ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is the most prevalent solid cancer among men in Western Countries. The clinical behavior of localized PCa is highly variable. Some cancers are aggressive leading to death, while others can even be monitored safely. Hence, there is a high clinical need for precise biomarkers for identification of aggressive disease in addition to established clinical parameters. OBJECTIVE: To develop an RNA expression-based score for the prediction of PCa prognosis that facilitates clinical decision making. DESIGN, SETTING, AND PARTICIPANTS: We assessed 233 tissue specimens of PCa patients with long-term follow-up data from fresh-frozen radical prostatectomies (RPs), from formalin-fixed and paraffin-embedded RP specimens and biopsies by transcriptome-wide next-generation sequencing and customized expression microarrays. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We applied Cox proportional hazard models to the cohorts from different platforms and specimen types. Evidence from these models was combined by fixed-effect meta-analysis to identify genes predictive of the time to death of disease (DoD). Genes were combined by a weighted median approach into a prognostic score called ProstaTrend and transferred for the prediction of biochemical recurrence (BCR) after RP in an independent cohort of The Cancer Genome Atlas (TCGA). RESULTS AND LIMITATIONS: ProstaTrend comprising ∼1400 genes was significantly associated with DoD in the training cohort of PCa patients treated by RP (leave-one-out cross-validation, Cox regression: p=2e-09) and with BCR in the TCGA validation cohort (Cox regression: p=3e-06). The prognostic impact persisted after multivariable Cox regression analysis adjusting for Gleason grading group (GG) ≥3 and resection status (p=0.001; DoD, training cohort) and for GG≥3, pathological stage ≥T3, and resection state (p=0.037; BCR, validation cohort). CONCLUSIONS: ProstaTrend is a transcriptome-based score that predicts DoD and BCR in cohorts of PCa patients treated with RP. PATIENT SUMMARY: ProstaTrend provides molecular patient risk stratification after radical prostatectomy.

uap: reproducible and robust HTS data analysis.

BACKGROUND: A lack of reproducibility has been repeatedly criticized in computational research. High throughput sequencing (HTS) data analysis is a complex multi-step process. For most of the steps a range of bioinformatic tools is available and for most tools manifold parameters need to be set. Due to this complexity, HTS data analysis is particularly prone to reproducibility and consistency issues. We have defined four criteria that in our opinion ensure a minimal degree of reproducible research for HTS data analysis. A series of workflow management systems is available for assisting complex multi-step data analyses. However, to the best of our knowledge, none of the currently available work flow management systems satisfies all four criteria for reproducible HTS analysis. RESULTS: Here we present uap, a workflow management system dedicated to robust, consistent, and reproducible HTS data analysis. uap is optimized for the application to omics data, but can be easily extended to other complex analyses. It is available under the GNU GPL v3 license at https://github.com/yigbt/uap. CONCLUSIONS: uap is a freely available tool that enables researchers to easily adhere to reproducible research principles for HTS data analyses.

Dilution of candidates: the case of iron-related genes in restless legs syndrome.

Restless legs syndrome (RLS) is a common multifactorial disease. Some genetic risk factors have been identified. RLS susceptibility also has been related to iron. We therefore asked whether known iron-related genes are candidates for association with RLS and, vice versa, whether known RLS-associated loci influence iron parameters in serum. RLS/control samples (n = 954/1814 in the discovery step, 735/736 in replication 1, and 736/735 in replication 2) were tested for association with SNPs located within 4 Mb intervals surrounding each gene from a list of 111 iron-related genes using a discovery threshold of P = 5 × 10(-4). Two population cohorts (KORA F3 and F4 with together n = 3447) were tested for association of six known RLS loci with iron, ferritin, transferrin, transferrin-saturation, and soluble transferrin receptor. Results were negative. None of the candidate SNPs at the iron-related gene loci was confirmed significantly. An intronic SNP, rs2576036, of KATNAL2 at 18q21.1 was significant in the first (P = 0.00085) but not in the second replication step (joint nominal P-value = 0.044). Especially, rs1800652 (C282Y) in the HFE gene did not associate with RLS. Moreover, SNPs at the known RLS loci did not significantly affect serum iron parameters in the KORA cohorts. In conclusion, the correlation between RLS and iron parameters in serum may be weaker than assumed. Moreover, in a general power analysis, we show that genetic effects are diluted if they are transmitted via an intermediate trait to an end-phenotype. Sample size formulas are provided for small effect sizes.

Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation.

