The second part of that statement is true, the first it not. SR assumes a few things distributionaly, normality is not one of them. It assumes that the distribution has a second moment (e.g. no Cauchy...) and that the distibution's small sample properties are such that sample first and second moment estimates are reliable. It implicitly, for sample sizes typical of markets, assumes that the distribution is symetrical (e.g log-symmetric distros). If the distro is symmetric but leptokurtotic, the small sample properties of the sharpe will be even worse than usual.Sharpe Ratio assumes that your returns follow a normal distribution and that you have sufficient sample.
That said, the gist of your posts is spot on. I have often thought that penalizing upside semi-variance (upside sd in the denominatior of sharpe) gives a better estimate of out-of-sample performance of optimized back-tested models than either variance or downside semi-variance.