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Archive for the ‘Aging’ Category

In everyday life, your muscles, metabolism, and nervous system work together to ensure that your cerebral blood flow meets the metabolic needs of your various brain regions. So if you are trying to scrutinize an impressionist painting, your body will likely relocate more blood flow to your visual cortex.

Following a stroke, this cerebral blood flow regulation is impaired. But, the degree and spread of the impairment is unknown. To investigate this, Hu et al. measured systemic blood pressure (BP) and used a transcranial doppler to measure cerebral blood flow velocity (BFV) at the same time.

In their model, better regulation of cerebral blood flow corresponds to a sharper phase shift between blood pressure (BP) and cerebral blood flow velocity (BFV). Individuals with the highest score of a 9 on their autoregulation index (ARI) have more regulation than those with the lowest score of 0, which corresponds to no phase shift.

When they compared patients who had experienced MCA infarcts (a common type of stroke) and healthy controls, they found that stroke patients had significantly less phase coupling between blood pressure and cerebral blood flow. This effect was pronounced over a wide range of blood pressure oscillation frequencies.

Given enough time and the right conditions, can the body repair its ability to regulate cerebral blood flow following a stroke? When the researchers examined this, they found no statistically significant difference between the BFV-BP phase difference and time since stroke.

But, that doesn’t mean that there’s a statistically significant lack of difference. So, further longitudinal studies will be needed to help clarify whether, in certain people in certain environments, the brain improves its cerebral regulation following stroke.

Reference

Hu K, Lo M-T, Peng C-K, Liu Y, Novak V (2012) A Nonlinear Dynamic Approach Reveals a Long-Term Stroke Effect on Cerebral Blood Flow Regulation at Multiple Time Scales. PLoS Comput Biol 8(7): e1002601. doi:10.1371/journal.pcbi.1002601

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How does the connection morphology of motor neuron axons and muscle fiber endplates change with age? Chai et al recently published some results addressing, in part, this question.

Their study compared young 3 month and geriatric 29 month old mice, which, as the authors note, correspond to roughly 20 and 80 years in humans, respectively. However, it’s always important to keep in mind that mice differ from humans in many important ways.

The researchers cut out muscle tissue, sectioned it in 20 um segments, and double stained with antibodies for both synaptophysin (to detect pre-synaptic nerve terminals) and α-bungarotoxin (to detect postsynaptic muscle endplates).

They then classified neuromuscular junctions that stained positive for both synaptophysin and α-bungarotoxin as innervated, and classified junctions positive for α-bungarotoxin only as denervated. Below is an example of a confocal image of a double stained tissue slice.

EDL = extensor digitorum longus; synaptophysin = red; α-bungarotoxin = green; overlay = yellow; white circle = example of endplate positive for only α-bungarotoxin; scale bars = 75 um; doi:10.1371/journal.pone.0028090.g002 part d-f

Across all samples analyzed, ~7 +/- 2% of neuromuscular junctions were fully denervated in 3 month old mice and ~20 +/- 3% of neuromuscular junctions were fully denervated in 29 month old mice. Such denervation could help account for any age-related decrease in muscle function.

Interestingly and importantly, the researchers did not find a similar trend in the soleus. The lack of concordance underscores some of the variability across tissues of the same type in aging.

Reference

Chai RJ, Vukovic J, Dunlop S, Grounds MD, Shavlakadze T (2011) Striking Denervation of Neuromuscular Junctions without Lumbar Motoneuron Loss in Geriatric Mouse Muscle. PLoS ONE 6(12): e28090. doi:10.1371/journal.pone.0028090

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In their review of the “neuroproteome” associated with aging and cognitive decline, VanGuilder and Freeman discuss some of the technical approaches and findings in the field.

