Consider what you're asking. If you just want to know if the overall p-value for the effect of status passes some some sort of arbitrary cutoff value, like 0.05, then that's easy. First, you want to find out the overall effect. You could get that from
m <- lmer(...) #just run your lmer command but save the model
Now you have an F value. You can take that and look it up in some F tables. Just pick the lowest possible denom. degrees of freedom. The cutoff there is going to be around 20. Your F may be larger than that but I could be wrong. Even if it's not, look at the number of degrees of freedom from a conventional ANOVA calculation here using the number of experiments you have. Sticking that value in you're down to about 5 for a cutoff. Now you easily pass it in your study. The 'true' df for your model will be something higher than that because you're modelling every data point as opposed to aggregate values that an ANOVA would model.
If you actually want an exact p-value there's no such thing unless you're willing to make a theoretical statement about it. If you read Pinheiro & Bates (2001, and perhaps some more books on the subject... see other links in these answers) and you come away with an argument for a specific df then you could use that. But you're not actually looking for an exact p-value anyway. I mention this because you therefore shouldn't report an exact p-value, only that your cutoff is passed.
You should really consider the Mike Lawrence answer because the whole idea of just sticking with a pass point for p-values as the final and most important information to extract from your data is generally misguided (but might not be in your case since we don't really have enough information to know). Mike is using a pet version of LR calculation that is interesting, but it may be hard to find a lot of documentation on it. If you look into model selection and interpretation using AIC you may like it.