Impact of High Light Intensity and Low Temperature on the Growth and Phenylpropanoid Profile of Azolla filiculoides

Abstract

Exposure to high light intensity (HL) and cold treatment (CT) induces reddish pigmentation in Azolla filiculoides, an aquatic fern. Nevertheless, how these conditions, alone or in combination, influence Azolla growth and pigment synthesis remains to be fully elucidated. Likewise, the regulatory network underpinning the accumulation of flavonoids in ferns is still unclear. Here, we grew A. filiculoides under HL and/or CT conditions for 20 days and evaluated the biomass doubling time, relative growth rate, photosynthetic and non-photosynthetic pigment contents, and photosynthetic efficiency by chlorophyll fluorescence measurements. Furthermore, from the A. filiculoides genome, we mined the homologs of MYB, bHLH, and WDR genes, which form the MBW flavonoid regulatory complex in higher plants, to investigate their expression by qRT-PCR. We report that A. filiculoides optimizes photosynthesis at lower light intensities, regardless of the temperature. In addition, we show that CT does not severely hamper Azolla growth, although it causes the onset of photoinhibition. Coupling CT with HL stimulates the accumulation of flavonoids, which likely prevents irreversible photoinhibition-induced damage. Although our data do not support the formation of MBW complexes, we identified candidate MYB and bHLH regulators of flavonoids. Overall, the present findings are of fundamental and pragmatic relevance to Azolla’s biology.

High-resolution metabolite imaging: luteolinidin accumulates at the host-cyanobiont interface during cold-acclimation inAzollasymbioses

“…Af DFR2 (Afi_v2_s5G012490.4) was, however, downregulated in cold ( Figure 6A ), whilst Af LAR was upregulated ( Table 2 ). Reduced LAR expression correlated with less soluble PAs (Cannavò et al, 2023). Thus, when Azolla stops growing in cold, increased Af LAR may alter the degree of polymerization of existing PA-subunits (Liu et al, 2016), instead of channeling more substrate via Af DFR2.…”

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions

“…Recent literature has seen a surge in studies investigating the properties and behaviors of MB through the lens of machine learning (ML) algorithms. Kooh et al [13] employ supervised ML algorithms to model MB dye adsorption by Azolla [14] pinnata, aiming for accurate predictions of adsorption capacity across various conditions. SVR-RBF [15] emerges as the top-performing algorithm, achieving an R value of 0.994 with minimal error.…”