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Archive for the ‘Neurogenetics’ Category

How neurons remain neurons

A short Feb ’11 review by Oliver Hobert (HT: J Snyder) explains the process. A particular protein called a terminal selector coordinates it, and acts by binding to DNA sequences. One might describe the process as involving three main steps:

1) Initiation. Initiation occurs when neuroblasts terminally divide. An initiator protein binds to the DNA upstream of the gene encoding the terminal selector (in particular, to the “cis-regulatory element” of the DNA). This activates transcription of the terminal selector, and thus its translation as well. Crucially, the initiator protein is itself only expressed for short window of time.

2) Propagation. The terminal selector binds to the cis-regulatory elements upstream of “terminal differentiation genes,” activating their expression. These genes are involved in neural function, such as neurotransmitter metabolism and ion channels. Some also presumably act to arrest the cell’s growth phase in G0.

3) Maintenance. Through a common mechanism known as transcriptional autoregulation, the terminal selector gene maintains its levels by binding to a cis-regulatory element upstream of its own gene, thus activating its own expression. So, long after the initiator protein is no longer present (and indeed for the lifespan of the animal), the expression of the terminator selector gene will remain high, and it will, in turn, continue to activate the expression of the terminal differentiation genes.

This is also an interesting case study in the interplay between chromatin states and the action of transcription factors. New (“de novo”) events of transcription factor binding require the chromatin to “open up” to allow them to bind the DNA. An individual transcription factor protein molecule probably only binds to the DNA for short periods of time (low dissociation constants suggest it’s often on the scale of milliseconds). This also leads to remodeling of the chromatin state via histone modifications, which over the long run might make binding of the transcription factors easier.

But how important are the relative contributions of de novo transcription factor binding, histone modifications, and the initial chromatin state of DNA upstream the terminator selector and terminal differentiation genes? As far as I can tell, these remain somewhat pressing and open questions.

References

Holbert O, 2011, Maintaining a memory by transcriptional autoregulation, Current Biology. doi:10.1016/j.cub.2011.01.005

Kiełbasa SM, Vingron M (2008) Transcriptional Autoregulatory Loops Are Highly Conserved in Vertebrate Evolution. PLoS ONE 3(9): e3210. doi:10.1371/journal.pone.0003210

Wang Y, et al. 2009 Quantitative Transcription Factor Binding Kinetics at the Single-Molecule Level. Biophys Journal 10.1016/j.bpj.2008.09.040.

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French et al explore this link, looking at the correlation between gene expression in the mouse in and connectivity in homologous brain regions of the rat. This is their conceptual scheme:

Of their 142 common regions, 112 have efferent (outgoing) connections, and 141 have afferent (incoming) connections. There are 5216 outgoing connections and 6110 incoming connections.

To see whether the similarity of the connectivity between two regions is related to the similarity of gene expression between those two regions, they looked at the correlation between the connectivity and gene expression matrices. Specifically, they used a Mantel test, which measures the Pearson correlation between every entry in the matrices, and gets significance by permuting the order of the entries and recalculating the correlation after each permutation.

They find that brain regions with similar connectivity tend to have similar gene expression. The Mantel correlation between expression and incoming connectivity patterns (141 regions) is 0.248, with a permutation (exact) p-value < 0.0001, and for outgoing the correlation is 0.226, p also very low.

This is a really good proof of principle that expression and brain connectivity feed back upon one another. As the gene expression and connectivity measures become more precise, these types of analyses will yield valuable insights related to neural development and disease.

Reference

French L, Pavlidis P (2011) Relationships between Gene Expression and Brain Wiring in the Adult Rodent Brain. PLoS Comput Biol 7(1): e1001049. doi:10.1371/journal.pcbi.1001049

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In order to gather data on the gene expression characteristics of the brain, Cahoy et al purified cell suspensions of astrocytes, neurons, and oligodendrocyte lineage cells. They then used dChip software (here) to determine the gene expression of each cell type at various days postnatal.  Here’s a dendogram showing the hierarchical clustering of cell types based on gene expression:

similarity of gene expression between cell types shown via the vertical distances of each branch; doi:10.1523/JNEUROSCI.4178-07.2008

Two take aways from this are that 1) astrocytes are just as dissimilar to oligodendrocytes as they are to neurons, meaning that the concept of glial cells is outdated and 2) maturing cell gene expression is closer to mature expression than it is to immature expression in any given cell.

Here are the top 40 genes whose expression is particularly upregulated in neurons:

doi:10.1523/JNEUROSCI.4178-07.2008

The top ones are neurod6 which is involved in cell differentation during nervous system devo, glycine receptor subunit-2, nov which codes for a protein that might be in cell adhesion signaling, prdm8 which is important in developing cell morphology, and sla which is apparently involved in T cell receptor signaling. The function of most of these genes has yet to be elucidated much.

Here are the top 40 genes particularly upregulated in astrocytes:

doi:10.1523/JNEUROSCI.4178-07.2008

Genes involved in a number of metabolic pathways are upregulated in astrocytes, such as amino acid (especially glutamate) synthesis and glycolysis. Many genes involved in phagocytotic pathways are also upregulated, suggesting astrocyte roles in breaking down apoptotic cells and in axon pruning. These two roles are presumably interrelated.

Reference

Cahoy JD, et al. 2008 A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function. J Neuro doi:10.1523/JNEUROSCI.4178-07.2008

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Using RNAi to knock down the expression of particular receptors and signaling molecules is an intriguing pathway for neural engineering. For example, reducing the expression of neuropilin-2, a molecule that acts as a axon guiding cue, may improve axon outgrowth following injury.

