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Archive for the ‘Molecular Neuroscience’ Category

Cells can die for a variety of reasons. Some of them are intentional (“programmed”) in response to exposure to external stressors (like viral or toxic molecules) or internal problems (like DNA damage). And some of them are unintentional (“non-programmed”), which often involves the premature breakdown of cell membranes and loss of cell contents.

Necroptosis_Pathway_Diagram

The totally simple necroptosis cell death pathway, from the NIH via Wikipedia

Caccamo et al recently published a paper suggesting that one specific type of programmed cell death, necroptosis, might be a key part of what mediates neuron death in Alzheimer’s disease (AD).

Their evidence spanned multiple human data sets of postmortem AD brains and mouse models (5XFAD), and showed that markers for necroptosis (MLKL, RIP1, RIP3) were often significantly correlated with the degree of AD neuropathology seen in those brains.

Notably, their data didn’t provide strong evidence to exclude apoptosis and non-necroptosis necrotic cell death pathways as also contributory to cell death in AD.

So, another study that would also be interesting would be to see a more global comparison of all different types of cell death, to see which markers correlate the strongest with AD neuropathologic changes.

On the other hand, the authors note in their discussion that there is a lot of cross-talk between necrosis and apoptosis, which means that it may be difficult or not make sense to distinguish between them in this way.

Even if necroptosis is the mechanism of cell death in AD, that doesn’t mean that we can just turn off this cell death pathway and rescue neurons and memory. If anything, it suggests that the neurodegeneration itself is intentional, likely helpful to mitigate even more damage, and that changes to stop AD will have to occur much farther up to pathogenic cascade.

Still, it’s critical to understand exactly what is the pathway of degeneration in AD so that we can figure out what to target, and this study might be an important part of that.

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A really nice article from Tyssowski et al. The authors did RNAseq on neurons that either were or were not stimulated with neural activity. They found that a subset of proteins (251) that have been previously described as “[neuronal] activity regulated genes” were able to predict the stimulation state of those neurons well above chance. Specifically: 92% of the time using nearest neighbor classification as measured by leave one out cross validation.

I’m interested in the broad question of “which RNAs/proteins are important for neuronal activity” and this set of activity regulated genes is pretty clearly within that set. Interestingly, it seems that the expression of these genes is pretty highly correlated (very similar chromatin states, transcription factors, etc), so I don’t think you would have to perfectly preserve ALL of them in order to allow for a high-fidelity preservation of information.

On that note, it’d be interesting if someone were to use this data to try to predict neuron stimulation state using the smallest set of activity regulated genes as necessary. For example, the 19 rapidly-induced activity regulated genes, including the non-transcription factors Arc and Amigo3, seem like they would punch above their weight in terms of predicting neuronal activation state.

Figure 6 from the paper

Arc expression and enhancer acetylation is stimulated after only 10 minutes of neuronal activity; http://biorxiv.org/content/early/2017/06/05/146282.full.pdf+html

It also suggests an experiment for any brain preservation procedure that purports to preserve gene expression important for neural activity: stimulate neural activity on a subset of neurons (probably in vitro, since it’s easier and should yield the same result), perform your brain preservation processing steps, attempt to measure the expression of these genes, and then see if you can distinguish between which neurons were stimulated or not on the basis of those measurements.

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What is the mechanism by which dendritic spines can change structure over a rapid time course? Though this may seem esoteric, it is probably how memories form and is thus utterly essential to neuroscience. Two new papers present some relevant data.

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Two-photon imaging data of dendritic spines, from Wikipedia User:Tmhoogland

First, as has been shown several times, Harward et al show that glutamate uncaging at single dendritic spines leads to a rapid increase in spine volume after only ~ 1 minute that degrades over a period of several more minutes:

screen-shot-2016-10-14-at-8-06-49-am

Harward et al; doi:10.1038/nature19766

Along the same time course as the dendritic spine volume increase, these authors also detected TrkB activation (using their amazing new FRET sensor), which was largely in the activated spine but also traveled to nearby spines and the dendrite itself:

screen-shot-2016-10-14-at-8-10-04-am

Harward et al; doi:10.1038/nature19766

 

In what is to me probably their most compelling experiment, they show that hippocampal slices without BDNF have highly impaired volume changes in response to glutamate, and that this can be rescued by the addition of BDNF:

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Harward et al; doi:10.1038/nature19766

They also present several lines of evidence that this is an autocrine mechanism, with BDNF released from spines by exosomes and binding to TrkB receptors on the same spine.

In a separate article in which most of the same authors contributed, they show that another protein, Rac1, is activated (ie, GTP-bound, leading to fluorescence) very quickly following glutamate uncaging at single spines:

 

screen-shot-2016-10-14-at-9-18-25-am

Hedrick et al; doi:10.1038/nature19784

They also show that a similar rapid course of activation following glutamate uncaging occurs for the other Rho GTPases Cdc42 and RhoA.

