Soil genesis and mineralogy alter the stability and activity of hydrolytic enzymes

Hydrolases are a main group of enzymes that catalyze soil organic matter (SOM) decomposition, thus influencing the fate of organic carbon and nutrient release rates in soils. The rate of enzyme-catalyzed processes depends on the pool size and lifespan of enzymes. Both are strongly affected by soil genesis and mineralogy, yet this remains poorly documented by experimental data. In this study, we added three pure enzymes (β-glucosidase, acid phosphatase, and leucine aminopeptidase) to three soil horizons from a podzolic chronosequence with contrasting pedogenic characteristics: BC horizon: mainly primary minerals; Ae: quartz and organic matter enriched; and Bf: organo-metallic complexes and iron oxide enriched. Although the addition of pure enzymes increased enzyme activities by 1.3–2.3 times, only 7–22% of enzymes remained active one day after their addition into soil, moreover the active portion of added enzymes dropped to 5–12% over one week. The decay of enzymes followed the first-order model with rates ranging from 0.047 to 0.104 day^–1. The lack of enzyme stabilization processes in BC horizon mainly comprised of primary minerals with lower specific surface area and reactivity led to greater activity loss of acid phosphatase compared to horizons enriched with organic matter (Ae) and/or pedogenic iron subproducts (Bf). The adsorption of leucine aminopeptidase on the surface of iron oxides in Bf horizon decreased enzyme activity but prolonged the persistence of enzyme activity. However, the catalytic efficiency of enzymes adsorbed on the surface of iron oxides was lower than that of enzymes associated with organic matter (Ae) or existed in a free form (BC). Our findings highlight the need to (i) further investigate the relationship between enzyme activity and SOM decomposition rate, especially if soil minerals reduce enzyme catalytic efficiency, and (ii) carefully consider incorporating soil genesis into enzyme activity-based models to improve the predictions, for example, of SOM decomposition.