In order to study one method, Ehlert et al injected short hairpin RNAs encoded by a lentiviral vector into the dorsal root ganglia at L4 and L5 of the spinal cord in rats. They used two types of short hairpin RNAs, one of which (A) was able to reduce the expression of Npn-2 as compared to controls.  They measured Npn-2 mRNA via hapten-labeled RNA probes and GFP immunofluorescence:

A - C = control, D - F = RNAi, Npn-2 mRNA = red, in G Npn-2 expressing cells are only those that are GFP positive ; doi:10.1186/1471-2202-11-20

The authors do encounter lots of the typical in vivo problems, especially cytotoxicity, probably due to saturation of the cell’s micro RNA synthesizing molecules. Still, these RNAi experiments are always interesting.

Reference

Elhert EM, et al. 2010 Cellular toxicity following application of adeno-associated viral vector-mediated RNA interference in the nervous system. BMC Neuro doi:10.1186/1471-2202-11-20

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MicroRNA’s are non-coding strands of ~20 nucleotides that regulate mRNA activity by partially base pairing to certain complementary strands and inhibiting translation. Mutations in miRNA’s have been linked to schizophrenia by Feng et al, who found that 8 out of 193 patients with schizophrenia had a mutation in a miRNA on their X chromosome as opposed to only 1 out of 191 control patients. Now Olsen et al have shown via miRNA isolation in rat brains that the distribution of micro RNA’s is variable throughout the brain and is clustered according to biological activity. The researchers conducted unsupervised hierarchical clustering on their normalized expression data to compute the similarity of miRNA expression data in various regions of the brain. Longer branch lengths correspond to more variability in branch length. Here are the results of their cluster dendogram:

Olsen et al, 2009. doi:10.1371/journal.pone.0007225

Olsen et al, 2009. doi:10.1371/journal.pone.0007225

“Cb” refers to cerebellum, “hip” to hippocampus, “am” to amygdala, “hyp” to hypothalamus, and “sn” to substantia nigra.

This may be an evolutionary accident but more likely the miRNA’s regulate cerebellar or forebrain-specific activities. The authors suggest particularly that the miRNA’s may have a role in neural development, having some sort of interaction with insulin-like growth factor 1. But if miRNA’s are important in the function of the active adult human brain this seems like the kind of thing whose activity would be difficult to simulate in a computer.

References

Feng J, et al. 2009 Evidence for X-chromosomal schizophrenia associated with microRNA alterations. PLoS One Online. PubMed.

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Interesting article by Kawakubo et al correlating the level of hemoglobin in the prefrontal cortex during a letter fluency task using near infrared spectroscopy. Via twin studies autism is already believed to have a genetic aspect but this kind of dose-dependent effect is illuminating nonetheless. Here’s the meaty image, and note that the fluency task started at t = 30 s:

Kawakubo et al, doi:10.1371/journal.pone.0006881

Kawakubo et al, doi:10.1371/journal.pone.0006881

The correlation was only significant before Bonferonni correction, but it seems fair to speculate on a few of the patterns regardless. Based on the progression from children to adults, more of the prefrontal cortex begins to be recruited for this cognitive task in control subjects, as indicated by higher levels of oxygenated hemoglobin, but not in individuals with autistic spectrum disorders. Moreover, siblings of individuals with ASD are on average more similar to those with ASD than control subjects are, further indicating the genetic basis of ASD.

In Create Your Own Economy, Tyler Cowen argues that autism represents a different cognitive style which has some benefits to be exploited. Although “moderating social behavior” and “decision making” are in some ways clearly useful byproducts of the prefrontal cortex, there are some other contexts in which these attributes might not be as favorable. That might include scientific discovery, wherein personal ambitions and ties to ones colleagues should be secondary to the quest for the underlying reality.

Reference

Kawakubo Y, Kuwabara H, Watanabe K-i, Minowa M, Someya T, et al. (2009) Impaired Prefrontal Hemodynamic Maturation in Autism and Unaffected Siblings. PLoS ONE 4(9): e6881. doi:10.1371/journal.pone.0006881

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In this fascinating piece of work, Okaty et al recently set out to describe the various upregulations and downregulations of genes in genetically labeled, fast-spiking, parvalbumin-expressing, rodent GABAergic interneurons during the first 6 postnatal weeks. Because they restricted their genetic analysis to one subset of cells, their results are more specific than if they had conducted a transcriptome wide assay on a given tissue.

Contrary to the old dogma that maturation involves simply an increase in the total number of ion channels and an increase in excitability, their results suggest a more nuanced stance. In this new approach, subsets of certain ion channels may emerge and recede as dictated by necessity. Certain channels that are useful in development may no longer serve a purpose in mature cells (so the driving force for degradation would be energetics), or may even hinder the mature cell’s function.

Specifically, of the downregulated ion channels they analyzed, seven of the thirteen subunits are associated with channels that flux calcium. For example, Cacng5 mRNA was downregulated about 89 fold. As these fast-spiking interneurons develop, their intracellular calcium concentration is liable to spiral out of control, as the increased number of action potentials will lower the average membrane potential and activate voltage-gated calcium channels much more often. So the downregulation of calcium channels could be a plausible mechanism for maintaining homeostasis in the cell. It would be interesting to study whether the mRNA downregulation is intrinsic to the cell type or if is a negative feedback mechanism triggered in response to increased intracellular calcium concentrations; one might do this by optogenetically controlling access to extracellular calcium.

The researchers also consider some synaptic properties of these interneurons. All in all, a very thoroughly researched paper that demonstrates both the complexity of the developmental genetic code and our increasing ingenuity in cracking it.

Reference

Okaty BW, et al. 2009 Transcriptional and electrophysiological maturation of neocortical fast-spiking GABAergic interneurons. The Journal of Neuroscience, 29. 7040-7052; doi:10.1523/JNEUROSCI.0105-09.2009.

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