Interestingly, they also show that these proteins mediate synaptic crosstalk, whereby the activation of one dendritic spines causes nearby dendritic spines to increase in strength. After several more experiments, here is their diagram explaining this mechanism:

screen-shot-2016-10-14-at-9-24-27-am

Hedrick et al; doi:10.1038/nature19784

Overall I find their data trustworthy and important. The most interesting subsequent question for me is whether endogenous amounts of CaMKII, BDNF, TrkB, and Rho GTPase signaling components (e.g., Cdc42, RhoA, Rac1) vary across dendritic spines, and whether this helps mediate variability in spine-specific and spine neighbor-specific degrees of plasticity. My guess is that they do, but AFAICT it remains to be shown.

If it is true that spines, dendrites, and neurons vary in the expression and distribution of these proteins, then any attempt to build models of the brain, as well as models of individual brains that have any sort of dynamic component, probably need to measure and model the local densities of these protein mediators of plasticity.

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CSF- and serum-borne autoantibodies against brain proteins are known to cause a wide range of cognitive sequelae due autoimmune attack. For example, when antibodies are raised against the protein LGI1, which is thought to act as a voltage-gated K+ channel, a common result is encephalopathy.

As a result, LGI1 is often included in autoimmune panels, along with several other proteins including CASPR2, NMDA and AMPA subunits, GABA-B receptors, GAD65, CRMp-5, ANNA-1, and ANNA-2.

Recently, Ariño et al presented a summary of 76 patients with LGI1-associated cognitive deterioration, 13% of which had forms of cognitive deterioration distinct from limbic encephalitis. At 2 years their major outcomes were:

  • 35% fully recovered
  • 35% regained independence but to baseline levels
  • 23% required assistance due to cognitive defects
  • 6% died

In mice, LGI1 is primary expressed at the RNA level in neurons at the RNA level, while in humans its expressed in both mature astrocytes and neurons (data from here and here), eg in the Darmanis et al 2015 human data set its actually expressed higher in astrocytes:

screen-shot-2016-10-10-at-6-46-25-pm

It might be interesting to see whether encephalopathies are generally only caused by autoantibodies against proteins expressed in neurons, or whether or cell type-expressed proteins can also lead to a similar clinical outcome.

 

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A cool study shows that knocking out the regulatory protein Epac2 in mice has large effects on the structural stability of basal dendrites in pyramidal neurons.

This is not the first study to have demonstrated this type of selective regulation, but it’s still surprising. The apical dendrites are so similar and close to the basal ones; why wouldn’t a regulatory molecule affect both classes?

The fact that they are regulated differentially shows that each structural component of neurons is finely tuned. This is weak evidence in favor of the theory that the neuronal morphology carries lots of information.

Reference

Srivastava DP, Woolfrey KM, Jones KA, Anderson CT, Smith KR, et al. (2012) An Autism-Associated Variant of Epac2 Reveals a Role for Ras/Epac2 Signaling in Controlling Basal Dendrite Maintenance in Mice. PLoS Biol 10(6): e1001350. doi:10.1371/journal.pbio.1001350

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What has been the growth rate of computing power, multi-neuron recording, and DNA sequencing over the past decade? Konrad Kording provides an illuminating chart pertaining to this question:

neurons recorded = the number of neurons that can be recorded from simultaneously; the neuron and computer scales are exponential fits to data; doi:10.1371/journal.pcbi.1002291

Given the above DNA sequencing trends, it’s no surprise that groups in many different fields are developing strategies to turn the problem they are trying to study into a sequencing problem.

See, for example, Jonathan Weissman’s talk on ribosome profiling, which is an elegant way to use DNA sequencing of mRNA molecules tethered to the ribosome as a way to study translation.

In his article, Kording touches on a couple of intriguing sequencing technologies that might help make the “data-out” step of a given neuroscience experiment more high-throughput.

The method for connectomics he describes is particularly fascinating. The idea is to assign neurons a unique DNA barcode that is spread to each of its synaptic partners via a transsynaptic virus, and then sequence the set of barcodes from a given group of cells.

One aspect that I think Kording might have underemphasized is that these technologies would improve greatly if we improved our ability to sequence the DNA of individual neurons.

For example, typical protocols for probing the expression of intermediate early genes rely on harvesting cells from mass culture or coarse brain regions before sequencing. This is powerful, but it would be much more so if we could analyze the distribution of gene expression between cells rather than across them.

Single-cell genomics is advancing, but it is not yet at the point of routine laboratory use for a typical sequencing experiment. And in order to really take advantage of DNA sequencing technology in understanding how networks of neurons work together, it will presumably need to reach that point.

References

Kording KP (2011) Of Toasters and Molecular Ticker Tapes. PLoS Comput Biol 7(12): e1002291. doi:10.1371/journal.pcbi.1002291

Link to Jonathan Weissman’s 11/16/11 talk.

Oyibo H, et al. 2011 Probing the connectivity of neural circuits at single-neuron resolution using high-throughput DNA sequencing. Presentation at Computational and Systems Neuroscience Meeeting, pdf.

Saha RN, et al. 2011 Rapid activity-induced transcription of arc and other IEGs relies on poised RNA polymerase II. doi: 10.1038/nn.2839.

Kalisky T, et al. 2011 Single-cell genomics. doi:10.1038/nmeth0411-311

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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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