L-Asparaginase-producing Rouxiella Species Isolation, Antileukemia Activity Evaluation, and Enzyme Production Optimization

Abstract

Background: L-Asparaginase (L-Asp) is used as an efficient anti-cancer drug, especially for acute lymphoblastic leukemia (ALL). Currently, two bacterial asparaginase isoenzymes are used for cancer treatment. Therefore, this research focused on isolating native bacteria with the ability to produce L-Asp. Materials and Methods: L-Asp producing bacteria were isolated from soil samples on 9K medium supplemented with L-Asp as nitrogen source. Detection of L-Asp activity was performed by observing color change of the agar medium from yellow to orange due to the release of ammonia around the colonies. After the isolation and identification of the bacterium, L-Asp production was first optimized by the one factor-at-the-time (OFAT) technique followed by the response surface method. Next, the enzyme was extracted, purified, and assessed for antileukemia activity on U937 and MRC-5 cell lines. Results: The results revealed that L-Asp produced by Rouxiella sp. AF1 significantly inhibited the growth of U937 cells at a dose of up to 0.04 IU/ml, while MRC-5 was not affected at any enzyme doses. The final purification of the enzyme was achieved by column chromatography (Sephadex G-100) at approximately 0.31 mg/ml, and its specific activity was determined to be 0.51 IU/mg. The OFAT optimization experiments were performed primarily to determine optimal enzyme conditions, which were found to be neutral pH (pH7), 30 ∘C temperature, and 3 % NaCl, 1 % peptone, and 1% glucose concentrations. Statistical optimization was based on five factors obtained from OFAT, and response surface method (RSM) analysis introduced a quadratic model for enzyme production at the optimal range of these variables. This model provided an equation for measuring the effect of physiochemical conditions on final enzyme production. Conclusion: We showed that native bacteria may be novel candidates for isolating new metabolites such as L-Asp. Because many bacteria grow in unknown environments with unique ecological properties, the probability of discovering novel bacterial species producing bioactive compounds is high.

Caracterización bioinformática y producción de L-asparaginasa de Bacillus sp. M62 aislado de las salinas de Maras, Cusco, Perú

“…También, Bacillus aryabhattai B8W22, obtenido del lago salado de Urmia, produjo 61.70 U/mL con NaCl 30 g/L (Shirazian et al 2016). De igual forma, Gilavand et al (2019) reportaron mejores niveles de producción a NaCl 30 g/L usando Rouxiella AF1. Sin embargo, en el presente estudio se obtuvo 0.44 U/mL con mínima actividad L-glutaminasa a NaCl 50 g/L.…”

“…La L-asparaginasa es producida por microorganismos, vegetales y animales, siendo los primeros la fuente más importante debido a su rápida producción, además de permitir una eficiente purificación de la enzima y su escalamiento (Cachumba et al 2016). Entre los microorganismos utilizados en la producción de L-asparaginasa se encuentran los Streptomyces, Bacillus, Lysinibacillus, Staphylococcus, Rouxiella, Erwinia, Escherichia, Acinetobacter, Pseudomonas (Erva et al 2018;Arévalo-Tristancho et al 2019;Gilavand et al 2019;Prihanto et al 2019;Setiawan & Larasati 2019). Sin embargo, para lograr altos niveles de producción se requiere buena estabilidad en condiciones extremas, como la que han demostrado bacterias que mantienen actividad con alta eficiencia y termoestabilidad (Zuo et al 2015).…”

Complete genome sequence of Australian soil bacterium Rouxiella badensis DAR84756 resolved with Oxford Nanopore long-read and Illumina sequences

“…Rouxiella badensis is a Gram-negative bacterium in the family Yersiniaceae ( 1 ) found associated with soil and plant ecosystems ( 2 ), with emerging applications including post-harvest preservation of berries ( 3 ), a human antidepressant probiotic ( 4 – 6 ), biosynthesis of L-asparaginase ( 7 ), and uncharacterized antibiotics ( 8 ). High-quality genome sequences, such as described here, allow improved identification of metabolic pathways and secondary metabolite prediction ( 9 , 10 ).…”