The transcriptional repressor B lymphocyte-induced maturation protein-1 (Blimp-1) has crucial roles in the control of plasma cell differentiation and in maintaining survival of plasma cells. However, how Blimp-1 ensures the survival of plasma cell malignancy, multiple myeloma (MM), has remained elusive. Here we identified Aiolos, an anti-apoptotic transcription factor of MM cells, as a Blimp-1-interacting protein by mass spectrometry. ChIP coupled with DNA microarray was used to profile the global binding of Aiolos and Blimp-1 to endogenous targets in MM cells, which revealed their co-binding to a large number of genes, including apoptosis-related genes. Accordingly, Blimp-1 and Aiolos regulate similar transcriptomes in MM cells. Analysis of the binding motifs for Blimp-1 and Aiolos uncovered a partial motif that was similar across sites for both proteins. Aiolos promotes the binding of Blimp-1 to target genes and thereby enhances Blimp-1-dependent transcriptional repression. Furthermore, treatment with an anti-MM agent, lenalidomide, caused ubiquitination and proteasomal degradation of Blimp-1, leading to the de-repression of a new Blimp-1 direct target, CULLIN 4A (CUL4A), and reduced Aiolos levels. Accordingly, lenalidomide-induced cell death was partially rescued by reintroduction of Blimp-1 or knockdown of CUL4A. Thus, we demonstrated the functional impacts and underlying mechanisms of the interaction between Aiolos and Blimp-1 in maintaining MM cell survival. We also showed that interruption of Blimp-1/Aiolos regulatory pathways contributes to lenalidomide-mediated anti-MM activity.

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Artificial selection can provide insights into how insecticide resistance mechanisms evolve in populations. The underlying basis of such phenomena can involve complex interactions of multiple genes, and the resolution of this complexity first necessitates confirmation that specific genes are involved in resistance mechanisms. Here, we used a novel approach invoking a constrained RNA sequencing analysis to refine the discovery of specific genes involved in insecticide resistance. Specifically, for gene discovery, an additional constraint was added to the traditional comparisons of susceptible vs. resistant flies by the incorporation of a line in which insecticide susceptibility was 'recovered' within a resistant line by the removal of insecticide stress. In our analysis, the criterion for the classification of any gene as related to insecticide resistance was based on evidence for differential expression in the resistant line as compared with both the susceptible and recovered lines. The incorporation of this additional constraint reduced the number of differentially expressed genes putatively involved in resistance to 464, compared with more than 1000 that had been identified previously using this same species. In addition, our analysis identified several key genes involved in metabolic detoxification processes that showed up-regulated expression. Furthermore, the involvement of acetylcholinesterase, a known target for modification in insecticide resistance, was associated with three key nonsynonymous amino acid substitutions within our data. In conclusion, the incorporation of an additional constraint using a 'recovered' line for gene discovery provides a higher degree of confidence in genes identified to be involved in insecticide resistance phenomena.

By binding to short and highly conserved DNA sequences in genomes, DNA-binding proteins initiate, enhance or repress biological processes. Accurately identifying such binding sites, often represented by position weight matrices (PWMs), is an important step in understanding the control mechanisms of cells. When given coordinates of a DNA-binding domain (DBD) bound with DNA, a potential function can be used to estimate the change of binding affinity after base substitutions, where the changes can be summarized as a PWM. This technique provides an effective alternative when the chromatin immunoprecipitation data are unavailable for PWM inference. To facilitate the procedure of predicting PWMs based on protein–DNA complexes or even structures of the unbound state, the web server, DBD2BS, is presented in this study. The DBD2BS uses an atom-level knowledge-based potential function to predict PWMs characterizing the sequences to which the query DBD structure can bind. For unbound queries, a list of 1066 DBD–DNA complexes (including 1813 protein chains) is compiled for use as templates for synthesizing bound structures. The DBD2BS provides users with an easy-to-use interface for visualizing the PWMs predicted based on different templates and the spatial relationships of the query protein, the DBDs and the DNAs. The DBD2BS is the first attempt to predict PWMs of DBDs from unbound structures rather than from bound ones. This approach increases the number of existing protein structures that can be exploited when analyzing protein–DNA interactions. In a recent study, the authors showed that the kernel adopted by the DBD2BS can generate PWMs consistent with those obtained from the experimental data. The use of DBD2BS to predict PWMs can be incorporated with sequence-based methods to discover binding sites in genome-wide studies.Available at: http://dbd2bs.csie.ntu.edu.tw/, http://dbd2bs.csbb.ntu.edu.tw/, and http://dbd2bs.ee.ncku.edu.tw.