RNA-Seq is a recently developed sequencing technology, that through the analysis of cDNA allows for unique insights into the transcriptome of a cell. The data generated by RNA-Seq provides information on gene expression, alternative splicing events and the presence of non-coding RNAs. It has been realised non-coding RNAs are more then just artefacts of erroneous transcription and play vital regulatory roles at the genomic, transcriptional and translational level. Transcription of the DNA sense strand produces antisense transcripts. This is known as antisense transcription and often results in the production of non-coding RNAs that are complementary to their associated sense transcripts. Antisense tran-scription has been identified in bacteria, fungi, protozoa, plants, invertebrates and mammals. It seems that antisense tran-scriptional ‘hot spots’ are located around nucleosome-free regions such as those associated with promoters, indicating that it is likely that antisense transcripts carry out important regulatory functions. This underlines the importance of identifying the presence and understanding the function of these antisense non-coding RNAs. The information concerning strand ori-gin is often lost during conventional RNA-Seq; capturing this information would substantially increase the worth of any RNA-Seq experiment. By manipulating the input cDNA during the template preparation stage it is possible to retain this vital information. This forms the basis of strand-specific RNA-Seq. With an ability to unlock immense portions of new in-formation surrounding the transcriptome, this cutting edge technology may hold the key to developing a greater under-standing of the transcriptome. Drosophila melanogaster has approximately half this amount, while the salamander has twenty times this number of genes [As sequencing techniques become more sophisticated and our understanding of molecular biology increases, it has become apparent that the pathway from gene to protein is an intricate and multifaceted process. Organism complexity highlights this; there is no correlation between genome size or number of genes and the complexity of an organism . Intuitiof genes -4. One tof genes . Up 98% of genes -8. It isOne component of the transcriptome of particular interest is transcription from the DNA sense (or plus) strand. Transcription from this strand produces transcripts known as antisense transcripts or antisense RNAs (asRNAs) . CurrentRNA-Seq is a next-generation sequencing technique that allows for an in depth look into the transcriptome . It is p2. 2.1. DNA exists as a double stranded molecule; one strand is known as the sense (or plus) strand the other is known as the antisense (or minus) strand. The antisense strand contains all of the pertinent information for the formation of proteins; it was originally thought that this strand was the only strand that underwent transcription. Transcription from the sense strand is less common and it will produce what is knows as antisense transcripts or asRNAs. It was previously thought that this form of transcription was an aberration of the norm, however antisense transcription has been identified at higher then expected levels in prokaryotes and eukaryotes; including humans and other mammals , 17-21. 2.2. There exist a variety of pathways in which antisense transcripts can act as regulatory elements. It is possible to divide these pathways into three broad categories; transcription modulation, hybridization of sense-antisense RNA partners and chromatin modification. As the field of non-coding RNA is still relatively young and the field of asRNA comparatively unexplored, it is likely that more mechanisms of regulation by these elements will be elucidated in the future.2.2.1. Saccharomyces cerevisiae and bioinformatic studies have suggested that it does occur in mice and humans [The act of antisense transcription, rather than asRNA molecule itself can modulate gene expression levels. During transcription RNA polymerase binds to the promoter region of the gene and proceeds along the strand. If transcription occurs on the DNA sense strand and antisense strand simultaneously it can result in the RNA polymerases colliding. This is known as the transcriptional collision model . While id humans , 26. Thed humans . As expe2.2.2. Antisense transcripts are complementary to their sense partners; this means that there is always a chance of hybridization, and the formation of a RNA duplex. Duplex formation can exert regulatory functions in diverse ways. Factors that impact on the regulatory implications of the RNA duplex include length of hybridization, location along the RNA transcript of hybridization and whether the duplex forms in the nucleus or the cytoplasm. The formation of RNA duplexes is probably the main way in which antisense transcripts exert regulatory functions.THRA) has splicing patterns that can be altered by the presence of its antisense transcript partner [Antisense transcripts have the potential to mask many regulatory components of sense RNA transcripts. Alternative splicing is a process by which exons and introns of primary transcripts can be included, excluded or skipped to form unique mRNAs. Splicing is controlled by the presence of exonic splicing enhancers/silencers and intronic enhancer/silencers, the ratios of these elements impact on the splicing pattern . These e partner . It can  partner . The numSPI1 or PU.1) its non-coding antisense transcript partner stalls translation between the initiation and elongation steps [BACE1) has been associated with Alzheimer’s disease (AD) [APP), leading to a build up amyloid-β peptides. It has been shown that the antisense transcript of BACE1 is up-regulated in AD brains [BACE1 antisense transcript and BACE1 will form a duplex and this increases the stability of the transcript through the formation of secondary and tertiary structures. Formation of these secondary and tertiary structures protects the transcript from degradation while still allowing it to function as a cleaving enzyme. Furthermore, as amyloid-β peptides levels are increased the levels of BACE1 antisense transcripts are also elevated, creating a positive feedback loop [RNA duplex formation in the cytoplasm may alter the ability of a transcript to be translated. It is possible that the duplex formation blocks the ability of the transcript to associate with the ribosome hence altering the efficiency of the translation machinery. In the case of the gene spleen focus forming virus proviral integration oncogene spi1 (on steps . Furtherase (AD) . It playD brains . It has ack loop .2.2.3. Genomic DNA in eukaryotic cells must be assembled appropriately to fit within the nucleus of the cell. Generally, genomic DNA in its packaged state exists as 146bp segments tightly encompassing a histone protein octamer and these units are known as nucleosomes . Each nuIt is thought that long non-coding RNAs are involved in the regulation of chromatin, possessing the ability to remodel the nucleosome complex , 36. HisXIST) [XIST coats one of the X-chromosomes, the coated X will go on to become the inactive X [XIST known as TSIX, is a 40 kb RNA that is located 15 kb downstream of XIST [TSIX is suspected to play a role in X-inactivation, by keeping XIST from coating both X-chromosomes. It is suspected that TSIX silences XIST through the modification of the chromatin structure by recruiting protein complexes that are involved in heterochromatinization [XIST is repressed by TSIX it follows that the X-chromosome will remain active. Strand specific FISH probes have demonstrated that TSIX expression is associated with the active X, while no TSIX is found in cells that have entered the X-inactivation pathway [Perhaps the best example of an antisense transcript being involved in chromatin remodeling is X-inactivation . X-inactXIST) . The higactive X . The asR of XIST . TSIX isnization . If XIST pathway .3. RNA-Seq is a recently developed next-generation sequencing technology, that through the analysis of cDNA allows for unique insights into the transcriptome of a cell. The data generated by RNA-Seq provides information on gene expression, alternative splicing events, locations of transcription factor binding sites and the presence of non-coding RNAs. It has been realised non-coding RNAs are more than just artefacts of erroneous transcription and play vital regulatory roles at the genomic, transcriptional and translational level. It is thought that these additional levels of regulation are the greatest contributors to the complexity seen in higher eukaryotic organisms such as humans , 42. Thiet al., 2012 [http://galaxyproject.org/) have been developed to make these programs and various other more complex tools user friendly and accessible to all wet-lab research scientists even those with little or no bioinformatics training [The data generated by RNA-Seq can then be analyzed using free, open source bioinformatics software tools such as TopHat and Cufflinks . TopHat,l., 2012 . Open, wtraining -48.RNA-Seq has several advantages over other gene expression quantification platforms such as exon arrays and reverse transcription quantitative real-time PCR. RNA-Seq is a high throughput method that requires low levels of initial input RNA, it has a high resolution in terms of determination of transcripts structure and their quantification, low levels of background noise and produces fewer false positive results . Further4. http://www.illumina.com/products/truseq_rna_sample_prep_kit_v2.ilmn). This method makes use of a bead capture procedure, where the beads will selectively bind rRNA molecules, the RNA bound beads are then removed using a magnet. This leaves the diverse RNA species representing an intact transcriptome.Despite differences in sequencing reaction chemistries and base calling all of the RNA-Seq platforms share commonalities in terms of major steps in template preparation . The conAfter selection of the appropriate RNA fraction is completed, the molecules must be fragmented into smaller pieces, to a size between 200-500bp depending on the sequencing platform being used . This frhttp://www.illumina.com/products/tru seq_rna_sample_prep_kit_v2.ilmn).For the direct fragmenting of double stranded (ds) cDNA, first the RNA must be reverse transcribed. This can be done using either random hexamers or oligo(dT) primers. Again oligo(dT) primers will fail to pick up any RNAs that are not polyadenylated. The ds cDNA is then fragmented and primer adapters are ligated . Alterna5. The standard method of RNA-Seq library generation fails to preserve the information pertaining to which DNA strand was the original template during transcription and subsequent synthesis of the mRNA transcript. Since antisense transcripts are likely to have regulatory roles that are distinctly different from their protein coding complement, this loss of strand information results in an incomplete understanding of the transcriptome , 17, 53.1 and Figure 1.Methods for the construction of strand-specific RNA-Seq libraries can be split into two categories (i) the use of known orientation strand-specific adapters, and (ii) the chemical modification of strands . The mai5.1. Adapter methods for strand-specific RNA-Seq are conceptually difficult and require more consideration and planning when compared to standard RNA-Seq. There has been an array of different protocols outlined. These methods utilize the known sequences at the 5’ or 3’ end and the relative orientations of the RNA transcript to derive strand information.1A) [In the strand-specific 3’-end RNA-Seq method anchored oligo(dT) primers are first used to select for mRNA, which results in production of double-stranded cDNA molecules . Adapter1A) . This pr1A) . It is a1B) [Another strand-specific RNA-Seq protocol makes use of single-stranded (ss) cDNA and Illumina adapters  . The sta1B) . These s1B) . 