Renal blood flow in heart failure patients during exercise

During exercise, reflex renal vasoconstriction maintains blood pressure and helps in redistributing blood flow to the contracting muscle. Exercise intolerance in heart failure (HF) is thought to involve diminished perfusion in active muscle. We studied the temporal relationship between static handgrip (HG) and renal blood flow velocity (RBV; duplex ultrasound) in 10 HF and in 9 matched controls during 3 muscle contraction paradigms. Fatiguing HG (protocol 1) at 40% of maximum voluntary contraction led to a greater reduction in RBV in HF compared with controls (group main effect: P <0.05). The reduction in RBV early in HG tended to be more prominent during the early phases of protocol 1. Similar RBV was observed in the two groups during post-HG circulatory arrest (isolating muscle metaboreflex). Short bouts (15 s) of HG at graded intensities (protocol 2; engages muscle mechanoreflex and/or central command) led to greater reductions in RBV in HF than controls (P <0.03). Protocol 3, voluntary and involuntary biceps contraction (eliminates central command), led to similar increases in renal vasoconstriction in HF (n=4). Greater reductions in RBV were found in HF than in controls during the early phases of exercise. This effect was not likely due to a metaboreflex or central command. Thus our data suggest that muscle mechanoreflex activity is enhanced in HF and serves to vigorously vasoconstrict the kidney. We believe this compensatory mechanism helps preserve blood flow to exercising muscle in HF.     

On the twin risk in autism

Autism is considered by many to be the most strongly genetically influenced multifactorial childhood psychiatric disorder. In the absence of any known gene or genes, the main support for this is derived from family and twin studies. Two recent studies (Greenberg et al. 2001; Betancur et al. 2002) suggested that the twinning process itself is an important risk factor in the development of autism. If true, this would have major consequences for the interpretation of twin studies. Both studies compared the number of affected twin pairs among affected sib pairs to expected values in two separate samples of multiplex families and reported a substantial and significant excess of twin pairs. Using data from our epidemiological study in Western Australia, we investigated the possibility of an increased rate of autism in twins. All children born between 1980 and 1995 with autism, Asperger syndrome, or pervasive developmental disorder not otherwise specified (PDD-NOS) were ascertained. Of the 465 children with a diagnosis, 14 were twin births (rate 30.0/1,000) compared to 9,640 children of multiple births out of a total of 386,637 births in Western Australia between 1980 and 1995 (twin rate weighted to number of children with autism or PDD per year 26.3/1,000). These data clearly do not support twinning as a substantial risk factor in the etiology of autism. We demonstrate that the high proportion of twins found in affected-sib-pair studies can be adequately explained by the high ratio of concordance rates in monozygotic (MZ) twins versus siblings and the distribution of family size in the population studied. Our results are in agreement with those of two similar studies by Croen et al. (2002) in California and Hultman et al. (2002) in Sweden.     

On the Use of Bayesian Methods for Evaluating Compartmental Neural Models

Computational modeling is being used increasingly in neuroscience. In deriving such models, inference issues such as model selection, model complexity, and model comparison must be addressed constantly. In this article we present briefly the Bayesian approach to inference. Under a simple set of commonsense axioms, there exists essentially a unique way of reasoning under uncertainty by assigning a degree of confidence to any hypothesis or model, given the available data and prior information. Such degrees of confidence must obey all the rules governing probabilities and can be updated accordingly as more data becomes available. While the Bayesian methodology can be applied to any type of model, as an example we outline its use for an important, and increasingly standard, class of models in computational neuroscience—compartmental models of single neurons. Inference issues are particularly relevant for these models: their parameter spaces are typically very large, neurophysiological and neuroanatomical data are still sparse, and probabilistic aspects are often ignored. As a tutorial, we demonstrate the Bayesian approach on a class of one-compartment models with varying numbers of conductances. We then apply Bayesian methods on a compartmental model of a real neuron to determine the optimal amount of noise to add to the model to give it a level of spike time variability comparable to that found in the real cell.     

Auxin biogenesis: subcellular compartmentation of indoleacetaldehyde reductases in cucumber seedlings

Subcellular fractionation of cucumber (Cucumis sativus L.) seedlings was achieved, and two of the enzymes in the auxin biosynthetic pathway were localized. NADH-specific indoleacetaldehyde reductase activity was observed only in the cytosol fractions obtained from separated hypocotyl and cotyledon tissue. In contrast, a portion of the NADPH-specific indoleacetaldehyde reductase activity was associated with a microsomal fraction derived from these tissues. The NADPH-specific indoleacetaldehyde reductase was consistently found to be more firmly associated with the microsomal fraction derived from hypocotyls than with that from the cotyledons. These results indicate a division of the terminal steps of auxin biogenesis into at least two subcellular compartments.     

Mechanochemical coupling in spin-labeled, active, isometric muscle

Observed effects of inorganic phosphate (P(i)) on active isometric muscle may provide the answer to one of the fundamental questions in muscle biophysics: how are the free energies of the chemical species in the myosin-catalyzed ATP hydrolysis (ATPase) reaction coupled to muscle force?. Pflugers Arch. 414:73-81) showed that active, isometric muscle force varies logarithmically with [P(i)]. Here, by simultaneously measuring electron paramagnetic resonance and the force of spin-labeled muscle fibers, we show that, in active, isometric muscle, the fraction of myosin heads in any given biochemical state is independent of both [P(i)] and force. These direct observations of mechanochemical coupling in muscle are immediately described by a muscle equation of state containing muscle force as a state variable. These results challenge the conventional assumption mechanochemical coupling is localized to individual myosin heads in muscle.     

Real-time imaging of the dynamics of secretory granules in growth cones

Secretory granules containing a hybrid protein consisting of the regulated secretory protein tissue plasminogen activator and an enhanced form of green fluorescent protein were tracked at high spatial resolution in growth cones of differentiated PC12 cells. Tracking shows that granules, unlike synaptic vesicles, generally are mobile in growth cones. Quantitative analysis of trajectories generated by granules revealed two dominant modes of motion: diffusive and directed. Diffusive motion was observed primarily in central and peripheral parts of growth cones, where most granules diffused two to four orders of magnitude more slowly than comparably sized spheres in dilute solution. Directed motion was observed primarily in proximal parts of growth cones, where a subset of granules underwent rapid, directed motion at average speeds comparable to those observed for granules in neurites. This high-resolution view of the dynamics of secretory granules in growth cones provides insight into granule organization and release at nerve terminals. In particular, the mobility of granules suggests that granules, unlike synaptic vesicles, are not tethered stably to cytoskeletal structures in nerve terminals. Moreover, the slow diffusive nature of this mobility suggests that secretory responses involving centrally distributed granules in growth cones will occur slowly, on a time scale of minutes or longer.