This illustrative figure shows some of the major cellular players involved and lists some example proteins involved in four important pathways:

"numerous cell types (microglia (green), astrocytes (orange), oligodendrocytes (blue), and neurons (violet)) and subcellular components (mitochondria (brown), endoplasmic reticulum (green), cytoskeleton (orange/red), and synaptic machinery) are affected by brain aging"; doi: 10.3389/fnagi.2011.00008

As you can see, many proteins have been implicated, although the degree of up-/down-regulation of these proteins is not fully elucidated.

The authors mention the value of standardizing efforts to profile the proteome in important brain regions across the lifespan of rodent models. This step would make these results more robustly quantitative and help iterate towards a consensus.

Reference

VanGuilder H. D. and Freeman W. M (2011) The hippocampal neuroproteome with aging and cognitive decline: past progress and future directions. Front. Ag. Neurosci. 3:8. doi: 10.3389/fnagi.2011.00008

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More neurons are born than necessary, and synaptic pruning is the process by which neurons that have not made as many functional synaptic connections with other neurons are preferentially degraded.

Abitz et al counted cells in the medial thalamus of newborn and adult brains using a optical fractionator and Giemsa staining which binds to phosphate groups of DNA. They distinguished small neurons from glial cells on the basis of chromatin pattern, the size / shape of the nucleus, and the visibility of the nucleolus. Here’s an example of the Giemsa stained  cells via micrographs:

scale bar = 10 micrometers, doi:10.1093/cercor/bhl163

They found an average of 11.2 million neurons in the newborn MD thalamus, which decreased to an average of 6.43 million neurons in adults, probably as a result of synaptic pruning. On the other hand, they found 36.3 million glial cells in adults, much higher than the 10.6 million they found in newborns, suggesting that glial progenitor cells still have a few proliferation cycles to undergo in development.

Elsewhere, Elston et al measured the number of spines in the average pyramidal cell of macaque brains in the primary visual cortex (V1), the inferior temporal gyrus (TE), and the prefrontal cortex (PFC) at different stages of development. They found an inverted U shaped curve of spine number with log age:

doi:10.1523/JNEUROSCI.5216-08.2009

The authors conclude that “synaptic activity thresholds that reinforce synapses and stabilize dendritic spines may vary across cortex.” It is interesting that the regions follow the same general trend in each region, peaking at 3.5 months.

References

Maja Abitz , Rune Damgaard Nielsen , Edward G. Jones , Henning Laursen , Niels Graem , and Bente Pakkenberg. Excess of Neurons in the Human Newborn Mediodorsal Thalamus Compared with That of the Adult. Cerebral Cortex Advance Access published on January 11, 2007, DOI 10.1093/cercor/bhl163.

Guy N. Elston, Tomofumi Oga, and Ichiro Fujita. Spinogenesis and Pruning Scales across Functional Hierarchies.  J. Neurosci. 29: 3271-3275; doi:10.1523/JNEUROSCI.5216-08.2009

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Scholz et al performed diffusion tensor imaging on 48 adults randomly placed in either a juggling or control group. By the end of the 6-week training each of the adults in the juggling group could perform 2 cycles of the 3 ball cascade, which is somewhat but not overly impressive. As compared to their pre-scanning fractional anisotropy, a somewhat loose measure of myelination, fiber density, and axon diameter, the juggling group had a percent increase of ~ 5.5 +/- 1.5 % immediately following the training, and a percent increase of ~ 4 +/- 1 % four weeks later. The control group had no real increase following training, which makes sense because they didn’t do anything!

The increase four weeks post-training indicates that although the effects of the training diminish somewhat over time, they should last for at least a little while. Perhaps further studies could continue to perform diffusion tensor imaging on the adults to see when the percent increases due to training are extinguished, if ever. A back of the envelope calculation based on a linear trend would suggest that after ~ 15 weeks following training the increased would be gone. But the reality may be wildly different.

Reference

Scholz J et al. 2009 Training induces changes in white-matter architecture. Nature Neuroscience 12 1370-1371. doi:10.1038/nn.2412

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Most naturally occuring amino acids in animals are of the L stereoisomerism, but D-serine is an amino acid that does have biological activity. It is known to activate NMDA receptors and induce NDMA receptor-dependent synaptic plasticity. And, there is evidence that deficiencies in D-serine are involved in the decline in hippocampus-dependent memory that occurs during aging.