1C). [Flowcell reverse transcription sequencing (FRT-Seq), is another category of RNA-Seq that maintains strand information. Of all the methods discussed it diverges the greatest amount from the standard RNA-Seq protocol, but it can be loosely classified as an adapter method . , 59. FRTet al. 2010 [http://www.illumina.com/products/truseq_small_rna_sample_prep_kit.ilmn), and the Life Technologies  SOLiD® Total RNA-Seq Kit that preserves strand specificity through the addition of adapters in a directional manner (http://products.invitrogen.com/ivgn/product/ 44453 74).There are other adapter methods for strand-specific RNA-Seq. These include direct strand-specific sequencing (DSSS) developed by Vivancos, al. 2010 , which i5.2. Another method of carrying out strand-specific RNA-Seq involves the marking the RNA strand through chemical modification, either on the RNA itself or during second strand cDNA synthesis . These m1D). Through the addition of bisulfite mix, all cytidine residues of the RNA strand are changed to uridine residues [The first method of chemical modification involves marking the original RNA template through the use of bisulfite treatment  [The second method of chemical modification, known as dUTP second strand, involves a modified second-strand synthesis step. During this step dUTPs are incorporated into reverse transcription reaction, this results in ds cDNA where the original strand has deoxythymidine residues while the complementary strand contains deoxyuridine residues. Through the use of Uracil-DNA-Glycosylase (UDG) these residues are degraded, which leaves the first strand intact  -66.et al., 2012 [® Stranded Total RNA Sample Prep Kit  uses a similar method to maintain strand information. For the Illumina protocol second-strand synthesis also involves the incorporation of dUTPs, however instead of using UDG to degrade the residues a special polymerase is used during amplification that will not incorporate these nucleotides. In a comprehensive comparison of various strand-specific RNA-Seq protocols using RNA extracted from Saccharomyces cerevisiae the dUTP second strand method was identified as the leading protocol [When sequencing proceeds the polarity information from the original RNA molecules is maintained. A recent protocol by Zhang l., 2012  further protocol  and it iprotocol -66.6. Antisense transcription and the expression of antisense transcripts add another layer of complexity to the transcriptome and gene regulation. The modes of regulation by antisense transcripts are diverse and, as a greater amount of research is directed to this area, it is expected that more modes of regulation will be revealed. Strand-specific RNA-Seq holds the key to fully understand the antisense transcriptome. When using RNA-Seq to annotate the transcriptome of higher eukaryotic organisms the following might be suggested. Ribosomal RNA depletion should be used for the selection of the RNA fraction to be sequenced, random hexamers for the generation of double-stranded cDNA libraries and strand-specific RNA-Seq for sequencing. By following these guidelines the entirety of the transcriptome can be annotated including the vital non-processed RNAs, such as ncRNAs and the strand of origin information will be retained. These recommendations might help to avoid generation of a limited snapshot of the transcriptome consisting almost entirely of protein coding mRNAs and processed RNAs, which does not fully demonstrate the complexity of the transcriptome.


Drosophila lacks telomerase and fly telomeres are elongated by occasional transposition of three specialized retroelements. Drosophila telomeres do not terminate with GC-rich repeats and are assembled independently of the sequence of chromosome ends. Recent work has shown that Drosophila telomeres are capped by the terminin complex, which includes the fast-evolving proteins HOAP, HipHop, Moi, and Ver. These proteins, which are not conserved outside Drosophilidae and closely related Diptera, localize and function exclusively at telomeres, protecting them from fusion events. Other proteins required to prevent end-to-end fusion in flies include HP1, Eff/UbcD1, ATM, the components of the Mre11-Rad50-Nbs (MRN) complex, and the Woc transcription factor. These proteins do not share the terminin properties; they are evolutionarily conserved non-fast-evolving proteins that do not accumulate only at telomeres and do not serve telomere-specific functions. We propose that following telomerase loss, Drosophila rapidly evolved terminin to bind chromosome ends in a sequence-independent manner. This hypothesis suggests that terminin is the functional analog of the shelterin complex that protects human telomeres. The non-terminin proteins are instead likely to correspond to ancestral telomere-associated proteins that did not evolve as rapidly as terminin because of the functional constraints imposed by their involvement in diverse cellular processes. Thus, it appears that the main difference between Drosophila and human telomeres is in the protective complexes that specifically associate with the DNA termini. We believe that Drosophila telomeres offer excellent opportunities for investigations on human telomere biology. The identification of additional Drosophila genes encoding non-terminin proteins involved in telomere protection might lead to the discovery of novel components of human telomeres. Drosophila, telomerase is absent and telomeres are elongated by targeted transposition of specialized retroelements to chromosome ends. As a consequence, Drosophila telomeres do not terminate with GC-rich repeats and are assembled in a sequence-independent fashion  retrotransposons called HeT-A, TART, and TAHRE (collectively abbreviated as HTT). Retrotransposition occurs through an RNA intermediate and each transposition event leads to an increase in the copy number of the element. The HTT elements transpose independently of each other only to chromosome ends with no sequence specificity for the attachment site, and target individual telomeres at rates ranging from 10−2 to 10−4 per fly generation. However the terminal elements loose approximately 70 bp at each fly generation due to replication-dependent loss of telomeric DNA. Thus, Drosophila telomere length homeostasis depends on the balance between the frequency of transposition events and replication-dependent terminal DNA shortening. As a result, Drosophila chromosomes terminate with HTT arrays of variable length, in which transposable elements are always arranged head-to-tail with the 3′ end of the most proximal element attached to the end of the chromosome  activity; HeT-A harbors a gag gene but does not contain an RT-coding gene, and must therefore rely on an RT encoded by another element. Telomere targeting of the HTT elements depends at least in part on the GAG proteins they encode. The transcripts of the HTT elements associate with these GAGs, which in turn associate with interphase telomeres facilitating targeted transposition. Although it lacks an RT-coding sequence, HeT-A is the most abundant element at Drosophila telomeres. Consistent with this finding, the HeT-A GAG specifically associates with telomeres and mediates telomeric localization of TART and TAHRE GAGs, which do not have intrinsic abilities to target chromosome ends 205 and Z4 (or pzg) increase the rate of HeT-A transcription leading to telomere elongation; Su(var)205 and Z4 encode HP1 and a zinc finger protein involved in chromatin remodeling, respectively  and Tel, that are not yet characterized at the molecular level and by the Ku70/Ku80 complex. Dominant mutations in the E(tc) and Tel cause dramatic elongations of the HTT arrays of all telomeres. E(tc) enhances terminal gene conversion with no effect on HTT transposition; the mechanism of Tel action has not been determined  generated by the enzymatic cut method showed that neotelomere formation occurs rather frequently in wild type males and is facilitated by partial disruption of DNA repair functions such as those of Mu2/MDC1, Rad51, ATRIP, Nbs, or ATM  machinery lead to persistence of DSBs allowing more time for de novo telomere assembly at the broken chromosome ends  termed shelterin. TRF1 and TRF2 specifically bind the TTAGGG duplex, and POT1 binds the G-overhang. TIN2 and TPP1 do not directly bind DNA but interconnect TRF1/TRF2 with POT1, linking the telomere duplex with the single stranded G-overhang; TRF2 also binds hRap1, a distant homolog of Although the shelterin subunits form a single six-subunit complex, deletions of individual shelterin components result in different phenotypes. Loss of TRF2 activates ATM signaling and the NHEJ DNA repair pathway leading to telomeric fusions (TFs). Depletion of either POT1 or TPP1 activates the ATR kinase and causes NHEJ-mediated TF formation  is the major capping complex in Drosophila telomere protection were identified by molecular cloning of genes specified by mutations causing TFs in larval brain cells , modigliani (moi), and verrocchio (ver) cause very high frequencies of TFs (∼5 per cell) and produce multicentric chromosomes that resemble little “trains” of chromosomes. The names of these genes reflect this phenotype, as three Italian trains are dubbed with the names of these famous artists. cav encodes HP1/ORC-associated protein (HOAP)  specifies a protein that contains an OB-fold domain structurally