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

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

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

Multitrophic Interactions in the Sea: Assessing the Effect of Infochemical-Mediated Foraging in a 1-d Spatial Model

The release of chemicals following herbivore grazing on primary producers may provide feeding cues to carnivorous predators, thereby promoting multitrophic interactions. In particular, chemicals released following grazing on phytoplankton by microzooplankton herbivores have been shown to elicit a behavioural foraging response in carnivorous copepods, which may use this chemical information as a mechanism to locate and remain within biologically productive patches of the ocean. In this paper, we use a 1D spatial reaction-diffusion model to simulate a tri-trophic planktonic system in the water column, where predation at the top trophic level (copepods) is affected by infochemicals released by the primary producers forming the bottom trophic level. The effect of the infochemical-mediated predation is investigated by comparing the case where copepods forage randomly to the case where copepods adjust their vertical position to follow the distribution of grazing-induced chemicals. Results indicate that utilization of infochemicals for foraging provides fitness benefits to copepods and stabilizes the system at high nutrient load, whilst also forming a possible mechanism for phytoplankton bloom formation. We also investigate how the copepod efficiency to respond to infochemicals affects the results, and show that small increases (2%) in the ability of copepods to sense infochemicals can promote their persistence in the system. Finally we argue that effectively employing infochemicals for foraging can be an evolutionarily stable strategy for copepods

Developmental temperature affects the expression of ejaculatory traits and the outcome of sperm competition in Callosobruchus maculatus

The outcome of post-copulatory sexual selection is determined by a complex set of interactions between the primary reproductive traits of two or more males and their interactions with the reproductive traits of the female. Recently, a number of studies have shown the primary reproductive traits of both males and females express phenotypic plasticity in response to the thermal environment experienced during ontogeny. However, how plasticity in these traits affects the dynamics of sperm competition remains largely unknown. Here, we demonstrate plasticity in testes size, sperm size and sperm number in response to developmental temperature in the bruchid beetle Callosobruchus maculatus. Males reared at the highest temperature eclosed at the smallest body size and had the smallest absolute and relative testes size. Males reared at both the high- and low-temperature extremes produced both fewer and smaller sperm than males reared at intermediate temperatures. In the absence of sperm competition, developmental temperature had no effect on male fertility. However, under conditions of sperm competition, males reared at either temperature extreme were less competitive in terms of sperm offence (P2), whereas those reared at the lowest temperature were less competitive in terms of sperm defence (P1). This suggests the developmental pathways that regulate the phenotypic expression of these ejaculatory traits are subject to both natural and sexual selection: natural selection in the pre-ejaculatory environment and sexual selection in the post-ejaculatory environment. In nature, thermal heterogeneity during development is commonplace. Therefore, we suggest the interplay between ecology and development represents an important, yet hitherto underestimated component of male fitness via post-copulatory sexual selection

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]

The sperm factor: paternal impact beyond genes

The fact that sperm carry more than the paternal DNA has only been discovered just over a decade ago. With this discovery, the idea that the paternal condition may have direct implications for the fitness of the offspring had to be revisited. While this idea is still highly debated, empirical evidence for paternal effects is accumulating. Male condition not only affects male fertility but also offspring early development and performance later in life. Several factors have been identified as possible carriers of non-genetic information, but we still know little about their origin and function and even less about their causation. I consider four possible non-mutually exclusive adaptive and non-adaptive explanations for the existence of paternal effects in an evolutionary context. In addition, I provide a brief overview of the main non-genetic components found in sperm including DNA methylation, chromatin modifications, RNAs and proteins. I discuss their putative functions and present currently available examples for their role in transferring non-genetic information from the father to the offspring. Finally, I identify some of the most important open questions and present possible future research avenues

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