human immunodeficiency virus-tested TB and non-TB patients. The data were analyzed using principal-component analysis, discriminant function analysis, and artificial neural networks. The EN differentiated between different Mycobacterium spp. and between mycobacteria and other lung pathogens both in culture and in spiked sputum samples. The detection limit in culture and spiked sputa was found to be 1 × 104 mycobacteria ml−1. After training of the neural network with 196 sputum samples, 134 samples (55 M. tuberculosis culture-positive samples and 79 culture-negative samples) were used to challenge the model. The EN correctly predicted 89% of culture-positive patients; the six false negatives were the four ZN-negative and two ZN-positive patients. The specificity and sensitivity of the described method were 91% and 89%, respectively, compared to culture. At present, the reasons for the false negatives and false positives are unknown, but they could well be due to the nonoptimized system used here. This study has shown the ability of an electronic nose to detect M. tuberculosis in clinical specimens and opens the way to making this method a rapid and automated system for the early diagnosis of respiratory infections.

De novo derivation of proteomes from transcriptomes for transcript and protein identification

Identification of proteins by tandem mass spectrometry requires a reference protein database, but these are only available for model species. Here we demonstrate that, for a non-model species, the sequencing of expressed mRNA can generate a protein database for mass spectrometry–based identification. This combination of high-throughput sequencing and protein identification technologies allows detection of genes and proteins. We use human cells infected with human adenovirus as a complex and dynamic model to demonstrate the robustness of this approach. Our proteomics informed by transcriptomics (PIT) technique identifies >99% of over 3,700 distinct proteins identified using traditional analysis that relies on comprehensive human and adenovirus protein lists. We show that this approach can also be used to highlight genes and proteins undergoing dynamic changes in post-transcriptional protein stability.

MRMaid, the Web-based Tool for Designing Multiple Reaction Monitoring (MRM) Transitions

Multiple reaction monitoring (MRM) of peptides uses tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential studies. Using this technique, the specificity of precursor to product transitions is harnessed for quantitative analysis of multiple proteins in a single sample. The design of transitions is critical for the success of MRM experiments, but predicting signal intensity of peptides and fragmentation patterns ab initio is challenging given existing methods. The tool presented here, MRMaid (pronounced “mermaid”) offers a novel alternative for rapid design of MRM transitions for the proteomics researcher. The program uses a combination of knowledge of the properties of optimal MRM transitions taken from expert practitioners and literature with MS/MS evidence derived from interrogation of a database of peptide identifications and their associated mass spectra. The tool also predicts retention time using a published model, allowing ordering of transition candidates. By exploiting available knowledge and resources to generate the most reliable transitions, this approach negates the need for theoretical prediction of fragmentation and the need to undertake prior “discovery” MS studies. MRMaid is a modular tool built around the Genome Annotating Proteomic Pipeline framework, providing a web-based solution with both descriptive and graphical visualizations of transitions. Predicted transition candidates are ranked based on a novel transition scoring system, and users may filter the results by selecting optional stringency criteria, such as omitting frequently modified residues, constraining the length of peptides, or omitting missed cleavages. Comparison with published transitions showed that MRMaid successfully predicted the peptide and product ion pairs in the majority of cases with appropriate retention time estimates. As the data content of the Genome Annotating Proteomic Pipeline repository increases, the coverage and reliability of MRMaid are set to increase further. MRMaid is freely available over the internet as an executable web-based service at www.mrmaid.info.

Multivariate Data Analysis in Electroanalytical Chemistry

Data analysis is becoming an increasingly important aspect of electroanalytical chemistry, as voltammetric techniques and electrode arrays become ever more popular as diagnostic tools. Modern data analysis techniques promise to help us make full use of the large amounts of data collected, allowing electroanalytical chemists to get more out of their existing instruments, and paving the way for new measurement approaches. This article provides an overview of the most widely used multivariate techniques in electroanalysis, citing specific examples of how they have been applied, and looking at their relative merits. As in other areas of analytical science, no single technique is applicable to all applications and the running of controls and appreciation of the applications and limitations of each technique is essential.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

Free computational resources for designing selected reaction monitoring transitions

Selected reaction monitoring (SRM) is a technique for quantifying specific proteins using triple quadrupole MS. Proteins are digested into peptides and fed into MS following HPLC separation. The stream of ionized peptides is filtered by m/z ratio so only specific peptide targets enter the collision cell, where they are fragmented into product ions. A specific product ion is then filtered from the cell and its intensity measured. By spiking an isotopically labeled version of each target peptide into a sample, both native and surrogate peptides enter MS, pass the filters and transition into product ions in tandem; thus the quantity of the native peptide may be calculated by examining the relative intensities of the native and surrogate signals. The choice of precursor‐to‐product ion transitions is critical for SRM, but predicting the best candidates is challenging and time‐consuming. To alleviate this problem, software tools for designing and optimizing transitions have recently emerged, predominantly driven by data from public proteomics repositories, such as the Global Proteome Machine and PeptideAtlas. In this review, we provide an overview of the state‐of‐the‐art in automated SRM transition design tools in the public domain, explaining how the systems work and how to use them.

Electronic-Nose Technology Using Sputum Samples in Diagnosis of Patients with Tuberculosis

ABSTRACT We investigated the potential of two different electronic noses (EN; code named “Rob” and “Walter”) to differentiate between sputum headspace samples from tuberculosis (TB) patients and non-TB patients. Only samples from Ziehl-Neelsen stain (ZN)- and Mycobacterium tuberculosis culture-positive (TBPOS) sputum samples and ZN- and culture-negative (TBNEG) samples were used for headspace analysis; with EN Rob, we used 284 samples from TB suspects (56 TBPOS and 228 TBNEG samples), and with EN Walter, we used 323 samples from TB suspects (80 TBPOS and 243 TBNEG samples). The best results were obtained using advanced data extraction and linear discriminant function analysis, resulting in a sensitivity of 68%, a specificity of 69%, and an accuracy of 69% for EN Rob; for EN Walter, the results were 75%, 67%, and 69%, respectively. Further research is still required to improve the sensitivity and specificity by choosing more selective sensors and type of sampling technique.

The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics

The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS)1 leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml.