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Relly Brandman

Publications and source records attributed to Relly Brandman.

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Peptides derived from the C2 domain of protein kinase C epsilon (epsilon PKC) modulate epsilon PKC activity and identify potential protein-protein interaction surfaces.

Peptides derived from protein kinase C (PKC) modulate its activity by interfering with critical protein-protein interactions within PKC and between PKC and PKC-binding proteins (Souroujon, M. C., and Mochly-Rosen, D. (1998) Nat. Biotechnol. 16, 919-924). We previously demonstrated that the C2 domain of PKC plays a critical role in these interactions. By focusing on epsilonPKC and using a rational approach, we then identified one C2-derived peptide that acts as an isozyme-selective activator and another that acts as a selective inhibitor of epsilonPKC. These peptides were used to identify the role of epsilonPKC in protection from cardiac and brain ischemic damage, in prevention of complications from diabetes, in reducing pain, and in protecting transplanted hearts. The efficacy of these two peptides led us to search for additional C2-derived peptides with PKC-modulating activities. Here we report on the activity of a series of 5-9-residue peptides that are derived from regions that span the length of the C2 domain of epsilonPKC. These peptides were tested for their effect on PKC activity in cells in vivo and in an ex vivo model of acute ischemic heart disease. Most of the peptides acted as activators of PKC, and a few peptides acted as inhibitors. PKC-dependent myristoylated alanine-rich C kinase substrate phosphorylation in epsilonPKC knock-out cells revealed that only a subset of the peptides were selective for epsilonPKC over other PKC isozymes. These epsilonPKC-selective peptides were also protective of the myocardium from ischemic injury, an epsilonPKC-dependent function (Liu, G. S., Cohen, M. V., Mochly-Rosen, D., and Downey, J. M. (1999) J. Mol. Cell. Cardiol. 31, 1937-1948), and caused selective translocation of epsilonPKC over other isozymes when injected systemically into mice. Examination of the structure of the C2 domain from epsilonPKC revealed that peptides with similar activities clustered into discrete regions within the domain. We propose that these regions represent surfaces of protein-protein interactions within epsilonPKC and/or between epsilonPKC and other partner proteins; some of these interactions are unique to epsilonPKC, and others are common to other PKC isozymes.

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A simple model of long-term spike train regularization.

A simple model of spike generation is described that gives rise to negative correlations in the interspike interval (ISI) sequence and leads to long-term spike train regularization. This regularization can be seen by examining the variance of the kth-order interval distribution for large k (the times between spike i and spike i + k). The variance is much smaller than would be expected if successive ISIs were uncorrelated. Such regularizing effects have been observed in the spike trains of electrosensory afferent nerve fibers and can lead to dramatic improvement in the detectability of weak signals encoded in the spike train data (Ratnam & Nelson, 2000). Here, we present a simple neural model in which negative ISI correlations and long-term spike train regularization arise from refractory effects associated with a dynamic spike threshold. Our model is derived from a more detailed model of electrosensory afferent dynamics developed recently by other investigators (Chacron, Longtin, St.-Hilaire, & Maler, 2000;Chacron, Longtin, & Maler, 2001). The core of this model is a dynamic spike threshold that is transiently elevated following a spike and subsequently decays until the next spike is generated. Here, we present a simplified version-the linear adaptive threshold model-that contains a single state variable and three free parameters that control the mean and coefficient of variation of the spontaneous ISI distribution and the frequency characteristics of the driven response. We show that refractory effects associated with the dynamic threshold lead to regularization of the spike train on long timescales. Furthermore, we show that this regularization enhances the detectability of weak signals encoded by the linear adaptive threshold model. Although inspired by properties of electrosensory afferent nerve fibers, such regularizing effects may play an important role in other neural systems where weak signals must be reliably detected in noisy spike trains. When modeling a neuronal system that exhibits this type of ISI correlation structure, the linear adaptive threshold model may provide a more appropriate starting point than conventional renewal process models that lack long-term regularizing effects.

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