Long-term potentiation and dual-component quantal signaling in the dentate gyrus

Long-term potentiation (LTP) of excitatory transmission is an important candidate cellular mechanism for the storage of memories in the mammalian brain. The subcellular phenomena that underlie the persistent increase in synaptic strength, however, are incompletely understood. A potentially powerful method to detect a presynaptic increase in glutamate release is to examine the effect of LTP induction on the rate at which the use-dependent blocker MK-801 attenuates successive N-methyl-d-aspartic acid (NMDA) receptor-mediated synaptic signals. This method, however, has given apparently contradictory results when applied in hippocampal CA1. The inconsistency could be explained if NMDA receptors were opened by glutamate not only released from local presynaptic terminals, but also diffusing from synapses on neighboring cells where LTP was not induced. Here we examine the effect of pairing-induced LTP on the MK-801 blocking rate in two afferent inputs to dentate granule cells. LTP in the medial perforant path is associated with a significant increase in the MK-801 blocking rate, implying a presynaptic increase in glutamate release probability. An enhanced MK-801 blocking rate is not seen, however, in the lateral perforant path. This result still could be compatible with a presynaptic contribution to LTP in the lateral perforant path if intersynaptic cross-talk occurred. In support of this hypothesis, we show that NMDA receptors consistently sense more quanta of glutamate than do α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors. In the medial perforant path, in contrast, there is no significant difference in the number of quanta mediated by the two receptors. These results support a presynaptic contribution to LTP and imply that differences in intersynaptic cross-talk can complicate the interpretation of experiments designed to detect changes in transmitter release.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

Ultrasensitive detection of pathological prion protein aggregates by dual-color scanning for intensely fluorescent targets

A definite diagnosis of prion diseases such as Creutzfeldt–Jakob disease (CJD) relies on the detection of pathological prion protein (PrPSc). However, no test for PrPSc in cerebrospinal fluid (CSF) has been available thus far. Based on a setup for confocal dual-color fluorescence correlation spectroscopy, a technique suitable for single molecule detection, we developed a highly sensitive detection method for PrPSc. Pathological prion protein aggregates were labeled by specific antibody probes tagged with fluorescent dyes, resulting in intensely fluorescent targets, which were measured by dual-color fluorescence intensity distribution analysis in a confocal scanning setup. In a diagnostic model system, PrPSc aggregates were detected down to a concentration of 2 pM PrPSc, corresponding to an aggregate concentration of approximately 2 fM, which was more than one order of magnitude more sensitive than Western blot analysis. A PrPSc-specific signal could also be detected in a number of CSF samples from patients with CJD but not in control samples, providing the basis for a rapid and specific test for CJD and other prion diseases. Furthermore, this method could be adapted to the sensitive detection of other disease-associated amyloid aggregates such as in Alzheimer's disease.

Nonlinear indicial response of complex nonstationary oscillations as pulmonary hypertension responding to step hypoxia

This paper is devoted to the quantization of the degree of nonlinearity of the relationship between two biological variables when one of the variables is a complex nonstationary oscillatory signal. An example of the situation is the indicial responses of pulmonary blood pressure (P) to step changes of oxygen tension (ΔpO2) in the breathing gas. For a step change of ΔpO2 beginning at time t1, the pulmonary blood pressure is a nonlinear function of time and ΔpO2, which can be written as P(t-t1 | ΔpO2). An effective method does not exist to examine the nonlinear function P(t-t1 | ΔpO2). A systematic approach is proposed here. The definitions of mean trends and oscillations about the means are the keys. With these keys a practical method of calculation is devised. We fit the mean trends of blood pressure with analytic functions of time, whose nonlinearity with respect to the oxygen level is clarified here. The associated oscillations about the mean can be transformed into Hilbert spectrum. An integration of the square of the Hilbert spectrum over frequency yields a measure of oscillatory energy, which is also a function of time, whose mean trends can be expressed by analytic functions. The degree of nonlinearity of the oscillatory energy with respect to the oxygen level also is clarified here. Theoretical extension of the experimental nonlinear indicial functions to arbitrary history of hypoxia is proposed. Application of the results to tissue remodeling and tissue engineering of blood vessels is discussed.