homologous to the STN1 OB-fold , while mutations in moi or ver have little or no ability to trigger these checkpoints. However, moi and ver are essential to hide chromosome ends from the DNA repair machineries that mediate telomere fusion  gene  gene encodes a zinc finger protein that interacts with HP1c and functions both in transcriptional regulation and telomere capping (Raffa et al., cav mutations and mutations in woc do not affect HOAP localization at chromosome ends (Raffa et al., Drosophila brains are all conserved in humans. In addition, there is evidence the human homologs of HP1, Mre11, Rad50, Nbs, Tefu/ATM, Mei-41/ATR, and Mus-304/ATRIP associate with human telomeres and play telomere-related functions. Eff/UbcD1 and Woc are also conserved but it is currently unknown whether their human counterparts have roles at telomeres.In summary, the nine non-terminin proteins required for telomere capping in Drosophila lost the shelterin and the CST homologs that bind DNA in a sequence-specific fashion, and evolved terminin to bind chromosome ends independently of the DNA sequence. It is indeed conceivable that the transition from a telomerase-driven to a transposon-driven telomere elongation mechanism resulted in a divergence of terminal DNA sequences, accompanied by a strong selective pressure toward the evolution of sequence-independent telomere-binding factors. We also hypothesized that the non-terminin proteins did not evolve as rapidly as terminin because of the functional constraints imposed by their involvement in diverse cellular processes (Raffa et al., While the non-terminin proteins are evolutionarily conserved, none of the terminin proteins, with the possible exception of Ver, has homologs in yeasts, mammals or plants. In addition, all terminin proteins exhibit very high rates of non-synonymous substitution per non-synonymous site, and are therefore fast-evolving proteins; non-terminin proteins are not fast-evolving and exhibit relatively low non-synonymous substitution rates (Gao et al., Drosophila and human telomeres is in the protective complexes that specifically associate with the DNA termini. It has been estimated that the Drosophila genome contains at least 40 genes required to prevent telomere fusion (Cenci et al., Drosophila genes encoding non-terminin proteins involved in telomere protection will lead to the discovery of novel human telomere components.Our hypothesis on terminin evolution suggests that non-terminin proteins correspond to ancestral telomere-associated proteins. Indeed, of the nine non-terminin proteins so far identified, seven are implicated in human telomere maintenance. Thus, it appears that the main difference between The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect.Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. Microarray-based assays of gene expression have become a mainstay of basic and translational cancer research. A significant number of modern investigations rely on these tools to inform hypothesis generation Against this backdrop, the importance of accurate interpretation of microarray results and the significant consequences of systematic analytic errors becomes apparent. In the early days of microarray analysis, high experimental costs and significant technical variability limited the available information with which comprehensive analyses of the practical effects of subtle biases in microarray data or in its interpretation could be studied One common assumption is that data from microarray-based genome expression analyses conform to a standard Gaussian  distribution. This assumption is rarely explicit but rather is most commonly made implicitly when investigators apply analytic algorithms predicated upon the Gaussian assumption. Distribution-related assumptions are relevant to at least two, distinct sets of expression data generated in microarray analyses, and the normality assumption has been variably (often implicitly) applied to both The first dataset to which distribution is relevant comprises the complete set of individual expression values across all genes and all samples in a given experiment. For example, in a study examining the expression of 25,000 genes in 100 tumors, this is the set of all 2.5 million gene expression values. The distribution of this composite dataset may be particularly relevant to downstream clustering and class discrimination analyses, as many of these algorithms are typically applied to the entire dataset as a whole. When algorithms predicated upon a standard Gaussian distribution are used, the normal assumption is implicitly introduced.The second dataset to which distribution is relevant is the dataset comprising the individual expression values for a single gene across the entire range of experimental samples. Continuing the previous example, this experiment would generate 25,000 such datasets, each with 100 data points. The distribution of these 100 data points may be particularly relevant to studies that examine the consistency of behavior of a specific gene in a specific tumor type or analyze the pattern of its change across a range of “classes” or “grades” of a specific tumor. Here the distribution may provide a useful description of the behavior of this single gene across multiple independent samples, but the normal assumption may be implicitly introduced if algorithms used to analyze the behavior of this gene are predicated upon a standard Gaussian distribution.The assumption of normality has been explicitly investigated in gene expression analysis, although to a limited degree. While it initially appeared to have both theoretical The possibility that gene expression data violate the normality assumption may be of considerable significance to clinical and translational investigators. Most current and proposed medical applications of microarray expression data are derived from analyses predicated upon this assumption, many of which have relied upon parametric statistics for gene calling and class discovery The present investigation has two objectives and has been structured in two parts: the first is theoretical – to study the distributions of cancer gene expression data – both at the individual gene and at the complete dataset level – and to assess the extent to which these deviate from normality. This provides the foundation for the second, translational objective: to study the implications of non-Gaussian gene expression distributions on clinically-oriented genomic analyses. The experimental model has been deliberately designed to recapitulate faithfully the workflow of a typical, translational pipeline for gene expression analysis .F-test of the variance demonstrates statistically-significant departures from normality for all samples  distribution.We also examined the data distributions of individual genes across the 180 samples of each of the 5 cancer data sets. Many investigators examining data from an experiment containing microarrays of multiple, similar tumors may assume that an “overexpressed” gene would exhibit a Gaussian distribution centered around a positive mean value, an “underexpressed” gene will have a similar distribution around a negative value, and a gene whose expression is unchanged will have a Gaussian distribution centered around zero. Our analysis, however, demonstrates that variable degrees of skewness and kurtosis as well as marked deviations from unity among the standard deviations are characteristic of the expression distributions for individual genes. Empiric curve fitting was used to further investigate the actual morphology of the cancer gene expression distributions , S5, S6.While these distributions fit the data more accurately than the normal distribution, KS testing indicates that they are imperfect fits . MoreoveUp to this point the analysis has been focused on investigating the actual distributions of gene expression datasets and comparing these to a theoretical, normal distribution. This analysis has demonstrated that human cancer gene expression data is not normally-distributed, either on the experiment or on the single-gene level. An appropriate next question would be whether these deviations from normality affect commonly-performed gene expression analytics, including molecular classification, gene calling, and functional annotation.Class 1) and another subset of eight oligodendrogliomas with chromosome 1p/19q codeletions Class 2), was used to study the effects of the data distribution on identification genes that are differentially-expressed between known tumor subsets. This was accomplished by applying a uniform transform , including a unique subset of eleven tumors with intact chromosomes 1p and 19q (arbitrarily designated  Box-Cox  to the e Box-Cox . In thist-test with a standard Bonferroni correction (p<0.01), identified 50 differentially-expressed genes between Class 1 and Class 2 using the parent distribution and 55 using the transformed distribution (9.1% difference). Forty-nine (49) of 56 total differentially-expressed genes were common to both lists (87.5%), while 7 were uniquely identified in only one of the two lists (12.5%) (The two-sided student's  (12.5%) , S3.t-test is a parametric test that makes assumptions regarding the shape of the underlying distribution. To eliminate this effect, we applied two, nonparametric methods for gene calling. A two-class, unpaired significance analysis of microarrays (SAM) Even with the stringent Bonferroni correction, the t-test. While this method assumes normality of the underlying data, it is viewed by many to be superior to standard and corrected t-tests and is considered robust to a variety of confounding mathematical and statistical effects An alternate strategy for gene calling uses linear modeling for microarrays (LIMMA) The effects of the distribution on functional annotation were studied first by using DAVID a priori knowledge Class 1 and Class 2) to simulate the classification process and to study the relationship of the results to the shape of the underlying data distribution. Discriminant analysis (DA) and k-nearest neighbors (KNN) classifiers were trained on a subset of the tumors with representatives from each class and were then used to classify ten, novel tumors into one of the two classes. Identical analyses were performed on data from the parent and transformed distributions. The results of these analyses demonstrate a 20% difference in class assignment (2/10 samples) for the DA and 30% (3/10 samples) for the KNN classifier when used with the parent data but identical classifications for both models when used with the transformed dataset (t-test)  .The distribution of gene