journal article·2024-06Open access

Adaptive Laboratory Evolution of Probiotics toward Oxidative Stress Using a Microfluidic‐Based Platform

Ann V. Nguyen, Mohammad Yaghoobi, Shiying Zhang, Peilong Li, Qike Li, Belgin Dogan, Gianna P. Ahnrud, Genevieve Flock, Patrick Marek, Kenneth W. Simpson, Alireza Abbaspourrad

Small· Volume 20· Issue 26

Abstract

Abstract Adaptive laboratory evolution (ALE) can be used to make bacteria less susceptible to oxidative stress. An alternative to large batch scale ALE cultures is to use microfluidic platforms, which are often more economical and more efficient. Microfluidic ALE platforms have shown promise, but many have suffered from subpar cell passaging mechanisms and poor spatial definition. A new approach is presented using a microfluidic Evolution on a Chip (EVoc) design which progressively drives microbial cells from areas of lower H 2 O 2 concentration to areas of higher concentration. Prolonged exposure, up to 72 h, revealed the survival of adaptive strains of Lacticaseibacillus rhamnosus GG, a beneficial probiotic often included in food products. After performing ALE on this microfluidic platform, the bacteria persisted under high H 2 O 2 concentrations in repeated trials. After two progressive exposures, the ability of L. rhamnosus to grow in the presence of H 2 O 2 increased from 1 m m H 2 O 2 after a lag time of 31 h to 1 m m after 21 h, 2 m m after 28 h, and 3 m m after 42 h. The adaptive strains have different morphology, and gene expression compared to wild type, and genome sequencing revealed a potentially meaningful single nucleotide mutation in the protein omega‐amidase.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Probiotics and Fermented Foods · Bacterial Genetics and Biotechnology · Microbial Metabolic Engineering and Bioproduction

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Food Science College of Agricultural and Life Sciences Cornell University Stocking Hall Ithaca NY 14853 USA

Department of Clinical Sciences College of Veterinary Medicine Cornell University 602 Tower Rd. Ithaca NY 14853 USA

DEVCOM Soldier Center Soldier Sustainment Directorate Combat Feeding Division Food Protection & Innovative Packaging Team Natick MA 01760 USA

Funding

National Institute of Food and Agriculture · U.S. Army Natick Soldier Research, Development and Engineering Center · National Science Foundation

References (46)

Show source-provided references
1. 10.1007/s00253-012-4241-710.1007/s00253-012-4241-7 ↗
2. 10.1080/19490976.2020.180194410.1080/19490976.2020.1801944 ↗
3. 10.1039/C2IB20095C10.1039/C2IB20095C ↗
4. 10.1007/s12257-011-0346-610.1007/s12257-011-0346-6 ↗
5. 10.1007/s12602-021-09892-310.1007/s12602-021-09892-3 ↗
6. 10.3389/fmicb.2018.0278110.3389/fmicb.2018.02781 ↗
7. 10.1007/s12010-020-03264-310.1007/s12010-020-03264-3 ↗
8. 10.1128/mBio.03337-2010.1128/mBio.03337-20 ↗
9. 10.1038/s41467-017-00222-210.1038/s41467-017-00222-2 ↗
10. 10.3390/metabo201017810.3390/metabo2010178 ↗
11. 10.1016/j.copbio.2018.08.00410.1016/j.copbio.2018.08.004 ↗
12. 10.1016/j.copbio.2018.08.01110.1016/j.copbio.2018.08.011 ↗
13. 10.1021/acs.analchem.0c0524810.1021/acs.analchem.0c05248 ↗
14. 10.1371/journal.pone.027229410.1371/journal.pone.0272294 ↗
15. 10.1038/s44172-023-00064-510.1038/s44172-023-00064-5 ↗
16. 10.1021/acs.est.9b0088110.1021/acs.est.9b00881 ↗
17. 10.1126/science.120874710.1126/science.1208747 ↗
18. 10.1002/smll.20200716610.1002/smll.202007166 ↗
19. 10.1007/s00253-020-10703-y10.1007/s00253-020-10703-y ↗
20. 10.3389/fimmu.2022.97322410.3389/fimmu.2022.973224 ↗
21. 10.1016/j.tim.2021.03.01010.1016/j.tim.2021.03.010 ↗
22. 10.1016/j.ijfoodmicro.2013.11.01910.1016/j.ijfoodmicro.2013.11.019 ↗
23. 10.1016/j.foodchem.2008.09.03310.1016/j.foodchem.2008.09.033 ↗
24. 10.1016/j.jff.2015.01.04310.1016/j.jff.2015.01.043 ↗
25. Google C https://colab.research.google.com/drive/1RaFrHrgFpWwWQ6t39dHTmZZ9zTFql5bc#scrollTo=tuSNCBZkPe80 (accessed: January 2024).
26. Mohammad Yaghoobi; 2022; hydraulic network solver; github https://doi.org/10.5281/zenodo.6989769.
27. 10.3390/foods1006120310.3390/foods10061203 ↗
28. 10.1038/s41592-022-01488-110.1038/s41592-022-01488-1 ↗
29. 10.1007/s11011-013-9444-910.1007/s11011-013-9444-9 ↗
30. 10.1016/j.biochi.2009.07.00210.1016/j.biochi.2009.07.002 ↗
31. 10.1073/pnas.161373611410.1073/pnas.1613736114 ↗
32. 10.1016/j.freeradbiomed.2007.12.00810.1016/j.freeradbiomed.2007.12.008 ↗
33. 10.1016/j.jprot.2022.10466310.1016/j.jprot.2022.104663 ↗
34. 10.1111/jam.1285310.1111/jam.12853 ↗
35. 10.1111/jam.1252010.1111/jam.12520 ↗
36. 10.1111/jam.1228510.1111/jam.12285 ↗
37. 10.1186/1475-2859-6-2910.1186/1475-2859-6-29 ↗
38. 10.1016/j.carbpol.2011.07.05610.1016/j.carbpol.2011.07.056 ↗
39. 10.1016/j.foodhyd.2016.09.04110.1016/j.foodhyd.2016.09.041 ↗
40. O.Ronneberger P.Fischer T.Brox inMedical Image Computing and Computer‐Assisted Intervention–MICCAI 2015: 18th International Conference (Eds:N.Navab J.Hornegger W.Wells A.Frangi) Proceedings Part III 18 Springer Cham2015 pp.234–241.10.1007/978-3-319-24574-4_28 ↗
41. 10.3168/jds.2018-1619210.3168/jds.2018-16192 ↗
42. H.Li arXiv preprint arXiv:1303.39972013.
43. 10.1093/bioinformatics/btp35210.1093/bioinformatics/btp352 ↗
44. 10.1093/bioinformatics/btr50910.1093/bioinformatics/btr509 ↗
45. 10.1038/nbt.175410.1038/nbt.1754 ↗
46. 10.1093/bioinformatics/btq03310.1093/bioinformatics/btq033 ↗