Nanobody-based sensors reveal a high proportion of mGlu heterodimers in the brain.

Membrane proteins, including ion channels, receptors and transporters, are often composed of multiple subunits and can form large complexes. Their specific composition in native tissues is difficult to determine and remains largely unknown. In this study, we developed a method for determining the subunit composition of endogenous cell surface protein complexes from isolated native tissues. Our method relies on nanobody-based sensors, which enable proximity detection between subunits in time-resolved Förster resonance energy transfer (FRET) measurements. Additionally, given conformation-specific nanobodies, the activation of these complexes can be recorded in native brain tissue. Applied to the metabotropic glutamate receptors in different brain regions, this approach revealed the clear existence of functional metabotropic glutamate (mGlu)2-mGlu4 heterodimers in addition to mGlu2 and mGlu4 homodimers. Strikingly, the mGlu4 subunits appear to be mainly heterodimers in the brain. Overall, these versatile biosensors can determine the presence and activity of endogenous membrane proteins in native tissues with high fidelity and convenience.

Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1-3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.

Meta-analytic and multiwave comparison of emotional support and instrumental support in the workplace.

Two complementary studies were conducted to compare emotional support and instrumental support in the workplace. Study 1 included meta-analyses with 142 independent samples containing 68,354 participants and tested the moderation effects of source of support (supervisor vs. coworker) and support scale type (received vs. availability). Study 2 incorporated a two-wave survey design and objective ratings of participant job demands. Overall, emotional support and instrumental support were strongly correlated and demonstrated a similar pattern of effects with work criteria. However, the emotional support-instrumental support relationship is stronger within occupations higher in emotional labor demands. Moderation effects of support on stressor-criteria relationships were also reviewed among the primary studies in the meta-analysis. For both emotional support and instrumental support, buffering effects and reverse buffering effects were commonly found, which indicates that contextual factors need to be considered to determine when support mitigates or exacerbates the effect of stressors on work criteria. Moderation effects of source of support (supervisor vs. coworker) and support scale type (received vs. availability) were also tested. In general, support was more strongly correlated with criteria when the source of support was the supervisor and the scale included items about the availability of support. The findings from the two studies provide researchers and practitioners a guide for when emotional support and instrumental support converge or diverge. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Allosteric nanobodies uncover a role of hippocampal mGlu2 receptor homodimers in contextual fear consolidation.

Antibodies have enormous therapeutic and biotechnology potential. G protein-coupled receptors (GPCRs), the main targets in drug development, are of major interest in antibody development programs. Metabotropic glutamate receptors are dimeric GPCRs that can control synaptic activity in a multitude of ways. Here we identify llama nanobodies that specifically recognize mGlu2 receptors, among the eight subtypes of mGluR subunits. Among these nanobodies, DN10 and 13 are positive allosteric modulators (PAM) on homodimeric mGlu2, while DN10 displays also a significant partial agonist activity. DN10 and DN13 have no effect on mGlu2-3 and mGlu2-4 heterodimers. These PAMs enhance the inhibitory action of the orthosteric mGlu2/mGlu3 agonist, DCG-IV, at mossy fiber terminals in the CA3 region of hippocampal slices. DN13 also impairs contextual fear memory when injected in the CA3 region of hippocampal region. These data highlight the potential of developing antibodies with allosteric actions on GPCRs to better define their roles in vivo.