expression data is typically assumed to conform to a standard Gaussian  distribution 8 features per dataset to allow statistical significance assessments of seemingly-minor deviations from normality. In so doing, it reveals that these deviations achieve a high degree of statistical significance for all of the first four central moments. This provides convincing evidence that these cancer gene expression data do not conform to a standard Gaussian distribution  and have performed a comprehensive analysis of the higher-order central moments for these distributions. This analysis exploits the availability of nearly 10ribution , Table 1Empiric curve fitting identifies, in an unbiased fashion, distributions that most accurately model the observed distributions of the expression data. Analysis of the empirically-fit distributions provides additional information regarding data distribution and can be used to draw general conclusions regarding the types of downstream analyses that may be applicable to these datasets. This analysis demonstrates that the expression distributions are not well modeled by simplified, two- parameter distributions  but instead require distributions with multiple (3–4) shape parameters to model the data accurately. Several derivatives of the β-prime distribution   . The extibutions , S6. Conibutions , S5. AltNonparametric methods may not be sufficient, however, to completely offset the effect of non-Gaussian distributions on gene calling. SAM, a nonparametric algorithm This simulation illustrates the potential effects of non-Gaussian distributions on gene calling. More importantly, it provides direct evidence against two common beliefs regarding gene expression analysis: that “small” departures from normality are insignificant to practical gene expression analyses and that “robust” gene calling algorithms can compensate completely for these effects.a priori knowledge of the clinical phenotype of interest  to train molecular classifiers that are subsequently capable of making phenotypic predictions for novel tumors using gene expression data. This process is mathematically-complex, and we performed a series of simulations to analyze the extent to which these training and prospective classification strategies may be affected by non-Gaussian data distributions.An important objective of translational oncology research is to identify patterns of gene expression that distinguish phenotypically-significant subclasses of malignant disease. A common strategy is to use gene expression profiles in conjunction with We used a representative subset of 13 randomly-selected LGGs as the training set for two “robust” molecular classifiers, the parametric DA classifier and the nonparametric KNN classifier. Each classification strategy was used in conjunction with both the parent and the transformed data distributions, allowing the effects of the shape of the distribution on the classification algorithm to be examined. The results of this analysis  demonstrdifferences in the classifications attributable to the shape of the data distribution rather than to compare the classification accuracy. While both classifiers resulted in classifications that better fit our current disease models when used in conjunction with the transformed data, it is impossible to know if this truly represents an improvement in accuracy without knowing with certainty that our current disease models accurately reflect the true nature of the disease. At present the classification scheme applied in this analysis differences in classification (regardless of the ultimate accuracy), and we have illustrated that the potential for such differences exists for both parametric and nonparametric, prospective, molecular classifiers.It is important to note that the goal of this analysis was to perform a semi-quantitative analysis of the t-test strategies, including Bonferroni corrections or linear modeling (LIMMA). The effects may be mitigated in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis therefore provides direct evidence refuting two, common beliefs in translational cancer gene expression analysis: that, “small,” departures from normality in the expression data distributions are insignificant in practice and that “robust” gene calling algorithms can fully compensate for these effects.Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. They exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of these data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects are not fully corrected by multiple-testing and other “robust” modifications to normal distribution will refer in this manuscript to the special instance of the Gaussian distribution with mean (μ) equal to zero and standard deviation (σ) equal to unity . The term first central moment will be taken to mean the first moment, which describes the mean of a distribution. All other references to higher-order central moments will be used literally.The term We examined the distributions of cancer gene expression data and investigated the translational implications of deviations from normality. To increase the practical applicability of these results, the experimental model has been structured to simulate the flow of expression data through a prototypic analytic pipeline, similar to those used in modern basic and translational gene expression investigations. An overview of the pipeline, the statistical methods used at each step, and the corresponding analyses of the data distributions and their implications are presented in Five publicly-available gene expression datasets derived from five unique, primary human cancer types were analyzed, including brain (glioblastoma), breast (adenocarcinoma), colon (adenocarcinoma), gastric (adenocarcinoma), and ovarian (adenocarcinoma) cancers, each representing the largest available dataset meeting inclusion criteria . These fTo control for potential effects of cross-platform and cross-assay variability, all included expression data were assayed using the Affymetrix® Human Genome U133A-Plus 2.0 array. Because the five datasets contained a variable number of arrays, sample size-related bias was controlled by setting the experimental sample size to 180 for each group . A total of 180 samples for each of the five cancer types were therefore selected at random from the parent datasets. These five sets of 180 samples  comprised the experimental datasets. A sixth, smaller dataset containing expression data for 23 low-grade gliomas was also used for analyses of gene calling, functional annotation, and tumor classification. This dataset has been previously generated by our group and comprises at least two molecular tumor subclasses, thereby facilitating this analysis Similarly, gene expression data from normal corresponding tissues, assayed with the U133A-Plus 2.0 array, were analyzed and used as the normal controls in the Log-subtraction-based analyses.2 converted form.To simulate typical analytic workflow of a translational molecular oncology investigation, raw expression data were normalized using the RMA method 2-converted and then subtracted from the Log2-converted expression of the corresponding feature in each tumor sample. This is standard practice in many semi-quantitative gene expression analyses; see the Text S1 for further details.Remaining consistent with a prototypic translational analysis workflow, we next calculated expression ratios for each feature  using Log-subtraction. The gene expression profiles for normal tissue corresponding to each tumor type were calculated by averaging three, comparable gene expression profiles from the appropriate normal tissue, and the average value was Log7 elements . Means were compared using both parametric and nonparametric tests (student's t-test and KW), variances were compared using the F-test, and skew and kurtosis were compared using standard error and Fisher's coefficients of skewness and kurtosis p-value of the KS calculation  by first constructing expression density histograms for each of the datasets and then by empiric fitting and parameter estimation for each of 57 potential distributions  on this dataset, again suggesting that it was not normally-distributed (data not shown). A systematic, Box-Cox transformation ribution .a priori definition of the two, genotypically- and phenotypically-distinct tumor subsets contained within the LGG dataset to define two classes Class 1 for the intact gliomas and Class 2 for the codeleted oligodendrogliomas. We applied four different statistical tests to identify genes with statistically-significant differences between the two subsets: one nonparameric method, the two-class, unpaired student's t-test with a standard Bonferroni correction for multiple testing (p<0.01) t-test that assumes normality but is considered robust p<0.01) GO Next, a series of identical classification and gene calling procedures was performed on the pre- and post-transformation datasets and the results were compared to estimate, in a semi-quantitative fashion, the effects of the shape of the underlying distribution on identification of differentially-expressed genes, functional annotation, and molecular classification. We used the The effect of the distribution on the accuracy of prospective, molecular classification algorithms was tested using a DA classifier and a KNN classifier. The DA p<0.01) was first used to eliminate genes whose expression did not differ significantly from control. Next, a training set consisting of 8 samples randomly selected from Class 1 and 5 from Class 2  was used to train the classifier, which was then tested against a novel set known to contain 7 Class 1 and 3 Class 2 tumors . These classification strategies were applied to both the parent and to the transformed distributions, and the classification results were compared in a semi-quantitative fashion  data normalized using the Robust Multichip Average (RMA) method.Distribution analysis for Log The source data for these graphs are the Log2 (unsbtracted) datasets. Bin widths have been set between 190–250 to improve visualization. Red curves represent the best-fit normal distribution. The primary image gives the histogram with the superimposed theoretical normal curve. The inset presents the quantile-quantile (QQ) plot, where deviation from the line  illustrates deviation of the empiric from the theoretical normal distribution. A: Brain; B: Breast; C: Colon; D: Gastric; E: Ovarian.