Behavioural response of the scyphozoan jellyfish Aurelia aurita (L.) upon contact with the predatory jellyfish Cyanea capillata (L.)

Underwater manipulative experiments were carried out in situ to investigate the sensibility of the jellyfish Amelia aurita (L.) to contact with the tentacles of Cyanea capillata (L), commonly known as a predator on A. aurita. Movements of individual medusae touched by tentacles of C. capillata and other objects were video‐recorded during SCUBA dives. The behavioural variable studied was change in swim pulse frequency. The results showed that A. aurita was highly susceptible to the tentacles of C. capillata and responded with an increased swim pulse frequency when touched at the umbrellar margin but not at the central exumbrella. Contact with other objects also induced a behavioural response in A. aurita.     

Impact of Temperature Dependent Sampling Procedures in Proteomics and Peptidomics – A Characterization of the Liver and Pancreas Post Mortem Degradome

Little is known about the nature of post mortem degradation of proteins and peptides on a global level, the so-called degradome. This is especially true for nonneural tissues. Degradome properties in relation to sampling procedures on different tissues are of great importance for the studies of, for instance, post translational modifications and/or the establishment of clinical biobanks. Here, snap freezing of fresh (<2 min post mortem time) mouse liver and pancreas tissue is compared with rapid heat stabilization with regard to effects on the proteome (using two-dimensional differential in-gel electrophoresis) and peptidome (using label free liquid chromatography). We report several proteins and peptides that exhibit heightened degradation sensitivity, for instance superoxide dismutase in liver, and peptidyl-prolyl cis-trans isomerase and insulin C-peptides in pancreas. Tissue sampling based on snap freezing produces a greater amount of degradation products and lower levels of endogenous peptides than rapid heat stabilization. We also demonstrate that solely snap freezing related degradation can be attenuated by subsequent heat stabilization. We conclude that tissue sampling involving a rapid heat stabilization step is preferable to freezing with regard to proteomic and peptidomic sample quality.The evolving maturation of the field of proteomics has, in the same way as in genomics, highlighted the need of better sampling procedures and sample preparation methodologies to minimize the effect of post mortem alterations. The aspect of sample quality is not new in any way and is relevant in most biomedical fields but has only lately started to receive adequate attention. The main factors influencing sample quality is storage temperature of the body until tissue removal (foremost a problem in clinical settings and extraction of less accessible tissue samples from model organisms) and post mortem interval (PMI)¹ (1–3). Post mortem degradation in during PMI is a well known compromising problem when studying endogenous peptides (2, 3) and has also been proven to affect the results of polypeptide (here defined as proteins larger than 10 kDa) studies (3–8). PMI degradation has mainly been studied on human or mouse brain tissue, using two-dimensional electrophoresis (2-DE), SDS-PAGE, and immunoblotting (1, 3–12). There are also a few proteomic studies on muscle tissue degradation in livestock (13–16).We and others have previously explored the effect of focused microwave irradiation with regard to sample quality, demonstrating that this method is more reliable than snap freezing in liquid nitrogen, especially with regard to post-translational modification (PTM) stability (2, 3, 17–20). An alternative method based on cryostat dissection with subsequent heat treatment through boiling has also been reported to improve endogenous peptide sample quality (21). Besides focused microwave irradiation, which is specifically used for rodent brain tissue sampling, we have also demonstrated the efficiency of rapid heat stabilization through conductivity with regard to sample degradation (3, 22). Although somewhat constrained by its dependence on how quickly the tissue is harvested from the body, the latter procedure has the added advantage that it can be used on any type of tissue and species, fresh as well as frozen. This study will compare effects of sampling procedures on the liver and pancreas degradome following rapid heat stabilization, the more traditional snap freezing, or the combination of snap freezing with subsequent heat stabilization.To summarize, this study investigated the effects of post mortem degradation in pancreas and liver. Both tissues are well studied because of their multiple functions in the body and their involvement in different diseases such as diabetes or hepatocarcinoma. Pancreas is especially interesting in this context as it displays endocrine secretion of peptides, and exocrine secretion of digestive enzymes, the later making it a protease rich tissue. We used both two-dimensional difference in gel electrophoresis (2D-DIGE) and label free liquid chromatography mass spectrometry (LC-MS) based differential peptide display (2, 18), the later to better investigate changes in small molecular fragment that are not easily detectable by gel-based methods. 2D-DIGE is an unrivaled methodology to characterize alterations in isoform patterns, which is an important aspect considering that post-translational modifications (PTMs) such as phosphorylations are especially sensitive to post mortem influence within a few minutes PMI (3). The peptidomics approach has been used in several studies to point out early post mortem changes and protein degradation that tissue undergo following sampling and is therefore a well-suited method (3, 18, 22).     

MOCAT: A Metagenomics Assembly and Gene Prediction Toolkit

MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.     

Development and Evaluation of Normalization Methods for Label-free Relative Quantification of Endogenous Peptides

The performances of 10 different normalization methods on data of endogenous brain peptides produced with label-free nano-LC-MS were evaluated. Data sets originating from three different species (mouse, rat, and Japanese quail), each consisting of 35–45 individual LC-MS analyses, were used in the study. Each sample set contained both technical and biological replicates, and the LC-MS analyses were performed in a randomized block fashion. Peptides in all three data sets were found to display LC-MS analysis order-dependent bias. Global normalization methods will only to some extent correct this type of bias. Only the novel normalization procedure RegrRun (linear regression followed by analysis order normalization) corrected for this type of bias. The RegrRun procedure performed the best of the normalization methods tested and decreased the median S.D. by 43% on average compared with raw data. This method also produced the smallest fraction of peptides with interblock differences while producing the largest fraction of differentially expressed peaks between treatment groups in all three data sets. Linear regression normalization (Regr) performed second best and decreased median S.D. by 38% on average compared with raw data. All other examined methods reduced median S.D. by 20–30% on average compared with raw data.Peptidomics is defined as the analysis of the peptide content within an organism, tissue, or cell (1–3). The proteome and peptidome have common features, but there are also prominent differences. Proteomics generally identifies proteins by using the information of biologically inactive peptides derived from tryptic digestion, whereas peptidomics tries to identify endogenous peptides using single peptide sequence information only (4). Endogenous neuropeptides are peptides used for intracellular signaling that can act as neurotransmitters or neuromodulators in the nervous system. These polypeptides of 3–100 amino acids can be abundantly produced in large neural populations or in trace levels from single neurons (5) and are often generated through the cleavage of precursor proteins. However, unwanted peptides can also be created through post-mortem induced proteolysis (6). The later aspect complicates the technical analysis of neuropeptides as post-mortem conditions increase the number of degradation peptides. The possibility to detect, identify, and quantify lowly expressed neuropeptides using label-free LC-MS techniques has improved with the development of new sample preparation techniques including rapid heating of the tissue, which prevents protein degradation and inhibition of post-mortem proteolytic activity (7, 8).It has been suggested by us (4, 5) and others (9) that comparing the peptidome between samples of e.g. diseased and normal tissue may lead to the discovery of biologically relevant peptides of certain pathological or pharmacological events. However, differences in relative peptide abundance measurements may not only originate from biological differences but also from systematic bias and noise. To reduce the effects of experimentally induced variability it is common to normalize the raw data. This is a concept well known in the area of genomics studies using gene expression microarrays (10–12). As a consequence, many methods developed for microarray data have also been adapted for normalizing peptide data produced with LC-MS techniques (10–16). Normally the underlying assumption for applying these techniques is that the total or mean/median peak abundances should be equal across different experiments, in this case between LC-MS analyses. Global normalization methods refer to cases where all peak abundances are used to determine a single normalization factor between experiments (13, 15, 16), a subset of peaks assumed to be similarly abundant between experiments (16) is used, or spiked-in peptides are used as internal standards. In a study by Callister et al. (14), normalization methods for tryptic LC-FTICR-MS peptide data were compared. The authors concluded that global or iterative linear regression works best in most cases but also recommended that the best procedure should be selected for each data set individually. Methods used for normalizing LC-MS data have been reviewed previously (14, 17, 18), but to our knowledge only Callister et al. (14) have used small data sets to systematically evaluate such methods. None of these studies have targeted data of endogenous peptides.In this study, the effects of 10 different normalization methods were evaluated on data produced by a nano-LC system coupled to an electrospray Q-TOF or linear trap quadrupole (LTQ)¹ mass spectrometer. Normalization methods that originally were developed for gene expression data were used, and one novel method, linear regression followed by analysis order normalization (RegrRun), is presented. The normalization methods were evaluated using three data sets of endogenous brain peptides originating from three different species (mouse, rat, and Japanese quail), each consisting of 35–45 individual LC-MS analyses. Each data set contained both technical and biological replicates.     

