The WISER metadatabase: the key to more than 100 ecological datasets from European rivers, lakes and coastal waters

In ecological sciences, the role of metadata (i.e. key information about a dataset) to make existing datasets visible and discoverable has become increasingly important. Within the EU-funded WISER project (Water bodies in Europe: Integrative Systems to assess Ecological status and Recovery), we designed a metadatabase to allow scientists to find the optimal data for their analyses. An online questionnaire helped to collect metadata from the data providers and an online query tool (http://www.wiser.eu/results/meta-database/) facilitated data evaluation. The WISER metadatabase currently holds information on 114 datasets (22 river, 71 lake, 1 general freshwater and 20 coastal/transitional datasets), which also can be accessed by external scientists. We evaluate if generally used metadata standards (e.g. Darwin Core, ISO 19115, CSDGM, EML) are suitable for such specific purposes as WISER and suggest at least the linkage with standard metadata fields. Furthermore, we discuss whether the simple metadata documentation is enough for others to reuse a dataset and why there is still reluctance to publish both metadata and primary research data (i.e. time and financial constraints, misuse of data, abandoning intellectual property rights). We emphasise that metadata publication has major advantages as it makes datasets detectable by other scientists and generally makes a scientist’s work more visible.     

Synthesis of DNA oligonucleotides containing C5-ethynylbenzenesulfonamide-modified nucleotides (EBNA) by polymerases towards the construction of base functionalized nucleic acids

C5-Ethynylbenzenesulfonamide-modified nucleotide (EBNA) was investigated as substrate of various DNA polymerases. The experiments revealed that KOD, Phusion and Klenow DNA polymerases successfully accepted EBNA-T nucleotide as a substrate and yielded the fully extended DNA. KOD DNA polymerase was found to be the most efficient enzyme to furnish EBNA-T containing DNA in good yields. Phusion DNA polymerase efficiently amplified the template containing EBNA-T nucleotides by PCR.     

Synthesis and biological evaluation of potential threonine synthase inhibitors: Rhizocticin A and Plumbemycin A

Rhizocticins and Plumbemycins are natural phosphonate antibiotics produced by the bacterial strains Bacillus subtilis ATCC 6633 and Streptomyces plumbeus, respectively. Up to now, these potential threonine synthase inhibitors have only been synthesized under enzymatic catalysis. Here we report the chemical stereoselective synthesis of the non-proteinogenic (S,Z)-2-amino-5-phosphonopent-3-enoic acid [(S,Z)-APPA] and its use for the synthesis of Rhizocticin A and Plumbemycin A. In this work, (S,Z)-APPA was synthesized via the Still–Gennari olefination starting from Garner’s aldehyde. The Michaelis–Arbuzov reaction was used to form the phosphorus–carbon bond. Oligopeptides were prepared using liquid phase peptide synthesis (LPPS) and were tested against selected bacteria and fungi.     

Anchorless surface associated glycolytic enzymes from Lactobacillus plantarum 299v bind to epithelial cells and extracellular matrix proteins

An important criterion for the selection of a probiotic bacterial strain is its ability to adhere to the mucosal surface. Adhesion is usually mediated by proteins or other components located on the outer cell surface of the bacterium. In the present study we characterized the adhesive properties of two classical intracellular enzymes glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and enolase (ENO) isolated from the outer cell surface of the probiotic bacterium Lactobacillus plantarum 299v. None of the genes encoded signal peptides or cell surface anchoring motifs that could explain their extracellular location on the bacterial surface. The presence of the glycolytic enzymes on the outer surface was verified by western blotting using polyclonal antibodies raised against the specific enzymes. GAPDH and ENO showed a highly specific binding to plasminogen and fibronectin whereas GAPDH but not ENO showed weak binding to mucin. Furthermore, a pH dependent and specific binding of GAPDH and ENO to intestinal epithelial Caco-2 cells at pH 5 but not at pH 7 was demonstrated. The results showed that these glycolytic enzymes could play a role in the adhesion of the probiotic bacterium L. plantarum 299v to the gastrointestinal tract of the host. Finally, a number of probiotic as well non-probiotic Lactobacillus strains were analyzed for the presence of GAPDH and ENO on the outer surface, but no correlation between the extracellular location of these enzymes and the probiotic status of the applied strains was demonstrated.     

Selectively dispersed isotope labeling for protein structure determination by magic angle spinning NMR

The power of nuclear magnetic resonance spectroscopy derives from its site-specific access to chemical, structural and dynamic information. However, the corresponding multiplicity of interactions can be difficult to tease apart. Complimentary approaches involve spectral editing on the one hand and selective isotope substitution on the other. Here we present a new “redox” approach to the latter: acetate is chosen as the sole carbon source for the extreme oxidation numbers of its two carbons. Consistent with conventional anabolic pathways for the amino acids, [1-¹³C] acetate does not label α carbons, labels other aliphatic carbons and the aromatic carbons very selectively, and labels the carboxyl carbons heavily. The benefits of this labeling scheme are exemplified by magic angle spinning spectra of microcrystalline immunoglobulin binding protein G (GB1): the elimination of most J-couplings and one- and two-bond dipolar couplings provides narrow signals and long-range, intra- and inter-residue, recoupling essential for distance constraints. Inverse redox labeling, from [2-¹³C] acetate, is also expected to be useful: although it retains one-bond couplings in the sidechains, the removal of CA–CO coupling in the backbone should improve the resolution of NCACX spectra.     

