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Mesopontine cholinergic neurons have been implicated in the initiation and maintenance of rapid eye movement sleep via their efferent connections to the thalamus and the medial pontine reticular formation. As a first step toward understanding how these modulatory neurons integrate synaptic input, we have investigated the dendritic architecture of laterodorsal tegmental nucleus neurons. The principal cells of the guinea-pig laterodorsal tegmental nucleus were identified electrophysiologically in a brain slice preparation, then were intracellularly injected with biocytin and reconstructed using a computer-aided tracing system. The somata were large (27 +/- 3 microns; n = 11) and gave rise to an average of 4.8 primary dendrites which, in most cases, emerged from the soma in a pattern that was radially symmetric in the plane of the slice. Primary dendrites had an average of 3.7 endings. A single axon arose from either the soma or a proximal dendrite and exited the nucleus with a medial and/or lateral trajectory. Some axons also gave rise to a local terminal plexus composed of fine fibers bearing numerous punctate swellings that ramified profusely within the neuron's dendritic field. Total dendritic area averaged about 10(5) microns2, and therefore the average contribution of the soma to the total surface area (20%) was significantly larger than the values reported for many other cell types. Dendritic diameters were non-uniform in three respects. Some processes were sparsely spiny. Most processes were varicose, with the degree of varicosity increasing substantially in secondary and tertiary dendritic segments. There was also a large degree of taper in dendritic processes; those processes with a non-negative taper had an average diameter decrease of 40 +/- 25%. Dendritic processes deviated from the criteria necessary for a Rall equivalent cylinder approximation due to non-uniformity in morphotonic path length, failure to conform to the Rall 3/2 branching rule and non-uniformity of dendritic diameter. An analysis was done to assess the impact of dendritic varicosities on the extraction of cable parameters for these cells. Voltage traces were simulated by solving the cable equation for a varicose dendrite and then membrane parameters were recovered using an equivalent cylinder model. Errors in the extracted values of specific membrane conductance and specific membrane capacitance were quite small (or = 5%), while larger errors were seen for electrotonic length (or = 21%) and intracellular resistivity (or = 5%). These data indicate that the principal cells of the laterodorsal tegmental nucleus, while possessing a relatively simple dendritic structure in terms of number and branchiness of dendrites, display a heterogeneity of dendritic process types. Processes range from smooth to markedly varicose, and can be aspiny or sparsely spiny. The possibility that the dendritic varicosities function as sites of either electrical or chemical compartmentalization is discussed. The degree of error resulting from a Rall equivalent cylinder approximation in light of these varicosities indicated that a generalized cable model approach may prove more effective in estimating their cable parameters.

We propose that protein translocation across membranes is driven by biased random thermal motion. This "Brownian ratchet" mechanism depends on chemical asymmetries between the cis and trans sides of the membrane. Several mechanisms could contribute to rectifying the thermal motion of the protein, such as binding and dissociation of chaperonins to the translocating chain, chain coiling induced by pH and/or ionic gradients, glycosylation, and disulfide bond formation. This helps explain the robustness and promiscuity of these transport systems.

The empirical differential equation that describes the kinetics of monazomycin-induced voltage-dependent conductance is derived using a standard chemical kinetic formulation and the assumption that monazomycin entry into and exit from the membrane is autocatalytic. The predicted form of gating currents is shown and numerical calculations for this process are made using a range of values for two unmeasured variables. A form of "memory" is then demonstrated, along with the ability of the theoretical equation to explain the nature of the memory.