The RNA genome of the human T-cell leukemia virus type 1 (HTLV-1) codes for proteins involved in infectivity, replication, and transformation. We report in this study the characterization of a novel viral protein encoded by the complementary strand of the HTLV-1 RNA genome. This protein, designated HBZ (for HTLV-1 bZIP factor), contains a N-terminal transcriptional activation domain and a leucine zipper motif in its C terminus. We show here that HBZ is able to interact with the bZIP transcription factor CREB-2 (also called ATF-4), known to activate the HTLV-1 transcription by recruiting the viral trans -activator Tax on the Tax-responsive elements (TxREs). However, we demonstrate that the HBZ/CREB-2 heterodimers are no more able to bind to the TxRE and cyclic AMP response element sites. Taking these findings together, the functional inactivation of CREB-2 by HBZ is suggested to contribute to regulation of the HTLV-1 transcription. Moreover, the characterization of a minus-strand gene protein encoded by HTLV-1 has never been reported until now.

Significance: Circadian clocks orchestrate daily rhythms in behavior and physiology using temporal regulation of gene expression to control core clock genes and rhythmic output programs. Although transcription regulation was shown to drive extensive diurnal mRNA rhythms, less is known about the proteins. Here, we provide a proteome-wide study of rhythmic protein accumulation in mouse liver, showing that proteins preferentially accumulate in the morning and during the night. About one-half of these rhythmic proteins could not be explained by rhythmic mRNAs, suggesting that translational or posttranslational regulation plays an important role. Moreover, such rhythms involved many secreted proteins and were clock-independent. Hence, these findings indicate that feeding behavior might determine the rhythmic functions of circulating proteins in the blood.

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them.