BACKGROUND: Biogeochemical elemental cycling is driven by primary production of biomass via phototrophic phytoplankton growth, with 40% of marine productivity being assigned to diatoms. Phytoplankton growth is widely limited by the availability of iron, an essential component of the photosynthetic apparatus. The oceanic diatom Thalassiosira oceanica shows a remarkable tolerance to low-iron conditions and was chosen as a model for deciphering the cellular response upon shortage of this essential micronutrient. RESULTS: The combined efforts in genomics, transcriptomics and proteomics reveal an unexpected metabolic flexibility in response to iron availability for T. oceanica CCMP1005. The complex response comprises cellular retrenchment as well as remodeling of bioenergetic pathways, where the abundance of iron-rich photosynthetic proteins is lowered, whereas iron-rich mitochondrial proteins are preserved. As a consequence of iron deprivation, the photosynthetic machinery undergoes a remodeling to adjust the light energy utilization with the overall decrease in photosynthetic electron transfer complexes. CONCLUSIONS: Beneficial adaptations to low-iron environments include strategies to lower the cellular iron requirements and to enhance iron uptake. A novel contribution enhancing iron economy of phototrophic growth is observed with the iron-regulated substitution of three metal-containing fructose-bisphosphate aldolases involved in metabolic conversion of carbohydrates for enzymes that do not contain metals. Further, our data identify candidate components of a high-affinity iron-uptake system, with several of the involved genes and domains originating from duplication events. A high genomic plasticity, as seen from the fraction of genes acquired through horizontal gene transfer, provides the platform for these complex adaptations to a low-iron world.

PredAlgo: a new subcellular localization prediction tool dedicated to green algae.

The unicellular green alga Chlamydomonas reinhardtii is a prime model for deciphering processes occurring in the intracellular compartments of the photosynthetic cell. Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. We thus developed a new tool called PredAlgo that predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway. At its core, a neural network, trained using carefully curated sets of C. reinhardtii proteins, divides the N-terminal sequence into overlapping 19-residue windows and scores the probability that they belong to a cleavable targeting sequence for one of the aforementioned organelles. A targeting prediction is then deduced for the protein, and a likely cleavage site is predicted based on the shape of the scoring function along the N-terminal sequence. When assessed on an independent benchmarking set of C. reinhardtii sequences, PredAlgo showed a highly improved discrimination capacity between chloroplast- and mitochondrion-localized proteins. Its predictions matched well the results of chloroplast proteomics studies. When tested on other green algae, it gave good results with Chlorophyceae and Trebouxiophyceae but tended to underpredict mitochondrial proteins in Prasinophyceae. Approximately 18% of the nuclear-encoded C. reinhardtii proteome was predicted to be targeted to the chloroplast and 15% to the mitochondrion.

pymzML--Python module for high-throughput bioinformatics on mass spectrometry data.

SUMMARY: pymzML is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data, the standard data format in MS and (iii) a set of functions to compare or handle spectra. AVAILABILITY AND IMPLEMENTATION: pymzML requires Python2.6.5+ and is fully compatible with Python3. The module is freely available on http://pymzml.github.com or pypi, is published under LGPL license and requires no additional modules to be installed. CONTACT: christian@fufezan.net.

Protein phosphorylation is a key event of flagellar disassembly revealed by analysis of flagellar phosphoproteins during flagellar shortening in Chlamydomonas.

Cilia are disassembled prior to cell division, which is proposed to regulate proper cell cycle progression. The signaling pathways that regulate cilia disassembly are not well-understood. Recent biochemical and genetic data demonstrate that protein phosphorylation plays important roles in cilia disassembly. Here, we analyzed the phosphoproteins in the membrane/matrix fraction of flagella undergoing shortening as well as flagella from steady state cells of Chlamydomonas. The phosphopeptides were enriched by a combination of IMAC and titanium dioxide chromatography with a strategy of sequential elution from IMAC (SIMAC) and analyzed by tandem mass spectrometry. A total of 224 phosphoproteins derived from 1296 spectral counts of phosphopeptides were identified. Among the identified phosphoproteins are flagellar motility proteins such as outer dynein arm, intraflagellar transport proteins as well as signaling molecules including protein kinases, phosphatases, G proteins, and ion channels. Eighty-nine of these phosphoproteins were only detected in shortening flagella, whereas 29 were solely in flagella of steady growing cells, indicating dramatic changes of protein phosphorylation during flagellar shortening. Our data indicates that protein phosphorylation is a key event in flagellar disassembly, and paves the way for further study of flagellar assembly and disassembly controlled by protein phosphorylation.

The chloroplast proteome: a survey from the Chlamydomonas reinhardtii perspective with a focus on distinctive features.

