Effect of Grazing-Mediated Dimethyl Sulfide (DMS) Production on the Swimming Behavior of the Copepod Calanus helgolandicus

Chemical interactions play a fundamental role in the ecology of marine foodwebs. Dimethyl sulfide (DMS) is a ubiquitous marine trace gas that acts as a bioactive compound by eliciting foraging behavior in a range of marine taxa including the copepod Temora longicornis. Production of DMS can rapidly increase following microzooplankton grazing on phytoplankton. Here, we investigated whether grazing-induced DMS elicits an increase in foraging behavior in the copepod Calanus helgolandicus. We developed a semi-Automated method to quantify the effect of grazing-mediated DMS on the proportion of the time budget tethered females allocate towards slow swimming, typically associated with feeding. The pooled data showed no differences in the proportion of the 25 min time budget allocated towards slow swimming between high (23.6 ± 9.74%) and low (29.1 ± 18.33%) DMS treatments. However, there was a high degree of variability between behavioral responses of individual copepods. We discuss the need for more detailed species-specific studies of individual level responses of copepods to chemical signals at different spatial scales to improve our understanding of chemical interactions between copepods and their prey. © 1996-2013 MDPI AG

The Plastic and Evolutionary Responses of Fish to Anthropogenic Stressors

Ecosystems are being altered at unprecedented rates with little knowledge of the potential impacts on biodiversity. Two of the most pressing contemporary anthropogenic stressors are pollution and global warming. Species can respond to these stressors via dispersal, phenotypic plasticity, or evolutionary adaptation. Many species, especially aquatic organisms, experience ecological or physical barriers to dispersal and will therefore have to respond via phenotypic plasticity or evolutionary responses. I examined the responses of multiple traits associated with fitness in fish to pollution and increased temperature using a 2 × 2 common garden experimental design. I examined the effects of pollution on behaviour in a natural population of brown bullheads (Ameiurus nebulosus), and increased temperature on population demographics, life history traits, reproductive traits, and the immune response in experimental populations of guppies (Poecilia reticulata). The plastic responses to pollution were increased locomotion and decreased aggression and the plastic responses to increased temperature were decreased age at maturity, sperm length, sperm velocity, and sperm path linearity. These results are indicative of stress responses by the fish and could potentially decrease reproductive success and survival. Next, I measured reproductive output in experimental populations of guppies and found that, after many generations in elevated temperature, females produced fewer, smaller broods than control populations. However, I found no effect of temperature on census population size, survivorship, sex ratio, size-at-age, or the immune response, indicating that, despite the decreased reproductive output, guppies appear to cope with the increased temperature. Additionally, genetic diversity in the elevated temperature populations decreased more rapidly than control populations, and was equivalent to one quarter the effective population size relative to the controls. This latter result shows a clear signature of selection. Indeed, I found that sperm length displayed an evolutionary response in an estimated 2-3 generations. And in a natural population of bullheads, after an estimated 33 generations, I found an evolutionary response in locomotion and aggression. However, the reduced genetic diversity could lower the adaptive potential of populations to future stressors. I discuss these results in the context of the scope of organisms to rapidly respond to anthropogenic stressors

A Bioconductor workflow for processing and analysing spatial proteomics data

Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.LMB and CMM are supported by a Wellcome Trust Technology Development Grant (grant number 108441/Z/15/Z). KSL is a Wellcome Trust Joint Investigator (110170/Z/15/Z). LG is supported by the BBSRC Strategic Longer and Larger grant (Award BB/L002817/1)

Role of infochemical mediated zooplankton grazing in a phytoplankton competition model

Infochemicals released by marine phytoplankton play important roles in food web interactions by influencing the feeding behavior and selectivity of zooplanktonic predators. Recent modeling efforts have focused on the role of such chemicals as toxic grazing deterrents in phytoplankton competition. However, infochemicals may also be utilized as grazing cues, leading predators to profitable foraging patches. Here we investigate the role of infochemical mediated zooplankton grazing in a standard 3-species phytoplankton competition model, with the aim of further elucidating the ecological role of phytoplankton derived infochemicals. We then extend this to consider a more realistic 4-species model. The models produce a range of solutions depending on the strength of competition and microzooplankton grazing selectivity. Our key result is that infochemical chemoattractants, which increase the susceptibility of the producer to grazing, can provide a refuge for both competing phytoplankton species by attracting carnivorous copepods to consume microzooplankton grazers in a multi-trophic interaction. Our results indicate that infochemicals potentially have important consequences for the dynamics of marine food webs. © 2012 Elsevier B.V

