Back

Phytohormonal cross-talk modulate Bipolaris sorokiniana (Scc.)interaction with Zea mays

Yousaf, M. J.; Hussain, A.; Hamayun, M.; Iqbal, A.; Irshad, M.; Ahmad, A.; Lee, I.-J.

2019-11-19 physiology
10.1101/847061 bioRxiv
Show abstract

Besides acting as growth inducing molecule, Gibberellin (GA3) also confers the compatibility of microbial interactions with host. We inoculated 11 days old Z. mays seedlings grown under hydroponic conditions and high GA3 levels with Bipolaris sorokiniana (BIPOL) at the spore density (SD) of OD0.6. The high level of GA3 negatively affected the growth of the seedlings, accompanied by the high level of stress deducing secondary metabolites (proline, total flavanoids, phenylpropanoids, and glucosinolides). Moreover, high level of GA3 produced a hypersensitive response (HR) in the seedlings. The HR developed cross talks with IAA and trans-zeatins and triggered higher production of hypersensitive inducing biomolecules. The other HR co-related biological processes were demonstrated by high phytoalexins level and high protease activities. Such activities ultimately inhibited the colonization of BIPOL on the roots of maize seedlings. The products of the genes expressed at high GA3 also conferred the deterrence of BIPOL colonization at SD = OD0.6. Intriguingly, when we inhibited GA3 biosynthesis in the seedlings with aerially sprayed uniconizole, prior to BIPOL treatment, the BIPOL colonized and subsequently promoted the seedling growth. This low level of GA3 after BIPOL treatment checked the high level of secondary metabolites and hypersensitivity inducing molecules. The results, thus suggested that the aforementioned processes only happened in the BIPOL at SD (OD0.6), whereas the SD at lower levels (OD0.2 or OD0.4) neither promoted the growth of uniconizole pre-treated seedlings nor produced HR in control seedlings of maize plant.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.