Nucleosome conformation dictates the histone code

Histone post-translational modifications (PTMs) play a critical role in chromatin regulation. It has been proposed that these PTMs form localized 'codes' that are read by specialized regions (reader domains) in chromatin-associated proteins (CAPs) to regulate downstream function. Substantial effort has been made to define [CAP: histone PTM] specificities, and thus decipher the histone code and guide epigenetic therapies. However, this has largely been done using the reductive approach of isolated reader domains and histone peptides, which cannot account for any higher-order factors. Here, we show that the [BPTF PHD finger and bromodomain: histone PTM] interaction is dependent on nucleosome context. The tandem reader selectively associates with nucleosomal H3K4me3 and H3K14ac or H3K18ac, a combinatorial engagement that despite being in cis is not predicted by peptides. This in vitro specificity of the BPTF tandem reader for PTM-defined nucleosomes is recapitulated in a cellular context. We propose that regulatable histone tail accessibility and its impact on the binding potential of reader domains necessitates we refine the 'histone code' concept and interrogate it at the nucleosome level

ADP-Ribosylation Factor (ARF) Interaction Is Not Sufficient for Yeast GGA Protein Function or Localization

Golgi-localized γ-ear homology domain, ADP-ribosylation factor (ARF)-binding proteins (GGAs) facilitate distinct steps of post-Golgi traffic. Human and yeast GGA proteins are only ∼25% identical, but all GGA proteins have four similar domains based on function and sequence homology. GGA proteins are most conserved in the region that interacts with ARF proteins. To analyze the role of ARF in GGA protein localization and function, we performed mutational analyses of both human and yeast GGAs. To our surprise, yeast and human GGAs differ in their requirement for ARF interaction. We describe a point mutation in both yeast and mammalian GGA proteins that eliminates binding to ARFs. In mammalian cells, this mutation disrupts the localization of human GGA proteins. Yeast Gga function was studied using an assay for carboxypeptidase Y missorting and synthetic temperature-sensitive lethality between GGAs and VPS27. Based on these assays, we conclude that non-Arf-binding yeast Gga mutants can function normally in membrane trafficking. Using green fluorescent protein-tagged Gga1p, we show that Arf interaction is not required for Gga localization to the Golgi. Truncation analysis of Gga1p and Gga2p suggests that the N-terminal VHS domain and C-terminal hinge and ear domains play significant roles in yeast Gga protein localization and function. Together, our data suggest that yeast Gga proteins function to assemble a protein complex at the late Golgi to initiate proper sorting and transport of specific cargo. Whereas mammalian GGAs must interact with ARF to localize to and function at the Golgi, interaction between yeast Ggas and Arf plays a minor role in Gga localization and function

State-space models for bio-loggers: A methodological road map

Ecologists have an unprecedented array of bio-logging technologies available to conduct in situ studies of horizontal and vertical movement patterns of marine animals. These tracking data provide key information about foraging, migratory, and other behaviours that can be linked with bio-physical datasets to understand physiological and ecological influences on habitat selection. In most cases, however, the behavioural context is not directly observable and therefore, must be inferred. Animal movement data are complex in structure, entailing a need for stochastic analysis methods. The recent development of state-space modelling approaches for animal movement data provides statistical rigor for inferring hidden behavioural states, relating these states to bio-physical data, and ultimately for predicting the potential impacts of climate change. Despite the widespread utility, and current popularity, of state-space models for analysis of animal tracking data, these tools are not simple and require considerable care in their use. Here we develop a methodological “road map” for ecologists by reviewing currently available state-space implementations. We discuss appropriate use of state-space methods for location and/or behavioural state estimation from different tracking data types. Finally, we outline key areas where the methodology is advancing, and where it needs further developmen