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Animals that live in changing environments need to adjust their metabolism to maintain body functions, and sensing these changing conditions is essential for mediating the short- and long-term physiological and behavioral responses that make these adjustments. Previous research on nematodes and insects facing changing oxygen levels has shown that these animals rapidly respond using atypical soluble guanylyl cyclases (sGCs) as oxygen sensors connected to downstream cGMP pathways, and they respond more slowly using hypoxia-inducible transcription factors (HIFs) that are further modulated by oxygen-sensing prolyl hydroxylases (PHs). Crustaceans are known to respond in different ways to hypoxia, but the mechanisms responsible for sensing oxygen levels are more poorly understood than in nematodes and insects. Our paper reviews the functions of and mechanisms underlying oxygen sensing in crustaceans. Furthermore, using the oxygen sensing abilities of nematodes and insects as guides in analyzing available crustacean transcriptomes, we identified orthologues of atypical sGCs, HIFs, and PHs in crustaceans, including in their chemosensory organs and neurons. These molecules include atypical sGCs activated by hypoxia (Gyc-88E/GCY-31 and Gyc-89D/GCY-33) but not those activated by hyperoxia (GCY-35, GCY-36), as well as orthologues of HIF-α, HIF-β, and PH. We offer possible directions for future research on oxygen sensing by crustaceans. [ABSTRACT FROM AUTHOR]

Motivation Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. Results mEpigram outperforms both MEME and DREME on finding modified motifs in simulated data that mimics various motif enrichment scenarios. Furthermore we were able to identify methylated motifs in Arabidopsis DNA affinity purification sequencing (DAP-seq) data that were previously demonstrated to contain such motifs. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis -elements by collaborative proteins. Availability and implementation The mEpigram program is available at http://wanglab.ucsd.edu/star/mEpigram. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: We performed a loss-of-function RNA interference screen to define therapeutic targets in multiple myeloma, a genetically diverse plasma cell malignancy. Unexpectedly, we discovered that all myeloma lines require caspase-10 for survival irrespective of their genetic abnormalities. The transcription factor IRF4 induces both caspase-10 and its associated protein cFLIPL in myeloma, generating a protease that does not induce apoptosis but rather blocks an autophagy-dependent cell death pathway. Caspase-10 inhibits autophagy by cleaving the BCL2-interacting protein BCLAF1, itself a strong inducer of autophagy that acts by displacing beclin-1 from BCL2. While myeloma cells require a basal level of autophagy for survival, caspase-10 tempers this response to avoid cell death. Drugs that disrupt this vital balance may have therapeutic potential in myeloma. [Copyright &y& Elsevier]

The transcription factor IRF4 (interferon regulatory factor 4) is required during an immune response for lymphocyte activation and the generation of immunoglobulin-secreting plasma cells. Multiple myeloma, a malignancy of plasma cells, has a complex molecular aetiology with several subgroups defined by gene expression profiling and recurrent chromosomal translocations. Moreover, the malignant clone can sustain multiple oncogenic lesions, accumulating genetic damage as the disease progresses. Current therapies for myeloma can extend survival but are not curative. Hence, new therapeutic strategies are needed that target molecular pathways shared by all subtypes of myeloma. Here we show, using a loss-of-function, RNA-interference-based genetic screen, that IRF4 inhibition is toxic to myeloma cell lines, regardless of transforming oncogenic mechanism. Gene expression profiling and genome-wide chromatin immunoprecipitation analysis uncovered an extensive network of IRF4 target genes and identified MYC as a direct target of IRF4 in activated B cells and myeloma. Unexpectedly, IRF4 was itself a direct target of MYC transactivation, generating an autoregulatory circuit in myeloma cells. Although IRF4 is not genetically altered in most myelomas, they are nonetheless addicted to an aberrant IRF4 regulatory network that fuses the gene expression programmes of normal plasma cells and activated B cells. [ABSTRACT FROM AUTHOR]

Genomics has provided a lever to pry open lymphoid cells and examine their regulatory biology. The large body of available gene expression data has also allowed us to define the of coordinately expressed genes, termed gene expression signatures, which characterize the states of cellular physiology that reflect cellular differentiation, activation of signaling pathways, and the action of transcription factors. Gene expression signatures that reflect the action of individual transcription factors can be defined by perturbing transcription factor function using RNA interference (RNAi), small-molecule inhibition, and dominant-negative approaches. We have used this methodology to define gene expression signatures of various transcription factors controlling B-cell differentiation and activation, including BCL-6, B lymphocyte-induced maturation protein-1 (Blimp-1), X-box binding protein-1 (XBP1), nuclear factor-κB (NF-κB), and c-myc. We have also curated a wide variety of gene expression signatures from the literature and assembled these into a signature database. Statistical methods can define whether any signature in this database is differentially expressed in independent biological samples, an approach we have used to gain mechanistic insights into the origin and clinical behavior of B-cell lymphomas. We also discuss the use of genomic-scale RNAi libraries to identify genes and pathways that may serve as therapeutic targets in B-cell malignancies. [ABSTRACT FROM AUTHOR]