Developmental switch in the kinase dependency of long-term potentiation depends on expression of GluA4 subunit-containing AMPA receptors

The AMPA-receptor subunit GluA4 is expressed transiently in CA1 pyramidal neurons at the time synaptic connectivity is forming, but its physiological significance is unknown. Here we show that GluA4 expression is sufficient to alter the signaling requirements of long-term potentiation (LTP) and can fully explain the switch in the LTP kinase dependency from PKA to Ca2(+)/calmodulin-dependent protein kinase II during synapse maturation. At immature synapses, activation of PKA leads to a robust potentiation of AMPA-receptor function via the mobilization of GluA4. Analysis of GluA4-deficient mice indicates that this mechanism is critical for neonatal PKA-dependent LTP. Furthermore, lentiviral expression of GluA4 in CA1 neurons conferred a PKA-dependent synaptic potentiation and LTP regardless of the developmental stage. Thus, GluA4 defines the signaling requirements for LTP and silent synapse activation during a critical period of synapse development.

Storytelling and story testing in domestication

The domestication of plants and animals marks one of the most significant transitions in human, and indeed global, history. Traditionally, study of the domestication process was the exclusive domain of archaeologists and agricultural scientists; today it is an increasingly multidisciplinary enterprise that has come to involve the skills of evolutionary biologists and geneticists. Although the application of new information sources and methodologies has dramatically transformed our ability to study and understand domestication, it has also generated increasingly large and complex datasets, the interpretation of which is not straightforward. In particular, challenges of equifinality, evolutionary variance, and emergence of unexpected or counter-intuitive patterns all face researchers attempting to infer past processes directly from patterns in data. We argue that explicit modeling approaches, drawing upon emerging methodologies in statistics and population genetics, provide a powerful means of addressing these limitations. Modeling also offers an approach to analyzing datasets that avoids conclusions steered by implicit biases, and makes possible the formal integration of different data types. Here we outline some of the modeling approaches most relevant to current problems in domestication research, and demonstrate the ways in which simulation modeling is beginning to reshape our understanding of the domestication process.