If the X axis is reversed, this looks like a classic case of heteroskedastic data in which the variance increases with X. I'd define Z = 1/X and plot Y vs. Z (or use 1/(X+1) if X=0 is a possible value). You can then use all the standard variance stabilizing transformations (AKA, normalizing transformations) such as Z-->log(Z) or Z-->sqrt(Z). Depending on the meaning of X, you could also try just a simple reflection such as W = 300000 - x and then work with W. As Steve mentions, transformations often work best when they are meaningfully related to the data. So ask yourself, is "natural" way to flip around the X axis?
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