Serine racemase is the enzyme that converts the naturally occuring L-serine to D-serine. Turpin et al looked at the mRNA and protein levels of D-serine in young and old Wistar rats as well as young and old Lou/C/Jall rats, which represent a model of aging without memory decline. D-serine levels were significantly reduced only in the hippocampus of aged Wistar rats as compared to young ones, −47.8% for mRNA levels and −25.1% for protein levels. When the researchers induced isolated NMDA receptor based field excitatory postsynaptic potentials on transverse hippocampal slices in Wistar rats, the recording was weaker in old animals than young ones. This difference between old and young was not apparent in the recordings from Lou/C/Jall rats. Crucially, when exogenous D-serine was added to the cerebrospinal fluid of Wistar rats, the age-related decrease in isolated NMDA receptor mediated synaptic potentials was rescued and there were no longer any signifcant difference between young and old rats. This strongly suggests that diminished D-serine can be responsible for lowered activity by NMDA receptors in the hippocampus.

Interestingly, the authors note that Lou/C/Jall rats have a reduced oxidative metabolism and less ROS production as compared to other strains (i.e., Wistar), don’t show any age-dependent reductions in the expression of serine racemase, and are generally a model for healthy aging without cognitive decline. Thus, the serine racemase gene may be a common and/or prototypical target of DNA-based oxidative damage in the aging brain.

Reference

Turpin FR, et al. 2009 Reduced serine racemase expression contributes to age-related deficits in hippocampal cognitive function. Neurobiology of Aging, Article in Press. doi:10.1016/j.neurobiolaging.2009.09.001.

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Beta amyloid precursor promotes synaptogenesis

Amyloid precursor protein (APP) is heavily implicated in the progression of Alzheimer’s disease. When proteolytically processed they yield 40 and 42 amino acid amyloid peptides which form the beta amyloid plaques one often hears about.  APP/APLP2double knock-out mice have reduced protein expression at the neuromuscular synapse and have generally defective synapses. Wang et al have been studying this defect and have found that:

  • APP synthesized in muscle and motor neurons end up in the pre and post synaptic sites at 1:1 stoichiometry, on the basis of antibody immunoreactivity.. This indicates that its expression at both sites is necessary for the proper development of the neuromuscular synapse.
  • Postsynaptic APP deletion inhibits presynaptic vescicle release, indicating that the defects in synapse function are bidirectional.
  • At embryonic day 12.5, APP expression is low, and nerve endings are not yet in contact with muscle. But at embryonic day 14.5 when synaptogenesis begins, APP expression spikes in both neural and muscle tissue. Major defects in the APP/APLP2 double knock out mutants don’t begin until embryonic day 16.5, perhaps because interaction between proteins across the synapse is necessary for proper function.
  • After transfecting an APP expression construct into HEK293 cells with hippocampal neurons, the area of the cells covered by synaptophysin increased as compared to negative control, as did the number of synaptic puncta, both indicating that APP acts as a synaptic adhesion protein. Double knock out APP/APLP2 neurons had ~ 3 +/- 1 synaptic puncta per HEK293 cell as compared to ~ 10 +/- 1 for controls, further supporting the characterization of APP as necessary for synpatogenesis.

Downregulation of this synaptic adhesion property, which could possibly be inhibited by the beta amyloid plaques, would lead to the synaptic disfunction associated with Alzheimer’s pathogenesis. Perhaps a drug that inhibits the proteolytic enzyme that cleaves APP into amyloid peptides could act as a preventative drug for the disease for individuals with warning signs.

Reference

Wang et al. 2009 Presynaptic and postsynaptic interaction of the amyloid precursor protein promotes peripheral and central synaptogenesis. Journal of Neuroscience 29:10788-10801. doi:10.1523/JNEUROSCI.2132-09.2009.

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