The unicellular green alga Chlamydomonas reinhardtii has emerged to be an important model organism for the study of oxygenic eukaryotic photosynthesis as well as other processes occurring in the chloroplast. However, the chloroplast proteome in C. reinhardtii has only recently been comprehensively characterized, made possible by proteomics emerging as an accessible and powerful tool over the last decade. In this review, we introduce a compiled list of 996 experimentally chloroplast-localized proteins for C. reinhardtii, stemming largely from our previous proteomic dataset comparing chloroplasts and mitochondria samples to localize proteins. In order to get a taste of some cellular functions taking place in the C. reinhardtii chloroplast, we will focus this review particularly on metabolic differences between chloroplasts of C. reinhardtii and higher plants. Areas that will be covered are photosynthesis, chlorophyll biosynthesis, carbon metabolism, fermentative metabolism, ferredoxins and ferredoxin-interacting proteins.

The neuronal transporter gene SLC6A15 confers risk to major depression.

Major depression (MD) is one of the most prevalent psychiatric disorders and a leading cause of loss in work productivity. A combination of genetic and environmental risk factors probably contributes to MD. We present data from a genome-wide association study revealing a neuron-specific neutral amino acid transporter (SLC6A15) as a susceptibility gene for MD. Risk allele carrier status in humans and chronic stress in mice were associated with a downregulation of the expression of this gene in the hippocampus, a brain region implicated in the pathophysiology of MD. The same polymorphisms also showed associations with alterations in hippocampal volume and neuronal integrity. Thus, decreased SLC6A15 expression, due to genetic or environmental factors, might alter neuronal circuits related to the susceptibility for MD. Our convergent data from human genetics, expression studies, brain imaging, and animal models suggest a pathophysiological mechanism for MD that may be accessible to drug targeting.

Concerted action of the new Genomic Peptide Finder and AUGUSTUS allows for automated proteogenomic annotation of the Chlamydomonas reinhardtii genome.

The use and development of post-genomic tools naturally depends on large-scale genome sequencing projects. The usefulness of post-genomic applications is dependent on the accuracy of genome annotations, for which the correct identification of intron-exon borders in complex genomes of eukaryotic organisms is often an error-prone task. Although automated algorithms for predicting intron-exon structures are available, supporting exon evidence is necessary to achieve comprehensive genome annotation. Besides cDNA and EST support, peptides identified via MS/MS can be used as extrinsic evidence in a proteogenomic approach. We describe an improved version of the Genomic Peptide Finder (GPF), which aligns de novo predicted amino acid sequences to the genomic DNA sequence of an organism while correcting for peptide sequencing errors and accounting for the possibility of splicing. We have coupled GPF and the gene finding program AUGUSTUS in a way that provides automatic structural annotations of the Chlamydomonas reinhardtii genome, using highly unbiased GPF evidence. A comparison of the AUGUSTUS gene set incorporating GPF evidence to the standard JGI FM4 (Filtered Models 4) gene set reveals 932 GPF peptides that are not contained in the Filtered Models 4 gene set. Furthermore, the GPF evidence improved the AUGUSTUS gene models by altering 65 gene models and adding three previously unidentified genes.

Proteomics to go: Proteomatic enables the user-friendly creation of versatile MS/MS data evaluation workflows.

UNLABELLED: We present Proteomatic, an operating system independent and user-friendly platform that enables the construction and execution of MS/MS data evaluation pipelines using free and commercial software. Required external programs such as for peptide identification are downloaded automatically in the case of free software. Due to a strict separation of functionality and presentation, and support for multiple scripting languages, new processing steps can be added easily. AVAILABILITY AND IMPLEMENTATION: Proteomatic is implemented in C++/Qt, scripts are implemented in Ruby, Python and PHP. All source code is released under the LGPL. Source code and installers for Windows, Mac OS X, and Linux are freely available at http://www.proteomatic.org. CONTACT: michael.specht@uni-muenster.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterizing the anaerobic response of Chlamydomonas reinhardtii by quantitative proteomics.

The versatile metabolism of the green alga Chlamydomonas reinhardtii is reflected in its complex response to anaerobic conditions. The anaerobic response is also remarkable in the context of renewable energy because C. reinhardtii is able to produce hydrogen under anaerobic conditions. To identify proteins involved during anaerobic acclimation as well as to localize proteins and pathways to the powerhouses of the cell, chloroplasts and mitochondria from C. reinhardtii in aerobic and anaerobic (induced by 8 h of argon bubbling) conditions were isolated and analyzed using comparative proteomics. A total of 2315 proteins were identified. Further analysis based on spectral counting clearly localized 606 of these proteins to the chloroplast, including many proteins of the fermentative metabolism. Comparative quantitative analyses were performed with the chloroplast-localized proteins using stable isotopic labeling of amino acids ([(13)C(6)]arginine/[(12)C(6)]arginine in an arginine auxotrophic strain). The quantitative data confirmed proteins previously characterized as induced at the transcript level as well as identified several new proteins of unknown function induced under anaerobic conditions. These proteins of unknown function provide new candidates for further investigation, which could bring insights for the engineering of hydrogen-producing alga strains.

