Detecting ALDH2 activity in live cells conditional metabolic labeling.

Detecting enzyme activity that catalyzes subtle functional group transformations in live cells remains a major challenge. We introduce a conditional metabolic labeling strategy for enzymatic activity detection (cMLEAD), which harnesses cellular metabolic pathways to deliver indirect yet reliable activity readouts. Unlike traditional metabolic labeling approaches relying on nonspecific incorporation of tagged biomolecules, cMLEAD employs a tagged precursor whose metabolic incorporation is strictly dependent on specific enzymatic activity, effectively transforming a metabolic labeling event into an enzyme-activity measurement. Using aldehyde dehydrogenase 2 (ALDH2) as a proof of concept, we demonstrate the robustness of the strategy. cMLEAD for ALDH2 employs azido-tagged acetaldehyde, metabolized by ALDH2 into azidoacetate, which feeds into the acetyl-CoA biosynthetic pathway and is incorporated into lysine acylation, enabling fluorescence-based detection click chemistry. The assay reliably reports ALDH2 activity, as validated through genetic and pharmacological modulation. cMLEAD further revealed suppressed ALDH2 activity under cellular senescence and oxidative stress, with direct inhibition by HO likely contributing in part. Notably, cMLEAD is complementary to conventional assays and advantageous in preserving the native enzyme context. Leveraging this advantage, we developed a screening platform that identified sennoside A as a candidate ALDH2 activator, which alleviated light-induced retinal degeneration in mice. This study establishes cMLEAD as a robust and versatile platform for probing ALDH2 activity in pathophysiologically relevant contexts and facilitating therapeutic discovery. We envision the conceptual framework of cMLEAD may be adapted to other enzymes whose catalytic products feed into detectable metabolic incorporation.