Neuronal junctophilins recruit specific Cav and RyR isoforms to ER-PM junctions and functionally alter Cav2.1 and Cav2.2

Junctions between the endoplasmic reticulum and plasma membrane that are induced by the neuronal junctophilins are of demonstrated importance, but their molecular architecture is still poorly understood and challenging to address in neurons. This is due to the small size of the junctions and the multiple isoforms of candidate junctional proteins in different brain areas. Using colocalization of tagged proteins expressed in tsA201 cells, and electrophysiology, we compared the interactions of JPH3 and JPH4 with different calcium channels. We found that JPH3 and JPH4 caused junctional accumulation of all the tested high-voltage-activated CaV isoforms, but not a low-voltage-activated CaV. Also, JPH3 and JPH4 noticeably modify CaV2.1 and CaV2.2 inactivation rate. RyR3 moderately colocalized at junctions with JPH4, whereas RyR1 and RyR2 did not. By contrast, RyR1 and RyR3 strongly colocalized with JPH3, and RyR2 moderately. Likely contributing to this difference, JPH3 binds to cytoplasmic domain constructs of RyR1 and RyR3, but not of RyR2

Junctophilins 1, 2, and 3 all support voltage-induced Ca2+ release despite considerable divergence

In skeletal muscle, depolarization of the plasma membrane (PM) causes conformational changes of the calcium channel CaV1.1 that then activate RYR1 to release calcium from the SR. Being independent of extracellular calcium entry, this process is termed voltage-induced calcium release. In skeletal muscle, junctophilins (JPHs) 1 and 2 form the SR–PM junctions at which voltageinduced calcium release occurs. Previous work demonstrated that JPH2 is able to recapitulate voltage-induced calcium release when expressed in HEK293 cells together with CaV1.1, β1a, Stac3, and RYR1. However, it is unknown whether JPH1 and the more distantly related neuronal JPH3 and JPH4 might also function in this manner, a question of interest because different JPH isoforms diverge in their interactions with RYR1. Here, we show that, like JPH2, JPH1 and JPH3, coexpressed with CaV1.1, β1a, Stac3, and RYR1 in HEK293 cells, cause colocalization of CaV1.1 and RYR1 at ER–PM junctions. Furthermore, potassium depolarization elicited cytoplasmic calcium transients in cells in which WT CaV1.1 was replaced with the calcium impermeant mutant CaV1.1(N617D), indicating that JPH1, JPH2, and JPH3 can all support voltage-induced calcium release, despite sequence divergence and differences in interaction with RYR1. Conversely, JPH4-induced ER–PM junctions contain CaV1.1 but not RYR1, and cells expressing JPH4 are unable to produce depolarization-induced calcium transients. Thus, JPHs seem to act primarily to form ER–PM junctions and to recruit the necessary signaling proteins to these junctions but appear not to be directly involved in the functional interactions between these proteins

De novo reconstitution reveals the proteins required for skeletal muscle voltage-induced Ca2+ release

Skeletal muscle contraction is triggered by Ca2+ release from the sarcoplasmic reticulum (SR) in response to plasma membrane (PM) excitation. In vertebrates, this depends on activation of the RyR1 Ca2+ pore in the SR, under control of conformational changes of CaV1.1, located ∼12 nm away in the PM. Over the last ∼30 y, gene knockouts have revealed that CaV1.1/RyR1 coupling requires additional proteins, but leave open the possibility that currently untested proteins are also necessary. Here, we demonstrate the reconstitution of conformational coupling in tsA201 cells by expression of CaV1.1, β1a, Stac3, RyR1, and junctophilin2. As in muscle, depolarization evokes Ca2+ transients independent of external Ca2+ entry and having amplitude with a saturating dependence on voltage. Moreover, freeze-fracture electron microscopy indicates that the five identified proteins are sufficient to establish physical links between CaV1.1 and RyR1. Thus, these proteins constitute the key elements essential for excitation-contraction coupling in skeletal muscle

Is Brain Gym an Effective Educational Intervention?

Brain Gym® (BG) (BGI, 2008) is a popular commercial program sold by Brain Gym® International (BGI). Making extravagant claims for improved intellectual and physical development, it used in more than 80 countries. While BGI’s claims are persuasive, to date there is little empirical evidence validating the approach. We examine some theoretical assumptions from which BGI was developed, review the efficacy literature, and provide suggestions for making informed decisions about the judiciousness of investing time and resources in this program

The use of mid-infrared spectrometry to predict body energy status of Holstein cows

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application