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The radula is the central foraging organ and apomorphy of the Mollusca. However, in contrast to other innovations, including the mollusk shell, genetic underpinnings of radula formation remain virtually unknown. Here, we present the first radula formative tissue transcriptome using the viviparous freshwater snail Tylomelania sarasinorum and compare it to foot tissue and the shell-building mantle of the same species. We combine differential expression, functional enrichment, and phylostratigraphic analyses to identify both specific and shared genetic underpinnings of the three tissues as well as their dominant functions and evolutionary origins. Gene expression of radula formative tissue is very distinct, but nevertheless more similar to mantle than to foot. Generally, the genetic bases of both radula and shell formation were shaped by novel orchestration of preexisting genes and continuous evolution of novel genes. A significantly increased proportion of radula-specific genes originated since the origin of stem-mollusks, indicating that novel genes were especially important for radula evolution. Genes with radula-specific expression in our study are frequently also expressed during the formation of other lophotrochozoan hard structures, like chaetae (hes1, arx), spicules (gbx), and shells of mollusks (gbx, heph) and brachiopods (heph), suggesting gene co-option for hard structure formation. Finally, a Lophotrochozoa-specific chitin synthase with a myosin motor domain (CS-MD), which is expressed during mollusk and brachiopod shell formation, had radula-specific expression in our study. CS-MD potentially facilitated the construction of complex chitinous structures and points at the potential of molecular novelties to promote the evolution of different morphological innovations.
High-throughput RNA sequencing (RNAseq) produces large data sets containing expression levels of thousands of genes. The analysis of RNAseq data leads to a better understanding of gene functions and interactions, which eventually helps to study diseases like cancer and develop effective treatments. Large-scale RNAseq expression studies on cancer comprise samples from multiple cancer types and aim to identify their distinct molecular characteristics. Analyzing samples from different cancer types implies analyzing samples from different tissue origin. Such multi-tissue RNAseq data sets require a meaningful analysis that accounts for the inherent tissue-related bias: The identified characteristics must not originate from the differences in tissue types, but from the actual differences in cancer types. However, current analysis procedures do not incorporate that aspect. As a result, we propose to integrate a tissue-awareness into the analysis of multi-tissue RNAseq data. We introduce an extension for gene selection that provides a tissue-wise context for every gene and can be flexibly combined with any existing gene selection approach. We suggest to expand conventional evaluation by additional metrics that are sensitive to the tissue-related bias. Evaluations show that especially low complexity gene selection approaches profit from introducing tissue-awareness.