Effect of Stacked Insecticidal Cry Proteins from Maize Pollen on Nurse Bees (Apis mellifera carnica) and Their Gut Bacteria

Honey bee pollination is a key ecosystem service to nature and agriculture. However, biosafety research on genetically modified crops rarely considers effects on nurse bees from intact colonies, even though they receive and primarily process the largest amount of pollen. The objective of this study was to analyze the response of nurse bees and their gut bacteria to pollen from Bt maize expressing three different insecticidal Cry proteins (Cry1A.105, Cry2Ab2, and Cry3Bb1). Naturally Cry proteins are produced by bacteria (Bacillus thuringiensis). Colonies of Apis mellifera carnica were kept during anthesis in flight cages on field plots with the Bt maize, two different conventionally bred maize varieties, and without cages, 1-km outside of the experimental maize field to allow ad libitum foraging to mixed pollen sources. During their 10-days life span, the consumption of Bt maize pollen had no effect on their survival rate, body weight and rates of pollen digestion compared to the conventional maize varieties. As indicated by ELISA-quantification of Cry1A.105 and Cry3Bb1, more than 98% of the recombinant proteins were degraded. Bacterial population sizes in the gut were not affected by the genetic modification. Bt-maize, conventional varieties and mixed pollen sources selected for significantly different bacterial communities which were, however, composed of the same dominant members, including Proteobacteria in the midgut and Lactobacillus sp. and Bifidobacterium sp. in the hindgut. Surprisingly, Cry proteins from natural sources, most likely B. thuringiensis, were detected in bees with no exposure to Bt maize. The natural occurrence of Cry proteins and the lack of detectable effects on nurse bees and their gut bacteria give no indication for harmful effects of this Bt maize on nurse honey bees.     

Label‐free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis

The aims of the study were to: (i) identify differentially regulated proteins in cerebrospinal fluid (CSF) between multiple sclerosis (MS) patients and non‐MS controls; (ii) examine the effect of matching the CSF samples on either total protein amount or volume, and compare four protein normalization strategies for CSF protein quantification. CSF from MS patients (n = 37) and controls (n = 64), consisting of other noninflammatory neurological diseases (n = 50) and non neurological spinal anesthetic subjects (n = 14), were analyzed using label‐free proteomics, quantifying almost 800 proteins. In total, 122 proteins were significantly regulated (p < 0.05), where 77 proteins had p‐value <0.01 or AUC value >0.75. Hierarchical clustering indicated that there were two main groups of MS patients, those with increased levels of inflammatory response proteins and decreased levels of proteins involved in neuronal tissue development (n = 30), and those with normal protein levels for both of these protein groups (n = 7). The main subgroup of controls clustering with the MS patients showing increased inflammation and decreased neuronal tissue development were patients suffering from chronic fatigue. Our data indicate that the preferable way to quantify proteins in CSF is to first match the samples on total protein amount and then normalize the data based on the median intensities, preferably from the CNS‐enriched proteins.     

Downregulation of AKT3 Increases Migration and Metastasis in Triple Negative Breast Cancer Cells by Upregulating S100A4

Background

Treatment of breast cancer patients with distant metastases represents one of the biggest challenges in today’s gynecological oncology. Therefore, a better understanding of mechanisms promoting the development of metastases is of paramount importance. The serine/threonine kinase AKT was shown to drive cancer progression and metastasis. However, there is emerging data that single AKT isoforms (i.e. AKT1, AKT2 and AKT3) have different or even opposing functions in the regulation of cancer cell migration in vitro, giving rise to the hypothesis that inhibition of distinct AKT isoforms might have undesirable effects on cancer dissemination in vivo.

Methods

The triple negative breast cancer cell line MDA-MB-231 was used to investigate the functional roles of AKT in migration and metastasis. AKT single and double knockdown cells were generated using isoform specific shRNAs. Migration was analyzed using live cell imaging, chemotaxis and transwell assays. The metastatic potential of AKT isoform knockdown cells was evaluated in a subcutaneous xenograft mouse model in vivo.

Results

Depletion of AKT3, but not AKT1 or AKT2, resulted in increased migration in vitro. This effect was even more prominent in AKT2,3 double knockdown cells. Furthermore, combined downregulation of AKT2 and AKT3, as well as AKT1 and AKT3 significantly increased metastasis formation in vivo. Screening for promigratory proteins revealed that downregulation of AKT3 increases the expression of S100A4 protein. In accordance, depletion of S100A4 by siRNA approach reverses the increased migration induced by knockdown of AKT3.

Conclusions

We demonstrated that knockdown of AKT3 can increase the metastatic potential of triple negative breast cancer cells. Therefore, our results provide a rationale for the development of AKT isoform specific inhibitors.     

Integrating Genetic,Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.