Potential of fecal microbiota for early-stage detection of colorectal cancer

Several bacterial species have been implicated in the development of colorectal carcinoma (CRC), but CRC-associated changes of fecal microbiota and their potential for cancer screening remain to be explored. Here, we used metagenomic sequencing of fecal samples to identify taxonomic markers that distinguished CRC patients from tumor-free controls in a study population of 156 participants. Accuracy of metagenomic CRC detection was similar to the standard fecal occult blood test (FOBT) and when both approaches were combined, sensitivity improved > 45% relative to the FOBT, while maintaining its specificity. Accuracy of metagenomic CRC detection did not differ significantly between early- and late-stage cancer and could be validated in independent patient and control populations (N = 335) from different countries. CRC-associated changes in the fecal microbiome at least partially reflected microbial community composition at the tumor itself, indicating that observed gene pool differences may reveal tumor-related host–microbe interactions. Indeed, we deduced a metabolic shift from fiber degradation in controls to utilization of host carbohydrates and amino acids in CRC patients, accompanied by an increase of lipopolysaccharide metabolism.     

Faced with inequality: chicken do not have a general dosage compensation of sex-linked genes

Background  

The contrasting dose of sex chromosomes in males and females potentially introduces a large-scale imbalance in levels of gene expression between sexes, and between sex chromosomes and autosomes. In many organisms, dosage compensation has thus evolved to equalize sex-linked gene expression in males and females. In mammals this is achieved by X chromosome inactivation and in flies and worms by up- or down-regulation of X-linked expression, respectively. While otherwise widespread in systems with heteromorphic sex chromosomes, the case of dosage compensation in birds (males ZZ, females ZW) remains an unsolved enigma.     

Cnidae in the sea anemone Sagartiogeton viduatus (Muller, 1776) (Cnidaria,Anthozoa); A comparison to cnidae in the sea anemone Metridium senile (Linnaeus, 1761) (Cnidaria,Anthozoa)

The cnidom of the sea anemone Sagartiogeton viduatus (Muller, 1776) is described from interference‐contrast light micrographs (LMs) and scanning electron micrographs (SEMs). Special attention is given to nematocyst maturation, including the differentiation of the shaft into proximal and main regions as helical folding of the shaft wall proceeds. Comparisons are made with Metridium senile (Linnaeus, 1761), whose cnidom, with a few exceptions, is closely similar to that of S. viduatus. The two anemones possess b‐ and p‐mastigophores, p‐amastigophores, isorhizas and spirocysts. Although the majority of cnidae in S. viduatus is smaller than corresponding ones in M. senile, they are grouped into the same size classes as those of M. senile, namely small, medium and large. The main differences from M. senile cnidae are the followings: (1) Large acontia p‐amastigophores are the largest nematocysts in S. viduatus. (2) They are noticeably larger than the large acontia b‐mastigophores, and (3) they are separated from the p‐amastigophores of M. senile by the sinusoid pattern of their U‐shaped capsular matrix. (4) The large acontia b‐mastigophores are microbasic and not mesobasic as in M. Senile, and (5) they do not produce darts. (6) Another difference from M. senile is the absence of catch‐tentacle isorhizas.