(TIF)Click here for additional data file.Figure S22 (unsubtracted) data normalized using the DChip method.Distribution analysis for Log The source data for these graphs are the Log2 (unsbtracted) datasets. Bin widths have been set between 190–250 to improve visualization. Red curves represent the best-fit normal distribution. The primary image gives the histogram with the superimposed theoretical normal curve. The inset presents the quantile-quantile (QQ) plot, where deviation from the line  illustrates deviation of the empiric from the theoretical normal distribution. A: Brain; B: Breast; C: Colon; D: Gastric; E: Ovarian.(TIF)Click here for additional data file.Figure S3Distribution Fitting. Distribution fitting for the breast cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S4Distribution Fitting. Distribution fitting for the colon cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S5Distribution Fitting. Distribution fitting for the gastric cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S6Distribution Fitting. Distribution fitting for the ovarian cancer dataset for RMA (top) and DChip (bottom) normalized data. The three best-fit curves are superimposed on the histogram, and the normal distribution curve is included for comparison. The specific parameters for the best-fit distributions are given. The inset displays the quantile-quantile (QQ) plot for the best-fit and normal distributions. These charts demonstrate that multiparameter distributions capable of modeling skewness and kurtosis better characterize the data than the standard Gaussian  distribution.(TIF)Click here for additional data file.Figure S7Distribution-Dependent Effects on Molecular Tumor Subclassification Based on Two Different Methods of Gene Calling. Two different gene calling methods, the student's t-test (top) and the Significance Analysis for Microarrays  were used to verify that the gene calling method used in conjunction with the K-Nearest Neighbors classifier (KNN). Both analyses use the parent (untransformed) low-grade glioma expression datasets. Class 1 represents low-grade astrocytomas, and Class 2 represents chromosome 1p/19q codeleted, low-grade oligodendrogliomas. The topmost color bars represent the known class of each sample . The area below the color bars is a portion of the gene expression profile . Both gene calling algorithms lead to identical results, indicating that this is not the primary reason for the observed differences in molecular classification (see tion see .(TIF)Click here for additional data file.Table S12 (unsubtracted) data.Central Moments Analysis using Logf-test # = Standard Error of Skewness (Fisher's Skewness Index) † = Standard Error of Kurtosis (Fisher's Kurtosis Index) ‡ = p-value [95% CI] φ = p-value.(XLS)Click here for additional data file.Table S2Empiric Distributions. Categorized list of the 57 empiric distributions tested in the curve fitting analysis.(XLS)Click here for additional data file.Table S3Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by the student's t-test. Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions.(XLS)Click here for additional data file.Table S4Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by Significance Analysis for Microarrays (SAM). Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. FDR = False Discovery Rate.(XLS)Click here for additional data file.Table S5Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by the Kruskal-Wallis test. Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. FDR = False Discovery Rate.(XLS)Click here for additional data file.Table S6Differentially-Expressed Genes between Low-Grade Glioma Class 1 and Class 2, as identified by linear modeling (LIMMA). Affymetrix ID is the unique probeset identifier of the gene. The second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions.(XLS)Click here for additional data file.Table S7Functional Category Enrichment Analysis (SAM). Functional category (GO and KEGG) enrichment analysis for the list of genes differentially-expressed between low-grade glioma Class 1 and Class 2, as identified by the Significance Analysis for Microarrays (SAM) algorithm. Only categories with a Bonferroni-corrected p-value<0.01 are included. The GO or KEGG term is given in the left column, and the second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. Descriptive statistics are given at the bottom of the list.(XLS)Click here for additional data file.Table S8Functional Category Enrichment Analysis (KW). Functional category (GO and KEGG) enrichment analysis for the list of genes differentially-expressed between low-grade glioma Class 1 and Class 2, as identified by the Kruskal-Wallis test. Only categories with a Bonferroni-corrected p-value<0.01 are included. The GO or KEGG term is given in the left column, and the second column indicates whether each gene is present in the lists from both the parent (untransformed) and transformed (Box-Cox) distributions. Descriptive statistics are given at the bottom of the list.(XLS)Click here for additional data file.Table S9Datasets. These are the cancer gene expression datasets used in this analysis. GEO indicated data take from the Gene Expression Omnibus.(XLS)Click here for additional data file.Text S1(DOC)Click here for additional data file.


In Switzerland, people with a severe mental illness and unable to work receive disability benefits (‘IV-pension’). Once they are granted these benefits, the chances to regain competitive employment are usually small. However, previous studies have shown that individual placement and support (IPS) supports a successful reintegration into competitive employment. This study focuses on the integration of newly appointed IV-pensioners, who have received an IV-pension for less than a year.0-T4). A two-factor analysis of variance will be conducted with the two factors group (intervention versus control group) and outcome (employment yes/no). The main criterion of the two-factor analysis will be the number of competitive employment contracts in each group.The present pilot project ZHEPP  is a randomized controlled trial (RCT). The 250 participants will be randomized to either the intervention or the control group. The intervention group receives support of a job coach according to the approach of IPS. Participants in the control group do not receive IPS support. Participation takes a total of two years for each participant. Each group is interviewed every six months (TThis study will focus on the impact of IPS on new IV-pensioners and aims to identify predictors for a successful integration. Furthermore, we will examine the effect of IPS on stigma variables and recovery orientation.ISRCTN54951166 In general only 10 to 20% of people who suffer from severe mental illness are employed in the competitive employment market . AdditioIn Switzerland, people suffering from a physical or mental illness which prevents them from working receive a financial support  from the social services SVA Zurich . Over the past 12 years the IV-institution has registered a continual increase of people receiving an IV-pension because of mental illness. In the year 2000 around 77 000 people received this contribution in Switzerland but by 2011 the amount of IV-pensioners had increased to 102 000.Before receiving the IV-pension, the IV-institution offers several possibilities of supporting a reintegration into the competitive employment market . However, after receiving the approval for an IV-pension auxiliary measures to support a reintegration are limited. In consequence IV-pensioners may not feel sufficiently supported by the IV-insurance. During our information gatherings many IV-pensioners stated that the only thing they knew about the IV-insurance was that it provided assistance in finding sheltered work.Most mentally ill people including IV-pensioners state that they are not keen to work in such places and would rather be integrated into competitive employment . They feFurthermore, patients state that the integration into the competitive employment market leads to more self-esteem and an increased quality of life  due to rThe stigma associated with psychiatric disorders can have a major negative impact on individuals with mental illness. Stigma comes in two forms . First, There are two main reasons to examine stigma variables in the context of IPS : (i) feafirst train, then place[First train, then place has drawbacks, as people become ill and are not able to hold their jobs for a long time [The traditional approach in rehabilitating people with mental illness has been hen place. In thishen place. First tong time . In consong time .As frontline services to integrate IV-pensioners into the competitive employment market are almost non-existent, two problems emerge.Firstly, IV-pensioners often lack the knowledge of the legal framework and are unsure about their rights. Erroneously, they commonly believe that they are not allowed to apply for a job in the competitive employment market while receiving an IV-pension. The IV-insurance is addressing this issue by providing more information to their pensioners. Secondly, pensioners grow accustomed to not working, thereby increasing the risk of further chronification. To minimize such risks this study includes only new IV-pensioners who have received an IV-pension for less than one year.first train, then place-models [During the 1980s the concept of supported employment (SE) was developed in the USA  as a reae-models . In the e-models . Howevere-models . This stMoreover, EQOLISE demonstrated that working in a sheltered work place did not lead to a comparable stabilization of the affected persons . A betteThe supported employment concept IPS, which was used in the EQOLISE study as well as in the study presented here, ensures the immediate support of a job coach and the direct integration into competitive employment. Job coaches support the client by searching for vacant jobs, assisting applications, as well as coaching the client in working situations . If the The EQOLISE study was able to prove that the positive effects for the participants assigned to the IPS group are comparable to findings in the US. However, previous studies did not differentiate between participants with different characteristics and thus no conclusions for subgroups of clients such as persons with new-onset disability benefits can be drawn. After receiving an IV-pension the risk of further chronification of a mental illness increases over time, hence making a successful reintegration into the competitive employment market increasingly difficult.This study is a randomized controlled trial (RCT) and will investigate the effectiveness of SE for IV-pensioners receiving the IV-pension for less than a year.The main aim of this study is:(1) To determine if job coaching according to the IPS approach leads to a more successful