The Natural Variation of a Neural Code

The way information is represented by sequences of action potentials of spiking neurons is determined by the input each neuron receives, but also by its biophysics, and the specifics of the circuit in which it is embedded. Even the “code” of identified neurons can vary considerably from individual to individual. Here we compared the neural codes of the identified H1 neuron in the visual systems of two families of flies, blow flies and flesh flies, and explored the effect of the sensory environment that the flies were exposed to during development on the H1 code. We found that the two families differed considerably in the temporal structure of the code, its content and energetic efficiency, as well as the temporal delay of neural response. The differences in the environmental conditions during the flies' development had no significant effect. Our results may thus reflect an instance of a family-specific design of the neural code. They may also suggest that individual variability in information processing by this specific neuron, in terms of both form and content, is regulated genetically

Neural responses of the two fly families convey different information about the stimulus.

<p>(<b>A</b>) STA, spike-triggered average, the average stimulus that preceded the occurrence of a single spike, averaged in the four groups. Line width represents standard error of the mean. The STA of the blow fly group was significantly different from the STA of all flesh fly groups. (<b>B</b>) Mean firing rates and standard error of the four groups; again the blow fly exhibited significantly higher rates. (<b>C–F</b>) Four examples of word triggered averages (WTAs); the average stimulus that preceded the occurrence of each specific 8-letter word. Inset - corresponding probabilities that each word will appear in the response of each group, mean+SEM.</p

Word distributions of flies and a comparison between codewords of different fly families.

<p>(<b>A</b>) Mean and standard error values of the distribution of 8-letter words in the four groups. Words are arranged in decreasing rank order of the probability of words in the response of blow flies (note that the whole range of 256 possible words is not shown). Inset – mean and standard error of the probability of ‘00000000’ which was the most commonly used word. (<b>B</b>) Matrix of the Jensen Shannon distances between 8-letter word distributions of each pair of flies. Bars show mean values+SEM of three clusters: distance within each of the four groups, between fly families (groups B1 and F1) and flies from different environmental conditions (groups F2 and F3). (<b>C</b>) Matrix of the distances between the stimulus conditioned word distribution of each pair of flies, averaged across the stimulus presentation. Bars show mean+SEM values of the same clusters as in B. (<b>D</b>) The distance between each two flies was minimized by finding the time shift between their responses that resulted in the minimal value (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033149#s4" target="_blank">Methods</a>). Matrix displays the time shift in milliseconds between the responses of each pair of flies that minimized the distance. Bars show mean+SEM values of same clusters as in <b>B</b>. A similar analysis using 12-letter words produced qualitatively similar results.</p

Overexpression of UBA5 in Cells Mimics the Phenotype of Cells Lacking UBA5

Ufmylation is a posttranslational modification in which the modifier UFM1 is attached to target proteins. This conjugation requires the concerted work of three enzymes named UBA5, UFC1, and UFL1. Initially, UBA5 activates UFM1 in a process that ends with UFM1 attached to UBA5’s active site Cys. Then, in a trans-thiolation reaction, UFM1 is transferred from UBA5 to UFC1, forming a thioester bond with the latter. Finally, with the help of UFL1, UFM1 is transferred to the final destination—a lysine residue on a target protein. Therefore, not surprisingly, deletion of one of these enzymes abrogates the conjugation process. However, how overexpression of these enzymes affects this process is not yet clear. Here we found, unexpectedly, that overexpression of UBA5, but not UFC1, damages the ability of cells to migrate, in a similar way to cells lacking UBA5 or UFC1. At the mechanistic level, we found that overexpression of UBA5 reverses the trans-thiolation reaction, thereby leading to a back transfer of UFM1 from UFC1 to UBA5. This, as seen in cells lacking UBA5, reduces the level of charged UFC1 and therefore harms the conjugation process. In contrast, co-expression of UBA5 with UFM1 abolishes this effect, suggesting that the reverse transfer of UFM1 from UFC1 to UBA5 depends on the level of free UFM1. Overall, our results propose that the cellular expression level of the UFM1 conjugation enzymes has to be tightly regulated to ensure the proper directionality of UFM1 transfer