Gender-specific association of galanin polymorphisms with HPA-axis dysregulation, symptom severity, and antidepressant treatment response.

Galanin (GAL) is an estrogen-inducible neuropeptide, highly expressed in brain regions reported to be involved in regulation of mood and anxiety. GAL possibly has a direct modulatory effect on hypothalamic-pituitary-adrenal (HPA)-axis regulation. Recent data from pharmacological and genetic studies indicate a significant function of GAL in stress-related disorders. By using a tag SNP approach covering the locus encoding preprogalanin (PPGAL), earlier findings of female-specific associations of polymorphisms in this locus with panic disorder were expanded to a larger sample of 268 outpatients with anxiety disorders (ADs). Within a larger sample of 541 inpatients with major depressive disorder (MDD), we then tested associations of one PPGAL tag SNP with specific depression symptom clusters and HPA-axis activity assessed by the combined dexamethasone-suppression/CRH-stimulation test both at inpatient admission and discharge (n=298). Gender specificity as well as dependence of the association on levels of circulating estrogens was analyzed. Genotyping revealed high linkage disequilibrium in the promoter area of the PPGAL gene, which includes several estrogen-response elements. Confirming earlier results, rs948854, tagging this promoter region, was associated with more severe anxiety pathology in female AD patients, but not in males. In premenopausal female MDD patients, the same allele of rs948854 was associated with more severe vegetative but not cognitive depressive symptoms at discharge and worse treatment response on antidepressant medication. Furthermore, this allele was associated with higher HPA-axis activity at admission. No significant case-control associations could be observed. However, because of power limitations of both patient samples, small effects cannot be excluded. The reported associations in independent samples of AD and MDD support an estrogen-dependent function of GAL in pathophysiology of anxiety and depression, affecting response to antidepressant treatment.

p3d--Python module for structural bioinformatics.

BACKGROUND: High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code. RESULTS: p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures. CONCLUSION: p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.

Polymorphisms in the GAD2 gene-region are associated with susceptibility for unipolar depression and with a risk factor for anxiety disorders.

Glutamate decarboxylase (GAD) is the rate limiting enzyme for conversion of glutamic acid to gamma-aminobutyric acid (GABA). The GAD 65 kDa isoform is encoded by the gene GAD2 and is mainly expressed in synaptic terminals. It serves as an apoenzyme, which shows enhanced availability in situations of stress, responding to short-term demands for GABA. We analyzed 18 single nucleotide polymorphisms (SNPs) in the GAD2-gene region for associations with psychiatric diagnosis and behavioral inhibition (BI) derived from the personality traits neuroticism and extraversion as defined by the Eysenck Personality Questionaire (EPQ). A total of 268 patients with anxiety disorder (AD), 541 with unipolar depression (MD), and 541 healthy controls were included. We observe associations for five tag-SNPs with BI in the AD- and control samples as well as two additional case-control associations in the MD-sample. The associated SNPs lie within a 16KB linkage disequilibrium-block, including putative 5' GAD2-promoter-elements as well as the 3' end of the gene MYO3A. Using open access mRNA-expression data, we could show that BI-associated SNPs appear to be associated with differences in MYO3A- but not GAD2 lymphoblastoid-mRNA expression levels. These results support earlier studies that suggest associations of polymorphisms within the GAD2 locus with anxiety and affective disorders. However, data from expression studies imply that these polymorphisms could tag functional effects on the neighboring gene MYO3A, which is also expressed in the brain, including the cingulate cortex and the amygdala.

Polymorphisms in the drug transporter gene ABCB1 predict antidepressant treatment response in depression.

The clinical efficacy of a systemically administered drug acting on the central nervous system depends on its ability to pass the blood-brain barrier, which is regulated by transporter molecules such as ABCB1 (MDR1). Here we report that polymorphisms in the ABCB1 gene predict the response to antidepressant treatment in those depressed patients receiving drugs that have been identified as substrates of ABCB1 using abcb1ab double-knockout mice. Our results indicate that the combined consideration of both the medication's capacity to act as an ABCB1-transporter substrate and the patient's ABCB1 genotype are strong predictors for achieving a remission. This finding can be viewed as a further step into personalized antidepressant treatment.

Graph-based analysis and visualization of experimental results with ONDEX.

MOTIVATION: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. RESULTS: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function.