It is expressed as experimental over control.

Positive logFC values mean induction (due to the experimental conditions),

negative logFC values mean repression,

and values close to zero mean not much difference between experimental and control.

Volcano plots are just a tool to display expected differences between two conditions (like experimental vs. control) along with a measure of the statistical significance associated with these differences.

There is no principal difference in "wet bench data" and "omics data", and both are (should) be treated statistically in a similar way. For most biological data the values vary over a large range, and the "biological signal" or "effect" (like the change in gene expression or metabolic rate or immune cell attraction or neuronal EPSP density or ...) is typically proportional to the value itself. This means that a stimulus changes the values by an amount that is equal to the starting value multiplied with some factor. We say that the impact of the stimulus is multiplicative. However, standard statistical methods assume that signals/effects are independent of the values, i.e. that such signals are just "adding" to the value. Logarithms transform multiplicative effects into additive effects (adding logarithms is like multiplying the original values). This is the reason why statistics should (usually) be done using the logarithms. It is also adviseable to present the logarithms, because this gives a mor intuitive picture of the biological relevance of the effects.

Jochen Willhelm, to clarify, the host factor in question is over expressed or enriched? Thank you for your informative response. 

"AGFG1 has a log10 FC of -0.5. I think that would mean 10-0.5 fold change, or 0.316."

Yes, that is correct.

"How is this ratio being expressed: experimental over control or vice versa?"

Typically a fold-change (or fold-difference) is expressed as "experimental over control", "diseased over healthy" or similar.

If the (log) fold-change of AGFG1 from your example was calculated this way, then the negative log indicates a lower expression in "experimental" compared to "control": the protein expression is down-regulated, suppressed, or silenced due to the experimental condition. The expression of this protein under experimental conditions is 0.3 times as high as the expression under control conditions. If you take the expression under control conditions as 100%, then the experimental conditions cause a 70% reduction in the expression of this protein (its expression is reduced by 70% or reduced to 30% of the value under control conditions).

If the log fold-change was positive (e.g. +0.5), the protein expression would be induced by the treatment. The fold-chage would be 10+0.5 = 3.2, indicating an expression under experimental conditions that is 3.2 times as high as the expression under control conditions. If you again take control as 100%, the experimental condition increased the expression by 220% or increased it to 320% of the expression under control conditions.

Note: it seems plausible that both regulations (0.3-fold as well as 3-fold) have a similar biological relevance. But the value "3.0" is much farther away from the "equality point" (1.0) than the value "0.3". This seems to somehow indicate a "higher" relevance of the induction - but that is misleading. The biologically relevant picture is better represented by the log fold-changes, where both values have the same size (0.5) but different signs (- or +), indicating a similar biological relevance but with the opposite direction of the effect. I would therefore always look at, report, and interpret log fold-changes rather than fold-changes.