integration of new IV-pensioners into competitive employment.In addition, the following issues will be analyzed:(2) Possible enhancement of quality of life, state of mental health and level of self-esteem by finding competitive employment(3) Chances of maintaining an employment and increasing the workload, if the participant desires this and has the ability(4) Cost-benefit equation of job coaching and potential saving schemes as compared to the current handling(5) Predictors for a successful coaching after receiving an IV-pension(6) The effect of IPS on stigma variables  and recovery orientation(7) Stigma variables and recovery orientation as predictors of finding competitive employment during IPSTo determine interest in participating in the pilot study ZHEPP, the project has been divided into two phases. The first phase was implemented to ascertain whether enough IV-pensioners were interested in participating in this study. The first phase involved the recruitment of 40 participants and those assigned to the intervention group involved in job coaching. The number of participants was easily reached and the first phase was successfully completed by the summer of 2011. The second phase was then initiated. A further 210 participants had to be recruited to reach the final sample size of 250. This recruitment took place until September 2012. After being assigned to the control or intervention group the participants will be followed up over the next two years. Hence the total duration of the study will be three years and nine months (January 2011 until end of September 2014).Persons who recently received the approval of the IV-pension were invited to an information event. The aim was to inform about the project as well as the supported employment concept IPS. Additionally the job coaches were introduced to the potential participants.0-T4) . During the following two years the participants will be interviewed every six months concerning their well-being, self-esteem, stigma and recovery variables as well as job status (Tgroup (intervention versus control group) and outcome (employment yes/no).The ZHEPP project is a randomized control trial (RCT) with two factors, The participants of the intervention group are supported by a job coach according to the approach of IPS. Those randomized to the control group are not subject to any intervention by our job coaches, but are merely invited to an interview every six months. The catchment area is the canton of Zurich, Switzerland.The target study population consists of 250 IV-pensioners, who recently (not longer than a year) received the approval of the IV. In total, 126 persons were assigned to the intervention group, where they receive the coaching according to the IPS approach. The other 124 were assigned to the control group.The sample size was calculated through power analysis. A medium effect size (0.42 SD) should be detected with a power of 95% at a two tailed significance level of 0.05.Ideally, participants assigned to the intervention group can find employment in the competitive employment market and may subsequently become independent of social benefits subsequently.Inclusion criteria:● IV-pension due to a mental disorder (full or part time pension)● The participant does not receive the IV-pension for longer than a year● The IV-pensioner`s goal is to work in the competitive employment market● Ability to give written informed consent● Working age (18–60)Exclusion criteria:● Organic mental disorder (-10: F0)Diagnosed to ICD● Mental retardation (Diagnosed to ICD-10: F7)0-T4).Based on the EQOLISE study the following instruments (German version) are used to quantify the outcome of the general well-being of the IV-pensioners. The participants are interviewed every 6 months by research workers (T(1) Incentive Focus Scale (IF): assessment of the participant`s motivation (2) Client Sociodemographic and Service Receipt Inventory (CSSRI- EU): assessment of sociodemographic facts and the medical supply ,27(3) Lancashire Quality of Life Profile (LQoLP): assessment of the participant`s quality of live (4) Symptom-Checklist 90-Revised (SCL-90-R): assessment of the participant`s psychological symptoms (5) Rosenberg self-esteem scale (RSES): assessment of the participant`s self-esteem (6) Groningen Social Disability Scale (GSDS-II): assessment of social disabilities, fulfil their social role as expected (7) Global Assessment of Functioning (GAF): brief assessment of social, occupational and psychological functioning of the participant (8) Recovery Assessment Scale (RAS), 24-item short version: assessment of participant`s feeling towards him/herself and his/her life (9) Center for Epidemiological Studies Depression Scale (CES-D), German 15-item version: assessment of depressive symptoms 0, T2 and T4):This study will also assess stigma and recovery variables, using the following validated self-report measures administered once a year (T(1) Internalized Stigma of Mental Illness Scale (ISMI), 29 items (2) Cognitive appraisal of stigma as a stressor, 8 items ,13(3) Perceived Devaluation-Discrimination Questionnaire (PDDQ), 12 items, measuring perceived public stigma Additionally, for those who worked at least one day in the last year.0). Every six months (T0-T4) the current situation of employment and, if applicable, the participant`s satisfaction with his/her employment are assessed.Experienced discrimination at work, 5 items . Furthermore, job preferences as well as the participant`s expectation concerning the process of finding an employment will be evaluated in the first interview (T(1) Client Job Status: statement of the ongoing working situation of the participant(2) Job Preferences: assessment of the participant`s job preferences(3) Indiana Job-Satisfaction/Termination Scale (IJSS/IJTS): assessment of the participant`s satisfaction with his/her employment at the beginning, while having a job and if applicable after termination of her/his contract of employmentMoreover, job coaches are required to complete the IPS-Fidelity scale  every thThis study aims to determine, whether job coaching according to IPS enables the successful integration of IV-pensioners into competitive employment.The total N of this study is 250 participants. Through a Bernoulli randomization the participants are assigned to either the control or the intervention group.The intervention’s effectiveness is tested by interview based instruments . Binary, nominal and interval-scaled data are collected.group (intervention versus control group) and outcome (employment yes/no) will be conducted to control the effectiveness of the intervention. The main criterion of the two-factor analysis will be the quantity of jobs received.A two-factor analysis of variance with the two factors As parameters vary on more than one level a simultaneous comparison in a multilevel model is carried out.Moreover, the study contains two group comparisons. The persistence of being employed or not in both groups is examined with help of a Cox-regression.A significance level < 0.05, thus, a confidence interval of 95% is to be achieved.The study was approved by the ethics committee of the canton of Zurich (KEK) und the reference number KEK-ZH-NR: 2010-0311/0. Research is carried out in compliance with the Declaration of Helsinki of the World Medical Association (WMA).Whilst there has been research about specific age groups , the effZHEPP is a randomized controlled trial (RCT). The participants of the study are randomized to either the control group or the intervention group. We hypothesize that job coaching according to IPS will lead to a more successful integration of new IV-pensioners into competitive employment. If our hypothesis is supported, IPS may in the future be offered to new Swiss IV-pensioners on a routine basis.ZHEPP: Zürcher Eingliederungs Pilot Projekt engl.: Zurich integration pilot project; EQOLISE: Enhancing the Quality of Life and Independence of Persons Disabled by Severe Mental Illness through Supported Employment; IV-pension: Pension for disabled person; SVA: Sozialversicherungsanstalt social contribution centre of Zurich; SE: Supported Employment; IPS: Individual Placement and Support; RCT: Randomized control trial; KEK: Ethics committee of canton Zurich.The authors declare that they have no competing interests.WK, BB, NR, and WR were responsible for the conception of the project and drafting the first study proposal. SV wrote the first draft and SV, WK and CO were involved in writing the manuscript. All authors critically revised and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/13/195/prepub


A major bottleneck in the manufacture of recombinant therapeutic proteins is the time and effort needed for the generation of stable, high-producing mammalian cell lines. Conventional gene transfer methods for stable cell line generation rely on random transgene integration, resulting in unpredictable and highly variable levels of expression of the transgene in individual clones ,2. As a We initially determined the efficiency of the PB system in the generation of stable lines using suspension adapted mammalian cells. CHO and HEK 293 cells were co-transfected with an eGFP-bearing donor plasmid along with the PB helper plasmid. The cells were then grown in the absence of puromycin, and the percentage of GFP-expressing cells in each culture was determined by flow cytometry on a daily basis. By 21 days post-transfection, the remaining GFP-positive cells were assumed to be recombinant. Compared to conventional transfection of plasmid DNA, PB transposition resulted in an improvement in the efficiency of stable cell line generation up to 20-fold for both CHO and HEK 293 cells Figure .To further evaluate the PB system, CHO cells expressing a tumour necrosis factor receptor:Fc fusion protein (TNFR:Fc) were generated either by PB-transposition or by conventional transfection. Clonal cell lines were recovered following selection in 50 or 10 µg/mL puromycin for two weeks. Recovered lines were grown in suspension culture for 7 days in 24-well plates after which the medium was analyzed by ELISA to determine TNFR:Fc productivity. Transposition increased the frequency of high-producing clones in the transfected population Figure . To furtIn conclusion our results demonstrate that stable cell lines derived by PB transposition are efficiently generated and are more productive than cell lines generated by conventional transfection methods. Therefore, the PB system represents a valuable and practical alternative to standard plasmid transfection to efficiently generate cell clones with stable and enhanced transgene expression.