The effect of organelle discovery upon sub-cellular protein localisation.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]

A Bioconductor workflow for processing and analysing spatial proteomics data [version 2; referees: 2 approved]

Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular

The effect of oil sands process-affected water and model naphthenic acids on photosynthesis and growth in Emiliania huxleyi and Chlorella vulgaris

Naphthenic acids (NAs) are among the most toxic organic pollutants present in oil sands process waters (OSPW) and enter marine and freshwater environments through natural and anthropogenic sources. We investigated the effects of the acid extractable organic (AEO) fraction of OSPW and individual surrogate NAs, on maximum photosynthetic efficiency of photosystem II (PSII) (FV/FM) and cell growth in Emiliania huxleyi and Chlorella vulgaris as representative marine and freshwater phytoplankton. Whilst FV/FM in E. huxleyi and C. vulgaris was not inhibited by AEO, exposure to two surrogate NAs: (4'-n-butylphenyl)-4-butanoic acid (n-BPBA) and (4'-tert-butylphenyl)-4-butanoic acid (tert-BPBA), caused complete inhibition of FV/FM in E. huxleyi (≥10 mg L-1 n-BPBA; ≥50 mg L-1 tert-BPBA) but not in C. vulgaris. Growth rates and cell abundances in E. huxleyi were also reduced when exposed to ≥10 mg L-1 n- and tert-BPBA; however, higher concentrations of n- and tert-BPBA (100 mg L-1) were required to reduce cell growth in C. vulgaris. AEO at ≥10 mg L-1 stimulated E. huxleyi growth rate (p ≤ 0.002), yet had no apparent effect on C. vulgaris. In conclusion, E. huxleyi was generally more sensitive to NAs than C. vulgaris. This report provides a better understanding of the physiological responses of phytoplankton to NAs which will enable improved monitoring of NA pollution in aquatic ecosystems in the future

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data [version 1; peer review: 2 approved]

Background: Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods: Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilizes open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results: The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions: A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses

A draft map of the mouse pluripotent stem cell spatial proteome.

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data.The authors thank Andreas Hühmer, Philip Remes, Jesse Canterbury and Graeme McAlister of Thermo Fisher Scientific, San Jose, CA, USA, for their advice regarding operation of the Orbitrap Fusion. We also thank Mike Deery for assistance with checking sample integrity on the mass spectrometers in the Cambridge Centre for Proteomics on equipment purchased via a Wellcome Trust grant (099135/Z/12/Z ), and Brian Hendrich of the Wellcome Trust-MRC Stem Cell Institute in Cambridge and Sean Munro of the MRC Laboratory of Molecular Biology in Cambridge for insightful comments about the data. AC was supported by BBSRC grant (BB/D526088/1). C.M.M. and L.G. were supported by European Union 7th Framework Program (PRIMEXS project, grant agreement number 262067), L.M.B was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1), and P.C.H. was supported by an ERC Advanced Investigator grant to A.M.A. A.G. was funded through the Alexander S. Onassis Public Benefit Foundation, the Foundation for Education and European Culture (IPEP) and the Embiricos Trust Scholarship of Jesus College Cambridge. T.H. was supported by Commonwealth Split Site PhD Scholarship. T.N. was supported by an ERASMUS Placement scholarshipThis is the final version of the article. It was first available from NPG via http://dx.doi.org/10.1038/ncomms999

A foundation for reliable spatial proteomics data analysis.

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis..G., C.M.M., and M.F. were supported by the European Union 7th Framework Program (PRIME-XS Project, Grant No. 262067). L.M.B. was supported by a BBSRC Tools and Resources Development Fund (Award No. BB/K00137X/1). T.B. was supported by the Proteomics French Infrastructure (ProFI, ANR-10-INBS-08). A.C. was supported by BBSRC Grant No. BB/D526088/1. A.J.G. was supported by BBSRC Grant No. BB/E024777/ and a generous gift from King Abdullah University for Science and Technology, Saudi Arabia. D.J.N.H. was supported by a BBSRC CASE studentship (BB/I016147/1)