Hick and Radhakrishnan on Religious Diversity: Back to the Kantian Noumenon

We shall examine some conceptual tensions in Hick’s ‘pluralism’ in the light of S. Radhakrishnan’s reformulation of classical Advaita. Hick himself often quoted Radhakrishnan’s translations from the Hindu scriptures in support of his own claims about divine ineffability, transformative experience and religious pluralism. However, while Hick developed these themes partly through an adaptation of Kantian epistemology, Radhakrishnan derived them ultimately from Śaṁkara (c.800 CE), and these two distinctive points of origin lead to somewhat different types of reconstruction of the diversity of world religions. Our argument will highlight the point that Radhakrishnan is not a ‘pluralist’ in terms of Hick’s understanding of the Real. The Advaitin ultimate, while it too like Hick’s Real cannot be encapsulated by human categories, is, however, not strongly ineffable, because some substantive descriptions, according to the Advaitic tradition, are more accurate than others. Our comparative analysis will reveal that they differ because they are located in two somewhat divergent metaphysical schemes. In turn, we will be able to revisit, through this dialogue between Hick and Radhakrishnan, the intensely vexed question of whether Hick’s version of pluralism is in fact a form of covert exclusivism.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s11841-015-0459-

Improved annotation with <i>de novo</i> transcriptome assembly in four social amoeba species

Background: Annotation of gene models and transcripts is a fundamental step in genome sequencing projects. Often this is performed with automated prediction pipelines, which can miss complex and atypical genes or transcripts. RNA sequencing (RNA-seq) data can aid the annotation with empirical data. Here we present de novo transcriptome assemblies generated from RNA-seq data in four Dictyostelid species: D. discoideum, P. pallidum, D. fasciculatum and D. lacteum. The assemblies were incorporated with existing gene models to determine corrections and improvement on a whole-genome scale. This is the first time this has been performed in these eukaryotic species. Results: An initial de novo transcriptome assembly was generated by Trinity for each species and then refined with Program to Assemble Spliced Alignments (PASA). The completeness and quality were assessed with the Benchmarking Universal Single-Copy Orthologs (BUSCO) and Transrate tools at each stage of the assemblies. The final datasets of 11,315-12,849 transcripts contained 5,610-7,712 updates and corrections to >50% of existing gene models including changes to hundreds or thousands of protein products. Putative novel genes are also identified and alternative splice isoforms were observed for the first time in P. pallidum, D. lacteum and D. fasciculatum. Conclusions: In taking a whole transcriptome approach to genome annotation with empirical data we have been able to enrich the annotations of four existing genome sequencing projects. In doing so we have identified updates to the majority of the gene annotations across all four species under study and found putative novel genes and transcripts which could be worthy for follow-up. The new transcriptome data we present here will be a valuable resource for genome curators in the Dictyostelia and we propose this effective methodology for use in other genome annotation projects

Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS