Incorporation of DPP6a and DPP6K Variants in Ternary Kv4 Channel Complex Reconstitutes Properties of A-type K Current in Rat Cerebellar Granule Cells

Dipeptidyl peptidase-like protein 6 (DPP6) proteins co-assemble with Kv4 channel α-subunits and Kv channel-interacting proteins (KChIPs) to form channel protein complexes underlying neuronal somatodendritic A-type potassium current (ISA). DPP6 proteins are expressed as N-terminal variants (DPP6a, DPP6K, DPP6S, DPP6L) that result from alternative mRNA initiation and exhibit overlapping expression patterns. Here, we study the role DPP6 variants play in shaping the functional properties of ISA found in cerebellar granule (CG) cells using quantitative RT-PCR and voltage-clamp recordings of whole-cell currents from reconstituted channel complexes and native ISA channels. Differential expression of DPP6 variants was detected in rat CG cells, with DPP6K (41±3%)>DPP6a (33±3%)>>DPP6S (18±2%)>DPP6L (8±3%). To better understand how DPP6 variants shape native neuronal ISA, we focused on studying interactions between the two dominant variants, DPP6K and DPP6a. Although previous studies did not identify unique functional effects of DPP6K, we find that the unique N-terminus of DPP6K modulates the effects of KChIP proteins, slowing recovery and producing a negative shift in the steady-state inactivation curve. By contrast, DPP6a uses its distinct N-terminus to directly confer rapid N-type inactivation independently of KChIP3a. When DPP6a and DPP6K are co-expressed in ratios similar to those found in CG cells, their distinct effects compete in modulating channel function. The more rapid inactivation from DPP6a dominates during strong depolarization; however, DPP6K produces a negative shift in the steady-state inactivation curve and introduces a slow phase of recovery from inactivation. A direct comparison to the native CG cell ISA shows that these mixed effects are present in the native channels. Our results support the hypothesis that the precise expression and co-assembly of different auxiliary subunit variants are important factors in shaping the ISA functional properties in specific neuronal populations

An externally validated age-related model of mean follicle density in the cortex of the human ovary

The population of non-growing follicles present in the ovary is defined as the ovarian reserve. This underpins the reproductive lifespan in women, with its depletion determining age at loss of fertility and the menopause. Data amassed from published results of indirect invasive and non-invasive procedures has resulted in the generation of predictive models which estimate the ovarian reserve from conception throughout adult life. The distribution of follicles in the ovary is not uniform, with the great majority of NGFs located in the cortex, which is the region normally biopsied and used for fertility preservation. Previous models have however analysed whole ovary NGF populations and ovarian volumes, but not cortical NGF density. In this study we compared mean non-growing follicle density values obtained from tissue samples from 13 ovarian cortical biopsies (16-37 years) against age- matched model-predicted values generated from population and ovarian volume models, taking into account the proportion of the ovary that is cortex. A mean non-growing follicle density was calculated for each patient by counting all follicles in a given volume of freshly biopsied ovarian cortical tissue. These values were compared to age-matched model generated densities and the correlation between data sets tested. Non-growing follicle density values obtained from fresh biopsied ovarian cortex samples closely matched model generated data with low mean difference, tight agreement limits and no proportional error between the observed and predicted results. These findings validate the use of the population and ovarian volume models to accurately predict mean follicle density in the ovarian cortex of adult women.Publisher PDFPeer reviewe

Establishing a National Strategy for Shared Research Resources

President Biden’s renewed push to develop cures for society’s most devastating diseases including cancer and Alzheimer’s, in tandem with infrastructure investments to “Build Back Better,” represents an opportunity to harness our nation’s critical shared research resources (SRRs). For over 40 years, SRRs have played a key role in accelerating biomedical research discoveries and innovations by providing widespread access to cutting-edge technologies, services, and scientific expertise. Yet a national strategy that addresses how to leverage these resources to ensure new treatments, cures, and economic vitality is noticeably absent. A national strategy for SRRs—led by the National Institutes of Health (NIH)—is crucial to advance key national initiatives and enable long-term efficiency, coordination, and economic impact of these critical assets