Characterising a human endogenous retrovirus(HERV)-derived tumour-associated antigen: enriched RNA-Seq analysis of HERV-K(HML-2) in mantle cell lymphoma cell lines.

BACKGROUND: The cell-surface attachment protein (Env) of the HERV-K(HML-2) lineage of endogenous retroviruses is a potentially attractive tumour-associated antigen for anti-cancer immunotherapy. The human genome contains around 100 integrated copies (called proviruses or loci) of the HERV-K(HML-2) virus and we argue that it is important for therapy development to know which and how many of these contribute to protein expression, and how this varies across tissues. We measured relative provirus expression in HERV-K(HML-2), using enriched RNA-Seq analysis with both short- and long-read sequencing, in three Mantle Cell Lymphoma cell lines (JVM2, Granta519 and REC1). We also confirmed expression of the Env protein in two of our cell lines using Western blotting, and analysed provirus expression data from all other relevant published studies. RESULTS: Firstly, in both our and other reanalysed studies, approximately 10% of the transcripts mapping to HERV-K(HML-2) came from Env-encoding proviruses. Secondly, in one cell line the majority of the protein expression appears to come from one provirus (12q14.1). Thirdly, we find a strong tissue-specific pattern of provirus expression. CONCLUSIONS: A possible dependency of Env expression on a single provirus, combined with the earlier observation that this provirus is not present in all individuals and a general pattern of tissue-specific expression among proviruses, has serious implications for future HERV-K(HML-2)-targeted immunotherapy. Further research into HERV-K(HML-2) as a possible tumour-associated antigen in blood cancers requires a more targeted, proteome-based, screening protocol that will consider these polymorphisms within HERV-K(HML-2). We include a plan (and necessary alignments) for such work

Medicine and improvement in the Scots Magazine; and Edinburgh Literary Miscellany, 1804-17

Megan Coyer’s chapter engages with periodical print as a vehicle for an improving medical culture in Scotland, concentrating on the second series of the Scots Magazine. Coyer demonstrates how the Scottish press often complemented improving civic initiatives like the Edinburgh Lunatic Asylum campaign. She focuses attention on the distinctive national dynamics associated with medical improvement efforts in early nineteenth-century Scotland, with the Scots Magazine ‘providing a public forum for the expression of a national medical identity’. This identity, as Coyer shows, had an ideology of improvement at its core. This work recovers the cultural significance of the Scots Magazine as ‘the third major player in popular periodical culture in Romantic-era Scotland’; a status overshadowed by the recent critical attention devoted to the second Edinburgh Review and Blackwood’s in Scottish Romantic studies. Coyer also shows how the efforts of public health reformers highlight the complexity of improvement as both a material and moral process. She argues that print efforts dedicated to improving public health represent a ‘discursive strand in the magazine identifying a lack of cleanliness … as a moral and material blight on an otherwise improving Scottish society’. This bringing together of moral and practical aspects of improvement in the Scots also finds expression in the magazine’s series of Scottish medical biographies, whose narratives, Coyer notes, provide ‘ideal exemplars of lives dedicated to a culture of improvement’

Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen

The purpose of this work was to model the acoustic emission (AE) flaw growth data that resulted from the tensile test of a unidirectional fiberglass/epoxy specimen. The data collected and stored during the test were the six standard AE quantification parameters for each event. A classification neural network was used to sort the data into five failure mechanism clusters. The resulting frequency histograms of the sorted data were then mathematically modeled herein using the three types of Johnson distributions: bounded, lognormal, and unbounded. These provided a reasonably good fit for all six AE parameter distributions for each of the five failure mechanisms

Representative transcript sets for evaluating a translational initiation sites predictor

<p>Abstract</p> <p>Background</p> <p>Translational initiation site (TIS) prediction is a very important and actively studied topic in bioinformatics. In order to complete a comparative analysis, it is desirable to have several benchmark data sets which can be used to test the effectiveness of different algorithms. An ideal benchmark data set should be reliable, representative and readily available. Preferably, proteins encoded by members of the data set should also be representative of the protein population actually expressed in cellular specimens.</p> <p>Results</p> <p>In this paper, we report a general algorithm for constructing a reliable sequence collection that only includes mRNA sequences whose corresponding protein products present an average profile of the general protein population of a given organism, with respect to three major structural parameters. Four representative transcript collections, each derived from a model organism, have been obtained following the algorithm we propose. Evaluation of these data sets shows that they are reasonable representations of the spectrum of proteins obtained from cellular proteomic studies. Six state-of-the-art predictors have been used to test the usefulness of the construction algorithm that we proposed. Comparative study which reports the predictors' performance on our data set as well as three other existing benchmark collections has demonstrated the actual merits of our data sets as benchmark testing collections.</p> <p>Conclusion</p> <p>The proposed data set construction algorithm has demonstrated its property of being a general and widely applicable scheme. Our comparison with published proteomic studies has shown that the expression of our data set of transcripts generates a polypeptide population that is representative of that obtained from evaluation of biological specimens. Our data set thus represents "real world" transcripts that will allow more accurate evaluation of algorithms dedicated to identification of TISs, as well as other translational regulatory motifs within mRNA sequences. The algorithm proposed by us aims at compiling a redundancy-free data set by removing redundant copies of homologous proteins. The existence of such data sets may be useful for conducting statistical analyses of protein sequence-structure relations. At the current stage, our approach's focus is to obtain an "average" protein data set for any particular organism without posing much selection bias. However, with the three major protein structural parameters deeply integrated into the scheme, it would be a trivial task to extend the current method for obtaining a more selective protein data set, which may facilitate the study of some particular protein structure.</p

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

Factors influencing success of clinical genome sequencing across a broad spectrum of disorders

To assess factors influencing the success of whole-genome sequencing for mainstream clinical diagnosis, we sequenced 217 individuals from 156 independent cases or families across a broad spectrum of disorders in whom previous screening had identified no pathogenic variants. We quantified the number of candidate variants identified using different strategies for variant calling, filtering, annotation and prioritization. We found that jointly calling variants across samples, filtering against both local and external databases, deploying multiple annotation tools and using familial transmission above biological plausibility contributed to accuracy. Overall, we identified disease-causing variants in 21% of cases, with the proportion increasing to 34% (23/68) for mendelian disorders and 57% (8/14) in family trios. We also discovered 32 potentially clinically actionable variants in 18 genes unrelated to the referral disorder, although only 4 were ultimately considered reportable. Our results demonstrate the value of genome sequencing for routine clinical diagnosis but also highlight many outstanding challenges