In vitro and ex vivo proteomics of Mycobacterium marinum biofilms and the development of biofilm-binding synthetic nanobodies.
Milka Marjut HammarénHanna LuukinenAlina SillanpääKim RemansKarine LapougeTânia CustódioChristian LöwHenna MyllymäkiToni MontonenMarkus A SeegerJoseph RobertsonTuula Anneli NymanSavijoki KirsiMataleena ParikkaPublished in: mSystems (2023)
The antibiotic-tolerant biofilms present in tuberculous granulomas add an additional layer of complexity when treating mycobacterial infections, including tuberculosis (TB). For a more efficient treatment of TB, the biofilm forms of mycobacteria warrant specific attention. Here, we used Mycobacterium marinum (Mmr) as a biofilm-forming model to identify the abundant proteins covering the biofilm surface. We used biotinylation/streptavidin-based proteomics on the proteins exposed at the Mmr biofilm matrices in vitro to identify 448 proteins and ex vivo proteomics to detect 91 Mmr proteins from the mycobacterial granulomas isolated from adult zebrafish. In vitro and ex vivo proteomics data are available via ProteomeXchange with identifier PXD033425 and PXD039416, respectively. Data comparisons pinpointed the molecular chaperone GroEL2 as the most abundant Mmr protein within the in vitro and ex vivo proteomes, while its paralog, GroEL1, with a known role in biofilm formation, was detected with slightly lower intensity values. To validate the surface exposure of these targets, we created in-house synthetic nanobodies (sybodies) against the two chaperones and identified sybodies that bind the mycobacterial biofilms in vitro and those present in ex vivo granulomas. Taken together, the present study reports a proof-of-concept showing that surface proteomics in vitro and ex vivo proteomics combined are a valuable strategy to identify surface-exposed proteins on the mycobacterial biofilm. Biofilm-surface-binding nanobodies could be eventually used as homing agents to deliver biofilm-targeting treatments to the sites of persistent biofilm infection.
Keyphrases
- candida albicans
- biofilm formation
- pseudomonas aeruginosa
- staphylococcus aureus
- mycobacterium tuberculosis
- mass spectrometry
- cystic fibrosis
- escherichia coli
- label free
- emergency department
- electronic health record
- young adults
- machine learning
- pulmonary tuberculosis
- deep learning
- artificial intelligence
- small molecule
- cancer therapy
- replacement therapy