Back

Estimation of the biosorption potential of certain representatives of the genus Bacillus in interaction with lead cation in vitro

Galaktionova, L.; Sizentsov, A. N.; Davydova, O. K.; Nikiyan, H. N.; Sizentsov, Y. A.

2021-12-27 ecology
10.1101/2021.12.27.474245 bioRxiv
Show abstract

The article presents data on the physicochemical and metabolically dependent mechanisms of detoxification by microorganisms of heavy metals that enter the environment during anthropogenic pollution. The taxonomic and physiological-biochemical diversity of microorganisms capable of neutralizing toxicants has been demonstrated. In the experimental part of the study, the combination of the methods used made it possible to fully assess the degree of toxicity and the effect of lead cations on the growth of bacteria of the genus Bacillus in a model experiment. Thus, the use of atomic absorption spectrophotometry and atomic force microscopy gave an idea of the level of biosorption of a given xenobiotic element from a substrate with localization of inactive forms of lead. The data obtained indicate the presence of an inhibitory effect of Pb(NO3)2 and Pb(CH3COO)2 in concentrations from 1 mM to 0.063 mM about the test organisms under study. The presence of cations with a nutrient substrate gives a false-positive idea of the degree of influence of lead on the growth of the studied microorganisms, since an increase in optical density in test samples is due to high sorption characteristics and, as a consequence, is characterized by an increase in relative optical density. An assessment of the detoxification mechanisms, which is expressed by active bioaccumulation of lead on the surface elements of the microbial cell up to 65% at the point of entering the stationary growth phase, indicates the promising use of representatives of this group of microorganisms as microbial bioremediation and correctors of excess content of this element in the body or ecosystem as a whole.

Matching journals

The top 2 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.