Bathymodiolus azoricus is a deep-sea hydrothermal vent mussel found in association with large faunal communities living in chemosynthetic environments at the bottom of the sea floor near the Azores Islands. Investigation of the exceptional physiological reactions that vent mussels have adopted in their habitat, including responses to environmental microbes, remains a difficult challenge for deep-sea biologists. In an attempt to reveal genes potentially involved in the deep-sea mussel innate immunity we carried out a high-throughput sequence analysis of freshly collected B. azoricus transcriptome using gills tissues as the primary source of immune transcripts given its strategic role in filtering the surrounding waterborne potentially infectious microorganisms. Additionally, a substantial EST data set was produced and from which a comprehensive collection of genes coding for putative proteins was organized in a dedicated database, "DeepSeaVent" the first deep-sea vent animal transcriptome database based on the 454 pyrosequencing technology.A normalized cDNA library from gills tissue was sequenced in a full 454 GS-FLX run, producing 778,996 sequencing reads. Assembly of the high quality reads resulted in 75,407 contigs of which 3,071 were singletons. A total of 39,425 transcripts were conceptually translated into amino-sequences of which 22,023 matched known proteins in the NCBI non-redundant protein database, 15,839 revealed conserved protein domains through InterPro functional classification and 9,584 were assigned with Gene Ontology terms. Queries conducted within the database enabled the identification of genes putatively involved in immune and inflammatory reactions which had not been previously evidenced in the vent mussel. Their physical counterpart was confirmed by semi-quantitative quantitative Reverse-Transcription-Polymerase Chain Reactions (RT-PCR) and their RNA transcription level by quantitative PCR (qPCR) experiments.We have established the first tissue transcriptional analysis of a deep-sea hydrothermal vent animal and generated a searchable catalog of genes that provides a direct method of identifying and retrieving vast numbers of novel coding sequences which can be applied in gene expression profiling experiments from a non-conventional model organism. This provides the most comprehensive sequence resource for identifying novel genes currently available for a deep-sea vent organism, in particular, genes putatively involved in immune and inflammatory reactions in vent mussels.B. azoricus transcriptome will facilitate research into biological processes underlying physiological adaptations to hydrothermal vent environments and will provide a basis for expanding our understanding of genes putatively involved in adaptations processes during post-capture long term acclimatization experiments, at "sea-level" conditions, using B. azoricus as a model organism.The characterization of the  Deep-sea hydrothermal vent ecosystems are driven by unique physical, geochemical and biological processes with specialized energy sources at the origin of the trophic web. Since the discovery of hydrothermal vents and their associated fauna in the Galapagos Rift, evidence of the establishment of dense faunal communities based on chemosynthesis have mounted over the past decades, and generally in relation to areas where tectonic movements and deep ocean volcanism are active . HydrothBathymodiolus are biomass dominant at many known deep-sea hydrothermal vent and cold seep habitats. Survival in such extreme conditions requires unique anatomical and physiological adaptations. For example the development of specialized gill epithelial cells harboring methanotrophic and thiotrophic endosymbiont bacteria constitutes one the best recognized adaptation strategies to chemosynthetic environments [Bathymodiolus azoricus subsists at vent sites, amid unusual levels of heavy metals, pH, temperature, CO2, methane and sulfide, while coping successfully with environmental microbes [Mussels in the genus ronments . Dual syronments -7. Near microbes .Bathymodiolus species. Gene expression profiles are limited to a few EST projects mainly originated from the Evolution and Genetics of Marine Populations team at the Biological Station of Roscoff, France. In a recent analysis, the screening of cDNA libraries from whole bodies of B. azoricus resulted in 362 contigs and 1,918 singletons. Many genes known to be involved in both metallic and oxidative stress responses were then identified [B. thermophilus was investigated by means of subtractive suppression hybridization experiments aimed at the identification of genes differentially expressed in response to different temperatures regimes [Despite its prominence as a model to study physiological adaptation to extreme physical and chemical conditions , there ientified . However regimes .B. azoricus genes in the public database and to discover new deep-sea vent adaptation-related genes in B. azoricus, and particularly for immune-related genes we have conducted a high-throughput experimental approach using pyrosequencing, on the 454 GS FLX (Roche-454 Life Sciences) with Titanium chemistry, to sequence the transcriptome of B. azoricus gill tissues. In the absence of a reference genome, this sequence method, which has not yet been widely applied to hydrothermal vent animals, holds great potential for discovery of genes and genetic markers in unconventional model species through de novo transcriptome sequence assembly. The assembled and annotated sequences were produced and have been organized in a dedicated database, accessible through the website, http://transcriptomics.biocant.pt:8080/deepSeaVent providing an extensive catalog of genes expressed in gill tissues harboring immune cells, the hemocytes, of the deep-sea vent mussel Bathymodiolus azoricus.Thus far, knowledge of deep-sea biology or of the molecules involved in the maintenance of homeostasis in hydrothermal vent animals has been limited in part by the lack of information about their genomes and systematic sequencing of expressed sequence tags to identify protein coding genes on a large scale. The deep-sea vent biological systems represent the opportunity to study and provide new insights into the basic physiological principles that govern the defense mechanisms in vent animals and to understand how they cope with microbial infections. The problem of microbial threat and the need for immunity exist in both deep sea and shallow water bivalves however differences in the genes of marine organisms living in so distinct habitats are likely to occur. In order to significantly increase the number of Bathymodiolus azoricus collected at deep-sea vents and sequenced in a single GS FLX Titanium plate. A total of 778,996 raw nucleotide reads were produced with an average length of 283 bp, corresponding to 223.7 Mb. After removal of the SMART adaptors, by a custom script, the sequences were assembled with the MIRA software. A total of 582,650 quality reads were assembled into 75,407 contigs of which 3,071 were singletons corresponding to a total of 38,8 Mb. The length of the consensus sequences varied from 40 bp to 3,400 bp, with an average length of 509 bp. A summary of data is indicated in Table A cDNA library was constructed from mRNA of fresh gill tissues from 3,416 contigs were longer than 1 Kb, and 270 longer than 1.5 Kb. The distribution of contig length and EST assembly by contig are shown in Fig -6. The process was repeated for contigs without hits, increasing the E-value cut-off to 10-2, and resulting in the identification of 3,616 additional contigs. The remaining contigs were processed through the ESTscan application [http://www.ebi.ac.uk/InterPro[Bathymodiolus contigs to one of the major GO annotation categories, i.e., Biological Processes, Cellular Components and Molecular Functions in a species-independent manner [The contigs were annotated taking into consideration the identity of the translation frame, the conserved protein domains and Gene Ontology (GO) terms. To obtain the translation frame, all contigs were queried against the NCBI protein database (nr) using BLASTx algorithm , resultilication , from wh/InterPro,15, fromB. azoricus from the Lucky Strike hydrothermal vent field.The largest proportion of GO assigned sequences fell into broad categories for all three major GO functional domains as presented in Fig Bathymodiolus species, b) represent mostly 3' or 5' untranslated regions (UTRs) that would lack protein matches as they are non-coding or c) contain sequences to short to result in significant hits.The contigs without any homology may correspond to one of the following categories: a) novel or diverged amino acid coding sequences that are specific to Despite that the Gene Ontology project is aimed at describing gene product characteristics and gene product annotation data, by ways of a direct acyclic graph (DAG) structure of controlled vocabularies, the process of assigning GO terms to gene products might prove difficult given the current GO structured vocabulary and the systematic relationship between the GO terms. This follows from the fact that GO describes how gene products behave in a cellular context and thus a gene product might be associated with or located in one or more cellular components; it is active in one or more biological processes, during which it performs one or more molecular functions.B. azoricus has been the subject of previous studies, in our laboratory, aiming at the characterization of its cellular and humoral immune reactions as a means to better understand physiological adaptations to vent environments. Given the limited immune gene expression information available for this non-conventional model organism, a query-based strategy was used to identify coding sequences within the DeepSeaVent database, for the selection and retrieval of nucleic acid information of cDNAs corresponding to genes potentially involved in immune and inflammatory reactions. Therefore, the