Facioscapulohumeral dystrophy (FSHD) is one of the most common inherited muscular dystrophies. The causative gene remains controversial and the mechanism of pathophysiology unknown. Here we identify genes associated with germline and early stem cell development as targets of the DUX4 transcription factor, a leading candidate gene for FSHD. The genes regulated by DUX4 are reliably detected in FSHD muscle but not in controls, providing direct support for the model that misexpression of DUX4 is a causal factor for FSHD. Additionally, we show that DUX4 binds and activates LTR elements from a class of MaLR endogenous primate retrotransposons and suppresses the innate immune response to viral infection, at least in part through the activation of DEFB103, a human defensin that can inhibit muscle differentiation. These findings suggest specific mechanisms of FSHD pathology and identify candidate biomarkers for disease diagnosis and progression.

Key Points• A total of 38% of patients who achieved CR (13 of 34) on brentuximab vedotin have remained in remission for .5 years and may be cured. • Nine of the 13 patients (9% of all enrolled patients) have remained in long-term remission without a consolidative allogeneic transplant.Presented here are the 5-year end-of-study results from the pivotal phase 2 trial of brentuximab vedotin in patients with relapsed/refractory (R/R) Hodgkin lymphoma (HL) after failed hematopoietic autologous stem cell transplantation. At 5 years, the overall patient population (N 5 102) had an estimated overall survival (OS) rate of 41% (95% confidence interval [CI]: 31-51) and progression-free survival (PFS) rate of 22% (95% CI: 13-31). Patients who achieved a complete response (CR) to brentuximab vedotin (N 5 34) had estimated OS and PFS rates of 64% (95% CI: 48-80%) and 52% (95% CI: 34-69%), respectively. The median OS and PFS were not reached in CR patients, with 13 patients (38% of all CR patients) remaining in follow-up and in remission at study closure. Of the 13 patients, 4 received consolidative hematopoietic allogeneic stem cell transplant, and 9 (9% of all enrolled patients) remain in sustained CR without receiving any further anticancer therapy after treatment with brentuximab vedotin. Of the patients who experienced treatment-emergent peripheral neuropathy, 88% experienced either resolution (73%) or improvement (14%) in symptoms. These 5-year follow-up data demonstrate that a subset of patients with R/R HL who obtained CR with single-agent brentuximab vedotin achieved long-term disease control and may potentially be cured. The trial was registered at www.clinicaltrials.gov as #NCT00848926. (Blood. 2016;128(12):1562-1566

The binding of transcription factors to specific DNA target sequences is the fundamental basis of gene regulatory networks. Chromatin immunoprecipitation combined with DNA tiling arrays or high-throughput sequencing-ChIP-chip and ChIP-seq-has produced many recent studies that detail the binding sites of various transcription factors. Surprisingly, data from a variety of model organisms and tissues have demonstrated that transcription factors vary greatly in their number of genomic binding sites, and that binding events can significantly exceed the number of known or possible direct gene targets. Thus, our current understanding of transcription factor function must expand to encompass what role, if any, binding might play outside of direct transcriptional target regulation. Here, we discuss the biological significance of genome-wide binding of transcription factors and present models that can account for this phenomenon. Regulatory networks and the core model of gene regulationThe complex interactions between multiple transcription factors and gene targets across various tissues, cellular contexts, and time points are termed `transcriptional regulatory networks' (Box 1). It has been stated that a truly thorough understanding of such interactions should theoretically explain how an organism is `computed' from its DNA [1]. The core model of gene regulation posits that transcription factors recruit a polymerase complex to the transcriptional start site [2]. Transcription factors initiate this by binding at nearby or distant DNA sequences and directly interacting with components of the polymerase complex or with complexes that indirectly mediate the polymerase interaction. In eukaryotes, the latter may include chromatin remodelers or modifiers that facilitate access or increase protein-protein affinities via histone modifications [3,4]. The simplest view of the core model would suggest that factor binding directly correlates with transcriptional regulation. However, numerous examples of the separate regulation of factor binding and transcriptional activation suggest otherwise [5][6][7]. For example, recent studies indicate that the sequence of the DNA binding site can induce conformational changes in the bound transcription factor that permits transcriptional regulation by subsets of a transcription factor family that can bind to similar sites [8,9].

SUMMARY The regulatory networks of differentiation programs have been partly characterized; however, the molecular mechanisms of lineage-specific gene regulation by highly similar transcription factors remain largely unknown. Here we compare the genome-wide binding and transcription profiles of NEUROD2-mediated neurogenesis with MYOD-mediated myogenesis. We demonstrate that NEUROD2 and MYOD bind a shared CAGCTG E-box motif and E-box motifs specific for each factor: CAGGTG for MYOD and CAGATG for NEUROD2. Binding at factor-specific motifs is associated with gene transcription, whereas binding at shared sites is associated with regional epigenetic modifications but not as strongly associated with gene transcription. Binding is largely constrained to E-boxes pre-set in an accessible chromatin context that determines the set of target genes activated in each cell type. These findings demonstrate that the differentiation program is genetically determined by E-box sequence whereas cell lineage epigenetically determines the availability of E-boxes for each differentiation program.