Genetic screens are powerful tools to identify the genes required for a given biological process. However, for technical reasons, comprehensive screens have been restricted to very few model organisms. Therefore, although deep sequencing is revealing the genes of ever more insect species, the functional studies predominantly focus on candidate genes previously identified in Drosophila, which is biasing research towards conserved gene functions. RNAi screens in other organisms promise to reduce this bias. Here we present the results of the iBeetle screen, a large-scale, unbiased RNAi screen in the red flour beetle, Tribolium castaneum, which identifies gene functions in embryonic and postembryonic development, physiology and cell biology. The utility of Tribolium as a screening platform is demonstrated by the identification of genes involved in insect epithelial adhesion. This work transcends the restrictions of the candidate gene approach and opens fields of research not accessible in Drosophila.

BackgroundInsect pest control is challenged by insecticide resistance and negative impact on ecology and health. One promising pest specific alternative is the generation of transgenic plants, which express double stranded RNAs targeting essential genes of a pest species. Upon feeding, the dsRNA induces gene silencing in the pest resulting in its death. However, the identification of efficient RNAi target genes remains a major challenge as genomic tools and breeding capacity is limited in most pest insects impeding whole-animal-high-throughput-screening.ResultsWe use the red flour beetle Tribolium castaneum as a screening platform in order to identify the most efficient RNAi target genes. From about 5,000 randomly screened genes of the iBeetle RNAi screen we identify 11 novel and highly efficient RNAi targets. Our data allowed us to determine GO term combinations that are predictive for efficient RNAi target genes with proteasomal genes being most predictive. Finally, we show that RNAi target genes do not appear to act synergistically and that protein sequence conservation does not correlate with the number of potential off target sites.ConclusionsOur results will aid the identification of RNAi target genes in many pest species by providing a manageable number of excellent candidate genes to be tested and the proteasome as prime target. Further, the identified GO term combinations will help to identify efficient target genes from organ specific transcriptomes. Our off target analysis is relevant for the sequence selection used in transgenic plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1880-y) contains supplementary material, which is available to authorized users.

Motile multiciliated cells (MCCs) have critical roles in respiratory health and disease and are essential for cleaning inhaled pollutants and pathogens from airways. Despite their significance for human disease, the transcriptional control that governs multiciliogenesis remains poorly understood. Here we identify TP73, a p53 homolog, as governing the program for airway multiciliogenesis. Mice with TP73 deficiency suffer from chronic respiratory tract infections due to profound defects in ciliogenesis and complete loss of mucociliary clearance. Organotypic airway cultures pinpoint TAp73 as necessary and sufficient for basal body docking, axonemal extension, and motility during the differentiation of MCC progenitors. Mechanistically, cross-species genomic analyses and complete ciliary rescue of knockout MCCs identify TAp73 as the conserved central transcriptional integrator of multiciliogenesis. TAp73 directly activates the key regulators FoxJ1, Rfx2, Rfx3, and miR34bc plus nearly 50 structural and functional ciliary genes, some of which are associated with human ciliopathies. Our results position TAp73 as a novel central regulator of MCC differentiation.

TFClass (http://tfclass.bioinf.med.uni-goettingen.de/) provides a comprehensive classification of human transcription factors based on their DNA-binding domains. Transcription factors constitute a large functional family of proteins directly regulating the activity of genes. Most of them are sequence-specific DNA-binding proteins, thus reading out the information encoded in cis-regulatory DNA elements of promoters, enhancers and other regulatory regions of a genome. TFClass is a database that classifies human transcription factors by a six-level classification schema, four of which are abstractions according to different criteria, while the fifth level represents TF genes and the sixth individual gene products. Altogether, nine superclasses have been identified, comprising 40 classes and 111 families. Counted by genes, 1558 human TFs have been classified so far or >2900 different TFs when including their isoforms generated by alternative splicing or protein processing events. With this classification, we hope to provide a basis for deciphering protein–DNA recognition codes; moreover, it can be used for constructing expanded transcriptional networks by inferring additional TF-target gene relations.