selection of candidate genes presented in Table The innate immune system acts to protect the individual from invasive bacteria, viruses, and eukaryotic pathogens by detecting molecular signatures of infection and initiating effector responses. It probably originated early in animal phylogeny and is intimately related with wound healing and tissue maintenance functions among invertebrates . This deB. azoricus were classified within the context of GO representation and to one of the following immune categories: immune recognition, signal transduction, transcription and effector molecules  between the two databases corresponded to the categories, Cell Killing, Cellular Component Biogenesis, Macromolecular Complex and Structural Molecule Activity , during the MAR08 cruise (July 9th - August 16th 2008) led by Chief Scientist Dr Anna-Louise Reysenbach. Once the mussels were brought to the surface, they were immediately processed onboard for subsequent manipulation of RNA or immediately stored at -80°C for long-term preservation of tissue samples.Mussels were collected from the hydrothermal vent field Lucky Strike , on the Mid-Atlantic Ridge (MAR), with the American R/V (r), Austin, TX). The quality of total RNA was verified on a 1.4% (w/v) agarose-MOPS-formaldehyde denaturating gels and by assessing the A260/280 and A260/230 ratios using the NanoVue spectrophotometer . Poly-A RNA was extracted from each total RNA sample using the Poly(A)Purist(tm) mRNA Purification Kit according to manufacturer's instructions . The mRNA from each gill tissue sample from all 6 animals (approximately 20-50 μg/ml) was pooled and used as the source of starting material for cDNA synthesis and the production of normalized cDNA intended for 454 sequencing. The normalization process was performed by Evrogen  and based on the SMART double-stranded cDNA synthesis methodology using a modified template-switching approach that allows the introduction of known adapter sequences to both ends of the first-strand cDNA. cDNA was further amplified by PCR and normalized using Duplex-Specific Nuclease-technology [Gill tissues from 6 different animals were dissected from -80°C preserved animals and processed for total RNA extraction using the RiboPure(tm) kit  at Biocant .Following 454 sequencing, the quality trimming and size selection of reads were determined by the 454 software after which the SMART adaptor sequences were removed from reads using a custom script and the poly-A masked using MIRA, to assure correct assembly of raw sequencing reads . A total-6 and assessing the best twenty five hits. Contigs without hits were submitted again to BLASTx homology searches against the NCBI nr database with a higher E-value cut-off set at 10-2. Sequences with a translation frame identification derived from the two previous searches were used to establish the preferential codon usage in B. azoricus based on which the software ESTScan detected further potential transcripts from the two previous sets of sequences with yet no BLASTx matches. This procedure originated a third set of sequences with putative amino-acid translation. To illustrate the outline flow for the transcriptome annotation procedure a diagram is shown in Fig The translation frame of the contigs was determined through queries against the NCBI non redundant protein database using BLASTx with an E-value of 10The entire collection of sequences of at least 30 amino-acid long, resulting from the BLASTx and the ESTScan procedures, was processed by InterProScan for the prediction of protein domain signatures and Gene Ontology terms. All the results were compiled into a SQL database developed as an information management system.The distribution of sequences into GO categories was calculated at each level and were passed to the parent GO at the top of the broad ontology domains, considering that each single assignment into a GO child was only counted once in the total sum. This information was also useful to establish the number of amino-acid sequences shared between the two Mytibase and DeepSeaVent databases.The retrieval of sequence information matching bacterial phylotypes was based on a convergent analysis of bacterial hits among the best BLASTx hits. The number of positive hits was calculated and plotted according to the position of best hit ranging from the first to the twentieth. A convergent number of bacterial hits was found at 3,522 contigs Fig . The uniHM756110 to HM756130; HM756132 to HM756134; HM756136 to HM756149; HM756151 and HM756152  was performed to confirm the physical identity of the selected genes recognized among the 454-ESTs using the normalized cDNA. 2 μg of total RNA, initially used to generate the transcriptome library, were reverse transcribed using the ThermoScript™ RT-PCR system  following manufacturer's instructions. All the contig sequences from putative immune genes were submitted to GenBank and assigned the accession numbers http://www.ncbi.nlm.nih.gov/, specifying an expected PCR product of 200-300 bp and primer annealing temperatures between 56°C and 58°C. 25 μl PCR volume reactions were set with 1 μl of each forward and reverse primers  and using a 2× PCR mix from PROMEGA . PCR cycling conditions were according to Bettencourt et al. 2009 [Primers targeting the immune candidate genes were designed using the Primer-Blast  from NCBal. 2009 . PCR proT) for the relative quantification of gene expression was used for assessing the normalized expression value of immune-related genes using the 28S rRNA as the control transcript . Data were transferred to Excel files and plotted as histograms of normalized fold expression of target genes.Quantitative PCR (qPCR) was further used to assess and quantify the relative expression of the candidate genes previously tested on semi-quantitative RT-PCR. The non-normalized cDNA was obtained as previously described and consisting of the same cDNA utilized for subsequent normalization and 454 sequencing procedure. Quantitative PCR reactions were performed on the CFX96™ Real Time PCR System mounted onto the C1000 Thermal Cycling platform . Amplifications were carried out using 0.5 μl (10 μM) of the specific primers as for semi-quantitative PCR and mixed to 10 μl of SsoFast™ Eva Green SuperMix  and 50 ng of cDNA in a final volume of 20 μl. PCR cycling conditions were 95°C for 3 min, followed by 35 cycles of 95 °C 10 s, 58 °C 15 s and 72 °C 30 s. 6 replicates were performed for each gene tested in real time PCR reactions. Melt curves profiles were analyzed for each gene tested. The 28S rRNA gene was used as the housekeeping gene and for normalization of expression of gene of interest or immune-related target genes. The comparative CT method (ΔΔCB. azoricus and participated in the discussion of sequencing results and manuscript drafting. RS contributed to the general coordination of the study in the Azores and helped drafting the final manuscript. All authors read and approved the final manuscript.RB conceived and designed the study, collected samples, participated in the data analysis and drafted the manuscript. MP developed the pipe-line analysis for all functional annotation and developed the SQL database. CE helped conceive the study and the manuscript drafting, oversaw sequencing reaction and participated in sequence analyses. PG participated in the preparation of cDNA for 454 sequencing reaction and set-up of the 454 equipment. MA participated in the gene confirmation experiments. TS provided logistics and details for sampling of


Cinaciguat (BAY 58-2667) is an nitric oxide (NO)-independent and heme-independent soluble guanylate cyclase (sGC) activator. Cinaciguat preferentially activates sGC in its oxidized or heme-free state, when the enzyme is insensitive to its endogenous ligand NO and exogenous nitrovasodilators .Endothelium-derived NO is one of the mechanisms by which platelet aggregation and thrombus formation is prevented by the intact blood vessel wall. Pharmacological stimulation of sGC in platelets correlates with inhibition of aggregation, platelet cGMP increase, prolongation of bleeding time and antithrombotic effects in vitro . This prThis non-randomized, open-label trial was conducted from September to November 2001 in a single center in Germany. It was carried out in accordance with the Declaration of Helsinki and adhered to the International Conference of Harmonization good clinical practice guidelines and the German drug law (AMG). The study protocol was approved by the Ethics Committee of the North-Rhine Medical Council, Duesseldorf, Germany.The study consisted of a 9-day treatment phase with 100 mg oral acetylsalicylic acid (aspirin) once daily administered in the morning in the fasting state and, after the last dose of aspirin, four sequential oral doses of 1.5 mg cinaciguat administered in the fasting state at 1-hour intervals. The first dose of cinaciguat was administered together with the last dose of aspirin. This cinaciguat dosing schedule was used to mimic a prolonged plasma concentration over time profile.12 healthy men  were enrolled and completed the study according to protocol.Low-dose aspirin treatment in healthy subjects resulted in the expected inhibition of platelet aggregation. 4 oral doses of 1.5 mg cinaciguat administered at 1-hour intervals did not significantly affect platelet aggregation induced by collagen and the thromboxane A2 mimetic U 46619 or bleeding time. These results are in line with the findings of a single dose escalation study.Plasma pharmacokinetics of cinaciguat following 4 oral doses of 1.5 mg cinaciguat administered at 1-hour intervals showed pronounced inter-individual variability. There was no major pharmacokinetic interaction detected between aspirin and cinaciguat, compared with the data of a previous study, which applied the same dosing regimen.Coadministration of cinaciguat and aspirin did not reveal any pharmacodynamic interaction with respect of platelet aggregation or clinically relevant pharmacokinetic interaction.